Stay strong, fair-weather friends--you can keep biking to work even through the darkest, coldest days. Biking to work is a thing. A regular bike commute gives you the chance to squeeze in extra cardio, and that extra exercise can do remarkable things for your health. Startling research has discovered that cyclists have about a 41 percent lower risk of dying overall (assuming you stay safe, obviously!), a 46 percent lower risk of cardiovascular disease, a 45 percent lower risk of cancer incidence, compared with non-active commuters. Swapping car trips for bike rides cuts fuel and parking costs; results in fewer sick days and higher productivity; and is great for your carbon footprint, besides easing congestion and improving air quality. Then the idea of commuting by bike becomes a whole lot less appealing, even if it good for you. That's why we wrote this guide to the best bike gear for winter commuting. Instead, we just want you to stay warm, safe, and dry. Be sure to also check out our other outdoor buying guides, including, Best Bike Lights, Best Electric Bikes, Best Laptop Backpacks for Work, Best Rain Jackets and Best Base Layers .

A bird's ultrablack feathers inspired this versatile material. Breakthroughs, discoveries, and DIY tips sent every weekday. There is black, and then there is The shade defined as a black that reflects less than 0.5 percent of the light that hits it, is used on everything from telescopes to cameras. This uniquely dark color is not easy to produce and may appear less black when it is viewed at an angle. To find a better way to reproduce this cool color, a team at Cornell University looked to nature.

Parameters can be learned using standard methods based on back-propagation, at a cost that scales linearly in the fabric size.

We thank all the reviewers for their constructive comments. Making predictions directly on a pixel level without the intermediate structures won't be Still, we follow the reviewers' suggestion by including an additional baseline that predicts directly over the pixels. The above figure shows the results. Dreamer's prediction deviates from the ground truth and quickly becomes blurry, Baselines, even with graph-structured prediction models, cannot cope with such out of distribution generalization. Applicability of the proposed method (R4, R1).

Concretely, we have from [ 41, Thm.

Abstract-- Soft robots employing compliant materials and deformable structures offer great potential for wearable devices that are comfortable and safe for human interaction. However, achieving both structural integrity and compliance for comfort remains a significant challenge. In this study, we present a novel fabrication and design method that combines the advantages of origami structures with the material programmability and wearability of knitted fabrics. We introduce a general design method that translates origami patterns into knit designs by programming both stitch and material patterns. The method creates folds in preferred directions while suppressing unintended buckling and bending by selectively incorporating heat fusible yarn to create rigid panels around compliant creases. We experimentally quantify folding moments and show that stitch patterning enhances folding directionality while the heat fusible yarn (1) keeps geometry consistent by reducing edge curl and (2) prevents out-of-plane deformations by stiffening panels. We demonstrate the framework through the successful reproduction of complex origami tessellations, including Miura-ori, Y oshimura, and Kresling patterns, and present a wearable knitted Kaleidocycle robot capable of locomotion. The combination of structural reconfigurability, material programmability, and potential for manufacturing scalability highlights knitted origami as a promising platform for next-generation wearable robotics. I. INTRODUCTION Soft robots operate effectively in human environments by conforming to their surroundings using their material compliance [1], [2], [3].

By stripping away the fabric tape that's held zippers together for a hundred years, Japanese clothing giant YKK is designing the future of seamless clothing. For more than a century, the zipper has stayed more or less the same: two interlocking rows of teeth, a sliding pull, and the fabric tape that holds it together. Billions are used every day, yet few people ever stop to think about how they work. Now, after a hundred years of stasis, YKK, the Japanese company that makes roughly half the world's zippers, has decided it's time to rethink the mechanism that holds much of modern clothing together. Their new AiryString zipper looks ordinary at first glance.

Named-entity recognition (NER) in low-resource languages is usually tackled by finetuning very large multilingual LMs, an option that is often infeasible in memory- or latency-constrained settings. We ask whether small decoder LMs can be pretrained so that they adapt quickly and transfer zero-shot to languages unseen during pretraining. To this end we replace part of the autoregressive objective with first-order model-agnostic meta-learning (MAML). Tagalog and Cebuano are typologically similar yet structurally different in their actor/non-actor voice systems, and hence serve as a challenging test-bed. Across four model sizes (11 M - 570 M) MAML lifts zero-shot micro-F1 by 2-6 pp under head-only tuning and 1-3 pp after full tuning, while cutting convergence time by up to 8%. Gains are largest for single-token person entities that co-occur with Tagalog case particles si/ni, highlighting the importance of surface anchors.

We develop Morphlux, a server-scale programmable photonic fabric to interconnect accelerators within servers. We show that augmenting state-of-the-art torus-based ML data-centers with Morphlux can improve the bandwidth of tenant compute allocations by up to 66%, reduce compute fragmentation by up to 70%, and minimize the blast radius of chip failures. We develop a novel end-to-end hardware prototype of Morphlux to demonstrate these performance benefits which translate to 1.72X improvement in training throughput of ML models. By rapidly programming the server-scale fabric in our hardware testbed, Morphlux can replace a failed accelerator chip with a healthy one in 1.2 seconds.

We thank all the reviewers for their constructive comments. Making predictions directly on a pixel level without the intermediate structures won't be Still, we follow the reviewers' suggestion by including an additional baseline that predicts directly over the pixels. The above figure shows the results. Dreamer's prediction deviates from the ground truth and quickly becomes blurry, Baselines, even with graph-structured prediction models, cannot cope with such out of distribution generalization. Applicability of the proposed method (R4, R1).