Federated learning is a learning paradigm to enable collaborative learning across different parties without revealing raw data. Notably, vertical federated learning (VFL), where parties share the same set of samples but only hold partial features, has a wide range of real-world applications.

Very recently, a variety of vision transformer architectures for dense prediction tasks have been proposed and they show that the design of spatial attention is critical to their success in these tasks. In this work, we revisit the design of the spatial attention and demonstrate that a carefully devised yet simple spatial attention mechanism performs favorably against the state-of-the-art schemes. As a result, we propose two vision transformer architectures, namely, Twins-PCPVT and Twins-SVT. Our proposed architectures are highly efficient and easy to implement, only involving matrix multiplications that are highly optimized in modern deep learning frameworks. More importantly, the proposed architectures achieve excellent performance on a wide range of visual tasks including image-level classification as well as dense detection and segmentation. The simplicity and strong performance suggest that our proposed architectures may serve as stronger backbones for many vision tasks.

This paper explores the generalization loss of linear regression in variably parameterized families of models, both under-parameterized and over-parameterized. We show that the generalization curve can have an arbitrary number of peaks, and moreover, the locations of those peaks can be explicitly controlled. Our results highlight the fact that both the classical U-shaped generalization curve and the recently observed double descent curve are not intrinsic properties of the model family. Instead, their emergence is due to the interaction between the properties of the data and the inductive biases of learning algorithms.

Collaborative robots and machine learning-based virtual agents are increasingly entering the human workspace with the aim of increasing productivity and enhancing safety. Despite this, we show in a ubiquitous experimental domain, Overcooked-AI, that state-of-the-art techniques for human-machine teaming (HMT), which rely on imitation or reinforcement learning, are brittle and result in a machine agent that aims to decouple the machine and human's actions to act independently rather than in a synergistic fashion. To remedy this deficiency, we develop HMT approaches that enable iterative, mixed-initiative team development allowing end-users to interactively reprogram interpretable AI teammates. Our 50-subject study provides several findings that we summarize into guidelines. While all approaches underperform a simple collaborative heuristic (a critical, negative result for learning-based methods), we find that white-box approaches supported by interactive modification can lead to significant team development, outperforming white-box approaches alone, and that black-box approaches are easier to train and result in better HMT performance highlighting a tradeoff between explainability and interactivity versus ease-of-training.

With this paper, the design of a biomimetic robotic squid (dubbed URSULA) developed for dexterous underwater manipulation is presented. The robot serves as a test bed for several novel underwater technologies such as soft manipulators, propeller-less propulsion, model mediated tele-operation with video and haptic feedback, sonar-based underwater mapping, localization, and navigation, and high bandwidth visible light communications. Following the finalization of the detailed design, a prototype is manufactured and is currently undergoing pool tests.

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.

We as a Digitalist Group & Network are opening our doors and building a dynamic community here in San Francisco. If it's a creative, professional environment that you are looking for, we are it! Our company and wider community is comprised of businesses and experts in the AI, ML and high tech fields. Digitalist San Francisco is located in the Financial District of downtown San Francisco. The montly rent is $600 USD per desk, per person, per month.

There will be many people who will say it does exist and has working technologies, hardware and software. It is an interesting error in thinking to focus on closed system devices/products as to what Ubiquity (IoT3) is. Devices are used to get across the point of various types of connections and networks being accessed. But more importantly in a full implementation of the concept of Ubiquity (often described as the IoT) devices may not even be owned anymore. The ownership of devices ceases to be important if you can own your digital identity, can verify it and establish your own ecosystem of assets in Blockchain.

Graphic design used to require physical work. To compose letterheads, business cards, brochures, magazines, books, and posters, you hunched over a desk or a light table. You cut and pasted paper or assembled metal type on a printing press. You processed 35mm film by hand, developing pictures in a darkroom with chemicals. Jason Tselentis is an educator, writer, and designer.

Editor: On "Learning Language" I was dismayed by the inclusion of William Katke's article ("Learning Language Using A Pattern Recognition Approach," Spring 1985). Usually you do an excellent job of representing "the current state of the art in Artificial Intelligence" (to quote your Editorial Policy), but I consider this article an exception. First of all, although the article claims to be on "Learning Language," what it presents is at best a knowledge-free approach to learning syntax. I saw no evidence that the induced syntax is useful for anything, and good reasons to believe that it is not, such as the unmnemonic category names and the intrinsic limitations of finite state grammars. Second, this kind of stuff has been done before, and it didn't work too well then either; for a useful overview of the field and pointers into the literature, see the article on "Grammatical Inference" in Volume 3 of The Handbook of The plete specifications and the verification of proposed impleideas and issues presented were firmly focused on a conven-mentations, we should concentrate more on incremental tional view of the design process-a view I can caricaturize development of specifications as a result of assessment of as the SPIV methodology: performance.