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CUPERTINO, CALIFORNIA Apple today announced iPhone 17e, a powerful and more affordable addition to the iPhone 17 lineup. At the heart of iPhone 17e is the latest-generation A19, which delivers exceptional performance for everything users do. iPhone 17e also features C1X, the latest-generation cellular modem designed by Apple, which is up to 2x faster than C1 in iPhone 16e. The 48MP Fusion camera captures stunning photos, including next-generation portraits, and 4K Dolby Vision video. It also enables an optical-quality 2x Telephoto — like having two cameras in one. The 6.1-inch Super Retina XDR display features Ceramic Shield 2, offering 3x better scratch resistance than the previous generation and reduced glare.1 With MagSafe, users can enjoy fast wireless charging and access to a vast ecosystem of accessories like chargers and cases. And when iPhone 17e users are outside of cellular and Wi-Fi coverage, Apple’s groundbreaking satellite features — including Emergency SOS, Roadside Assistance, Messages, and Find My via satellite — help them stay connected when it matters most.2
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Abstract:Humans shift between different personas depending on social context. Large Language Models (LLMs) demonstrate a similar flexibility in adopting different personas and behaviors. Existing approaches, however, typically adapt such behavior through external knowledge such as prompting, retrieval-augmented generation (RAG), or fine-tuning. We ask: do LLMs really need external context or parameters to adapt to different behaviors, or do they already have such knowledge embedded in their parameters? In this work, we show that LLMs already contain persona-specialized subnetworks in their parameter space. Using small calibration datasets, we identify distinct activation signatures associated with different personas. Guided by these statistics, we develop a masking strategy that isolates lightweight persona subnetworks. Building on the findings, we further discuss: how can we discover opposing subnetwork from the model that lead to binary-opposing personas, such as introvert-extrovert? To further enhance separation in binary opposition scenarios, we introduce a contrastive pruning strategy that identifies parameters responsible for the statistical divergence between opposing personas. Our method is entirely training-free and relies solely on the language model's existing parameter space. Across diverse evaluation settings, the resulting subnetworks exhibit significantly stronger persona alignment than baselines that require external knowledge while being more efficient. Our findings suggest that diverse human-like behaviors are not merely induced in LLMs, but are already embedded in their parameter space, pointing toward a new perspective on controllable and interpretable personalization in large language models.
Mashable's tech editor Timothy Beck Werth saw the colors in person at Apple's launch event in New York City. The blush model reads as an extremely subtle pink, he said, while the citrus version is much brighter in person and "really pops."。关于这个话题，币安_币安注册_币安下载提供了深入分析
Варвара Кошечкина (редактор отдела оперативной информации)
马年春晚，具身智能以前所未有的姿态统治了视觉中心。。safew官方下载对此有专业解读