Search: NIR - ai.jp.net

Research Paper #Optics, Fiber Optics, Structured Light 🔬 ResearchAnalyzed: Jan 3, 2026 06:24

Tunable Generation of Structured Light Beams with Fiber Grating

Published:Dec 31, 2025 13:12

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1 min read

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ArXiv

Analysis

This paper demonstrates a method for generating and manipulating structured light beams (vortex, vector, flat-top) in the near-infrared (NIR) and visible spectrum using a mechanically tunable long-period fiber grating. The ability to control beam profiles by adjusting the grating's applied force and polarization offers potential applications in areas like optical manipulation and imaging. The use of a few-mode fiber allows for the generation of complex beam shapes.

Key Takeaways

•Demonstrates tunable generation of structured light beams (vortex, vector, flat-top).
•Utilizes a mechanically induced long-period fiber grating for beam manipulation.
•Achieves propagation-invariant vector flat-top beams.
•Works in both NIR (1060nm) and visible (532nm) wavelengths.
•Offers potential for applications in optical manipulation and imaging.

Reference

“By precisely tuning the intensity ratio between fundamental and doughnut modes, we arrive at the generation of propagation-invariant vector flat-top beams for more than 5 m.”

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Research Paper #Computer Vision, 3D Visual Grounding, Roadside Infrastructure, Multi-modal Learning 🔬 ResearchAnalyzed: Jan 3, 2026 08:53

MoniRefer: A New Dataset for 3D Visual Grounding in Roadside Infrastructure

Published:Dec 31, 2025 03:56

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1 min read

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ArXiv

Analysis

This paper introduces a novel dataset, MoniRefer, for 3D visual grounding specifically tailored for roadside infrastructure. This is significant because existing datasets primarily focus on indoor or ego-vehicle perspectives, leaving a gap in understanding traffic scenes from a broader, infrastructure-level viewpoint. The dataset's large scale and real-world nature, coupled with manual verification, are key strengths. The proposed method, Moni3DVG, further contributes to the field by leveraging multi-modal data for improved object localization.

Key Takeaways

•Introduces MoniRefer, a new large-scale dataset for 3D visual grounding in roadside infrastructure.
•Addresses the gap in existing datasets by focusing on infrastructure-level understanding of traffic scenes.
•Proposes Moni3DVG, a new end-to-end method for multi-modal feature learning and 3D object localization.
•The dataset and code will be released, promoting further research in this area.

Reference

““...the first real-world large-scale multi-modal dataset for roadside-level 3D visual grounding.””

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Research Paper #Materials Science, Photonics 🔬 ResearchAnalyzed: Jan 3, 2026 15:52

High-Entropy Perovskites for Broadband NIR Photonics

Published:Dec 30, 2025 16:30

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1 min read

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ArXiv

Analysis

This paper introduces a novel approach to create robust and functionally rich photonic materials for near-infrared (NIR) applications. By leveraging high-entropy halide perovskites, the researchers demonstrate ultrabroadband NIR emission and enhanced environmental stability. The work highlights the potential of entropy engineering to improve material performance and reliability in photonic devices.

Key Takeaways

•High-entropy halide perovskites are used to achieve ultrabroadband NIR emission.
•The materials exhibit improved stability against humidity and oxygen.
•The research demonstrates a practical strategy to couple ultrabroadband photonic function with environmental stability.

Reference

“The paper demonstrates device-relevant ultrabroadband near-infrared (NIR) photonics by integrating element-specific roles within an entropy-stabilized lattice.”

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Research Paper #Cross-Modal Retrieval, Noisy Labels, Robust Learning 🔬 ResearchAnalyzed: Jan 3, 2026 17:04

Neighbor-aware Instance Refining for Cross-Modal Retrieval with Noisy Labels

Published:Dec 30, 2025 08:19

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1 min read

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ArXiv

Analysis

This paper addresses the problem of noisy labels in cross-modal retrieval, a common issue in multi-modal data analysis. It proposes a novel framework, NIRNL, to improve retrieval performance by refining instances based on neighborhood consensus and tailored optimization strategies. The key contribution is the ability to handle noisy data effectively and achieve state-of-the-art results.

Key Takeaways

•Addresses the problem of noisy labels in cross-modal retrieval.
•Proposes a novel framework, NIRNL, for robust learning.
•Employs Cross-modal Margin Preserving (CMP) and Neighbor-aware Instance Refining (NIR).
•Achieves state-of-the-art performance on benchmark datasets.

Reference

“NIRNL achieves state-of-the-art performance, exhibiting remarkable robustness, especially under high noise rates.”

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Research Paper #Computer Vision, Fire Detection, Deep Learning 🔬 ResearchAnalyzed: Jan 3, 2026 18:37

Fire Detection in RGB-NIR Cameras

Published:Dec 29, 2025 16:48

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1 min read

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ArXiv

Analysis

This paper addresses the challenge of fire detection, particularly at night, using RGB-NIR cameras. It highlights the limitations of existing models in distinguishing fire from artificial lights and proposes solutions including a new NIR dataset, a two-stage detection model (YOLOv11 and EfficientNetV2-B0), and Patched-YOLO for improved accuracy, especially for small and distant fire objects. The focus on data augmentation and addressing false positives is a key strength.

Key Takeaways

•Addresses the problem of fire detection in RGB-NIR cameras, particularly at night.
•Proposes a two-stage detection model to reduce false positives from artificial lights.
•Introduces Patched-YOLO to improve detection of small and distant fire objects.
•Emphasizes the importance of data augmentation for improved performance.

Reference

“The paper introduces a two-stage pipeline combining YOLOv11 and EfficientNetV2-B0 to improve night-time fire detection accuracy while reducing false positives caused by artificial lights.”

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Research Paper #Uncertainty Quantification, Regression, Machine Learning 🔬 ResearchAnalyzed: Jan 3, 2026 18:49

Calibrating Uncertainty in Regression Models

Published:Dec 29, 2025 13:02

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1 min read

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ArXiv

Analysis

This paper addresses a crucial aspect of machine learning: uncertainty quantification. It focuses on improving the reliability of predictions from multivariate statistical regression models (like PLS and PCR) by calibrating their uncertainty. This is important because it allows users to understand the confidence in the model's outputs, which is critical for scientific applications and decision-making. The use of conformal inference is a notable approach.

Key Takeaways

•Proposes a method to calibrate uncertainty in multivariate statistical regression models.
•Method is inspired by conformal inference.
•Tested on both traditional and kernelized versions of PLS and PCR.
•Demonstrated on synthetic and real-world datasets (NIR and hyperspectral data).
•Achieves accurate prediction intervals, matching the desired confidence level.

Reference

“The model was able to successfully identify the uncertain regions in the simulated data and match the magnitude of the uncertainty. In real-case scenarios, the optimised model was not overconfident nor underconfident when estimating from test data: for example, for a 95% prediction interval, 95% of the true observations were inside the prediction interval.”

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Research Paper #Medical AI, ECG Analysis, Deep Learning 🔬 ResearchAnalyzed: Jan 3, 2026 16:07

ECG Generalization with Morphology-Rhythm Disentanglement

Published:Dec 29, 2025 10:14

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1 min read

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ArXiv

Analysis

This paper addresses the challenge of generalizing ECG classification across different datasets, a crucial problem for clinical deployment. The core idea is to disentangle morphological features and rhythm dynamics, which helps the model to be less sensitive to distribution shifts. The proposed ECG-RAMBA framework, combining MiniRocket, HRV, and a bi-directional Mamba backbone, shows promising results, especially in zero-shot transfer scenarios. The introduction of Power Mean pooling is also a notable contribution.

Key Takeaways

•Proposes ECG-RAMBA, a framework for ECG classification that disentangles morphology and rhythm.
•Employs MiniRocket for morphological features, HRV for rhythm descriptors, and a bi-directional Mamba backbone for long-range context.
•Introduces Power Mean pooling to improve sensitivity to transient abnormalities.
•Demonstrates strong performance in zero-shot transfer, outperforming baseline models.

Reference

“ECG-RAMBA achieves a macro ROC-AUC ≈ 0.85 on the Chapman--Shaoxing dataset and attains PR-AUC = 0.708 for atrial fibrillation detection on the external CPSC-2021 dataset in zero-shot transfer.”

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Paper #Image Registration 🔬 ResearchAnalyzed: Jan 3, 2026 19:10

Domain-Shift Immunity in Deep Registration

Published:Dec 29, 2025 02:10

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1 min read

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ArXiv

Analysis

This paper challenges the common belief that deep learning models for deformable image registration are highly susceptible to domain shift. It argues that the use of local feature representations, rather than global appearance, is the key to robustness. The authors introduce a framework, UniReg, to demonstrate this and analyze the source of failures in conventional models.

Key Takeaways

•Deep deformable registration models can be inherently robust to domain shift.
•Local feature consistency is a key driver of robustness.
•Dataset-induced biases in early convolutional layers can cause failures under modality shift.
•UniReg framework demonstrates domain-shift immunity using fixed, pre-trained feature extractors.

Reference

“UniReg exhibits robust cross-domain and multi-modal performance comparable to optimization-based methods.”

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Research Paper #Nanomaterials, Copper Selenide, Plasmonics, Optoelectronics 🔬 ResearchAnalyzed: Jan 3, 2026 20:12

Synthesis and Properties of Quasi-2D Copper Selenide Nanocrystals

Published:Dec 26, 2025 17:15

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1 min read

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ArXiv

Analysis

This paper presents a novel synthesis method for producing quasi-2D klockmannite copper selenide nanocrystals, a material with interesting semiconducting and metallic properties. The study focuses on controlling the shape and size of the nanocrystals and investigating their optical and photophysical properties, particularly in the near-infrared (NIR) region. The use of computational modeling (CSDDA) to understand the optical anisotropy and the exploration of ultrafast photophysical behavior are key contributions. The findings highlight the importance of crystal anisotropy in determining the material's nanoscale properties, which is relevant for applications in optoelectronics and plasmonics.

Key Takeaways

•Introduces a thiol-free colloidal synthesis for quasi-2D klockmannite copper selenide nanocrystals.
•Achieves shape control by tuning injection temperature and precursor concentrations.
•Produces large nanosheets and uniform nanoplatelets with strong NIR plasmonic absorption.
•Utilizes CSDDA calculations to analyze optical anisotropy and hyperbolic regime.
•Examines ultrafast photophysical behavior, including hot-hole cooling and coherent phonons generation.
•Highlights the role of crystal anisotropy in governing nanoscale properties.

Reference

“The study reveals pronounced optical anisotropy and the emergence of hyperbolic regime in the NIR.”

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Research #mathematics 🔬 ResearchAnalyzed: Jan 4, 2026 06:59

On general Caffarelli-Kohn-Nirenberg type inequalities involving non-doubling weights in the case of $p=1$

Published:Dec 25, 2025 03:57

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1 min read

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ArXiv

Analysis

This article focuses on a specific mathematical topic: Caffarelli-Kohn-Nirenberg inequalities. The title indicates the research explores these inequalities under specific conditions: non-doubling weights and the case where p=1. This suggests a highly specialized and technical piece of research likely aimed at mathematicians or researchers in related fields. The use of 'non-doubling weights' implies a focus on more complex and potentially less well-understood scenarios than standard cases. The mention of p=1 further narrows the scope, indicating a specific parameter value within the inequality framework.

Key Takeaways

•The research investigates Caffarelli-Kohn-Nirenberg inequalities.
•It focuses on cases with non-doubling weights.
•The specific case of p=1 is considered.

Reference

“The title itself provides the core information about the research's focus: a specific type of mathematical inequality under particular conditions.”

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Research #Recognition 🔬 ResearchAnalyzed: Jan 10, 2026 07:41

UniRec-0.1B: Compact Model for Unified Text and Formula Recognition

Published:Dec 24, 2025 10:35

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1 min read

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ArXiv

Analysis

This research introduces UniRec-0.1B, a lightweight model capable of recognizing both text and formulas. The model's small size (0.1B parameters) makes it potentially efficient for resource-constrained environments.

Key Takeaways

•The model offers unified recognition of both text and mathematical formulas.
•The small parameter size (0.1B) suggests potential for deployment on edge devices.
•The ArXiv source indicates this is a research paper providing initial results.

Reference

“UniRec-0.1B is a unified text and formula recognition model with 0.1B parameters.”

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Research #Astronomy 🔬 ResearchAnalyzed: Jan 10, 2026 09:47

AI Method Classifies Galaxies Using JWST Data and Contrastive Learning

Published:Dec 19, 2025 01:44

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1 min read

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ArXiv

Analysis

This research explores a novel application of AI, specifically contrastive learning, for astronomical image analysis. The study's focus on JWST data suggests a potential for significant advancements in galaxy classification capabilities.

Key Takeaways

•Applies dual-encoding contrastive learning for galaxy morphological classification.
•Uses data from the James Webb Space Telescope (JWST).
•Employs multi-clustering voting for robust classification.

Reference

“The research utilizes JWST/NIRCam images.”

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Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 09:56

UniRel-R1: RL-tuned LLM Reasoning for Knowledge Graph Relational Question Answering

Published:Dec 18, 2025 20:11

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1 min read

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ArXiv

Analysis

The article introduces UniRel-R1, a system that uses Reinforcement Learning (RL) to improve the reasoning capabilities of Large Language Models (LLMs) for answering questions about knowledge graphs. The focus is on relational question answering, suggesting a specific application domain. The use of RL implies an attempt to optimize the LLM's performance in a targeted manner, likely addressing challenges in accurately extracting and relating information from the knowledge graph.

•The build vs. buy decision is a key consideration for companies deploying machine learning.
•Experiment management is becoming a standard requirement.
•Software-based approaches may offer advantages over cloud vendor solutions in certain scenarios.

Reference

“The episode explores how companies should think about building vs buying and integration.”

Permalink Practical AI

Tunable Generation of Structured Light Beams with Fiber Grating

Analysis

Key Takeaways

MoniRefer: A New Dataset for 3D Visual Grounding in Roadside Infrastructure

Analysis

Key Takeaways

High-Entropy Perovskites for Broadband NIR Photonics

Analysis

Key Takeaways

Neighbor-aware Instance Refining for Cross-Modal Retrieval with Noisy Labels

Analysis

Key Takeaways

Fire Detection in RGB-NIR Cameras

Analysis

Key Takeaways

Calibrating Uncertainty in Regression Models

Analysis

Key Takeaways

ECG Generalization with Morphology-Rhythm Disentanglement

Analysis

Key Takeaways

Domain-Shift Immunity in Deep Registration

Analysis

Key Takeaways

Synthesis and Properties of Quasi-2D Copper Selenide Nanocrystals

Analysis

Key Takeaways

On general Caffarelli-Kohn-Nirenberg type inequalities involving non-doubling weights in the case of $p=1$

Analysis

Key Takeaways

UniRec-0.1B: Compact Model for Unified Text and Formula Recognition

Analysis

Key Takeaways

AI Method Classifies Galaxies Using JWST Data and Contrastive Learning

Analysis

Key Takeaways

UniRel-R1: RL-tuned LLM Reasoning for Knowledge Graph Relational Question Answering

Analysis

Key Takeaways

Sceniris: A Fast Procedural Scene Generation Framework

Analysis

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Nirvana Insurance Raises $100M Series D, Valuation Nearly Doubles to $1.5B

Analysis

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How much gas and dust is in the $z=5.7$ Lyman Break Galaxy HZ10? An ALMA Band 10 to 4 and JWST/NIRSpec study of its interstellar medium

Analysis

Key Takeaways

Movie Mindset 33 - Casino feat. Felix

Analysis

Key Takeaways

Buy AND Build for Production Machine Learning with Nir Bar-Lev - #488

Analysis

Key Takeaways

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