pwc-archive/papers-with-abstracts huggingface dataset:
Skin Sympathetic Nerve Activity Driver Extraction through Non-Negative Sparse Decomposition
Authors: Farnoush Baghestani, Youngsun Kong, Ki H. Chon
Date: 2024-11-11 00:00:00

In recent years, skin sympathetic nerve activity (SKNA) extracted from electrocardiogram has gained attention as a novel noninvasive measure of the sympathetic nervous system (SNS), while electrodermal activity (EDA) has long served this purpose. SparsEDA is a sparse deconvolution technique originally developed for EDA to extract phasic drivers indicating the start of sympathetic burst responses. Our focus is on applying this method to preprocessed SKNA signals, justified by both SKNA and EDA signals' connection to sympathetic nerve activity and prior observed similarities. In a thermal-grill pain experiment, 16 subjects underwent six stimulations each to elicit SNS responses, with simultaneous recording of EDA and SKNA. We confirmed the method's accuracy in identifying stimuli initiation. Results were assessed for burst detection and accuracy of driver placement compared to annotated labels. The SKNA drivers achieved an RMSE of 0.42 from annotated stimulations, a 97% hit rate in detecting applied stimuli, and minimal false alarms during the 2-minute control period and interstimulus intervals.
Equilibrium Analysis of Discrete Stochastic Population Models with Gamma Distribution
Authors: HaiYan Wang
Date: 2024-11-24 00:00:00

This paper analyzes the stationary distributions of populations governed by the discrete stochastic logistic and Ricker difference equations at equilibrium examines with the gamma distribution. We identify mathematical relationships between the intrinsic growth rate in the stochastic equations and the parameters of the gamma distribution with a small stochastic perturbation. We present the biological significance of these relationships, emphasizing how the stochastic perturbation and shape parameter of the gamma distribution influence population dynamics at equilibrium. Furthermore, we identify two branches of the intrinsic growth rate, representing alternative stable states corresponding to higher and lower growth rates. This duality provides deeper insights into population stability and resilience under stochastic conditions.
Is there a robust effect of mainland mutualism rates on species richness of oceanic islands?
Authors: Maximilian Pichler, Florian Hartig
Date: 2024-11-22 00:00:00
Tasks: Diversity

In island biogeography, it is widely accepted that species richness on island depends on the area and isolation of the island as well as the species pool on the mainland. Delavaux et al. (2024) suggest that species richness on oceanic islands also depends on the proportion of mutualists on the mainland, based on the idea that mutualists require specific interaction partners for their survival and thus have lower chances of establishment after successful immigration. As the proportion of mutualists increases towards the tropics, this effect could explain a weaker latitudinal diversity gradient (LDG) for oceanic islands. However, after re-analyzing their data, we have doubts if these conclusions are supported by the available data.
Population dynamics of multiple ecDNA types
Authors: Elisa Scanu, Benjamin Werner, Weini Huang
Date: 2024-11-21 00:00:00

Extrachromosomal DNA (ecDNA) can drive oncogene amplification, gene expression and intratumor heterogeneity, representing a major force in cancer initiation and progression. The phenomenon becomes even more intricate as distinct types of ecDNA present within a single cancer cell. While exciting as a new and significant observation across various cancer types, there is a lack of a general framework capturing the dynamics of multiple ecDNA types theoretically. Here, we present novel mathematical models investigating the proliferation and expansion of multiple ecDNA types in a growing cell population. By switching on and off a single parameter, we model different scenarios including ecDNA species with different oncogenes, genotypes with same oncogenes but different point mutations and phenotypes with identical genetic compositions but different functions. We analyse the fraction of ecDNA-positive and free cells as well as how the mean and variance of the copy number of cells carrying one or more ecDNA types change over time. Our results showed that switching does not play a role in the fraction and copy number distribution of total ecDNA-positive cells, if selection is identical among different ecDNA types. In addition, while cells with multiple ecDNA cannot be maintained in the scenario of ecDNA species without extra fitness advantages, they can persist and even dominate the ecDNA-positive population if switching is possible.
Optimization of Bloch-Siegert B1 Mapping Sequence for Maximum Signal to Noise
Authors: M. Mehdi Khalighi, Doug Kelley, Jason H. Su, Brian K. Rutt, Adam B. Kerr
Date: 2024-11-24 00:00:00

Adiabatic Bloch-Siegert B1+ mapping method addresses the long TE and high RF power deposition problems of conventional Bloch-Siegert B1+ mapping by introducing short frequency-swept ABS pulses with maximum sensitivity. Here, it is shown how maximum signal to noise ratio can be achieved in adiabatic Bloch-Siegert B1+ mapping. Signal to noise ratio of B1+ maps is maximized by optimizing the adiabatic pulse parameters such as width, amplitude and shape of the Bloch-Siegert pulse within a specified scan time and under approved SAR guidelines. Equations for optimized Bloch-Siegert pulse parameters are derived, which are dependent on the base pulse sequence used for B1+ mapping as well as tissue properties and transmit coil configuration. It is shown that by this optimization it is more efficient to increase TR rather than using the averaging method to increase signal to noise ratio.
Multi-layer matrix factorization for cancer subtyping using full and partial multi-omics dataset
Authors: Yingxuan Ren, Fengtao Ren, Bo Yang
Date: 2024-11-18 00:00:00
Tasks: Clustering, Data Integration, Missing Values

Cancer, with its inherent heterogeneity, is commonly categorized into distinct subtypes based on unique traits, cellular origins, and molecular markers specific to each type. However, current studies primarily rely on complete multi-omics datasets for predicting cancer subtypes, often overlooking predictive performance in cases where some omics data may be missing and neglecting implicit relationships across multiple layers of omics data integration. This paper introduces Multi-Layer Matrix Factorization (MLMF), a novel approach for cancer subtyping that employs multi-omics data clustering. MLMF initially processes multi-omics feature matrices by performing multi-layer linear or nonlinear factorization, decomposing the original data into latent feature representations unique to each omics type. These latent representations are subsequently fused into a consensus form, on which spectral clustering is performed to determine subtypes. Additionally, MLMF incorporates a class indicator matrix to handle missing omics data, creating a unified framework that can manage both complete and incomplete multi-omics data. Extensive experiments conducted on 10 multi-omics cancer datasets, both complete and with missing values, demonstrate that MLMF achieves results that are comparable to or surpass the performance of several state-of-the-art approaches.
Evaluación del Cambio en la Musculatura y Adiposidad y su Relación con la Recurrencia del Carcinoma de Cabeza y Cuello mediante PET/CT y MRI
Authors: Virginia del Campo, Iker Malaina
Date: 2024-11-25 00:00:00
Tasks: Prognosis

This study investigates the impact of changes in body composition and follow-up imaging modalities on recurrence and prognosis in patients with head and neck squamous cell carcinoma (HNSCC). The results indicate that an increase in the adiposity index post-radiotherapy is significantly associated with higher recurrence and mortality rates. Additionally, the combined evaluation of muscle and adipose status reveals that patients with muscle loss and adipose gain have the lowest survival probabilities and the highest risk of recurrence. These findings underscore the importance of monitoring adiposity and muscle status, as well as the strategic use of advanced imaging techniques such as PET/CT, MRI, and CT. However, this work represents a preliminary analysis, and further detailed studies are necessary to confirm these results and develop more effective strategies.
Pricing Multi-strike Quanto Call Options on Multiple Assets with Stochastic Volatility, Correlation, and Exchange Rates
Authors: Boris Ter-Avanesov, Gunter A. Meissner
Date: 2024-11-25 00:00:00

Quanto options allow the buyer to exchange the foreign currency payoff into the domestic currency at a fixed exchange rate. We investigate quanto options with multiple underlying assets valued in different foreign currencies each with a different strike price in the payoff function. We carry out a comparative performance analysis of different stochastic volatility (SV), stochastic correlation (SC), and stochastic exchange rate (SER) models to determine the best combination of these models for Monte Carlo (MC) simulation pricing. In addition, we test the performance of all model variants with constant correlation as a benchmark. We find that a combination of GARCH-Jump SV, Weibull SC, and Ornstein Uhlenbeck (OU) SER performs best. In addition, we analyze different discretization schemes and their results. In our simulations, the Milstein scheme yields the best balance between execution times and lower standard deviations of price estimates. Furthermore, we find that incorporating mean reversion into stochastic correlation and stochastic FX rate modeling is beneficial for MC simulation pricing. We improve the accuracy of our simulations by implementing antithetic variates variance reduction. Finally, we derive the correlation risk parameters Cora and Gora in our framework so that correlation hedging of quanto options can be performed.
Predictive Power of LLMs in Financial Markets
Authors: Jerick Shi, Burton Hollifield
Date: 2024-11-25 00:00:00

Predicting the movement of the stock market and other assets has been valuable over the past few decades. Knowing how the value of a certain sector market may move in the future provides much information for investors, as they use that information to develop strategies to maximize profit or minimize risk. However, market data are quite noisy, and it is challenging to choose the right data or the right model to create such predictions. With the rise of large language models, there are ways to analyze certain data much more efficiently than before. Our goal is to determine whether the GPT model provides more useful information compared to other traditional transformer models, such as the BERT model. We shall use data from the Federal Reserve Beige Book, which provides summaries of economic conditions in different districts in the US. Using such data, we then employ the LLM's to make predictions on the correlations. Using these correlations, we then compare the results with well-known strategies and determine whether knowing the economic conditions improves investment decisions. We conclude that the Beige Book does contain information regarding correlations amongst different assets, yet the GPT model has too much look-ahead bias and that traditional models still triumph.
Do Activists Align with Larger Mutual Funds?
Authors: Manish Jha
Date: 2024-11-25 00:00:00

This paper demonstrates that hedge funds tend to design their activist campaigns to align with the preferences and ideologies of institutions holding large stakes in the target company. I estimate these preferences by analyzing the institutions' previous proxy voting behavior. The results reveal that activists benefit from this approach. Campaigns with a stronger positive correlation between the preferences of larger institutions and activist communications attract more shareholder attention, receive more votes, and are more likely to succeed.
Markov-Functional Models with Local Drift
Authors: ShengQuan Zhou
Date: 2024-11-22 00:00:00

We introduce a Markov-functional approach to construct local volatility models that are calibrated to a discrete set of marginal distributions. The method is inspired by and extends the volatility interpolation of Bass (1983) and Conze and Henry-Labord\`ere (2022). The method is illustrated with efficient numerical algorithms in the cases where the constructed local volatility functions are: (1) time-homogeneous between or (2) continuous across, the successive maturities. The step-wise time-homogeneous construction produces a parsimonious representation of the local volatility term structure.
Research on Optimal Portfolio Based on Multifractal Features
Authors: Yong Li
Date: 2024-11-24 00:00:00
Tasks: Time Series

Providing optimal portfolio selection for investors has always been one of the hot topics in academia. In view of the traditional portfolio model could not adapt to the actual capital market and can provide erroneous results. This paper innovatively constructs a mean-detrended cross-correlation portfolio model (M-DCCP model), This model is designed to embed detrended cross-correlation between different simultaneously recorded time series in the presence of nonstationary into the reward-risk criterion. We illustrate the model's effectiveness by selected five composite indexes (SSE 50, CSI 300, SSE 500, CSI 1000 and CSI 2000) in China A-share market. The empirical results show that compared with traditional mean-variance portfolio model (M-VP model), the M-DCCP model is more conducive for investors to construct optimal portfolios under the different fluctuation exponent preference and time scales preference, so as to improve portfolio's performance.
Diversification quotient based on expectiles
Authors: Xia Han, Liyuan Lin, Hao Wang, Ruodu Wang
Date: 2024-11-22 00:00:00

A diversification quotient (DQ) quantifies diversification in stochastic portfolio models based on a family of risk measures. We study DQ based on expectiles, offering a useful alternative to conventional risk measures such as Value-at-Risk (VaR) and Expected Shortfall (ES). The expectile-based DQ admits simple formulas and has a natural connection to the Omega ratio. Moreover, the expectile-based DQ is not affected by small-sample issues faced by VaR-based or ES-based DQ due to the scarcity of tail data. The expectile-based DQ exhibits pseudo-convexity in portfolio weights, allowing gradient descent algorithms for portfolio selection. We show that the corresponding optimization problem can be efficiently solved using linear programming techniques in real-data applications. Explicit formulas for DQ based on expectiles are also derived for elliptical and multivariate regularly varying distribution models. Our findings enhance the understanding of the DQ's role in financial risk management and highlight its potential to improve portfolio construction strategies.
Forecasting the Price of Rice in Banda Aceh after Covid-19
Authors: Fadhlul Mubarak, Vinny Yuliani Sundara, Nurniswah
Date: 2024-11-21 00:00:00
Tasks: Imputation, Missing Values

This research aims to predict the price of rice in Banda Aceh after the occurrence of Covid-19. The last observation carried forward (LOCF) imputation technique has been used to solve the problem of missing values from this research data. Furthermore, the technique used to forecast rice prices in Banda Aceh is auto-ARIMA which is the best ARIMA model based on AIC, AICC, or BIC values. The results of this research show that the ARIMA model (0,0,5) is the best model to predict the prices of lower quality rice I (BKB1), lower quality rice II (BKB2), medium quality rice I (BKM1), medium quality rice II (BKM2), super quality rice I (BKS1), and super quality rice II (BKS2). Based on this model, the results of forecasting rice prices for all qualities show that there was a decline for some time (between September 1, 2023 and September 6, 2023) and then remained constant (between September 6, 2023 and December 31, 2023).
MarketGPT: Developing a Pre-trained transformer (GPT) for Modeling Financial Time Series
Authors: Aaron Wheeler, Jeffrey D. Varner
Date: 2024-11-25 00:00:00
Tasks: Time Series

This work presents a generative pre-trained transformer (GPT) designed for modeling financial time series. The GPT functions as an order generation engine within a discrete event simulator, enabling realistic replication of limit order book dynamics. Our model leverages recent advancements in large language models to produce long sequences of order messages in a steaming manner. Our results demonstrate that the model successfully reproduces key features of order flow data, even when the initial order flow prompt is no longer present within the model's context window. Moreover, evaluations reveal that the model captures several statistical properties, or 'stylized facts', characteristic of real financial markets and broader macro-scale data distributions. Collectively, this work marks a significant step toward creating high-fidelity, interactive market simulations.
Decentralizing Test-time Adaptation under Heterogeneous Data Streams
Authors: Zixian Su, Jingwei Guo, Xi Yang, Qiufeng Wang, Kaizhu Huang
Date: 2024-11-16 00:00:00
Tasks: Test-time Adaptation

While Test-Time Adaptation (TTA) has shown promise in addressing distribution shifts between training and testing data, its effectiveness diminishes with heterogeneous data streams due to uniform target estimation. As previous attempts merely stabilize model fine-tuning over time to handle continually changing environments, they fundamentally assume a homogeneous target domain at any moment, leaving the intrinsic real-world data heterogeneity unresolved. This paper delves into TTA under heterogeneous data streams, moving beyond current model-centric limitations. By revisiting TTA from a data-centric perspective, we discover that decomposing samples into Fourier space facilitates an accurate data separation across different frequency levels. Drawing from this insight, we propose a novel Frequency-based Decentralized Adaptation (FreDA) framework, which transitions data from globally heterogeneous to locally homogeneous in Fourier space and employs decentralized adaptation to manage diverse distribution shifts.Interestingly, we devise a novel Fourier-based augmentation strategy to assist in decentralizing adaptation, which individually enhances sample quality for capturing each type of distribution shifts. Extensive experiments across various settings (corrupted, natural, and medical environments) demonstrate the superiority of our proposed framework over the state-of-the-arts.
Harnessing Scale and Physics: A Multi-Graph Neural Operator Framework for PDEs on Arbitrary Geometries
Authors: Zhihao LI, Haoze Song, Di Xiao, Zhilu Lai, Wei Wang
Date: 2024-11-18 00:00:00
Tasks: Management

Partial Differential Equations (PDEs) underpin many scientific phenomena, yet traditional computational approaches often struggle with complex, nonlinear systems and irregular geometries. This paper introduces the \textbf{AMG} method, a \textbf{M}ulti-\textbf{G}raph neural operator approach designed for efficiently solving PDEs on \textbf{A}rbitrary geometries. AMG leverages advanced graph-based techniques and dynamic attention mechanisms within a novel GraphFormer architecture, enabling precise management of diverse spatial domains and complex data interdependencies. By constructing multi-scale graphs to handle variable feature frequencies and a physics graph to encapsulate inherent physical properties, AMG significantly outperforms previous methods, which are typically limited to uniform grids. We present a comprehensive evaluation of AMG across six benchmarks, demonstrating its consistent superiority over existing state-of-the-art models. Our findings highlight the transformative potential of tailored graph neural operators in surmounting the challenges faced by conventional PDE solvers. Our code and datasets are available on \url{https://github.com/lizhihao2022/AMG}.
Forecasting Application Counts in Talent Acquisition Platforms: Harnessing Multimodal Signals using LMs
Authors: Md Ahsanul Kabir, Kareem Abdelfatah, Shushan He, Mohammed Korayem, Mohammad Al Hasan
Date: 2024-11-19 00:00:00
Tasks: Job classification, Time Series, Time Series Forecasting

As recruitment and talent acquisition have become more and more competitive, recruitment firms have become more sophisticated in using machine learning (ML) methodologies for optimizing their day to day activities. But, most of published ML based methodologies in this area have been limited to the tasks like candidate matching, job to skill matching, job classification and normalization. In this work, we discuss a novel task in the recruitment domain, namely, application count forecasting, motivation of which comes from designing of effective outreach activities to attract qualified applicants. We show that existing auto-regressive based time series forecasting methods perform poorly for this task. Henceforth, we propose a multimodal LM-based model which fuses job-posting metadata of various modalities through a simple encoder. Experiments from large real-life datasets from CareerBuilder LLC show the effectiveness of the proposed method over existing state-of-the-art methods.
Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation
Authors: Minguk Jang, Hye Won Chung
Date: 2024-11-20 00:00:00
Tasks: Pseudo Label, Test-time Adaptation

Test-time adaptation (TTA) is an effective approach to mitigate performance degradation of trained models when encountering input distribution shifts at test time. However, existing TTA methods often suffer significant performance drops when facing additional class distribution shifts. We first analyze TTA methods under label distribution shifts and identify the presence of class-wise confusion patterns commonly observed across different covariate shifts. Based on this observation, we introduce label Distribution shift-Aware prediction Refinement for Test-time adaptation (DART), a novel TTA method that refines the predictions by focusing on class-wise confusion patterns. DART trains a prediction refinement module during an intermediate time by exposing it to several batches with diverse class distributions using the training dataset. This module is then used during test time to detect and correct class distribution shifts, significantly improving pseudo-label accuracy for test data. Our method exhibits 5-18% gains in accuracy under label distribution shifts on CIFAR-10C, without any performance degradation when there is no label distribution shift. Extensive experiments on CIFAR, PACS, OfficeHome, and ImageNet benchmarks demonstrate DART's ability to correct inaccurate predictions caused by test-time distribution shifts. This improvement leads to enhanced performance in existing TTA methods, making DART a valuable plug-in tool.