Search

Satellite-based greenhouse gases (GHG) sensing technologies play a critical role in the study of global carbon emissions and climate change. However, none of the existing satellite-based GHG sensing technologies can achieve the measurement of broad bandwidth, high temporal-spatial resolution, and high sensitivity at the same time. Recently, dual-comb spectroscopy (DCS) has been proposed as a superior candidate technology for GHG sensing because it can measure broadband spectra with high temporal-spatial resolution and high sensitivity. The main barrier to DCS's display on satellites is its short measurement distance in open air achieved thus far. Prior research has not been able to implement DCS over 20 km of open-air path. Here, by developing a bistatic setup using time-frequency dissemination and high-power optical frequency combs, we have implemented DCS over a 113 km turbulent horizontal open-air path. Our experiment successfully measured GHG with 7 nm spectral bandwidth and a 10 kHz frequency and achieved a CO2 sensing precision of <2 ppm in 5 minutes and <0.6 ppm in 36 minutes. Our results represent a significant step towards advancing the implementation of DCS as a satellite-based technology and improving technologies for GHG monitoring, Comment: 24 pages, 6 figures

At present, the mainstream artificial intelligence generally adopts the technical path of "attention mechanism + deep learning" + "reinforcement learning". It has made great progress in the field of AIGC (Artificial Intelligence Generated Content), setting off the technical wave of big models[ 2][13 ]. But in areas that need to interact with the actual environment, such as elderly care, home nanny, agricultural production, and vehicle driving, trial and error are expensive and a reinforcement learning process that requires much trial and error is difficult to achieve. Therefore, in order to achieve Artificial General Intelligence(AGI) that can be applied to any field, we need to use both existing technologies and solve the defects of existing technologies, so as to further develop the technological wave of artificial intelligence. In this paper, we analyze the limitations of the technical route of large models, and by addressing these limitations, we propose solutions, thus solving the inherent defects of large models. In this paper, we will reveal how to achieve true AGI step by step., Comment: 30 pages, 2 figures, 1 table

Optical clock networks play important roles in various fields, such as precise navigation, redefinition of "second" unit, and gravitational tests. To establish a global-scale optical clock network, it is essential to disseminate time and frequency with a stability of $10^{-19}$ over a long-distance free-space link. However, such attempts were limited to dozens of kilometers in mirror-folded configuration. Here, we take a crucial step toward future satellite-based time-frequency disseminations. By developing the key technologies, including high-power frequency combs, high-stability and high-efficiency optical transceiver systems, and efficient linear optical sampling, we demonstrate free-space time-frequency dissemination over two independent links with femtosecond time deviation, $3\times10^{-19}$ at 10,000 s residual instability and $1.6\times10^{-20}\pm 4.3\times10^{-19}$ offset. This level of the stability retains for an increased channel loss up to 89 dB. Our work can not only be directly used in ground-based application, but also firmly laid the groundwork for future satellite time-frequency dissemination., Comment: 27 pages, 13 figures, 2 tables

New categories can be discovered by transforming semantic features into synthesized visual features without corresponding training samples in zero-shot image classification. Although significant progress has been made in generating high-quality synthesized visual features using generative adversarial networks, guaranteeing semantic consistency between the semantic features and visual features remains very challenging. In this paper, we propose a novel zero-shot learning approach, GAN-CST, based on class knowledge to visual feature learning to tackle the problem. The approach consists of three parts, class knowledge overlay, semi-supervised learning and triplet loss. It applies class knowledge overlay (CKO) to obtain knowledge not only from the corresponding class but also from other classes that have the knowledge overlay. It ensures that the knowledge-to-visual learning process has adequate information to generate synthesized visual features. The approach also applies a semi-supervised learning process to re-train knowledge-to-visual model. It contributes to reinforcing synthesized visual features generation as well as new category prediction. We tabulate results on a number of benchmark datasets demonstrating that the proposed model delivers superior performance over state-of-the-art approaches.

Suffering from the semantic insufficiency and domain-shift problems, most of existing state-of-the-art methods fail to achieve satisfactory results for Zero-Shot Learning (ZSL). In order to alleviate these problems, we propose a novel generative ZSL method to learn more generalized features from multi-knowledge with continuously generated new semantics in semantic-to-visual embedding. In our approach, the proposed Multi-Knowledge Fusion Network (MKFNet) takes different semantic features from multi-knowledge as input, which enables more relevant semantic features to be trained for semantic-to-visual embedding, and finally generates more generalized visual features by adaptively fusing visual features from different knowledge domain. The proposed New Feature Generator (NFG) with adaptive genetic strategy is used to enrich semantic information on the one hand, and on the other hand it greatly improves the intersection of visual feature generated by MKFNet and unseen visual faetures. Empirically, we show that our approach can achieve significantly better performance compared to existing state-of-the-art methods on a large number of benchmarks for several ZSL tasks, including traditional ZSL, generalized ZSL and zero-shot retrieval.

Although zero-shot learning (ZSL) has an inferential capability of recognizing new classes that have never been seen before, it always faces two fundamental challenges of the cross modality and crossdomain challenges. In order to alleviate these problems, we develop a generative network-based ZSL approach equipped with the proposed Cross Knowledge Learning (CKL) scheme and Taxonomy Regularization (TR). In our approach, the semantic features are taken as inputs, and the output is the synthesized visual features generated from the corresponding semantic features. CKL enables more relevant semantic features to be trained for semantic-to-visual feature embedding in ZSL, while Taxonomy Regularization (TR) significantly improves the intersections with unseen images with more generalized visual features generated from generative network. Extensive experiments on several benchmark datasets (i.e., AwA1, AwA2, CUB, NAB and aPY) show that our approach is superior to these state-of-the-art methods in terms of ZSL image classification and retrieval.

Time and frequency transfer lies at the heart of the field of metrology. Compared to current microwave dissemination such as GPS, optical domain dissemination can provide more than one order of magnitude in terms of higher accuracy, which allows for many applications such as the redefinition of the second, tests of general relativity and fundamental quantum physics, precision navigation and quantum communication. Although optical frequency transfer has been demonstrated over thousand kilometers fiber lines, intercontinental time comparison and synchronization still requires satellite free space optical time and frequency transfer. Quite a few pioneering free space optical time and frequency experiments have been implemented at the distance of tens kilometers at ground level. However, there exists no detailed analysis or ground test to prove the feasibility of satellite-based optical time-frequency transfer. Here, we analyze the possibility of this system and then provide the first-step ground test with high channel loss. We demonstrate the optical frequency transfer with an instability of $10^{-18}$ level in 8,000 seconds across a 16-km free space channel with a loss of up to 70~dB, which is comparable with the loss of a satellite-ground link at medium earth orbit (MEO) and geostationary earth orbit (GEO)., Comment: 19 pages, 4 figures