pwc-archive/papers-with-abstracts huggingface dataset:
0-1 laws for pattern occurrences in phylogenetic trees and networks
Authors: François Bienvenu, Mike Steel
Date: 2024-02-07 00:00:00
Tasks: 10-shot image generation, 3D Anomaly Detection and Segmentation, 3D Face Reconstruction, Articles, Automated Theorem Proving, DeepFake Detection, Face Swapping, Informativeness, Object Detection, Object Tracking, Shadow Removal, Style Transfer, Text-to-Image Generation, Unconditional Video Generation, Unsupervised Domain Adaptation, Video Object Detection, Weakly-Supervised Semantic Segmentation, Zero-shot 3D Point Cloud Classification

In a recent paper, the question of determining the fraction of binary trees that contain a fixed pattern known as the snowflake was posed. We show that this fraction goes to 1, providing two very different proofs: a purely combinatorial one that is quantitative and specific to this problem; and a proof using branching process techniques that is less explicit, but also much more general, as it applies to any fixed patterns and can be extended to other trees and networks. In particular, it follows immediately from our second proof that the fraction of $d$-ary trees (resp. level-$k$ networks) that contain a fixed $d$-ary tree (resp. level-$k$ network) tends to $1$ as the number of leaves grows.
Stochastic matrix metapopulation models with fast migration: re-scaling survival to the fast scale
Authors: Luis Sanz, Rafael Bravo de la Parra
Date: 2024-02-06 00:00:00

In this work we address the analysis of discrete-time models of structured metapopulations subject to environmental stochasticity. Previous works on these models made use of the fact that migrations between the patches can be considered fast with respect to demography (maturation, survival, reproduction) in the population. It was assumed that, within each time step of the model, there are many fast migration steps followed by one slow demographic event. This assumption allowed one to apply approximate reduction techniques that eased the model analysis. It is however a questionable issue in some cases since, in particular, individuals can die at any moment of the time step. We propose new non-equivalent models in which we re-scale survival to consider its effect on the fast scale. We propose a more general formulation of the approximate reduction techniques so that they also apply to the proposed new models. We prove that the main asymptotic elements in this kind of stochastic models, the Stochastic Growth Rate (SGR) and the Scaled Logarithmic Variance (SLV), can be related between the original and the reduced systems, so that the analysis of the latter allows us to ascertain the population fate in the first. Then we go on to considering some cases where we illustrate the reduction technique and show the differences between both modelling options. In some cases using one option represents exponential growth, whereas the other yields extinction.
Basic principles drive self-organization of brain-like connectivity structure
Authors: Carlos Calvo Tapia, Valeriy A. Makarov Slizneva, Cees van Leeuwen
Date: 2024-02-05 00:00:00
Tasks: Anatomy, Functional Connectivity

The brain can be considered as a system that dynamically optimizes the structure of anatomical connections based on the efficiency requirements of functional connectivity. To illustrate the power of this principle in organizing the complexity of brain architecture, we portray the functional connectivity as diffusion on the current network structure. The diffusion drives adaptive rewiring, resulting in changes to the network to enhance its efficiency. This dynamic evolution of the network structure generates, and thus explains, modular small-worlds with rich club effects, f eatures commonly observed in neural anatomy. Taking wiring length and propagating waves into account leads to the morphogenesis of more specific neural structures that are stalwarts of the detailed brain functional anatomy, such as parallelism, divergence, convergence, super-rings, and super-chains. By showing how such structures emerge, largely independently of their specific biological realization, we offer a new conjecture on how natural and artificial brain-like structures can be physically implemented.
Tweet Influence on Market Trends: Analyzing the Impact of Social Media Sentiment on Biotech Stocks
Authors: C. Sarai R. Avila
Date: 2024-01-26 00:00:00
Tasks: Decision Making, Sentiment Analysis, Stock Price Prediction

This study investigates the relationship between tweet sentiment across diverse categories: news, company opinions, CEO opinions, competitor opinions, and stock market behavior in the biotechnology sector, with a focus on understanding the impact of social media discourse on investor sentiment and decision-making processes. We analyzed historical stock market data for ten of the largest and most influential pharmaceutical companies alongside Twitter data related to COVID-19, vaccines, the companies, and their respective CEOs. Using VADER sentiment analysis, we examined the sentiment scores of tweets and assessed their relationships with stock market performance. We employed ARIMA (AutoRegressive Integrated Moving Average) and VAR (Vector AutoRegression) models to forecast stock market performance, incorporating sentiment covariates to improve predictions. Our findings revealed a complex interplay between tweet sentiment, news, biotech companies, their CEOs, and stock market performance, emphasizing the importance of considering diverse factors when modeling and predicting stock prices. This study provides valuable insights into the influence of social media on the financial sector and lays a foundation for future research aimed at refining stock price prediction models.
PhosNetVis: a web-based tool for fast kinase-substrate enrichment analysis and interactive 2D/3D network visualizations of phosphoproteomics data
Authors: Osho Rawal, Berk Turhan, Irene Font Peradejordi, Shreya Chandrasekar, Selim Kalayci, Sacha Gnjatic, Jeffrey Johnson, Mehdi Bouhaddou, Zeynep H. Gümüş
Date: 2024-02-07 00:00:00

Protein phosphorylation involves the reversible modification of a protein (substrate) residue by another protein (kinase). Liquid chromatography-mass spectrometry studies are rapidly generating massive protein phosphorylation datasets across multiple conditions. Researchers then must infer kinases responsible for changes in phosphosites of each substrate. However, tools that infer kinase-substrate interactions (KSIs) are not optimized to interactively explore the resulting large and complex networks, significant phosphosites, and states. There is thus an unmet need for a tool that facilitates user-friendly analysis, interactive exploration, visualization, and communication of phosphoproteomics datasets. We present PhosNetVis, a web-based tool for researchers of all computational skill levels to easily infer, generate and interactively explore KSI networks in 2D or 3D by streamlining phosphoproteomics data analysis steps within a single tool. PhostNetVis lowers barriers for researchers in rapidly generating high-quality visualizations to gain biological insights from their phosphoproteomics datasets. It is available at: https://gumuslab.github.io/PhosNetVis/
Immunogenic cell death triggered by pathogen ligands via host germ line-encoded receptors
Authors: Chuang Li, Yichen Wei, Chao Qin, ShiFan Chen, Xiaolong Shao
Date: 2024-02-06 00:00:00

The strategic induction of cell death serves as a crucial immune defense mechanism for the eradication of pathogenic infections within host cells. Investigating the molecular mechanisms underlying immunogenic cell pathways has significantly enhanced our understanding of the host's immunity. This review provides a comprehensive overview of the immunogenic cell death mechanisms triggered by pathogen infections, focusing on the critical role of pattern recognition receptors. In response to infections, host cells dictate a variety of cell death pathways, including apoptosis, pyroptosis, necrosis, and lysosomal cell death, which are essential for amplifying immune responses and controlling pathogen dissemination. Key components of these mechanisms are host cellular receptors that recognize pathogen-associated ligands. These receptors activate downstream signaling cascades, leading to the expression of immunoregulatory genes and the production of antimicrobial cytokines and chemokines. Particularly, the inflammasome, a multi-protein complex, plays a pivotal role in these responses by processing pro-inflammatory cytokines and inducing pyroptotic cell death. Pathogens, in turn, have evolved strategies to manipulate these cell death pathways, either by inhibiting them to facilitate their replication or by triggering them to evade host defenses. This dynamic interplay between host immune mechanisms and pathogen strategies highlights the intricate co-evolution of microbial virulence and host immunity.
Motion Mapping Cognition: A Nondecomposable Primary Process in Human Vision
Authors: Zhenping Xie
Date: 2024-02-02 00:00:00
Tasks: Image Segmentation, Object Recognition, Object Tracking, Semantic Segmentation

Human intelligence seems so mysterious that we have not successfully understood its foundation until now. Here, I want to present a basic cognitive process, motion mapping cognition (MMC), which should be a nondecomposable primary function in human vision. Wherein, I point out that, MMC process can be used to explain most of human visual functions in fundamental, but can not be effectively modelled by traditional visual processing ways including image segmentation, object recognition, object tracking etc. Furthermore, I state that MMC may be looked as an extension of Chen's theory of topological perception on human vision, and seems to be unsolvable using existing intelligent algorithm skills. Finally, along with the requirements of MMC problem, an interesting computational model, quantized topological matching principle can be derived by developing the idea of optimal transport theory. Above results may give us huge inspiration to develop more robust and interpretable machine vision models.
Use of a Golden Gate plasmid set enabling scarless MoClo-compatible transcription unit assembly
Authors: Stijn T. de Vries, Laura Kley, Daniel Schindler
Date: 2024-02-05 00:00:00

Golden Gate cloning has become a powerful and widely used DNA assembly method. Its modular nature and the reusability of standardized parts allow rapid construction of transcription units and multi-gene constructs. Importantly, its modular structure makes it compatible with laboratory automation, allowing for systematic and highly complex DNA assembly. Golden Gate cloning relies on Type IIS enzymes that cleave an adjacent undefined sequence motif at a defined distance from the directed enzyme recognition motif. This feature has been used to define hierarchical Golden Gate assembly standards with defined overhangs ("fusion sites") for defined part libraries. The simplest Golden Gate standard would consist of three part libraries, namely promoter, coding and terminator sequences, respectively. Each library would have defined fusion sites, allowing a hierarchical Golden Gate assembly to generate transcription units. Typically, Type IIS enzymes are used, which generate four nucleotide overhangs. This results in small scar sequences in hierarchical DNA assemblies, which can affect the functionality of transcription units. However, there are enzymes that generate three nucleotide overhangs, such as SapI. Here we provide a step-by-step protocol on how to use SapI to assemble transcription units using the start and stop codon for scarless transcription unit assembly. The protocol also provides guidance on how to perform multi-gene Golden Gate assemblies with the resulting transcription units using the Modular Cloning standard. The transcription units expressing fluorophores are used as an example.
A Combined Experimental and Mathematical Study of The Evolution of Microbial Community Composed of Interacting Staphylococcus Strains
Authors: Nouf Alghamdi, Mal Horsburgh, Bakhtier Vasiev
Date: 2024-02-07 00:00:00

The emergence of the phenomenon known as ABR (anti-bacterial resistance), is the result of the gradual decrease in the efficacy of antibiotics and the increase in the cost of producing new antibiotics. Hence, alternative solutions to prevent the spread of the pathogenic species are required. Here we present a combined experimental and mathematical study of the evolution of microbial communities. The aim was to investigate the role of skin bacteria invasion and competition in limiting pathogenic species growth and colonisation, and to determine and reveal factors and conditions that alter and influence the dynamics of interactions between species. The focus in this study was Staphylococcus aureus as it is considered a major human pathogen that shows colonisation traits distinct from the more abundant skin antimicrobial-secreting residents, S. epidermidis and S. hominis. The method adopted when conducting this study was based on two approaches: experimental and mathematical. The novelty and significance of this study lies in the fact that, unlike that found in a previous studies the manipulation of spatial structures, the level of toxicity, and initial frequencies did not prevent the emergence of resistance in the evolved S. aureus populations. The evolved S. aureus populations were able to dominate their opponents regardless of the environmental conditions. However, it was found that the level of toxicity and environmental regulations made it harder for evolved S. aureus populations to recover.
Mutant fate in spatially structured populations on graphs: connecting models to experiments
Authors: Alia Abbara, Lisa Pagani, Celia García-Pareja, Anne-Florence Bitbol
Date: 2024-02-06 00:00:00

In nature, most microbial populations have complex spatial structures that can affect their evolution. Evolutionary graph theory predicts that some spatial structures modelled by placing individuals on the nodes of a graph affect the probability that a mutant will fix. Evolution experiments are beginning to explicitly address the impact of graph structures on mutant fixation. However, the assumptions of evolutionary graph theory differ from the conditions of modern evolution experiments, making the comparison between theory and experiment challenging. Here, we aim to bridge this gap by using our new model of spatially structured populations. This model considers connected subpopulations that lie on the nodes of a graph, and allows asymmetric migrations. It can handle large populations, and explicitly models serial passage events with migrations, thus closely mimicking experimental conditions. We analyze recent experiments in light of this model. We suggest useful parameter regimes for future experiments, and we make quantitative predictions for these experiments. In particular, we propose experiments to directly test our recent prediction that the star graph with asymmetric migrations suppresses natural selection and can accelerate mutant fixation or extinction, compared to a well-mixed population.
Learning immune receptor representations with protein language models
Authors: Andreas Dounas, Tudor-Stefan Cotet, Alexander Yermanos
Date: 2024-02-06 00:00:00
Tasks: Diversity, Drug Discovery, Protein Design, Specificity

Protein language models (PLMs) learn contextual representations from protein sequences and are profoundly impacting various scientific disciplines spanning protein design, drug discovery, and structural predictions. One particular research area where PLMs have gained considerable attention is adaptive immune receptors, whose tremendous sequence diversity dictates the functional recognition of the adaptive immune system. The self-supervised nature underlying the training of PLMs has been recently leveraged to implement a variety of immune receptor-specific PLMs. These models have demonstrated promise in tasks such as predicting antigen-specificity and structure, computationally engineering therapeutic antibodies, and diagnostics. However, challenges including insufficient training data and considerations related to model architecture, training strategies, and data and model availability must be addressed before fully unlocking the potential of PLMs in understanding, translating, and engineering immune receptors.
CNN-DRL with Shuffled Features in Finance
Authors: Sina Montazeri, Akram Mirzaeinia, Amir Mirzaeinia
Date: 2024-01-16 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning

In prior methods, it was observed that the application of Convolutional Neural Networks agent in Deep Reinforcement Learning to financial data resulted in an enhanced reward. In this study, a specific permutation was applied to the feature vector, thereby generating a CNN matrix that strategically positions more pertinent features in close proximity. Our comprehensive experimental evaluations unequivocally demonstrate a substantial enhancement in reward attainment.
Prioritizing Investments in Cybersecurity: Empirical Evidence from an Event Study on the Determinants of Cyberattack Costs
Authors: Daniel Celeny, Loïc Maréchal, Evgueni Rousselot, Alain Mermoud, Mathias Humbert
Date: 2024-02-07 00:00:00

Along with the increasing frequency and severity of cyber incidents, understanding their economic implications is paramount. In this context, listed firms' reactions to cyber incidents are compelling to study since they (i) are a good proxy to estimate the costs borne by other organizations, (ii) have a critical position in the economy, and (iii) have their financial information publicly available. We extract listed firms' cyber incident dates and characteristics from newswire headlines. We use an event study over 2012--2022, using a three-day window around events and standard benchmarks. We find that the magnitude of abnormal returns around cyber incidents is on par with previous studies using newswire or alternative data to identify cyber incidents. Conversely, as we adjust the standard errors accounting for event-induced variance and residual cross-correlation, we find that the previously claimed significance of abnormal returns vanishes. Given these results, we run a horse race of specifications, in which we test for the marginal effects of type of cyber incidents, target firm sector, periods, and their interactions. Data breaches are the most detrimental incident type with an average loss of -1.3\% or (USD -1.9 billion) over the last decade. The health sector is the most sensitive to cyber incidents, with an average loss of -5.21\% (or USD -1.2 billion), and even more so when these are data breaches. Instead, we cannot show any time-varying effect of cyber incidents or a specific effect of the type of news as had previously been advocated.
Token vs Equity for Startup Financing
Authors: Guangye Cao
Date: 2024-02-07 00:00:00

Why would a blockchain-based startup and its venture capital investors choose to finance by issuing tokens instead of equity? What would be their rates of return for each asset? This paper focuses on the liquidity difference between the two fundraising methods. I build a three-period model of an entrepreneur, two types of investors, and users. Some investors have unforeseen liquidity needs in the middle period that can only be met with tokens. The entrepreneur obtains higher payoff by issuing tokens instead of equity, and the payoff difference increases with investors risk-aversion and need for liquidity in the middle period, as well as the depth of the token market.
Cyber risk and the cross-section of stock returns
Authors: Daniel Celeny, Loïc Maréchal
Date: 2024-02-07 00:00:00

We extract firms' cyber risk with a machine learning algorithm measuring the proximity between their disclosures and a dedicated cyber corpus. Our approach outperforms dictionary methods, uses full disclosure and not devoted-only sections, and generates a cyber risk measure uncorrelated with other firms' characteristics. We find that a portfolio of US-listed stocks in the high cyber risk quantile generates an excess return of 18.72% p.a. Moreover, a long-short cyber risk portfolio has a significant and positive risk premium of 6.93% p.a., robust to all factors' benchmarks. Finally, using a Bayesian asset pricing method, we show that our cyber risk factor is the essential feature that allows any multi-factor model to price the cross-section of stock returns.
Measuring the performance of investments in information security startups: An empirical analysis by cybersecurity sectors using Crunchbase data
Authors: Loïc Maréchal, Alain Mermoud, Dimitri Percia David, Mathias Humbert
Date: 2024-02-07 00:00:00

Early-stage firms play a significant role in driving innovation and creating new products and services, especially for cybersecurity. Therefore, evaluating their performance is crucial for investors and policymakers. This work presents a financial evaluation of early-stage firms' performance in 19 cybersecurity sectors using a private-equity dataset from 2010 to 2022 retrieved from Crunchbase. We observe firms, their primary and secondary activities, funding rounds, and pre and post-money valuations. We compare cybersecurity sectors regarding the amount raised over funding rounds and post-money valuations while inferring missing observations. We observe significant investor interest variations across categories, periods, and locations. In particular, we find the average capital raised (valuations) to range from USD 7.24 mln (USD 32.39 mln) for spam filtering to USD 45.46 mln (USD 447.22 mln) for the private cloud sector. Next, we assume a log process for returns computed from post-money valuations and estimate the expected returns, systematic and specific risks, and risk-adjusted returns of investments in early-stage firms belonging to cybersecurity sectors. Again, we observe substantial performance variations with annualized expected returns ranging from 9.72\% for privacy to 177.27\% for the blockchain sector. Finally, we show that overall, the cybersecurity industry performance is on par with previous results found in private equity. Our results shed light on the performance of cybersecurity investments and, thus, on investors' expectations about cybersecurity.
Exploring the Impact: How Decentralized Exchange Designs Shape Traders' Behavior on Perpetual Future Contracts
Authors: Erdong Chen, Mengzhong Ma, Zixin Nie
Date: 2024-02-06 00:00:00

In this paper, we analyze traders' behavior within both centralized exchanges (CEXs) and decentralized exchanges (DEXs), focusing on the volatility of Bitcoin prices and the trading activity of investors engaged in perpetual future contracts. We categorize the architecture of perpetual future exchanges into three distinct models, each exhibiting unique patterns of trader behavior in relation to trading volume, open interest, liquidation, and leverage. Our detailed examination of DEXs, especially those utilizing the Virtual Automated Market Making (VAMM) Model, uncovers a differential impact of open interest on long versus short positions. In exchanges which operate under the Oracle Pricing Model, we find that traders primarily act as price takers, with their trading actions reflecting direct responses to price movements of the underlying assets. Furthermore, our research highlights a significant propensity among less informed traders to overreact to positive news, as demonstrated by an increase in long positions. This study contributes to the understanding of market dynamics in digital asset exchanges, offering insights into the behavioral finance for future innovation of decentralized finance.