Target-Tailored Source-Transformation for Scene Graph Generation

Scene graph generation aims to provide a semantic and structural description of an image, denoting the objects (with nodes) and their relationships (with edges). The best performing works to date are based on exploiting the context surrounding objects or relations,e.g., by passing information among objects. In these approaches, to transform the representation of source objects is a critical process for extracting information for the use by target objects. In this work, we argue that a source object should give what tar-get object needs and give different objects different information rather than contributing common information to all targets. To achieve this goal, we propose a Target-TailoredSource-Transformation (TTST) method to efficiently propagate information among object proposals and relations. Particularly, for a source object proposal which will contribute information to other target objects, we transform the source object feature to the target object feature domain by simultaneously taking both the source and target into account. We further explore more powerful representations by integrating language prior with the visual context in the transformation for the scene graph generation. By doing so the target object is able to extract target-specific information from the source object and source relation accordingly to refine its representation. Our framework is validated on the Visual Genome bench-mark and demonstrated its state-of-the-art performance for the scene graph generation. The experimental results show that the performance of object detection and visual relation-ship detection are promoted mutually by our method

Mean-reverting behavior of consumption-income ratio in OECD countries: evidence from SURADF panel unit root tests

This paper examines the existence of the mean-reverting behavior of the consumption-income ratio from a panel of 24 OECD countries through the application of the series-specific SURADF panel unit root test. The results show that the consumption-income ratios in 22 OECD countries exhibit mean-reverting behavior. Furthermore, the half-life of the consumption-income ratio for these 22 OECD countries is between 0.28 to 3.48 years. This implies that policy shocks in industrialized economies are not likely to have permanent effects on the consumption-income ratio.Mean reversion; Consumption-income ratio; SURADF; Half-life

Suppression approach to main-beam deceptive jamming in FDA-MIMO radar using nonhomogeneous sample detection

Suppressing the main-beam deceptive jamming in traditional radar systems is challenging. Furthermore, the observations corrupted by false targets generated by smart deceptive jammers, which are not independent and identically distributed because of the pseudo-random time delay. This in turn complicates the task of jamming suppression. In this paper, a new main-beam deceptive jamming suppression approach is proposed, using nonhomogeneous sample detection in the frequency diverse array-multiple-input and multiple-output radar with non-perfectly orthogonal waveforms. First, according to the time delay or range difference, the true and false targets are discriminated in the joint transmit-receive spatial frequency domain. Subsequently, due to the range mismatch, the false targets are suppressed through a transmit-receive 2-D matched filter. In particular, in order to obtain the jamming-plus-noise covariance matrix with high accuracy, a nonhomogeneous sample detection method is developed. Simulation results are provided to demonstrate the detection performance of the proposed approach

Exploring the Semantics for Visual Relationship Detection

Scene graph construction / visual relationship detection from an image aims to give a precise structural description of the objects (nodes) and their relationships (edges). The mutual promotion of object detection and relationship detection is important for enhancing their individual performance. In this work, we propose a new framework, called semantics guided graph relation neural network (SGRN), for effective visual relationship detection. First, to boost the object detection accuracy, we introduce a source-target class cognoscitive transformation that transforms the features of the co-occurent objects to the target object domain to refine the visual features. Similarly, source-target cognoscitive transformations are used to refine features of objects from features of relations, and vice versa. Second, to boost the relation detection accuracy, besides the visual features of the paired objects, we embed the class probability of the object and subject separately to provide high level semantic information. In addition, to reduce the search space of relationships, we design a semantics-aware relationship filter to exclude those object pairs that have no relation. We evaluate our approach on the Visual Genome dataset and it achieves the state-of-the-art performance for visual relationship detection. Additionally, Our approach also significantly improves the object detection performance (i.e. 4.2\% in mAP accuracy)

Discovering sequential rental patterns by fleet tracking

© Springer International Publishing Switzerland 2015. As one of the most well-known methods on customer analysis, sequential pattern mining generally focuses on customer business transactions to discover their behaviors. However in the real-world rental industry, behaviors are usually linked to other factors in terms of actual equipment circumstance. Fleet tracking factors, such as location and usage, have been widely considered as important features to improve work performance and predict customer preferences. In this paper, we propose an innovative sequential pattern mining method to discover rental patterns by combining business transactions with the fleet tracking factors. A novel sequential pattern mining framework is designed to detect the effective items by utilizing both business transactions and fleet tracking information. Experimental results on real datasets testify the effectiveness of our approach

Novel sulfamoylamino-containing cephalosporin derivatives, and their in vitro antibacterial properties

Purpose: To prepare and develop new antibacterial agents with novel molecular structures. Method: A series of novel sulfamoylamino-containing cephalosporin derivatives were synthesized. The in vitro antibacterial effects of the derivatives against Gram-positive bacteria (S. aureus, S. pneumonia and S. epidermidis), and Gram-negative bacteria (E. coli, P. aeruginosa, and K. pneumonia) were investigated. Results: Compounds 13a and 13b exhibited excellent antibacterial effects against all the Gram-positive and Gram-negative bacteria tested, when compared with other cephalosporin derivatives. Conclusion: Of these new cephalosporin derivatives, compounds 13a and 13b show the most potent antibacterial activity and would need to be further investigated