Accelerating discovery in India through AI-powered science and education
Google DeepMind brings National Partnerships for AI initiative to India, scaling AI for science and education
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ContraLog: Log File Anomaly Detection with Contrastive Learning and Masked Language Modeling
Log files record computational events that reflect system state and behavior, making them a primary source of operational insights in modern computer systems. Automated anomaly detection on logs is therefore critical, yet most established methods rely on log parsers that collapse messages into discrete templates, discarding variable values and semantic content. We propose ContraLog, a parser-free and self-supervised method that reframes log anomaly detection as predicting continuous message embeddings rather than discrete template IDs. ContraLog combines a message […]
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Machine Learning in Epidemiology
arXiv:2602.16352v1 Announce Type: new Abstract: In the age of digital epidemiology, epidemiologists are faced by an increasing amount of data of growing complexity and dimensionality. Machine learning is a set of powerful tools that can help to analyze such enormous amounts of data. This chapter lays the methodological foundations for successfully applying machine learning in epidemiology. It covers the principles of supervised and unsupervised learning and discusses the most important machine learning methods. Strategies for model evaluation and […]
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Realistic Curriculum Reinforcement Learning for Autonomous and Sustainable Marine Vessel Navigation
arXiv:2601.10911v1 Announce Type: new Abstract: Sustainability is becoming increasingly critical in the maritime transport, encompassing both environmental and social impacts, such as Greenhouse Gas (GHG) emissions and navigational safety. Traditional vessel navigation heavily relies on human experience, often lacking autonomy and emission awareness, and is prone to human errors that may compromise safety. In this paper, we propose a Curriculum Reinforcement Learning (CRL) framework integrated with a realistic, data-driven marine simulation environment and a machine learning-based fuel consumption […]
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Discrete Diffusion with Sample-Efficient Estimators for Conditionals
arXiv:2602.20293v1 Announce Type: cross Abstract: We study a discrete denoising diffusion framework that integrates a sample-efficient estimator of single-site conditionals with round-robin noising and denoising dynamics for generative modeling over discrete state spaces. Rather than approximating a discrete analog of a score function, our formulation treats single-site conditional probabilities as the fundamental objects that parameterize the reverse diffusion process. We employ a sample-efficient method known as Neural Interaction Screening Estimator (NeurISE) to estimate these conditionals in the diffusion […]
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Reinforcement Learning-Based Energy-Aware Coverage Path Planning for Precision Agriculture
Coverage Path Planning (CPP) is a fundamental capability for agricultural robots; however, existing solutions often overlook energy constraints, resulting in incomplete operations in large-scale or resource-limited environments. This paper proposes an energy-aware CPP framework grounded in Soft Actor-Critic (SAC) reinforcement learning, designed for grid-based environments with obstacles and charging stations. To enable robust and adaptive decision-making under energy limitations, the framework integrates Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal […]
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Minimax and Bayes Optimal Best-Arm Identification
arXiv:2506.24007v4 Announce Type: replace-cross Abstract: This study investigates minimax and Bayes optimal strategies for fixed-budget best-arm identification. We consider an adaptive procedure consisting of a sampling phase followed by a recommendation phase, and we design an adaptive experiment within this framework to efficiently identify the best arm, defined as the one with the highest expected outcome. In our proposed strategy, the sampling phase consists of two stages. The first stage is a pilot phase, in which we allocate […]
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High Convergence Rates of CMOS Invertible Logic Circuits Based on Many-Body Hamiltonians
arXiv:2602.15033v1 Announce Type: new Abstract: This paper introduces CMOS invertible-logic (CIL) circuits based on many-body Hamiltonians. CIL can realize probabilistic forward and backward operations of a function by annealing a corresponding Hamiltonian using stochastic computing. We have created a Hamiltonian that includes three-body interaction of spins (probabilistic nodes). It provides some degrees of freedom to design a simpler landscape of Hamiltonian (energy) than that of the conventional two-body Hamiltonian. The simpler landscape makes it easier to reach the […]
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How Clarus Care uses Amazon Bedrock to deliver conversational contact center interactions
This post was cowritten by Rishi Srivastava and Scott Reynolds from Clarus Care. Many healthcare practices today struggle with managing high volumes of patient calls efficiently. From appointment scheduling and prescription refills to billing inquiries and urgent medical concerns, practices face the challenge of providing timely responses while maintaining quality patient care. Traditional phone systems often lead to long hold times, frustrated patients, and overwhelmed staff who manually process and prioritize hundreds of calls daily. These communication bottlenecks […]
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Limits of Residual-Based Detection for Physically Consistent False Data Injection
arXiv:2602.10162v1 Announce Type: new Abstract: False data injection attacks (FDIAs) pose a persistent challenge to AC power system state estimation. In current practice, detection relies primarily on topology-aware residual-based tests that assume malicious measurements can be distinguished from normal operation through physical inconsistency reflected in abnormal residual behavior. This paper shows that this assumption does not always hold: when FDIA scenarios produce manipulated measurements that remain on the measurement manifold induced by AC power flow relations and measurement […]
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