Bioptimus Launches STELA Initiative To Build World’s Largest Multimodal Atlas For AI Driven Biology - BioPharma APAC
<a href="https://news.google.com/rss/articles/CBMi2gFBVV95cUxNWVdxRVFlVHhIVmhDNnliOXA0dnp0ZHE3WHBuS3ZOMk8zVXRwRGJLMW8yazBxamlTbE9lUlNxcDBWZEcxbExwUFlhRVZiTE13YXU4V29kUjR5UVNMOWVKc0xQdGxJSjlFTlRJd2x6Q01kdDByUThDbHprekRUZUJob0M1Ul96ZE82Qi0xaVhXMm9DbWtTOTloM1hPZFBpa1JrRHcxd0wzSFZFWXpwUGkyWUVjQ1dPY0sybDF1S0tqZFJrOUJKcUg1LWFXTTlHODFkVEFvS2FPaTFTZw?oc=5" target="_blank">Bioptimus Launches STELA Initiative To Build World’s Largest Multimodal Atlas For AI Driven Biology</a> <font color="#6f6f6f">BioPharma APAC</font>
Could not retrieve the full article text.
Read on GNews AI multimodal →Sign in to highlight and annotate this article

Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
launchmultimodalDubai launches AI-powered digital ecosystem to drive $2.72bn growth in two years - Arabian Business
<a href="https://news.google.com/rss/articles/CBMiugFBVV95cUxPbXJLRFR2anI4cDJWNG10WnJXZGY1U0lHVHA0YzJBU1lvRndBU2NiX3VQeC16Tl9tSnhVTWZGRjRGdUZFLW43bU1TeV9nUXZERHRYOW1FMGg4UndjZXQ5RnZHS1p2QVpjenFnSGlLTExUTG15N1BmN1h2cXRBcW9YdWRWZ3ZnWmFMNHF3UEFZNTJmNFpVeTB0VXgzSXUzVnFCUTM2LUZUbXNUamFoREhIWDY4b2h4VllQMkE?oc=5" target="_blank">Dubai launches AI-powered digital ecosystem to drive $2.72bn growth in two years</a> <font color="#6f6f6f">Arabian Business</font>

Advancing Complex Video Object Segmentation via Tracking-Enhanced Prompt: The 1st Winner for 5th PVUW MOSE Challenge
arXiv:2604.00395v1 Announce Type: new Abstract: In the Complex Video Object Segmentation task, researchers are required to track and segment specific targets within cluttered environments, which rigorously tests a method's capability for target comprehension and environmental adaptability. Although SAM3, the current state-of-the-art solution, exhibits unparalleled segmentation performance and robustness on conventional targets, it underperforms on tiny and semantic-dominated objects. The root cause of this limitation lies in SAM3's insufficient comprehension of these specific target types. To address this issue, we propose TEP: Advancing Complex Video Object Segmentation via Tracking-Enhanced Prompts. As a training-free approach, TEP leverages external tracking models and Multimodal Large
Auto-Slides: An Interactive Multi-Agent System for Creating and Customizing Research Presentations
arXiv:2509.11062v3 Announce Type: replace-cross Abstract: The rapid progress of large language models (LLMs) has opened new opportunities for education. While learners can interact with academic papers through LLM-powered dialogue, limitations still exist: the lack of structured organization and the heavy reliance on text can impede systematic understanding and engagement with complex concepts. To address these challenges, we propose Auto-Slides, an LLM-driven system that converts research papers into pedagogically structured, multimodal slides (e.g., diagrams and tables). Drawing on cognitive science, it creates a presentation-oriented narrative and allows iterative refinement via an interactive editor to better match learners' knowledge level and goals. Auto-Slides further incorporates v
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Releases
Dubai launches AI-powered digital ecosystem to drive $2.72bn growth in two years - Arabian Business
<a href="https://news.google.com/rss/articles/CBMiugFBVV95cUxPbXJLRFR2anI4cDJWNG10WnJXZGY1U0lHVHA0YzJBU1lvRndBU2NiX3VQeC16Tl9tSnhVTWZGRjRGdUZFLW43bU1TeV9nUXZERHRYOW1FMGg4UndjZXQ5RnZHS1p2QVpjenFnSGlLTExUTG15N1BmN1h2cXRBcW9YdWRWZ3ZnWmFMNHF3UEFZNTJmNFpVeTB0VXgzSXUzVnFCUTM2LUZUbXNUamFoREhIWDY4b2h4VllQMkE?oc=5" target="_blank">Dubai launches AI-powered digital ecosystem to drive $2.72bn growth in two years</a> <font color="#6f6f6f">Arabian Business</font>
The Chronicles of RiDiC: Generating Datasets with Controlled Popularity Distribution for Long-form Factuality Evaluation
arXiv:2604.00019v1 Announce Type: new Abstract: We present a configurable pipeline for generating multilingual sets of entities with specified characteristics, such as domain, geographical location and popularity, using data from Wikipedia and Wikidata. These datasets are intended for evaluating the factuality of LLMs' long-form generation, thereby complementing evaluation based on short-form QA datasets. We present the RiDiC dataset as an example of this approach. RiDiC contains 3,000 entities from three domains -- rivers, natural disasters, and car models -- spanning different popularity tiers. Each entity is accompanied by its geographical location, English and Chinese names (if available) and relevant English and Chinese Wikipedia content, which is used to evaluate LLMs' responses. Gen
Neural Reconstruction of LiDAR Point Clouds under Jamming Attacks via Full-Waveform Representation and Simultaneous Laser Sensing
arXiv:2604.00371v1 Announce Type: new Abstract: LiDAR sensors are critical for autonomous driving perception, yet remain vulnerable to spoofing attacks. Jamming attacks inject high-frequency laser pulses that completely blind LiDAR sensors by overwhelming authentic returns with malicious signals. We discover that while point clouds become randomized, the underlying full-waveform data retains distinguishable signatures between attack and legitimate signals. In this work, we propose PULSAR-Net, capable of reconstructing authentic point clouds under jamming attacks by leveraging previously underutilized intermediate full-waveform representations and simultaneous laser sensing in modern LiDAR systems. PULSAR-Net adopts a novel U-Net architecture with axial spatial attention mechanisms specific
Revisiting Human-in-the-Loop Object Retrieval with Pre-Trained Vision Transformers
arXiv:2604.00809v1 Announce Type: cross Abstract: Building on existing approaches, we revisit Human-in-the-Loop Object Retrieval, a task that consists of iteratively retrieving images containing objects of a class-of-interest, specified by a user-provided query. Starting from a large unlabeled image collection, the aim is to rapidly identify diverse instances of an object category relying solely on the initial query and the user's Relevance Feedback, with no prior labels. The retrieval process is formulated as a binary classification task, where the system continuously learns to distinguish between relevant and non-relevant images to the query, through iterative user interaction. This interaction is guided by an Active Learning loop: at each iteration, the system selects informative sample
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!