Domain adaptation aims to transfer knowledge from a domain with adequate labeled samples scarce samples. Prior research has introduced various open set settings in the literature extend applications of methods real-world scenarios. This paper focuses on type setting where target both private ('unknown classes') label space and shared ('known space. However, source only 'known classes' Prevalent distribution-matching are inadequate such that demands smaller larger diverse more classes. For...

Eph receptors orchestrate cell positioning during normal and oncogenic development. Their function is spatially temporally controlled by protein tyrosine phosphatases (PTPs), but the underlying mechanisms are unclear identity of most regulatory PTPs unknown. We demonstrate here that PTP1B governs signaling biological activity EphA3. Changes in expression significantly affect duration amplitude EphA3 phosphorylation function, whereas confocal fluorescence lifetime imaging microscopy (FLIM)...

Soil moisture, soil organic carbon, and nitrogen content prediction are considered significant fields of study as they directly related to plant health food production. Direct estimation these properties with traditional methods, for example, the oven-drying technique chemical analysis, is a time resource-consuming approach can predict only smaller areas. With development remote sensing hyperspectral (HS) imaging technologies, be estimated over vast This paper presents generalized predicting...

Estimating various properties of soil, including moisture, carbon, and nitrogen, is crucial for studying their correlation with plant health food production. However, conventional methods such as oven-drying chemical analysis are laborious, expensive, only feasible a limited land area. With the advent remote sensing technologies like multi/hyperspectral imaging, it now possible to predict soil non-invasive cost-effectively large expanse bare land. Recent research shows possibility predicting...

This paper introduces a new ensemble approach, Feature-Subspace Aggregating (Feating), which builds local models instead of global models. Feating is generic approach that can enhance the predictive performance both stable and unstable learners. In contrast, most existing approaches improve learners only. Our analysis shows reduces execution time to generate model in an through increased level localisation Feating. empirical evaluation performs significantly better than Boosting, Random...

Network representation learning (NRL) is an effective graph analytics technique and promotes users to deeply understand the hidden characteristics of data. It has been successfully applied in many real-world tasks related network science, such as social data processing, biological information recommender systems. Deep Learning a powerful tool learn features. However, it non-trivial generalize deep graph-structured since different from regular pictures having spatial sounds temporal...

Multi-modal image registration has received significant research attention over the past decade. Symmetric-SIFT is a recently proposed local description technique that can be used for registering multi-modal images. It based on well-known general named Scale Invariant Feature Transform (SIFT). Symmetric-SIFT, however, achieves this invariance to multi-modality at cost of losing important information. In paper, we show how loss may adversely affect accuracy results. We then propose an...

Existing automatic building extraction methods are not effective in extracting buildings which small size and have transparent roofs. The application of large area threshold prohibits detection the use ground points generating mask prevents buildings. In addition, existing numerous parameters to extract complex environments, e.g., hilly high vegetation. However, empirical tuning number reduces robustness methods. This paper proposes a novel Gradient-based Building Extraction (GBE) method...

Convolutional Neural Network is good at image classification. However, it found to be vulnerable quality degradation. Even a small amount of distortion such as noise or blur can severely hamper the performance these CNN architectures. Most work in literature strives mitigate this problem simply by fine-tuning pre-trained on mutually exclusive union set distorted training data. This iterative process with all known types exhaustive and network struggles handle unseen distortions. In work, we...

Attention is a very popular and effective mechanism in artificial neural network-based sequence-to-sequence models. In this survey paper, comprehensive review of the different attention models used developing automatic speech recognition systems provided. The paper focuses on how have grown changed for offline streaming recurrent networks Transformer-based systems.

Abstract Background Due to the large number of genes in a typical microarray dataset, feature selection looks set play an important role reducing noise and computational cost gene expression-based tissue classification while improving accuracy at same time. Surprisingly, this does not appear be case for all multiclass datasets. The reason is that many techniques applied on datasets are either rank-based hence do take into account correlations between genes, or wrapper-based, which require...

Multimodal image registration has received significant research attention over the past decade, and majority of techniques are global in nature. Although local widely used for general registration, there only limited studies on them multimodal registration. Scale invariant feature transform (SIFT) is a well-known technique. However, SIFT descriptors not to multimodality. We propose SIFT-based technique that modality still retains strengths techniques. Moreover, our proposed histogram...