Many monitors advertise HDR support but lack essential hardware like sufficient brightness (1000 nits recommended) and proper local dimming to deliver true HDR experiences. PCWorld highlights that even high-end models like the Alienware AW3225QF may only achieve 250 nits in full-screen HDR despite 1000-nit peak claims. Consumers should prioritize raw specifications over marketing terms, as technologies like OLED or Mini-LED with strong contrast ratios are crucial for authentic HDR performance. Imagine you just bought a brand-new monitor that prominently advertises its HDR capabilities. You hop over to YouTube and stream the first "4K HDR" video you find but it looks washed out or barely any different from the non-HDR display you had before.

In multi-vehicle cooperative driving tasks involving high-frequency continuous control, traditional state-based reward functions suffer from the issue of vanishing reward differences. This phenomenon results in a low signal-to-noise ratio (SNR) for policy gradients, significantly hindering algorithm convergence and performance improvement. To address this challenge, this paper proposes a novel Hybrid Differential Reward (HDR) mechanism. We first theoretically elucidate how the temporal quasi-steady nature of traffic states and the physical proximity of actions lead to the failure of traditional reward signals. Building on this analysis, the HDR framework innovatively integrates two complementary components: (1) a Temporal Difference Reward (TRD) based on a global potential function, which utilizes the evolutionary trend of potential energy to ensure optimal policy invariance and consistency with long-term objectives; and (2) an Action Gradient Reward (ARG), which directly measures the marginal utility of actions to provide a local guidance signal with a high SNR. Furthermore, we formulate the cooperative driving problem as a Multi-Agent Partially Observable Markov Game (POMDPG) with a time-varying agent set and provide a complete instantiation scheme for HDR within this framework. Extensive experiments conducted using both online planning (MCTS) and Multi-Agent Reinforcement Learning (QMIX, MAPPO, MADDPG) algorithms demonstrate that the HDR mechanism significantly improves convergence speed and policy stability. The results confirm that HDR guides agents to learn high-quality cooperative policies that effectively balance traffic efficiency and safety.

Console gamers have a leg up on PC gamers when it comes to ease of use. Turn it on and your settings are mostly chosen for you with little fuss. From game to game, the overall experience is standardized -- no need to worry if your PlayStation 5 can handle the latest releases. As PC gamers, we may have more options, but that comes at a cost. There's a real learning curve when it comes to configuring hardware and software to run games optimally, and developers have to play it safer as they're forced to support a much wider range of specs.

With summer gone and the skies already greying over in preparation for six months of uncontested rain, you may well be thinking more seriously about video games. September and October tend to see the biggest releases of the year, so you can expect many evenings spent hiding from the world while playing Call of Duty: Black Ops 6 or Mario Party Jamboree. If your gaming set-up is looking a little tired and you want to treat yourself to a serious seasonal upgrade, here are some suggestions. If you have a PS5 or Xbox Series X, you'll want a 4K TV at the very least, but also a model that supports a 120-Hz refresh rate, which provides incredibly smooth, fluid image quality for compatible games. The TV should have at least one HDMI 2.1 port and you'll need a 2.1 HDMI-compatible cable to plug in your console.

Ensuring the safe alignment of large language models (LLMs) with human values is critical as they become integral to applications like translation and question answering. Current alignment methods struggle with dynamic user intentions and complex objectives, making models vulnerable to generating harmful content. We propose Safety Arithmetic, a training-free framework enhancing LLM safety across different scenarios: Base models, Supervised fine-tuned models (SFT), and Edited models. Safety Arithmetic involves Harm Direction Removal to avoid harmful content and Safety Alignment to promote safe responses. Additionally, we present NoIntentEdit, a dataset highlighting edit instances that could compromise model safety if used unintentionally. Our experiments show that Safety Arithmetic significantly improves safety measures, reduces over-safety, and maintains model utility, outperforming existing methods in ensuring safe content generation.

In this paper, we use matrix information theory as an analytical tool to analyze the dynamics of the information interplay between data representations and classification head vectors in the supervised learning process. Specifically, inspired by the theory of Neural Collapse, we introduce matrix mutual information ratio (MIR) and matrix entropy difference ratio (HDR) to assess the interactions of data representation and class classification heads in supervised learning, and we determine the theoretical optimal values for MIR and HDR when Neural Collapse happens. Our experiments show that MIR and HDR can effectively explain many phenomena occurring in neural networks, for example, the standard supervised training dynamics, linear mode connectivity, and the performance of label smoothing and pruning. Additionally, we use MIR and HDR to gain insights into the dynamics of grokking, which is an intriguing phenomenon observed in supervised training, where the model demonstrates generalization capabilities long after it has learned to fit the training data. Furthermore, we introduce MIR and HDR as loss terms in supervised and semi-supervised learning to optimize the information interactions among samples and classification heads. The empirical results provide evidence of the method's effectiveness, demonstrating that the utilization of MIR and HDR not only aids in comprehending the dynamics throughout the training process but can also enhances the training procedure itself.

Samsung just provided availability details for a full range of new TVs as part of its Unbox & Discover event. These include Neo QLED 8K models, Neo QLED 4K models and various OLED boxes. Many of these TVs were originally teased at CES, but now preorders are actually open for consumers looking for a new way to watch that upcoming Star Wars show. Samsung calls the NEO QLED QN900D the "slimmest, most premium 8K TV to ever hit the market." It's got everything you can think of, including AI to upscale content to 8K.

Conversion rate (CVR) prediction is one of the core components in online recommender systems, and various approaches have been proposed to obtain accurate and well-calibrated CVR estimation. However, we observe that a well-trained CVR prediction model often performs sub-optimally during sales promotions. This can be largely ascribed to the problem of the data distribution shift, in which the conventional methods no longer work. To this end, we seek to develop alternative modeling techniques for CVR prediction. Observing similar purchase patterns across different promotions, we propose reusing the historical promotion data to capture the promotional conversion patterns. Herein, we propose a novel \textbf{H}istorical \textbf{D}ata \textbf{R}euse (\textbf{HDR}) approach that first retrieves historically similar promotion data and then fine-tunes the CVR prediction model with the acquired data for better adaptation to the promotion mode. HDR consists of three components: an automated data retrieval module that seeks similar data from historical promotions, a distribution shift correction module that re-weights the retrieved data for better aligning with the target promotion, and a TransBlock module that quickly fine-tunes the original model for better adaptation to the promotion mode. Experiments conducted with real-world data demonstrate the effectiveness of HDR, as it improves both ranking and calibration metrics to a large extent. HDR has also been deployed on the display advertising system in Alibaba, bringing a lift of $9\%$ RPM and $16\%$ CVR during Double 11 Sales in 2022.

Finding a suitable TV for your PlayStation or Xbox used to require a careful look at spec sheets. But that's not the case these days as the best TVs for gaming are usually the best TVs you can buy, period. While nobody needs a fancy TV to enjoy a good video game, the right set can help you maximize your gaming hardware's potential. If you're unsure of where to start, we've laid out some helpful advice for buying the right model below, along with a few recommendations for the best gaming TVs you can buy today. Whether you use it for gaming or not, all good TVs are built on the same foundations.

Meta wants to make it clear it's not giving up on high-end VR experiences yet. So, in a rare move, the company is spilling the beans on several VR headset prototypes at once. The goal, according to CEO Mark Zuckerberg, is to eventually craft something that could pass the "visual Turing Test," or the point where virtual reality is practically indistinguishable from the real world. That's the Holy Grail for VR enthusiasts, but for Meta's critics, it's another troubling sign that the company wants to own reality (even if Zuckerberg says he doesn't want to completely own the metaverse). As explained by Zuckerberg and Michael Abrash, Chief Scientist of Meta's Reality Labs, creating the perfect VR headset involves perfecting four basic concepts.