pwc-archive/papers-with-abstracts huggingface dataset:
Advancing Brain-Computer Interface System Performance in Hand Trajectory Estimation with NeuroKinect
Authors: Sidharth Pancholi, Amita Giri
Date: 2023-08-16 00:00:00
Tasks: Brain Computer Interface, Computational Efficiency, EEG

Brain-computer interface (BCI) technology enables direct communication between the brain and external devices, allowing individuals to control their environment using brain signals. However, existing BCI approaches face three critical challenges that hinder their practicality and effectiveness: a) time-consuming preprocessing algorithms, b) inappropriate loss function utilization, and c) less intuitive hyperparameter settings. To address these limitations, we present \textit{NeuroKinect}, an innovative deep-learning model for accurate reconstruction of hand kinematics using electroencephalography (EEG) signals. \textit{NeuroKinect} model is trained on the Grasp and Lift (GAL) tasks data with minimal preprocessing pipelines, subsequently improving the computational efficiency. A notable improvement introduced by \textit{NeuroKinect} is the utilization of a novel loss function, denoted as $\mathcal{L}_{\text{Stat}}$. This loss function addresses the discrepancy between correlation and mean square error in hand kinematics prediction. Furthermore, our study emphasizes the scientific intuition behind parameter selection to enhance accuracy. We analyze the spatial and temporal dynamics of the motor movement task by employing event-related potential and brain source localization (BSL) results. This approach provides valuable insights into the optimal parameter selection, improving the overall performance and accuracy of the \textit{NeuroKinect} model. Our model demonstrates strong correlations between predicted and actual hand movements, with mean Pearson correlation coefficients of 0.92 ($\pm$0.015), 0.93 ($\pm$0.019), and 0.83 ($\pm$0.018) for the X, Y, and Z dimensions. The precision of \textit{NeuroKinect} is evidenced by low mean squared errors (MSE) of 0.016 ($\pm$0.001), 0.015 ($\pm$0.002), and 0.017 ($\pm$0.005) for the X, Y, and Z dimensions, respectively.
IRS Assisted MIMO Full Duplex: Rate Analysis and Beamforming Under Imperfect CSI
Authors: Chandan Kumar Sheemar, Sourabh Solanki, Jorge Querol, Sumit Kumar, Symeon Chatzinotas
Date: 2023-08-16 00:00:00

Intelligent reflecting surfaces (IRS) have emerged as a promising technology to enhance the performance of wireless communication systems. By actively manipulating the wireless propagation environment, IRS enables efficient signal transmission and reception. In recent years, the integration of IRS with full-duplex (FD) communication has garnered significant attention due to its potential to further improve spectral and energy efficiencies. IRS-assisted FD systems combine the benefits of both IRS and FD technologies, providing a powerful solution for the next generation of cellular systems. In this manuscript, we present a novel approach to jointly optimize active and passive beamforming in a multiple-input-multiple-output (MIMO) FD system assisted by an IRS for weighted sum rate (WSR) maximization. Given the inherent difficulty in obtaining perfect channel state information (CSI) in practical scenarios, we consider imperfect CSI and propose a statistically robust beamforming strategy to maximize the ergodic WSR. Additionally, we analyze the achievable WSR for an IRS-assisted MIMO FD system under imperfect CSI by deriving both the lower and upper bounds. To tackle the problem of ergodic WSR maximization, we employ the concept of expected weighted minimum mean squared error (EWMMSE), which exploits the information of the expected error covariance matrices and ensures convergence to a local optimum. We evaluate the effectiveness of our proposed design through extensive simulations. The results demonstrate that our robust approach yields significant performance improvements compared to the simplistic beamforming approach that disregards CSI errors, while also outperforming the robust half-duplex (HD) system considerably
Permutationally Invariant Networks for Enhanced Sampling (PINES): Discovery of Multi-Molecular and Solvent-Inclusive Collective Variables
Authors: Nicholas S. M. Herringer, Siva Dasetty, Diya Gandhi, Junhee Lee, Andrew L. Ferguson
Date: 2023-08-16 00:00:00
Tasks: Translation

The typically rugged nature of molecular free energy landscapes can frustrate efficient sampling of the thermodynamically relevant phase space due to the presence of high free energy barriers. Enhanced sampling techniques can improve phase space exploration by accelerating sampling along particular collective variables (CVs). A number of techniques exist for data-driven discovery of CVs parameterizing the important large scale motions of the system. A challenge to CV discovery is learning CVs invariant to symmetries of the molecular system, frequently rigid translation, rigid rotation, and permutational relabeling of identical particles. Of these, permutational invariance have proved a persistent challenge in frustrating the the data-driven discovery of multi-molecular CVs in systems of self-assembling particles and solvent-inclusive CVs for solvated systems. In this work, we integrate Permutation Invariant Vector (PIV) featurizations with autoencoding neural networks to learn nonlinear CVs invariant to translation, rotation, and permutation, and perform interleaved rounds of CV discovery and enhanced sampling to iteratively expand sampling of configurational phase space and obtain converged CVs and free energy landscapes. We demonstrate the Permutationally Invariant Network for Enhanced Sampling (PINES) approach in applications to the self-assembly of a 13-atom Argon cluster, association/dissociation of a NaCl ion pair in water, and hydrophobic collapse of a C45H92 n-pentatetracontane polymer chain. We make the approach freely available as a new module within the PLUMED2 enhanced sampling libraries.
Genomic reproducibility in the bioinformatics era
Authors: Pelin Icer Baykal, Paweł P. Łabaj, Florian Markowetz, Lynn M. Schriml, Daniel J. Stekhoven, Serghei Mangul, Niko Beerenwinkel
Date: 2023-08-18 00:00:00
Tasks: scientific discovery

In biomedical research, validation of a new scientific discovery is tied to the reproducibility of its experimental results. However, in genomics, the definition and implementation of reproducibility still remain imprecise. Here, we argue that genomic reproducibility, defined as the ability of bioinformatics tools to maintain consistent genomics results across technical replicates, is key to generating scientific knowledge and enabling medical applications. We first discuss different concepts of reproducibility and then focus on reproducibility in the context of genomics, aiming to establish clear definitions of relevant terms. We then focus on the role of bioinformatics tools and their impact on genomic reproducibility and assess methods of evaluating bioinformatics tools in terms of genomic reproducibility. Lastly, we suggest best practices for enhancing genomic reproducibility, with an emphasis on assessing the performance of bioinformatics tools through rigorous testing across multiple technical replicates.
Examination of Some Nut Traits and Release From Dormancy Along With Germination Capacity in Some Bitter Almond Genotypes
Authors: Anwar Mohammed Raouf, Kocher Omer Salih, Aram Akram Mohammad
Date: 2023-08-18 00:00:00
Tasks: Sand

This study was conducted at College of Agricultural Engineering Sciences, University of Sulaimani, Kurdistan Region-Iraq so as to investigate some nut traits in 10 bitter almond genotypes, capacity of them to release from dormancy and finally germination ability. Nut traits were calculated, and stratified in a sand medium at 6 C in a refrigerator for 55 days, then they were sown in fine sand on August 22, 2021 for 29 days to calculate germination percentage. There were great discrepancies among genotypes in nut traits. Nut length was between (23.66-32.73 mm), nut width (18.77-21.84 mm), nut size (3.16-4.26 cm3), nut weight (2.67-4.13 g), kernel weight (0.6-0.99 g), shell weight (1.94-3.27 g) and shell thickness (2.31-3.37 mm). The results of release from dormancy and calculation of germination percentage trials showed that the highest nut numbers from G6 (100%), G4 (86.11%) and G2 (65.22%) were released from dormancy and the same genotypes gave the best germination percentage, particularly G6 and G2 both gave (73.33%) germination. Depending on the results of release percentage from dormancy and germination percentage, G6 and G2 along with G4 were the best genotypes.
A discrete-time dynamical model of prey and stage-structured predator with juvenile hunting incorporating negative effects of prey refuge
Authors: Debasish Bhattacharjee, Nabajit Ray, Dipam Das, Hemanta Kumar Sarmah
Date: 2023-08-17 00:00:00

This paper examines a discrete predator-prey model that incorporates prey refuge and its detrimental impact on the growth of the prey population. Age structure is taken into account for predator species. Furthermore, juvenile hunting as well as prey counter-attack are also considered. This paper provides a comprehensive analysis of the existence and stability conditions pertaining to all possible fixed points. The analytical and numerical investigation into the occurrence of different bifurcations, such as the Neimark-Sacker bifurcation and period-doubling bifurcation, in relation to various parameters is discussed. The impact of the parameters reflecting prey growth and prey refuge is thoroughly addressed. Numerous numerical simulations are presented in order to validate the theoretical findings.
A new multi-metric approach for quantifying global biodiscovery and conservation priorities reveals overlooked hotspots for amphibians
Authors: Sky Button, Amaël Borzée
Date: 2023-08-17 00:00:00

Undocumented species represent one of the largest hurdles for conservation efforts due to the uncertainty they introduce into conservation planning. Until the distribution of earth's biodiversity is better understood, substantial conjecture will continue to be required for protecting species from anthropogenic extinction. Therefore, we developed a novel approach for identifying regions with promising biodiscovery prospects, linked to integrative conservation priorities, which we illustrate using amphibians. Our approach builds on previous estimates of biodiscovery priorities by simultaneously (1) considering linkages between spatio-environmental variables and biodiversity, (2) accounting for the negative relationship between past sampling intensity and future biodiscovery potential, (3) incorporating a priori knowledge about global species distribution patterns, (4) addressing spatial autocorrelation in community composition, and (5) weighting theoretical undocumented species by their predicted levels of conservation need. Using boosted regression trees and 50km^2 map pixels spread across the global range of amphibians, we identified several regions likely to contain many undocumented amphibian species and conservation needs, including the Southeast Asian Archipelago, humid portions of sub-Saharan Africa, and undersampled portions of the Amazon, Andes Mountains, and Central America. We also ranked top-scoring ecoregions by their mean and maximum biodiscovery potential and found that the top-20 ranked ecoregions were most concentrated in the Southeast Asian Archipelago and tropical Africa for undocumented species richness, and in tropical Africa and tropical South America for integrative undocumented amphibian conservation needs. However, high-scoring pixels tended to be widely distributed across different ecoregions for both biodiscovery scoring approaches.
Evaluating the potential impacts of grey seal predation and fishery bycatch/discards on cod productivity on the Western Scotian Shelf and in the Bay of Fundy
Authors: Steven P. Rossi, Yanjun Wang, Cornelia E. den Heyer, Hugues P. Benoît
Date: 2023-08-16 00:00:00

The recovery of many groundfish stocks throughout the Northwest Atlantic has been impeded by elevated natural (i.e., non-fishing) mortality (M) among older/larger individuals. The causes of elevated mortality are not well known, though predation by rapidly growing grey seal herds and unreported fishing are thought to be possible drivers of mortality for Atlantic Cod (Gadus morhua) on the Western Scotian Shelf and in the Bay of Fundy (known as "4X5Y cod") and in nearby ecosystems. We developed a statistical catch-at-age model for 4X5Y cod that accounted for both grey seal predation and estimated bycatch/discards to evaluate the degree to which either of these factors may influence cod mortality. The model was fit over a range of predation and discarding scenarios to account for uncertainties and a lack of data for these processes. We found that most cod M remained unaccounted for unless cod comprised a large proportion (>0.45) of the grey seal diet by weight. If the reported bycatch estimates are taken as accurate, then the magnitude of cod discards from non-directed fisheries was minor, though these estimates are highly uncertain.
Modeling Biphasic, Non-Sigmoidal Dose-Response Relationships: Comparison of Brain-Cousens and Cedergreen Models for a Biochemical Dataset
Authors: Venkat D. Abbaraju, Tamaraty L. Robinson, Brian P. Weiser
Date: 2023-08-16 00:00:00

Biphasic, non-sigmoidal dose-response relationships are frequently observed in biochemistry and pharmacology, but they are not always analyzed with appropriate statistical methods. Here, we examine curve fitting methods for "hormetic" dose-response relationships where low and high doses of an effector produce opposite responses. We provide the full dataset used for modeling, and we provide the code for analyzing the dataset in SAS using two established mathematical models of hormesis, the Brain-Cousens model and the Cedergreen model. We show how to obtain and interpret curve parameters such as the ED50 that arise from modeling, and we discuss how curve parameters might change in a predictable manner when the conditions of the dose-response assay are altered. In addition to modeling the raw dataset that we provide, we also model the dataset after applying common normalization techniques, and we indicate how this affects the parameters that are associated with the fit curves. The Brain-Cousens and Cedergreen models that we used for curve fitting were similarly effective at capturing quantitative information about the biphasic dose-response relationships.
Semi-analytic pricing of American options in time-dependent jump-diffusion models with exponential jumps
Authors: Andrey Itkin
Date: 2023-08-17 00:00:00

In this paper we propose a semi-analytic approach to pricing American options for time-dependent jump-diffusions models with exponential jumps The idea of the method is to further generalize our approach developed for pricing barrier, [Itkin et al., 2021], and American, [Carr and Itkin, 2021; Itkin and Muravey, 2023], options in various time-dependent one factor and even stochastic volatility models. Our approach i) allows arbitrary dependencies of the model parameters on time; ii) reduces solution of the pricing problem for American options to a simpler problem of solving a system of an algebraic nonlinear equation for the exercise boundary and a linear Fredholm-Volterra equation for the the option price; iii) the options Greeks solve a similar Fredholm-Volterra linear equation obtained by just differentiating Eq. (25) by the required parameter. Once done, the American option price is presented in close form.
Detecting Financial Market Manipulation with Statistical Physics Tools
Authors: Haochen Li, Maria Polukarova, Carmine Ventre
Date: 2023-08-16 00:00:00
Tasks: Anomaly Detection

We take inspiration from statistical physics to develop a novel conceptual framework for the analysis of financial markets. We model the order book dynamics as a motion of particles and define the momentum measure of the system as a way to summarise and assess the state of the market. Our approach proves useful in capturing salient financial market phenomena: in particular, it helps detect the market manipulation activities called spoofing and layering. We apply our method to identify pathological order book behaviours during the flash crash of the LUNA cryptocurrency, uncovering widespread instances of spoofing and layering in the market. Furthermore, we establish that our technique outperforms the conventional Z-score-based anomaly detection method in identifying market manipulations across both LUNA and Bitcoin cryptocurrency markets.
When to efficiently rebalance a portfolio
Authors: Masayuki Ando, Masaaki Fukasawa
Date: 2023-08-17 00:00:00

A constant weight asset allocation is a popular investment strategy and is optimal under a suitable continuous model. We study the tracking error for the target continuous rebalancing strategy by a feasible discrete-in-time rebalancing under a general multi-dimensional Brownian semimartingale model of asset prices. In a high-frequency asymptotic framework, we derive an asymptotically efficient sequence of simple predictable strategies.
Black-Litterman, Bayesian Shrinkage, and Factor Models in Portfolio Selection: You Can Have It All
Authors: Kwong Yu Chong
Date: 2023-08-18 00:00:00
Tasks: All, Management

Mean-variance analysis is widely used in portfolio management to identify the best portfolio that makes an optimal trade-off between expected return and volatility. Yet, this method has its limitations, notably its vulnerability to estimation errors and its reliance on historical data. While shrinkage estimators and factor models have been introduced to improve estimation accuracy through bias-variance trade-offs, and the Black-Litterman model has been developed to integrate investor opinions, a unified framework combining three approaches has been lacking. Our study debuts a Bayesian blueprint that fuses shrinkage estimation with view inclusion, conceptualizing both as Bayesian updates. This model is then applied within the context of the Fama-French approach factor models, thereby integrating the advantages of each methodology. Finally, through a comprehensive empirical study in the US equity market spanning a decade, we show that the model outperforms both the simple $1/N$ portfolio and the optimal portfolios based on sample estimators.
Recurrent Neural Networks with more flexible memory: better predictions than rough volatility
Authors: Damien Challet, Vincent Ragel
Date: 2023-08-04 00:00:00
Tasks: Time Series

We extend recurrent neural networks to include several flexible timescales for each dimension of their output, which mechanically improves their abilities to account for processes with long memory or with highly disparate time scales. We compare the ability of vanilla and extended long short term memory networks (LSTMs) to predict asset price volatility, known to have a long memory. Generally, the number of epochs needed to train extended LSTMs is divided by two, while the variation of validation and test losses among models with the same hyperparameters is much smaller. We also show that the model with the smallest validation loss systemically outperforms rough volatility predictions by about 20% when trained and tested on a dataset with multiple time series.
Effects of Daily News Sentiment on Stock Price Forecasting
Authors: S. Srinivas, R. Gadela, R. Sabu, A. Das, G. Nath, V. Datla
Date: 2023-08-02 00:00:00
Tasks: Articles

Predicting future prices of a stock is an arduous task to perform. However, incorporating additional elements can significantly improve our predictions, rather than relying solely on a stock's historical price data to forecast its future price. Studies have demonstrated that investor sentiment, which is impacted by daily news about the company, can have a significant impact on stock price swings. There are numerous sources from which we can get this information, but they are cluttered with a lot of noise, making it difficult to accurately extract the sentiments from them. Hence the focus of our research is to design an efficient system to capture the sentiments from the news about the NITY50 stocks and investigate how much the financial news sentiment of these stocks are affecting their prices over a period of time. This paper presents a robust data collection and preprocessing framework to create a news database for a timeline of around 3.7 years, consisting of almost half a million news articles. We also capture the stock price information for this timeline and create multiple time series data, that include the sentiment scores from various sections of the article, calculated using different sentiment libraries. Based on this, we fit several LSTM models to forecast the stock prices, with and without using the sentiment scores as features and compare their performances.
A Benchmarking Protocol for Pansharpening: Dataset, Preprocessing, and Quality Assessment
Authors: Gemine Vivone, Mauro Dalla Mura, Andrea Garzelli, Fabio Pacifici
Date: 2021-06-07 00:00:00
Proceeding: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021 6
Tasks: Benchmarking, Pansharpening

Comparative evaluation is a requirement for reproducible science and objective assessment of new algorithms. Reproducible research in the field of pansharpening of very high resolution images is a difficult task due to the lack of openly available reference datasets and protocols. The contribution of this article is threefold, and it defines a benchmarking framework to evaluate pansharpening algorithms. First, it establishes a reference dataset, named PAirMax, composed of 14 panchromatic and multispectral image pairs collected over heterogeneous landscapes by different satellites. Second, it standardizes various image preprocessing steps, such as filtering, upsampling, and band coregistration, by providing a reference implementation. Third, it details the quality assessment protocols for reproducible algorithm evaluation.
Multiscale patch-based feature graphs for image classification
Authors: Todescato, Matheus; Garcia, Luan; Balreira, Dennis; Carbonera, Joel
Date: 2023-08-08 00:00:00
Proceeding: Expert Systems with Applications 2023 8
Tasks: Classification, Fine-Grained Image Classification, image-classification, Image Classification, Transfer Learning

Deep learning architectures have demonstrated outstanding results in image classification in the last few years. However, applying sophisticated neural network architectures in small datasets remains challenging. In this context, transfer learning is a promising approach for dealing with this scenario. Generally, the available pre-trained architectures adopt a standard fixed input, which usually implies resizing and cropping the input images in the preprocessing phase, causing information loss. Besides, images present visual features in different scales in real-world scenarios, and most common approaches do not consider this fact. In this paper, we propose an approach that applies transfer learning for dealing with small datasets and leverages visual features extracted by pre-trained models from different scales. We based our approach on graph convolutional networks (GCN) that take graphs representing the images in different scales as input and whose nodes are characterized by features extracted by pre-trained models from regular image patches of different scales. Since GCN can deal with graphs with different numbers of nodes, our approach can deal naturally with images of heterogeneous sizes without discarding relevant information. We evaluated our approach in two datasets: a set of geological images and a publicly available dataset, both presenting characteristics that challenge traditional approaches. We tested our approach by adopting three different pre-trained models as feature extractors: two efficient pre-trained CNN models (DenseNet and ResNeXt) and one Vision Transformer (CLIP). We compared our approach with two conventional approaches for dealing with image classification. The experiments show that our approach achieves better results than the conventional approaches for this task.