Under Display Camera (UDC) is a novel imaging system that mounts a digital camera lens beneath a display panel with the panel covering the camera.

Do you need more sleep in fall and winter? Less sunlight, colder weather, and diet changes make us sleepier--and that's OK. Winter mornings make staying under the covers feel impossible to resist. Breakthroughs, discoveries, and DIY tips sent every weekday. It's a crisp, fall day in mid-November, and though your calendar is filled with evening get-togethers and morning runs, you're feeling sluggish.

When you purchase through links in our articles, we may earn a small commission. A solar panel on a security cam is nothing new, but the panel on this one tracks the sun, rotating to gain maximum exposure. If you can mount it where it can harvest a steady supply of sunlight, the Baseus Security S2 Outdoor Camera 4K's tracking solar panel makes it one of the few outdoor cameras that can run truly unattended, capturing crisp 4K-resolution video as a bonus. For many households, outdoor cameras are the front line of home security. The devices watch over driveways, porches, and backyards, catching activity that doorbell cameras often miss.

Improving robotic navigation is critical for extending exploration range and enhancing operational efficiency. Vision-based navigation relying on traditional CCD or CMOS cameras faces major challenges when complex illumination conditions are paired with motion, limiting the range and accessibility of mobile planetary robots. In this study, we propose a novel approach to planetary navigation that leverages the unique imaging capabilities of Single-Photon Avalanche Diode (SPAD) cameras. We present the first comprehensive evaluation of single-photon imaging as an alternative passive sensing technology for robotic exploration missions targeting perceptually challenging locations, with a special emphasis on high-latitude lunar regions. We detail the operating principles and performance characteristics of SPAD cameras, assess their advantages and limitations in addressing key perception challenges of upcoming exploration missions to the Moon, and benchmark their performance under representative illumination conditions.

Grounding large language models (LLMs) in external knowledge sources is a promising method for faithful prediction. While existing grounding approaches work well for simple queries, many real-world information needs require synthesizing multiple pieces of evidence. We introduce "integrative grounding" -- the challenge of retrieving and verifying multiple inter-dependent pieces of evidence to support a hypothesis query. To systematically study this problem, we repurpose data from four domains for evaluating integrative grounding capabilities. Our investigation reveals two critical findings: First, in groundedness verification, while LLMs are robust to redundant evidence, they tend to rationalize using internal knowledge when information is incomplete. Second, in examining retrieval planning strategies, we find that undirected planning can degrade performance through noise introduction, while premise abduction emerges as a promising approach due to its logical constraints. Additionally, LLMs' zero-shot self-reflection capabilities consistently improve grounding quality. These insights provide valuable direction for developing more effective integrative grounding systems.

We provide an attention-level control method for the task of coupled image generation, where "coupled" means that multiple simultaneously generated images are expected to have the same or very similar backgrounds. While backgrounds coupled, the centered objects in the generated images are still expected to enjoy the flexibility raised from different text prompts. The proposed method disentangles the background and entity components in the model's cross-attention modules, attached with a sequence of time-varying weight control parameters depending on the time step of sampling. We optimize this sequence of weight control parameters with a combined objective that assesses how coupled the backgrounds are as well as text-to-image alignment and overall visual quality. Empirical results demonstrate that our method outperforms existing approaches across these criteria.

Large Language Models (LLMs) have achieved remarkable success across various domains. However, they still face significant challenges, including high computational costs for training and limitations in solving complex reasoning problems. Although existing methods have extended the reasoning capabilities of LLMs through structured paradigms, these approaches often rely on task-specific prompts and predefined reasoning processes, which constrain their flexibility and generalizability. To address these limitations, we propose a novel framework that leverages graph learning to enable more flexible and adaptive reasoning capabilities for LLMs. Specifically, this approach models the reasoning process of a problem as a graph and employs LLM-based graph learning to guide the adaptive generation of each reasoning step. To further enhance the adaptability of the model, we introduce a Graph Neural Network (GNN) module to perform representation learning on the generated reasoning process, enabling real-time adjustments to both the model and the prompt. Experimental results demonstrate that this method significantly improves reasoning performance across multiple tasks without requiring additional training or task-specific prompt design. Code can be found in https://github.com/zch65458525/L2T.

The EufyCam S3 Pro 2-Cam Kit delivers sharp, reliable, and fully independent home security without locking you into ongoing fees. Cloud subscriptions that lock your security camera footage behind a monthly fee are a frustrating reality for homeowners. The EufyCam S3 Pro 2-Cam Kit offers a way out. With 4K video resolution, smart AI detection, and solar panels integrated into the two cameras, it delivers top-shelf performance without roping you into a payment plan. Eufy does offer cloud storage as an option, but the cameras in this offering store their recordings locally on Eufy's HomeBase 3 hub--a NAS box (network-attached storage), essentially--enhancing your privacy while saving you money on subscription fees.

Keep your home comfortable without using a single watt of electricity. Have you ever wondered how a pine cone knows when to open and close? Now, researchers have taken this cue from nature to create something pretty cool for our homes. Let's dive into how this revolutionary window technology works, keeping your home comfortable without using a single watt of electricity. GET SECURITY ALERTS, EXPERT TIPS - SIGN UP FOR KURT'S NEWSLETTER - THE CYBERGUY REPORT HERE Pine cones have these amazing scales that respond to moisture.

Scientists have discovered a technique whereby light can be bent around corners, inspired by the way clouds scatter sunlight. This type of light-bending could lead to advances in medical imaging, electronics cooling and even nuclear reactor design. Daniele Faccio at the University of Glasgow, UK, and his colleagues say they are shocked this type of light scattering wasn't noticed before. It works on the same basis as clouds, snow and other white materials that absorb light: once photons hit the surface of such a material, they are scattered in all directions, barely penetrating at all and getting reflected out the way they came. For instance, when sunlight hits a tall cumulonimbus cloud, it bounces off the top, making this part of the cloud appear bright white.