pwc-archive/papers-with-abstracts huggingface dataset:
The Effects of Unilateral Slope Loading on Lower Limb Plantar Flexor Muscle EMG Signals in Young Healthy Males
Authors: Xinyu Zhou, Gengshang Dong, Pengxuan Zhang
Date: 2024-09-06 00:00:00
Tasks: Electromyography (EMG)

Different loading modes can significantly affect human gait, posture, and lower limb biomechanics. This study investigated the muscle activity intensity of the lower limb soleus muscle in the slope environment of young healthy adult male subjects under unilateral loading environment. Ten subjects held dumbbells equal to 5% and 10% of their body weight (BW) and walked at a fixed speed on a slope of 5 degree and 10 degree, respectively. The changes of electromyography (EMG) of bilateral soleus muscles of the lower limbs were recorded. Experiments were performed using one-way analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA) to examine the relationship between load weight, slope angle, and muscle activity intensity. The data provided by this research can help to promote the development of the field of lower limb assist exoskeleton. The research results fill the missing data when loading on the slope side, provide data support for future assistance systems, and promote the formation of relevant data sets, so as to improve the terrain recognition ability and the movement ability of the device wearer.
Enhancing Clinical Data Warehouses with Provenance and Large File Management: The gitOmmix Approach for Clinical Omics Data
Authors: Maxime Wack, Adrien Coulet, Anita Burgun, Bastien Rance
Date: 2024-09-05 00:00:00
Tasks: Management

Background. Clinical data warehouses (CDWs) are essential in the reuse of hospital data in observational studies or predictive modeling. However, state of-the-art CDW systems present two drawbacks. First, they do not support the management of large data files, what is critical in medical genomics, radiology, digital pathology, and other domains where such files are generated. Second, they do not provide provenance management or means to represent longitudinal relationships between patient events. Indeed, a disease diagnosis and its follow-up rely on multiple analyses. In these cases no relationship between the data (e.g., a large file) and its associated analysis and decision can be documented.Method. We introduce gitOmmix, an approach that overcomes these limitations, and illustrate its usefulness in the management of medical omics data. gitOmmix relies on (i) a file versioning system: git, (ii) an extension that handles large files: git-annex, (iii) a provenance knowledge graph: PROV-O, and (iv) an alignment between the git versioning information and the provenance knowledge graph.Results. Capabilities inherited from git and git-annex enable retracing the history of a clinical interpretation back to the patient sample, through supporting data and analyses. In addition, the provenance knowledge graph, aligned with the git versioning information, enables querying and browsing provenance relationships between these elements.Conclusion. gitOmmix adds a provenance layer to CDWs, while scaling to large files and being agnostic of the CDW system. For these reasons, we think that it is a viable and generalizable solution for omics clinical studies.
Bumblebees Exhibit Adaptive Flapping Responses to Air Disturbances
Authors: Tim Jakobi, Simon Watkins, Alex Fisher, Sridhar Ravi
Date: 2024-09-02 00:00:00

Insects excel in trajectory and attitude handling during flight, yet the specific kinematic behaviours they use for maintaining stability in air disturbances are not fully understood. This study investigates the adaptive strategies of bumblebees when exposed to gust disturbances directed from three different angles within a plane cross-sectional to their flight path. By analyzing characteristic wing motions during gust traversal, we aim to uncover the mechanisms that enable bumblebees to maintain control in unsteady environments. We utilised high-speed cameras to capture detailed flight paths, allowing us to extract dynamic information. Our results reveal that bees make differential bilateral kinematic adjustments based on gust direction: sideward gusts elicit posterior shifts in the wing closest to the gust, while upward gusts trigger coordinated posterior shifts in both wings. Downward gusts prompted broader flapping and increased flapping frequencies, along with variations in flap timing and sweep angle. Stroke sweep angle was a primary factor influencing recovery responses, coupled with motion around the flap axis. The adaptive behaviours strategically position the wings to optimize gust reception and enhance wing-generated forces. These strategies can be distilled into specific behavioural patterns for analytical modelling to inform the design of robotic flyers. We observed a characteristic posterior shift of wings when particular counteractive manoeuvres were required. This adjustment reduced the portion of the stroke during which the wing receiving gust forces was positioned in front of the centre of gravity, potentially enhancing manoeuvrability and enabling more effective recovery manoeuvres. These findings deepen our understanding of insect flight dynamics and offer promising strategies for enhancing the stability and manoeuvrability of MAVs in turbulent environments.
Exploring Neurofunctional Phase Transition Patterns in Autism Spectrum Disorder: A Thermodynamics Parameters Analysis Approach
Authors: Dayu Qin, Yuzhe Chen, Ercan Engin Kuruoglu
Date: 2024-09-02 00:00:00

Designing network parameters that can effectively represent complex networks is of significant importance for the analysis of time-varying complex networks. This paper introduces a novel thermodynamic framework for analyzing complex networks, focusing on Spectral Core Entropy (SCE), Node Energy, internal energy and temperature to measure structural changes in dynamic complex network. This framework provides a quantitative representation of network characteristics, capturing time-varying structural changes. We apply this framework to study brain activity in autism versus control subjects, illustrating its potential to identify significant structural changes and brain state transitions. By treating brain networks as thermodynamic systems, important parameters such as node energy and temperature are derived to depict brain activities. Our research has found that in our designed framework the thermodynamic parameter-temperature, is significantly correlated with the transitions of brain states. Statistical tests confirm the effectiveness of our approach. Moreover, our study demonstrates that node energy effectively captures the activity levels of brain regions and reveals biologically proven differences between autism patients and controls, offering a powerful tool for exploring the characteristics of complex networks in various applications.
Associations between exposure to OPEs and rheumatoid arthritis risk among adults in NHANES, 2011-2018
Authors: Sneha Singh, Elsa Pirouz, Amir Shahmoradi
Date: 2024-09-01 00:00:00
Tasks: Additive models, Nutrition

Rheumatoid arthritis (RA) has an intricate etiology that includes environmental factors as well as genetics. Organophosphate esters (OPEs) are frequently used as chemical additives in many personal care products and household items. However, there has been limited research on their potential effects on rheumatoid arthritis (RA). The specific associations between OPEs and RA remain largely unexplored. This study investigates any potential associations between adult rheumatoid arthritis risk and exposure to OPEs. We investigated data from the National Health and Nutrition Examination Survey (NHANES) 2011-2018 among participants over 20 years old. In two models, multivariable logistic regression was utilized to investigate the relationship between exposure to OPEs and RA. Furthermore, subgroup analyses stratified by age, gender, and dose exposure response were evaluated. Generalized additive models and smooth curve fits were used to characterize the nonlinear relationship between RA and OPEs. In conclusion, 5490 individuals (RA: 319, Non-RA: 5171) were analyzed. Higher quantiles (Q4) of DPHP and DBUP showed a higher prevalence of RA than the lowest quantiles. Our findings show that adult RA prevalence is higher in those who have been exposed to OPEs (DPHP, DBUP). These correlations seem to be stronger among women, the elderly, those with higher BMIs, and those who have diabetes. The dose-response curve for DPHP and DBUP demonstrated an upward-sloping trend. In contrast, BCEP and BCPP showed a U-shaped relationship and an inverted U-shaped relationship with the probability of RA. BDCPP demonstrates a complex relationship with a peak at lower concentrations followed by a decrease. Our study concludes that exposure to OPEs plays a crucial role in the pathogenesis of RA.
Causal loop and Stock-Flow Modeling of Signal Transduction Pathways
Authors: Sadegh Sulaimany, Gholamreza Bidkhori, Sarbaz H. A. Khoshnaw
Date: 2024-09-02 00:00:00

System dynamics is a popular approach in many fields of science and technology, but it has not been investigated for cell signaling pathways yet. It is a well formulated methodology used to analyze the components of a system considering the cause-effect relationships. The two main components of system dynamics modeling, Causal Loop and Stocks-Flow diagrams, make possible to model the dynamics of the system with the ability to analyze it both qualitatively and quantitatively. In this paper, after introducing the system dynamics modeling approach, and giving a simple example from its usage for Michaelis Menton reactions, a three-step process is proposed for signal transduction modeling. Then in a complete example, it is applied to a case study in cell signaling pathways, namely the RKIP Influence on the ERK signaling pathway and the results is compared with an integrative modeling approach based on Petri net and ODEs. Computational simulations show the success of the system dynamic in easier and effective modeling of the cell signaling pathways, in addition to its diverse options for understanding and testing the pathway quantitatively and quantitatively at the same time in relation to other methods. More intestinally, the suggested approach here helps one to identify all biochemical reaction paths and loops for complex cell signaling pathways. It will be a good step forward to define dominant systems for such complex cell signaling pathways.
Robust financial calibration: a Bayesian approach for neural SDEs
Authors: Christa Cuchiero, Eva Flonner, Kevin Kurt
Date: 2024-09-10 00:00:00
Tasks: Time Series

The paper presents a Bayesian framework for the calibration of financial models using neural stochastic differential equations (neural SDEs), for which we also formulate a global universal approximation theorem based on Barron-type estimates. The method is based on the specification of a prior distribution on the neural network weights and an adequately chosen likelihood function. The resulting posterior distribution can be seen as a mixture of different classical neural SDE models yielding robust bounds on the implied volatility surface. Both, historical financial time series data and option price data are taken into consideration, which necessitates a methodology to learn the change of measure between the risk-neutral and the historical measure. The key ingredient for a robust numerical optimization of the neural networks is to apply a Langevin-type algorithm, commonly used in the Bayesian approaches to draw posterior samples.
Advancing Financial Forecasting: A Comparative Analysis of Neural Forecasting Models N-HiTS and N-BEATS
Authors: Mohit Apte, Yashodhara Haribhakta
Date: 2024-08-31 00:00:00
Tasks: Decision Making, Time Series

In the rapidly evolving field of financial forecasting, the application of neural networks presents a compelling advancement over traditional statistical models. This research paper explores the effectiveness of two specific neural forecasting models, N-HiTS and N-BEATS, in predicting financial market trends. Through a systematic comparison with conventional models, this study demonstrates the superior predictive capabilities of neural approaches, particularly in handling the non-linear dynamics and complex patterns inherent in financial time series data. The results indicate that N-HiTS and N-BEATS not only enhance the accuracy of forecasts but also boost the robustness and adaptability of financial predictions, offering substantial advantages in environments that require real-time decision-making. The paper concludes with insights into the practical implications of neural forecasting in financial markets and recommendations for future research directions.
Semi-analytical pricing of options written on SOFR futures
Authors: Andrey Itkin, Yerkin Kitapbayev
Date: 2024-09-07 00:00:00

In this paper, we propose a semi-analytical approach to pricing options on SOFR futures where the underlying SOFR follows a time-dependent CEV model. By definition, these options change their type at the beginning of the reference period: before this time, this is an American option written on a SOFR forward price as an underlying, and after this point, this is an arithmetic Asian option with an American style exercise written on the daily SOFR rates. We develop a new version of the GIT method and solve both problems semi-analytically, obtaining the option price, the exercise boundary, and the option Greeks. This work is intended to address the concern that the transfer from LIBOR to SOFR has resulted in a situation in which the options of the key money market (i.e., futures on the reference rate) are options without any pricing model available. Therefore, the trading in options on 3M SOFR futures currently ends before their reference quarter starts, to eliminate the final metamorphosis into exotic options.
Pricing and hedging of decentralised lending contracts
Authors: Lukasz Szpruch, Marc Sabaté Vidales, Tanut Treetanthiploet, Yufei Zhang
Date: 2024-09-06 00:00:00

We study the loan contracts offered by decentralised loan protocols (DLPs) through the lens of financial derivatives. DLPs, which effectively are clearinghouses, facilitate transactions between option buyers (i.e. borrowers) and option sellers (i.e. lenders). The loan-to-value at which the contract is initiated determines the option premium borrowers pay for entering the contract, and this can be deduced from the non-arbitrage pricing theory. We show that when there are no market frictions, and there is no spread between lending and borrowing rates, it is optimal to never enter the lending contract. Next, by accounting for the spread between rates and transactional costs, we develop a deep neural network-based algorithm for learning trading strategies on the external markets that allow us to replicate the payoff of the lending contracts that are not necessarily optimally exercised. This allows hedge the risk lenders carry by issuing options sold to the borrowers, which can complement (or even replace) the liquidations mechanism used to protect lenders' capital. Our approach can also be used to exploit (statistical) arbitrage opportunities that may arise when DLP allow users to enter lending contracts with loan-to-value, which is not appropriately calibrated to market conditions or/and when different markets price risk differently. We present thorough simulation experiments using historical data and simulations to validate our approach.
MoA is All You Need: Building LLM Research Team using Mixture of Agents
Authors: Sandy Chen, Leqi Zeng, Abhinav Raghunathan, Flora Huang, Terrence C. Kim
Date: 2024-09-04 00:00:00
Tasks: All, RAG, Retrieval-augmented Generation

Large Language Models (LLMs) research in the financial domain is particularly complex due to the sheer number of approaches proposed in literature. Retrieval-Augmented Generation (RAG) has emerged as one of the leading methods in the sector due to its inherent groundedness and data source variability. In this work, we introduce a RAG framework called Mixture of Agents (MoA) and demonstrate its viability as a practical, customizable, and highly effective approach for scaling RAG applications. MoA is essentially a layered network of individually customized small language models (Hoffmann et al., 2022) collaborating to answer questions and extract information. While there are many theoretical propositions for such an architecture and even a few libraries for generally applying the structure in practice, there are limited documented studies evaluating the potential of this framework considering real business constraints such as cost and speed. We find that the MoA framework, consisting of small language models (Hoffmann et al., 2022), produces higher quality and more grounded responses across various financial domains that are core to Vanguard's business while simultaneously maintaining low costs.
Attention-Based Reading, Highlighting, and Forecasting of the Limit Order Book
Authors: Jiwon Jung, Kiseop Lee
Date: 2024-09-03 00:00:00
Tasks: Time Series, Time Series Forecasting

Managing high-frequency data in a limit order book (LOB) is a complex task that often exceeds the capabilities of conventional time-series forecasting models. Accurately predicting the entire multi-level LOB, beyond just the mid-price, is essential for understanding high-frequency market dynamics. However, this task is challenging due to the complex interdependencies among compound attributes within each dimension, such as order types, features, and levels. In this study, we explore advanced multidimensional sequence-to-sequence models to forecast the entire multi-level LOB, including order prices and volumes. Our main contribution is the development of a compound multivariate embedding method designed to capture the complex relationships between spatiotemporal features. Empirical results show that our method outperforms other multivariate forecasting methods, achieving the lowest forecasting error while preserving the ordinal structure of the LOB.
Information Asymmetry Index: The View of Market Analysts
Authors: Roberto Frota Decourt, Heitor Almeida, Philippe Protin, Matheus R. C. Gonzalez
Date: 2024-09-10 00:00:00

The purpose of the research was to build an index of informational asymmetry with market and firm proxies that reflect the analysts' perception of the level of informational asymmetry of companies. The proposed method consists of the construction of an algorithm based on the Elo rating and captures the perception of the analyst that choose, between two firms, the one they consider to have better information. After we have the informational asymmetry index, we run a regression model with our rating as dependent variable and proxies used by the literature as the independent variable to have a model that can be used for other researches that need to measure the level of informational asymmetry of a company. Our model presented a good fit between our index and the proxies used to measure informational asymmetry and we find four significant variables: coverage, volatility, Tobin q, and size.
A market resilient data-driven approach to option pricing
Authors: Anindya Goswami, Nimit Rana
Date: 2024-09-12 00:00:00
Tasks: Domain Adaptation

In this paper, we present a data-driven ensemble approach for option price prediction whose derivation is based on the no-arbitrage theory of option pricing. Using the theoretical treatment, we derive a common representation space for achieving domain adaptation. The success of an implementation of this idea is shown using some real data. Then we report several experimental results for critically examining the performance of the derived pricing models.
Irreversible investment under weighted discounting: effects of decreasing impatience
Authors: Pengyu Wei, Wei Wei
Date: 2024-09-02 00:00:00

This paper employs an intra-personal game-theoretic framework to investigate how decreasing impatience influences irreversible investment behaviors in a continuous-time setting. We consider a capacity expansion problem under weighted discount functions, a class of nonexponential functions that exhibit decreasing impatience, including the hyperbolic discount function as a special case. By deriving the Bellman system that characterizes the equilibrium, we establish the framework for analyzing investment behaviors of agents subject to decreasing impatience. From an economic perspective, we demonstrates that decreasing impatience prompts early investment. From a technical standpoint, we warn that decreasing impatience can lead to the failure of the smooth pasting principle.
Long-term decomposition of robust pricing kernels under G-expectation
Authors: Jaehyun Kim, Hyungbin Park
Date: 2024-08-31 00:00:00

This study develops a BSDE method for the long-term decomposition of pricing kernels under the G-expectation framework. We establish the existence, uniqueness, and regularity of solutions to three types of quadratic G-BSDEs: finite-horizon G-BSDEs, infinite-horizon G-BSDEs, and ergodic G-BSDEs. Moreover, we explore the Feynman--Kac formula associated with these three types of quadratic G-BSDEs. Using these results, a pricing kernel is uniquely decomposed into four components: an exponential discounting component, a transitory component, a symmetric G-martingale, and a decreasing component that captures the volatility uncertainty of the G-Brownian motion. Furthermore, these components are represented through a solution to a PDE. This study extends previous findings obtained under a single fixed probability framework to the G-expectation context.
Betting Against (Bad) Beta
Authors: Miguel C. Herculano
Date: 2024-08-31 00:00:00

Frazzini and Pedersen (2014) Betting Against Beta (BAB) factor is based on the idea that high beta assets trade at a premium and low beta assets trade at a discount due to investor funding constraints. However, as argued by Campbell and Vuolteenaho (2004), beta comes in "good" and "bad" varieties. While gaining exposure to low-beta, BAB factors fail to recognize that such a portfolio may tilt towards bad-beta. We propose a Betting Against Bad Beta factor, built by double-sorting on beta and bad-beta and find that it improves the overall performance of BAB strategies though its success relies on proper transaction cost mitigation.
A functional variational approach to pricing path dependent insurance policies
Authors: David R. Baños, Salvador Ortiz-Latorre, Oriol Zamora Font
Date: 2024-09-01 00:00:00

The main purpose of this work is the derivation of a functional partial differential equation (FPDE) for the calculations of equity-linked insurance policies, where the payment stream may depend on the whole past history of the financial asset. To this end, we employ variational techniques from the theory of functional It\^o calculus.
Pareto-Optimal Peer-to-Peer Risk Sharing with Robust Distortion Risk Measures
Authors: Mario Ghossoub, Michael B. Zhu, Wing Fung Chong
Date: 2024-09-08 00:00:00

We study Pareto optimality in a decentralized peer-to-peer risk-sharing market where agents' preferences are represented by robust distortion risk measures that are not necessarily convex. We obtain a characterization of Pareto-optimal allocations of the aggregate risk in the market, and we show that the shape of the allocations depends primarily on each agent's assessment of the tail of the aggregate risk. We quantify the latter via an index of probabilistic risk aversion, and we illustrate our results using concrete examples of popular families of distortion functions. As an application of our results, we revisit the market for flood risk insurance in the United States. We present the decentralized risk sharing arrangement as an alternative to the current centralized market structure, and we characterize the optimal allocations in a numerical study with historical flood data. We conclude with an in-depth discussion of the advantages and disadvantages of a decentralized insurance scheme in this setting.