Class Prior-Free Positive-Unlabeled Learning with Taylor Variational Loss for Hyperspectral Remote Sensing Imagery

Positive-unlabeled learning (PU learning) in hyperspectral remote sensing imagery (HSI) is aimed at learning a binary classifier from positive and unlabeled data, which has broad prospects in various earth vision applications. However, when PU learning meets limited labeled HSI, the unlabeled data may dominate the optimization process, which makes the neural networks overfit the unlabeled data. In this paper, a Taylor variational loss is proposed for HSI PU learning, which reduces the weight of the gradient of the unlabeled data by Taylor series expansion to enable the network to find a balance between overfitting and underfitting. In addition, the self-calibrated optimization strategy is designed to stabilize the training process. Experiments on 7 benchmark datasets (21 tasks in total) validate the effectiveness of the proposed method. Code is at: https://github.com/Hengwei-Zhao96/T-HOneCls.Comment: Accepted to ICCV 202

Learning One-Class Hyperspectral Classifier from Positive and Unlabeled Data for Low Proportion Target

Hyperspectral imagery (HSI) one-class classification is aimed at identifying a single target class from the HSI by using only positive labels, which can significantly reduce the requirements for annotation. However, HSI one-class classification is far more challenging than HSI multi-class classification, due the lack of negative labels and the low target proportion, which are issues that have rarely been considered in the previous HSI classification studies. In this paper, a weakly supervised HSI one-class classifier, namely HOneCls is proposed to solve the problem of under-fitting of the positive class occurs in the HSI data with low target proportion, where a risk estimator -- the One-Class Risk Estimator -- is particularly introduced to make the full convolutional neural network (FCN) with the ability of one class classification. The experimental results obtained on challenging hyperspectral classification datasets, which includes 20 kinds of ground objects with very similar spectra, demonstrate the efficiency and feasibility of the proposed One-Class Risk Estimator. Compared with the state-of-the-art one-class classifiers, the F1-score is improved significantly in the HSI data with low target proportion

Mapping the distribution of invasive tree species using deep one-class classification in the tropical montane landscape of Kenya

Some invasive tree species threaten biodiversity and cause irreversible damage to global ecosystems. The key to controlling and monitoring the propagation of invasive tree species is to detect their occurrence as early as possible. In this regard, one-class classification (OCC) shows potential in forest areas with abundant species richness since it only requires a few positive samples of the invasive tree species to be mapped, instead of all the species. However, the classical OCC method in remote sensing is heavily dependent on manually designed features, which have a limited ability in areas with complex species distributions. Deep learning based tree species classification methods mostly focus on multi-class classification, and there have been few studies of the deep OCC of tree species. In this paper, a deep positive and unlabeled learning based OCC framework—ITreeDet—is proposed for identifying the invasive tree species of Eucalyptus spp. (eucalyptus) and Acacia mearnsii (black wattle) in the Taita Hills of southern Kenya. In the ITreeDet framework, an absNegative risk estimator is designed to train a robust deep OCC model by fully using the massive unlabeled data. Compared with the state-of-the-art OCC methods, ITreeDet represents a great improvement in detection accuracy, and the F1-score was 0.86 and 0.70 for eucalyptus and black wattle, respectively. The study area covers 100 km2 of the Taita Hills, where, according to our findings, the total area of eucalyptus and black wattle is 1.61 km2 and 3.24 km2, respectively, which represent 6.78% and 13.65% of the area covered by trees and forest. In addition, both invasive tree species are located in the higher elevations, and the extensive spread of black wattle around the study area confirms its invasive tendency. The maps generated by the use of the proposed algorithm will help local government to develop management strategies for these two invasive species.Peer reviewe

The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection from Multi-Source Satellite Imagery

The scientific outcomes of the 2022 Landslide4Sense (L4S) competition organized by the Institute of Advanced Research in Artificial Intelligence (IARAI) are presented here. The objective of the competition is to automatically detect landslides based on large-scale multiple sources of satellite imagery collected globally. The 2022 L4S aims to foster interdisciplinary research on recent developments in deep learning (DL) models for the semantic segmentation task using satellite imagery. In the past few years, DL-based models have achieved performance that meets expectations on image interpretation, due to the development of convolutional neural networks (CNNs). The main objective of this article is to present the details and the best-performing algorithms featured in this competition. The winning solutions are elaborated with state-of-the-art models like the Swin Transformer, SegFormer, and U-Net. Advanced machine learning techniques and strategies such as hard example mining, self-training, and mix-up data augmentation are also considered. Moreover, we describe the L4S benchmark data set in order to facilitate further comparisons, and report the results of the accuracy assessment online. The data is accessible on Future Development Leaderboard for future evaluation at https://www.iarai.ac.at/landslide4sense/challenge/ , and researchers are invited to submit more prediction results, evaluate the accuracy of their methods, compare them with those of other users, and, ideally, improve the landslide detection results reported in this article

Altered intestinal microbiome and epithelial damage aggravate intestinal graft-versus-host disease

ABSTRACTDespite significant achievements in hematopoietic stem cell transplantation (HSCT), graft-versus-host disease (GVHD), especially intestinal GVHD, remains a major obstacle to this procedure. GVHD has long been regarded as a pathogenic immune response, and the intestine has been simply considered as a target of immune attack. In effect, multiple factors contribute to intestinal damage after transplantation. Impaired intestinal homeostasis including altered intestinal microbiome and epithelial damage results in delayed wound healing, amplified immune response and sustained tissue destruction, and it may not fully recover following immunosuppression. In this review, we summarize factors leading to intestinal damage and discuss the relationship between intestinal damage and GVHD. We also describe the great potential of remodeling intestinal homeostasis in GVHD management

Molecular Dynamics Investigation of the Influence of Voids on the Impact Mechanical Behavior of NiTi Shape-Memory Alloy

To date, research on the physical and mechanical behavior of nickel-titanium shape-memory alloy (NiTi SMA) has focused on the macroscopic physical properties, equation of state, strength constitution, phase transition induced by temperature and stress under static load, etc. The behavior of a NiTi SMA under high-strain-rate impact and the influence of voids have not been reported. In this present work, the behavior evolution of (100) single-crystal NiTi SMA and the influencing characteristics of voids under a shock wave of 1.2 km/s are studied by large-scale molecular dynamics calculation. The results show that only a small amount of B2 austenite is transformed into B19’ martensite when the NiTi sample does not pass through the void during impact compression, whereas when the shock wave passes through the hole, a large amount of martensite phase transformation and plastic deformation is induced around the hole; the existence of phase transformation and phase-transformation-induced plastic deformation greatly consumes the energy of the shock wave, thus making the width of the wave front in the subsequent propagation process wider and the peak of the foremost wave peak reduced. In addition, the existence of holes disrupts the orderly propagation of shock waves, changes the shock wave front from a plane to a concave surface, and reduces the propagation speed of shock waves. The calculation results show that the presence of pores in a porous NiTi SMA leads to significant martensitic phase transformation and plastic deformation induced by phase transformation, which has a significant buffering effect on shock waves. The results of this study provide great guidance for expanding the application of NiTi SMA in the field of shock

Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors

Anomaly segmentation in high spatial resolution (HSR) remote sensing imagery is aimed at segmenting anomaly patterns of the earth deviating from normal patterns, which plays an important role in various Earth vision applications. However, it is a challenging task due to the complex distribution and the irregular shapes of objects, and the lack of abnormal samples. To tackle these problems, an anomaly segmentation model based on pixel descriptors (ASD) is proposed for anomaly segmentation in HSR imagery. Specifically, deep one-class classification is introduced for anomaly segmentation in the feature space with discriminative pixel descriptors. The ASD model incorporates the data argument for generating virtual abnormal samples, which can force the pixel descriptors to be compact for normal data and meanwhile to be diverse to avoid the model collapse problems when only positive samples participated in the training. In addition, the ASD introduced a multi-level and multi-scale feature extraction strategy for learning the low-level and semantic information to make the pixel descriptors feature-rich. The proposed ASD model was validated using four HSR datasets and compared with the recent state-of-the-art models, showing its potential value in Earth vision applications

Molecular Structure Underlying the Allosteric Mechanism of Caffeine Detection in Taste Sensor

The use of taste sensors with lipid/polymer membranes is one of the methods to evaluate taste. As previously reported, taste sensors can detect non-charged substances such as caffeine by modifying the lipid/polymer membranes with hydroxybenzoic acids (HBAs). The mechanism of caffeine detection by taste sensors was identified to be an allosteric one. Generally, the allosteric mechanism, defined as “regulation at distant sites”, is used to describe the regulation process for proteins. In this study, to improve the sensitivity of taste sensors to caffeine and its analogs using the allosteric mechanism, we used various modifiers of lipid/polymer membranes, and we detected caffeine using taste sensors with the modified membranes. The detection of the caffeine analogs theophylline and theobromine was also analyzed. The results of caffeine detection clarified that the molecular structure underlying the allosteric mechanism capable of effective caffeine detection involves both the carboxyl and hydroxyl groups, where the hydroxyl group can form intermolecular H bonds with caffeine. Furthermore, the taste sensors with a modifier, which has the molecular structure underlying the allosteric mechanism, showed high sensitivity to caffeine and caffeine analogs. The use of an allosteric mechanism may help improve the sensitivity of taste sensors to other non-charged pharmaceutical substances, such as dexamethasone and prednisolone, in the future

Molecular Structure Underlying the Allosteric Mechanism of Caffeine Detection in Taste Sensor

The use of taste sensors with lipid/polymer membranes is one of the methods to evaluate taste. As previously reported, taste sensors can detect non-charged substances such as caffeine by modifying the lipid/polymer membranes with hydroxybenzoic acids (HBAs). The mechanism of caffeine detection by taste sensors was identified to be an allosteric one. Generally, the allosteric mechanism, defined as “regulation at distant sites”, is used to describe the regulation process for proteins. In this study, to improve the sensitivity of taste sensors to caffeine and its analogs using the allosteric mechanism, we used various modifiers of lipid/polymer membranes, and we detected caffeine using taste sensors with the modified membranes. The detection of the caffeine analogs theophylline and theobromine was also analyzed. The results of caffeine detection clarified that the molecular structure underlying the allosteric mechanism capable of effective caffeine detection involves both the carboxyl and hydroxyl groups, where the hydroxyl group can form intermolecular H bonds with caffeine. Furthermore, the taste sensors with a modifier, which has the molecular structure underlying the allosteric mechanism, showed high sensitivity to caffeine and caffeine analogs. The use of an allosteric mechanism may help improve the sensitivity of taste sensors to other non-charged pharmaceutical substances, such as dexamethasone and prednisolone, in the future