pwc-archive/papers-with-abstracts huggingface dataset:
Decoding the Molecular Universe -- Workshop Report
Authors: Thomas O. Metz, Joshua N. Adkins, Peter B. Armentrout, Patrick Chain, Fanny Chu, Courtney D Corley, John R. Cort, Elizabeth Denis, Daniel Drell, Katherine R. Duncan, Robert G. Ewing, Facundo M. Fernandez, Oliver Fiehn, Neha Garg, Stefan Grimme, Christopher Henry, Robert L. Hettich, Tobias Kind, Roger G. Linington, Gary W. Miller, Trent Northen, Kirsten Overdahl, Ari Patrinos, Daniel Raftery, Paul Rigor, Richard D. Smith, Jon Sobus, Justin Teeguarden, Akos Vertes, Katrina Waters, Bobbie-Jo Webb-Robertson, Antony Williams, David Wishart
Date: 2023-11-19 00:00:00
Tasks: Cloud Computing, Computational chemistry

On August 9-10, 2023, a workshop was convened at the Pacific Northwest National Laboratory (PNNL) in Richland, WA that brought together a group of internationally recognized experts in metabolomics, natural products discovery, chemical ecology, chemical and biological threat assessment, cheminformatics, computational chemistry, cloud computing, artificial intelligence, and novel technology development. These experts were invited to assess the value and feasibility of a grand-scale project to create new technologies that would allow the identification and quantification of all small molecules, or to decode the molecular universe. The Decoding the Molecular Universe project would extend and complement the success of the Human Genome Project by developing new capabilities and technologies to measure small molecules (defined as non-protein, non-polymer molecules less than 1500 Daltons) of any origin and generated in biological systems or produced abiotically. Workshop attendees 1) explored what new understanding of biological and environmental systems could be revealed through the lens of small molecules; 2) characterized the similarities in current needs and technical challenges between each science or mission area for unambiguous and comprehensive determination of the composition and quantities of small molecules of any sample; 3) determined the extent to which technologies or methods currently exist for unambiguously and comprehensively determining the small molecule composition of any sample and in a reasonable time; and 4) identified the attributes of the ideal technology or approach for universal small molecule measurement and identification. The workshop concluded with a discussion of how a project of this scale could be undertaken, possible thrusts for the project, early proof-of-principle applications, and similar efforts upon which the project could be modeled.
Sweetwater: An interpretable and adaptive autoencoder for efficient tissue deconvolution
Authors: Jesus de la Fuente, Naroa Legarra, Guillermo Serrano, Irene Marin-Goni, Aintzane Diaz-Mazkiaran, Markel Benito Sendin, Ana Garcia Osta, Krishna R. Kalari, Carlos Fernandez-Granda, Idoia Ochoa, Mikel Hernaez
Date: 2023-11-20 00:00:00
Tasks: Feature Importance

Single-cell RNA-sequencing (scRNA-seq) stands as a powerful tool for deciphering cellular heterogeneity and exploring gene expression profiles at high resolution. However, its high cost renders it impractical for extensive sample cohorts within routine clinical care, hindering its broader applicability. Hence, many methodologies have recently arised to estimate cell type proportions from bulk RNA-seq samples (known as deconvolution methods). However, they have several limitations: Many depend on selecting a robust scRNA-seq reference dataset, which is often challenging. Secondly, building reliable pseudobulk samples requires determining the optimal number of genes or cells involved in the simulated data generation process, which has not been studied in depth. Moreover, pseudobulk and bulk RNA-seq samples often exhibit distribution shifts. Finally, most modern deconvolution approaches behave as a black box, and the underlying mechanisms of the deconvolution task are still unknown, which can compromise the reliability of the results. In this work, we present Sweetwater, an adaptive and interpretable autoencoder able to efficiently deconvolve bulk RNA-seq and microarray samples leveraging multiple classes of reference data, such as scRNA-seq and single-nuclei RNA-seq. Moreover, it can be trained on a mixture of FACS-sorted FASTQ files, which we newly propose to use as this reduces platform-specific biases and may potentially outperform single-cell-based references. Also, we demonstrate that Sweetwater effectively uncovers biologically meaningful patterns during the training process, increasing the reliability of the results. Sweetwater is available at https://github.com/ubioinformat/Sweetwater, and we anticipate will facilitate and expedite the accurate examination of high-throughput clinical data across diverse applications.
Low-dimensional controllability of brain networks
Authors: Remy Ben Messaoud, Vincent Le Du, Brigitte Charlotte Kaufmann, Baptiste Couvy-Duchesne, Lara Migliaccio, Paolo Bartolomeo, Mario Chavez, Fabrizio De Vico Fallani
Date: 2023-11-18 00:00:00

Network controllability is a powerful tool to study causal relationships in complex systems and identify the driver nodes for steering the network dynamics into desired states. However, due to ill-posed conditions, results become unreliable when the number of drivers becomes too small compared to the network size. This is a very common situation, particularly in real-world applications, where the possibility to access multiple nodes at the same time is limited by technological constraints, such as in the human brain. Although targeting smaller network parts might improve accuracy, challenges may remain for extremely unbalanced situations, when for example there is one single driver. To address this problem, we developed a mathematical framework that combines concepts from spectral graph theory and modern network science. Instead of controlling the original network dynamics, we aimed to control its low-dimensional embedding into the topological space derived from the network Laplacian. By performing extensive simulations on synthetic networks, we showed that a relatively low number of projected components is enough to improve the overall control accuracy, notably when dealing with very few drivers. Based on these findings, we introduced alternative low-dimensional controllability metrics and used them to identify the main driver areas of the human connectome obtained from N=6134 healthy individuals in the UK-biobank cohort. Results revealed previously unappreciated influential regions compared to standard approaches, enabled to draw control maps between distinct specialized large-scale brain systems, and yielded an anatomically-based understanding of hemispheric functional lateralization. Taken together, our results offered a theoretically-grounded solution to deal with network controllability in real-life applications and provided insights into the causal interactions of the human brain.
Basic concepts for the Kermack and McKendrick model with static heterogeneity
Authors: Hisashi Inaba
Date: 2023-11-19 00:00:00

In this paper, we consider the infection-age-dependent Kermack--McKendrick model in which host individuals are distributed in a continuous state space. To provide a mathematical foundation for the heterogeneous model, we develop a $L^1$-framework to formulate basic epidemiological concepts. First, we show the mathematical well-posedness of the basic model under appropriate conditions allowing the unbounded parameters with non-compact domain. Next we define the basic reproduction number and prove the pandemic threshold theorem. We then present a systematic procedure to compute the effective reproduction number and the herd immunity threshold. Finally we give some illustrative examples and concrete results by using the separable mixing assumption.
Densely sampled phylogenies frequently deviate from maximum parsimony in simple and local ways
Authors: William Howard-Snyder, Will Dumm, Mary Barker, Ognian Milanov, Claris Winston, David H. Rich, Frederick A Matsen IV
Date: 2023-11-17 00:00:00

Why do phylogenetic algorithms fail when they return incorrect answers? This simple question has not been answered in detail, even for maximum parsimony (MP), the simplest phylogenetic criterion. Understanding MP has recently gained relevance in the regime of extremely dense sampling, where each virus sample commonly differs by zero or one mutation from another previously sampled virus. Although recent research shows that evolutionary histories in this regime are close to being maximally parsimonious, the structure of their deviations from MP is not yet understood. In this paper, we develop algorithms to understand how the correct tree deviates from being MP in the densely sampled case. By applying these algorithms to simulations that realistically mimic the evolution of SARS-CoV-2, we find that simulated trees frequently only deviate from maximally parsimonious trees locally, through simple structures consisting of the same mutation appearing independently on sister branches.
A Foundation Model for Cell Segmentation
Authors: Uriah Israel, Markus Marks, Rohit Dilip, Qilin Li, Morgan Schwartz, Elora Pradhan, Edward Pao, Shenyi Li, Alexander Pearson-Goulart, Pietro Perona, Georgia Gkioxari, Ross Barnowski, Yisong Yue, David Van Valen
Date: 2023-11-18 00:00:00
Tasks: Cell Segmentation, model, Prompt Engineering, Segmentation

Cells are the fundamental unit of biological organization, and identifying them in imaging data - cell segmentation - is a critical task for various cellular imaging experiments. While deep learning methods have led to substantial progress on this problem, models that have seen wide use are specialist models that work well for specific domains. Methods that have learned the general notion of "what is a cell" and can identify them across different domains of cellular imaging data have proven elusive. In this work, we present CellSAM, a foundation model for cell segmentation that generalizes across diverse cellular imaging data. CellSAM builds on top of the Segment Anything Model (SAM) by developing a prompt engineering approach to mask generation. We train an object detector, CellFinder, to automatically detect cells and prompt SAM to generate segmentations. We show that this approach allows a single model to achieve state-of-the-art performance for segmenting images of mammalian cells (in tissues and cell culture), yeast, and bacteria collected with various imaging modalities. To enable accessibility, we integrate CellSAM into DeepCell Label to further accelerate human-in-the-loop labeling strategies for cellular imaging data. A deployed version of CellSAM is available at https://label-dev.deepcell.org/.
Fast and Stable Credit Gamma of CVA
Authors: Roberto Daluiso
Date: 2023-11-20 00:00:00
Tasks: Management

Credit Valuation Adjustment is a balance sheet item which is nowadays subject to active risk management by specialized traders. However, one of the most important risk factors, which is the vector of default intensities of the counterparty, affects in a non-differentiable way the most general Monte Carlo estimator of the adjustment, through simulation of default times. Thus the computation of first and second order (pure and mixed) sensitivities involving these inputs cannot rely on direct path-wise differentiation, while any approach involving finite differences shows very high statistical noise. We present ad hoc analytical estimators which overcome these issues while offering very low runtime overheads over the baseline computation of the price adjustment. We also discuss the conversion of the so-obtained sensitivities to model parameters (e.g. default intensities) into sensitivities to market quotes (e.g. Credit Default Swap spreads).
Arguably Adequate Aqueduct Algorithm: Crossing A Bridge-Less Block-Chain Chasm
Authors: Ravi Kashyap
Date: 2023-09-12 00:00:00
Tasks: Management

We consider the problem of being a cross-chain wealth management platform with deposits, redemptions and investment assets across multiple networks. We discuss the need for blockchain bridges to facilitates fund flows across platforms. We point out several issues with existing bridges. We develop an algorithm - tailored to overcome current constraints - that dynamically changes the utilization of bridge capacities and hence the amounts to be transferred across networks. We illustrate several scenarios using numerical simulations.
Examining the Effect of Monetary Policy and Monetary Policy Uncertainty on Cryptocurrencies Market
Authors: Mohammadreza Mahmoudi
Date: 2023-10-25 00:00:00

This study investigates the influence of monetary policy and monetary policy uncertainties on Bitcoin returns, utilizing monthly data of BTC, and MPU from July 2010 to August 2023, and employing the Markov Switching Means VAR (MSM-VAR) method. The findings reveal that Bitcoin returns can be categorized into two distinct regimes: 1) regime 1 with low volatility, and 2) regime 2 with high volatility. In both regimes, an increase in MPU leads to a decline in Bitcoin returns: -0.028 in regime 1 and -0.44 in regime 2. This indicates that monetary policy uncertainty exerts a negative influence on Bitcoin returns during both downturns and upswings. Furthermore, the study explores Bitcoin's sensitivity to Federal Open Market Committee (FOMC) decisions.
Large Language Models in Finance: A Survey
Authors: Yinheng Li, Shaofei Wang, Han Ding, Hang Chen
Date: 2023-09-28 00:00:00
Tasks: Few-Shot Learning, Survey

Recent advances in large language models (LLMs) have opened new possibilities for artificial intelligence applications in finance. In this paper, we provide a practical survey focused on two key aspects of utilizing LLMs for financial tasks: existing solutions and guidance for adoption. First, we review current approaches employing LLMs in finance, including leveraging pretrained models via zero-shot or few-shot learning, fine-tuning on domain-specific data, and training custom LLMs from scratch. We summarize key models and evaluate their performance improvements on financial natural language processing tasks. Second, we propose a decision framework to guide financial professionals in selecting the appropriate LLM solution based on their use case constraints around data, compute, and performance needs. The framework provides a pathway from lightweight experimentation to heavy investment in customized LLMs. Lastly, we discuss limitations and challenges around leveraging LLMs in financial applications. Overall, this survey aims to synthesize the state-of-the-art and provide a roadmap for responsibly applying LLMs to advance financial AI.
Cryptocurrency in the Aftermath: Unveiling the Impact of the SVB Collapse
Authors: Qin Wang, Guangsheng Yu, Shiping Chen
Date: 2023-09-15 00:00:00

In this paper, we explore the aftermath of the Silicon Valley Bank (SVB) collapse, with a particular focus on its impact on crypto markets. We conduct a multi-dimensional investigation, which includes a factual summary, analysis of user sentiment, and examination of market performance. Based on such efforts, we uncover a somewhat counterintuitive finding: the SVB collapse did not lead to the destruction of cryptocurrencies; instead, they displayed resilience.
The independence of Central Banks, a reductio ad impossibile
Authors: Ion Pohoata, Delia-Elena Diaconasu, Ioana Negru
Date: 2023-08-22 00:00:00

This paper testifies to the fact that the independence of the Central Banks, as stated by its founding fathers, is nothing more than a chimera. We demonstrate that the hypothesis inflation is a purely monetary phenomenon does not support the plea for independence. Moreover, we show that the conservative central banker, the imaginary Principal-Agent contract, the alleged financial autonomy, just like the ban on budgetary financing, are all arguments that lack logic. We equally show that the idea of independence is not convincing because its operational toolbox, as well as the system of rules it relies on, lack well-defined outlines.
Diversifying an Index
Authors: Johannes Ruf
Date: 2023-07-16 00:00:00

In July 2023, Nasdaq announced a `Special Rebalance' of the Nasdaq-100 index to reduce the index weights of its large constituents. A rebalance as suggested currently by Nasdaq index methodology may have several undesirable effects. These effects can be avoided by a different, but simple rebalancing strategy. Such rebalancing is easily computable and guarantees (a) that the maximum overall index weight does not increase through the rebalancing and (b) that the order of index weights is preserved.
Approximation of supply curves
Authors: Andres M. Alonso, Zehang Li
Date: 2023-10-24 00:00:00

In this note, we illustrate the computation of the approximation of the supply curves using a one-step basis. We derive the expression for the L2 approximation and propose a procedure for the selection of nodes of the approximation. We illustrate the use of this approach with three large sets of bid curves from European electricity markets.
Application Research of Spline Interpolation and ARIMA in the Field of Stock Market Forecasting
Authors: Xitai Yu
Date: 2023-11-14 00:00:00
Tasks: Time Series, Time Series Forecasting

The ARIMA (Autoregressive Integrated Moving Average model) has extensive applications in the field of time series forecasting. However, the predictive performance of the ARIMA model is limited when dealing with data gaps or significant noise. Based on previous research, we have found that cubic spline interpolation performs well in capturing the smooth changes of stock price curves, especially when the market trends are relatively stable. Therefore, this paper integrates the two approaches by taking the time series data in stock trading as an example, establishes a time series forecasting model based on cubic spline interpolation and ARIMA. Through validation, the model has demonstrated certain guidance and reference value for short-term time series forecasting.
Earnings Prediction Using Recurrent Neural Networks
Authors: Moritz Scherrmann, Ralf Elsas
Date: 2023-11-10 00:00:00
Tasks: Prediction

Firm disclosures about future prospects are crucial for corporate valuation and compliance with global regulations, such as the EU's MAR and the US's SEC Rule 10b-5 and RegFD. To comply with disclosure obligations, issuers must identify nonpublic information with potential material impact on security prices as only new, relevant and unexpected information materially affects prices in efficient markets. Financial analysts, assumed to represent public knowledge on firms' earnings prospects, face limitations in offering comprehensive coverage and unbiased estimates. This study develops a neural network to forecast future firm earnings, using four decades of financial data, addressing analysts' coverage gaps and potentially revealing hidden insights. The model avoids selectivity and survivorship biases as it allows for missing data. Furthermore, the model is able to produce both fiscal-year-end and quarterly earnings predictions. Its performance surpasses benchmark models from the academic literature by a wide margin and outperforms analysts' forecasts for fiscal-year-end earnings predictions.
Analysis of frequent trading effects of various machine learning models
Authors: Jiahao Chen, Xiaofei Li
Date: 2023-09-14 00:00:00

In recent years, high-frequency trading has emerged as a crucial strategy in stock trading. This study aims to develop an advanced high-frequency trading algorithm and compare the performance of three different mathematical models: the combination of the cross-entropy loss function and the quasi-Newton algorithm, the FCNN model, and the vector machine. The proposed algorithm employs neural network predictions to generate trading signals and execute buy and sell operations based on specific conditions. By harnessing the power of neural networks, the algorithm enhances the accuracy and reliability of the trading strategy. To assess the effectiveness of the algorithm, the study evaluates the performance of the three mathematical models. The combination of the cross-entropy loss function and the quasi-Newton algorithm is a widely utilized logistic regression approach. The FCNN model, on the other hand, is a deep learning algorithm that can extract and classify features from stock data. Meanwhile, the vector machine is a supervised learning algorithm recognized for achieving improved classification results by mapping data into high-dimensional spaces. By comparing the performance of these three models, the study aims to determine the most effective approach for high-frequency trading. This research makes a valuable contribution by introducing a novel methodology for high-frequency trading, thereby providing investors with a more accurate and reliable stock trading strategy.
Harnessing Deep Q-Learning for Enhanced Statistical Arbitrage in High-Frequency Trading: A Comprehensive Exploration
Authors: Soumyadip Sarkar
Date: 2023-09-13 00:00:00
Tasks: Decision Making, Q-Learning, Reinforcement Learning (RL)

The realm of High-Frequency Trading (HFT) is characterized by rapid decision-making processes that capitalize on fleeting market inefficiencies. As the financial markets become increasingly competitive, there is a pressing need for innovative strategies that can adapt and evolve with changing market dynamics. Enter Reinforcement Learning (RL), a branch of machine learning where agents learn by interacting with their environment, making it an intriguing candidate for HFT applications. This paper dives deep into the integration of RL in statistical arbitrage strategies tailored for HFT scenarios. By leveraging the adaptive learning capabilities of RL, we explore its potential to unearth patterns and devise trading strategies that traditional methods might overlook. We delve into the intricate exploration-exploitation trade-offs inherent in RL and how they manifest in the volatile world of HFT. Furthermore, we confront the challenges of applying RL in non-stationary environments, typical of financial markets, and investigate methodologies to mitigate associated risks. Through extensive simulations and backtests, our research reveals that RL not only enhances the adaptability of trading strategies but also shows promise in improving profitability metrics and risk-adjusted returns. This paper, therefore, positions RL as a pivotal tool for the next generation of HFT-based statistical arbitrage, offering insights for both researchers and practitioners in the field.