An Efficient Approach for Geo-Multimedia Cross-Modal Retrieval

Due to the rapid development of mobile Internet techniques, such as online social networking and location-based services, massive amount of multimedia data with geographical information is generated and uploaded to the Internet. In this paper, we propose a novel type of cross-modal multimedia retrieval, called geo-multimedia cross-modal retrieval, which aims to find a set of geo-multimedia objects according to geographical distance proximity and semantic concept similarity. Previous studies for cross-modal retrieval and spatial keyword search cannot address this problem effectively because they do not consider multimedia data with geo-tags (geo-multimedia). Firstly, we present the definition of k NN geo-multimedia cross-modal query and introduce relevant concepts such as spatial distance and semantic similarity measurement. As the key notion of this work, cross-modal semantic representation space is formulated at the first time. A novel framework for geo-multimedia cross-modal retrieval is proposed, which includes multi-modal feature extraction, cross-modal semantic space mapping, geo-multimedia spatial index and cross-modal semantic similarity measurement. To bridge the semantic gap between different modalities, we also propose a method named cross-modal semantic matching (CoSMat for shot) which contains two important components, i.e., CorrProj and LogsTran, which aims to build a common semantic representation space for cross-modal semantic similarity measurement. In addition, to implement semantic similarity measurement, we employ deep learning based method to learn multi-modal features that contains more high level semantic information. Moreover, a novel hybrid index, GMR-Tree is carefully designed, which combines signatures of semantic representations and R-Tree. An efficient GMR-Tree based k NN search algorithm called k GMCMS is developed. Comprehensive experimental evaluations on real and synthetic datasets clearly demonstrate that our approach outperforms the-state-of-the-art methods

Min-max minimal disks with free boundary in Riemannian manifolds

In this paper, we establish a min-max theory for constructing minimal disks with free boundary in any closed Riemannian manifold. The main result is an effective version of the partial Morse theory for minimal disks with free boundary established by Fraser. Our theory also includes as a special case the min-max theory for Plateau problem of minimal disks, which can be used to generalize the famous work by Morse-Thompkins and Shiffman on minimal surfaces in $\mathbf{R}^n$ to the Riemannian setting. More precisely, we generalize the min-max construction of minimal surfaces using harmonic replacement introduced by Colding and Minicozzi to the free boundary setting. As a key ingredient to this construction, we show an energy convexity for weakly harmonic maps with mixed Dirichlet and free boundaries from the half unit $2$-disk in $\mathbf{R}^2$ into any closed Riemannian manifold, which in particular yields the uniqueness of such weakly harmonic maps. This is a free boundary analogue of the energy convexity and uniqueness for weakly harmonic maps with Dirichlet boundary on the unit $2$-disk proved by Colding and Minicozzi

Identifying Spatial Determinants of Rice Yields in Main Producing Areas of China Using Geospatial Machine Learning

Rice yield is essential to global food security under increasingly frequent and severe climate change events. Spatial analysis of rice yields becomes more critical for regional action to ensure yields and reduce climate impacts. However, the understanding of the spatially varied geographical, climate, soil, and environmental factors of rice yields needs to be improved, leading to potentially biased local rice yield prediction and responses to climate change. This study develops a spatial machine learning-based approach that integrates machine learning and spatial stratified heterogeneity models to identify the determinants and spatial interactions of rice yields in the main rice-producing areas of China, the world’s largest rice-producing nation. A series of satellite remote sensing-derived variables are collected to characterize varied geographical, climate, soil, and environmental conditions and explain the spatial disparities of rice yields. The first step is to explore the spatial clustering patterns of the rice yield distributions using spatially global and local autocorrelation models. Next, a Geographically Optimal Zones-based Heterogeneity (GOZH) model, which integrates spatial stratified heterogeneity models and machine learning, is employed to explore the power of determinants (PD) of individual spatial variables in influencing the spatial disparities of rice yields. Third, geographically optimal zones are identified with the machine learning-derived optimal spatial overlay of multiple geographical variables. Finally, the overall PD of various variables affecting rice yield distributions is calculated using the multiple variables-determined geographically optimal zones and the GOZH model. The comparison between the developed spatial machine learning-based approach and previous related models demonstrates that the GOZH model is an effective and robust approach for identifying the spatial determinants and their spatial interactions with rice yields. The identified spatial determinants and their interactions are essential for enhancing regional agricultural management practices and optimizing resource allocation within diverse main rice-producing regions. The comprehensive understanding of the spatial determinants and heterogeneity of rice yields of this study has a broad impact on agricultural strategies and food security

Min-max minimal disks with free boundary in Riemannian manifolds

In this paper, we establish a min-max theory for constructing minimal disks with free boundary in any closed Riemannian manifold. The main result is an effective version of the partial Morse theory for minimal disks with free boundary established by Fraser. Our theory also includes as a special case the min-max theory for Plateau problem of minimal disks, which can be used to generalize the famous work by Morse-Thompkins and Shiffman on minimal surfaces in $\mathbf{R}^n$ to the Riemannian setting. More precisely, we generalize the min-max construction of minimal surfaces using harmonic replacement introduced by Colding and Minicozzi to the free boundary setting. As a key ingredient to this construction, we show an energy convexity for weakly harmonic maps with mixed Dirichlet and free boundaries from the half unit $2$-disk in $\mathbf{R}^2$ into any closed Riemannian manifold, which in particular yields the uniqueness of such weakly harmonic maps. This is a free boundary analogue of the energy convexity and uniqueness for weakly harmonic maps with Dirichlet boundary on the unit $2$-disk proved by Colding and Minicozzi

Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia Retrieval

Cross-modal hashing technology is a key technology for real-time retrieval of large-scale multimedia data in real-world applications. Although the existing cross-modal hashing methods have achieved impressive accomplishment, there are still some limitations: (1) some cross-modal hashing methods do not make full consider the rich semantic information and noise information in labels, resulting in a large semantic gap, and (2) some cross-modal hashing methods adopt the relaxation-based or discrete cyclic coordinate descent algorithm to solve the discrete constraint problem, resulting in a large quantization error or time consumption. Therefore, in order to solve these limitations, in this paper, we propose a novel method, named Discrete Semantics-Guided Asymmetric Hashing (DSAH). Specifically, our proposed DSAH leverages both label information and similarity matrix to enhance the semantic information of the learned hash codes, and the ℓ2,1 norm is used to increase the sparsity of matrix to solve the problem of the inevitable noise and subjective factors in labels. Meanwhile, an asymmetric hash learning scheme is proposed to efficiently perform hash learning. In addition, a discrete optimization algorithm is proposed to fast solve the hash code directly and discretely. During the optimization process, the hash code learning and the hash function learning interact, i.e., the learned hash codes can guide the learning process of the hash function and the hash function can also guide the hash code generation simultaneously. Extensive experiments performed on two benchmark datasets highlight the superiority of DSAH over several state-of-the-art methods

Fuzzy-rough feature selection based on λ-partition differentiation entropy

Fuzzy-rough set theory is proven as an effective tool for feature selection. Whilst promising, many state-of-the-art fuzzy-rough feature selection algorithms are time-consuming when dealing with the datasets which have a large quantity of features. In order to address this issue, a λ-partition differentiation entropy fuzzy-rough feature selection (LDE-FRFS) method is proposed in this paper. Such λ-partition differentiation entropy extends the concept of partition differentiation entropy from rough sets to fuzzy-rough sets on the view of a partition of the information system. In this case, it can efficiently gauge the significance of features. Experimental results demonstrate that, by such λ-partition differentiation entropy-based attribute significance, LDE-FRFS outperforms the competitors in terms of both the size of the reduced datasets and the execute time

Convolutional Neural Network-Based Travel Mode Recognition Based on Multiple Smartphone Sensors

Nowadays, large-scale human mobility has led to increasingly severe traffic congestion in cities, how to accurately identify people’s travel mode has become particularly important for urban traffic planning and management. However, traditional methods are based on telephone interviews or questionnaires, which makes it difficult to obtain accurate and effective data. Nowadays, numbers of smartphones are equipped with various sensors, including accelerometers, gyroscopes, and GPS, providing a novel social sensing data source to detect people’s travel modes. The fusion of multiple sensor data is a promising way for travel mode detection. However, how to use these sensor data to accurately detect travel mode is still a challenging task. In this paper, we presented a light-weight method for travel mode detection based on four types of smartphone sensor data collected from an accelerometer, gyroscope, magnetometer, and barometer, and a prototype application was developed. Then, a novel convolutional neural network (CNN) was designed to identify five representative travel modes (walk, bicycle, bus, car, and metro). We compared the overall performance of the proposed method via different hyperparameters, and the experimental results show that the F value of the proposed method reaches 97%, which verified the effectiveness of the proposed method for travel mode classification

TRAF6 promotes chemoresistance to paclitaxel of triple negative breast cancer via regulating PKM2‐mediated glycolysis

Abstract Ample evidence reveals that glycolysis is crucial to tumor progression; however, the underlying mechanism of its drug resistance is still worth being further explored. TRAF6, an E3 ubiquitin ligase, is well recognized to overexpress in various types of cancer, which predicts a poor prognosis. In our study, we discovered that TRAF6 was expressed more significantly in the case of triple‐negative breast cancer (TNBC) than in other of breast cancers, promoting chemoresistance to paclitaxel; that inhibited TRAF6 expression in the chemoresistant TNBC (TNBC‐CR) cells enhanced the sensitivity by decreasing glucose uptake and lactate production; that TRAF6 regulated glycolysis and facilitated chemoresistance via binding directly to PKM2; and that overexpressing PKM2 in the TNBC‐CR cells with TRAF6 knocked down regained significantly TRAF6‐dependent drug resistance and glycolysis. Additionally, we verified that TRAF6 could facilitate PKM2‐mediated glycolysis and chemoresistance in animal models and clinical tumor tissues. Thus, we identified the novel function of TRAF6 to promote glycolysis and drug resistance in TNBC with the regulation of PKM2, which could provide a potential molecular target for TNBC treatment

Generation of Wheat Transcription Factor FOX Rice Lines and Systematic Screening for Salt and Osmotic Stress Tolerance.

Transcription factors (TFs) play important roles in plant growth, development, and responses to environmental stress. In this study, we collected 1,455 full-length (FL) cDNAs of TFs, representing 45 families, from wheat and its relatives Triticum urartu, Aegilops speltoides, Aegilops tauschii, Triticum carthlicum, and Triticum aestivum. More than 15,000 T0 TF FOX (Full-length cDNA Over-eXpressing) rice lines were generated; of these, 10,496 lines set seeds. About 14.88% of the T0 plants showed obvious phenotypic changes. T1 lines (5,232 lines) were screened for salt and osmotic stress tolerance using 150 mM NaCl and 20% (v/v) PEG-4000, respectively. Among them, five lines (591, 746, 1647, 1812, and J4065) showed enhanced salt stress tolerance, five lines (591, 746, 898, 1078, and 1647) showed enhanced osmotic stress tolerance, and three lines (591, 746, and 1647) showed both salt and osmotic stress tolerance. Further analysis of the T-DNA flanking sequences showed that line 746 over-expressed TaEREB1, line 898 over-expressed TabZIPD, and lines 1812 and J4065 over-expressed TaOBF1a and TaOBF1b, respectively. The enhanced salt and osmotic stress tolerance of lines 898 and 1812 was confirmed by retransformation of the respective genes. Our results demonstrate that a heterologous FOX system may be used as an alternative genetic resource for the systematic functional analysis of the wheat genome