NEC Corporation is a global leader in IT and network technologies, providing advanced solutions in AI, biometrics, smart cities, and communications. It drives innovation for social value creation and infrastructure resilience. As part of the broader NEC family, NECLA frequently collaborates with NEC Corporation on next-generation networking, AI, and secure computing systems. Our joint efforts span fundamental research to real-world deployments, including innovations in optical networks, data science platforms, and trusted AI frameworks. Please read about our latest news and collaborative publications with NEC Corporation.

Posts

NEC Laboratories America: Celebrating 23 Years of Research Innovation!

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NEC Laboratories America celebrates 23 years of pioneering research and innovation. Emerging from the 2002 merger of NEC Research Institute and NEC C&C Research Laboratories, NECLA has become the U.S. hub for NEC’s global R&D network. Under the leadership of Dr. Christopher White, NECLA bridges the gap between scientific discovery and market-ready technology. With groundbreaking work in AI, optical networking, sensing, and system architecture, our teams continue to drive world-class innovation that shapes industries and connects the world.

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Sound Event Classification meets Data Assimilation with Distributed Fiber-Optic Sensing

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Distributed Fiber-Optic Sensing (DFOS) is a promising technique for large-scale acoustic monitoring. However, its wide variation in installation environments and sensor characteristics causes spatial heterogeneity. This heterogeneity makes it difficult to collect representative training data. It also degrades the generalization ability of learning-based models, such as fine-tuning methods, under a limited amount of training data. To address this, we formulate Sound Event Classification (SEC) as data assimilation in an embedding space. Instead of training models, we infer sound event classes by combining pretrained audio embeddings with simulated DFOS signals. Simulated DFOS signals are generated by applying various frequency responses and noise patterns to microphone data, which allows for diverse prior modeling of DFOS conditions. Our method achieves out-of-domain (OOD) robust classification without requiring model training. The proposed method achieved accuracy improvements of 6.42, 14.11, and 3.47 percentage points compared with conventional zero-shot and two types of fine-tune methods, respectively. By employing the simulator in the framework of data assimilation, the proposed method also enables precise estimation of physical parameters from observed DFOS signals.

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Optical Network Tomography over Live Production Network in Multi-Domain Environment

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We report the first trial of network tomography over a live network in a multi-domain environ­ment. We visualize end-to-end optical powers along multiple routes across multiple domains solely from a commercial B00G transponder, enabling performance bottleneck localization, power and routing opti­mization, and lightpath provisioning.

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Observing the Worst- and Best-Case Line-System Transmission Conditions in a C-Band Variable Spectral Load Scenario

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We experimentally investigated variable spectral loading in an OMS, identifying performance under best and worst transmission conditions. Metrics and data visualization allowed correlation between channel configurations and OSNR variations, enabling the derivation of a simple spectrum allocation rule.

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Energy-based Generative Models for Distributed Acoustic Sensing Event Classification in Telecom Networks

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Distributed fiber-optic sensing combined with machine learning enables continuous monitoring of telecom infrastructure. We employ generative modeling for event classification, supporting semi­ supervised learning, uncertainty calibration, and noise resilience. Our approach offers a scalable, data-efficient solution for real-world deployment in complex environments.

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ICeTEA: Mixture of Detectors for Metric-Log Anomaly Detection

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Anomaly detection is essential for identifying unusual system behaviors and has wide-ranging applications, from fraud detection to system monitoring. In web servers, anomalies are typically detected using two types of data: metrics (numerical indicators of performance) and logs (records of system events). While correlations between metrics and logs in real-world scenarios highlight the need for joint analysis, which is termed the “metric-log anomaly detection” problem, it has not been fully explored yet due to inherent differences between metrics and logs. In this paper, we propose ICeTEA, a novel system for metric-log anomaly detection that integrates three detectors: a metric-log detector based on a multimodal Variational Autoencoder (VAE), and two individual metric and log detectors. By leveraging the ensemble technique to combine outputs of these detectors, ICeTEA enhances the effectiveness and robustness of metric-log anomaly detection. Case studies demonstrate two key functionalities of ICeTEA: data visualization and rankings of contributions to anomaly scores. Experiments demonstrate that our proposed ICeTEA accurately detects true anomalies while significantly reducing false positives.

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On Synthesizing Data for Context Attribution in Question Answering

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Question Answering (QA) accounts for a significantportion of LLM usage “in the wild”.However, LLMs sometimes produce false ormisleading responses, also known as hallucinations.Therefore, grounding the generatedanswers in contextually provided information—i.e., providing evidence for the generated text—is paramount for LLMs’ trustworthiness. Providingthis information is the task of context attribution.In this paper, we systematically studyLLM-based approaches for this task, namelywe investigate (i) zero-shot inference, (ii) LLMensembling, and (iii) fine-tuning of small LMson synthetic data generated by larger LLMs.Our key contribution is SYNQA: a novel generativestrategy for synthesizing context attributiondata. Given selected context sentences, anLLM generates QA pairs that are supported bythese sentences. This leverages LLMs’ naturalstrengths in text generation while ensuring clearattribution paths in the synthetic training data.We show that the attribution data synthesizedvia SYNQA is highly effective for fine-tuningsmall LMs for context attribution in differentQA tasks and domains. Finally, with a userstudy, we validate the usefulness of small, efficientLMs (fine-tuned on synthetic data fromSYNQA) in context attribution for QA.

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Uncertainty Propagation on LLM Agent

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Large language models (LLMs) integrated into multi-step agent systems enable complex decision-making processes across various applications. However, their outputs often lack reliability, making uncertainty estimation crucial. Existing uncertainty estimation methods primarily focus on final-step outputs, which fail to account for cumulative uncertainty over the multi-step decision-making process and the dynamic interactions between agents and their environments. To address these limitations, we propose SAUP (Situation Awareness Uncertainty Propagation), a novel framework that propagates uncertainty through each step of an LLM-based agent’s reasoning process. SAUP incorporates situational awareness by assigning situational weights to each step’s uncertainty during the propagation. Our method, compatible with various one-step uncertainty estimation techniques, provides a comprehensive and accurate uncertainty measure. Extensive experiments on benchmark datasets demonstrate that SAUP significantly outperforms existing state-of-the-art methods, achieving up to 20% improvement in AUROC.

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Toward Intelligent and Efficient Optical Networks: Performance Modeling, Co-existence, and Field Trials

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Optical transmission networks require intelligent traffic adaptation and efficient spectrum usage. We present scalable machine learning (ML) methods for network performance modeling, andfield trials of distributed fiber sensing and classic optical network traffic coexistence.

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First City-Scale Deployment of DASs with Satellite Imagery and AI for Live Telecom Infrastructure Management

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We demonstrate real-time fiber risk assessment and dynamic network routing in live metro networks using deployed DASs, satellite imagery, and large-scale AI, achieving the first significantreduction in fiber failures in four years

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