Giant electric‐field–induced strain of 0.70%, corresponding to a d33* value of 1400 pm V−1, is observed in a lead‐free (Bi1/2Na1/2)TiO3‐based polycrystalline ceramic. This is comparable to the ...properties of single crystals. An in situ transmission electron microscopy study indicates that the excellent performance originates from phase transitions under the applied electric fields.
This paper explores multi-task learning (MTL) for face recognition. First, we propose a multi-task convolutional neural network (CNN) for face recognition, where identity classification is the main ...task and pose, illumination, and expression (PIE) estimations are the side tasks. Second, we develop a dynamic-weighting scheme to automatically assign the loss weights to each side task, which solves the crucial problem of balancing between different tasks in MTL. Third, we propose a pose-directed multi-task CNN by grouping different poses to learn pose-specific identity features, simultaneously across all poses in a joint framework. Last but not least, we propose an energy-based weight analysis method to explore how CNN-based MTL works. We observe that the side tasks serve as regularizations to disentangle the PIE variations from the learnt identity features. Extensive experiments on the entire multi-PIE dataset demonstrate the effectiveness of the proposed approach. To the best of our knowledge, this is the first work using all data in multi-PIE for face recognition. Our approach is also applicable to in-the-wild data sets for pose-invariant face recognition and achieves comparable or better performance than state of the art on LFW, CFP, and IJB-A datasets.
This study tends to explore the impact of brand knowledge and organizational loyalty under the mediating role of organizational culture on employee-based brand equity (EBBE). For this purpose, ...employees of the hospitality sector were contacted to collect data through personally administrated questionnaires. Already established scales were used to devise instruments. Data were collected in two waves to minimize the common method bias. In the first wave, a total of 600 questionnaires were distributed, out of which 400 were received back, while in the second wave, remaining respondents were approached who have filled the survey in the first wave, and only 320 were received back, from which the partial and incomplete questionnaires were discarded, and at the end, 306 questionnaires were left. These final and completed responses were used for the data analysis and inferential purpose in this study. Collected data have been analyzed through Structural Equation Modeling by using Smart PLS 3 software. The assessment of measurement and structural model indicated a good model fit, and results indicate that EBBE is influenced by organizational loyalty and brand knowledge positively. Moreover, the mediating role of organizational culture has also been proved.
ABSTRACT
The purpose of the dbNSFP is to provide a one‐stop resource for functional predictions and annotations for human nonsynonymous single‐nucleotide variants (nsSNVs) and splice‐site variants ...(ssSNVs), and to facilitate the steps of filtering and prioritizing SNVs from a large list of SNVs discovered in an exome‐sequencing study. A list of all potential nsSNVs and ssSNVs based on the human reference sequence were created and functional predictions and annotations were curated and compiled for each SNV. Here, we report a recent major update of the database to version 3.0. The SNV list has been rebuilt based on GENCODE 22 and currently the database includes 82,832,027 nsSNVs and ssSNVs. An attached database dbscSNV, which compiled all potential human SNVs within splicing consensus regions and their deleteriousness predictions, add another 15,030,459 potentially functional SNVs. Eleven prediction scores (MetaSVM, MetaLR, CADD, VEST3, PROVEAN, 4× fitCons, fathmm‐MKL, and DANN) and allele frequencies from the UK10K cohorts and the Exome Aggregation Consortium (ExAC), among others, have been added. The original seven prediction scores in v2.0 (SIFT, 2× Polyphen2, LRT, MutationTaster, MutationAssessor, and FATHMM) as well as many SNV and gene functional annotations have been updated. dbNSFP v3.0 is freely available at http://sites.google.com/site/jpopgen/dbNSFP.
The purpose of the dbNSFP is to provide a one‐stop resource for functional predictions and annotations for human non‐synonymous single‐nucleotide variants (nsSNVs) and splice site variants (ssSNVs), and to facilitate the steps of filtering and prioritizing SNVs from a large list of SNVs discovered in an exome‐sequencing study. Here we report a recent major update of the database to version 3.0 and some preliminary analyses comparing the 24 functional prediction scores and conservation scores in dbNSFP v3.0.
Inferring the demographic histories of populations has wide applications in population, ecological, and conservation genomics. We present Stairway Plot 2, a cross-platform program package for this ...task using SNP frequency spectra. It is based on a nonparametric method with the capability of handling folded SNP frequency spectra (that is, when the ancestral alleles of the SNPs are unknown) of thousands of samples produced with genotyping-by-sequencing technologies; therefore, it is particularly suitable for nonmodel organisms.
Discriminative Face Alignment Liu, Xiaoming
IEEE transactions on pattern analysis and machine intelligence,
11/2009, Letnik:
31, Številka:
11
Journal Article
Recenzirano
This paper proposes a discriminative framework for efficiently aligning images. Although conventional Active Appearance Models (AAMs)-based approaches have achieved some success, they suffer from the ...generalization problem, i.e., how to align any image with a generic model. We treat the iterative image alignment problem as a process of maximizing the score of a trained two-class classifier that is able to distinguish correct alignment (positive class) from incorrect alignment (negative class). During the modeling stage, given a set of images with ground truth landmarks, we train a conventional Point Distribution Model (PDM) and a boosting-based classifier, which acts as an appearance model. When tested on an image with the initial landmark locations, the proposed algorithm iteratively updates the shape parameters of the PDM via the gradient ascent method such that the classification score of the warped image is maximized. We use the term Boosted Appearance Models (BAMs) to refer to the learned shape and appearance models, as well as our specific alignment method. The proposed framework is applied to the face alignment problem. Using extensive experimentation, we show that, compared to the AAM-based approach, this framework greatly improves the robustness, accuracy, and efficiency of face alignment by a large margin, especially for unseen data.
As a new generation of porous materials, porous organic polymers (POPs), have recently emerged as a powerful platform of heterogeneous photocatalysis. POPs are constructed using extensive organic ...synthesis methodologies, with various functional organic units being connected
via
high-energy covalent bonds. This review systematically presents the recent advances in POPs for visible-light driven organic transformations. Herein, we firstly summarize the common construction strategies for POP-based photocatalysts based on two major approaches: pre-design and post-modification; secondly, we categorize and summarize the synthesis methods and organic reaction types for constructing various types of POPs. We then classify and introduce the specific reactions of current light-driven POP-mediated organic transformations. Finally, we outline the current state of development and the problems faced in light-driven organic transformations by POPs, and we present some perspectives to motivate the reader to explore solutions to these problems and confront the present challenges in the development process.
Porous organic polymers (POPs), with their high specific surface area, low density, good stability, uniform pore size, structural versatility, and designability, have recently emerged as a powerful platform of heterogeneous photocatalysis.
Recently, Convolutional Neural Network (CNN) based models have achieved great success in Single Image Super-Resolution (SISR). Owing to the strength of deep networks, these CNN models learn an ...effective nonlinear mapping from the low-resolution input image to the high-resolution target image, at the cost of requiring enormous parameters. This paper proposes a very deep CNN model (up to 52 convolutional layers) named Deep Recursive Residual Network (DRRN) that strives for deep yet concise networks. Specifically, residual learning is adopted, both in global and local manners, to mitigate the difficulty of training very deep networks, recursive learning is used to control the model parameters while increasing the depth. Extensive benchmark evaluation shows that DRRN significantly outperforms state of the art in SISR, while utilizing far fewer parameters. Code is available at https://github.com/tyshiwo/DRRN_CVPR17.
Recently, very deep convolutional neural networks (CNNs) have been attracting considerable attention in image restoration. However, as the depth grows, the longterm dependency problem is rarely ...realized for these very deep models, which results in the prior states/layers having little influence on the subsequent ones. Motivated by the fact that human thoughts have persistency, we propose a very deep persistent memory network (MemNet) that introduces a memory block, consisting of a recursive unit and a gate unit, to explicitly mine persistent memory through an adaptive learning process. The recursive unit learns multi-level representations of the current state under different receptive fields. The representations and the outputs from the previous memory blocks are concatenated and sent to the gate unit, which adaptively controls how much of the previous states should be reserved, and decides how much of the current state should be stored. We apply MemNet to three image restoration tasks, i.e., image denosing, super-resolution and JPEG deblocking. Comprehensive experiments demonstrate the necessity of the MemNet and its unanimous superiority on all three tasks over the state of the arts. Code is available at https://github.com/tyshiwo/MemNet.
In silico tools have been developed to predict variants that may have an impact on pre-mRNA splicing. The major limitation of the application of these tools to basic research and clinical practice is ...the difficulty in interpreting the output. Most tools only predict potential splice sites given a DNA sequence without measuring splicing signal changes caused by a variant. Another limitation is the lack of large-scale evaluation studies of these tools. We compared eight in silico tools on 2959 single nucleotide variants within splicing consensus regions (scSNVs) using receiver operating characteristic analysis. The Position Weight Matrix model and MaxEntScan outperformed other methods. Two ensemble learning methods, adaptive boosting and random forests, were used to construct models that take advantage of individual methods. Both models further improved prediction, with outputs of directly interpretable prediction scores. We applied our ensemble scores to scSNVs from the Catalogue of Somatic Mutations in Cancer database. Analysis showed that predicted splice-altering scSNVs are enriched in recurrent scSNVs and known cancer genes. We pre-computed our ensemble scores for all potential scSNVs across the human genome, providing a whole genome level resource for identifying splice-altering scSNVs discovered from large-scale sequencing studies.
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