pwc-archive/papers-with-abstracts huggingface dataset:
LifeGPT: Topology-Agnostic Generative Pretrained Transformer Model for Cellular Automata
Authors: Jaime A. Berkovich, Markus J. Buehler
Date: 2024-09-03 00:00:00
Tasks: Large Language Model

Conway's Game of Life (Life), a well known algorithm within the broader class of cellular automata (CA), exhibits complex emergent dynamics, with extreme sensitivity to initial conditions. Modeling and predicting such intricate behavior without explicit knowledge of the system's underlying topology presents a significant challenge, motivating the development of algorithms that can generalize across various grid configurations and boundary conditions. We develop a decoder-only generative pretrained transformer (GPT) model to solve this problem, showing that our model can simulate Life on a toroidal grid with no prior knowledge on the size of the grid, or its periodic boundary conditions (LifeGPT). LifeGPT is topology-agnostic with respect to its training data and our results show that a GPT model is capable of capturing the deterministic rules of a Turing-complete system with near-perfect accuracy, given sufficiently diverse training data. We also introduce the idea of an `autoregressive autoregressor' to recursively implement Life using LifeGPT. Our results pave the path towards true universal computation within a large language model framework, synthesizing of mathematical analysis with natural language processing, and probing AI systems for situational awareness about the evolution of such algorithms without ever having to compute them. Similar GPTs could potentially solve inverse problems in multicellular self-assembly by extracting CA-compatible rulesets from real-world biological systems to create new predictive models, which would have significant consequences for the fields of bioinspired materials, tissue engineering, and architected materials design.
Optimal Investment with Costly Expert Opinions
Authors: Christoph Knochenhauer, Alexander Merkel, Yufei Zhang
Date: 2024-09-17 00:00:00

We consider the Merton problem of optimizing expected power utility of terminal wealth in the case of an unobservable Markov-modulated drift. What makes the model special is that the agent is allowed to purchase costly expert opinions of varying quality on the current state of the drift, leading to a mixed stochastic control problem with regular and impulse controls involving random consequences. Using ideas from filtering theory, we first embed the original problem with unobservable drift into a full information problem on a larger state space. The value function of the full information problem is characterized as the unique viscosity solution of the dynamic programming PDE. This characterization is achieved by a new variant of the stochastic Perron's method, which additionally allows us to show that, in between purchases of expert opinions, the problem reduces to an exit time control problem which is known to admit an optimal feedback control. Under the assumption of sufficient regularity of this feedback map, we are able to construct optimal trading and expert opinion strategies.
Design of Ligand-Binding Proteins with Atomic Flow Matching
Authors: Junqi Liu, Shaoning Li, Chence Shi, Zhi Yang, Jian Tang
Date: 2024-09-18 00:00:00

Designing novel proteins that bind to small molecules is a long-standing challenge in computational biology, with applications in developing catalysts, biosensors, and more. Current computational methods rely on the assumption that the binding pose of the target molecule is known, which is not always feasible, as conformations of novel targets are often unknown and tend to change upon binding. In this work, we formulate proteins and molecules as unified biotokens, and present AtomFlow, a novel deep generative model under the flow-matching framework for the design of ligand-binding proteins from the 2D target molecular graph alone. Operating on representative atoms of biotokens, AtomFlow captures the flexibility of ligands and generates ligand conformations and protein backbone structures iteratively. We consider the multi-scale nature of biotokens and demonstrate that AtomFlow can be effectively trained on a subset of structures from the Protein Data Bank, by matching flow vector field using an SE(3) equivariant structure prediction network. Experimental results show that our method can generate high fidelity ligand-binding proteins and achieve performance comparable to the state-of-the-art model RFDiffusionAA, while not requiring bound ligand structures. As a general framework, AtomFlow holds the potential to be applied to various biomolecule generation tasks in the future.
k-mer-based approaches to bridging pangenomics and population genetics
Authors: Miles D. Roberts, Olivia Davis, Emily B. Josephs, Robert J. Williamson
Date: 2024-09-18 00:00:00
Tasks: Data Compression, Diversity

Many commonly studied species now have more than one chromosome-scale genome assembly, revealing a large amount of genetic diversity previously missed by approaches that map short reads to a single reference. However, many species still lack multiple reference genomes and correctly aligning references to build pangenomes is challenging, limiting our ability to study this missing genomic variation in population genetics. Here, we argue that $k$-mers are a crucial stepping stone to bridging the reference-focused paradigms of population genetics with the reference-free paradigms of pangenomics. We review current literature on the uses of $k$-mers for performing three core components of most population genetics analyses: identifying, measuring, and explaining patterns of genetic variation. We also demonstrate how different $k$-mer-based measures of genetic variation behave in population genetic simulations according to the choice of $k$, depth of sequencing coverage, and degree of data compression. Overall, we find that $k$-mer-based measures of genetic diversity scale consistently with pairwise nucleotide diversity ($\pi$) up to values of about $\pi = 0.025$ ($R^2 = 0.97$) for neutrally evolving populations. For populations with even more variation, using shorter $k$-mers will maintain the scalability up to at least $\pi = 0.1$. Furthermore, in our simulated populations, $k$-mer dissimilarity values can be reliably approximated from counting bloom filters, highlighting a potential avenue to decreasing the memory burden of $k$-mer based genomic dissimilarity analyses. For future studies, there is a great opportunity to further develop methods to identifying selected loci using $k$-mers.
Allium Vegetables Intake and Digestive System Cancer Risk: A Study Based on Mendelian Randomization, Network Pharmacology and Molecular Docking
Authors: Shuhao Li, Jingwen Lou, Yelina Mulatihan, Yuhang Xiong, Yao Li, Qi Xu
Date: 2024-09-16 00:00:00
Tasks: Molecular Docking

Background: Allium vegetables (garlic and onion) are one of the flavorings in people's daily diets. Observational studies suggest that intake of allium vegetables may be correlated with a lower incidence of digestive system cancers. However, the existence of a causal relationship is still controversial due to confounding factors and reverse causation. Therefore, we explored the causal relationship between intake of allium vegetables and digestive system cancers using Mendelian randomization approach. Methods: First, we performed Mendelian randomization analyses using inverse variance weighting (IVW), weighted median, and MR-Egger approaches, and demonstrated the reliability of the results in the sensitivity step. Second, Multivariable Mendelian randomization was applied to adjust for smoking and alcohol consumption. Third, we explored the molecular mechanisms behind the positive results through network pharmacology and molecular docking methods. Results: The study suggests that increased intake of garlic reduced gastric cancer risk. However, onion intake was not statistically associated with digestive system cancer. Conclusion: Garlic may have a protective effect against gastric cancer.
Early reduced dopaminergic tone mediated by D3 receptor and dopamine transporter in absence epileptogenesis
Authors: Fanny Cavarec, Philipp Krauss, Tiffany Witkowski, Alexis Broisat, Catherine Ghezzi, Stéphanie de Gois, Bruno Giros, Antoine Depaulis, Colin Deransart
Date: 2024-09-18 00:00:00

Abstract Objective In Genetic Absence Epilepsy Rats From Strasbourg ( GAERS s), epileptogenesis takes place during brain maturation and correlates with increased mRNA expression of D3 dopamine receptors (D3R). Whether these alterations are the consequence of seizure repetition or contribute to the development of epilepsy remains to be clarified. Here, we addressed the involvement of the dopaminergic system in epilepsy onset in GAERS s. Methods Experiments were performed using rats at different stages of brain maturation from three strains according to their increasing propensity to develop absence seizures: nonepileptic control rats ( NEC s), Wistar Hannover rats, and GAERS s. Changes in dopaminergic neurotransmission were investigated using different behavioral and neurochemical approaches: autoradiography of D3R and dopamine transporter, single photon emission computed tomographic imaging, acute and chronic drug effects on seizure recordings (dopaminergic agonists and antagonists), quinpirole-induced yawns and dopamine synaptosomal uptake, microdialysis, brain tissue monoamines, and brain-derived neurotrophic factor quantification. Results Autoradiography revealed an increased expression of D3R in 14-day-old GAERS s, before absence seizure onset, that persists in adulthood, as compared to age-matched NEC s. This was confirmed by increased yawns, a marker of D3R activity, and increased seizures when animals were injected with quinpirole at low doses to activate D3R. We also observed a concomitant increase in the expression and activity of the dopamine transporter in GAERS s before seizure onset, consistent with both lowered dopamine basal level and increased phasic responses. Significance Our data show that the dopaminergic system is persistently altered in GAERS s, which may contribute not only to behavioral comorbidities but also as an etiopathogenic factor in the development of epilepsy. The data suggest that an imbalanced dopaminergic tone may contribute to absence epilepsy development and seizure onset, as its reversion by a chronic treatment with a dopamine stabilizer significantly suppressed epileptogenesis. Our data suggest a potential new target for antiepileptic therapies and/or improvement of quality of life of epileptic patients.
Compatibility studies of loquat scions with loquat and quince rootstocks
Authors: Rasul Rafiq Aziz, Fakhraddin Mustafa Hama Salih, Ibrahim Maaroof Noori
Date: 2024-09-17 00:00:00
Tasks: Sand

Experiment 1. Rooting of quince hardwood cuttings: Rooting success was influenced by both the concentrations of IBA and the selection of rooting media. However, the control group (without IBA) notably enhanced rooting when compared to the various IBA concentrations. Cuttings in the control group (without IBA) and those planted in river sand exhibited notably high percentages of successful rooting, underscoring the importance of the selected planting medium. Experiment 2. Bench grafting of loquat: The success of grafting loquat cutting stocks varied based on grafting dates, types of cuttings, and concentrations of IBA. However, IBA at different concentrations did not have a significant impact. Notably, certain interactions such as grafting on February 20 with loquat stock cuttings, yielded higher percentages of successful graft bud sprouting. Experiment 3. Performance of grafting loquats onto different rootstocks: Grafting success was notably influenced by the selection of rootstock, with loquat rootstock demonstrating superior performance compared to quince. The highest significant levels of successful grafting were attained on February 20, underscoring the crucial role of grafting dates. Experiment 4. Impact of tree stock types on grafting success: Grafting success percentage was higher in loquat tree stock when compared to quince. The consistency of grafting success percentages across three dates underscores the significant influence of rootstock type. Experiment 5. Bench grafting of loquat cutting stocks: Graft bud sprout percentages exhibited variations, with loquat stock cuttings surpassing quince. Grafting success demonstrated a consistent increase from February 20 to March 30, underscoring the importance of selecting appropriate grafting dates.
Opportunities and challenges of mRNA technologies in development of Dengue Virus Vaccine
Authors: Xiaoyang Liu, Daniel Salmon
Date: 2024-09-17 00:00:00

Dengue virus (DENV) is a mosquito-borne virus with a significant human health concern. With 390 million infections annually and 96 million showing clinical symptoms, severe dengue can lead to life-threatening conditions like dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS). The only FDA-approved vaccine, Dengvaxia, has limitations due to antibody-dependent enhancement (ADE), necessitating careful administration. The recent pre-approval of TAK-003 by WHO in 2024 highlights ongoing efforts to improve vaccine options. This review explores recent advancements in dengue vaccine development, emphasizing potential utility of mRNA-based vaccines. By examining current clinical trial data and innovations, we aim to identify promising strategies to address the limitations of existing vaccines and enhance global dengue prevention efforts.
Dynamics of solutions to a multi-patch epidemic model with a saturation incidence mechanism
Authors: Yawo Ezunkpe, Cynthia T. Nnolum, Rachidi B. Salako, Shuwen Xue
Date: 2024-09-17 00:00:00

This study examines the behavior of solutions in a multi-patch epidemic model that includes a saturation incidence mechanism. When the fatality rate due to the disease is not null, our findings show that the solutions of the model tend to stabilize at disease-free equilibria. Conversely, when the disease-induced fatality rate is null, the dynamics of the model become more intricate. Notably, in this scenario, while the saturation effect reduces the basic reproduction number $\mathcal{R}_0$, it can also lead to a backward bifurcation of the endemic equilibria curve at $\mathcal{R}_0=1$. Provided certain fundamental assumptions are satisfied, we offer a detailed analysis of the global dynamics of solutions based on the value of $\mathcal{R}_0$. Additionally, we investigate the asymptotic profiles of endemic equilibria as population dispersal rates tend to zero. To support and illustrate our theoretical findings, we conduct numerical simulations.
Foundations of ecological and evolutionary change
Authors: A. Bradley Duthie, Victor J. Luque
Date: 2024-09-16 00:00:00

Biological evolution is realised through the same mechanisms of birth and death that underlie change in population density. The deep interdependence between ecology and evolution is well-established, and recent models focus on integrating eco-evolutionary dynamics to demonstrate how ecological and evolutionary processes interact and feed back upon each other. Nevertheless, a gap remains between the logical foundations of ecology and evolution. Both population ecology and evolution have fundamental equations that define how the size of a population (ecology) and the average characteristic within a population (evolution) change, respectively, over time. These fundamental equations are a complete and exact description of change for any closed population, but how they are formally linked remains unclear. We link the fundamental equations of population ecology and evolution with an equation that sums how individual characteristics interact with individual fitness in a population. From this equation, we derive the fundamental equations of population ecology and evolutionary biology (the Price equation). We thereby identify an overlooked bridge between ecology and biological evolution. Our unification formally recovers the equivalence between mean population growth rate and evolutionary fitness and links this change to ecosystem function. We outline how our framework can be used to further develop eco-evolutionary theory. .
A computational framework for optimal and Model Predictive Control of stochastic gene regulatory networks
Authors: Hamza Faquir, Manuel Pájaro, Irene Otero-Muras
Date: 2024-09-17 00:00:00
Tasks: Computational Efficiency, Model Predictive Control

Engineering biology requires precise control of biomolecular circuits, and Cybergenetics is the field dedicated to achieving this goal. A significant challenge in developing controllers for cellular functions is designing systems that can effectively manage molecular noise. To address this, there has been increasing effort to develop model-based controllers for stochastic biomolecular systems, where a major difficulty lies in accurately solving the chemical master equation. In this work we develop a framework for optimal and Model Predictive Control of stochastic gene regulatory networks with three key advantageous features: high computational efficiency, the capacity to control the overall probability density function enabling the fine-tuning of the cell population to obtain complex shapes and behaviors (including bimodality and other emergent properties), and the capacity to handle high levels of intrinsic molecular noise. Our method exploits an efficient approximation of the Chemical Master Equation using Partial Integro-Differential Equations, which additionally enables the development of an effective adjoint-based optimization. We illustrate the performance of the methods presented through two relevant studies in Synthetic Biology: shaping bimodal cell populations and tracking moving target distributions via inducible gene regulatory circuits.
Comorbid anxiety predicts lower odds of depression improvement during smartphone-delivered psychotherapy
Authors: Morgan B. Talbot, Jessica M. Lipschitz, Omar Costilla-Reyes
Date: 2024-09-16 00:00:00
Tasks: Interpretable Machine Learning, Variable Selection

Comorbid anxiety disorders are common among patients with major depressive disorder (MDD), and numerous studies have identified an association between comorbid anxiety and resistance to pharmacological depression treatment. However, the impact of anxiety on the effectiveness of non-pharmacological interventions for MDD is not as well understood. In this study, we applied machine learning techniques to predict treatment responses in a large-scale clinical trial (n=493) of individuals with MDD, who were recruited online and randomly assigned to one of three smartphone-based interventions. Our analysis reveals that a baseline GAD-7 questionnaire score in the moderate to severe range (>10) predicts reduced probability of recovery from MDD. Our findings suggest that depressed individuals with comorbid anxiety face lower odds of substantial improvement in the context of smartphone-based therapeutic interventions for depression. Our work highlights a methodology that can identify simple, clinically useful "rules of thumb" for treatment response prediction using interpretable machine learning models.
DeFi Arbitrage in Hedged Liquidity Tokens
Authors: Maxim Bichuch, Zachary Feinstein
Date: 2024-09-17 00:00:00
Tasks: Position

Empirically, the prevailing market prices for liquidity tokens of the constant product market maker (CPMM) -- as offered in practice by companies such as Uniswap -- readily permit arbitrage opportunities by delta hedging the risk of the position. Herein, we investigate this arbitrage opportunity by treating the liquidity token as a derivative position in the prices of the underlying assets for the CPMM. In doing so, not dissimilar to the Black-Scholes result, we deduce risk-neutral pricing and hedging formulas for these liquidity tokens. Furthermore, with our novel pricing formula, we construct a method to calibrate a volatility to data which provides an updated (non-market) price which would not permit arbitrage if quoted by the CPMM. We conclude with a discussion of novel AMM designs which would bring the pricing of liquidity tokens into the modern financial era.
Macroscopic properties of equity markets: stylized facts and portfolio performance
Authors: Steven Campbell, Qien Song, Ting-Kam Leonard Wong
Date: 2024-09-17 00:00:00
Tasks: Diversity, Econometrics

Macroscopic properties of equity markets affect the performance of active equity strategies but many are not adequately captured by conventional models of financial mathematics and econometrics. Using the CRSP Database of the US equity market, we study empirically several macroscopic properties defined in terms of market capitalizations and returns, and highlight a list of stylized facts and open questions motivated in part by stochastic portfolio theory. Additionally, we present a systematic backtest of the diversity-weighted portfolio under various configurations and study its performance in relation to macroscopic quantities. All of our results can be replicated using codes made available on our online repository.
Optimizing Performance: How Compact Models Match or Exceed GPT's Classification Capabilities through Fine-Tuning
Authors: Baptiste Lefort, Eric Benhamou, Jean-Jacques Ohana, David Saltiel, Beatrice Guez
Date: 2024-08-22 00:00:00
Tasks: Sentiment Analysis, text-classification, Text Classification, Zero-Shot Learning

In this paper, we demonstrate that non-generative, small-sized models such as FinBERT and FinDRoBERTa, when fine-tuned, can outperform GPT-3.5 and GPT-4 models in zero-shot learning settings in sentiment analysis for financial news. These fine-tuned models show comparable results to GPT-3.5 when it is fine-tuned on the task of determining market sentiment from daily financial news summaries sourced from Bloomberg. To fine-tune and compare these models, we created a novel database, which assigns a market score to each piece of news without human interpretation bias, systematically identifying the mentioned companies and analyzing whether their stocks have gone up, down, or remained neutral. Furthermore, the paper shows that the assumptions of Condorcet's Jury Theorem do not hold suggesting that fine-tuned small models are not independent of the fine-tuned GPT models, indicating behavioural similarities. Lastly, the resulted fine-tuned models are made publicly available on HuggingFace, providing a resource for further research in financial sentiment analysis and text classification.
Reproduction of IVFS algorithm for high-dimensional topology preservation feature selection
Authors: Zihan Wang
Date: 2024-08-23 00:00:00
Tasks: feature selection

Feature selection is a crucial technique for handling high-dimensional data. In unsupervised scenarios, many popular algorithms focus on preserving the original data structure. In this paper, we reproduce the IVFS algorithm introduced in AAAI 2020, which is inspired by the random subset method and preserves data similarity by maintaining topological structure. We systematically organize the mathematical foundations of IVFS and validate its effectiveness through numerical experiments similar to those in the original paper. The results demonstrate that IVFS outperforms SPEC and MCFS on most datasets, although issues with its convergence and stability persist.