MOTIVATION: Chemical genomics is a powerful high-throughput approach to systematically link phenotypes to genotypes. However, the vast datasets generated remain challenging to explore due to the lack of integrated, interactive tools for visualization and analysis. Existing workflows often require multiple independent software tools, limiting data accessibility and collaboration. Therefore, we created a user-friendly platform that enables efficient exploration and sharing of chemical genomics data. RESULTS: We developed ChemGenXplore, a web-based Shiny application designed to streamline the visualization and analysis of chemical genomic screens. It offers two primary functionalities: one for exploring pre-implemented datasets and another for analysing user-uploaded datasets. ChemGenXplore enables users to visualize phenotypic profiles, assess gene-gene and condition-condition correlations, perform GO and KEGG enrichment analysis, and generate customizable, interactive heatmaps. To further support collaborative research, ChemGenXplore also facilitates the comparative analysis of chemical genomic and other omics datasets. By consolidating these features into a single interactive and accessible tool, ChemGenXplore facilitates data sharing, enhances reproducibility, and promotes collaboration within the research community. AVAILABILITY AND IMPLEMENTATION: ChemGenXplore is freely accessible as a web application at https://chemgenxplore.kaust.edu.sa/. Source code and documentation, including instructions for local installation, are provided on GitHub (https://github.com/Hudaahmadd/ChemGenXplore). A Docker image is also available on DockerHub (https://hub.docker.com/r/hudaahmad/chemgenxplore) to ensure reproducibility and simplify installation.

ABSTRACT Tooth instance segmentation in panoramic radiographs is crucial for computer‐aided dental diagnosis, yet remains challenging due to low contrast, structural similarity and the coexistence of permanent and deciduous teeth. In this paper, we propose BEHA‐Net, a Bilateral Edge‐Augmented Recurrent Horizontal Feature‐Shift Aggregation Network for joint tooth instance segmentation and Fédération Dentaire Internationale (FDI)‐compliant tooth numbering. BEHA‐Net integrates two key components: (1) a recurrent horizontal feature‐shift aggregation (HFA) module that captures cross‐tooth contextual correlations via leveraging the horizontal arrangement topology of dental arches and aggregating multi‐scale dependencies across adjacent and non‐adjacent teeth; and (2) a bilateral edge‐augmented (BEA) module that refines boundary representations by edge detail enhancement and noise suppression. Extensive experiments on the Tufts and O 2 PR benchmarks demonstrate the superiority of our method over existing approaches.

Recurrent Neural Networks (RNNs) are widely used in control system due to their dynamic capabilities. However, the control accuracy of RNN-based systems can be compromised by noise interference, and there has been little research on RNN-based control in disturbed multi-agent systems. To address this, we developed an enhanced Distributed RNN (DRNN) structure and proposed a Novel DRNN-based Control Protocol (NDRNN-CP). This enhancement involves introducing a time-delay component, allowing the protocol to adaptively learn noise variation patterns. As a result, the NDRNN-CP effectively resists various periodic noise interferences and achieves more precise control of each agent. Additionally, our optimized activation function ensures that all agents reach consensus within a predefined time. To demonstrate the advantages of NDRNN-CP, we conducted extensive experiments that confirmed its significant improvements in noise signal resistance and convergence performance.

Meta-learning has become a widely used method for the user cold-start problem in recommendation systems, as it allows the model to learn from similar learning tasks and transfer the knowledge to new tasks. However, most existing meta-learning methods do not consider the temporal factor of users' preferences, which is crucial for news recommendation scenarios where news streams change dynamically over time. In this paper, we propose Time-Aware Meta-Learning (TAML), a novel framework that focuses on cold-start users in news recommendation systems. TAML factorizes user preferences into time-specifc and time-shift representations that jointly affect users' news preferences. These temporal factors are further incorporated into the meta-learning framework to achieve accurate and timely cold-start recommendations. Extensive experiments are conducted on two real-world datasets, demonstrating the superior performance of TAML over state-of-the-art methods.

This article is concerned with the optimal tracking performance on linear time-invariant (LTI) discrete-time multi-input–multi-output (MIMO) systems based on quantization, channel noise, and encoding–decoding, as well as bandwidth constraints. Meanwhile, power constraint, feedback and feedforward loops constraints of the systems are also taken into account. The tracking performance expression is described by inner–outer factorization, which is obtained by all stabilizing two-degree-of-freedom compensators. The results will illustrate that tracking performance limitation is decided by nonminimum phase zeros and unstable poles of the given plant. Moreover, time delay, encoding–decoding, channel noise, quantization, and bandwidth constraints also impact the performance. The accuracy of the method is verified by simulation examples.

Various robots are playing critical roles in many areas such as industrial manufacturing, disaster rescuing, unmanned vehicle, and science exploration. Because of the uncertain environment, changed resource, or dynamic system structure at runtime, traditional methods such as testing, model checking, and static analysis used in the development stage are not enough to ensure that the executions of robot control software satisfy specified properties. In this paper, we propose a runtime verification approach called Robots Monitor on Multilayer (RMoM) based on robot operating system (ROS) for monitoring whether the running of a robot swarm violates given temporal properties. To monitor robot system in a comprehensive manner over multiple layers, RMoM unifies resource, communication, robot, and swarm properties into a systemic, hierarchical monitoring framework. A discrete-time Metric Temporal Logic (MTL3) RMoM is proposed for specifying properties with timed and parameterized characteristics in robot swarms. Then, the corresponding three-valued semantics is defined for MTL3-RMoM to generate impartial and anticipatory monitors. Moreover, a hierarchical monitoring specification language high-level specification language (HSL)-RMoM and a series of monitor construction algorithms are proposed to automatically generate monitors for MTL3-RMoM properties on ROS platform. The experiments show that the method can automatically generate the monitors for detecting properties of robot swarms.

The synthesis problem is to construct a system fulfilling some specific requirements when interacting with the environment, which is one of the most crucial and challenging tasks in robotics. In comparison to the case of dealing with a single robot, synthesizing of a system constituted with multiple robots is, in general, much more involved. Actually, information is shared among robots in the latter case, and for a fixed robot, when fictively merging the rest ones into its environment, we are confronted with imperfect description of environments. In this article, we present an iterative controller synthesis approach to dealing with multirobot systems. In our model, the behaviors and outputs of one robot can be observed by the other ones, as a part of their inputs. To make our model more flexible, we allow the mechanism of “partial observation,” namely, only a part of outputs can be observed by other robots on some particular sensors. Our synthesis approach is conductive in an iterative manner. By analyzing the dependence among robots, we first try to synthesize controllers for some of them and then extract a set of invariants from the solved part to refine other ones. Repeatedly and iteratively using this way, we may arrive at a complete solution. Meanwhile, in comparison to the monolithic approach, using the iterative manner usually produces a much more compact result, which means that the size of the controller is smaller.

In this paper, we discuss the optical fiber interconnection technologies applied in the two types of parallel processing systems: 1) a backplane interconnection in a parallel processor array system and 2) a computing cluster network. We have set up a parallel processor array system using optical fiber to make point-to-point interconnection between processor elements and are developing a low-cost virtual parallel optical fiber interconnection link (VPOFLink) complying with peripheral component interconnect (PCI) local bus specifications for the computing cluster. VPOFLink is integrated with the popular PCI bus interface in order to make the link hold the same bandwidth as that of the PCI bus. It was fabricated as an available peripheral device that can been inserted into the bus slots of commercial computers directly and can operate under the control of PCI bus. Also in this paper, we demonstrate the optical fiber link for a ring network and the architecture of the ring network.