arXiv:2602.09155v1 Announce Type: new Abstract: Colorectal cancer (CRC) remains a significant cause of cancer-related mortality, despite the widespread implementation of prophylactic initiatives aimed at detecting and removing precancerous polyps. Although screening effectively reduces incidence, a notable portion of patients initially diagnosed with low-grade adenomatous polyps will still develop CRC later in life, even without the presence of known high-risk syndromes. Identifying which low-risk patients are at higher risk of progression is a critical unmet need for tailored surveillance […]

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arXiv:2601.18979v1 Announce Type: new Abstract: Extended Reality in early childhood education presents high-risk challenges due to children’s rapid developmental changes. While augmented and virtual reality offer immersive pedagogical benefits, they often impose excessive cognitive load or sensory conflict. We introduce the Augmented Human Development (AHD) framework to model these interactions through cognitive, sensory, environmental, and developmental parameters. To ground this framework, we conducted a Systematization of Knowledge (SoK) of 111 peer-reviewed studies involving children aged 3 – 8. […]

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arXiv:2603.02232v1 Announce Type: new Abstract: Reward modeling is crucial for aligning large language models with human preferences, yet current approaches lack a principled mathematical framework for leveraging ordinal preference data. When human annotators provide graded preferences on a Likert scale (e.g., significantly better, better, slightly better, negligibly better), existing methods typically apply ad-hoc heuristics, such as margin terms or scaling factors, to loss functions derived from binary preference models like Bradley-Terry. These approaches lack an underlying mathematical model […]

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The Pentagon is playing chicken with Anthropic over who gets to control how the military uses AI while communities across the country are blocking data center construction. As the AI debate has been flattened to “doomers versus boomers,” one state legislator is attempting to walk a middle road.  On this episode of TechCrunch’s Equity podcast, Rebecca Bellan sits down with Alex Bores, a New York State Assemblymember and candidate for U.S. Congress. Bores sponsored New York’s […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## MongoDB vs ScyllaDB: Architecture Comparison By @scylladb [ 10 Min read ] A deep architectural comparison of MongoDB and ScyllaDB, revealing why their designs lead to very different performance and scalability. Read More. Your AI Model Isn’t Broken. Your Data Is By @melissaindia [ […]

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Anthropic published a lawyer-related plugin on 30 January, and within four to five days, software stocks had lost billions of dollars. The pace of the collapse left everyone shocked, as the whole industry had been operating on a fundamental misperception of how the AI economy works. Why Everyone Thought They Were Safe The narrative that dominated the technological field held that AI companies would build baseline models, which would then serve as a foundation for software companies to […]

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arXiv:2601.04200v1 Announce Type: new Abstract: Product information extraction is crucial for e-commerce services, but obtaining high-quality labeled datasets remains challenging. We present a systematic approach for generating synthetic e-commerce product data using Large Language Models (LLMs), introducing a controlled modification framework with three strategies: attribute-preserving modification, controlled negative example generation, and systematic attribute removal. Using a state-of-the-art LLM with attribute-aware prompts, we enforce store constraints while maintaining product coherence. Human evaluation of 2000 synthetic products demonstrates high effectiveness, […]

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arXiv:2601.09579v1 Announce Type: cross Abstract: Kernel-based methods are used in the context of Granger Causality to enable the identification of nonlinear causal relationships between time series variables. In this paper, we show that two state of the art kernel-based Granger Causality (GC) approaches can be theoretically unified under the framework of Kernel Principal Component Regression (KPCR), and introduce a method based on this unification, demonstrating that this approach can improve causal identification. Additionally, we introduce a Gaussian Process […]

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Neural machine translation (NMT) has revolutionized the field of natural language processing by enabling high-quality automatic translation between languages using deep neural networks. This comprehensive survey examines the evolution of machine translation from statistical methods to modern neural approaches, with particular emphasis on the transformer architecture and its variants that have dominated the field since 2017. We systematically review the fundamental architectures including encoder-decoder models, attention mechanisms, and transformer-based systems, analyzing their theoretical foundations and practical implementations. The […]

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