Oldest and Highest Rated Papers

Date: Oldest Rating: Highest Clear all

Advanced filtersTopic: All

Experimental

Topic

Topics are auto-detected from title, abstract, and metadata and may be imperfect.

Publication Date

Newest Oldest Clear

Average Rating

Highest Lowest Clear

All Too Well: The Short Film – a deep dive - irene

Substack

Substack·2025

As Taylor Swift prepares to grace us once again with her directorial skills, I shall take it as my duty to remind people of her remarkable visual storytelling prowess. Yes, it is that serious to me.

No ratings yet

View paper →

irene (@ireneplm): "Started writing this rather impulsively and ended up spending an embarrasing amount of time on it for me not to post it. enjoy."

Substack

Substack·2025

Started writing this rather impulsively and ended up spending an embarrasing amount of time on it for me not to post it. enjoy.

No ratings yet

View paper →

Sensitivity Analysis for Causal ML: A Use Case at Booking.com

Philipp Bach, Victor Chernozhukov, Carlos Cinelli, Lin Jia, Sven Klaassen, Nils Skotara, Martin Spindler

arXiv·2025

Causal Machine Learning has emerged as a powerful tool for flexibly estimating causal effects from observational data in both industry and academia. However, causal inference from observational data relies on untestable assumptions about the data-generating process, such as the absence of unobserved confounders. When these assumptions are violated, causal effect estimates may become biased, undermining the validity of research findings. In these contexts, sensitivity analysis plays a crucial role, by enabling data scientists to assess the robustness of their findings to plausible violations of unconfoundedness. This paper introduces sensitivity analysis and demonstrates its practical relevance through a (simulated) data example based on a use case at Booking.com. We focus our presentation on a recently proposed method by Chernozhukov et al. (2023), which derives general non-parametric bounds on biases due to omitted variables, and is fully compatible with (though not limited to) modern inferential tools of Causal Machine Learning. By presenting this use case, we aim to raise awareness of sensitivity analysis and highlight its importance in real-world scenarios.

No ratings yet

View paper →

Interactions between plasmids and other mobile genetic elements affect their transmission and persistence

Francisco Dionisio, Rita Zilhão, João Alves Gama

Plasmid·2019·99 citations

No abstract available

No ratings yet

View paper →

Mobile genetic elements: the agents of open source evolution

Laura S. Frost, Raphael Leplae, Anne O. Summers, Ariane Toussaint

Nature Reviews Microbiology·2005·1466 citations

No abstract available

No ratings yet

View paper →

PreviousPage 160 of 467Next

Pattern recognition in the nucleation kinetics of non-equilibrium self-assembly

Constantine Glen Evans, Jackson O’Brien, Erik Winfree, Arvind Murugan

Nature·2024·55 citations

AbstractInspired by biology’s most sophisticated computer, the brain, neural networks constitute a profound reformulation of computational principles1–3. Analogous high-dimensional, highly interconnected computational architectures also arise within information-processing molecular systems inside living cells, such as signal transduction cascades and genetic regulatory networks4–7. Might collective modes analogous to neural computation be found more broadly in other physical and chemical processes, even those that ostensibly play non-information-processing roles? Here we examine nucleation during self-assembly of multicomponent structures, showing that high-dimensional patterns of concentrations can be discriminated and classified in a manner similar to neural network computation. Specifically, we design a set of 917 DNA tiles that can self-assemble in three alternative ways such that competitive nucleation depends sensitively on the extent of colocalization of high-concentration tiles within the three structures. The system was trained in silico to classify a set of 18 grayscale 30 × 30 pixel images into three categories. Experimentally, fluorescence and atomic force microscopy measurements during and after a 150 hour anneal established that all trained images were correctly classified, whereas a test set of image variations probed the robustness of the results. Although slow compared to previous biochemical neural networks, our approach is compact, robust and scalable. Our findings suggest that ubiquitous physical phenomena, such as nucleation, may hold powerful information-processing capabilities when they occur within high-dimensional multicomponent systems.

No ratings yet

View paper →

Gradient Estimation Using Stochastic Computation Graphs

John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel

arXiv·2015

In a variety of problems originating in supervised, unsupervised, and reinforcement learning, the loss function is defined by an expectation over a collection of random variables, which might be part of a probabilistic model or the external world. Estimating the gradient of this loss function, using samples, lies at the core of gradient-based learning algorithms for these problems. We introduce the formalism of stochastic computation graphs---directed acyclic graphs that include both deterministic functions and conditional probability distributions---and describe how to easily and automatically derive an unbiased estimator of the loss function's gradient. The resulting algorithm for computing the gradient estimator is a simple modification of the standard backpropagation algorithm. The generic scheme we propose unifies estimators derived in variety of prior work, along with variance-reduction techniques therein. It could assist researchers in developing intricate models involving a combination of stochastic and deterministic operations, enabling, for example, attention, memory, and control actions.

No ratings yet

View paper →

Monitoring microplastics in live reef-building corals with microscopic laser particles

Vera M. Titze, Jessica Reichert, Marcel Schubert, Malte C. Gather

arXiv·2025

Micro- and nanoplastics pose a growing threat to marine organisms, such as reef-building corals. Yet, our understanding of microplastic uptake, interaction with coral tissue, and incorporation into coral skeletons remains limited, mainly due to the invasiveness of existing methods for detecting microplastics. Here, we exploit optical resonances in polymer spheres to transform microplastic particles into microscopic lasers. The bright, distinctive, and stable spectral signatures emitted by the microscopic laser particles function as optical barcodes, allowing extended tracking of microplastics transport through optically opaque coral tissue. Simultaneously, the lasers provide real-time sensing of dynamic changes at the microplastic surface with nanoscale resolution. Using confocal hyperspectral imaging, we establish the technical and analytical framework to capture coral anatomy and combine tracking and surface sensing into an integrated, non-invasive approach. With this, we explore the transport and internalization of individual microplastics in live corals, opening new avenues for understanding their ecological impact.

No ratings yet

View paper →

Oldest and Highest Rated Papers

All Too Well: The Short Film – a deep dive - irene

irene (@ireneplm): "Started writing this rather impulsively and ended up spending an embarrasing amount of time on it for me not to post it. enjoy."

Sensitivity Analysis for Causal ML: A Use Case at Booking.com

Interactions between plasmids and other mobile genetic elements affect their transmission and persistence

Mobile genetic elements: the agents of open source evolution

A Cyborg Manifesto

Episodic memory

The Elements of a Business Plan: First Steps for New Entrepreneurs

Pattern recognition in the nucleation kinetics of non-equilibrium self-assembly

Gradient Estimation Using Stochastic Computation Graphs

Monitoring microplastics in live reef-building corals with microscopic laser particles

AIDS-Videoaktivismus und die Entstehung des "Archivs" - Deutsche Digitale Bibliothek