Employing MODIS Cloud Regimes to Find Signals of Aerosol-Cloud-Precipitation Interactions

No abstract availabl

Confidence-Based Feature Imputation for Graphs with Partially Known Features

This paper investigates a missing feature imputation problem for graph learning tasks. Several methods have previously addressed learning tasks on graphs with missing features. However, in cases of high rates of missing features, they were unable to avoid significant performance degradation. To overcome this limitation, we introduce a novel concept of channel-wise confidence in a node feature, which is assigned to each imputed channel feature of a node for reflecting certainty of the imputation. We then design pseudo-confidence using the channel-wise shortest path distance between a missing-feature node and its nearest known-feature node to replace unavailable true confidence in an actual learning process. Based on the pseudo-confidence, we propose a novel feature imputation scheme that performs channel-wise inter-node diffusion and node-wise inter-channel propagation. The scheme can endure even at an exceedingly high missing rate (e.g., 99.5\%) and it achieves state-of-the-art accuracy for both semi-supervised node classification and link prediction on various datasets containing a high rate of missing features. Codes are available at https://github.com/daehoum1/pcfi.Comment: Accepted to ICLR 2023. 28 page

Class-Attentive Diffusion Network for Semi-Supervised Classification

Recently, graph neural networks for semi-supervised classification have been widely studied. However, existing methods only use the information of limited neighbors and do not deal with the inter-class connections in graphs. In this paper, we propose Adaptive aggregation with Class-Attentive Diffusion (AdaCAD), a new aggregation scheme that adaptively aggregates nodes probably of the same class among K-hop neighbors. To this end, we first propose a novel stochastic process, called Class-Attentive Diffusion (CAD), that strengthens attention to intra-class nodes and attenuates attention to inter-class nodes. In contrast to the existing diffusion methods with a transition matrix determined solely by the graph structure, CAD considers both the node features and the graph structure with the design of our class-attentive transition matrix that utilizes a classifier. Then, we further propose an adaptive update scheme that leverages different reflection ratios of the diffusion result for each node depending on the local class-context. As the main advantage, AdaCAD alleviates the problem of undesired mixing of inter-class features caused by discrepancies between node labels and the graph topology. Built on AdaCAD, we construct a simple model called Class-Attentive Diffusion Network (CAD-Net). Extensive experiments on seven benchmark datasets consistently demonstrate the efficacy of the proposed method and our CAD-Net significantly outperforms the state-of-the-art methods. Code is available at https://github.com/ljin0429/CAD-Net.Comment: Accepted to AAAI 202

A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classification

A self-organizing map (SOM) is a type of competitive artificial neural network, which projects the high dimensional input space of the training samples into a low dimensional space with the topology relations preserved. This makes SOMs supportive of organizing and visualizing complex data sets and have been pervasively used among numerous disciplines with different applications. Notwithstanding its wide applications, the self-organizing map is perplexed by its inherent randomness, which produces dissimilar SOM patterns even when being trained on identical training samples with the same parameters every time, and thus causes usability concerns for other domain practitioners and precludes more potential users from exploring SOM based applications in a broader spectrum. Motivated by this practical concern, we propose a deterministic approach as a supplement to the standard self-organizing map. In accordance with the theoretical design, the experimental results with satellite cloud data demonstrate the effective and efficient organization as well as simplification capabilities of the proposed approach

Contrasting the Co-Variability of Daytime Cloud and Precipitation over Tropical Land and Ocean

The co-variability of cloud and precipitation in the extended tropics (35 deg N35 deg S) is investigated using contemporaneous datasets for a 13-year period. The goal is to quantify potential relationships between cloud type amounts and precipitation events of particular strength. Particular attention is paid to whether the relationships exhibit different characteristics over tropical land and ocean. A primary analysis metric is the correlation coefficient between fractions of individual cloud types and frequencies within precipitation histogram bins that have been matched in time and space. The cloud type fractions are derived from Moderate Resolution Imaging Spectroradiometer (MODIS) joint histograms of cloud top pressure and cloud optical thickness in one-degree grid cells, and the precipitation frequencies come from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) dataset aggregated to the same grid. It is found that the strongest coupling (positive correlation) between clouds and precipitation occurs over ocean for cumulonimbus clouds and the heaviest rainfall. While the same cloud type and rainfall bin are also best correlated over land compared to other combinations, the correlation magnitude is weaker than over ocean. The difference is attributed to the greater size of convective systems over ocean. It is also found that both over ocean and land the anti-correlation of strong precipitation with weak (i.e., thin and/or low) cloud types is of greater absolute strength than positive correlations between weak cloud types and weak precipitation. Cloud type co-occurrence relationships explain some of the cloud-precipitation anti-correlations. Weak correlations between weaker rainfall and clouds indicate poor predictability for precipitation when cloud types are known, and this is even more true over land than over ocean

RoCOCO: Robust Benchmark MS-COCO to Stress-test Robustness of Image-Text Matching Models

Recently, large-scale vision-language pre-training models and visual semantic embedding methods have significantly improved image-text matching (ITM) accuracy on MS COCO 5K test set. However, it is unclear how robust these state-of-the-art (SOTA) models are when using them in the wild. In this paper, we propose a novel evaluation benchmark to stress-test the robustness of ITM models. To this end, we add various fooling images and captions to a retrieval pool. Specifically, we change images by inserting unrelated images, and change captions by substituting a noun, which can change the meaning of a sentence. We discover that just adding these newly created images and captions to the test set can degrade performances (i.e., Recall@1) of a wide range of SOTA models (e.g., 81.9% $\rightarrow$ 64.5% in BLIP, 66.1% $\rightarrow$ 37.5% in VSE$\infty$). We expect that our findings can provide insights for improving the robustness of the vision-language models and devising more diverse stress-test methods in cross-modal retrieval task. Source code and dataset will be available at https://github.com/pseulki/rococo

MODIS Dynamical and Microphysical Regimes as Viewed by AIRS

No abstract availabl

Antioxidant, inhibition of α-glucosidase and suppression of nitric oxide production in LPS-induced murine macrophages by different fractions of Actinidia arguta stem

AbstractIn traditional systems of medicine, fruits, leaves, and stems of Actinidia arguta (Sieb. et Zucc.) Planch. ex Miq. have been used to treat various inflammatory diseases. The present study determined the proximate composition, antioxidant, anti-inflammatory, and hypoglycemic potential of A. arguta stem. Phenolic composition of hot water extract and its sub-fractions was determined by Folin–Ciocalteu’s reagent method. In vitro antioxidant activities of the samples were evaluated using 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS) radical scavenging assays. Anti-inflammatory activity of different fractions was investigated through the inhibition of nitric oxide (NO) production in lipopolysaccharide (1μg/ml) stimulated RAW 264.7 cells. In addition, inhibition of α-glucosidase activity of hot water extract was determined using p-nitrophenyl-α-d-glucopyranoside (pNPG) as a substrate. Ethyl acetate (557.23mgGAE/g) fraction contains higher level of total phenolic content. The antioxidant activity evaluated by DPPH radical scavenging assay showed a strong activity for ethyl acetate (IC50 of 14.28μg/ml) and n-butanol fractions (IC50 of 48.27μg/ml). Further, ethyl acetate fraction effectively inhibited NO production in RAW 264.7 cells induced by lipopolysaccharide (LPS) than other fractions (nitrite level to 32.14μM at 200μg/ml). In addition, hot water extract of A. arguta stem exhibited appreciable inhibitory activity against α-glucosidase enzyme with IC50 of 1.71mg/ml. The obtained results have important consequence of using A. arguta stem toward the development of effective anti-inflammatory drugs

Accept or Refuse? A Pilot Study of Patients' Perspective on Participating as Imaginary Research Subjects in Schizophrenia

Objective The goal of the present study was to evaluate demographic and clinical factors that affect the intention to participate in commonly-conducted research in patients with schizophrenia. Methods Thirty-four outpatients with a diagnosis of schizophrenia were enrolled in this study. They were asked whether they would have any intention to participate in four imaginary studies: a simple questionnaire, a genetic study, a Study of complex tasks and a risky study. We analyzed the differences in general psychopathology, insight and demographic characteristics of the participants according to their responses (acceptance or refusal) to the four proposed studies. Results Younger and better-educated patients tended to decline participation in a risky study. Patients with a longer duration of regular psychiatric follow-ups tended to willingly participate in the simple questionnaire. There were no overall statistical differences in general psychopathology and insight between patients who agreed or declined to participate in studies. Conclusion Age and education level may be factors that influence decisions to participate in schizophrenia Studies. Further research is needed to confirm and expand on the current findings