Putting on Project Moohan, an upcoming XR headset developed by Google, Samsung, and Qualcomm, for the first time felt strangely familiar. From twisting the head-strap knob on the back to slipping the standalone battery pack into my pants pocket, my mind was transported back to February 2024, when I tried on the Apple Vision Pro on launch day. Also: I tried Google's XR glasses and they already beat my Meta Ray-Bans in 3 ways Only this time, the headset was powered by Android XR, Google's newest operating system built around Gemini, the same AI model that dominated the Google I/O headlines throughout this week. The difference in software was immediately noticeable -- from the home grid of Google apps like Photos, Maps, and YouTube (which VisionOS still lacks) to prompting for Gemini instead of Siri with a long press of the headset's multifunctional key. While my demo with Project Moohan lasted only about 10 minutes, it gave me a clear understanding of how it's challenging Apple's Vision Pro and how Google, Samsung, and Qualcomm plan to convince the masses that the future of spatial computing does, in fact, live in a bulkier space-helmet-like device.

If there's one thing that turns people off from adopting new tech, it's being forced to look silly and feel uncomfortable for extended lengths of time. It was always the Achilles' heel for 3D in the past, and it remains the primary hurdle for VR headsets and goofy-looking smart glasses. Laptops, tablets, and even computer monitors have started embracing a new form of 3D technology that solves this problem entirely, without giving up just how compelling 3D can look. I've used the latest iteration of the technology and spoke with the creators--this might finally be the version of 3D that sticks. I was skeptical when I first saw this next generation of 3D technology. Interest in 3D comes in waves.

Multiple clustering aims to discover various latent structures of data from different aspects. Deep multiple clustering methods have achieved remarkable performance by exploiting complex patterns and relationships in data. However, existing works struggle to flexibly adapt to diverse user-specific needs in data grouping, which may require manual understanding of each clustering. To address these limitations, we introduce Multi-Sub, a novel end-to-end multiple clustering approach that incorporates a multi-modal subspace proxy learning framework in this work. Utilizing the synergistic capabilities of CLIP and GPT-4, Multi-Sub aligns textual prompts expressing user preferences with their corresponding visual representations. This is achieved by automatically generating proxy words from large language models that act as subspace bases, thus allowing for the customized representation of data in terms specific to the user's interests. Our method consistently outperforms existing baselines across a broad set of datasets in visual multiple clustering tasks.

We study theoretical properties of a broad class of regularized algorithms with vector-valued output. These spectral algorithms include kernel ridge regression, kernel principal component regression and various implementations of gradient descent.

While state-of-the-art diffusion models (DMs) excel in image generation, concerns regarding their security persist. Earlier research highlighted DMs' vulnerability to data poisoning attacks, but these studies placed stricter requirements than conventional methods like'BadNets' in image classification. This is because the art necessitates modifications to the diffusion training and sampling procedures. Unlike the prior work, we investigate whether BadNets-like data poisoning methods can directly degrade the generation by DMs. In other words, if only the training dataset is contaminated (without manipulating the diffusion process), how will this affect the performance of learned DMs?

The lack of standardized benchmarks for reinforcement learning (RL) in sustainability applications has made it difficult to both track progress on specific domains and identify bottlenecks for researchers to focus their efforts. In this paper, we present SustainGym, a suite of five environments designed to test the performance of RL algorithms on realistic sustainable energy system tasks, ranging from electric vehicle charging to carbon-aware data center job scheduling. The environments test RL algorithms under realistic distribution shifts as well as in multi-agent settings. We show that standard off-the-shelf RL algorithms leave significant room for improving performance and highlight the challenges ahead for introducing RL to real-world sustainability tasks.

Serialization-based methods, which serialize the 3D voxels and group them into multiple sequences before inputting to Transformers, have demonstrated their effectiveness in 3D object detection. However, serializing 3D voxels into 1D sequences will inevitably sacrifice the voxel spatial proximity. Such an issue is hard to be addressed by enlarging the group size with existing serializationbased methods due to the quadratic complexity of Transformers with feature sizes. Inspired by the recent advances of state space models (SSMs), we present a Voxel SSM, termed as Voxel Mamba, which employs a group-free strategy to serialize the whole space of voxels into a single sequence. The linear complexity of SSMs encourages our group-free design, alleviating the loss of spatial proximity of voxels. To further enhance the spatial proximity, we propose a Dual-scale SSM Block to establish a hierarchical structure, enabling a larger receptive field in the 1D serialization curve, as well as more complete local regions in 3D space. Moreover, we implicitly apply window partition under the group-free framework by positional encoding, which further enhances spatial proximity by encoding voxel positional information. Our experiments on Waymo Open Dataset and nuScenes dataset show that Voxel Mamba not only achieves higher accuracy than state-of-the-art methods, but also demonstrates significant advantages in computational efficiency.

Buried in the Republican budget bill is a proposal that will radically change how artificial intelligence develops in the U.S., according to both its supporters and critics. The provision would ban states from regulating AI for the next decade. Opponents say the moratorium is so broadly written that states wouldn't be able to enact protections for consumers affected by harmful applications of AI, like discriminatory employment tools, deepfakes, and addictive chatbots. Instead, consumers would have to wait for Congress to pass its own federal legislation to address those concerns. Currently it has no draft of such a bill.

Dru Smith, Chief Geodesist of the National Geodetic Survey stands near a measurement device used to survey the height of the Washington Monument in 2017.Susan Walsh/AP This story was originally published by Wired and is reproduced here as part of the Climate Desk collaboration. Cuts made by the Trump administration are threatening the function of a tiny but crucial office within the National Oceanic and Atmospheric Administration that maintains the US framework of spatial information: latitudes, longitudes, vertical measurements like elevation, and even measurements of Earth's gravitational field. Staff losses at the National Geodetic Survey (NGS), the oldest scientific agency in the US, could further cripple its mission and activities, including a long-awaited project to update the accuracy of these measurements, former employees and experts say. As the world turns more and more toward operations that need precise coordinate systems like the ones NGS provides, the science that underpins this office's activities, these experts say, is becoming even more crucial. The work of NGS, says Tim Burch, the executive director of the National Society of Professional Surveyors, "is kind of like oxygen. You don't know you need it until it's not there."

Is there a label or target associated with each instance? No, there is no label or target associated with each instance as our focus is not supervised learning settings. Is any information missing from individual instances? Yes, it is common to have missing values in financial datasets. We provide scripts to preprocess and conduct data imputation with diffusion models [26]. Are relationships between individual instances made explicit?