Echocardiography is a cornerstone for managing heart failure (HF), with Left Ventricular Ejection Fraction (LVEF) being a critical metric for guiding therapy. However, manual LVEF assessment suffers from high inter-observer variability, while existing Deep Learning (DL) models are often computationally intensive and data-hungry "black boxes" that impede clinical trust and adoption. Here, we propose a backpropagation-free multi-task Green Learning (MTGL) framework that performs simultaneous Left Ventricle (LV) segmentation and LVEF classification. Our framework integrates an unsupervised VoxelHop encoder […]

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arXiv:2602.12354v1 Announce Type: new Abstract: LinkedIn Feed enables professionals worldwide to discover relevant content, build connections, and share knowledge at scale. We present Feed Sequential Recommender (Feed-SR), a transformer-based sequential ranking model for LinkedIn Feed that replaces a DCNv2-based ranker and meets strict production constraints. We detail the modeling choices, training techniques, and serving optimizations that enable deployment at LinkedIn scale. Feed-SR is currently the primary member experience on LinkedIn’s Feed and shows significant improvements in member engagement […]

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The sixth Information-Theoretic Cryptography (ITC) conference will be held at UC Santa Barbara, California, on August 16-17, 2025. The conference is affiliated with CRYPTO 2025, and will take place in the same location just before CRYPTO. Information-theoretic cryptography deals with the design and implementation of cryptographic protocols and primitives with unconditional security guarantees and the usage of information-theoretic tools and techniques in achieving other forms of security. The conference takes a broad interpretation of this theme and encourages submissions […]

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Dubai, UAE – December 2026: The global crypto community is about to witness a groundbreaking celebration of innovation, transparency, and true community spirit with the launch of iCrypto Awards: People’s Choice 2026, the world’s first blockchain and crypto awards determined entirely by the public.  Organized by iEvents PRO, the iCrypto Awards recognize the most outstanding projects, exchanges, companies, media outlets, and individuals shaping the future of blockchain, Web3, DeFi, NFTs, metaverse, AI innovation, tokenization, Layer 2 technologies, and […]

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In this paper, we present a reproducible benchmarking framework that systematically compares QML models with architecture-matched classical counterparts across three financial tasks: (i) directional return prediction on U.S. and Turkish equities, (ii) live-trading simulation with Quantum LSTMs versus classical LSTMs on the S&P 500, and (iii) realized volatility forecasting using Quantum Support Vector Regression. By standardizing data splits, features, and evaluation metrics, our study provides a fair assessment of when current-generation QML models can match or exceed classical […]

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Technology was supposed to make life easier. And it did. Food arrives without effort. Navigation requires no memory. Recommendations eliminate decision fatigue. Smart systems adjust lighting, temperature, and even our daily routines. Everything moves smoothly, efficiently, and with minimal friction. Convenience has become the defining feature of modern innovation. But what if convenience, while solving one problem, is quietly creating another? Not environmental pollution. Not data pollution. ==Behavioral pollution.== The Comfort of Effortless Living Convenience reduces friction. And […]

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arXiv:2406.04112v3 Announce Type: replace-cross Abstract: While overparameterization in machine learning models offers great benefits in terms of optimization and generalization, it also leads to increased computational requirements as model sizes grow. In this work, we show that by leveraging the inherent low-dimensional structures of data and compressible dynamics within the model parameters, we can reap the benefits of overparameterization without the computational burdens. In practice, we demonstrate the effectiveness of this approach for deep low-rank matrix completion as […]

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arXiv:2602.20396v1 Announce Type: new Abstract: Explainable artificial intelligence promises to yield insights into relevant features, thereby enabling humans to examine and scrutinize machine learning models or even facilitating scientific discovery. Considering the widespread technique of Shapley values, we find that purely data-driven operationalization of multivariate feature importance is unsuitable for such purposes. Even for simple problems with two features, spurious associations due to collider bias and suppression arise from considering one feature only in the observational context of […]

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