Task scheduling has become increasingly critical for embodied AI, where agents need to follow natural language instructions and execute actions efficiently in 3D physical worlds. Existing datasets for task planning in 3D environments often simplify the problem, lacking operations research knowledge for task scheduling and 3D grounding for real-world applications. In this work, we propose Operations Research Knowledge-based 3D Grounded Task Scheduling (OKS3D), a new task that requires synerization of language understanding, 3D grounding, and efficiency optimization for embodied agents. OKS3D reflects real-world demands by requiring agents to generate efficient, step-by-step schedules that are grounded in 3D space. To facilitate research on OKS3D, we construct a large-scale dataset called OKS3D-60K, comprising 60K tasks across 4K real-world scenes. Furthermore, we propose GRANT, an embodied multi-modal large language model equipped with a simple yet effective scheduling token mechanism to generate efficient task schedules and grounded actions. Extensive experiments on the OKS3D-60K dataset validate the effectiveness of GRANT across language understanding, 3D grounding, and scheduling efficiency.

MOTIVATION: Macrocyclic peptides hold great promise as therapeutics targeting intracellular proteins. This stems from their remarkable ability to bind flat protein surfaces with high affinity and specificity while potentially traversing the cell membrane. Research has already explored their use in developing inhibitors for intracellular proteins, such as KRAS, a well-known driver in various cancers. However, computational approaches for de novo macrocyclic peptide design remain largely unexplored. RESULTS: Here, we introduce HELM-GPT, a novel method that combines the strength of the hierarchical editing language for macromolecules (HELM) representation and generative pre-trained transformer (GPT) for de novo macrocyclic peptide design. Through reinforcement learning (RL), our experiments demonstrate that HELM-GPT has the ability to generate valid macrocyclic peptides and optimize their properties. Furthermore, we introduce a contrastive preference loss during the RL process, further enhanced the optimization performance. Finally, to co-optimize peptide permeability and KRAS binding affinity, we propose a step-by-step optimization strategy, demonstrating its effectiveness in generating molecules fulfilling both criteria. In conclusion, the HELM-GPT method can be used to identify novel macrocyclic peptides to target intracellular proteins. AVAILABILITY AND IMPLEMENTATION: The code and data of HELM-GPT are freely available on GitHub (https://github.com/charlesxu90/helm-gpt).

Accurate estimates of aboveground biomass (AGB) are valuable for monitoring forest degradation and carbon stocks on Earth. However, the validity of multiple data types and diverse data combinations for AGB estimation is unclear. In this study, recursive feature elimination (RFE) combined with machine-learning regression models for AGB were developed using field data and multi-source remote sensing data, which included Sentinel-1, Sentinel-2, PALSAR, and DEM. The spatial distribution of AGB was mapped for the Daxing’anling region in the northernmost part of China at 30m resolution. We compared the ability of multiple data combinations to perform AGB estimation and found that using all four types of data combinations resulted in the highest estimation accuracy with fewer predictors. The combination of diverse data sources substantiates enhancements in the precision of AGB estimation, surpassing the utilization of singular or dual sensor modalities. In addition to the optical remote sensing data sentinel-2, topographic data has a non-negligible role in the AGB estimation in this study, even more than microwave remote sensing data. Finally, the extreme gradient boosting model (R2=0.67, RMSE=22.57 Mg/ha) based on the combination of all four data types had the highest accuracy and mapped the AGB of the study area. The results indicate that the AGB can be estimated with reasonable accuracy for the boreal forest region based on publicly available multi-source remote sensing data. This study proposes diverse data combinations as well as derived variables for AGB estimation, aiming to explore the possibilities of more remote sensing data in AGB studies.

SARS-CoV-2 caused the first severe pandemic of the digital era. Computational approaches have been ubiquitously used in an attempt to timely and effectively cope with the resulting global health crisis. In order to extensively assess such contribution, we collected, categorized and prioritized over 17 000 COVID-19-related research articles including both peer-reviewed and preprint publications that make a relevant use of computational approaches. Using machine learning methods, we identified six broad application areas i.e. Molecular Pharmacology and Biomarkers, Molecular Virology, Epidemiology, Healthcare, Clinical Medicine and Clinical Imaging. We then used our prioritization model as a guidance through an extensive, systematic review of the most relevant studies. We believe that the remarkable contribution provided by computational applications during the ongoing pandemic motivates additional efforts toward their further development and adoption, with the aim of enhancing preparedness and critical response for current and future emergencies.

The article aims to explore certain reliability probability models using a Hamiltonian Monte Carlo (HMC) sampler. There are some concerns with the classic HMC samplers. A notable aspect of the method comes from its acceptance probability whose value is constant, “1,” in theory. One practical problem is the possibility of divergence. A further issue emerges from the simulation trajectory, which essentially traverses on the last principal component. Also, the method is extremely sensitive to run-time parameters. As an improvement, Riemann Manifold HMC can travel along the first principal component. However, its overall accuracy on other components remains unclear. The no-U-turn sampler can adjust the distance traveled, but it may also suffer from excessive simulation steps. To address these concerns, this article proposes: to implement the conservation of energy by virtual collision among particles; to adopt constant simulation steps and adjust the acceptance probability and step size; and to alternate two kinds of trajectories to reconcile all principal components. Experiments show that the proposed algorithm is able to estimate the reliability and remaining useful life of the probability models. To bridge theory and practice, algorithm fragment is demonstrated with Mathematica language.