787 research outputs found
Scientific Language Modeling: A Quantitative Review of Large Language Models in Molecular Science
Efficient molecular modeling and design are crucial for the discovery and
exploration of novel molecules, and the incorporation of deep learning methods
has revolutionized this field. In particular, large language models (LLMs)
offer a fresh approach to tackle scientific problems from a natural language
processing (NLP) perspective, introducing a research paradigm called scientific
language modeling (SLM). However, two key issues remain: how to quantify the
match between model and data modalities and how to identify the
knowledge-learning preferences of models. To address these challenges, we
propose a multi-modal benchmark, named ChEBI-20-MM, and perform 1263
experiments to assess the model's compatibility with data modalities and
knowledge acquisition. Through the modal transition probability matrix, we
provide insights into the most suitable modalities for tasks. Furthermore, we
introduce a statistically interpretable approach to discover context-specific
knowledge mapping by localized feature filtering. Our pioneering analysis
offers an exploration of the learning mechanism and paves the way for advancing
SLM in molecular science
GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and Text
Large language models have made significant strides in natural language
processing, paving the way for innovative applications including molecular
representation and generation. However, most existing single-modality
approaches cannot capture the abundant and complex information in molecular
data. Here, we introduce GIT-Mol, a multi-modal large language model that
integrates the structure Graph, Image, and Text information, including the
Simplified Molecular Input Line Entry System (SMILES) and molecular captions.
To facilitate the integration of multi-modal molecular data, we propose
GIT-Former, a novel architecture capable of mapping all modalities into a
unified latent space. Our study develops an innovative any-to-language
molecular translation strategy and achieves a 10%-15% improvement in molecular
captioning, a 5%-10% accuracy increase in property prediction, and a 20% boost
in molecule generation validity compared to baseline or single-modality models.Comment: 16 pages, 5 figure
Effects of Perioperative Psychological Intervention on Rehabilitation Process of Patients with Total Knee Arthroplasty
Background: This study focuses on evaluating the effects of perioperative psychological intervention on rehabilitation process of patients with total knee arthroplasty (TKA). Method: We selected 40 patients randomly which all need to receive total knee arthroplasty in Nanjing Drum Tower Hospital during the period from January 2022 to March 2022. The patients were randomly assigned to two Groups (20 in each group): an intervention group (Psychological intervention combined with routine nursing, drug and rehabilitation therapy) and a control group (routine nursing, drug rehabilitation therapy). During each patients’ perioperative TKA surgeries, three scales (including VAS, ROM and ADL) are used to assess two groups. Result: After one week of psychological intervention, the pain score of the intervention group was lower than that of control group, the knee motion was greater than that of control group, and the ADL score was higher than that of control group. There was a significant difference in the treatment recovery between the two groups (P<0.05) Conclusions: Perioperative psychological intervention can promote the rehabilitation process of TKA patients, It can significantly improve pain, joint activity limitation, disuse muscle atrophy and other problems in a short period of time after surgery. Besides, it will effectively help patients to overcome the fear of movement, anxiety and improve patients' confidence, rehabilitation cooperation and prevention of complications, make patients adapt to the later rehabilitation life
Reliable and Safe Motion Control of Unmanned Vehicles
Unmanned vehicles (UVs) are playing an increasingly significant role in modern daily life. In the past decades, numerous commercial, scientific, and military communities across the world are developing fully autonomous UVs for a variety of applications, such as environmental monitoring and surveillance, post-disaster search and rescue, border patrol, natural resources exploration, and experimental platforms for new technologies verification. The excessive opportunities and threats that come along with these diverse applications have created a niche demand for UVs to extend their capabilities to perform more sophisticated and hazardous missions with greater autonomy, lower costs of development and operation, improved personnel safety and security, extended operational range (reliability) and precision, as well as increased flexibility in sophisticated environments including so-called dirty, dull, harsh, and dangerous missions.
In order to successfully and effectively execute missions and meet their corresponding performance criteria and overcome these ever-increasing challenges, greater autonomy together with more advanced reliable and safe motion control systems are required to offer the critical technologies for ensuring intelligent, safe, reliable, and efficient control of UVs in the presence of disturbances, actuator saturation, and even actuator faults, especially for practical applications.
This thesis concentrates on the development of different reliable and safe motion control algorithms/strategies applicable to UVs, in particular, unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs). A number of contributions pertaining to the fault detection and diagnosis (FDD), fault-tolerant control (FTC), disturbance estimation and compensation, and actuator saturation avoidance have been made in this thesis. In addition to the control problems, this thesis also presents several guidance-related contributions, including adaptive observer-based line-of-sight (LOS) guidance law, time-varying lookahead distance scheme, piecewise path switching criterion for guiding a single UV, as well as a proportional-integral (PI) type of leader-follower formation guidance strategy for a group of UVs
Asymptotic properties of spiked eigenvalues and eigenvectors of signal-plus-noise matrices with their applications
This paper is to consider a general low-rank signal plus noise model in high
dimensional settings. Specifically, we consider the noise with a general
covariance structure and the signal to be at the same magnitude as the noise.
Our study focuses on exploring various asymptotic properties related to the
spiked eigenvalues and eigenvectors. As applications, we propose a new
criterion to estimate the number of clusters, and investigate the properties of
spectral clustering
- …