Mapping China using MODIS data : Method, software and data products

Institute of Geographical Scienceand Natural Resources Reseach, Chinese Academy of Sciences2005 International Symposium on Environmental Mornitoring in East Asia -Remote Sensing and Forests-,Hosted The EMEA Project, Kanazawa University 21st=Century COE Program -Environmental Monitoring and Predicition of Long- and Short- Term Dynamics of Pan-Japan Sea Area- ,予稿集, EMEA 2005 in Kanazawa, 国際学術研究公開シンポジウム『東アジアの環境モニタリング』－リモートセンシングと森林－,年月日：200511月28日～29日, 場所：KKRホテル金沢, 金沢大学自然科学研究科, 主催：金沢大学EMEAプロジェクト, 共催：金沢大学21世紀COEプログラム「環日本海域の環境変動と長期・短期変動予測

Explainable and Transferable Adversarial Attack for ML-Based Network Intrusion Detectors

espite being widely used in network intrusion detection systems (NIDSs), machine learning (ML) has proven to be highly vulnerable to adversarial attacks. White-box and black-box adversarial attacks of NIDS have been explored in several studies. However, white-box attacks unrealistically assume that the attackers have full knowledge of the target NIDSs. Meanwhile, existing black-box attacks can not achieve high attack success rate due to the weak adversarial transferability between models (e.g., neural networks and tree models). Additionally, neither of them explains why adversarial examples exist and why they can transfer across models. To address these challenges, this paper introduces ETA, an Explainable Transfer-based Black-Box Adversarial Attack framework. ETA aims to achieve two primary objectives: 1) create transferable adversarial examples applicable to various ML models and 2) provide insights into the existence of adversarial examples and their transferability within NIDSs. Specifically, we first provide a general transfer-based adversarial attack method applicable across the entire ML space. Following that, we exploit a unique insight based on cooperative game theory and perturbation interpretations to explain adversarial examples and adversarial transferability. On this basis, we propose an Important-Sensitive Feature Selection (ISFS) method to guide the search for adversarial examples, achieving stronger transferability and ensuring traffic-space constraints

De novo SNP discovery and genetic linkage mapping in poplar using restriction site associated DNA and whole-genome sequencing technologies

Detailed information on genetic distance and linkage phase between adjacent SNP markers on the genetic linkage map of the female P. deltoides ‘I-69’. The corresponding identical SNPs identified based on the P. trichocarpa reference genome are also included. (XLS 452 kb

Carbonic Anhydrase I Is Recognized by an SOD1 Antibody upon Biotinylation of Human Spinal Cord Extracts

We recently reported the presence of a novel 32 kDa protein immunoreactive to a copper, zinc superoxide dismutase (SOD1) antibody within the spinal cord of patients with amyotrophic lateral sclerosis (ALS). This unique protein species was generated by biotinylation of spinal cord tissue extracts to detect conformational changes of SOD1 specific to ALS patients. To further characterize this protein, we enriched the protein by column chromatography and determined its protein identity by mass spectrometry. The protein that gave rise to the 32 kDa species upon biotinylation was identified as carbonic anhydrase I (CA I). Biotinylation of CA I from ALS spinal cord resulted in the generation of a novel epitope recognized by the SOD1 antibody. This epitope could also be generated by biotinylation of extracts from cultured cells expressing human CA I. Peptide competition assays identified the amino acid sequence in carbonic anhydrase I responsible for binding the SOD1 antibody. We conclude that chemical modifications used to identify pathogenic protein conformations can lead to the identification of unanticipated proteins that may participate in disease pathogenesis

Event-Triggered Relearning Modeling Method for Stochastic System with Non-Stationary Variable Operating Conditions

This study presents a novel event-triggered relearning framework for neural network modeling, designed to improve prediction precision in dynamic stochastic complex industrial systems under non-stationary and variable conditions. Firstly, a sliding window algorithm combined with entropy is applied to divide the input and output datasets across different operational conditions, establishing clear data boundaries. Following this, the prediction errors derived from the neural network under different operational states are harnessed to define a set of event-triggered relearning criteria. Once these conditions are triggered, the relevant dataset is used to recalibrate the model to the specific operational condition and predict the data under this operating condition. When the predicted data fall within the training input range of a pre-trained model, we switch to that model for immediate prediction. Compared with the conventional BP neural network model and random vector functional-link network, the proposed model can produce a better estimation accuracy and reduce computation costs. Finally, the effectiveness of our proposed method is validated through numerical simulation tests using nonlinear Hammerstein models with Gaussian noise, reflecting complex stochastic industrial processes

AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations

Multi-task learning (MTL) aims at enhancing the performance and efficiency of machine learning models by training them on multiple tasks simultaneously. However, MTL research faces two challenges: 1) modeling the relationships between tasks to effectively share knowledge between them, and 2) jointly learning task-specific and shared knowledge. In this paper, we present a novel model Adaptive Task-to-Task Fusion Network (AdaTT) to address both challenges. AdaTT is a deep fusion network built with task specific and optional shared fusion units at multiple levels. By leveraging a residual mechanism and gating mechanism for task-to-task fusion, these units adaptively learn shared knowledge and task specific knowledge. To evaluate the performance of AdaTT, we conduct experiments on a public benchmark and an industrial recommendation dataset using various task groups. Results demonstrate AdaTT can significantly outperform existing state-of-the-art baselines

The in Vitro Estrogenic Activities of Polyfluorinated Iodine Alkanes

Background: Polyfluorinated iodine alkanes (PFIs) are important intermediates in the synthesis of organic fluoride products. Recently, PFIs have been detected in fluoropolymers as residual raw materials, as well as in the ambient environment

Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies

We explore the trade-offs of performing linear algebra using Apache Spark, compared to traditional C and MPI implementations on HPC platforms. Spark is designed for data analytics on cluster computing platforms with access to local disks and is optimized for data-parallel tasks. We examine three widely-used and important matrix factorizations: NMF (for physical plausability), PCA (for its ubiquity) and CX (for data interpretability). We apply these methods to TB-sized problems in particle physics, climate modeling and bioimaging. The data matrices are tall-and-skinny which enable the algorithms to map conveniently into Spark's data-parallel model. We perform scaling experiments on up to 1600 Cray XC40 nodes, describe the sources of slowdowns, and provide tuning guidance to obtain high performance

Study and Discussion on Preparation of Hemihydrate Gypsum by Salt Solution Method

This is an article in the field of mineral materials. The utilization of desulfurized gypsum (FGD) to prepare more widely used hemihydrate gypsum plays a very important role in the resource utilization of industrial by-product gypsum. It can not only avoid the large-scale exploitation of natural gypsum, but also realize the resource utilization of desulfurized gypsum. In the process of converting FGD gypsum into hemihydrate gypsum, appropriate concentration of additive and sodium chloride were used as the reaction solution, and the process was heated and stirred under normal pressure. The effects of sodium chloride concentration, additive concentration, solid-liquid ratio, rotation speed and reaction temperature on the phase conversion time and crystal size of dihydrate to hemihydrate gypsum were studied. The increase of additive and sodium chloride concentration not only sped up the phase conversion process but also had a certain inhibitory effect on the average length and average aspect ratio of hemihydrate gypsum crystals. Higher or lower rotation speed hindered the nucleation and growth of hemihydrate gypsum crystals and affected the collision frequency of Ca2+ and SO42- in the NaCl added solution, thus delaying the formation of hemihydrate gypsum. Decreasing the solid-liquid ratio and increasing the temperature had a certain promoting effect on the phase conversion process. At a lower temperature, due to the insufficient driving force of the phase conversion process, it was difficult to transform FGD gypsum into hemihydrate gypsum. The optimal process conditions for preparing hemihydrate gypsum from FGD gypsum were determined as follows: sodium chloride concentration 10%, additive concentration 10%, solid-liquid ratio 1∶5, rotating speed 300 r/min, reaction temperature 100 ℃. Under the best process conditions, the reaction could be completed in 60 min. The average length of the prepared hemihydrate gypsum crystals was as high as 127 μm, and the average aspect ratio was as high as 19. At the same time, the relationship between the activity of water molecules in the solution, the degree of supersaturation and the reaction temperature was studied, and it was determined that the phase conversion process was determined by the temperature and the degree of supersaturation