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Research

NEDNet: Decomposing Network Effects for Causal Estimation under Interference

Researchers propose NEDNet, a neural network that decomposes outcome responses into baseline, treatment, and exposure pathways for causal effect estimation under network interference. It uses path-specific neighborhood contextualization and a reference-normalized decomposition to improve accuracy. Experiments on BlogCatalog and Flickr benchmarks show NEDNet achieves the lowest PEHE for individual, peer, and total effects.

arxiv14h agoScore 77
causal effect estimationnetwork datatreatment effects
Research

Hybrid LSTM-XGBoost Framework Predicts Landing-Phase Safety Events from Imbalanced Flight Data

This paper introduces an interpretable hybrid framework for predicting rare landing-phase safety events from imbalanced flight data. It fuses LSTM autoencoder reconstruction errors with statistical flight features and feeds them into a class-weighted XGBoost classifier. Evaluated on 53,750 NASA DASHlink approach windows, the method achieves mean macro recall above 0.90 across airports, with component comparisons and feature attribution analysis. The study offers practical guidance for adapting flight-data monitoring models to airport-level data availability.

arxiv14h agoScore 70
aviation safetymachine learninganomaly detection
Research

STEK: Structural-Terrain-Enhanced Koopman-Inspired Cross-Channel Regularization for Retrospective Battery Multi-Target Estimation

The paper introduces STEK, a framework for retrospective battery multi-target estimation that combines cell-level lifecycle dQ/dV terrain with endpoint-derived structural proxies via quality-proxy-conditioned gating. It uses bounded FiLM and a validity-gated residual head to refine endpoint-curve representations, plus a Koopman-inspired low-rank linear map for cross-channel consistency. Experiments under the MIX-battery protocol show improvements over endpoint-only baselines and direct concatenation, with graceful fallback when descriptors are removed.

arxiv1d agoScore 72
battery health estimationmachine learningKoopman theory
Industry

Alibaba Unveils Zhenwu M890 AI Chip and Qwen 3.7-Max Model to Power the Agentic Era

Hangzhou, China – May 20, 2026&nbsp;– Alibaba Group has officially announced the launch of its next-generation AI accelerator, the&nbsp;Zhenwu M890, alongside the debut of its flagship large model,&nbsp;Qwen 3.7-Max. Unveiled at the 2026 Alibaba Clou<!--autointro-->

AITopic existing contentMay 20, 2026Score 70
AlibabaAgentic AImachine learning
Industry

Temasek-Backed Temus Launches AI Foundry and Hires 50 AI Professionals

Singapore-based digital services firm Temus announced on May 20 the official launch of its new AI Foundry. Established with support from Digital Industry Singapore (DISG), this strategic initiative aims to recruit 50 local AI professionals to spearhe<!--autointro-->

AITopic existing contentMay 20, 2026Score 70
Artificial IntelligenceSingaporeEnterprise AI