Every April Fools’ Day, companies push the boundaries of creativity with bold, believable hoaxes. Led by Google, these pranks blend humor with real tech trends, making them both entertaining and surprisingly insightful. Discover the funniest examples and why they resonate so widely.
Distributed fiber optic sensing (DFOS) has emerged as a promising technology for wide-area monitoring by utilizing existing telecom cables as large-scale sensing media. This paper explores three sensing modalities, backscattering-based sensing, forward-transmission-based sensing, and hybrid sensing, and discusses their respective benefits, challenges, and application domains. Backscattering sensing demonstrates strong potential for applications such as road traffic monitoring, pavement condition assessment, intrusion detection, and cabledamage prevention but is constrained in amplified dense wavelength division multiplexing (DWDM) networks. Forward-transmission sensing enables sensing over operational telecom links with in-line amplification, extending sensing reach, although it involves trade-offs in spatial resolution and localization accuracy. To address these challenges, a hybrid sensing architecture that integrates backscattering and forward-transmission techniques is introduced, achieving enhanced sensing distance while maintaining high sensitivity and localization performance.In addition, this work incorporates artificial intelligence (AI) through a locally adaptive anomaly detection (LAAD) framework based on self-supervised representation learning. By leveraging location-based pretext tasks and unlabeled data, the proposed AI approach enables efficient adaptation across heterogeneous fiber routes and operational environments, significantly reducing reliance on labeled data while improving cross-domain generalization. Field trials over deployed telecom networks validate the feasibility and effectiveness of the proposedsensing and AI framework, demonstrating scalable, telecom-compatible DFOS for real-world infrastructure monitoring and intelligent network operations.
Automatically extracting workflows as procedural graphs from natural language is promising yet underexplored, demanding both structural validity and logical alignment. While recent large language models (LLMs) show potential for procedural graph extraction, they often produce ill-formed structures or misinterpret logical flows. We present text2flow, a multi-agent framework that formulates procedural graph extraction as a multi-round reasoning process with dedicated structural and logical refinement. The framework iterates through three stages: (1) a graph extraction phase with the graph builder agent, (2) a structural feedback phase in which a simulation agent diagnoses and explains structural defects, and (3) a logical feedback phase in which a semantic agent aligns semantics between flow logic and linguistic cues in the source text. Important feedback is prioritized and expressed in naturallanguage, which is injected into subsequent prompts, enabling interpretable and controllable refinement. This modular design allows agents to target distinct error types without supervision or parameter updates. Experiments demonstrate that text2flow achieves substantial improvements in both structural correctness and logical consistency over strong baselines.
Large Language Models (LLMs) excel at many reasoning tasks but struggle with knowledge-intensive queries due to their inability to dynamically access up-to-date or domain-specific information. Retrieval-Augmented Generation (RAG) has emerged as a promising solution, enabling LLMs to ground their responses in external sources. However, existing RAG methods lack fine-grained control over both the query and source sides, resulting in noisy retrieval, shallow reasoning, and limited adaptability to heterogeneous knowledge sources. In this work, we introduce DeepSieve, a novel RAG method that incorporates information sieving via LLM-as-a-knowledge-router. DeepSieve breaks down complex queries into structured sub-queries and recursively routes each to the most appropriate knowledge source, filtering out irrelevant information through a multi-stage information sieving process. This modular and transparent approach ensures that DeepSieve remains adaptable across diverse information needs. Experiments on three multi-hop QA benchmarks involving heterogeneous sources show that DeepSieve achieves greater reasoning depth, retrieval precision, and interpretability compared to conventional RAG approaches. Our codes are available at https://github.com/MinghoKwok/DeepSieve.
Time series data is ubiquitous across various domains, including manufacturing, finance, and healthcare. High-quality annotations are essential for effectively understanding time series and facilitating downstream tasks. However, obtaining such annotations is challenging, particularly in mission-critical domains. In this paper, we propose TESSA, a multi-agent system designed to automatically generate both general and domain-specific annotations for time series data. TESSA introduces two agents: a general annotation agent and a domain-specific annotation agent. The general agent captures common patterns and knowledge across multiple source domains, leveraging both time-series-wise and text-wise features to generate general annotations. Meanwhile, the domain-specific agent utilizes limited annotations from the target domain to learn domain-specific terminology and generate targeted annotations. Extensive experiments on multiple synthetic and real-world datasets demonstrate that TESSA effectively generates high-quality annotations, outperforming existing methods.
We introduce a framework to analyse interpretability in deep learning, by drawing on a formal notion of model semantics from the philosophy of science. We argue that interpretability is only one aspect of a models semantics and illustrate our framework with examples from biomedicine.
We propose a mobile orbital domain-based hierarchical routing scheme which addresses the challenges posed by constant satellite movement and the resulting dynamicnetwork topology, thus significantly improving the routing scalability and efficiency in satellite networks.
We propose a method to compensate the phase offset between samples from different tributaries in time-interleaved phase OTDR using nested phase reference channels. We demonstrate our method for a four-span bidirectional link with high-loss loopback.
We report the first field study of the phase and polarization dynamics of deployed antiresonant hollow core fiber cable in a data center interconnect for real-world vibration sensing,revealing enhanced phase sensitivity and significantly faster polarization angular rate compared with standard single mode fibers.
We demonstrate agnostic QoT probing for datacenter exchange in a metro network via receiver-side ASE loading. Knowing BER telemetry and the progressive ASEload, the device estimates GSNR, enabling IPoWDM operations and digital-twin calibration.
Princeton Office
4 Independence Way, Suite 200
Princeton, NJ 08540
+1 609-520-1555
San Jose Office
2033 Gateway Place, Suite 200
San Jose, CA 95110
+1 408-863-6007
NEC Laboratories America, Inc. (NEC Labs) is the US-based center for NEC Corporation’s global network of corporate research laboratories. Our diverse research groups collaborate with industry, academia and governments to provide disruptive solutions to complex problems. A leader in the integration of IT and network technologies with more than 100 years of expertise, NEC provides a combination of products and solutions that cross-utilize the company’s experience and global resources to meet the complex and ever-changing needs of its customers.