MOTIVATION: Proteins play a crucial role in biological systems, and accurate protein sequence prediction is essential for applications such as drug discovery. Existing inverse folding models primarily rely on protein backbone structure information, overlooking the biochemical properties embedded in protein surface data that constrain its functionality, leading to limited prediction accuracy. RESULTS: We propose a novel inverse folding framework, SurfFold, which integrates both protein backbone structure and surface information for sequence prediction. Additionally, it incorporates side-chain structural information and its interaction with surface information. Then, we introduce a Representation alignment module to better fuze structure and surface Representations. Experimental results demonstrate that SurfFold achieves state-of-the-art performance on the CATH4.2 dataset, and additional experiments validate the effectiveness of the proposed modules. Moreover, the homologous structure inverse folding experiment also demonstrates that SurfFold possesses excellent capability in homologous protein design. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/jiudizhengf/SurfFold.

OBJECTIVE: Accurately measuring patient similarity is essential for precision medicine, enabling personalized predictive modeling, disease subtyping, and individualized treatment by identifying patients with similar characteristics to an index patient. This study aims to develop an electronic health record-based patient similarity estimation framework to enhance personalized predictive modeling for Acute Kidney Injury (AKI), a complex and life-threatening condition where accurate prediction is critical for timely intervention. MATERIALS AND METHODS: We introduce Similarity Measurement for Acute Kidney Injury Risk Tracking (SMART), a new patient similarity estimation framework with 3 key enhancements: (1) overlap weighting to adjust similarity scores; (2) distance measure optimization; and (3) feature type weight optimization. These enhancements were evaluated using internal and external validation datasets from 2 tertiary academic hospitals to predict AKI risk across varying group sizes of similar patients. RESULTS: The study analyzed data from 8637 patients in the reference patient pool and 8542 patients in each of the internal and external test sets. Each enhancement was independently evaluated while controlling for other variables to determine its impact on prediction performance. SMART consistently outperformed 3 baseline models on both the internal and external test sets (P<.05) and demonstrated improved performance in certain subpopulations with unique health profiles compared to a traditional machine learning approach. DISCUSSION: SMART improves the identification of high-quality similar patient groups, enhancing the accuracy of personalized AKI prediction across various group sizes. By accurately identifying clinically relevant similar patients, clinicians can tailor treatments more effectively, advancing personalized care.

This study explores the effect of takeover time (TOT) on decision-making for Level-3 autonomous vehicles (L3-AVs). The existing research on L3-AV lacks an in-depth analysis of the mechanisms affecting TOT, ignores the importance of spatial and temporal variations in features for TOT prediction, and also lacks consideration of TOT in downstream trajectory planning tasks. This study proposed an exponential smoothing transformers (ETS) former model for TOT prediction, and then, the spatial-temporal predictive transformer (ST-Preformer) was employed to forecast the trajectories of surrounding vehicles, assess lane availability, and determine lane-changing probabilities. Ultimately, these evaluations contribute to the decision-making process of L3-AVs. The findings showed that the ETSformer was able to explain more than 83% of the characteristics of the TOT distribution in the TOT prediction task, effectively reducing the absolute percentage error by 0.7%, based on which the decision-making framework was able to make safe and comfortable optimal decisions. Decision-making is closely related to driving conditions and the surrounding traffic state, and TOT has a critical impact on the safety and stability of decision-making. A comprehensive understanding the impact of TOT on decision-making can help improve the safety of autonomous driving and provide guidance for improving decision-making techniques.

The sequential recommendation (also known as the next-item recommendation), which aims to predict the following item to recommend in a session according to users’ historical behavior, plays a critical role in improving session-based recommender systems. Most of the existing deep learning-based approaches utilize the recurrent neural network architecture or self-attention to model the sequential patterns and temporal influence among a user's historical behavior and learn the user's preference at a specific time. However, these methods have two main drawbacks. First, they focus on modeling users’ dynamic states from a user-centric perspective and always neglect the dynamics of items over time. Second, most of them deal with only the first-order user-item interactions and do not consider the high-order connectivity between users and items, which has recently been proved helpful for the sequential recommendation. To address the above problems, in this article, we attempt to model user-item interactions by a bipartite graph structure and propose a new recommendation approach based on a Position-enhanced and Time-aware Graph Convolutional Network (PTGCN) for the sequential recommendation. PTGCN models the sequential patterns and temporal dynamics between user-item interactions by defining a position-enhanced and time-aware graph convolution operation and learning the dynamic representations of users and items simultaneously on the bipartite graph with a self-attention aggregator. Also, it realizes the high-order connectivity between users and items by stacking multi-layer graph convolutions. To demonstrate the effectiveness of PTGCN, we carried out a comprehensive evaluation of PTGCN on three real-world datasets of different sizes compared with a few competitive baselines. Experimental results indicate that PTGCN outperforms several state-of-the-art sequential recommendation models in terms of two commonly-used evaluation metrics for ranking. In particular, it can make a better trade-off between recommendation performance and model training efficiency, which holds great potential for online session-based recommendation scenarios in the future.

The economic expenditure of mobile network operators includes two parts, namely CAPEX and OPEX. CAPEX mainly includes the huge amount of capital invested in network infrastructure construction, while operating expenditure mainly includes expenditure for daily operation and maintenance. In order to achieve continuous coverage of wireless network, CAPEX needed for base station procurement is indispensable. Operators need to adopt more intelligent and scaled means to optimize the maintenance process of wireless network so as to better achieve the goal of cost reduction and efficiency increase. In this paper, a scheme of cell boundary prediction and base station location information verification based on machine learning is proposed, which innovatively introduces the machine learning algorithm into network optimization analysis and improve the verification efficiency and reduce the input of manpower.

This paper presents an object classification method for vision and light detection and ranging (LIDAR) fusion of autonomous vehicles in the environment. This method is based on convolutional neural network (CNN) and image upsampling theory. By creating a point cloud of LIDAR data upsampling and converting into pixel-level depth information, depth information is connected with Red Green Blue data and fed into a deep CNN. The proposed method can obtain informative feature representation for object classification in autonomous vehicle environment using the integrated vision and LIDAR data. This method is also adopted to guarantee both object classification accuracy and minimal loss. Experimental results are presented and show the effectiveness and efficiency of object classification strategies.

The recommender systems community is paying great attention to diversity as key qualities beyond accuracy in real recommendation scenarios. Multifarious diversity-increasing approaches have been developed to enhance recommendation diversity in the related literature while making personalized recommendations to users. In this work, we present Gaussian Cloud Recommendation Algorithm (GCRA), a novel method designed to balance accuracy and diversity personalized top-N recommendation lists in order to capture the user's complete spectrum of tastes. Our proposed algorithm does not require semantic information. Meanwhile we propose a unified framework to extend the traditional CF algorithms via utilizing GCRA for improving the recommendation system performance. Our work builds upon prior research on recommender systems. Though being detrimental to average accuracy, we show that our method can capture the user's complete spectrum of interests. Systematic experiments on three real-world data sets have demonstrated the effectiveness of our proposed approach in learning both accuracy and diversity.

To support an efficient carpooling service in heavy urban traffic, we propose an intelligent routing scheme based on mining Global Position System trajectories from shared riders. The carpooling system provides many-to-many services with multiple pickup and dropping points. To join a daily carpooling group, the riders must accept a compromised route that is efficient after merging the routes that are preferred by all qualified riders. We developed three frequency-correlated algorithms for route mining, rider selection, and route merging in an urban carpool service. Our approach can cope with the traffic dynamics to yield a suboptimal shared route. Our scheme was successfully tested under heavy Beijing traffic over hundreds of riders. We developed performance metrics to measure the service cost and mileage saved. The ultimate goal is to minimize the riding distances and the transportation costs, and thus alleviate urban traffic jams.

In this work, we address a novel problem about tag co-occurrence relationship prediction across heterogeneous networks. Although tag co-occurrence has recently become a hot research topic, many studies mainly focus on how to produce the personalized recommendation leveraging the tag co-occurrence relationship and most of them are considered in a homogeneous network. So far, few studies pay attention to how to predict tag co-occurrence relationship across heterogeneous networks. In order to solve the aforementioned problem, we propose a novel two-step prediction approach. First, weight path-based topological features are systematically extracted from the network. Then, a supervised model is used to learn the best weights associated with different topological features in deciding the co-occurrence relationships. Experiments are performed on real-world dataset, the Flickr network, with comprehensive measurements. Experimental results demonstrate that weight path-based heterogeneous topological features have substantial advantages over commonly used link prediction approaches in predicting co-occurrence relations in information networks.

Granular computing is one of the important methods for extracting knowledge from data and has got great achievements. However, it is still a puzzle for granular computing researchers to imitate the human cognition process of choosing reasonable granularities automatically for dealing with difficult problems. In this paper, a Gaussian cloud transformation method is proposed to solve this problem, which is based on Gaussian Mixture Model and Gaussian Cloud Model. Gaussian Mixture Model (GMM) is used to transfer an original data set to a sum of Gaussian distributions, and Gaussian Cloud Model (GCM) is used to represent the extension of a concept and measure its confusion degree. Extensive experiments on data clustering and image segmentation have been done to evaluate this method and the results show its performance and validity.

Although recommendation techniques have achieved distinct developments over the decades, the data sparseness problem of the involved user-item matrix still seriously influences the recommendation quality. Most of the existing techniques for recommender systems cannot easily deal with users who have very few ratings. How to combine the increasing amount of different types of social information such as user generated content and social relationships to enhance the prediction precision of the recommender systems remains a huge challenge. In this paper, based on a factor graph model, we formalize the problem in a semi-supervised probabilistic model, which can incorporate different user information, user relationships, and user-item ratings for learning to predict the unknown ratings. We evaluate the method in two different genres of datasets, Douban and Last.fm. Experiments indicate that our method outperforms several state-of-the-art recommendation algorithms. Furthermore, a distributed learning algorithm is developed to scale up the approach to real large datasets.

Network interconnection, information interoperability, and crowds interaction on the Internet could inspire better computation models than Turing machine, since that human plays an important factor in Internet computing, so that the human-machine and machine-machine interactions have evolved to be the kernel of Internet computing. Internet has not been simply equivalent to a virtual huge computer, or a set of computers. On the Internet, human's behaviors are uncertain, the interactions and influence among people are also uncertain. These uncertainties cannot be described by Turing machine and traditional interaction machine. As a new computation platform, Internet computing requires new theories and methods. By combining topology in mathematics with the field theory in physics, we propose the topological potential approach, which set up a virtual field by the topological space to reflect individual activities, local effects and preferential attachment. This approach can be used to research the emergence of collective intelligence. Here, we introduce three case studies to illustrate the analysis on the collective intelligence on the Internet and discuss some potential applications of the topological potential approach.

Autonomous vehicles are very hot research area which advance artificial intelligent, pattern recognition, computer vision, sensor fusion and control theory. In the paper we demonstrate a kind of micro-autonomous vehicle, describing hardware and software architectures, perception system and control method-cloud control. Our goal is study interaction behaviors between autonomous vehicles and how to take appropriate measure to avoid driving conditions hazardous. We propose a new control alogrithm-cloud control algorithm and experiments show cloud control algorithm has good performance and flexibility.

Randomness and fuzziness are the two most important uncertainties inherent in human cognition, which have attracted great attention in artificial intelligence research. In this paper, regarding linguistic terms or concepts as the basic units of human cognition, we propose a new cognitive model—cloud model, which can synthetically describe the randomness and fuzziness of concepts and implement the uncertain transformation between a qualitative concept and its quantitative instantiations. Furthermore, by analyzing in detail the statistical properties of normal cloud model, that is, an important kind of cloud models based on normal distribution and Gauss membership function, we show that normal cloud model can not only be viewed as a generalized normal distribution with weak constraints but also avoid the flaw of fuzzy sets to quantify the membership degree of an element as an accurate value between 0 and 1 and, therefore, may be more adaptive for the uncertainty description of linguistic concepts. Finally, two demonstration examples about the fractal evolution of plants and network topologies based on cloud models are given to illustrate the promising applications of cloud models in some more complex knowledge representation tasks. © 2009 Wiley Periodicals, Inc.