Universal Adversarial Defense in Remote Sensing Based on Pre-trained Denoising Diffusion Models

Deep neural networks (DNNs) have achieved tremendous success in many remote sensing (RS) applications, in which DNNs are vulnerable to adversarial perturbations. Unfortunately, current adversarial defense approaches in RS studies usually suffer from performance fluctuation and unnecessary re-training costs due to the need for prior knowledge of the adversarial perturbations among RS data. To circumvent these challenges, we propose a universal adversarial defense approach in RS imagery (UAD-RS) using pre-trained diffusion models to defend the common DNNs against multiple unknown adversarial attacks. Specifically, the generative diffusion models are first pre-trained on different RS datasets to learn generalized representations in various data domains. After that, a universal adversarial purification framework is developed using the forward and reverse process of the pre-trained diffusion models to purify the perturbations from adversarial samples. Furthermore, an adaptive noise level selection (ANLS) mechanism is built to capture the optimal noise level of the diffusion model that can achieve the best purification results closest to the clean samples according to their Frechet Inception Distance (FID) in deep feature space. As a result, only a single pre-trained diffusion model is needed for the universal purification of adversarial samples on each dataset, which significantly alleviates the re-training efforts and maintains high performance without prior knowledge of the adversarial perturbations. Experiments on four heterogeneous RS datasets regarding scene classification and semantic segmentation verify that UAD-RS outperforms state-of-the-art adversarial purification approaches with a universal defense against seven commonly existing adversarial perturbations. Codes and the pre-trained models are available online (https://github.com/EricYu97/UAD-RS).Comment: Added the GitHub link to the abstrac

Research on Augmented Reality Technology and Build AR Application on Google Glass

This article introduces augmented reality technology, some current applications, and augmented reality technology for wearable devices. Then it introduces how to use NyARToolKit as a software library to build AR applications. The article also introduces how to design an AR application in Google Glass. The application can recognize two different images through NyARToolKit build-in function. After find match pattern files, the application will draw different 3D graphics according to different input images.Master of Science in Information Scienc

Automatic Detection of Hypoglycemic Events From the Electronic Health Record Notes of Diabetes Patients: Empirical Study

BACKGROUND: Hypoglycemic events are common and potentially dangerous conditions among patients being treated for diabetes. Automatic detection of such events could improve patient care and is valuable in population studies. Electronic health records (EHRs) are valuable resources for the detection of such events. OBJECTIVE: In this study, we aim to develop a deep-learning-based natural language processing (NLP) system to automatically detect hypoglycemic events from EHR notes. Our model is called the High-Performing System for Automatically Detecting Hypoglycemic Events (HYPE). METHODS: Domain experts reviewed 500 EHR notes of diabetes patients to determine whether each sentence contained a hypoglycemic event or not. We used this annotated corpus to train and evaluate HYPE, the high-performance NLP system for hypoglycemia detection. We built and evaluated both a classical machine learning model (ie, support vector machines [SVMs]) and state-of-the-art neural network models. RESULTS: We found that neural network models outperformed the SVM model. The convolutional neural network (CNN) model yielded the highest performance in a 10-fold cross-validation setting: mean precision=0.96 (SD 0.03), mean recall=0.86 (SD 0.03), and mean F1=0.91 (SD 0.03). CONCLUSIONS: Despite the challenges posed by small and highly imbalanced data, our CNN-based HYPE system still achieved a high performance for hypoglycemia detection. HYPE can be used for EHR-based hypoglycemia surveillance and population studies in diabetes patients

Long-term Orbital Period Variation of Hot Jupiters from Transiting Time Analysis using TESS Survey Data

Many hot Jupiters may experience orbital decays, which are manifested as long-term transit timing variations. We have analyzed 7068 transits from the Transiting Exoplanet Survey Satellite (TESS) for a sample of 326 hot Jupiters. These new mid-transit time data allow us to update ephemerides for these systems. By combining the new TESS transit timing data with archival data, we search for possible long-term orbital period variations in these hot Jupiters using a linear and a quadratic ephemeris model. We identified 26 candidates that exhibit possible long-term orbital period variations, including 18 candidates with decreasing orbital periods and 8 candidates with increasing orbital periods. Among them, 12 candidates have failed in our leave-one-out cross-validation (LOOCV) test and thus should be considered as marginal candidates. In addition to tidal interaction, alternative mechanisms such as apsidal precession, R{\o}mer effect, and Applegate effect could also contribute to the observed period variations. The ephemerides derived in this work are useful for scheduling follow-up observations for these hot Jupiters in the future. The Python code used to generate the ephemerides is made available online.Comment: Accepted for publication in ApJ

AI Security for Geoscience and Remote Sensing: Challenges and Future Trends

Recent advances in artificial intelligence (AI) have significantly intensified research in the geoscience and remote sensing (RS) field. AI algorithms, especially deep learning-based ones, have been developed and applied widely to RS data analysis. The successful application of AI covers almost all aspects of Earth observation (EO) missions, from low-level vision tasks like super-resolution, denoising and inpainting, to high-level vision tasks like scene classification, object detection and semantic segmentation. While AI techniques enable researchers to observe and understand the Earth more accurately, the vulnerability and uncertainty of AI models deserve further attention, considering that many geoscience and RS tasks are highly safety-critical. This paper reviews the current development of AI security in the geoscience and RS field, covering the following five important aspects: adversarial attack, backdoor attack, federated learning, uncertainty and explainability. Moreover, the potential opportunities and trends are discussed to provide insights for future research. To the best of the authors' knowledge, this paper is the first attempt to provide a systematic review of AI security-related research in the geoscience and RS community. Available code and datasets are also listed in the paper to move this vibrant field of research forward

Fine-Tuning Bidirectional Encoder Representations From Transformers (BERT)-Based Models on Large-Scale Electronic Health Record Notes: An Empirical Study

BACKGROUND: The bidirectional encoder representations from transformers (BERT) model has achieved great success in many natural language processing (NLP) tasks, such as named entity recognition and question answering. However, little prior work has explored this model to be used for an important task in the biomedical and clinical domains, namely entity normalization. OBJECTIVE: We aim to investigate the effectiveness of BERT-based models for biomedical or clinical entity normalization. In addition, our second objective is to investigate whether the domains of training data influence the performances of BERT-based models as well as the degree of influence. METHODS: Our data was comprised of 1.5 million unlabeled electronic health record (EHR) notes. We first fine-tuned BioBERT on this large collection of unlabeled EHR notes. This generated our BERT-based model trained using 1.5 million electronic health record notes (EhrBERT). We then further fine-tuned EhrBERT, BioBERT, and BERT on three annotated corpora for biomedical and clinical entity normalization: the Medication, Indication, and Adverse Drug Events (MADE) 1.0 corpus, the National Center for Biotechnology Information (NCBI) disease corpus, and the Chemical-Disease Relations (CDR) corpus. We compared our models with two state-of-the-art normalization systems, namely MetaMap and disease name normalization (DNorm). RESULTS: EhrBERT achieved 40.95% F1 in the MADE 1.0 corpus for mapping named entities to the Medical Dictionary for Regulatory Activities and the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT), which have about 380,000 terms. In this corpus, EhrBERT outperformed MetaMap by 2.36% in F1. For the NCBI disease corpus and CDR corpus, EhrBERT also outperformed DNorm by improving the F1 scores from 88.37% and 89.92% to 90.35% and 93.82%, respectively. Compared with BioBERT and BERT, EhrBERT outperformed them on the MADE 1.0 corpus and the CDR corpus. CONCLUSIONS: Our work shows that BERT-based models have achieved state-of-the-art performance for biomedical and clinical entity normalization. BERT-based models can be readily fine-tuned to normalize any kind of named entities

Color-gradient lattice Boltzmann modeling of immiscible two-phase flows on partially wetting surface

A zero-interfacial-force condition is derived and implemented to improve the wetting boundary scheme for a lattice Boltzmann color-gradient model. This new wetting boundary scheme is validated by two static problems, i.e. a droplet resting on a flat surface and a cylindrical surface, and one dynamic problem, i.e. the capillary filling in a 2 dimensional (2D) channel. In these simulations, we observe that non-physical mass transfer is suppressed and spurious velocities become smaller. Meanwhile, accurate results including dynamic contact line movement are achieved on a broad range of contact angles. The model is then applied to study displacement of immiscible fluids in a 2D channel. Both the displacement velocity and the change rate of finger length are found to exhibit a linear dependence on the contact angle at the viscosity ratio of unity. The displacement velocity decreases but the change rate of finger length increases with increasing capillary number, while the displacement velocity tends to be constant, i.e. two-third of the maximum inlet velocity, at high viscosity ratios or low capillary numbers. In contrast to the displacement velocity, the change rate of finger length is negligible at high viscosity ratios or low capillary numbers, where the finger length is in an equilibrium state, while the equilibrium finger length itself is smaller at a higher viscosity ratio or a lower capillary number

Dietary Supplementation With High Fiber Alleviates Oxidative Stress and Inflammatory Responses Caused by Severe Sepsis in Mice Without Altering Microbiome Diversity

In this study, we demonstrated the effects of a high-fiber diet on intestinal lesions, oxidative stress and systemic inflammation in a murine model of endotoxemia. C57BL/6 mice were randomly assigned to four groups: the control group (CONTROL), which received a commercial normal-fiber rodent diet comprising normal fiber; a CLP group, which received a commercial normal-fiber rodent diet and underwent caecal ligation puncture (CLP); a high-fiber group (HFG), which received a commercial high-fiber rodent diet; and a high fiber + CLP group (HFCLP) which received a commercial high-fiber rodent diet and underwent CLP (30%). The sepsis model was created via CLP after 2 weeks of dietary intervention. Notably, dietary high-fiber supplementation in HFCLP group improved survival rates and reduced bacterial loads, compared with CLP alone. In the HFCLP group, dietary fiber supplementation decreased the serum concentrations of pro-inflammatory cytokines such as tumor necrosis factor-α (TNF-α), interleukin 6 (IL-6) and high-mobility group protein 1 (HMG-1) but raised the concentration of interleukin 10 (IL-10), compared with the levels in CLP mice. Meanwhile, high-fiber supplementation increased the relative proportions of Akkermansia and Lachnospiraceae. These data show that dietary high-fiber supplementation may be therapeutic for sepsis-induced lesions