Dynamic match kernel with deep convolutional features for image retrieval

For image retrieval methods based on bag of visual words, much attention has been paid to enhancing the discriminative powers of the local features. Although retrieved images are usually similar to a query in minutiae, they may be significantly different from a semantic perspective, which can be effectively distinguished by convolutional neural networks (CNN). Such images should not be considered as relevant pairs. To tackle this problem, we propose to construct a dynamic match kernel by adaptively calculating the matching thresholds between query and candidate images based on the pairwise distance among deep CNN features. In contrast to the typical static match kernel which is independent to the global appearance of retrieved images, the dynamic one leverages the semantical similarity as a constraint for determining the matches. Accordingly, we propose a semantic-constrained retrieval framework by incorporating the dynamic match kernel, which focuses on matched patches between relevant images and filters out the ones for irrelevant pairs. Furthermore, we demonstrate that the proposed kernel complements recent methods, such as hamming embedding, multiple assignment, local descriptors aggregation, and graph-based re-ranking, while it outperforms the static one under various settings on off-the-shelf evaluation metrics. We also propose to evaluate the matched patches both quantitatively and qualitatively. Extensive experiments on five benchmark data sets and large-scale distractors validate the merits of the proposed method against the state-of-the-art methods for image retrieval

A hybrid simulated annealing for scheduling in dual-resource cellular manufacturing system considering worker movement

This paper presents a novel linear mathematical model for integrated cell formation and task scheduling in the cellular manufacturing system (CMS). It is suitable for the dual-resource constrained setting, such as garment process, component assembly, and electronics manufacturing. The model can handle the manufacturing project composing of some tasks with precedence constraints. It provides a method to assign the multi-skilled workers to appropriate machines. The workers are allowed to move among the machines such that the processing time of tasks might be reduced. A hybrid simulated annealing (HSA) is proposed to minimize the makespan of manufacturing project in the CMS. The approach combines the priority rule based heuristic algorithm (PRBHA) and revised forward recursion algorithm (RFRA) with conventional simulated annealing (SA). The result of extensive numerical experiments shows that the proposed HSA outperforms the conventional SA accurately and efficiently

Biochar has no effect on soil respiration across Chinese agricultural soils

This work was supported by NSFC (41371298 and 41371300), Ministry of Science and Technology (2013GB23600666 and 2013BAD11B00), and Ministry of Education of China (20120097130003). The international cooperation was funded under a “111” project by the State Agency of Foreign Expert Affairs of China and jointly supported under a grant for Priority Disciplines in Higher Education by the Department of Education, Jiangsu Province, China; The work was also a contribution to the cooperation project of “Estimates of Future Agricultural GHG Emissions and Mitigation in China” under the UK-China Sustainable Agriculture Innovation Network (SAIN). Pete Smith contributed to this work under a UK BBSRC China Partnership Award. The authors are grateful to Yuming Liu, Bin Zhang, Xiao Li, Gang Wu, Jinjin Qu and Yinxin Ye and Dongqi Liu for their contribution to field experiments, and to Rongjun Bian and Qaiser Hussain for their participation in discussions of the data analysis and interpretation, and to Xinyan Yu and Jiafang Wang for their assistance in lab works.Peer reviewedPostprin

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This paper studies the H-infinity stochastic control problem for a class of networked control systems (NCSs) with time delays and packet dropouts. The state feedback closed-loop NCS is modeled as a Markovian jump linear system. Through using a Lyapunov function, a sufficient condition is obtained, under which the system is stochastically exponential stability with a desired H-infinity disturbance attenuation level. The designed H-infinity controller is obtained by solving a set of linear matrix inequalities with some inversion constraints. An numerical example is presented to demonstrate the effectiveness of the proposed method

Bifurcated backbone strategy for RGB-D salient object detection

Multi-level feature fusion is a fundamental topic in computer vision. It has been exploited to detect, segment and classify objects at various scales. When multi-level features meet multi-modal cues, the optimal feature aggregation and multi-modal learning strategy become a hot potato. In this paper, we leverage the inherent multi-modal and multi-level nature of RGB-D salient object detection to devise a novel cascaded refinement network. In particular, first, we propose to regroup the multi-level features into teacher and student features using a bifurcated backbone strategy (BBS). Second, we introduce a depth-enhanced module (DEM) to excavate informative depth cues from the channel and spatial views. Then, RGB and depth modalities are fused in a complementary way. Our architecture, named Bifurcated Backbone Strategy Network (BBS-Net), is simple, efficient, and backbone-independent. Extensive experiments show that BBS-Net significantly outperforms eighteen SOTA models on eight challenging datasets under five evaluation measures, demonstrating the superiority of our approach ($\sim 4 \%$ improvement in S-measure $vs.$ the top-ranked model: DMRA-iccv2019). In addition, we provide a comprehensive analysis on the generalization ability of different RGB-D datasets and provide a powerful training set for future research.Comment: A preliminary version of this work has been accepted in ECCV 202

Genome-Wide Uncovering of STAT3-Mediated miRNA Expression Profiles in Colorectal Cancer Cell Lines

Colorectal cancer (CRC) is one of the most common malignancies resulting in high mortality worldwide. Signal transducer and activator of transcription 3 (STAT3) is an oncogenic transcription factor which is frequently activated and aberrantly expressed in CRC. MicroRNAs (miRNAs) are a class of small noncoding RNAs which play important roles in many cancers. However, little is known about the global miRNA profiles mediated by STAT3 in CRC cells. In the present study, we applied RNA interference to inhibit STAT3 expression and profiled the miRNA expression levels regulated by STAT3 in CRC cell lines with deep sequencing. We found that 26 and 21 known miRNAs were significantly overexpressed and downexpressed, respectively, in the STAT3-knockdown CRC cell line SW480 (SW480/STAT3-siRNA) compared to SW480 transfected with scrambled siRNAs (SW480/siRNA-control). The miRNA expression profiling was then validated by quantitative real-time PCR for selected known miRNAs. We further predicted the putative target genes for the dysregulated miRNAs and carried out functional annotation including GO enrichment and KEGG pathway analysis for selected miRNA targets. This study directly depicts STAT3-mediated miRNA profiles in CRC cells, which provides a possible way to discover biomarkers for CRC therapy

Simultaneous subspace clustering and cluster number estimating based on triplet relationship

In this paper we propose a unified framework to discover the number of clusters and group the data points into different clusters using subspace clustering simultaneously. Real data distributed in a high-dimensional space can be disentangled into a union of low-dimensional subspaces, which can benefit various applications. To explore such intrinsic structure, stateof- the-art subspace clustering approaches often optimize a selfrepresentation problem among all samples, to construct a pairwise affinity graph for spectral clustering. However, a graph with pairwise similarities lacks robustness for segmentation, especially for samples which lie on the intersection of two subspaces. To address this problem, we design a hyper-correlation based data structure termed as the triplet relationship, which reveals high relevance and local compactness among three samples. The triplet relationship can be derived from the self-representation matrix, and be utilized to iteratively assign the data points to clusters. Based on the triplet relationship, we propose a unified optimizing scheme to automatically calculate clustering assignments. Specifically, we optimize a model selection reward and a fusion reward by simultaneously maximizing the similarity of triplets from different clusters while minimizing the correlation of triplets from same cluster. The proposed algorithm also automatically reveals the number of clusters and fuses groups to avoid over-segmentation. Extensive experimental results on both synthetic and real-world datasets validate the effectiveness and robustness of the proposed method

Personalized Food Printing for Portrait Images

The recent development of 3D printing techniques enables novel applications in customized food fabrication. Based on a tailor-made 3D food printer, we present a novel personalized food printing framework driven by portrait images. Unlike common 3D printers equipped with materials such as ABS, Nylon and SLA, our printer utilizes edible materials such as maltose, chocolate syrup, jam to print customized patterns. Our framework automatically converts an arbitrary input image into an optimized printable path to facilitate food printing, while preserving the prominent features of the image. This is achieved based on two key stages. First, we apply image abstraction techniques to extract salient image features. Robust face detection and sketch synthesis are optionally involved to enhance face features for portrait images. Second, we present a novel path optimization algorithm to generate printing path for efficient and feature-preserving food printing. We demonstrate the efficiency and efficacy of our framework using a variety of images and also a comparison with non-optimized results

Subspace clustering via good neighbors

Finding the informative clusters of a high-dimensional dataset is at the core of numerous applications in computer vision, where spectral based subspace clustering algorithm is arguably the most widely-studied methods due to its empirical performance and provable guarantees under various assumptions. It is well-known that sparsity and connectivity of the affinity graph play important rules for effective subspace clustering. However, it is difficult to simultaneously optimize both factors due to their conflicting nature, and most existing methods are designed to deal with only one factor. In this paper, we propose an algorithm to optimize both sparsity and connectivity by finding good neighbors which induce key connections among samples within a subspace. First, an initial coefficient matrix is generated from the input dataset. For each sample, we find its good neighbors which not only have large coefficients but are strongly connected to each other. We reassign the coefficients of good neighbors and eliminate other entries to generate a new coefficient matrix, which can be used by spectral clustering methods. Experiments on five benchmark datasets show that the proposed algorithm performs favorably against the state-of-the-art methods in terms of accuracy with a negligible increase in speed

WSCNet: Weakly Supervised Coupled Networks for Visual Sentiment Classification and Detection

Automatic assessment of sentiment from visual content has gained considerable attention with the increasing tendency of expressing opinions online. In this paper, we solve the problem of visual sentiment analysis, which is challenging due to the high-level abstraction in the recognition process. Existing methods based on convolutional neural networks learn sentiment representations from the holistic image, despite the fact that different image regions can have different influence on the evoked sentiment. In this paper, we introduce a weakly supervised coupled convolutional network (WSCNet). Our method is dedicated to automatically selecting relevant soft proposals from weak annotations (e.g., global image labels), thereby significantly reducing the annotation burden, and encompasses the following contributions. First, WSCNet detects a sentiment-specific soft map by training a fully convolutional network with the cross spatial pooling strategy in the detection branch. Second, both the holistic and localized information are utilized by coupling the sentiment map with deep features for robust representation in the classification branch. We integrate the sentiment detection and classification branches into a unified deep framework, and optimize the network in an end-to-end way. Through this joint learning strategy, weakly supervised sentiment classification and detection benefit each other. Extensive experiments demonstrate that the proposed WSCNet outperforms the state-of-the-art results on seven benchmark datasets