13 papers · ranked by Valyu relevance
Albert A. Smith, Kai Zumpfe
Nuclear magnetic resonance is a powerful method for characterizing dynamics of biological systems in a native-like environment. Accurate dynamics characterization, however, often requires simulations of complex NMR experiments. While a number of simulation programs exist for NMR simulation (SIMPSON, Spinach…
Stephen Coshatt, He Yang, Shushan Wu, Jin Ye + 5 more
As machine learning and artificial intelligence are being integrated into cyber-physical systems, it is becoming important for engineers to know and understand these topics. In particular, sensor data is on the rise in these systems and therefore engineers need to understand which models are appropriate to time-series…
Julie R. Pivin-Bachler, Egon L. van den Broek
Title: Summary Machine learning struggles with imbalanced data. Although several mitigation approaches exist, their application depends on the extent of imbalance. To determine the latter, a protocol was developed. Across 428 synthetic and 70 real datasets, 8 imbalance measures were benchmarked and evaluated using…
Alexander Muacevic, John R Adler, Ankit Joshi, Kinju Adhikari + 6 more
Artificial intelligence (AI) and machine learning (ML) are transforming urological practice; however, most clinicians lack the technical background required to develop, evaluate, or critically appraise predictive models. Existing resources are often written by data scientists for a technical audience, highlighting the…
Han Zhang, John Jonides
We present PupEyes, an open-source Python package for preprocessing and visualizing pupil size and fixation data. PupEyes supports data collected from EyeLink and Tobii eye-trackers as well as any generic dataset that conforms to minimal formatting standards. Developed with current best practices, PupEyes provides a…
Hayoung K Donnelly, David Mandell, Sy Hwang, Emily Schriver + 11 more
Background The use of artificial intelligence (AI) to analyze health care data has become common in behavioral health sciences. However, the lack of training opportunities for mental health professionals limits clinicians’ ability to adopt AI in clinical settings. AI education is essential for trainees, equipping them…
Stephen R Piccolo, Harlan P Stevens
Using OpenAI’s Chat Completions API, we evaluated the ability to translate the example solutions and test code for the Python exercises to other programming languages: C++, Rust, Julia, and JavaScript. When invoking the API, we used version “gpt-4-0314” of the model and the default temperature setting of 0.7. For each…
Gino Carmona-Díaz, William Jiménez-Leal, María Alejandra Grisales, Chandra Sripada + 3 more
Analyzing texts such as open-ended responses, headlines, or social media posts is a time- and labor-intensive process highly susceptible to bias. However, large language models (LLMs) are promising tools for text analysis, using either a predefined (top-down) or a data-driven (bottom-up) taxonomy, without sacrificing…
Yuanyi Ding, Yipeng Zhang, Chenda Duan, Atsuro Daida + 7 more
Objective. Accurate detection and classification of high-frequency oscillations (HFOs) in electroencephalography (EEG) recordings have become increasingly important for identifying epileptogenic zones in patients with drug-resistant epilepsy. However, few open-source platforms offer both state-of-the-art computational…
Augusto Borges, Jerónimo R. Miranda-Rodríguez, Alberto S. Ceccarelli, Guilherme Ventura + 3 more
Title: Summary Stress inference offers a rapid computational approach for estimating the mechanical state of a tissue without requiring invasive experiments. Here, we present a protocol for inferring mechanical stresses in tissues using ForSys, an open-source Python tool. We describe steps for preparing the Python…
Indrajeet Mandal, Jitendra Soni, Mohd Zaki, Morten M. Smedskjaer + 4 more
Large language models (LLMs) are transforming laboratory automation by enabling self-driving laboratories (SDLs) that could accelerate materials research. However, current SDL implementations rely on rigid protocols that fail to capture the adaptability and intuition of expert scientists in dynamic experimental…
Jaka Kokošar, Ela Praznik, Martin Špendl, Nancy P. Moreno + 4 more
In biomedicine, survival analysis addresses time-to-event data to study outcomes like patient survival and treatment response, and supports biomarker discovery. Yet, teaching this analysis is often hindered by mathematical and programming barriers. We present a structured, hands-on tutorial that goes beyond a typical…
Jonas Engicht, R. Maximilian Bee, Tobias Koch
This paper introduces an open-source Python package for simplified, customizable computerized adaptive testing (CAT) using Bayesian methods for ability estimation. It addresses the lack of sophisticated packages for CAT in the Python programming language. Moreover, it bridges the gap between the construction and…