Integrated sensing and communication (ISAC) is an encouraging wireless technology which can simultaneously perform both radar and communication functionalities by sharing the same transmit waveform, spectral resource, and hardware platform. Recently emerged symbol-level precoding (SLP) technique exhibits advancement in ISAC systems by leveraging the waveform design degrees of freedom (DoFs) in both temporal and spatial domains. However, traditional SLP-based ISAC systems are designed in a modular paradigm, which potentially limits the overall performance of communication and radar sensing. The high complexity of existing SLP design algorithms is another issue that hurdles the practical deployment. To break through the bottleneck of these approaches, in this paper we propose an end-to-end approach to jointly design the SLP-based dual-functional transmitter and receivers of communication and radar sensing. In particular, we aim to utilize deep learning-based methods to minimize the symbol error rate (SER) of communication users, maximize the detection probability, and minimize the root mean square error (RMSE) of the target angle estimation. Multi-layer perceptron (MLP) networks and a long short term memory (LSTM) network are respectively applied to the transmitter, communication users and radar receiver. Simulation results verify the feasibility of the proposed deep-learning-based end-to-end optimization for ISAC systems and reveal the effectiveness of the proposed neural networks for the end-to-end design.

MOTIVATION: Computational inference of genome organization based on Hi-C sequencing has greatly aided the understanding of chromatin and nuclear organization in three dimensions (3D). However, existing computational methods fail to address the cell population heterogeneity. Here we describe a probabilistic-modeling-based method called CscoreTool-M that infers multiple 3D genome sub-compartments from Hi-C data. RESULTS: The compartment scores inferred using CscoreTool-M represents the probability of a genomic region locating in a specific sub-compartment. Compared to published methods, CscoreTool-M is more accurate in inferring sub-compartments corresponding to both active and repressed chromatin. The compartment scores calculated by CscoreTool-M also help to quantify the levels of heterogeneity in sub-compartment localization within cell populations. By comparing proliferating cells and terminally differentiated non-proliferating cells, we show that the proliferating cells have higher genome organization heterogeneity, which is likely caused by cells at different cell-cycle stages. By analyzing 10 sub-compartments, we found a sub-compartment containing chromatin potentially related to the early-G1 chromatin regions proximal to the nuclear lamina in HCT116 cells, suggesting the method can deconvolve cell cycle stage-specific genome organization among asynchronously dividing cells. Finally, we show that CscoreTool-M can identify sub-compartments that contain genes enriched in housekeeping or cell-type-specific functions. AVAILABILITY AND IMPLEMENTATION: https://github.com/scoutzxb/CscoreTool-M.

Monitoring equipment conditions is of great value in manufacturing, which can not only reduce unplanned downtime by early detecting anomalies of equipment but also avoid unnecessary routine maintenance. With the coming era of Industry 4.0 (or industrial internet), more and more assets and machines in plants are equipped with various sensors and information systems, which brings an unprecedented opportunity to capture large-scale and fine-grained data for effective on-line equipment condition monitoring. However, due to the lack of systematic methods, analysts still find it challenging to carry out efficient analyses and extract valuable information from the mass volume of data collected, especially for process industry (e.g., a petrochemical plant) with complex manufacturing procedures. In this paper, we report the design and implementation of an interactive visual analytics system, which helps managers and operators at manufacturing sites leverage their domain knowledge and apply substantial human judgements to guide the automated analytical approaches, thus generating understandable and trustable results for real-world applications. Our system integrates advanced analytical algorithms (e.g., Gaussian mixture model with a Bayesian framework) and intuitive visualization designs to provide a comprehensive and adaptive semi-supervised solution to equipment condition monitoring. The example use cases based on a real-world manufacturing dataset and interviews with domain experts demonstrate the effectiveness of our system.

Summary: The genome-wide chromosome conformation capture (Hi-C) has revealed that the eukaryotic genome can be partitioned into A and B compartments that have distinctive chromatin and transcription features. Current Principle Component Analyses (PCA)-based method for the A/B compartment prediction based on Hi-C data requires substantial CPU time and memory. We report the development of a method, CscoreTool, which enables fast and memory-efficient determination of A/B compartments at high resolution even in datasets with low sequencing depth. Availability and implementation: https://github.com/scoutzxb/CscoreTool. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

With the acceleration of urbanization and modern civilization, more and more complex regions are formed in urban area. Although understanding these regions could provide huge insights to facilitate valuable applications for urban planning and business intelligence, few methods have been developed to effectively capture the rapid transformation of urban regions. In recent years, the widely applied location-acquisition technologies offer a more effective way to capture the dynamics of a city through analyzing people's movement activities based on mobility data. However, several challenges exist, including data sparsity and difficulties in result understanding and validation. To tackle these challenges, in this paper, we propose MobiSeg, an interactive visual analytics system, which supports the exploration of people's movement activities to segment the urban area into regions sharing similar activity patterns. A joint analysis is conducted on three types of heterogeneous mobility data (i.e., taxi trajectories, metro passenger RFID card data, and telco data), which can complement each other and provide a full picture of people's activities in a region. In addition, advanced analytical algorithms (e.g., non-negative matrix factorization (NMF) based method to capture latent activity patterns, as well as metric learning to calibrate and supervise the underlying analysis) and novel visualization designs are integrated into our system to provide a comprehensive solution to region segmentation in urban areas. We demonstrate the effectiveness of our system via case studies with real-world datasets and qualitative interviews with domain experts.

Centralized matching is a ubiquitous resource allocation problem. In a centralized matching problem, each agent has a preference list ranking the other agents and a central planner is responsible for matching the agents manually or with an algorithm. While algorithms can find a matching which optimizes some performance metrics, they are used as a black box and preclude the central planner from applying his domain knowledge to find a matching which aligns better with the user tasks. Furthermore, the existing matching visualization techniques (i.e. bipartite graph and adjacency matrix) fail in helping the central planner understand the differences between matchings. In this paper, we present VisMatchmaker, a visualization system which allows the central planner to explore alternatives to an algorithm-generated matching. We identified three common tasks in the process of matching adjustment: problem detection, matching recommendation and matching evaluation. We classified matching comparison into three levels and designed visualization techniques for them, including the number line view and the stacked graph view. Two types of algorithmic support, namely direct assignment and range search, and their interactive operations are also provided to enable the user to apply his domain knowledge in matching adjustment.

Collective behavior is an important concept defined to capture behavioral patterns emerged among the crowd spontaneously. In social science, people's behaviors can be regarded as temporal transitions between a set of typical states (e.g., home and work) which are always associated with certain locations. This fact leads to an interesting research topic in developing ways to explore people's collective behavior patterns through movement analysis, which is our focus in this paper. In recent years, massive volumes of spatiotemporal data generated by mobile phones, called telco data, bring an unprecedented opportunity to study collective behaviors in terms of large coverage and fine-grained resolution. However, distilling valuable collective behavior patterns from the large scale of telco data is not an easy task. The challenge is rooted in two aspects, including the data uncertainty as well as the lack of methods to characterize, compare and understand dynamic crowd behaviors, which triggers the use of visual analytics to take full advantage of machines' computational power as well as human's domain knowledge and cognitive abilities. In this paper, we propose TelcoFlow, a comprehensive visual analytics system which incorporates advanced quantitative analyses (e.g., statebased behavior model) and intuitive visualizations (e.g., an extended flow view embedded with state glyphs) to support an efficient and in-depth analysis of collective behaviors based on telco data. Case studies with a real-world dataset and expert interviews are carried out to demonstrate the effectiveness of our system for analysts to gain insights into collective behaviors and facilitate various analytical tasks.

Nowadays, various data collected in urban context provide unprecedented opportunities for building a smarter city through urban computing. However, due to heterogeneity, high complexity and large volumes of these urban data, analyzing them is not an easy task, which often requires integrating human perception in analytical process, triggering a broad use of visualization. In this survey, we first summarize frequently used data types in urban visual analytics, and then elaborate on existing visualization techniques for time, locations and other properties of urban data. Furthermore, we discuss how visualization can be combined with automated analytical approaches. Existing work on urban visual analytics is categorized into two classes based on different outputs of such combinations: 1) For data exploration and pattern interpretation, we describe representative visual analytics tools designed for better insights of different types of urban data. 2) For visual learning, we discuss how visualization can help in three major steps of automated analytical approaches (i.e., cohort construction; feature selection & model construction; result evaluation & tuning) for a more effective machine learning or data mining process, leading to sort of artificial intelligence, such as a classifier, a predictor or a regression model. Finally, we outlook the future of urban visual analytics, and conclude the survey with potential research directions.

Understanding co-occurrence in urban human mobility (i.e. people from two regions visit an urban place during the same time span) is of great value in a variety of applications, such as urban planning, business intelligence, social behavior analysis, as well as containing contagious diseases. In recent years, the widespread use of mobile phones brings an unprecedented opportunity to capture large-scale and fine-grained data to study co-occurrence in human mobility. However, due to the lack of systematic and efficient methods, it is challenging for analysts to carry out in-depth analyses and extract valuable information. In this paper, we present TelCoVis, an interactive visual analytics system, which helps analysts leverage their domain knowledge to gain insight into the co-occurrence in urban human mobility based on telco data. Our system integrates visualization techniques with new designs and combines them in a novel way to enhance analysts' perception for a comprehensive exploration. In addition, we propose to study the correlations in co-occurrence (i.e. people from multiple regions visit different places during the same time span) by means of biclustering techniques that allow analysts to better explore coordinated relationships among different regions and identify interesting patterns. The case studies based on a real-world dataset and interviews with domain experts have demonstrated the effectiveness of our system in gaining insights into co-occurrence and facilitating various analytical tasks.

The availability of massive volumes of trajectory data has made it convenient for the study of different types of movement behaviors. Among them, bi-directional movement behaviors exist ubiquitously in our daily life, from urban traffic to animal migration, and from sports to wars. To analyze bi-directional movement behaviors, people need to compare movements in two directions simultaneously for detecting similarities or differences in the movement patterns. If the movement involves tens of thousands items like vehicles or bird migration during a ten-year time span, the comparisons need to be done at both macro level and micro level. Due to the complexities of data and the challenges of analytical tasks, visual analytics is often used to take full advantage of machines' computational power as well as human's domain knowledge and cognitive abilities. In this paper, we present a comprehensive visual analytics system with three major visualization modules, including Global View, OD-pair Flow View and Isotime Storyline View, to depict bi-directional movement behaviors in a novel way, which enables a three-level exploration to help users gain insights into both macro and micro patterns. Quantitative analyses (e.g. movement model construction, modular Dol specification and key node extraction) and intuitive visualizations (e.g. parallelized flow map, bidirectional storyline chart with contour map and multi-layer heat map) are integrated into our system to provide an efficient and intuitive solution to the analysis of bi-directional movement behaviors based on big movement data. Case studies with two real-world datasets and expert interviews are carried out to demonstrate the effectiveness and usefulness of our system.