280 research outputs found
A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land
Cloud and Aerosol Imager (CAI) onboard the Greenhouse Gases Observing Satellite (GOSAT) is a multi-band sensor designed to observe and acquire information on clouds and aerosols. In order to retrieve aerosol optical depth (AOD) over land from the CAI sensor, a Dark Target (DT) algorithm for GOSAT CAI was developed based on the strategy of the Moderate Resolution Imaging Spectroradiometer (MODIS) DT algorithm. When retrieving AOD from satellite platforms, determining surface contributions is a major challenge. In the MODIS DT algorithm, surface signals in the visible wavelengths are estimated based on the relationships between visible channels and shortwave infrared (SWIR) near the 2.1 µm channel. However, the CAI only has a 1.6 µm band to cover the SWIR wavelengths. To resolve the difficulties in determining surface reflectance caused by the lack of 2.1 μm band data, we attempted to analyze the relationship between reflectance at 1.6 µm and at 2.1 µm. We did this using the MODIS surface reflectance product and then connecting the reflectances at 1.6 µm and the visible bands based on the empirical relationship between reflectances at 2.1 µm and the visible bands. We found that the reflectance relationship between 1.6 µm and 2.1 µm is typically dependent on the vegetation conditions, and that reflectances at 2.1 µm can be parameterized as a function of 1.6 µm reflectance and the Vegetation Index (VI). Based on our experimental results, an Aerosol Free Vegetation Index (AFRI2.1)-based regression function connecting the 1.6 µm and 2.1 µm bands was summarized. Under light aerosol loading (AOD at 0.55 µm < 0.1), the 2.1 µm reflectance derived by our method has an extremely high correlation with the true 2.1 µm reflectance (r-value = 0.928). Similar to the MODIS DT algorithms (Collection 5 and Collection 6), a CAI-applicable approach that uses AFRI2.1 and the scattering angle to account for the visible surface signals was proposed. It was then applied to the CAI sensor for AOD retrieval; the retrievals were validated by comparisons with ground-level measurements from Aerosol Robotic Network (AERONET) sites. Validations show that retrievals from the CAI have high agreement with the AERONET measurements, with an r-value of 0.922, and 69.2% of the AOD retrieved data falling within the expected error envelope of ± (0.1 + 15% AODAERONET)
Combining Past, Present and Future: A Self-Supervised Approach for Class Incremental Learning
Class Incremental Learning (CIL) aims to handle the scenario where data of
novel classes occur continuously and sequentially. The model should recognize
the sequential novel classes while alleviating the catastrophic forgetting. In
the self-supervised manner, it becomes more challenging to avoid the conflict
between the feature embedding spaces of novel classes and old ones without any
class labels. To address the problem, we propose a self-supervised CIL
framework CPPF, meaning Combining Past, Present and Future. In detail, CPPF
consists of a prototype clustering module (PC), an embedding space reserving
module (ESR) and a multi-teacher distillation module (MTD). 1) The PC and the
ESR modules reserve embedding space for subsequent phases at the prototype
level and the feature level respectively to prepare for knowledge learned in
the future. 2) The MTD module maintains the representations of the current
phase without the interference of past knowledge. One of the teacher networks
retains the representations of the past phases, and the other teacher network
distills relation information of the current phase to the student network.
Extensive experiments on CIFAR100 and ImageNet100 datasets demonstrate that our
proposed method boosts the performance of self-supervised class incremental
learning. We will release code in the near future
Numerical simulation of the non-uniform flow in a full-annulus multi-stage axial compressor with the harmonic balance method
To improve the understanding of unsteady flow in modern advanced axial compressor, unsteady simulations on full-annulus multi-stage axial compressor are carried out with the harmonic balance method. Since the internal flow in turbomachinery is naturally periodic, the harmonic balance method can be used to reduce the computational cost. In order to verify the accuracy of the harmonic balance method, the numerical results are first compared with the experimental results. The results show that the internal flow field and the operating characteristics of the multi-stage axial compressor obtained by the harmonic balance method coincide with the experimental results with the relative error in the range of 3%. Through the analysis of the internal flow field of the axial compressor, it can be found that the airflow in the clearance of adjacent blade rows gradually changes from axisymmetric to non-axisymmetric and then returns to almost completely axisymmetric distribution before the downstream blade inlet, with only a slight non-axisymmetric distribution, which can be ignored. Moreover, the slight non-axisymmetric distribution will continue to accumulate with the development of the flow and, finally, form a distinct circumferential non-uniform flow field in latter stages, which may be the reason why the traditional single-passage numerical method will cause certain errors in multi-stage axial compressor simulations
Test-Time Training for Semantic Segmentation with Output Contrastive Loss
Although deep learning-based segmentation models have achieved impressive
performance on public benchmarks, generalizing well to unseen environments
remains a major challenge. To improve the model's generalization ability to the
new domain during evaluation, the test-time training (TTT) is a challenging
paradigm that adapts the source-pretrained model in an online fashion. Early
efforts on TTT mainly focus on the image classification task. Directly
extending these methods to semantic segmentation easily experiences unstable
adaption due to segmentation's inherent characteristics, such as extreme class
imbalance and complex decision spaces. To stabilize the adaptation process, we
introduce contrastive loss (CL), known for its capability to learn robust and
generalized representations. Nevertheless, the traditional CL operates in the
representation space and cannot directly enhance predictions. In this paper, we
resolve this limitation by adapting the CL to the output space, employing a
high temperature, and simplifying the formulation, resulting in a
straightforward yet effective loss function called Output Contrastive Loss
(OCL). Our comprehensive experiments validate the efficacy of our approach
across diverse evaluation scenarios. Notably, our method excels even when
applied to models initially pre-trained using domain adaptation methods on test
domain data, showcasing its resilience and adaptability.\footnote{Code and more
information could be found at~ \url{https://github.com/dazhangyu123/OCL}
Pre-training with Aspect-Content Text Mutual Prediction for Multi-Aspect Dense Retrieval
Grounded on pre-trained language models (PLMs), dense retrieval has been
studied extensively on plain text. In contrast, there has been little research
on retrieving data with multiple aspects using dense models. In the scenarios
such as product search, the aspect information plays an essential role in
relevance matching, e.g., category: Electronics, Computers, and Pet Supplies. A
common way of leveraging aspect information for multi-aspect retrieval is to
introduce an auxiliary classification objective, i.e., using item contents to
predict the annotated value IDs of item aspects. However, by learning the value
embeddings from scratch, this approach may not capture the various semantic
similarities between the values sufficiently. To address this limitation, we
leverage the aspect information as text strings rather than class IDs during
pre-training so that their semantic similarities can be naturally captured in
the PLMs. To facilitate effective retrieval with the aspect strings, we propose
mutual prediction objectives between the text of the item aspect and content.
In this way, our model makes more sufficient use of aspect information than
conducting undifferentiated masked language modeling (MLM) on the concatenated
text of aspects and content. Extensive experiments on two real-world datasets
(product and mini-program search) show that our approach can outperform
competitive baselines both treating aspect values as classes and conducting the
same MLM for aspect and content strings. Code and related dataset will be
available at the URL \footnote{https://github.com/sunxiaojie99/ATTEMPT}.Comment: accepted by cikm202
PathAsst: Redefining Pathology through Generative Foundation AI Assistant for Pathology
As advances in large language models (LLMs) and multimodal techniques
continue to mature, the development of general-purpose multimodal large
language models (MLLMs) has surged, with significant applications in natural
image interpretation. However, the field of pathology has largely remained
untapped in this regard, despite the growing need for accurate, timely, and
personalized diagnostics. To bridge the gap in pathology MLLMs, we present the
PathAsst in this study, which is a generative foundation AI assistant to
revolutionize diagnostic and predictive analytics in pathology. To develop
PathAsst, we collect over 142K high-quality pathology image-text pairs from a
variety of reliable sources, including PubMed, comprehensive pathology
textbooks, reputable pathology websites, and private data annotated by
pathologists. Leveraging the advanced capabilities of ChatGPT/GPT-4, we
generate over 180K instruction-following samples. Furthermore, we devise
additional instruction-following data, specifically tailored for the invocation
of the pathology-specific models, allowing the PathAsst to effectively interact
with these models based on the input image and user intent, consequently
enhancing the model's diagnostic capabilities. Subsequently, our PathAsst is
trained based on Vicuna-13B language model in coordination with the CLIP vision
encoder. The results of PathAsst show the potential of harnessing the
AI-powered generative foundation model to improve pathology diagnosis and
treatment processes. We are committed to open-sourcing our meticulously curated
dataset, as well as a comprehensive toolkit designed to aid researchers in the
extensive collection and preprocessing of their own datasets. Resources can be
obtained at
https://github.com/superjamessyx/Generative-Foundation-AI-Assistant-for-Pathology.Comment: 13 pages, 5 figures, conferenc
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