The inverse design of microstructures plays a pivotal role in optimizing metamaterials with specific, targeted physical properties. While traditional forward design methods are constrained by their inability to explore the vast combinatorial design space, inverse design offers a compelling alternative by directly generating structures that fulfill predefined performance criteria. However, achieving precise control over both geometry and material properties remains a significant challenge due to their intricate interdependence. Existing approaches, which typically rely on voxel or parametric representations, often limit design flexibility and structural diversity. In this work, we present a novel generative model that integrates latent diffusion with Holoplane, an advanced hybrid neural representation that simultaneously encodes both geometric and physical properties. This combination ensures superior alignment between geometry and properties. Our approach generalizes across multiple microstructure classes, enabling the generation of diverse, tileable microstructures with significantly improved property accuracy and enhanced control over geometric validity, surpassing the performance of existing methods. We introduce a multi-class dataset encompassing a variety of geometric morphologies, including truss, shell, tube, and plate structures, to train and validate our model. Experimental results demonstrate the model's ability to generate microstructures that meet target properties, maintain geometric validity, and integrate seamlessly into complex assemblies. Additionally, we explore the potential of our framework through the generation of new microstructures, cross-class interpolation, and the infilling of heterogeneous microstructures. The dataset and source code will be open-sourced upon publication.

Digital technologies have positively transformed society, but they have also led to undesirable consequences not anticipated at the time of design or development. We posit that insights into past undesirable consequences can help researchers and practitioners gain awareness and anticipate potential adverse effects. To test this assumption, we introduce BLIP, a system that extracts real-world undesirable consequences of technology from online articles, summarizes and categorizes them, and presents them in an interactive, web-based interface. In two user studies with 15 researchers in various computer science disciplines, we found that BLIP substantially increased the number and diversity of undesirable consequences they could list in comparison to relying on prior knowledge or searching online. Moreover, BLIP helped them identify undesirable consequences relevant to their ongoing projects, made them aware of undesirable consequences they "had never considered," and inspired them to reflect on their own experiences with technology.

In our era of rapid technological advancement, the research landscape for writing assistants has become increasingly fragmented across various research communities. We seek to address this challenge by proposing a design space as a structured way to examine and explore the multidimensional space of intelligent and interactive writing assistants. Through a large community collaboration, we explore five aspects of writing assistants: task, user, technology, interaction, and ecosystem. Within each aspect, we define dimensions (i.e., fundamental components of an aspect) and codes (i.e., potential options for each dimension) by systematically reviewing 115 papers. Our design space aims to offer researchers and designers a practical tool to navigate, comprehend, and compare the various possibilities of writing assistants, and aid in the envisioning and design of new writing assistants.

Tickets to the Willy's Chocolate Experience in Glasgow, Scotland, were marketed based on Artificial Intelligence (AI) images that sold parents on a magical experience, but when they showed up, it was anything but, according to reports. Parents called the police on Saturday because they felt scammed by the "absolute shambles of an event," the New York Post reported. The AI-generated images included giant candy displays and colorful lights, but children were instead greeted by a virtually empty warehouse with a printed AI background, a disappointing bouncy castle and hardly any candy. "Experience captivating live performances featuring charming characters singing original catchy tunes. This event guarantees an immersive and delightful entertainment experience suitable for aged 3 years old," according to the event website.

The human brain's plasticity allows for the integration of artificial body parts into the human body. Leveraging this, embodied systems realize intuitive interactions with the environment. We introduce a novel concept: embodied swarm robots. Swarm robots constitute a collective of robots working in harmony to achieve a common objective, in our case, serving as functional body parts. Embodied swarm robots can dynamically alter their shape, density, and the correspondences between body parts and individual robots. We contribute an investigation of the influence on embodiment of swarm robot-specific factors derived from these characteristics, focusing on a hand. Our paper is the first to examine these factors through virtual reality (VR) and real-world robot studies to provide essential design considerations and applications of embodied swarm robots. Through quantitative and qualitative analysis, we identified a system configuration to achieve the embodiment of swarm robots.

Maamar Ali Saud Al Tobi, Ph.D., is Assistant Professor and Deputy Head of the Mechanical and Industrial Engineering Department at the National University of Science and Technology, Muscat, Oman. His teaching and research areas include machine condition monitoring, vibration analysis, artificial intelligence, genetic algorithm, and maintenance management and strategies. He is author of numerous papers in international journals on fault diagnosis in rotating machinery using AI systems. Geraint Bevan, Ph.D., is Senior Lecturer in Applied Instrumentation and Control at the School of Computing, Engineering and Built Environment at Glasgow Caledonian University, Glasgow, Scotland. He is widely published on bond-graph modeling for control system design, design of automotive control systems, monitoring for nuclear safeguards, machine condition monitoring, and renewable energy.

A digital replica of Earth could help scientists better model the future of our planet and find solutions to problems wrought by climate change. The advanced model, dubbed Digital Twin Earth, is being developed by the European Space Agency (ESA) and its partners based on data and images from Earth-observation satellites and sensors on the ground. To run reliably, the project will require new advanced artificial intelligence algorithms and powerful supercomputers, which are currently being developed. ESA and its partners discussed their progress in the runup to the UN Climate Change Conference COP26, a two-week event that's currently taking place in Glasgow, Scotland. ESA launched the Digital Twin Earth project in 2020 and invited researchers and tech companies from across Europe to present their progress during an event called PhiWeek, which took place Oct. 11 to Oct. 15.

Most emerging infectious diseases of humans (like COVID-19) are zoonotic – caused by viruses originating from other animal species. Identifying high-risk viruses earlier can improve research and surveillance priorities. A study published in PLOS Biology on September 28th by Nardus Mollentze, Simon Babayan, and Daniel Streicker at University of Glasgow, United Kingdom suggests that machine learning (a type of artificial intelligence) using viral genomes may predict the likelihood that any animal-infecting virus will infect humans, given biologically relevant exposure. Identifying zoonotic diseases prior to emergence is a major challenge because only a small minority of the estimated 1.67 million animal viruses are able to infect humans. To develop machine learning models using viral genome sequences, the researchers first compiled a dataset of 861 virus species from 36 families.

Most emerging infectious diseases of humans (like COVID-19) are zoonotic – caused by viruses originating from other animal species. Identifying high-risk viruses earlier can improve research and surveillance priorities. A study publishing in PLOS Biology on September 28th by Nardus Mollentze, Simon Babayan, and Daniel Streicker at University of Glasgow, United Kingdom suggests that machine learning (a type of artifical intelligence) using viral genomes may predict the likelihood that any animal-infecting virus will infect humans, given biologically relevant exposure. Identifying zoonotic diseases prior to emergence is a major challenge because only a small minority of the estimated 1.67 million animal viruses are able to infect humans. To develop machine learning models using viral genome sequences, the researchers first compiled a dataset of 861 virus species from 36 families.

Most emerging infectious diseases of humans (like COVID-19) are zoonotic--caused by viruses originating from other animal species. Identifying high-risk viruses earlier can improve research and surveillance priorities. A study publishing in PLOS Biology on September 28th by Nardus Mollentze, Simon Babayan, and Daniel Streicker at University of Glasgow, United Kingdom suggests that machine learning (a type of artifical intelligence) using viral genomes may predict the likelihood that any animal-infecting virus will infect humans, given biologically relevant exposure. Identifying zoonotic diseases prior to emergence is a major challenge because only a small minority of the estimated 1.67 million animal viruses are able to infect humans. To develop machine learning models using viral genome sequences, the researchers first compiled a dataset of 861 virus species from 36 families.