An explainable transformer model for Alzheimer’s disease detection using retinal imaging - Nature
<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTE1rbmQ2NmdnTVZSTXZ6dmFWRlBnY3NfbjRDZ1hfX0dFTkpLeS1NOGUycFBLQklwRWRaMjJrbEQzQnBfUi10THVNWGs2VzBZZ3RsQVNKTkk1ZVZlR2s0WGxr?oc=5" target="_blank">An explainable transformer model for Alzheimer’s disease detection using retinal imaging</a> <font color="#6f6f6f">Nature</font>
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2026 US CIO 100 winners: Celebrating IT innovation and leadership
The annual US CIO 100 Awards recognizes CIOs and their organizations for translating technology strategy into measurable business impact at enterprise and public sector scale. This year’s recognized 100 organizations have put technology to use in innovative ways to deliver business value, whether by creating competitive advantage, optimizing business processes, enabling growth, or improving relationships with customers. The award is an acknowledged mark of enterprise IT excellence and leadership. Along with this year’s CIO Hall of Fame inductees , the 2026 CIO 100 US honorees will be recognized at the CIO 100 Awards & Conference taking place August 17-19, 2026, at the Omni PGA Frisco Resort & Spa in Frisco, Texas. The event brings together the most influential CIOs to share how they are na
Scaling a business: A leadership guide for the rest of us
Leadership is changing faster than most organizations can comfortably absorb. In 2026, senior leaders are being measured by a new mix of expectations, sharper accountability for performance, a more vocal and values-driven workforce and rising pressure to protect culture while navigating constant change. These shifts are not theoretical. They are already showing up in how people engage, how boards govern and how executive teams make decisions. At the same time, boards and senior executives are looking at leadership through a more disciplined lens, return on capital invested. Not just “Are we growing,” but “Are we getting more output, more resilience and more customer value from every dollar and every hour we put into the system?” In that context, scaling is not a vanity goal. It is a practi
"It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with Vision-Language Models
arXiv:2511.08917v3 Announce Type: replace Abstract: Vision-Language Models (VLMs) are increasingly used by blind and low-vision (BLV) people to identify and understand products in their everyday lives, such as food, personal care items, and household goods. Despite their prevalence, we lack an empirical understanding of how common image quality issues--such as blur, misframing, and rotation--affect the accuracy of VLM-generated captions and whether the resulting captions meet BLV people's information needs. Based on a survey of 86 BLV participants, we develop an annotated dataset of 1,859 product images from BLV people to systematically evaluate how image quality issues affect VLM-generated captions. While the best VLM achieves 98% accuracy on images with no quality issues, accuracy drops
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Your Divorce Attorney Wants You to Stop Using ChatGPT: Family Law, AI, and the Privilege You’re Giving Away - Ward and Smith, P.A.
<a href="https://news.google.com/rss/articles/CBMi1wFBVV95cUxOMjdYXzlSd3ZvOFRWZGpVZDNsQjZMQXgybWxCRDdPUjB4eldDSzhMSVlaczBTTGxVaTJiWEhpYUpQMnpBMmliVzFZbGZHczQ5ZkdESk1COXhBQTF6N2wzU3JrS2FwR2xsVDNfOGZCYXhjWnhoWXhUWG5wM1V2ZXRBMHBVRHprdHJ5eFc5bWExcDhyOTEtZmxSZmRUei1Yenk3MXlPaDhKNXlrcWs5all4dG1ENFZmVG9WWWd1d2FKdjlSQmFORTZ6TTcxUFVNWDdCSDU3TGFpSQ?oc=5" target="_blank">Your Divorce Attorney Wants You to Stop Using ChatGPT: Family Law, AI, and the Privilege You’re Giving Away</a> <font color="#6f6f6f">Ward and Smith, P.A.</font>
"It's trained by non-disabled people": Evaluating How Image Quality Affects Product Captioning with Vision-Language Models
arXiv:2511.08917v3 Announce Type: replace Abstract: Vision-Language Models (VLMs) are increasingly used by blind and low-vision (BLV) people to identify and understand products in their everyday lives, such as food, personal care items, and household goods. Despite their prevalence, we lack an empirical understanding of how common image quality issues--such as blur, misframing, and rotation--affect the accuracy of VLM-generated captions and whether the resulting captions meet BLV people's information needs. Based on a survey of 86 BLV participants, we develop an annotated dataset of 1,859 product images from BLV people to systematically evaluate how image quality issues affect VLM-generated captions. While the best VLM achieves 98% accuracy on images with no quality issues, accuracy drops
FIRMED: A Peak-Centered Multimodal Dataset with Fine-Grained Annotation for Emotion Recognition
arXiv:2507.02350v3 Announce Type: replace Abstract: Traditional video-induced physiological datasets usually rely on whole-trial labels, which introduce temporal label noise in dynamic emotion recognition. We present FIRMED, a peak-centered multimodal dataset based on an immediate-recall annotation paradigm, with synchronized EEG, ECG, GSR, PPG, and facial recordings from 35 participants. FIRMED provides event-centered timestamps, emotion labels, and intensity annotations, and its annotation quality is supported by subjective and physiological validation. Benchmark experiments show that FIRMED consistently outperforms whole-trial labeling, yielding an average gain of 3.8 percentage points across eight EEG-based classifiers, with further improvements under multimodal fusion. FIRMED provides
AI In Cybersecurity Education -- Scalable Agentic CTF Design Principles and Educational Outcomes
arXiv:2603.21551v2 Announce Type: replace Abstract: Large language models are rapidly changing how learners acquire and demonstrate cybersecurity skills. However, when human--AI collaboration is allowed, educators still lack validated competition designs and evaluation practices that remain fair and evidence-based. This paper presents a cross-regional study of LLM-centered Capture-the-Flag competitions built on the Cyber Security Awareness Week competition system. To understand how autonomy levels and participants' knowledge backgrounds influence problem-solving performance and learning-related behaviors, we formalize three autonomy levels: human-in-the-loop, autonomous agent frameworks, and hybrid. To enable verification, we require traceable submissions including conversation logs, agent
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