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Soil micro-ecological imbalance is one of the critical factors associated with peach replanting issues. In an effort to understand the primary driving force in shaping soil microbial community, we analyzed soil nutrient, pH value, and autotoxin benzoic acid (BA) content, as well as rhizosphere microbial structure of peach seedlings grown in replanted soil (RS) and non-replanted soil (NS), both of which were collected from a same orchard. The results showed that depletion of soil nutrients, decreased soil pH, the accumulation of BA, and the deterioration of the soil microbial community were consistently detected in RS compared with NS, and led to the poor growth performance of peach seedlings. High-throughput sequencing analysis indicated that the soil microbial composition differed significantly between the NS and RS samples. Over time, the rhizosphere microbes of peach seedlings in RS and NS exhibited a similar trend of increasing bacterial richness and decreasing fungal richness. Interestingly, the NS had a high level of Bacillus and Ascobolus, whereas the RS enriched with Burkholderia and pathogenic Fusarium. Furthermore, canonical correspondence analysis revealed that soil nutrient and pH predominated the microbial composition of NS, whereas high level of soil BA played a crucial role in forming the microbial composition of RS. Altogether, this study provides a novel insight into better understanding of soil bacterial and fungal community structure in the RS, and aid in developing more effective means to alleviate the peach replant problem.

Establishing semantic correspondence is a core problem in computer vision and remains challenging due to large intra-class variations and lack of annotated data. In this paper, we aim to incorporate global semantic context in a flexible manner to overcome the limitations of prior work that relies on local semantic representations. To this end, we first propose a context-aware semantic representation that incorporates spatial layout for robust matching against local ambiguities. We then develop a novel dynamic fusion strategy based on attention mechanism to weave the advantages of both local and context features by integrating semantic cues from multiple scales. We instantiate our strategy by designing an end-to-end learnable deep network, named as Dynamic Context Correspondence Network (DCCNet). To train the network, we adopt a multi-auxiliary task loss to improve the efficiency of our weakly-supervised learning procedure. Our approach achieves superior or competitive performance over previous methods on several challenging datasets, including PF-Pascal, PF-Willow, and TSS, demonstrating its effectiveness and generality., ICCV 2019

Signal processing over graphs has recently attracted significant attention for dealing with the structured data. Normal graphs, however, only model pairwise relationships between nodes and are not effective in representing and capturing some high-order relationships of data samples, which are common in many applications, such as Internet of Things (IoT). In this article, we propose a new framework of hypergraph signal processing (HGSP) based on the tensor representation to generalize the traditional graph signal processing (GSP) to tackle high-order interactions. We introduce the core concepts of HGSP and define the hypergraph Fourier space. We then study the spectrum properties of hypergraph Fourier transform (HGFT) and explain its connection to mainstream digital signal processing. We derive the novel hypergraph sampling theory and present the fundamentals of hypergraph filter design based on the tensor framework. We present HGSP-based methods for several signal processing and data analysis applications. Our experimental results demonstrate significant performance improvement using our HGSP framework over some traditional signal processing solutions.

Glioblastoma (GBM) is an incurable primary brain tumor that is highly resistant to current treatments. Glioma stem-like cells (GSCs) are an aggressive population of glioma cells that not only initiate malignant growth, but also promote therapeutic resistance. Thus, targeting GSCs is critical for improving GBM treatment and ensuring complete eradication of the tumor. Here, we show that NT5DC2 (5'-Nucleotidase Domain Containing 2), a functionally unknown protein, plays a crucial role in GSC tumor initiation via upregulating Fyn expression. NT5DC2 is preferentially expressed in GSCs relative to the non-stem tumor cells. Knockdown of NT5DC2 significantly inhibits the GSC tumorsphere formation and cell viability in vitro, and tumorigenesis in vivo, thus, prolonging animal survival. Moreover, disruption of NT5DC2 in GSCs markedly reduces the expression of Fyn, a Src family proto-oncogene that has been implicated in the regulation of GBM progression. Importantly, the expression of NT5DC2 strongly correlated with increased aggression of human gliomas, but not that of other brain tumors. Taken together, our results uncover the function of NT5DC2 in GSC maintenance and highlight NT5DC2 as a promising therapeutic target for GBM.

The brushless doubly-fed induction generator (BDFIG) is demonstrating great potentiality in variable speed constant frequency power generation applications owing to the absence of the brushes and slip rings. To take full advantage of the brushless structure, the sensorless direct voltage control method is an attractive method. However, due to the harmonics caused by nonlinear load, the performance of conventional control method may be degraded and the efficiency of the generators would be reduced. This paper proposes an improved sensorless direct voltage control of the stand-alone BDFIG feeding nonlinear loads. On the one hand, to cancel the effect of the harmonic components, the voltage decomposition has been employed in sensorless control to get the fundamental components. On the other hand, the harmonic voltage elimination with reduced load current sensors has been applied to the load side converter. Experimental results with 30 kVA BDFIG are presented to verify the proposed control method.

Cascading failure in multilayer complex systems draws significant attentions from both industry and academia nowadays. In this paper, we propose a scalable tensor-based framework to represent the interdependent multilayer network, and use this framework to analyze the failure propagation based on a susceptible-infectious-susceptible (SIS) epidemic model. Specifically, the transition equations and transition tensor are derived to characterize the behavior of failure propagation. We show that the spectral radius of transition tensor is a failure indicator with an explicit failure threshold to measure the system reliability. Moreover, to make the failure indicator analytically tractable and computationally efficient, we derive its upper and lower bounds, as well as its approximated expressions in special cases as functions of the adjacency tensor and epidemic parameters. Our analytical results are evaluated by simulations in a set of multilayer networks generated by random graphs, which show that our results can achieve the desired performance compared with other benchmark approximation methods.

Congestion control provides the endpoint the ability to probe available bandwidth and prevents the network falling into severe congestion. But loss based congestion control algorithms designed for ...

Although the recent success of convolutional neural network (CNN) advances state-of-the-art saliency prediction in static images, few work has addressed the problem of predicting attention in videos. On the other hand, we find that the attention of different subjects consistently focuses on a single face in each frame of videos involving multiple faces. Therefore, we propose in this paper a novel deep learning (DL) based method to predict salient face in multiple-face videos, which is capable of learning features and transition of salient faces across video frames. In particular, we first learn a CNN for each frame to locate salient face. Taking CNN features as input, we develop a multiple-stream long short-term memory (M-LSTM) network to predict the temporal transition of salient faces in video sequences. To evaluate our DL-based method, we build a new eye-tracking database of multiple-face videos. The experimental results show that our method outperforms the prior state-of-the-art methods in predicting visual attention on faces in multiple-face videos.

RNN-based approaches have achieved outstanding performance on action recognition with skeleton inputs. Currently these methods limit their inputs to coordinates of joints and improve the accuracy mainly by extending RNN models to spatial domains in various ways. While such models explore relations between different parts directly from joint coordinates, we provide a simple universal spatial modeling method perpendicular to the RNN model enhancement. Specifically, we select a set of simple geometric features, motivated by the evolution of previous work. With experiments on a 3-layer LSTM framework, we observe that the geometric relational features based on distances between joints and selected lines outperform other features and achieve state-of-art results on four datasets. Further, we show the sparsity of input gate weights in the first LSTM layer trained by geometric features and demonstrate that utilizing joint-line distances as input require less data for training.

Total organic carbon (TOC) is an important parameter for characterizing shale gas and oil reservoirs. Estimation of TOC from well logs has previously been achieved by an empirical model. The radial basis function (RBF) neural network is a new quantitative method that can generate a smooth and continuous function of several input variables to approximate the unknown forward model. We investigated the basic principles of the RBF including network structure, basis function, network training method, and its application in the TOC prediction. The nearest neighbor algorithm was selected for the network training. Then, the Gaussian width was investigated to improve the TOC prediction accuracy through leave-one-out cross-validation. Finally, field cases of organic shale were studied for the TOC prediction, and the prediction results using the RBF method were compared with those of the [Formula: see text] method. Furthermore, according to sensitive attribute ranking, the impacts of different input logs on the predicted results were also investigated through various experiments, and the best network model was finally chosen. The error analysis between the prediction results and lab-measured TOC in some examples indicated that the new approach is more accurate than a single empirical regression method and more flexible than the [Formula: see text] method.

With the explosive growth of semantic data on the Web over the past years, many large-scale RDF knowledge bases with billions of facts are generating. This poses significant challenges for the storage and query of big RDF graphs. Current systems still have many limitations in processing big RDF graphs including scalability and real-time. In this paper, we introduce the SparkRDF, an elastic discreted RDF graph processing engine with distributed memory. To reduce the high I/O and communication cost in distributed processing platforms, SparkRDF implements SPARQL query based on Spark, a novel in-memory distributed computing framework for big data processing. All the intermediate results are modeled as Resilient Discreted SubGraph, which are cached in the distributed memory to support fast iterative join operations. To cut down the search space and avoid the overhead of memory, we split the RDF graph into the small Multi-layer Elastic SubGraph based on the relations and classes. For SPARQL query optimization, SparkRDF deploys a serials of optimization strategies, leading to effective reduction on the size of intermediate results, the number of joins and the cost of communication. Our extensive evaluation demonstrates that SparkRDF can efficiently implement non-selective joins faster than both current state-of-the-art distributed and centralized stores, while being able to process other queries in real time, scaling linearly to the amount of data.

An effective communication protocol for wireless sensor networks should provide a good quality of service (QoS) for network data transfer, but the traditional layering protocol stack can't sufficiently meet this complex requirement. So a new cross-layer communication protocol - directed spanning tree-based power control (PCDST) is proposed. In the algorithm, a directed spanning tree routing algorithm is discussed to resolve unnecessary energy consumption during data transmission. Regarded as a key factor, transmission power of node should be controlled to decrease the intensity of communication contention. A NUM model is constructed to control power. Based on Prim-dual Algorithm, a Lagrange dual function is designed and optimal solution set of transmission power is deduced. Make use of these cross-layer optimal schemes, WSNs achieve energy efficiency and smooth communication mode. The simulation results show that the performance of the proposed protocol is much better in energy efficiency and improvement of communication throughput. Index Terms - Wireless sensor networks; communication protocol; cross-layer optimization; power control; spanning tree.

In order to study the effect of friction state on contact reaction force of rollers and bearing's inner ring and outer ring, contact analysis model was established by the local coordinate system and finite element method for rolling bearings of universal steel mill. Studied the changing rule of force parameters of rolling bearings during steel rolling, the results show that contact reaction is a steady increase with increasing of contact angle, with a step distribution. The rolling reaction force to achieve maximum near the region the body, when the magnitude of contact angle is 90°, within the scope of friction coefficient from 0.01 to 0.07, the fluctuation of rolling reaction force to achieve maximum near the region body when the magnitude of contact angle is 45°. It is important to define limit load of bearings and formulate rolling order.

We investigate video-aided grammar induction, which learns a constituency parser from both unlabeled text and its corresponding video. Existing methods of multi-modal grammar induction focus on learning syntactic grammars from text-image pairs, with promising results showing that the information from static images is useful in induction. However, videos provide even richer information, including not only static objects but also actions and state changes useful for inducing verb phrases. In this paper, we explore rich features (e.g. action, object, scene, audio, face, OCR and speech) from videos, taking the recent Compound PCFG model as the baseline. We further propose a Multi-Modal Compound PCFG model (MMC-PCFG) to effectively aggregate these rich features from different modalities. Our proposed MMC-PCFG is trained end-to-end and outperforms each individual modality and previous state-of-the-art systems on three benchmarks, i.e. DiDeMo, YouCook2 and MSRVTT, confirming the effectiveness of leveraging video information for unsupervised grammar induction., This paper is accepted by NAACL'21