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Discover how Giza is revolutionizing DeFi with AI agents, yield optimization, semantic abstraction, and secure smart accounts. Renç Korzay reveals what’s next.

This section evaluates how rhetorical role classifiers generalize across domains, showing that methods like prototypical learning and discourse-aware contrastive learning improve cross-domain performance. Prototypical models prove robust in preventing overfitting to domain-specific features.

Stables expands to North America with Equibase partnership, adding U.S. racehorses and gameplay upgrades to its Tezos-based fantasy horse racing platform.

Sentient’s Himanshu Tyagi reveals how blockchain and AI unite in a community-owned revolution, powering Sentient Chat and Dobby.

This section shows that contrastive learning, discourse-aware loss, and multi-prototype methods improve rhetorical role labeling by enhancing embeddings and addressing class imbalance, particularly in low-data settings.

Learn best practices for handling large files in data indexing systems. Understand processing granularity, fan-in/fan-out scenarios, and strategies for efficient processing of large datasets like patent XML files. Discover how CocoIndex helps manage memory pressure and ensures reliable processing.

AI companies like OpenAI are struggling to balance subscription costs and usage-based pricing. While DeepSeek claims a 545% profit margin, other companies face the high cost of GPU usage, leading to unsustainable pricing models. The future of AI pricing might lie in hybrid models — a flat fee plus pay-per-use for premium features.

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. By @dineshbesiahgari [ 17 Min read ] AutoResponder AI automates email replies using AWS & AI. Streamline inbox management, ...

Language models have shown effectiveness in a variety of software applications, particularly in tasks related to automatic workflow.

Annotators will annotate all mentions in a clinical note that indicate the presence (or absence) of present/past SI and SS.

We offer two open-source NLP systems with different approaches, as well as a manual annotation guideline for identifying SS and SI.

There were insufficient instances in the notes of the emotional support subcategories to evaluate the NLP systems.

Bitunix exchange has announced that it has launched the Ultra version of the K-line (candlesticks) on its mobile app integrated with TradingView. This advanced charting system transforms the mobile trading experience for cryptocurrency traders.

This study presents rule- and LLM-based NLP systems to identify fine-grained categories of SS and SI in clinical notes of psychiatric patients

SEED is the first Web3 gaming ecosystem on the Sui Foundation. SEED Go is the team's next big step in Play-to-Earn - a location-based game. Adventure Mode is where players hunt SEED Mons in the real world using their Mons. Mon Battle offers strategic combat against Mons for glory and rewards.

The demographic characteristics of patients within the annotated cohort are detailed in Table 2. Notably, the patient composition at MSHS was younger and more diverse as compared to patients at WCM.

Researchers introduced PANM, a plug-and-play memory module that mimics symbolic processing using pointers. PANM improves neural networks’ generalization to longer, unseen sequences and boosts performance in symbolic tasks, QA, and translation by modeling memory access more like a computer does—with physical addresses and pointer arithmetic.

Researchers introduced PANM, a plug-and-play memory module that mimics symbolic processing using pointers. PANM improves neural networks’ generalization to longer, unseen sequences and boosts performance in symbolic tasks, QA, and translation by modeling memory access more like a computer does—with physical addresses and pointer arithmetic.

Researchers introduced PANM, a plug-and-play memory module that mimics symbolic processing using pointers. PANM improves neural networks’ generalization to longer, unseen sequences and boosts performance in symbolic tasks, QA, and translation by modeling memory access more like a computer does—with physical addresses and pointer arithmetic.

Go’s concurrency model revolves around goroutines—lightweight, runtime-managed threads. Synchronization is achieved via Mutexes, WaitGroups, and Condition Variables, ensuring safe execution. The Go scheduler maps goroutines to OS threads for efficiency, while channels and locks prevent race conditions and deadlocks.

EXPLORER outperforms baselines by combining neural exploration and symbolic reasoning, excelling in text-based games with unseen entities.