Richardsonmcdougall4360

Simultaneously inferring the latent representations and optimizing the parameters are achieved using stochastic gradient variational inference, after which the target HR-HSI is retrieved via feedforward mapping. Though without supervised information about the HR-HSI, NVPGM still can be trained based on extra LR-HSI and HR-MSI data sets in advance unsupervisedly and processes the images at the test phase in real time. Three commonly used data sets are used to evaluate the effectiveness and efficiency of NVPGM, illustrating the outperformance of NVPGM in the unsupervised LR-HSI and HR-MSI fusion task.Model compression methods have become popular in recent years, which aim to alleviate the heavy load of deep neural networks (DNNs) in real-world applications. However, most of the existing compression methods have two limitations 1) they usually adopt a cumbersome process, including pretraining, training with a sparsity constraint, pruning/decomposition, and fine-tuning. Moreover, the last three stages are usually iterated multiple times. 2) The models are pretrained under explicit sparsity or low-rank assumptions, which are difficult to guarantee wide appropriateness. In this article, we propose an efficient decomposition and pruning (EDP) scheme via constructing a compressed-aware block that can automatically minimize the rank of the weight matrix and identify the redundant channels. Selleck Iclepertin Specifically, we embed the compressed-aware block by decomposing one network layer into two layers a new weight matrix layer and a coefficient matrix layer. By imposing regularizers on the coefficient matrix, the new weight matrix learns to become a low-rank basis weight, and its corresponding channels become sparse. In this way, the proposed compressed-aware block simultaneously achieves low-rank decomposition and channel pruning by only one single data-driven training stage. Moreover, the network of architecture is further compressed and optimized by a novel Pruning & Merging (PM) module which prunes redundant channels and merges redundant decomposed layers. Experimental results (17 competitors) on different data sets and networks demonstrate that the proposed EDP achieves a high compression ratio with acceptable accuracy degradation and outperforms state-of-the-arts on compression rate, accuracy, inference time, and run-time memory.Prostate Cancer (PCa) is one of the deadliest forms of Cancer among men. Early screening process for PCa is primarily conducted with the help of a FDA approved biomarker known as Prostate Specific Antigen (PSA). The PSA-based screening is challenged with the inability to differentiate between the cancerous PSA and Benign Prostatic Hyperplasia (BPH), resulting in high rates of false-positives. Optical techniques such as optical absorbance, scattering, surface plasmon resonance (SPR), and fluorescence have been extensively employed for Cancer diagnostic applications. One of the most important diagnostic applications involves utilization of nanoparticles (NPs) for highly specific, sensitive, rapid, multiplexed, and high performance Cancer detection and quantification. The incorporation of NPs with these optical biosensing techniques allow realization of low cost, point-of-care, highly sensitive, and specific early cancer detection technologies, especially for PCa. In this work, the current state-of-the-art, challenges, and efforts made by the researchers for realization of low cost, point-of-care (POC), highly sensitive, and specific NP enhanced optical biosensing technologies for PCa detection using PSA biomarker are discussed and analyzed.Online services are used for all kinds of activities, like news, entertainment, publishing content or connecting with others. But information technology enables new threats to privacy by means of global mass surveillance, vast databases and fast distribution networks. Current news are full of misuses and data leakages. In most cases, users are powerless in such situations and develop an attitude of neglect for their online behaviour. On the other hand, the GDPR (General Data Protection Regulation) gives users the right to request a copy of all their personal data stored by a particular service, but the received data is hard to understand or analyze by the common internet user. This paper presents TransparencyVis - a web-based interface to support the visual and interactive exploration of data exports from different online services. With this approach, we aim at increasing the awareness of personal data stored by such online services and the effects of online behaviour. This design study provides an online accessible prototype and a best practice to unify data exports from different sources.Kinship recognition is a prominent research aiming to find if kinship relation exists between two different individuals. In general, child closely resembles his/her parents more than others based on facial similarities. These similarities are due to genetically inherited facial features that a child shares with his/her parents. Most existing researches in kinship recognition focus on full facial images to find these kinship similarities. This paper first presents kinship recognition for similar full facial images using proposed Global-based dual-tree complex wavelet transform (G-DTCWT). We then present novel patch-based kinship recognition methods based on dual-tree complex wavelet transform (DT-CWT) Local Patch-based DT-CWT (LP-DTCWT) and Selective Patch-Based DT-CWT (SP-DTCWT). LP-DTCWT extracts coefficients for smaller facial patches for kinship recognition. SP-DTCWT is an extension to LP-DTCWT and extracts coefficients only for representative patches with similarity scores above a normalized cumulative threshold. This threshold is computed by a novel patch selection process. These representative patches contribute more similarities in parent/child image pairs and improve kinship accuracy. Proposed methods are extensively evaluated on different publicly available kinship datasets to validate kinship accuracy. Experimental results showcase efficacy of proposed methods on all kinship datasets. SP-DTCWT achieves competitive accuracy to state-of-the-art methods. Mean kinship accuracy of SP-DTCWT is 95.85% on baseline KinFaceW-I and 95.30% on KinFaceW-II datasets. Further, SP-DTCWT achieves the state-of-the-art accuracy of 80.49% on the largest kinship dataset, Families In the Wild (FIW).