You don't have to upload your video to the cloud or pay a monthly fee to secure your home. In the age of state surveillance, with big tech trampling our data privacy rights and gouging us for every penny, there are plenty of reasons to keep your security camera footage local. Whether you want to save money or ensure your video doesn't end up in the hands of persons (or AI) unknown, subscription-free security cameras are the way to go. The good news is that locally recording security cameras are better than ever. I've been testing security cameras for a decade, and the gap between the best cloud-connected and local cameras is closing. You don't necessarily have to give up the best features to shirk that subscription anymore.

Neural Radiance Fields (NeRFs) have shown remarkable performances in producing novel-view images from high-quality scene images. However, hand-held low-light photography challenges NeRFs as the captured images may simultaneously suffer from low visibility, noise, and camera shakes.While existing NeRF methods may handle either low light or motion, directly combining them or incorporating additional image-based enhancement methods does not work as these degradation factors are highly coupled.We observe that noise in low-light images is always sharp regardless of camera shakes, which implies an implicit order of these degradation factors within the image formation process.This inspires us to explore such an order to decouple and remove these degradation factors while training the NeRF.To this end, we propose in this paper a novel model, named LuSh-NeRF, which can reconstruct a clean and sharp NeRF from a group of hand-held low-light images.The key idea of LuSh-NeRF is to sequentially model noise and blur in the images via multi-view feature consistency and frequency information of NeRF, respectively.Specifically, LuSh-NeRF includes a novel Scene-Noise Decomposition (SND) module for decoupling the noise from the scene representation and a novel Camera Trajectory Prediction (CTP) module for the estimation of camera motions based on low-frequency scene information.To facilitate training and evaluations, we construct a new dataset containing both synthetic and real images.Experiments show that LuSh-NeRF outperforms existing approaches.

Subgraph-based methods have proven to be effective and interpretable in predicting drug-drug interactions (DDIs),which are essential for medical practice and drug development. Subgraph selection and encoding are critical stages in these methods, yet customizing these components remains underexplored due to the high cost of manual adjustments. In this study, inspired by the success of neural architecture search (NAS), we propose a method to search for data-specific components within subgraph-based frameworks. Specifically, we introduce extensive subgraph selection and encoding spaces that account for the diverse contexts of drug interactions in DDI prediction. To address the challenge of large search spaces and high sampling costs, we design a relaxation mechanism that uses an approximation strategy to efficiently explore optimal subgraph configurations. This approach allows for robust exploration of the search space. Extensive experiments demonstrate the effectiveness and superiority of the proposed method, with the discovered subgraphs and encoding functions highlighting the model's adaptability.

The app reads your email inbox and your meeting calendar, then gives you a short audio summary. It can help you spend less time scrolling, but of course, there are privacy drawbacks to consider.

How Bad Is Plagiarism, Really? From ancient Rome to the era of A.I., people have prized originality, but the line where influence ends and cribbing begins is notoriously blurry. One pleasing facet of plagiarism is that, in the eyes of the law, it doesn't exist. I could come over later, bring a few beers, and we could, you know, get down to some serious humanizing. Hard to resist, these days, given what's at stake. For students with assignments to complete, who have already vanquished their desolation by asking ChatGPT to compose an essay on their behalf, a humanizer is an A.I. tool that takes what has been produced, puts it through a further digital mill, and makes it sound as if it had emerged from a verifiable person. Among the companies that offer such tools are StealthWriter, HIX AI, and QuillBot. Anyone who has buttered and blitzed a mountain of mashed potatoes into a purée will understand.

Furious Trump issues threat to Iran demanding Strait of Hormuz is'FULLY OPENED' in hours or America will'obliterate their power plants'... and there's already a key target in sight Nancy Guthrie's desperate family releases emotional new statement as they plead for'renewed attention to our mom's case' 50 days after she vanished Chappell Roan accused of'leaving Jude Law's 11-year-old daughter in tears and using security guard to threaten her' I was the only one JFK Jr and Carolyn Bessette trusted when they burdened me with an extraordinarily intimate secret. How Iran's ruthless enforcers use rape to crush dissent: Brutal sex attacks on victims as young as 12 used to strike fear into protesters, rights groups reveal amid fury over sickening nurse gang rape Shia LaBeouf suffers public meltdown in Rome as he's caught screaming'f*** off' at woman... after battery arrests'He just didn't protect him': Insiders reveal REAL reason Justin Bieber and Usher's secret feud hit'boiling point' at Oscars I thought I was losing my mind... then doctors told me I had'exploding head syndrome'. America is about to be torn apart by a financial tsunami - and it's not just an oil crisis to fear. Denise Richards's plastic surgeon reveals stunning before-and-after photos of her facelift'Get the f*** out of my life,' JFK Jr screamed at Carolyn Bessette... what she cruelly told friends about his manhood... the cuckolding, cocaine - and moment that sent her truly psychotic: MAUREEN CALLAHAN has the untold REAL story'Meteorite' CRASHES into woman's home as residents are left terrified by massive sonic boom YouTuber who exposed Somali'fraudsters' in bombshell investigation reveals terrifying threats from left-wing activists... as he begs for cash to help pay for security Sabine Getty's gown gets STUCK in escalator at Oscars during dramatic moment Charlie's Angels bombshell Jaclyn Smith looks nowhere near her 80 years in Beverly Hills... see her now Florida's Olivier Rioux, tallest player in college basketball history, dwarfs 6ft8 March Madness rival as defending champs roll to win RFK Jr reveals his strange daily routine... including not eating until noon and meditating with'dead people' Iran ballistic missile hits Israeli city in terrifying strike near top-secret facility that is key to country's atomic weapons program Ted Cruz proposes dramatic change to ICE funding in repsonse to'extreme and unreasonable' Democrats in effort to end bitter standoff causing airport chaos And now it turns out you've probably been doing your laundry wrong this entire time. Experts at AO.com have revealed why you should never close the washing machine door after a wash.

Motion time series collected from low-power, always-on mobile and wearable devices such as smartphones and smartwatches offer significant insights into human behavioral patterns, with wide applications in healthcare, automation, IoT, and AR/XR. However, given security and privacy concerns, building large-scale motion time series datasets remains difficult, hindering the development of pre-trained models for human activity analysis. Typically, existing models are trained and tested on the same dataset, leading to poor generalizability across variations in device location, device mounting orientation, and human activity type. In this paper, we introduce UniMTS, the first unified pre-training procedure for motion time series that generalizes across diverse device latent factors and activities. Specifically, we employ a contrastive learning framework that aligns motion time series with text descriptions enriched by large language models.

Inspired by the success of foundation models in applications such as ChatGPT, as graph data has been ubiquitous, one can envision the far-reaching impacts that can be brought by Graph Foundation Models (GFMs) with broader applications in the areas such as scientific research, social network analysis, drug discovery, and e-commerce. Despite the significant progress of pre-trained graph neural networks, there haven't been GFMs that can achieve desired performance on various graph-learning-related tasks. Building GFMs may rely on a vocabulary that encodes transferable patterns shared among different tasks and domains. Unlike image and text, defining such transferable patterns for graphs remains an open question. In this paper, we aim to bridge this gap by rethinking the transferable patterns on graphs as computation trees -- i.e., tree structures derived from the message-passing process. Based on this insight, we propose a cross-task, cross-domain graph foundation model named GFT, short for Graph Foundation model with transferable Tree vocabulary. By treating computation trees as tokens within the transferable vocabulary, GFT improves model generalization and reduces the risk of negative transfer. The theoretical analyses and extensive experimental studies have demonstrated the transferability of computation trees and shown the effectiveness of GFT across diverse tasks and domains in graph learning.

Alexandre Rocha, a French father in Iceland, claims he lost parental rights after opposing his autistic son's sex reassignment, alleging courts prioritized ideology over his rights.

Recent works have shown that large language model (LLM) agents are able to improve themselves from experience, which is an important ability for continuous enhancement post-deployment. However, existing benchmarks primarily evaluate their innate capabilities and do not assess their ability to improve over time. To address this gap, we introduce StreamBench, a pioneering benchmark designed to evaluate the continuous improvement of LLM agents over an input-feedback sequence. StreamBench simulates an online learning environment where LLMs receive a continuous flow of feedback stream and iteratively enhance their performance. In addition, we propose several simple yet effective baselines for improving LLMs on StreamBench, and provide a comprehensive analysis to identify critical components that contribute to successful streaming strategies. Our work serves as a stepping stone towards developing effective online learning strategies for LLMs, paving the way for more adaptive AI systems in streaming scenarios.