pwc-archive/papers-with-abstracts huggingface dataset:
Autonomous self-evolving research on biomedical data: the DREAM paradigm
Authors: Luojia Deng, Yijie Wu, Yongyong Ren, Hui Lu
Date: 2024-07-18 00:00:00
Tasks: Articles

In contemporary biomedical research, the efficiency of data-driven approaches is hindered by large data volumes, tool selection complexity, and human resource limitations, necessitating the development of fully autonomous research systems to meet complex analytical needs. Such a system should include the ability to autonomously generate research questions, write analytical code, configure the computational environment, judge and interpret the results, and iteratively generate in-depth questions or solutions, all without human intervention. Here we developed DREAM, the first biomedical Data-dRiven self-Evolving Autonomous systeM, which can independently conduct scientific research without human involvement. Utilizing a clinical dataset and two omics datasets, DREAM demonstrated its ability to raise and deepen scientific questions, with difficulty scores for clinical data questions surpassing top published articles by 5.7% and outperforming GPT-4 and bioinformatics graduate students by 58.6% and 56.0%, respectively. Overall, DREAM has a success rate of 80% in autonomous clinical data mining. Certainly, human can participate in different steps of DREAM to achieve more personalized goals. After evolution, 10% of the questions exceeded the average scores of top published article questions on originality and complexity. In the autonomous environment configuration of the eight bioinformatics workflows, DREAM exhibited an 88% success rate, whereas GPT-4 failed to configure any workflows. In clinical dataset, DREAM was over 10,000 times more efficient than the average scientist with a single computer core, and capable of revealing new discoveries. As a self-evolving autonomous research system, DREAM provides an efficient and reliable solution for future biomedical research. This paradigm may also have a revolutionary impact on other data-driven scientific research fields.
Improving AlphaFlow for Efficient Protein Ensembles Generation
Authors: Shaoning Li, Mingyu Li, Yusong Wang, Xinheng He, Nanning Zheng, Jian Zhang, Pheng-Ann Heng
Date: 2024-07-08 00:00:00

Investigating conformational landscapes of proteins is a crucial way to understand their biological functions and properties. AlphaFlow stands out as a sequence-conditioned generative model that introduces flexibility into structure prediction models by fine-tuning AlphaFold under the flow-matching framework. Despite the advantages of efficient sampling afforded by flow-matching, AlphaFlow still requires multiple runs of AlphaFold to finally generate one single conformation. Due to the heavy consumption of AlphaFold, its applicability is limited in sampling larger set of protein ensembles or the longer chains within a constrained timeframe. In this work, we propose a feature-conditioned generative model called AlphaFlow-Lit to realize efficient protein ensembles generation. In contrast to the full fine-tuning on the entire structure, we focus solely on the light-weight structure module to reconstruct the conformation. AlphaFlow-Lit performs on-par with AlphaFlow and surpasses its distilled version without pretraining, all while achieving a significant sampling acceleration of around 47 times. The advancement in efficiency showcases the potential of AlphaFlow-Lit in enabling faster and more scalable generation of protein ensembles.
Design, Fabrication, and Characterization of a User-Friendly Microfluidic Device for Studying Liver Zonation-on-Chip (ZoC)
Authors: Reza Mahdavi, Sameereh Hashemi Najafabadi, Mohammad Adel Ghiass, Silmu Valaskivi, Hannu Välimäki, Joose Kreutzer, Charlotte Hamngren Blomqvist, Stefano Romeo, Pasi Kallio, Caroline Beck Adiels
Date: 2024-07-17 00:00:00

Liver zonation is a fundamental characteristic of hepatocyte spatial heterogeneity, which is challenging to recapitulate in traditional cell cultures. This study presents a novel microfluidic device designed to induce zonation in liver cell cultures by establishing an oxygen gradient using standard laboratory gases. The device consists of two layers; a bottom layer containing a gas channel network that delivers high and low oxygenated gases to create three distinct zones within the cell culture chamber in the layer above. Computational simulations and ratiometric oxygen sensing were employed to validate the oxygen gradient, demonstrating that stable oxygen levels were achieved within two hours. Liver zonation was confirmed using immunofluorescence staining, which showed zonated albumin production in HepG2 cells directly correlating with oxygen levels and mimicking in-vivo zonation behavior. This user-friendly device supports studies on liver zonation and related metabolic disease mechanisms in vitro. It can also be utilized for experiments that necessitate precise gas concentration gradients, such as hypoxia-related research areas focused on angiogenesis and cancer development.
Analyzing the birth-death model of Oncostreams in Glioma, and the effects of Cytochalasin D treatment
Authors: Kai Poffenbarger, Rohan Pandey
Date: 2024-06-14 00:00:00

This research project investigates the critical role of oncostreams in glioma aggressiveness, leveraging advanced ex-vivo 3D explants and in-vivo intravital imaging techniques to establish a direct correlation between oncostream density and cancer severity. The primary objective is to model the cell populations within oncostreams, with a specific focus on GFP+ NPA cells, to simulate cancer dynamics and provide insights into tumor behavior. The study employs a simple Birth-Death process to analyze cell population dynamics and treatment effects, building and solving Kolmogorov equations to predict changes in cell population over time. While the model could be expanded to include additional modulators such as morphological attributes and neurotransmitter exposure, the focus remains on cell population to maintain feasibility. The study also examines various treatment methods, finding that glutamate increases glioma cell movement while histamine reduces it. Collagenase treatment effectively dismantles oncostreams, suggesting a potential therapeutic strategy. For this paper, we specifically are going to be looking at Cytochalasin D, which shows promise in disrupting oncostreams and reducing glioma invasiveness. By integrating these treatment variables into the model, the research aims to understand their impact on glioma cell density within the oncostreams and aggressiveness, thereby contributing to improved cancer management strategies. This comprehensive approach is expected to enhance our understanding of glioma progression and inform the development of effective therapeutic interventions.
Autonomous Money Supply Strategy Utilizing Control Theory
Authors: Yuval Boneh
Date: 2024-07-18 00:00:00
Tasks: Management, Position

Decentralized Finance (DeFi) has reshaped the possibilities of reserve banking in the form of the Collateralized Debt Position (CDP). Key to the safety of CDPs is the money supply architecture that enables issued debt to maintain its value. In traditional markets, and with respect to the United States Dollar system, interest rates are set by the Federal Reserve in an attempt to influence the effects of excessive inflation. DeFi enables a more transparent approach that typically relies on interest rates or other debt recovery mechanisms being directly informed by asset price. This research investigates contemporary DeFi money supply and debt management strategies and their limitations. Furthermore, this paper introduces a time-weighted approach to interest rate management that implements a Proportional-Integral-Derivative control system to constantly adapt to market activities and protect the value of issued currency, while addressing observed limitations.
Leveraging Machine Learning for High-Dimensional Option Pricing within the Uncertain Volatility Model
Authors: Ludovic Goudenege, Andrea Molent, Antonino Zanette
Date: 2024-07-18 00:00:00

This paper explores the application of Machine Learning techniques for pricing high-dimensional options within the framework of the Uncertain Volatility Model (UVM). The UVM is a robust framework that accounts for the inherent unpredictability of market volatility by setting upper and lower bounds on volatility and the correlation among underlying assets. By integrating advanced Machine Learning algorithms, we aim to enhance the accuracy and efficiency of option pricing under the UVM, especially when the option price depends on a large number of variables, such as in basket or path-dependent options. In this paper, we consider two approaches based on Machine Learning. The first one, termed GTU, evolves backward in time, dynamically selecting at each time step the most expensive volatility and correlation for each market state. Specifically, it identifies the particular values of volatility and correlation that maximize the expected option value at the next time step, and therefore, an optimization problem must be solved. This is achieved through the use of Gaussian Process regression, the computation of expectations via a single step of a multidimensional tree and the Sequential Quadratic Programming optimization algorithm. The second approach, referred to as NNU, leverages neural networks and frames pricing in the UVM as a control problem. Specifically, we train a neural network to determine the most adverse volatility and correlation for each simulated market state, generated via random simulations. The option price is then obtained through Monte Carlo simulations, which are performed using the values for the uncertain parameters provided by the neural network. The numerical results demonstrate that the proposed approaches can significantly improve the precision of option pricing particularly in high-dimensional contexts.
Beyond Trend Following: Deep Learning for Market Trend Prediction
Authors: Fernando Berzal, Alberto Garcia
Date: 2024-06-10 00:00:00
Tasks: Deep Learning, Prediction

Trend following and momentum investing are common strategies employed by asset managers. Even though they can be helpful in the proper situations, they are limited in the sense that they work just by looking at past, as if we were driving with our focus on the rearview mirror. In this paper, we advocate for the use of Artificial Intelligence and Machine Learning techniques to predict future market trends. These predictions, when done properly, can improve the performance of asset managers by increasing returns and reducing drawdowns.
Unified Asymptotics For Investment Under Illiquidity: Transaction Costs And Search Frictions
Authors: Tae Ung Gang, Jin Hyuk Choi
Date: 2024-07-18 00:00:00

This paper investigates the optimal investment problem in a market with two types of illiquidity: transaction costs and search frictions. Extending the framework established by arXiv:2101.09936, we analyze a power-utility maximization problem where an investor encounters proportional transaction costs and trades only when a Poisson process triggers trading opportunities. We show that the optimal trading strategy is described by a no-trade region. We introduce a novel asymptotic framework applicable when both transaction costs and search frictions are small. Using this framework, we derive explicit asymptotics for the no-trade region and the value function along a specific parametric curve. This approach unifies existing asymptotic results for models dealing exclusively with either transaction costs or search frictions.
A Mean Field Game approach for pollution regulation of competitive firms
Authors: Gianmarco Del Sarto, Marta Leocata, Giulia Livieri
Date: 2024-07-17 00:00:00

We develop a model based on mean-field games of competitive firms producing similar goods according to a standard AK model with a depreciation rate of capital generating pollution as a byproduct. Our analysis focuses on the widely-used cap-and-trade pollution regulation. Under this regulation, firms have the flexibility to respond by implementing pollution abatement, reducing output, and participating in emission trading, while a regulator dynamically allocates emission allowances to each firm. The resulting mean-field game is of linear quadratic type and equivalent to a mean-field type control problem, i.e., it is a potential game. We find explicit solutions to this problem through the solutions to differential equations of Riccati type. Further, we investigate the carbon emission equilibrium price that satisfies the market clearing condition and find a specific form of FBSDE of McKean-Vlasov type with common noise. The solution to this equation provides an approximate equilibrium price. Additionally, we demonstrate that the degree of competition is vital in determining the economic consequences of pollution regulation.
Mean-Variance Optimization for Participating Life Insurance Contracts
Authors: Felix Fießinger, Mitja Stadje
Date: 2024-07-16 00:00:00

This paper studies the equity holders' mean-variance optimal portfolio choice problem for (non-)protected participating life insurance contracts. We derive explicit formulas for the optimal terminal wealth and the optimal strategy in the multi-dimensional Black-Scholes model, showing the existence of all necessary parameters. In incomplete markets, we state Hamilton-Jacobi-Bellman equations for the value function. Moreover, we provide a numerical analysis of the Black-Scholes market. The equity holders on average increase their investment into the risky asset in bad economic states and decrease their investment over time.
Credit Risk Assessment Model for UAE Commercial Banks: A Machine Learning Approach
Authors: Aditya Saxena, Dr Parizad Dungore
Date: 2024-07-02 00:00:00
Tasks: Dimensionality Reduction

Credit ratings are becoming one of the primary references for financial institutions of the country to assess credit risk in order to accurately predict the likelihood of business failure of an individual or an enterprise. Financial institutions, therefore, depend on credit rating tools and services to help them predict the ability of creditors to meet financial persuasions. Conventional credit rating is broadly categorized into two classes namely: good credit and bad credit. This approach lacks adequate precision to perform credit risk analysis in practice. Related studies have shown that data-driven machine learning algorithms outperform many conventional statistical approaches in solving this type of problem, both in terms of accuracy and efficiency. The purpose of this paper is to construct and validate a credit risk assessment model using Linear Discriminant Analysis as a dimensionality reduction technique to discriminate good creditors from bad ones and identify the best classifier for credit assessment of commercial banks based on real-world data. This will help commercial banks to avoid monetary losses and prevent financial crisis
Deep learning for quadratic hedging in incomplete jump market
Authors: Nacira Agram, Bernt Øksendal, Jan Rems
Date: 2024-06-12 00:00:00
Tasks: Deep Learning

We propose a deep learning approach to study the minimal variance pricing and hedging problem in an incomplete jump diffusion market. It is based upon a rigorous stochastic calculus derivation of the optimal hedging portfolio, optimal option price, and the corresponding equivalent martingale measure through the means of the Stackelberg game approach. A deep learning algorithm based on the combination of the feedforward and LSTM neural networks is tested on three different market models, two of which are incomplete. In contrast, the complete market Black-Scholes model serves as a benchmark for the algorithm's performance. The results that indicate the algorithm's good performance are presented and discussed. In particular, we apply our results to the special incomplete market model studied by Merton and give a detailed comparison between our results based on the minimal variance principle and the results obtained by Merton based on a different pricing principle. Using deep learning, we find that the minimal variance principle leads to typically higher option prices than those deduced from the Merton principle. On the other hand, the minimal variance principle leads to lower losses than the Merton principle.
The 2023/24 VIEWS Prediction Challenge: Predicting the Number of Fatalities in Armed Conflict, with Uncertainty
Authors: Håvard Hegre, Paola Vesco, Michael Colaresi, Jonas Vestby, Alexa Timlick, Noorain Syed Kazmi, Friederike Becker, Marco Binetti, Tobias Bodentien, Tobias Bohne, Patrick T. Brandt, Thomas Chadefaux, Simon Drauz, Christoph Dworschak, Vito D'Orazio, Cornelius Fritz, Hannah Frank, Kristian Skrede Gleditsch, Sonja Häffner, Martin Hofer, Finn L. Klebe, Luca Macis, Alexandra Malaga, Marius Mehrl, Nils W. Metternich, Daniel Mittermaier, David Muchlinski, Hannes Mueller, Christian Oswald, Paola Pisano, David Randahl, Christopher Rauh, Lotta Rüter, Thomas Schincariol, Benjamin Seimon, Elena Siletti, Marco Tagliapietra, Chandler Thornhill, Johan Vegelius, Julian Walterskirchen
Date: 2024-07-08 00:00:00
Tasks: Future prediction, Prediction

This draft article outlines a prediction challenge where the target is to forecast the number of fatalities in armed conflicts, in the form of the UCDP `best' estimates, aggregated to the VIEWS units of analysis. It presents the format of the contributions, the evaluation metric, and the procedures, and a brief summary of the contributions. The article serves a function analogous to a pre-analysis plan: a statement of the forecasting models made publicly available before the true future prediction window commences. More information on the challenge, and all data referred to in this document, can be found at https://viewsforecasting.org/research/prediction-challenge-2023.
Industrial-Grade Time-Dependent Counterfactual Root Cause Analysis through the Unanticipated Point of Incipient Failure: a Proof of Concept
Authors: Alexandre Trilla, Rajesh Rajendran, Ossee Yiboe, Quentin Possamaï, Nenad Mijatovic, Jordi Vitrià
Date: 2024-07-10 00:00:00
Tasks: counterfactual, Time Series

This paper describes the development of a counterfactual Root Cause Analysis diagnosis approach for an industrial multivariate time series environment. It drives the attention toward the Point of Incipient Failure, which is the moment in time when the anomalous behavior is first observed, and where the root cause is assumed to be found before the issue propagates. The paper presents the elementary but essential concepts of the solution and illustrates them experimentally on a simulated setting. Finally, it discusses avenues of improvement for the maturity of the causal technology to meet the robustness challenges of increasingly complex environments in the industry.
GPT Czech Poet: Generation of Czech Poetic Strophes with Language Models
Authors: Michal Chudoba, Rudolf Rosa
Date: 2024-06-18 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

High-quality automated poetry generation systems are currently only available for a small subset of languages. We introduce a new model for generating poetry in Czech language, based on fine-tuning a pre-trained Large Language Model. We demonstrate that guiding the generation process by explicitly specifying strophe parameters within the poem text strongly improves the effectiveness of the model. We also find that appropriate tokenization is crucial, showing that tokenization methods based on syllables or individual characters instead of subwords prove superior in generating poetic strophes. We further enhance the results by introducing \textit{Forced~generation}, adding explicit specifications of meter and verse parameters at inference time based on the already generated text. We evaluate a range of setups, showing that our proposed approach achieves high accuracies in rhyming and metric aspects of formal quality of the generated poems.
TourLLM: Enhancing LLMs with Tourism Knowledge
Authors: Qikai Wei, Mingzhi Yang, Jinqiang Wang, Wenwei Mao, Jiabo Xu, Huansheng Ning
Date: 2024-06-18 00:00:00
Tasks: Diversity

Recently, large language models (LLMs) have demonstrated their effectiveness in various natural language processing (NLP) tasks. However, the lack of tourism knowledge limits the performance of LLMs in tourist attraction presentations and travel planning. To address this challenge, we constructed a supervised fine-tuning dataset for the culture and tourism domain, named Cultour. This dataset consists of three parts: tourism knowledge base QA data, travelogues data, and tourism diversity QA data. Additionally, we propose TourLLM, a Qwen-based model supervised fine-tuned with Cultour, to improve the quality of the information provided about attractions and travel planning. To evaluate the performance of TourLLM, we employed both automatic and human evaluation, and we proposed a human evaluation criterion named CRA (Consistency, Readability, Availability). The experimental results demonstrate the effectiveness of the responses generated by the TourLLM. Our proposed Cultour is accessible at https://github.com/mrweiqk/Cultour.
Building Understandable Messaging for Policy and Evidence Review (BUMPER) with AI
Authors: Katherine A. Rosenfeld, Maike Sonnewald, Sonia J. Jindal, Kevin A. McCarthy, Joshua L. Proctor
Date: 2024-06-27 00:00:00

We introduce a framework for the use of large language models (LLMs) in Building Understandable Messaging for Policy and Evidence Review (BUMPER). LLMs are proving capable of providing interfaces for understanding and synthesizing large databases of diverse media. This presents an exciting opportunity to supercharge the translation of scientific evidence into policy and action, thereby improving livelihoods around the world. However, these models also pose challenges related to access, trust-worthiness, and accountability. The BUMPER framework is built atop a scientific knowledge base (e.g., documentation, code, survey data) by the same scientists (e.g., individual contributor, lab, consortium). We focus on a solution that builds trustworthiness through transparency, scope-limiting, explicit-checks, and uncertainty measures. LLMs are rapidly being adopted and consequences are poorly understood. The framework addresses open questions regarding the reliability of LLMs and their use in high-stakes applications. We provide a worked example in health policy for a model designed to inform measles control programs. We argue that this framework can facilitate accessibility of and confidence in scientific evidence for policymakers, drive a focus on policy-relevance and translatability for researchers, and ultimately increase and accelerate the impact of scientific knowledge used for policy decisions.
Data Generation Using Large Language Models for Text Classification: An Empirical Case Study
Authors: Yinheng Li, Rogerio Bonatti, Sara Abdali, Justin Wagle, Kazuhito Koishida
Date: 2024-06-27 00:00:00
Tasks: Diversity, Natural Language Understanding, text-classification, Text Classification

Using Large Language Models (LLMs) to generate synthetic data for model training has become increasingly popular in recent years. While LLMs are capable of producing realistic training data, the effectiveness of data generation is influenced by various factors, including the choice of prompt, task complexity, and the quality, quantity, and diversity of the generated data. In this work, we focus exclusively on using synthetic data for text classification tasks. Specifically, we use natural language understanding (NLU) models trained on synthetic data to assess the quality of synthetic data from different generation approaches. This work provides an empirical analysis of the impact of these factors and offers recommendations for better data generation practices.