25 papers · ranked by Valyu relevance
Sizhen Bian, Mengxi Liu, Lala Shakti Swarup Ray, Bo Zhou + 6 more
Sensor-based Human Activity Recognition (HAR) underpins many ubiquitous and wearable computing applications, yet current models remain limited by scarce labels, sensor heterogeneity, and weak generalization across users, devices, and contexts. Foundation models, which are generally pretrained at scale using…
Rishi Bommasani, Kevin Klyman, Shayne Longpre, Sayash Kapoor + 4 more
'Nestor Maslej' 'Betty Xiong' 'Daniel Zhang' 'Percy Liang'] Foundation models have rapidly permeated society, catalyzing a wave of generative AI applications spanning enterprise and consumer-facing contexts. While the societal impact of foundation models is growing, transparency is on the decline, mirroring the opacity…
Xiangrui Liu, Yuanyuan Zhang, Yingzhou Lu, Changchang Yin + 11 more
'Xiaoling Hu' 'Xiaoou Liu' 'Lulu Chen' 'Sheng Wang' 'Alexander Rodriguez' 'Huaxiu Yao' 'Yezhou Yang' 'Ping Zhang' 'Jintai Chen' 'Tianfan Fu' 'Xiao Wang'] > Arizona State University, Tempe, AZ, USA Purdue University, West Lafayette, IN, USA 3 Stanford University, Stanford, CA, USA The Ohio State University, Columbus…
Mariia Radova, Wojciech G. Stark, Connor S. Allen, Reinhard J. Maurer + 1 more
'Reinhard J. Maurer' 'Albert P. Bartók'] Machine-learned interatomic potentials are revolutionising atomistic materials simulations by providing accurate and scalable predictions within the scope covered by the training data. However, generation of an accurate and robust training data set remains a challenge, often…
Max S. Y. Lau, C. Jessica E. Metcalf, Zewen Liu, Bryan T. Grenfell + 1 more
Foundation models-large AI systems pretrained on broad, heterogeneous data-are transforming scientific discovery. These models (e.g., GPT, GenCast, AlphaFold) excel at learning generalizable representations and adapting to new tasks with limited data. Yet, epidemic modeling has not experienced a comparable…
Merten, Gaspard, Sakr, Mahmoud + 2 more
Foundation models are transformative in artificial intelligence, but building them from scratch, especially for mobility trajectories, is not yet clear or documented. This tutorial bridges this gap by demonstrating the steps and code of a minimal implementation of a trajectory-focused foundation model starting from…
Srijan Atti, Shankar Subramaniam
Recent applications of foundation models in biology have focused on pretraining using large-scale single-cell datasets comprising millions of cells, across diverse patho-physiological states. These models are then fine-tuned for downstream tasks such as cell-type classification. In this study, we evaluated the…
William G. Coon, Mattson Ogg
Accurate sleep assessment is critical to the practice of sleep medicine and sleep research. The recent availability of large quantities of publicly available sleep data, alongside recent breakthroughs in AI like transformer architectures, present novel opportunities for data-driven discovery efforts. Transformers are…
Shi-Shuenn Chen, Chi-Jou Kao, Jun-Yang Shi
Torsional vibration, considering soil-structure interaction, is essential to the dynamic response of most irregular structures. A systematic method is developed to seek the optimal simplified model among multiple model candidates for uniform soil on rigid base regarding dynamic soil-foundation interactions. A generic…
Daniel R. Wong, Abby S. Hill, Robert Moccia
Modeling genetic perturbations and their effect on the transcriptome is a key area of pharmaceutical research. Due to the complexity of the transcriptome, there has been much excitement and development in deep learning (DL) because of its ability to model complex relationships. In particular, the transformer-based…
Yi Jiang, Shuang Wang, Shaohong Feng, Cankun Wang + 5 more
Foundation models have transformed AI by leveraging large-scale data to efficiently perform diverse tasks, and their applications in bioinformatics are primarily focused on data-centric tasks like cell type annotation and gene expression analysis. However, their potential extends beyond data analysis, offering…
Xingcun Fan, Wenbin Liao, Luchi Xiao, Xuefeng Yan + 1 more
Pre-trained large models have emerged as a pivotal technological approach for foundational cell modeling. However, existing deep learning-based foundational models for cells have predominantly focused on human or murine systems, with a relative scarcity of research on model microorganisms such as Saccharomyces…
Authors not listed
While artificial intelligence has revolutionized the prediction of static protein structures, characterizing their dynamics and interactions with drug candidates remains a computational bottleneck. Here, we introduce FeNNix-Bio1, a foundation machine learning model designed to power accurate, reactive atomistic…
Zehui Ma, Junjie Wang, Xuefeng Huang, Zhifeng Ren + 2 more
'René de Borst'] The construction of a power grillage is of great significance for promoting local economic development. Identifying the characteristics of foundation damage is a prerequisite for ensuring the normal service of the power grillage. To investigate the bearing mechanism and failure mode of the grillage…
Authors not listed
The rapid advancement of machine learning in computational chemistry has opened new doors for designing molecules, predicting molecular properties, and discovering novel materials. However, building scalable and robust models for molecular property prediction remains a significant challenge due to the vast size and…
Yara Rizk, Praveen Venkateswaran, Vatche Isahagian, Vinod Muthusamy
The inception of large language models has helped advance state-of-the-art performance on numerous natural language tasks. This has also opened the door for the development of foundation models for other domains and data modalities such as images, code, and music. In this paper, we argue that business process data…
Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega-Guerrero, Berend Smit
Machine learning has revolutionized many fields and has recently found applications in chemistry and materials science. The small datasets commonly found in chemistry sparked the development of sophisticated machine-learning approaches that incorporate chemical knowledge for each application and, therefore, require…
Jonathan Amar, Edward Liu, Alessandra Breschi, Liangliang Zhang + 12 more
This paper introduces an innovative Electronic Health Record (EHR) foundation model that integrates Polygenic Risk Scores (PRS) as a foundational data modality, moving beyond traditional EHR-only approaches to build more holistic health profiles. Leveraging the extensive and diverse data from the All of Us (AoU)…
Mohammad Hashemi, Hossein Amiri, Andreas Zufle
With the rapid growth and continual updates of geospatial data from diverse sources, geospatial foundation model pre-training for urban representation learning has emerged as a key research direction for advancing data-driven urban planning. Spatial structure is fundamental to effective geospatial intelligence systems…
Authors not listed
Recent years have seen a growing interest in machine learning approaches for chemical tasks. The best existing methods focus on building base models that combine molecular graphs (“2D structures”) with atomic coordinates in 3D to predict molecular properties, typically through pre-training followed by fine-tuning on…
Authors not listed
Supervised deep learning has become a standard approach to deliver competitive predictive tools that allow relating the structure of molecules and their physicochemical features to properties such as binding to protein targets, performance as electronic materials, and reactivity. However, efforts to understand how…
Zheng Han, Haohui Ding, Hongdi Yan, Chuicheng Zeng + 4 more
'Wendu Xie' 'Bangjie Fu' 'Yange Li'] Bearing capacity degradation of foundations under the impact of the flood is one of the major reasons responsible for the collapse and damage to the rural buildings, posing a serious threat to the local village societies. Based on a case study of a rural building foundation had been…
Rachana Niranjan Murthy, Sai Teja Potu, Akhil Thomas, Lokesh Mishra + 2 more
Retrieving structured materials information from unstructured textual data is essential for data mining and automatically developing comprehensive ontologies. Information extraction is a complex task composed of multiple subtasks and thus often relies on systems of task-specialized language models. A foundation…
Chengyu Hong, Jinyang Zhang, Weibin Chen, Francesco Lamonaca
As the scale of foundation pit projects of subway stations in Shenzhen becomes larger, and the construction constraints become more and more complex, there is an urgent need for intelligent monitoring and safety management of foundation pits. In this study, an integrated intelligent approach for monitoring and…
O. Ebenhöh, M. van Aalst, N.P. Saadat, T. Nies + 1 more
The modelbase package is a free expandable Python package for building and analysing dynamic mathematical models of biological systems. Originally it was designed for the simulation of metabolic systems, but it can be used for virtually any deterministic chemical processes. modelbase provides easy construction methods…