Insilico Medicine's Shares Drop After Founder Rebuts Eli Lilly Buyout Rumor - Yicai Global
<a href="https://news.google.com/rss/articles/CBMiyAFBVV95cUxQNGRHRFh5c1diQ1JvOUg4a2R1SGRfcHhUcmtGZnF1VVpRY1VEQTNHVm9USms5MTU2djZPWjV6OW41TEtPZ2FEeWVfOUhNVFdPQ1o5b3pRMWhFcmFhWXFLZ2QxQ0Z0Y09yYVhQNzZPX01CSnRTemZDZC1yeUY3c3JVRERnWFJPU2xaTkhuNXJPdTd6OTFGejJyVUFNVFBia3dqTHdMZlBNd0UtN3NkaHZjNVZZV19sSm5qdTdYYjItcmp1OU9rYkYzMw?oc=5" target="_blank">Insilico Medicine's Shares Drop After Founder Rebuts Eli Lilly Buyout Rumor</a> <font color="#6f6f6f">Yicai Global</font>
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GazeCLIP: Gaze-Guided CLIP with Adaptive-Enhanced Fine-Grained Language Prompt for Deepfake Attribution and Detection
arXiv:2603.29295v1 Announce Type: new Abstract: Current deepfake attribution or deepfake detection works tend to exhibit poor generalization to novel generative methods due to the limited exploration in visual modalities alone. They tend to assess the attribution or detection performance of models on unseen advanced generators, coarsely, and fail to consider the synergy of the two tasks. To this end, we propose a novel gaze-guided CLIP with adaptive-enhanced fine-grained language prompts for fine-grained deepfake attribution and detection (DFAD). Specifically, we conduct a novel and fine-grained benchmark to evaluate the DFAD performance of networks on novel generators like diffusion and flow models. Additionally, we introduce a gaze-aware model based on CLIP, which is devised to enhance t

Monocular Building Height Estimation from PhiSat-2 Imagery: Dataset and Method
arXiv:2603.29245v1 Announce Type: new Abstract: Monocular building height estimation from optical imagery is important for urban morphology characterization but remains challenging due to ambiguous height cues, large inter-city variations in building morphology, and the long-tailed distribution of building heights. PhiSat-2 is a promising open-access data source for this task because of its global coverage, 4.75 m spatial resolution, and seven-band spectral observations, yet its potential has not been systematically evaluated. To address this gap, we construct a PhiSat-2-Height dataset (PHDataset) and propose a Two-Stream Ordinal Network (TSONet). PHDataset contains 9,475 co-registered image-label patch pairs from 26 cities worldwide. TSONet jointly models footprint segmentation and height
Africa holds just 1.3 percent of global data storage capacity - tanzaniatimes.net
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The Inside Story of the Greatest Deal Google Ever Made: Buying DeepMind - WSJ
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A Pragmatic View of AI Personhood - Google DeepMind
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Anthropic Dials Back AI Safety Commitments - WSJ
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Anthropic releases part of AI tool source code in 'error' - Indiana Gazette Online
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