The frequency-dependent beamforming technology has demonstrated outstanding performance in two-dimensional beam training for wideband THz systems, owing to its synchronized and high-resolution scanning capabilities. However, extending this technique to three-dimensional beam training poses challenges, as linearly split beams cannot provide comprehensive angular coverage in the candidate area. To this end, we propose a two-stage 3D beam training scheme, consisting of an initial stage and a refinement stage. In the initial stage, the angular coverage of frequency-dependent beamforming is modeled as a controllable rectangular region, and the codebook is designed by arranging this rectangular region within the candidate area. In the refinement stage, frequency-dependent beamforming is densely arranged around the candidate positions estimated in the initial stage to achieve precise localization. Additionally, we design a true-time delay network to facilitate frequencydependent beamforming for uniform planar arrays. Simulation results show that the proposed method approaches near-optimal performance in terms of achievable sum-rate while maintaining acceptable pilot overhead.

Biological networks describe complex relationships in biological systems, which represent biological entities as vertices and their underlying connectivity as edges. Ideally, for a complete analysis of such systems, domain experts need to visually integrate multiple sources of heterogeneous data , and visually, as well as numerically, probe said data in order to explore or validate (mechanistic) hypotheses. Such visual analyses require the coming together of biological domain experts, bioinformaticians, as well as network scientists to create useful visualization tools. Owing to the underlying graph data becoming ever larger and more complex, the visual representation of such biological networks has become challenging in its own right. This introduction and survey aims to describe the current state of biological network visualization in order to identify scientific gaps for visualization experts, network scientists, bioinformaticians, and domain experts, such as biologists, or biochemists, alike. Specifically, we revisit the classic visualization pipeline, upon which we base this paper’s taxonomy and structure, which in turn forms the basis of our literature classification. This pipeline describes the process of visualizing data, starting with the raw data itself, through the construction of data tables, to the actual creation of visual structures and views, as a function of task-driven user interaction. Literature was systematically surveyed using API-driven querying where possible, and the collected papers were manually read and categorized based on the identified sub-components of this visualization pipeline’s individual steps. From this survey, we highlight a number of exemplary visualization tools from multiple biological sub-domains in order to explore how they adapt these discussed techniques and why. Additionally, this taxonomic classification of the collected set of papers allows us to identify existing gaps in biological network visualization practices. We finally conclude this report with a list of open challenges and potential research directions. Examples of such gaps include (i) the overabundance of visualization tools using schematic or straight-line node-link diagrams, despite the availability of powerful alternatives, or (ii) the lack of visualization tools that also integrate more advanced network analysis techniques beyond basic graph descriptive statistics.

MOTIVATION: Gene regulatory networks (GRNs) encode gene regulation in living organisms, and have become a critical tool to understand complex biological processes. However, due to the dynamic and complex nature of gene regulation, inferring GRNs from scRNA-seq data is still a challenging task. Existing computational methods usually focus on the close connections between genes, and ignore the global structure and distal regulatory relationships. RESULTS: In this study, we develop a supervised deep learning framework, IGEGRNS, to infer GRNs from scRNA-seq data based on graph embedding. In the framework, contextual information of genes is captured by GraphSAGE, which aggregates gene features and neighborhood structures to generate low-dimensional embedding for genes. Then, the k most influential nodes in the whole graph are filtered through Top-k pooling. Finally, potential regulatory relationships between genes are predicted by stacking CNNs. Compared with nine competing supervised and unsupervised methods, our method achieves better performance on six time-series scRNA-seq datasets. AVAILABILITY AND IMPLEMENTATION: Our method IGEGRNS is implemented in Python using the Pytorch machine learning library, and it is freely available at https://github.com/DHUDBlab/IGEGRNS.

Multiclass contour visualization is often used to interpret complex data attributes in such fields as weather forecasting, computational fluid dynamics, and artificial intelligence. However, effective and accurate representations of underlying data patterns and correlations can be challenging in multiclass contour visualization, primarily due to the inevitable visual cluttering and occlusions when the number of classes is significant. To address this issue, visualization design must carefully choose design parameters to make visualization more comprehensible. With this goal in mind, we proposed a framework for multiclass contour visualization. The framework has two components: a set of four visualization design parameters, which are developed based on an extensive review of literature on contour visualization, and a declarative domain-specific language (DSL) for creating multiclass contour rendering, which enables a fast exploration of those design parameters. A task-oriented user study was conducted to assess how those design parameters affect users' interpretations of real-world data. The study results offered some suggestions on the value choices of design parameters in multiclass contour visualization.

We propose UNICON, a UNIform CONstraint based graph layout framework that supports both soft and hard constraints. We extend the stress model to accommodate soft constraints by incorporating them in the objective functions, optimized by stochastic gradient descent. For hard constraints, such as inequalities or equalities in the layout space, we utilize a gradient projection method to satisfy them. A visualization prototype system is implemented based on this framework for the user to interactively add or remove constraints to generate the desired layouts. We demonstrate the efficiency, quality, and flexibility of the framework and the system on a number of datasets with a wide range of user-defined constraints.