Small-period long-period fiber grating with improved refractive index sensitivity and dual-parameter sensing ability

We UV inscribe and characterize a long-period fiber grating with a period of 25 μm. A series of polarization-dependent dual-peak pairs can be seen in the transmission spectrum, even though only the symmetrical refractive index modification is introduced. The fabricated grating exhibits a lower temperature sensitivity compared with standard long-period gratings and an enhanced refractive index sensitivity of ∼312.5 nm?RIU averaged from 1.315 to 1.395, which is more than four-fold higher than standard long-period gratings in this range. The full width at half-maximum of the fabricated grating is only about 0.6 nm, allowing for high-resolution sensing. Moreover, the grating period is so small that the attenuation dip corresponding to a high-order Bragg resonance can also be seen, which can act as a monitor of the unwanted perturbation to realize dual-parameter sensing

Cost-effectiveness Analysis of CO2 Reduction in the Automobile Sector

Various problems relating to energy and the environment clearly exist, such as global warming and a steep rise in the price fossil fuels, and resources. These problems should be addressed in the medium term or long run. As for the abatement of greenhouse gas emission, active discussions have been held on the stage of world politics to achieve the long-term goal. Although various approaches have been proposed by several research institutions and countries, sufficient studies have not yet been conducted on the roles of individual countries and sectors. Specifically, in the automotive transportation sector wherein oil demand and CO2 emissions are estimated to rise in the future with the marked progress of motorization in developing countries, it is increasingly important to study these subjects. We focused on the automotive transportation sector and studied the CO2 abatement potential and its cost performance in this sector. This article reports the results of the study.energy and the environment, Climate change, automotive transportation

A Unified Model for Video Understanding and Knowledge Embedding with Heterogeneous Knowledge Graph Dataset

Video understanding is an important task in short video business platforms and it has a wide application in video recommendation and classification. Most of the existing video understanding works only focus on the information that appeared within the video content, including the video frames, audio and text. However, introducing common sense knowledge from the external Knowledge Graph (KG) dataset is essential for video understanding when referring to the content which is less relevant to the video. Owing to the lack of video knowledge graph dataset, the work which integrates video understanding and KG is rare. In this paper, we propose a heterogeneous dataset that contains the multi-modal video entity and fruitful common sense relations. This dataset also provides multiple novel video inference tasks like the Video-Relation-Tag (VRT) and Video-Relation-Video (VRV) tasks. Furthermore, based on this dataset, we propose an end-to-end model that jointly optimizes the video understanding objective with knowledge graph embedding, which can not only better inject factual knowledge into video understanding but also generate effective multi-modal entity embedding for KG. Comprehensive experiments indicate that combining video understanding embedding with factual knowledge benefits the content-based video retrieval performance. Moreover, it also helps the model generate better knowledge graph embedding which outperforms traditional KGE-based methods on VRT and VRV tasks with at least 42.36% and 17.73% improvement in HITS@10

Paragraph-to-Image Generation with Information-Enriched Diffusion Model

Text-to-image (T2I) models have recently experienced rapid development, achieving astonishing performance in terms of fidelity and textual alignment capabilities. However, given a long paragraph (up to 512 words), these generation models still struggle to achieve strong alignment and are unable to generate images depicting complex scenes. In this paper, we introduce an information-enriched diffusion model for paragraph-to-image generation task, termed ParaDiffusion, which delves into the transference of the extensive semantic comprehension capabilities of large language models to the task of image generation. At its core is using a large language model (e.g., Llama V2) to encode long-form text, followed by fine-tuning with LORA to alignthe text-image feature spaces in the generation task. To facilitate the training of long-text semantic alignment, we also curated a high-quality paragraph-image pair dataset, namely ParaImage. This dataset contains a small amount of high-quality, meticulously annotated data, and a large-scale synthetic dataset with long text descriptions being generated using a vision-language model. Experiments demonstrate that ParaDiffusion outperforms state-of-the-art models (SD XL, DeepFloyd IF) on ViLG-300 and ParaPrompts, achieving up to 15% and 45% human voting rate improvements for visual appeal and text faithfulness, respectively. The code and dataset will be released to foster community research on long-text alignment.Comment: The project website is at: https://weijiawu.github.io/ParaDiffusionPage/. Code: https://github.com/weijiawu/ParaDiffusio

Undamaged measurement of the sub-micron diaphragm and gap by tri-beam interference

A simple, high-accuracy and non-destructive method for the measurement of diaphragm thickness and microgap width based on modulated tri-beam interference is demonstrated. With this method, a theoretical estimation error less than 0.5% for a diaphragm thickness of ~1 μm is achievable. Several fiber-tip air bubbles with different diaphragm thicknesses (6.25, 5.0, 2.5 and 1.25 μm) were fabricated to verify our proposed measurement method. Furthermore, an improved technique was introduced by immersing the measured object into a liquid environment to simplify a four-beam interference into tri-beam one. By applying this improved technique, the diaphragm thickness of a fabricated in-fiber rectangular air bubble is measured to be about 1.47 μm, and the averaged microgap width of a standard silica capillary is measured to be about 10.07 μm, giving a corresponding measurement error only 1.27% compared with actual scanning electron microscope (SEM) results