The pervasive adoption of inverter-interfaced resources in 100% renewable power systems fundamentally alters voltage regulation dynamics and renders classical margin - estimation techniques computationally prohibitive. This paper introduces a unified, Jacobian-free Krylov-Arnoldi (JFKA) framework for automated estimation of system static voltage stability (SVS). First, inverter current-limiting behavior is captured by a smooth S-type function, preserving continuous power-flow structure across both grid-forming (GFM) and gridfollowing (GFL) control modes. Building on singularity theory, we derive a Jacobian-free stability indicator that pinpoints the onset of voltage collapse without explicit derivative evaluation. To efficiently solve the resulting large-scale, transcendental power-flow equations, we embed a reduced-order Arnoldi process within a Newton-Krylov solver, yielding rapid convergence and markedly lower memory footprint. Case studies on a modified IEEE-39 bus network with full renewable penetration demonstrate that our method accurately tracks voltage regulation limits under varied load-growth scenarios and automatic mode switches.

Open-vocabulary Scene Graph Generation (OV-SGG) overcomes the limitations of the closed-set assumption by aligning visual relationship representations with open-vocabulary textual representations. This enables the identification of novel visual relationships, making it applicable to real-world scenarios with diverse relationships. However, existing OV-SGG methods are constrained by fixed text representations, limiting diversity and accuracy in image-text alignment. To address these challenges, we propose the Relation-Aware Hierarchical Prompting (RAHP) framework, which enhances text representation by integrating subject-object and region-specific relation information. Our approach utilizes entity clustering to address the complexity of relation triplet categories, enabling the effective integration of subject-object information. Additionally, we utilize a large language model (LLM) to generate detailed region-aware prompts, capturing fine-grained visual interactions and improving alignment between visual and textual modalities. RAHP also introduces a dynamic selection mechanism within Vision-Language Models (VLMs), which adaptively selects relevant text prompts based on the visual content, reducing noise from irrelevant prompts. Extensive experiments on the Visual Genome and Open Images v6 datasets demonstrate that our framework consistently achieves state-of-the-art performance, demonstrating its effectiveness in addressing the challenges of open-vocabulary scene graph generation.

While Multimodal Large Language Models (MLLMs) have advanced GUI navigation agents, current approaches face limitations in cross-domain generalization and effective history utilization. We present a reasoning-enhanced framework that systematically integrates structured reasoning, action prediction, and history summarization. The structured reasoning component generates coherent Chain-of-Thought analyses combining progress estimation and decision reasoning, which inform both immediate action predictions and compact history summaries for future steps. Based on this framework, we train a GUI agent, GUI-Rise, through supervised fine-tuning on pseudo-labeled trajectories and reinforcement learning with Group Relative Policy Optimization (GRPO). This framework employs specialized rewards, including a history-aware objective, directly linking summary quality to subsequent action performance. Comprehensive evaluations on standard benchmarks demonstrate state-of-the-art results under identical training data conditions, with particularly strong performance in out-of-domain scenarios. These findings validate our framework's ability to maintain robust reasoning and generalization across diverse GUI navigation tasks.

The multimodal dynamic stability (MDS) is a critical issue for constructing power systems with 100% renewable energy (PSRE). This paper proposes a multimodal unified control (MUC) method for enhancing the MDS in PSRE. First, an improved Heffron-Phillips model is established to demonstrate the mechanism of multimodal dynamic instability (MDI). Next, a third-order external subsystem model of the grid-forming renewable energy source (GFM-RES) is derived by the Lie derivative. Then, the MUC architecture is designed by utilizing the Lyapunov theory in the third-order external system. Finally, the application of the proposed MUC method is verified by analyzing the pertinent results for the modified IEEE 11-bus system with a 100% renewable energy generation.Note to Practitioners—The construction of PSRE has attracted widespread attention from the academic and engineering communities. Dynamic stability is one of the key issues faced in building PSRE. This work presents a MUC of GFM-RESs for improving the MDS of the PSRE. Practitioners should be able to apply the MUC to GFM-RESs like battery energy storage system, wind turbines and photovoltaic units. The MUC is designed using the Lie derivative and Lyapunov theory. From a practical point of view, the MUC achieves the MDS from a control perspective, without additional investment required.

This paper proposes a novel voltage-adaptive strategy (VAS) considering current limits of renewable energy resources (RESs), to enhance the transient stability of the power system with 100% renewable energy (PSRE). First, taking the current limits into account, a new transient stability mechanism is revealed by deriving the fault critical clearing time (CCT) of a PSRE with two RESs. Next, leveraging the Lie derivative and the Lyapunov theory, a novel adaptive control method is proposed, which is more in line with the output saturation characteristic of RESs. Then, VAS is formulated based on the proposed adaptive control method for enhancing the transient stability of PSRE. Finally, the proposed VAS is verified on a modified IEEE 11-bus system with 100% renewable energy generation.Note to Practitioners—Transient control can be considered one of the challenges in power system field for constructing the PSRE, which has significant implications for reducing carbon emissions. This work presents a VAS of RESs taking the current limits into account for improving the transient characteristics of the PSRE. Practitioners should be able to apply the VAS to RESs represented by wind farms and photovoltaic power stations. The VAS implements the transient control through the Lie derivative and the proposed adaptive control law. From a practical point of view, it should be highly emphasized that the proposed VAS can achieve transient control of the PSRE without increasing any investment and bringing negative impacts.

Predicting the interactions between compounds and their potential target proteins is crucial in drug discovery. Existing methods often assume that each compound has an adequate number of target proteins available for model training. However, in practice, the number of target proteins associated with compounds is often limited, making it difficult to gather a sufficient number of training examples. This results in learning bias in the model, leading to poor performance on these compounds. Moreover, this issue is evident in widely used datasets, such as GPCR and kinase, where 77.51% and 12.71% of compounds, respectively, have only one target protein available for model training. However, the issue has not been fully explored, presenting a challenge for model development. In this research, we propose CPOne, a framework designed to address the aforementioned issue. CPOne is a meta-learning based approach for compound-protein interaction prediction in one-shot scenario where each compound has only one target protein available for model training. By utilizing a meta compound learner, CPOne extracts the meta representation of compound from each task. Through fast gradient updates, this representation is quickly adapted to generate a compound-specific representation for the current task, thereby improving performance in one-shot scenario. Through comprehensive experiments, we empirically validate the superiority of CPOne, which demonstrates a promising performance improvement over established methods.

The accurate identification of disease-associated genes is crucial for understanding the molecular mechanisms underlying various diseases. Most current methods focus on constructing biological networks and utilizing machine learning, particularly deep learning, to identify disease genes. However, these methods overlook complex relations among entities in biological knowledge graphs. Such information has been successfully applied in other areas of life science research, demonstrating their effectiveness. Knowledge graph embedding methods can learn the semantic information of different relations within the knowledge graphs. Nonetheless, the performance of existing representation learning techniques, when applied to domain-specific biological data, remains suboptimal. To solve these problems, we construct a biological knowledge graph centered on diseases and genes, and develop an end-to-end knowledge graph completion framework for disease gene prediction using interactional tensor decomposition named KDGene. KDGene incorporates an interaction module that bridges entity and relation embeddings within tensor decomposition, aiming to improve the representation of semantically similar concepts in specific domains and enhance the ability to accurately predict disease genes. Experimental results show that KDGene significantly outperforms state-of-the-art algorithms, whether existing disease gene prediction methods or knowledge graph embedding methods for general domains. Moreover, the comprehensive biological analysis of the predicted results further validates KDGene's capability to accurately identify new candidate genes. This work proposes a scalable knowledge graph completion framework to identify disease candidate genes, from which the results are promising to provide valuable references for further wet experiments. Data and source codes are available at https://github.com/2020MEAI/KDGene.

The accelerated proliferation of visual content and the rapid development of machine vision technologies bring significant challenges in delivering visual data on a gigantic scale, which shall be effectively represented to satisfy both human and machine requirements. In this work, we investigate how hierarchical representations derived from the advanced generative prior facilitate constructing an efficient scalable coding paradigm for human-machine collaborative vision. Our key insight is that by exploiting the StyleGAN prior, we can learn three-layered representations encoding hierarchical semantics, which are elaborately designed into the basic, middle, and enhanced layers, supporting machine intelligence and human visual perception in a progressive fashion. With the aim of achieving efficient compression, we propose the layer-wise scalable entropy transformer to reduce the redundancy between layers. Based on the multi-task scalable rate-distortion objective, the proposed scheme is jointly optimized to achieve optimal machine analysis performance, human perception experience, and compression ratio. We validate the proposed paradigm's feasibility in face image compression. Extensive qualitative and quantitative experimental results demonstrate the superiority of the proposed paradigm over the latest compression standard Versatile Video Coding (VVC) in terms of both machine analysis as well as human perception at extremely low bitrates (< 0.01 bpp), offering new insights for human-machine collaborative compression.

Remote sensing object detection is characterized by arbitrary direction, dense targets and variable scale. Like common object detection methods predict horizontal rectangle boxes directly, most of the existing remote sensing oriented object detection methods predict oriented rectangle boxes directly. i.e. Center point position, length, width and rotation angle of the oriented rectangle boxes. These models often need to design complex rotation detection module to adapt to the rotating characteristics of targets, they lack good performance on targets with large aspect ratio changes as well. In this paper, we propose a keypoint based oriented object detection method for aerial images named RotPointNet to improve detection accuracy of rotating targets with large aspect ratio change. RotPointNet first locates the two endpoints of rotating targets using keypoint detection method, and then constructs the remote sensing oriented target based on the endpoints and additional width information. In addition, a new method of matching target key points is proposed in this paper. We conduct experiments to prove, the target detection model RotPoint-Net and key point matching strategy proposed in this paper can achieve good detection results on remote sensing images, especially for the detection of targets with large aspect ratio changes.

Material screening entails a large number of electronic structure simulations. Traditionally, these simulation runs are treated separately as solving independent Kohn-Sham (KS) equations. In this paper, we formulate material screening as an inter-job scheduling problem for solving a system of KS equations, and in doing so allowing one to explore different scheduling methods that use the results of some equations to expedite the solution of others. We propose the concept of sharing iterative simulation and employ several optimization methods to initialize a simulation run using the distribution of particles from similar jobs as the initial condition. More specifically, we propose two similarity metrics, one qualitative and the other quantitative, to predict the simulation runtime of a material screen job based on its similarity to other jobs. Accordingly, we present two inter-job scheduling algorithms that make use the qualitative and quantitative similarity information. We conducted extensive experiments on the Sunway TaihuLight supercomputer for a practical material screening problem to evaluate the performance of the two scheduling algorithms using the proposed similarity metrics. We show that the total time required to run the large number of material screening jobs can be significantly reduced, and the algorithms are robust even with moderate inaccurate prediction on the simulation runtime. The quantitative algorithm achieves better results than the qualitative algorithm using more accurate prediction and thus achieving more significant runtime reduction.