Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Say More Safety Net Creator Hub Versus Gift Ideas For Everyone On Your List Mashable Selects Switch Off Trending Now In My Bag VidCon with Mashable All Series Act fast to save over $100. Hannah Hoolihan is a freelance writer with Mashable. She's written for various entertainment websites since 2017, covering everything from tech to games to film. You'll currently find her work on IGN and Fangoria alongside Mashable, but she also has bylines at Rock Paper Shotgun, Collider, Screen Rant, and more. When she's not writing, she enjoys catching up with the latest films and shows -- horror, in particular -- and has a deep love of FromSoft games, which she continues to happily replay.

Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Look Up Say More Creator Hub Gift Ideas For Everyone On Your List Mashable Selects Versus Switch Off Trending Now Safety Net In My Bag VidCon with Mashable All Series Christianna Silva is a senior culture reporter covering social platforms and the creator economy, with a focus on the intersection of social media, politics, and the economic systems that govern us. Since joining Mashable in 2021, they have reported extensively on meme creators, content moderation, and the nature of online creation under capitalism . All products featured here are independently selected by our editors and writers. If you buy something through links on our site, Mashable may earn an affiliate commission. I ran the New York City Marathon wearing seven fitness trackers to see how they compare, and I left with a few favorites. I received a lot of strange looks, but if shame is the price to pay for finding the best fitness tracker for running a marathon, so be it.

It will be updated monthly. California has launched a new portal, which tracks AI-related job losses in the state. According to the office of California Governor Gavin Newsom, it's meant to serve as an early warning system for widespread job cuts due to artificial intelligence, allowing the government to proactively determine where interventions may be needed the most. The website says Newsom's office worked with the California Employment Development Department, as well as with the California Policy Lab at the University of California to conduct research to measure AI-related job losses. They use Unemployment Insurance claims data combined with AI exposure measures to come up with the figures in the tracker.

Existing multi-modal object tracking approaches primarily focus on dual-modal paradigms, such as RGB-Depth or RGB-Thermal, yet remain challenged in complex scenarios due to limited input modalities. To address this gap, this work introduces a novel multi-modal tracking task that leverages three complementary modalities, including visible RGB, Depth (D), and Thermal Infrared (TIR), aiming to enhance robustness in complex scenarios. To support this task, we construct a new multi-modal tracking dataset, coined RGBDT500, which consists of 500 videos with synchronised frames across the three modalities. Each frame provides spatially aligned RGB, depth, and thermal infrared images with precise object bounding box annotations. Furthermore, we propose a novel multi-modal tracker, dubbed RDTTrack. RDTTrack integrates tri-modal information for robust tracking by leveraging a pretrained RGB-only tracking model and prompt learning techniques. In specific, RDTTrack fuses thermal infrared and depth modalities under a proposed orthogonal projection constraint, then integrates them with RGB signals as prompts for the pre-trained foundation tracking model, effectively harmonising tri-modal complementary cues. The experimental results demonstrate the effectiveness and advantages of the proposed method, showing significant improvements over existing dual-modal approaches in terms of tracking accuracy and robustness in complex scenarios.

Transformer-based algorithms, such as LoRAT, have significantly enhanced objecttracking performance. However, these approaches rely on a standard attention mechanism, which incurs quadratic token complexity, making real-time inference computationally expensive. In this paper, we introduce LoRATv2, a novel tracking framework that addresses these limitations with three main contributions. First, LoRATv2 integrates frame-wise causal attention, which ensures full selfattention within each frame while enabling causal dependencies across frames, significantly reducing computational overhead. Moreover, key-value (KV) caching is employed to efficiently reuse past embeddings for further speedup.

Researchers at the University of Cambridge are using satellite data and AI in an effort to slow the decline in Britain's hedgehog population. Using an AI tool called Tessera, which analyses detailed images of the UK gathered from space, experts can precisely determine locations of hedgehog habitats - and where these are disappearing. The resulting maps capture landscapes in minute detail, including down to individual hedgerows, while AI can accurately predict hedgehog-friendly places obscured by cloud cover. Those behind the project hope it will help to shed light not just on where hedgehogs live across the UK, but barriers preventing them from finding food and mates. The researchers say Tessera's outputs can be used to track the impact of new housing developments and other environmental changes on landscapes that could affect hedgehogs over time.

We study bilevel optimization with a fixed polyhedral lower feasible set. Such problems are challenging for two reasons: active-set changes can make the upper objective nonsmooth, and existing hypergradient methods typically require lower-Hessian inversions or equivalent linear solves, which are computationally expensive. To address these issues, we adopt a logarithmic barrier smoothing of the lower problem to obtain a differentiable approximation of the constrained bilevel objective, and develop a proxy-gradient algorithm for the resulting barrier-smoothed surrogate. The algorithm uses only gradients of the upper and lower objectives; its only second-order object is the explicit logarithmic barrier Hessian determined by the fixed polyhedral constraints. Barrier smoothing restores differentiability, but Euclidean smoothness constants are not uniformly bounded near the boundary. We therefore develop a local Dikin-geometry analysis in which the barrier-metric provides an oracle-free curvature scale near the moving lower centers. This leads to barrier-aware schedules that keep the iterates inside locally well-behaved regions. For the barrier-smoothed objective, we prove stationarity rates of $\widetilde{O}(K^{-2/3})$ in the deterministic setting and $\widetilde{O}(K^{-2/5})$ under upper-level-only bounded stochastic noise after $K$ outer iterations, together with quantitative bias control as the barrier parameter decreases.

Fi Mini and Tractive: Which Smart Cat Tracker Should You Buy? For months, I tested Tractive and Fi Mini smart collars on my cat to find the best for activity, sleep, and GPS tracking. Wearable health-monitoring devices, like smart rings, smartwatches, and fitness trackers, help people stay on top of key wellness markers. By providing data on steps, heart rate, sleep, and more, these gadgets allow people to better understand their health, along with the opportunity to improve it with lifestyle shifts. But why should humans have all the fun?