Multimodal large language models (MLLMs) are transforming the capabilities of graphical user interface (GUI) agents, facilitating their transition from controlled simulations to complex, real-world applications across various platforms. However, the effectiveness of these agents hinges on the robustness of their grounding capability. Current GUI agents predominantly utilize text-based representations such as HTML or accessibility trees, which, despite their utility, often introduce noise, incompleteness, and increased computational overhead. In this paper, we advocate a human-like embodiment for GUI agents that perceive the environment entirely visually and directly perform pixel-level operations on the GUI. The key is visual grounding models that can accurately map diverse referring expressions of GUI elements to their coordinates on the GUI across different platforms. We show that a simple recipe, which includes web-based synthetic data and slight adaptation of the LLaVA architecture, is surprisingly effective for training such visual grounding models. We collect the largest dataset for GUI visual grounding so far, containing 10M GUI elements and their referring expressions over 1.3M screenshots, and use it to train UGround, a strong universal visual grounding model for GUI agents. Empirical results on six benchmarks spanning three categories (grounding, offline agent, and online agent) show that 1) UGround substantially outperforms existing visual grounding models for GUI agents, by up to 20\% absolute, and 2) agents with UGround outperform state-of-the-art agents, despite the fact that existing agents use additional text-based input while ours only uses visual perception. These results provide strong support for the feasibility and promises of GUI agents that navigate the digital world as humans do.

Traditionally, multi-step commutation strategy is used in high frequency link matrix rectifiers (HFLMR), however, the multi-step will finally result in high switching loss and greatly influence the performance of converter. While the traditional one-step commutation strategy is neither suitable for HFLMR since the modulation includes zero vector and it requires extra hardware detection circuit. To solve this problem, a novel one-step commutation strategy is proposed to realize safe commutation strategy of high frequency link matrix rectifier, which can reduce switching loss and reduce the influence on the performance of converter to minimum. Moreover, the proposed strategy can reduce switching loss by controlling single switching tube as well as optimizing the sequence vector synthesis. Finally, the correctness and effectiveness of the improved commutation strategy is verified by the Matlab/Simulink simulation model. The simulation validates that the novel strategy can realize the safe commutation and improve its input and output features.

Querying complex graph databases such as knowledge graphs is a challenging task for non-professional users. In this demo, we present SLQ, a user-friendly graph querying system enabling schemales and structures graph querying, where a user need not describe queries precisely as required by most databases. SLQ system combines searching and ranking: it leverages a set of transformation functions, including abbreviation, ontology, synonym, etc., that map keywords and linkages from a query to their matches in a data graph, based on an automatically learned ranking model. To help users better understand search results at different levels of granularity, it supports effective result summarization with "drill-down" and "roll-up" operations. Better still, the architecture of SLQ is elastic for new transformation functions, query logs and user feedback, to iteratively refine the ranking model. SLQ significantly improves the usability of graph querying. This demonstration highlights (1) SLQ can automatically learn an effective ranking model, without assuming manually labeled training examples, (2) it can efficiently return top ranked matches over noisy, large data graphs, (3) it can summarize the query matches to help users easily access, explore and understand query results, and (4) its GUI can interact with users to help them construct queries, explore data graphs and inspect matches in a user-friendly manner.

Online news reading has become the major method to know about the world as web provide more information than other media like TV and radio. However, traditional online news reading interface is inconvenient for many types of people, especially for those who are disabled or taking a bus. This paper presents a mobile application iNewsBox enabling users to listen to news collected from the Internet. In order to simplify necessary interactions of getting valuable news, we also propose a framework for using implicit feedback to recommend news in this paper. Experiment shows our algorithms in iNewsBox are effective.