We introduce CzechLynx, the first large-scale, open-access dataset for individual identification, pose estimation, and instance segmentation of the Eurasian lynx (Lynx lynx). CzechLynx contains 39,760 camera trap images annotated with segmentation masks, identity labels, and 20-point skeletons and covers 319 unique individuals across 15 years of systematic monitoring in two geographically distinct regions: southwest Bohemia and the Western Carpathians. In addition to the real camera trap data, we provide a large complementary set of photorealistic synthetic images and a Unity-based generation pipeline with diffusion-based text-to-texture modeling, capable of producing arbitrarily large amounts of synthetic data spanning diverse environments, poses, and coat-pattern variations. To enable systematic testing across realistic ecological scenarios, we define three complementary evaluation protocols: (i) geo-aware, (ii) time-aware open-set, and (iii) time-aware closed-set, covering cross-regional and long-term monitoring settings. With the provided resources, CzechLynx offers a unique, flexible benchmark for robust evaluation of computer vision and machine learning models across realistic ecological scenarios.

Although recent large multimodal models (LMMs) demonstrate impressive progress on vision language tasks, their alignment with human centered (HC) principles, such as fairness, ethics, inclusivity, empathy, and robustness; remains poorly understood. We present HumaniBench, a unified evaluation framework designed to characterize HC alignment across realistic, socially grounded visual contexts. HumaniBench contains 32,000 expert-verified image question pairs derived from real world news imagery and spanning seven evaluation tasks: scene understanding, instance identity, multiple-choice visual question answering (VQA), multilinguality, visual grounding, empathetic captioning, and image resilience testing. Each task is mapped to one or more HC principles through a principled operationalization of metrics covering accuracy, harmful content detection, hallucination and faithfulness, coherence, cross lingual quality, empathy, and robustness.We evaluate 15 state-of-the-art LMMs under this framework and observe consistent cross model trade offs: proprietary systems achieve the strongest performance on ethics, reasoning, and empathy, while open-source models exhibit superior visual grounding and resilience. All models, however, show persistent gaps in fairness and multilingual inclusivity. We further analyze the effect of inference-time techniques, finding that chain of thought prompting and test-time scaling yield 8 to 12 % improvements on several HC dimensions. HumaniBench provides a reproducible, extensible foundation for systematic HC evaluation of LMMs and enables fine-grained analysis of alignment trade-offs that are not captured by conventional multimodal benchmarks. https://vectorinstitute.github.io/humanibench/

Sequential knowledge editing techniques aim to continuously update knowledge in large language models at low cost, preventing models from generating outdated or incorrect information. However, existing sequential editing methods suffer from a significant decline in editing success rates after long-term editing. Through theoretical analysis and experiments, our findings reveal that as the number of edits increases, the model's output increasingly deviates from the desired target, leading to a drop in editing success rates. We refer to this issue as the superimposed noise accumulation problem. Our further analysis demonstrates that the problem is related to the erroneous activation of irrelevant knowledge and conflicts between activated knowledge. Based on this analysis, a method named DeltaEdit is proposed that reduces conflicts between knowledge through dynamic orthogonal constraint strategies. Experiments show that DeltaEdit significantly reduces superimposed noise, achieving a 16.8% improvement in editing performance over the strongest baseline.

This paper explores the design of a propaganda detection tool using Large Language Models (LLMs). Acknowledging the inherent biases in AI models, especially in political contexts, we investigate how these biases might be leveraged to enhance critical think ing in news consumption. Countering the typical view of AI biases as detrimental, our research proposes strategies of user choice and personalization in response to a user's political stance, applying psychological concepts of confirmation bias and cogniti ve dissonance.

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Tesla's Optimus humanoid robots showcase abilities in construction, cooking and emergency response in Elon Musk's viral 38-second video that has reshaped robot discussions.

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It's time to lock in and let your winter arc begin Have you ever locked in? No, not finding yourself locked in a lift or a bathroom. We're talking about locking IN - the phrase you might have seen on social media or heard people saying lately. To lock in is to focus; to endure short-term pain for long-term gain - whether that be building your body or your business. Do it today - not tomorrow.

If you're in the market for a new camera, this holiday season is the time to buy. There are some great Black Friday camera deals available right now, including one on our favorite GoPro, the GoPro Hero 13 Black. There are plenty of other GoPro camera deals, along with some good buys on cameras from Insta360, DJI, Fujifilm, and more. Also, be sure to read our guide to the Best GoPros, Best 360 Cameras, Best Instax Cameras, Best Travel Cameras, and Best Mirrorless Cameras . Do You Need a New Camera? Probably what you need to do is spend a lot more time with the one you have, but if buying a new camera makes you feel like maybe you're getting better at photography, far be it for me to stop you.

Blanketed by frost in the Mongolian wilderness, a Pallas's cat endures the aftermath of a snowstorm at -35 C. Perfectly adapted to its frozen world, this elusive feline's dense fur, flattened ears, and high-set eyes allow it to vanish into the landscape. Breakthroughs, discoveries, and DIY tips sent every weekday. A herd of musk ox protect their young from a hungry arctic wolf, a flock of flamingoes chill near Dubai's imposing skyline, and a flying squirrel pops out for a quick hello. These are just a few of the stunning wildlife scenes captured in the 2025 Nature inFocus Photography Awards . This year, photographers from 38 countries submitted nearly 16,000 images.