pwc-archive/papers-with-abstracts huggingface dataset:
Image-based eeg classification of brain responses to song recordings
Authors: Adolfo G. Ramirez-Aristizabal, Mohammad K. Ebrahimpour, Christopher T. Kello
Date: 2022-01-31 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), Transfer Learning

Classifying EEG responses to naturalistic acoustic stimuli is of theoretical and practical importance, but standard approaches are limited by processing individual channels separately on very short sound segments (a few seconds or less). Recent developments have shown classification for music stimuli (~2 mins) by extracting spectral components from EEG and using convolutional neural networks (CNNs). This paper proposes an efficient method to map raw EEG signals to individual songs listened for end-to-end classification. EEG channels are treated as a dimension of a [Channel x Sample] image tile, and images are classified using CNNs. Our experimental results (88.7%) compete with state-of-the-art methods (85.0%), yet our classification task is more challenging by processing longer stimuli that were similar to each other in perceptual quality, and were unfamiliar to participants. We also adopt a transfer learning scheme using a pre-trained ResNet-50, confirming the effectiveness of transfer learning despite image domains unrelated from each other.
A rigorous stochastic theory for spike pattern formation in recurrent neural networks with arbitrary connection topologies
Authors: Maik Schünemann, Udo Ernst, Marc Kesseböhmer
Date: 2022-02-05 00:00:00
Tasks: Form

Cortical networks exhibit synchronized activity which often occurs in spontaneous events in the form of spike avalanches. Since synchronization has been causally linked to central aspects of brain function such as selective signal processing and integration of stimulus information, participating in an avalanche is a form of a transient synchrony which temporarily creates neural assemblies and hence might especially be useful for implementing flexible information processing. For understanding how assembly formation supports neural computation, it is therefore essential to establish a comprehensive theory of how network structure and dynamics interact to generate specific avalanche patterns and sequences. Here we derive exact avalanche distributions for a finite network of recurrently coupled spiking neurons with arbitrary non-negative interaction weights, which is made possible by formally mapping the model dynamics to a linear, random dynamical system on the $N$-torus and by exploiting self-similarities inherent in the phase space. We introduce the notion of relative unique ergodicity and show that this property is guaranteed if the system is driven by a time-invariant Bernoulli process. This approach allows us not only to provide closed-form analytical expressions for avalanche size, but also to determine the detailed set(s) of units firing in an avalanche (i.e., the avalanche assembly). The underlying dependence between network structure and dynamics is made transparent by expressing the distribution of avalanche assemblies in terms of the induced graph Laplacian. We explore analytical consequences of this dependence and provide illustrating examples.
Advection-enhanced kinetics in microtiter plates for improved surface assay quantitation and multiplexing capabilities
Authors: Iago Pereiro, Anna Fomitcheva Khartchenko, Robert D. Lovchik, Govind V. Kaigala
Date: 2022-02-07 00:00:00

Surface assays such as ELISA are pervasive in clinics and research and predominantly standardized in microtiter plates (MTP). MTPs provide many advantages but are often detrimental to surface assay efficiency due to inherent mass transport limitations. Microscale flows can overcome these and largely improve assay kinetics. However, the disruptive nature of microfluidics with existing labware and protocols has narrowed its transformative potential. We present WellProbe, a novel microfluidic concept compatible with MTPs. With it, we show that immunoassays become more sensitive at low concentrations (up to 9-fold signal improvement in 12-fold less time), richer in information with 3-4 different kinetic conditions, and can be used to estimate kinetic parameters, minimize washing steps and non-specific binding, and identify compromised results. We further multiplex single-well assays combining WellProbes kinetic regions with tailored microarrays. Finally, we demonstrate our system in a context of immunoglobulin subclass evaluation, increasingly regarded as clinically relevant.
Volumetric and structural connectivity abnormalities co-localise in TLE
Authors: Jonathan J. Horsley, Gabrielle M. Schroeder, Rhys H. Thomas, Jane de Tisi, Sjoerd B. Vos, Gavin P. Winston, John S. Duncan, Yujiang Wang, Peter N. Taylor
Date: 2022-02-05 00:00:00

Patients with temporal lobe epilepsy (TLE) exhibit both volumetric and structural connectivity abnormalities relative to healthy controls. How these abnormalities inter-relate and their mechanisms are unclear. We computed grey matter volumetric changes and white matter structural connectivity abnormalities in 144 patients with unilateral TLE and 96 healthy controls. Regional volumes were calculated using T1-weighted MRI, while structural connectivity was derived using white matter fibre tractography from diffusion-weighted MRI. For each regional volume and each connection strength, we calculated the effect size between patient and control groups in a group-level analysis. We then applied hierarchical regression to investigate the relationship between volumetric and structural connectivity abnormalities in individuals. Additionally, we quantified whether abnormalities co-localised within individual patients by computing Dice similarity scores. In TLE, white matter connectivity abnormalities were greater when joining two grey matter regions with abnormal volumes. Similarly, grey matter volumetric abnormalities were greater when joined by abnormal white matter connections. The extent of volumetric and connectivity abnormalities related to epilepsy duration, but co-localisation did not. Co-localisation was primarily driven by neighbouring abnormalities in the ipsilateral hemisphere. Overall, volumetric and structural connectivity abnormalities were related in TLE. Our results suggest that shared mechanisms may underlie changes in both volume and connectivity alterations in patients with TLE.
Agricultural quality matrix-based multiomics structural analysis of carrots in soils fertilized with thermophile-fermented compost
Authors: Hirokuni Miyamoto, Katsumi Shigeta, Wataru Suda, Yasunori Ichihashi, Naoto Nihei, Makiko Matsuura, Arisa Tsuboi, Naoki Tominaga, Masahiko Aono, Muneo Sato, Shunya Taguchi, Teruno Nakaguma, Naoko Tsuji, Chitose Ishii, Teruo Matsushita, Chie Shindo, Toshiaki Ito, Tamotsu Kato, Hiroshi Ohno, Atsushi Kurotani, Hideaki Shima, Shigeharu Moriya, Sankichi Horiuchi, Takashi Satoh, Kenichi Mori, Takumi Nishiuchi, Hisashi Miyamoto, Masahira Hattori, Hiroaki Kodama, Jun Kikuchi, Yumi Hirai
Date: 2022-02-07 00:00:00

Compost is used worldwide as a soil conditioner for crops, but its functions have still been explored. Here, the omics profiles of carrots were investigated, as a root vegetable plant model, in a field amended with compost fermented with thermophilic Bacillaceae for growth and quality indices. Exposure to compost significantly increased the productivity, antioxidant activity, red color, and taste of the carrot root and altered the soil bacterial composition with the levels of characteristic metabolites of the leaf, root, and soil. Based on the data, structural equation modeling (SEM) estimated that L-2-aminoadipate, phenylalanine, flavonoids and / or carotenoids in plants were optimally linked by exposure to compost. The SEM of the soil estimated that the genus Paenibacillus, L-2-aminoadipate and nicotinamide, and S-methyl L-cysteine were optimally involved during exposure. These estimates did not show a contradiction between the whole genomic analysis of compost-derived Paenibacillus isolates and the bioactivity data, inferring the presence of a complex cascade of plant growth-promoting effects and modulation of the nitrogen cycle by compost itself. These observations have provided information on the qualitative indicators of compost in complex soil-plant interactions and offer a new perspective for chemically independent sustainable agriculture through the efficient use of natural nitrogen.
Deep Learning-based Assessment of Hepatic Steatosis on chest CT
Authors: Zhongyi Zhang, Jakob Weiss, Jana Taron, Roman Zeleznik, Michael T. Lu, Hugo J. W. L. Aerts
Date: 2022-02-04 00:00:00
Tasks: Computed Tomography (CT), Deep Learning, Liver Segmentation

Purpose: Automatic methods are required for the early detection of hepatic steatosis to avoid progression to cirrhosis and cancer. Here, we developed a fully automated deep learning pipeline to quantify hepatic steatosis on non-contrast enhanced chest computed tomography (CT) scans. Materials and Methods: We developed and evaluated our pipeline on chest CT images of 1,431 randomly selected National Lung Screening Trial (NLST) participants. A dataset of 451 CT scans with volumetric liver segmentations of expert readers was used for training a deep learning model. For testing, in an independent dataset of 980 CT scans hepatic attenuation was manually measured by an expert reader on three cross-sectional images at different hepatic levels by selecting three circular regions of interest. Additionally, 100 randomly selected cases of the test set were volumetrically segmented by expert readers. Hepatic steatosis on the test set was defined as mean hepatic attenuation of < 40 Hounsfield unit. Spearman correlation was conducted to analyze liver fat quantification accuracy and the Cohen's Kappa coefficient was calculated for hepatic steatosis prediction reliability. Results: Our pipeline demonstrated strong performance and achieved a mean dice score of 0.970 for the volumetric liver segmentation. The spearman correlation of the liver fat quantification was 0.954 (P <0.0001) between the automated and expert reader measurements. The cohen's kappa coefficient was 0.875 for automatic assessment of hepatic steatosis. Conclusion: We developed a fully automatic deep learning-based pipeline for the assessment of hepatic steatosis in chest CT images. With the fast and cheap screening of hepatic steatosis, our pipeline has the potential to help initiate preventive measures to avoid progression to cirrhosis and cancer.
Dual-CLVSA: a Novel Deep Learning Approach to Predict Financial Markets with Sentiment Measurements
Authors: Jia Wang, Hongwei Zhu, Jiancheng Shen, Yu Cao, Benyuan Liu
Date: 2022-01-27 00:00:00

It is a challenging task to predict financial markets. The complexity of this task is mainly due to the interaction between financial markets and market participants, who are not able to keep rational all the time, and often affected by emotions such as fear and ecstasy. Based on the state-of-the-art approach particularly for financial market predictions, a hybrid convolutional LSTM Based variational sequence-to-sequence model with attention (CLVSA), we propose a novel deep learning approach, named dual-CLVSA, to predict financial market movement with both trading data and the corresponding social sentiment measurements, each through a separate sequence-to-sequence channel. We evaluate the performance of our approach with backtesting on historical trading data of SPDR SP 500 Trust ETF over eight years. The experiment results show that dual-CLVSA can effectively fuse the two types of data, and verify that sentiment measurements are not only informative for financial market predictions, but they also contain extra profitable features to boost the performance of our predicting system.
Withdrawal Success Estimation
Authors: Hayden Brown
Date: 2022-02-07 00:00:00

Given a geometric Levy alpha-stable wealth process, a log-Levy alpha-stable lower bound is constructed for the terminal wealth of a regular investing schedule. Using a transformation, the lower bound is applied to a schedule of withdrawals occurring after an initial investment. As a result, an upper bound is described on the probability to complete a given schedule of withdrawals. For withdrawals of a constant amount at equidistant times, necessary conditions are given on the initial investment and parameters of the wealth process such that $k$ withdrawals can be made with 95% confidence. When the initial investment is in the S&P Composite Index and $2\leq k\leq 16$, then the initial investment must be at least $k$ times the amount of each withdrawal.
A discussion of stochastic dominance and mean-risk optimal portfolio problems based on mean-variance-mixture models
Authors: Hasanjan Sayit
Date: 2022-02-05 00:00:00
Tasks: Form, Portfolio Optimization

The classical Markowitz mean-variance model uses variance as a risk measure and calculates frontier portfolios in closed form by using standard optimization techniques. For general mean-risk models such closed form optimal portfolios are difficult to obtain. In this note, assuming returns follow the class of normal mean-variance mixture (NMVM) distributions, we obtain closed form expressions for frontier portfolios under mean-risk criteria when risk is modeled by the general class of law invariant convex risk measures. To achieve this goal, we first present a sufficient condition for the stochastic dominance relation on NMVM models and we apply this result to derive closed form solution for frontier portfolios. Our main result in this paper states that when return vectors follow the class of NMVM distributions the associated mean-risk frontier portfolios can be obtained by optimizing a Markowitz mean-variance model with an appropriately adjusted return vector.
Hierarchical Risk Parity and Minimum Variance Portfolio Design on NIFTY 50 Stocks
Authors: Jaydip Sen, Sidra Mehtab, Abhishek Dutta, Saikat Mondal
Date: 2022-02-06 00:00:00

Portfolio design and optimization have been always an area of research that has attracted a lot of attention from researchers from the finance domain. Designing an optimum portfolio is a complex task since it involves accurate forecasting of future stock returns and risks and making a suitable tradeoff between them. This paper proposes a systematic approach to designing portfolios using two algorithms, the critical line algorithm, and the hierarchical risk parity algorithm on eight sectors of the Indian stock market. While the portfolios are designed using the stock price data from Jan 1, 2016, to Dec 31, 2020, they are tested on the data from Jan 1, 2021, to Aug 26, 2021. The backtesting results of the portfolios indicate while the performance of the CLA algorithm is superior on the training data, the HRP algorithm has outperformed the CLA algorithm on the test data.
Derivatives Risks as Costs in a One-Period Network Model
Authors: Dorinel Bastide, Stéphane Crépey, Samuel Drapeau, Mekonnen Tadese
Date: 2022-02-07 00:00:00

We present a one-period XVA model encompassing bilateral and centrally cleared trading in a unified framework with explicit formulas for most quantities at hand. We illustrate possible uses of this framework for running stress test exercises on a financial network from a clearing member's perspective or for optimizing the porting of the portfolio of a defaulted clearing member.
Financial Crisis in the Framework of Non-zero Temperature Balance Theory
Authors: MohammadReza Zahedian, Mahsa Bagherikalhor, Andrey Trufanov, G. Reza Jafari
Date: 2022-02-02 00:00:00
Tasks: Triplet

Financial crises are known as crashes that result in a sudden loss of value of financial assets in large part and they continue to occur from time to time surprisingly. In order to discover features of the financial network, the pairwise interaction of stocks has been considered in many research, but the existence of the strong correlation of stocks and their collective behavior in crisis made us address higher-order interactions. Hence, in this study, we investigate financial networks by triplet interaction in the framework of balance theory. Due to detecting the contribution of higher-order interactions in understanding the complex behavior of stocks we take the advantage of the orders parameters of the higher-order interactions. Looking at real data of financial market obtained from $S\&P500$ through the lens of balance theory for the quest of network structure in different periods of time near and far from crisis reveals the existence of a structural difference of the network that corresponds to different periods of time. Here, we address two well-known crises the Great regression (2008) and the Covid-19 recession (2020). Results show an ordered structure forms on-crisis in the financial network while stocks behave independently far from a crisis. The formation of the ordered structure of stocks in crisis makes the network resistant against disorder. The resistance of the ordered structure against applying a disorder (temperature) can measure the crisis strength and determine the temperature at which the network transits. There is a critical temperature, $T_{c}$, in the language of statistical mechanics and mean-field approach which above, the ordered structure destroys abruptly and a first-order phase transition occurs. The stronger the crisis, the higher the critical temperature.
Comparative Study of Machine Learning Models for Stock Price Prediction
Authors: Ogulcan E. Orsel, Sasha S. Yamada
Date: 2022-01-31 00:00:00
Tasks: BIG-bench Machine Learning, Stock Price Prediction, Time Series, Time Series Analysis

In this work, we apply machine learning techniques to historical stock prices to forecast future prices. To achieve this, we use recursive approaches that are appropriate for handling time series data. In particular, we apply a linear Kalman filter and different varieties of long short-term memory (LSTM) architectures to historical stock prices over a 10-year range (1/1/2011 - 1/1/2021). We quantify the results of these models by computing the error of the predicted values versus the historical values of each stock. We find that of the algorithms we investigated, a simple linear Kalman filter can predict the next-day value of stocks with low-volatility (e.g., Microsoft) surprisingly well. However, in the case of high-volatility stocks (e.g., Tesla) the more complex LSTM algorithms significantly outperform the Kalman filter. Our results show that we can classify different types of stocks and then train an LSTM for each stock type. This method could be used to automate portfolio generation for a target return rate.
Time-Series K-means in Causal Inference and Mechanism Clustering for Financial Data
Authors: Minheng Xiao
Date: 2022-01-24 00:00:00
Tasks: Causal Inference, Clustering, Dynamic Time Warping, Time Series

This paper investigates the application of Time Series K-means (TS-K-means) within the context of causal inference and mechanism clustering of financial time series data. Traditional clustering approaches like K-means often rely on static distance metrics, such as Euclidean distance, which inadequately capture the temporal dependencies intrinsic to financial returns. By incorporating Dynamic Time Warping (DTW) as a distance metric, TS-K-means addresses this limitation, improving the robustness of clustering in time-dependent financial data. This study extends the Additive Noise Model Mixture Model (ANM-MM) framework by integrating TS-K-means, facilitating more accurate causal inference and mechanism clustering. The approach is validated through simulations and applied to real-world financial data, demonstrating its effectiveness in enhancing the analysis of complex financial time series, particularly in identifying causal relationships and clustering data based on underlying generative mechanisms. The results show that TS-K-means outperforms traditional K-means, especially with smaller datasets, while maintaining robust causal direction detection as the dataset size changes.
HyFactor: Hydrogen-count labelled graph-based defactorization Autoencoder
Authors: Tagir Akhmetshin, Arkadii Lin, Daniyar Mazitov, Evgenii Ziaikin, Timur Madzhidov, Alexandre Varnek
Date: 2021-12-06 00:00:00
Proceeding: ChemRxiv 2021 12
Tasks: Benchmarking, Graph Learning, Graph Sampling, Molecular Graph Generation

Graph-based architectures are becoming increasingly popular as a tool for structure generation. Here, we introduce a novel open-source architecture HyFactor which is inspired by previously reported DEFactor architecture and based on the hydrogen labeled graphs. Since the original DEFactor code was not available, its new implementation (ReFactor) was prepared in this work for the benchmarking purpose. HyFactor demonstrates its high performance on the ZINC 250K MOSES and ChEMBL data set and in molecular generation tasks, it is considerably more effective than ReFactor. The code of HyFactor and all models obtained in this study are publicly available from our GitHub repository: https://github.com/Laboratoire-de-Chemoinformatique/hyfactor
Multiattention network for semantic segmentation of fine-resolution remote sensing images
Authors: Rui Li, Shunyi Zheng, Ce Zhang, Chenxi Duan, Jianlin Su, Libo Wang, Peter M. Atkinson
Date: 2021-05-15 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2021 5
Tasks: Management, Segmentation, Semantic Segmentation

Semantic segmentation of remote sensing images plays an important role in a wide range of applications, including land resource management, biosphere monitoring, and urban planning. Although the accuracy of semantic segmentation in remote sensing images has been increased significantly by deep convolutional neural networks, several limitations exist in standard models. First, for encoder–decoder architectures such as U-Net, the utilization of multiscale features causes the underuse of information, where low-level features and high-level features are concatenated directly without any refinement. Second, long-range dependencies of feature maps are insufficiently explored, resulting in suboptimal feature representations associated with each semantic class. Third, even though the dot-product attention mechanism has been introduced and utilized in semantic segmentation to model long-range dependencies, the large time and space demands of attention impede the actual usage of attention in application scenarios with large-scale input. This article proposed a multiattention network (MANet) to address these issues by extracting contextual dependencies through multiple efficient attention modules. A novel attention mechanism of kernel attention with linear complexity is proposed to alleviate the large computational demand in attention. Based on kernel attention and channel attention, we integrate local feature maps extracted by ResNet-50 with their corresponding global dependencies and reweight interdependent channel maps adaptively. Numerical experiments on two large-scale fine-resolution remote sensing datasets demonstrate the superior performance of the proposed MANet. Code is available at https://github.com/lironui/Multi-Attention-Network.