Patterns最新文献

The advent of easy-to-use large language models (LLMs) such as ChatGPT has started an arms race in academia between students who use AI and faculty trying to detect that use. This unproductive battle must end, and faculty can help broker peace by rethinking assignments and using LLMs where appropriate.

Current societal challenges exceed the capacity of humans operating either alone or collectively. As AI evolves, its role within human collectives will vary from an assistive tool to a participatory member. Humans and AI possess complementary capabilities that, together, can surpass the collective intelligence of either humans or AI in isolation. However, the interactions in human-AI systems are inherently complex, involving intricate processes and interdependencies. This review incorporates perspectives from complex network science to conceptualize a multilayer representation of human-AI collective intelligence, comprising cognition, physical, and information layers. Within this multilayer network, humans and AI agents exhibit varying characteristics; humans differ in diversity from surface-level to deep-level attributes, while AI agents range in degrees of functionality and anthropomorphism. We explore how agents' diversity and interactions influence the system's collective intelligence and analyze real-world instances of AI-enhanced collective intelligence. We conclude by considering potential challenges and future developments in this field.

Class imbalance is a challenge that affects the prediction rate on a minority class. To remedy this problem, various SMOTEs (synthetic minority over-sampling techniques) have been designed to populate synthetic minority instances. Some SMOTEs operate on the border of a minority class, while others concentrate on the class core. Unfortunately, it is difficult to put the right SMOTE to the right dataset because distributions of classes are varied and might not be obvious. This paper proposes a new technique, called FLEX-SMOTE, that is flexible enough to be used with all sorts of datasets. The key idea is that an over-sampled region is selected based on the characteristics of minority classes. This approach is based on a density function that is used to describe the distributions of minority classes. Herein, we have included experimental results showing that FLEX-SMOTE can significantly improve the predictive performance of a minority class.

The reuse of research software is central to research efficiency and academic exchange. The application of software enables researchers to reproduce, validate, and expand upon study findings. The analysis of open-source code aids in the comprehension, comparison, and integration of approaches. Often, however, no further use occurs because relevant software cannot be found or is incompatible with existing research processes. This results in repetitive software development, which impedes the advancement of individual researchers and entire research communities. In this article, the DataDesc (Data Description) framework is presented-an approach to describing data models of software interfaces with machine-actionable metadata. In addition to a specialized metadata schema, an exchange format and support tools for easy collection and the automated publishing of software documentation are introduced. This approach practically increases the FAIRness, i.e., findability, accessibility, interoperability, and reusability, of research software as well as effectively promotes its impact on research.

Organoids and 3D imaging techniques are crucial for studying human tissue structure and function, but traditional 3D reconstruction methods are expensive and time consuming, relying on complete z stack confocal microscopy data. This paper introduces VONet, a deep learning-based system for 3D organoid rendering that uses a fully convolutional neural network to reconstruct entire 3D structures from a minimal number of z stack images. VONet was trained on a library of over 39,000 virtual organoids (VOs) with diverse structural features and achieved an average intersection over union of 0.82 in performance validation. Remarkably, VONet can predict the structure of deeper focal plane regions, unseen by conventional confocal microscopy. This innovative approach and VO dataset offer significant advancements in 3D bioimaging technologies.

Manual review is an integral part of any study. As the cost of data generation continues to decrease, the rapid rise in large-scale multi-omic studies calls for a modular, flexible framework to perform what is currently a tedious, error-prone process. We developed AnnoMate, a Python-based package built with Plotly Dash that creates interactive, highly customizable dashboards for reviewing and annotating data. Its object-oriented framework enables easy development and modification of custom dashboards for specific manual review tasks. We utilized this framework to implement “reviewer” dashboards for various tasks often performed in cancer genome sequencing studies.

For over 30 years, synthetic data have been heralded as a solution to make sensitive datasets accessible. However, despite much research effort, its adoption as a tool for research with sensitive data is lacking. This article argues that to make progress in this regard, the data science community should focus on improving the accessibility of existing privacy-friendly synthesis techniques.

The “stacking cell puzzle” is a data visualization project consisting of a three-dimensional puzzle made with electron microscopy data of planarian cells.

The escalating integration of machine learning in high-stakes fields such as healthcare raises substantial concerns about model fairness. We propose an interpretable framework, fairness-aware interpretable modeling (FAIM), to improve model fairness without compromising performance, featuring an interactive interface to identify a “fairer” model from a set of high-performing models and promoting the integration of data-driven evidence and clinical expertise to enhance contextualized fairness. We demonstrate FAIM’s value in reducing intersectional biases arising from race and sex by predicting hospital admission with two real-world databases, the Medical Information Mart for Intensive Care IV Emergency Department (MIMIC-IV-ED) and the database collected from Singapore General Hospital Emergency Department (SGH-ED). For both datasets, FAIM models not only exhibit satisfactory discriminatory performance but also significantly mitigate biases as measured by well-established fairness metrics, outperforming commonly used bias mitigation methods. Our approach demonstrates the feasibility of improving fairness without sacrificing performance and provides a modeling mode that invites domain experts to engage, fostering a multidisciplinary effort toward tailored AI fairness.