An example prompt for constructing PDDL models of the action "close a

We introduce M$^2$Hub, a toolkit for advancing machine learning in materials discovery. Machine learning has achieved remarkable progress in modeling molecular structures, especially biomolecules for drug discovery. However, the development of machine learning approaches for modeling materials structures lag behind, which is partly due to the lack of an integrated platform that enables access to diverse tasks for materials discovery. To bridge this gap, M$^2$Hub will enable easy access to materials discovery tasks, datasets, machine learning methods, evaluations, and benchmark results that cover the entire workflow. Specifically, the first release of M$^2$Hub focuses on three key stages in materials discovery: virtual screening, inverse design, and molecular simulation, including 9 datasets that covers 6 types of materials with 56 tasks across 8 types of material properties. We further provide 2 synthetic datasets for the purpose of generative tasks on materials. In addition to random data splits, we also provide 3 additional data partitions to reflect the real-world materials discovery scenarios. State-of-the-art machine learning methods (including those are suitable for materials structures but never compared in the literature) are benchmarked on representative tasks. Our codes and library are publicly available at \url{https://github.com/yuanqidu/M2Hub}.

Global Routing (GR) is a core yet time-consuming task in VLSI systems.

The rise of model-sharing through frameworks and dedicated hubs makes Machine Learning significantly more accessible. Despite their benefits, these tools expose users to underexplored security risks, while security awareness remains limited among both practitioners and developers. To enable a more security-conscious culture in Machine Learning model sharing, in this paper we evaluate the security posture of frameworks and hubs, assess whether security-oriented mechanisms offer real protection, and survey how users perceive the security narratives surrounding model sharing. Our evaluation shows that most frameworks and hubs address security risks partially at best, often by shifting responsibility to the user. More concerningly, our analysis of frameworks advertising security-oriented settings and complete model sharing uncovered six 0-day vulnerabilities enabling arbitrary code execution. Through this analysis, we debunk the misconceptions that the model-sharing problem is largely solved and that its security can be guaranteed by the file format used for sharing. As expected, our survey shows that the surrounding security narrative leads users to consider security-oriented settings as trustworthy, despite the weaknesses shown in this work. From this, we derive takeaways and suggestions to strengthen the security of model-sharing ecosystems.

Register now free-of-charge to explore this white paper The future of robotics is being shaped by powerful technologies like AI, edge computing, and high-speed connectivity, driving smarter, more responsive machines across industries. Robots are no longer confined to static environments—they are evolving to interact dynamically with humans and their surroundings. This eBook explores the impact of robotics in diverse fields, from home automation and medical technology to automotive, data centers, and industrial applications. It highlights challenges like power efficiency, miniaturization, and ruggedization, while showcasing Molex’s innovative solutions tailored for each domain. Additionally, the eBook covers: Ruggedized connectors for harsh industrial settings Advanced power management for home robots Miniaturized systems for precision medical robotics 5G/6G-enabled autonomous vehicles High-speed data solutions for cloud infrastructure Download Whitepaper

Ranking words is an important way to summarize a text or to retrieve information. A word graph is a way to represent the words of a sentence or a text as the vertices of a graph and to show the relationship among the words. It is also useful to determine the relative importance of a word among the words in the word-graph. In this research, the ranking of Bangla words are calculated, representing Bangla words from a text in a word graph using various graph based ranking algorithms. There is a lack of a standard Bangla word database. In this research, the Indian Language POS-tag Corpora is used, which has a rich collection of Bangla words in the form of sentences with their parts of speech tags. For applying a word graph to various graph based ranking algorithms, several standard procedures are applied. The preprocessing steps are done in every word graph and then applied to graph based ranking algorithms to make a comparison among these algorithms. This paper illustrate the entire procedure of calculating the ranking of Bangla words, including the construction of the word graph from text. Experimental result analysis on real data reveals the accuracy of each ranking algorithm in terms of F1 measure.

Developing humanoid robots that can emulate the versatility, agility, and robustness of human movement in complex, unstructured environments has long been a central pursuit in robotics research [1, 2, 3, 4, 5, 6]. Achieving this vision requires not only the ability to execute diverse motor skills, but also the capacity to maintain balance under challenging conditions. Studies in neuroscience and motor control suggest that human balance relies on intricate sensorimotor loops involving the vestibular system, proprioception, and high-level planning [7, 8], making it a particularly demanding aspect of motor control to replicate in robotics. This difficulty is exemplified by the Swallow Balance task shown in Figure 1, in which a humanoid must maintain stability in an extreme single-legged pose with the upper body extended horizontally. Such movements require full-body coordination, precise control of the center of mass, and robustness to perturbations--highlighting the demanding nature of humanoid balance. In recent work on learning-based humanoid control [4, 5, 9, 10, 11, 12, 6], a common approach for enabling humanoids to perform diverse motions is to train a control policy to track reference poses.

Maybe it was the sight of Sengled users literally left in the dark by their useless Wi-Fi bulbs, maybe it was another price hike, or just an overall sense that my smart devices weren't truly under my control. Whatever the reason, I'd developed a growing desire to build a smart home setup that wasn't a hostage to the cloud. Specifically, I'm talking about a locally hosted smart home setup, and I'm currently in the process of building one. And while I'm a smart home expert thanks to my six years' experience here at TechHive, I'm quickly realizing how much I still don't know as I tackle the steep learning curve of a DIY smart home. This isn't a step-by-step guide of how to build your own smart home system--that might come later--but more of a journal about where I am in my self-hosted smart home journey, where I started, and what I'm hoping to achieve.