With the continuous advancement of sequencing technology, the scale of biological data has rapidly increased. BAM format, widely used for storing aligned sequence data, is very popular due to its ease of use and good compression ratio. However, existing BAM-format file I/O libraries often fail to fully leverage the computational power of modern multi-core platforms, resulting in low CPU utilization. To address this, we introduce RabbitBAM, a fast BAM-format file I/O library. RabbitBAM employs pre-parsing and parallel parsing techniques to eliminate parsing bottlenecks and improve parallel efficiency. Additionally, we optimize multi-threaded data handling through the use of dedicated lock-free queues and memory pools. RabbitBAM achieves 2.1-3.3x speedups on next-generation sequencing data and 1-2.2x speedups on third-generation sequencing data compared to state-of-the-art SAMtools (HTSlib). We also present two case studies (BAM file quality control and sorting) using RabbitBAM, demonstrating 1.4-2.4x speedups compared to other implementations.

Human mobility prediction is a fundamental task essential for various applications in urban planning, location-based services and intelligent transportation systems. Existing methods often ignore activity information crucial for reasoning human preferences and routines, or adopt a simplified representation of the dependencies between time, activities and locations. To address these issues, we present Hierarchical Graph Attention Recurrent Network (Hgarn) for human mobility prediction. Specifically, we construct a hierarchical graph based on past mobility records and employ a Hierarchical Graph Attention Module to capture complex time-activity-location dependencies. This way, Hgarn can learn representations with rich human travel semantics to model user preferences at the global level. We also propose a model-agnostic history-enhanced confidence (MaHec) label to incorporate each user’s individual-level preferences. Finally, we introduce a Temporal Module, which employs recurrent structures to jointly predict users’ next activities and their associated locations, with the former used as an auxiliary task to enhance the latter prediction. For model evaluation, we test the performance of Hgarn against existing state-of-the-art methods in both the recurring (i.e., returning to a previously visited location) and explorative (i.e., visiting a new location) settings. Overall, Hgarn outperforms other baselines significantly in all settings based on two real-world human mobility data benchmarks. These findings confirm the important role that human activities play in determining mobility decisions, illustrating the need to develop activity-aware intelligent transportation systems. Source codes of this study are available athttps://github.com/YihongT/HGARN

Short-term route prediction on road networks allows us to anticipate the future trajectories of road users, enabling various applications ranging from dynamic traffic control to personalized navigation. Despite recent advances in this area, existing methods focus primarily on learning sequential transition patterns, neglecting the inherent spatial relations in road networks that can affect human routing decisions. To fill this gap, this paper introduces RouteKG, a novel Knowledge Graph-based framework for route prediction. Specifically, we construct a Knowledge Graph on the road network to encode spatial relations, especially moving directions that are crucial for human navigation. Moreover, an n-ary tree-based algorithm is introduced to efficiently generate top-K routes in batch mode, enhancing computational efficiency. To further optimize prediction performance, a rank refinement module is incorporated to fine-tune candidate route rankings. The model performance is evaluated using two real-world vehicle trajectory datasets from two Chinese cities under various practical scenarios. The results demonstrate a significant improvement in accuracy over the baseline methods. We further validate the proposed method by utilizing the pre-trained model as a simulator for real-time traffic flow estimation at the link level. RouteKG has great potential to transform vehicle navigation, traffic management, and a variety of intelligent transportation tasks, playing a crucial role in advancing the core foundation of intelligent and connected urban systems. The source codes of RouteKG are available athttps://github.com/YihongT/RouteKG

Ovarian cancer remains a significant global health challenge and its survival prognosis plays an important role in clinic practice. In this paper, we design a broad learning model for survival prognosis (SPBL) to enhance the accuracy and ob-jectivity of survival predictions for ovarian cancer patients using machine learning techniques. Prognoses often rely exclusively on lesion information; however, our SPBL model integrates multi-source data. We also apply an interpretable SHAP model, to provide transparent and understandable explanations for the 3-year survival prognosis generated by the SPBL model. Based on the SEER database, we evaluate the propose models. The SPBL model exhibited notable improvements in performance, achieving increases in AUC, F1 score, and accuracy by 1.34%, 7.51%, and 6.08% compared with Random Forest, AdaBoost, GBDT, and XGBoost. The significance analysis suggest that our model can significantly enhance the prognosis process, leading to better-informed clinical decisions and optimized treatment strategies.

Traditional conflict analysis methods, relying on the assumption of constant velocity, often fall short in capturing the dynamic nature of driver behavior randomness during the interaction process. Predicting all potential collision trajectories proves crucial for comprehensive safety analysis. To address the challenge of accounting for trajectory randomness in car-following scenarios, this study introduces a noise-enhanced generative adversarial network, named Car-Following GAN, designed for predicting collision trajectories based on data from the Shanghai Naturalistic Driving Study (SH-NDS). The model employs an encoder-decoder framework, integrating a noise enhancement module to capture the intrinsic randomness of driving patterns. Demonstrating notable robustness across varying environmental conditions, our model showcases adaptability for trajectory prediction in diverse driving scenarios. A conflict measure, termed the Rear-end Collision Risk Index based on Car-Following GAN (RCRIC), is proposed to quantify the risk of a rear-end collision. Our approach conducts a comprehensive case analysis to assess the impact of various traffic risk factors on RCRIC. The results underscore that our noise-enhanced approach significantly improves the trajectory prediction accuracy of the model when compared to other noise addition methods. This enhancement is observed across various prediction time windows and under different weather conditions. Moreover, RCRIC, derived from the model employing our noise-enhanced approach, effectively mirrors the dynamics of rear-end collision risk by explicitly incorporating trajectory randomness into its assessment. Furthermore, the findings underscore the significant influence of light conditions, traffic density, and weather conditions on driving risk.

For bike sharing systems, demand prediction is crucial to ensure the timely re-balancing of available bikes according to predicted demand. Existing methods for bike sharing demand prediction are mostly based on its own historical demand variation, essentially regarding it as a closed system and neglecting the interaction between different transportation modes. This is particularly important for bike sharing because it is often used to complement travel through other modes (e.g., public transit). Despite some recent progress, no existing method is capable of leveraging spatiotemporal information from multiple modes and explicitly considers the distribution discrepancy between them, which can easily lead to negative transfer. To address these challenges, this study proposes a domain-adversarial multi-relational graph neural network (DA-MRGNN) for bike sharing demand prediction with multimodal historical data as input. A spatiotemporal adversarial adaptation network is introduced to extract shareable features from demand patterns of different modes. To capture correlations between spatial units across modes, we adapt a multi-relational graph neural network (MRGNN) considering both geographical proximity and mobility pattern similarity. Extensive experiments are conducted using real-world bike sharing, subway and ride-hailing data from New York City. The results demonstrate the superior performance of our proposed approach compared to existing methods and the effectiveness of different model components.

Support Vector Machines (SVMs) are a widely adopted data mining algorithm for binary and multi-class classification due to their ability to handle high-dimensional and non-linearly separable problems. However, SVM training is computationally expensive because of the heavy kernel matrix computation on large training datasets. Although much effort has been made to accelerate the training of SVMs, we find that existing libraries still suffer from inappropriate matrix multiplication methods and inefficient memory access patterns. In this paper, we propose a series of optimization approaches to address these limitations, including (i) matrix partitioning based on column density to achieve efficient kernel matrix computation; (ii) optimizing high latency memory access patterns; and (iii) dynamically selecting more suitable matrix multiplication methods based on the training dataset characteristics. Our proposed methods demonstrate significant improvements in SVM training performance without sacrificing accuracy, achieving a maximum speedup of 52x over the state-of-the-art SVMs on GPUs. These results highlight the effectiveness of our optimization in improving SVM training efficiency.

Assessing the quality of sequencing data plays a crucial role in downstream data analysis. However, existing tools often achieve sub-optimal efficiency, especially when dealing with compressed files or performing complicated quality control operations such as over-representation analysis. We present RabbitQCPlus, an ultra-efficient quality control tool for modern multi-core systems. RabbitQCPlus uses vectorization, memory copy reduction, parallel (de)compression, and optimized data structures to achieve substantial performance gains. It is 1.1 to 5.4 times faster when performing basic quality control operations compared to state-of-the-art applications yet requires fewer compute resources. Moreover, RabbitQCPlus is at least 4 times faster than other applications when processing gzip-compressed FASTQ files. Furthermore, it takes less than 4 minutes to process 280GB of plain FASTQ sequencing data, while other applications take at least 22 minutes on a 48-core server when enabling the per-read over-representation analysis. C++ sources are available at https://github.com/RabbitBio/RabbitQCPlus.

Trajectory prediction of vehicles in city-scale road networks is of great importance to various location-based applications such as vehicle navigation, traffic management, and location-based recommendations. Existing methods typically represent a trajectory as a sequence of grid cells, road segments or intention sets. None of them is ideal, as the cell-based representation ignores the road network structures and the other two are less efficient in analyzing city-scale road networks. Moreover, previous models barely leverage spatial dependencies or only consider them at the grid cell level, ignoring the non-Euclidean spatial structure shaped by irregular road networks. To address these problems, we propose a network-based vehicle trajectory prediction model named NetTraj, which represents each trajectory as a sequence of intersections and associated movement directions, and then feeds them into a LSTM encoder-decoder network for future trajectory generation. Furthermore, we introduce a local graph attention mechanism to capture network-level spatial dependencies of trajectories, and a temporal attention mechanism with a sliding context window to capture both short- and long-term temporal dependencies in trajectory data. Extensive experiments based on two real-world large-scale taxi trajectory datasets show that NetTraj outperforms the existing state-of-the-art methods for vehicle trajectory prediction, validating the effectiveness of the proposed trajectory representation method and spatiotemporal attention mechanisms.

Individual mobility is driven by demand for activities with diverse spatiotemporal patterns, but existing methods for mobility prediction often overlook the underlying activity patterns. Knowledge of activity patterns can improve the performance and interpretability of existing individual mobility models, leading to more informed policy design and better user experience in intelligent transportation systems. This study develops an activity-based modeling framework for individual mobility prediction in mass transit systems. Specifically, an input-output hidden Markov model (IOHMM) approach is proposed to simultaneously predict the (continuous) time and (discrete) location of an individual’s next trip using transit smart card data. The prediction task can be transformed into predicting the hidden activity duration and end location. Based on a case study of Hong Kong’s metro system, we show that the proposed model can achieve similar prediction performance as the state-of-the-art long short-term memory (LSTM) model. Unlike LSTM, the proposed IOHMM approach can also be used to analyze hidden activity patterns, which provides meaningful behavioral interpretation for why an individual makes a certain trip. Therefore, the activity-based prediction framework offers a way to preserve the predictive power of advanced machine learning methods while enhancing our ability to generate insightful behavioral explanations, which is useful for user-centric policy design and intelligent transportation applications such as personalized traveler information.

This paper presents a neural network-based approach for the path planning of arbitrary shaped mobile robots in complex environments, with the consideration of safety. A 2D workspace is discretized to a topologically organized map using a biological neural network, in which the dynamic neural activity landscape represents the environmental information. A set of kernel matrices are established to describe the shape and orientation features of the robot. Taking the safety factor into consideration, the translation and rotation performances of the robot on each neuron node of the workspace are determined using a convolutional neural network (CNN). Then, from the initial state of the robot to the target state, a node rooted tree is constructed by searching the adjacent neurons, and the moving path of the robot is generated by backward searching the node rooted tree. By changing the bias coefficient in the convolutional calculation, the clearance between the planned path and the obstacles can be conveniently adjusted. The effectiveness of the proposed method is demonstrated through several simulations conducted in both static and dynamic environments. The results show that the method can effectively solve the “path blocked” issue caused by small densely scattered obstacles, and also solve the “too close” and “too far” path planning problems.

This paper proposes boundary parallel-like index (BPI) to describe shape features for high-resolution remote sensing image classification. Parallel-like boundary is found to be a discriminating clue which can reveal the shape regularity of segmented objects. Therefore, multi-orientation distance projections were constructed to measure and quantify parallel-like information. The discriminating ability was tested using original and segmented ground objects, respectively. The proposed BPI showed better discrimination for both original and segmented data than for other shape features, especially for buildings. This was also confirmed by the considerably higher accuracy of BPI in building classification experiments of high-resolution remote sensing imagery. It suggests the proposed BPI is useful for building related applications.

Regularity is an important property of individual travel behavior, and the ability to measure it enables advances in behavior modeling, mobility prediction, and customer analytics. In this paper, we propose a methodology to measure travel behavior regularity based on the order in which trips or activities are organized. We represent individuals' travel over multiple days as sequences of “travel events”-discrete and repeatable behavior units explicitly defined based on the research question and the available data. We then present a metric of regularity based on entropy rate, which is sensitive to both the frequency of travel events and the order in which they occur. The methodology is demonstrated using a large sample of pseudonymised transit smart card transaction records from London, U.K. The entropy rate is estimated with a procedure based on the Burrows-Wheeler transform. The results confirm that the order of travel events is an essential component of regularity in travel behavior. They also demonstrate that the proposed measure of regularity captures both conventional patterns and atypical routine patterns that are regular but not matched to the 9-to-5 working day or working week. Unlike existing measures of regularity, our approach is agnostic to calendar definitions and makes no assumptions regarding periodicity of travel behavior. The proposed methodology is flexible and can be adapted to study other aspects of individual mobility using different data sources.

This paper has presented a wearable sleep apnea monitoring wrist watch, its micro controller controls sensors to detect user's respiratory airflow, blood oxygen saturation(SpO2), electrocardiogram(ECG) and breathing movement in real-time. Then our algorithm uses these physiological parameters to estimate whether the apnea occurs or not. In addition, this wrist watch provides motion state of users and some environmental parameters like temperature, humidity and atmospheric pressure. A liquid crystal display(LCD) module is used to show the parameters above. This wrist watch can send all the physiological parameters to the application on smartphone or computer by Bluetooth, where doctors can use these data for further analysis. Based on experimental test, the highly-integrated wrist watch can provide reliable measurement results. It avoids hospital's high cost and complicated procedures, and expands the screening scope of sleep apnea syndrome.

We developed and tested the Institute of Electronics, Chinese Academy of Sciences (IECAS) 3AHcare node, a health monitoring device capable of measuring a subject's ECG, blood pressure, blood oxygenation, respiration, temperature and motion - almost equivalent to the feature set of a hospital bedside patient monitor. The main contribution of this paper include: the device has been a highly integrated design incorporating the radio and all associated circuitry on a single PCB; a new noninvasive and cuff-less measurement of blood pressure using pulse wave transit time has been designed and validated. The device stores data locally on microSD flash and /or transmits via Bluetooth and/or Zigbee. We have developed a bandage vest which embeds reusable electrodes for data acquisition as well as a desktop and mobile application for real-time data telemetry. We have evaluated the performance of the device in capturing and recording ambulatory data and found the device easy to use and with high precision.