Tootfinder

We show here that a significant increase in SARS-CoV-2 aerostability results from a moderate increase in the atmospheric carbon dioxide concentration (e.g. 800 ppm),
an effect that is more marked than that observed for changes in relative humidity.
We model the likelihood of COVID-19 transmission on the ambient concentration of CO2,
concluding that even this moderate increase in CO2 concentration results in a significant increase in overall risk.
These observations …

Ambient carbon dioxide concentration correlates with SARS-CoV-2 aerostability and infection risk - Nature Communications
Carbon dioxide concentration has previously been used as a proxy for overall ventilation efficiency to indirectly estimate the risk of indoor SARS-CoV-2 transmission. Here, authors investigate whether the concentration of carbon dioxide also has a direct mechanistic role in improving transmission efficiency.

How to use and interpret activation patching
Stefan Heimersheim, Neel Nanda
https://arxiv.org/abs/2404.15255 https://arxiv.org/pdf/24…

How to use and interpret activation patching
Activation patching is a popular mechanistic interpretability technique, but has many subtleties regarding how it is applied and how one may interpret the results. We provide a summary of advice and best practices, based on our experience using this technique in practice. We include an overview of the different ways to apply activation patching and a discussion on how to interpret the results. We focus on what evidence patching experiments provide about circuits, and on the choice of metric and…

Curvature Augmented Manifold Embedding and Learning
Yongming Liu
https://arxiv.org/abs/2403.14813 https://arxiv.org/pdf/2403.14813

Curvature Augmented Manifold Embedding and Learning
A new dimensional reduction (DR) and data visualization method, Curvature-Augmented Manifold Embedding and Learning (CAMEL), is proposed. The key novel contribution is to formulate the DR problem as a mechanistic/physics model, where the force field among nodes (data points) is used to find an n-dimensional manifold representation of the data sets. Compared with many existing attractive-repulsive force-based methods, one unique contribution of the proposed method is to include a non-pairwise fo…

This https://arxiv.org/abs/2303.15232 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Effects of molecular noise on cell size control
Cells employ control strategies to maintain a stable size. Dividing at a target size (the `sizer' strategy) is thought to produce the tightest size distribution. However, this result follows from phenomenological models that ignore the molecular mechanisms required to implement the strategy. Here we investigate a simple mechanistic model for exponentially growing cells whose division is triggered at a molecular abundance threshold. We find that size noise inherits the molecular noise and is con…

Mechanistic Insights into Temperature Effects for Ionic Conductivity in Li6PS5Cl
Zicun Li, Jianxing Huang, Xinguo Ren, Jinbin Li, Ruijuan Xiao, Hong Li
https://arxiv.org/abs/2403.14116

Mechanistic Insights into Temperature Effects for Ionic Conductivity in Li6PS5Cl
Ensuring solid-state lithium batteries perform well across a wide temperature range is crucial for their practical use. Molecular dynamics (MD) simulations can provide valuable insights into the temperature dependence of the battery materials, however, the high computational cost of ab initio MD poses challenges for simulating ion migration dynamics at low temperatures. To address this issue, accurate machine-learning interatomic potentials were trained, which enable efficient and reliable simu…

Curvature Augmented Manifold Embedding and Learning
Yongming Liu
https://arxiv.org/abs/2403.14813 https://arxiv.org/pdf/2403.14813

Curvature Augmented Manifold Embedding and Learning
A new dimensional reduction (DR) and data visualization method, Curvature-Augmented Manifold Embedding and Learning (CAMEL), is proposed. The key novel contribution is to formulate the DR problem as a mechanistic/physics model, where the force field among nodes (data points) is used to find an n-dimensional manifold representation of the data sets. Compared with many existing attractive-repulsive force-based methods, one unique contribution of the proposed method is to include a non-pairwise fo…

Beyond Personhood: Agency, Accountability, and the Limits of Anthropomorphic Ethical Analysis
Jessica Dai
https://arxiv.org/abs/2404.13861 https://<…

Beyond Personhood: Agency, Accountability, and the Limits of Anthropomorphic Ethical Analysis
What is agency, and why does it matter? In this work, we draw from the political science and philosophy literature and give two competing visions of what it means to be an (ethical) agent. The first view, which we term mechanistic, is commonly--and implicitly--assumed in AI research, yet it is a fundamentally limited means to understand the ethical characteristics of AI. Under the second view, which we term volitional, AI can no longer be considered an ethical agent. We discuss the implications…

Semantic Goodhart's Law: as a descriptive concept becomes prescriptive, its meaning starts to blur and expand, eventually becoming an empty placeholder for "good". In advanced stages its valence flips and eliminativism arises.
cf. rigorous, formal, optimal, causal, mechanistic

Universal Differential Equations as a Common Modeling Language for Neuroscience
Ahmed ElGazzar, Marcel van Gerven
https://arxiv.org/abs/2403.14510 https://…

Universal Differential Equations as a Common Modeling Language for Neuroscience
The unprecedented availability of large-scale datasets in neuroscience has spurred the exploration of artificial deep neural networks (DNNs) both as empirical tools and as models of natural neural systems. Their appeal lies in their ability to approximate arbitrary functions directly from observations, circumventing the need for cumbersome mechanistic modeling. However, without appropriate constraints, DNNs risk producing implausible models, diminishing their scientific value. Moreover, the int…

Edge-wave phase-shifts versus normal-mode phase-tilts in an Eady problem with a sloping boundary
Julian Mak, Nili Harnik, Eyal Heifetz, Gautam Kumar, Ellie Q. Y. Ong
https://arxiv.org/abs/2404.12417

Edge-wave phase-shifts versus normal-mode phase-tilts in an Eady problem with a sloping boundary
One mechanistic interpretation of baroclinic instability is that of mutual constructive interference of Rossby edge-waves. While the two edge-waves and their relative phase-shifts are invoked as part of the mechanistic interpretation, the phase-tilts of the related normal modes are often presented instead. Here we highlight the differences between edge-wave phase-shifts and normal-mode phase-tilts, in the context of an Eady problem modified by the presence of a sloping boundary. We argue and pr…

How to think step-by-step: A mechanistic understanding of chain-of-thought reasoning
Subhabrata Dutta, Joykirat Singh, Soumen Chakrabarti, Tanmoy Chakraborty
https://arxiv.org/abs/2402.18312

How to think step-by-step: A mechanistic understanding of chain-of-thought reasoning
Despite superior reasoning prowess demonstrated by Large Language Models (LLMs) with Chain-of-Thought (CoT) prompting, a lack of understanding prevails around the internal mechanisms of the models that facilitate CoT generation. This work investigates the neural sub-structures within LLMs that manifest CoT reasoning from a mechanistic point of view. From an analysis of LLaMA-2 7B applied to multistep reasoning over fictional ontologies, we demonstrate that LLMs deploy multiple parallel pathways…

#LongCovid
Three papers of interest regarding Long Covid:
1.
Liew, F., Efstathiou, C., Fontanella, S. et al.
Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease.
Nat Immunol (2024).

Cooperation in a non-Ergodic World on a Network -- Insurance and Beyond
Tobias Wand, Oliver Kamps, Benjamin Skjold
https://arxiv.org/abs/2403.12095 https:/…

Cooperation in a non-Ergodic World on a Network -- Insurance and Beyond
Cooperation between individuals is emergent in all parts of society, yet mechanistic reasons for this emergence is ill understood in the literature. A specific example of this is insurance. Recent work has, though, shown that assuming the risk individuals face is proportional to their wealth and optimising the time average growth rate rather than the ensemble average results in a non-zero-sum game, where both parties benefit from cooperation through insurance contracts. In a recent paper, Peter…

PURE: Turning Polysemantic Neurons Into Pure Features by Identifying Relevant Circuits
Maximilian Dreyer, Erblina Purelku, Johanna Vielhaben, Wojciech Samek, Sebastian Lapuschkin
https://arxiv.org/abs/2404.06453

PURE: Turning Polysemantic Neurons Into Pure Features by Identifying Relevant Circuits
The field of mechanistic interpretability aims to study the role of individual neurons in Deep Neural Networks. Single neurons, however, have the capability to act polysemantically and encode for multiple (unrelated) features, which renders their interpretation difficult. We present a method for disentangling polysemanticity of any Deep Neural Network by decomposing a polysemantic neuron into multiple monosemantic "virtual" neurons. This is achieved by identifying the relevant sub-graph ("circu…

A mechanistic model for smallpox transmission via inhaled aerosols inside respiratory pathways
Saikat Basu, Abir Malakar, Mohammad Mehedi Hasan Akash
https://arxiv.org/abs/2403.04064

A mechanistic model for smallpox transmission via inhaled aerosols inside respiratory pathways
Investigations on airborne transmission of pathogens constitute a rapidly expanding field, primarily focused on understanding the expulsion patterns of respiratory particulates from infected hosts and their dispersion in confined spaces. Largely overlooked has been the crucial role of fluid dynamics in guiding inhaled virus-laden particulates within the respiratory cavity, thereby directing the pathogens to the infection-prone upper airway sites. Here, we discuss a multi-scale approach for mode…

This https://arxiv.org/abs/2305.18331 has been replaced.
initial toot: https://mastoxiv.page/@a…

Operando Characterization and Molecular Simulations Reveal the Growth Kinetics of Graphene on Liquid Copper during Chemical Vapor Deposition
In recent years, liquid metal catalysts have emerged as a compelling choice for the controllable, large-scale, and high-quality synthesis of two-dimensional materials. At present, there is little mechanistic understanding of the intricate catalytic process, though, of its governing factors or what renders it superior to growth at the corresponding solid catalysts. Here, we report on a combined experimental and computational study of the kinetics of graphene growth during chemical vapor depositi…

This https://arxiv.org/abs/2403.01678 has been replaced.
link: https://scholar.google.com/scholar?q=a

Structured methods for parameter inference and uncertainty quantification for mechanistic models in the life sciences
Parameter inference and uncertainty quantification are important steps when relating mathematical models to real-world observations, and when estimating uncertainty in model predictions. However, methods for doing this can be computationally expensive, particularly when the number of unknown model parameters is large. The aim of this study is to develop and test an efficient profile likelihood-based method, which takes advantage of the structure of the mathematical model being used. We do this …

From virtual patients to digital twins in immuno-oncology: lessons learned from mechanistic quantitative systems pharmacology modeling
Hanwen WangDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA, Theinmozhi ArulrajDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA, Alberto IppolitoDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA, A…

From virtual patients to digital twins in immuno-oncology: lessons learned from mechanistic quantitative systems pharmacology modeling
Virtual patients and digital patients/twins are two similar concepts gaining increasing attention in health care with goals to accelerate drug development and improve patients' survival, but with their own limitations. Although methods have been proposed to generate virtual patient populations using mechanistic models, there are limited number of applications in immuno-oncology research. Furthermore, due to the stricter requirements of digital twins, they are often generated in a study-specific…

This https://arxiv.org/abs/2310.14809 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_nli…

Learning spatio-temporal patterns with Neural Cellular Automata
Neural Cellular Automata (NCA) are a powerful combination of machine learning and mechanistic modelling. We train NCA to learn complex dynamics from time series of images and PDE trajectories. Our method is designed to identify underlying local rules that govern large scale dynamic emergent behaviours. Previous work on NCA focuses on learning rules that give stationary emergent structures. We extend NCA to capture both transient and stable structures within the same system, as well as learning …

Estimating time-varying exposure effects through continuous-time modelling in Mendelian randomization
Haodong Tian, Ashish Patel, Stephen Burgess
https://arxiv.org/abs/2403.05336 …

Estimating time-varying exposure effects through continuous-time modelling in Mendelian randomization
Mendelian randomization is an instrumental variable method that utilizes genetic information to investigate the causal effect of a modifiable exposure on an outcome. In most cases, the exposure changes over time. Understanding the time-varying causal effect of the exposure can yield detailed insights into mechanistic effects and the potential impact of public health interventions. Recently, a growing number of Mendelian randomization studies have attempted to explore time-varying causal effects…

Matter and cosmogenesis in Kant's Theory of the Heavens
Garance Benoit (UP1 UFR10)
https://arxiv.org/abs/2403.11710 https://arxiv…

Matter and cosmogenesis in Kant's Theory of the Heavens
In 1755, Kant published his General Natural History and Theory of the Heavens, in which he presented his hypothesis on the formation of the solar system, known as the primitive nebula hypothesis. This original theory of the heavens was written in dialogue with the conceptions of celestial matter of his time. On the one hand, Kant recognized Descartes' cosmological enterprise as a decisive mechanistic requirement for the intelligence of physics, but his vortices of matter fell into disrepute: Ne…

Dynamics of Polarization Under Normative Institutions and Opinion Expression Stewarding
Atrisha Sarkar, Gillian K. Hadfield
https://arxiv.org/abs/2403.06264

Dynamics of Polarization Under Normative Institutions and Opinion Expression Stewarding
Although there is mounting empirical evidence for the increase in affective polarization, few mechanistic models can explain its emergence at the population level. The question of how such a phenomenon can emerge from divergent opinions of a population on an ideological issue is still an open issue. In this paper, we establish that human normativity, that is, individual expression of normative opinions based on beliefs about the population, can lead to population-level polarization when ideolog…

This https://arxiv.org/abs/2401.08069 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Prediction challenge: First principles simulation of the ultrafast electron diffraction spectrum of cyclobutanone
Computer simulation has long been an essential partner of ultrafast experiments, allowing the assignment of microscopic mechanistic detail to low-dimensional spectroscopic data. However, the ability of theory to make a priori predictions of ultrafast experimental results is relatively untested. Herein, as a part of a community challenge, we attempt to predict the signal of an upcoming ultrafast photochemical experiment using state-of-the-art theory in the context of preexisting experimental dat…

DC-Augmented Dielectric Barrier Discharge (DCA-DBD)
Anthony Tang, Alberto Aliseda, Alexander Mamishev, Igor Novosselov
https://arxiv.org/abs/2403.18064 htt…

DC-Augmented Dielectric Barrier Discharge (DCA-DBD)
Time-dependent multiphysics interactions that drive the energy transfer in electromechanical systems are poorly understood. We probe dielectric barrier discharge (DBD) with an external DC-augmented (DCA) field to reveal new mechanistic insights. The biased HV DC electrode influences the interaction between the charged ions and the E-field, surface and space charge, and neutral molecules. Direct force measurement, velocity profiles, and time-resolved electrical and optical measurements of discha…

SimFair: Physics-Guided Fairness-Aware Learning with Simulation Models
Zhihao Wang, Yiqun Xie, Zhili Li, Xiaowei Jia, Zhe Jiang, Aolin Jia, Shuo Xu
https://arXiv.org/abs/2401.15270

SimFair: Physics-Guided Fairness-Aware Learning with Simulation Models
Fairness-awareness has emerged as an essential building block for the responsible use of artificial intelligence in real applications. In many cases, inequity in performance is due to the change in distribution over different regions. While techniques have been developed to improve the transferability of fairness, a solution to the problem is not always feasible with no samples from the new regions, which is a bottleneck for pure data-driven attempts. Fortunately, physics-based mechanistic mode…

Data-driven network analysis using local delay embeddings
Stefan Klus, Hongyu Zhu
https://arXiv.org/abs/2401.16848 https://arXiv.org/…

Data-driven network analysis using local delay embeddings
Data-driven methods for the identification of the governing equations of dynamical systems or the computation of reduced surrogate models play an increasingly important role in many application areas such as physics, chemistry, biology, and engineering. Given only measurement or observation data, data-driven modeling techniques allow us to gain important insights into the characteristic properties of a system, without requiring detailed mechanistic models. However, most methods assume that we h…

This https://arxiv.org/abs/2301.13368 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference
Simulation-based inference techniques are indispensable for parameter estimation of mechanistic and simulable models with intractable likelihoods. While traditional statistical approaches like approximate Bayesian computation and Bayesian synthetic likelihood have been studied under well-specified and misspecified settings, they often suffer from inefficiencies due to wasted model simulations. Neural approaches, such as sequential neural likelihood (SNL) avoid this wastage by utilising all mode…

Simulation of perovskite thin layer crystallization with varying evaporation rates
M. Majewski, S. Qiu, O. Ronsin, L. L\"uer, V. M. Le Corre, T. Du, C. J. Brabec, H. -J. Egelhaaf, J. Harting
https://arxiv.org/abs/2404.09739

Simulation of perovskite thin layer crystallization with varying evaporation rates
Perovskite solar cells (PSC) are promising potential competitors to established photovoltaic technologies due to their superior efficiency and low-cost solution processability. However, the limited understanding of the crystallization behaviour hinders the technological transition from lab-scale cells to modules. In this work, we perform Phase Field (PF) simulations of the doctor-bladed film formation to obtain mechanistic and morphological information that is experimentally challenging to acce…

This https://arxiv.org/abs/2308.00421 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Effect of Synaptic Heterogeneity on Neuronal Coordination
Recent advancements in measurement techniques have resulted in an increasing amount of data on neural activities recorded in parallel, revealing largely heterogeneous correlation patterns across neurons. Yet, the mechanistic origin of this heterogeneity is largely unknown because existing theoretical approaches linking structure and dynamics in neural circuits are restricted to population-averaged connectivity and activity. Here we present a systematic inclusion of heterogeneity in network conn…

Modeling of Zircaloy Oxidation Through Dynamic Mesh Deformation
A. Scolaro, E. Brunetto, C. Fiorina
https://arxiv.org/abs/2404.03454 https://

Modeling of Zircaloy Oxidation Through Dynamic Mesh Deformation
Zircaloy cladding corrosion in Light Water Reactors (LWRs) results in the formation of an outer oxide layer and in the thinning of the metallic portion of the cladding. At the École Polytechnique Fédérale de Lausanne (EPFL), we aim to investigate the impact of asymmetric oxide or crud layers and their feedback on thermal-hydraulics. To achieve this, we are implementing mechanistic capabilities for the treatment of corrosion within the open-source multi-dimensional code OFFBEAT. In this work,…

This https://arxiv.org/abs/2402.04498 has been replaced.
link: https://scholar.google.com/scholar?q=a

Pathspace Kalman Filters with Dynamic Process Uncertainty for Analyzing Time-course Data
Kalman Filter (KF) is an optimal linear state prediction algorithm, with applications in fields as diverse as engineering, economics, robotics, and space exploration. Here, we develop an extension of the KF, called a Pathspace Kalman Filter (PKF) which allows us to a) dynamically track the uncertainties associated with the underlying data and prior knowledge, and b) take as input an entire trajectory and an underlying mechanistic model, and using a Bayesian methodology quantify the different so…

Machine Learning, Density Functional Theory, and Experiments to Understand the Photocatalytic Reduction of CO$_2$ by CuPt/TiO$_2$
Vaidish Sumaria, Takat B. Rawal, Young Feng Li, David Sommer, Jake Vikoren, Robert J. Bondi, Matthias Rupp, Amrit Prasad, Deeptanshu Prasad
https://arxiv.org/abs/2402.08884

Machine Learning, Density Functional Theory, and Experiments to Understand the Photocatalytic Reduction of CO$_2$ by CuPt/TiO$_2$
The photoconversion of CO$_2$ to hydrocarbons is a sustainable route to its transformation into value-added compounds and, thereby, crucial to mitigating the energy and climate crises. CuPt nanoparticles on TiO$_2$ surfaces have been reported to show promising photoconversion efficiency. For further progress, a mechanistic understanding of the catalytic properties of these CuPt/TiO$_2$ systems is vital. Here, we employ \textit{ab-initio} calculations, machine learning, and photocatalysis experi…