Adaptive Tag Selection for Image Annotation

Not all tags are relevant to an image, and the number of relevant tags is image-dependent. Although many methods have been proposed for image auto-annotation, the question of how to determine the number of tags to be selected per image remains open. The main challenge is that for a large tag vocabulary, there is often a lack of ground truth data for acquiring optimal cutoff thresholds per tag. In contrast to previous works that pre-specify the number of tags to be selected, we propose in this paper adaptive tag selection. The key insight is to divide the vocabulary into two disjoint subsets, namely a seen set consisting of tags having ground truth available for optimizing their thresholds and a novel set consisting of tags without any ground truth. Such a division allows us to estimate how many tags shall be selected from the novel set according to the tags that have been selected from the seen set. The effectiveness of the proposed method is justified by our participation in the ImageCLEF 2014 image annotation task. On a set of 2,065 test images with ground truth available for 207 tags, the benchmark evaluation shows that compared to the popular top-$k$ strategy which obtains an F-score of 0.122, adaptive tag selection achieves a higher F-score of 0.223. Moreover, by treating the underlying image annotation system as a black box, the new method can be used as an easy plug-in to boost the performance of existing systems

AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates

Structured weight pruning is a representative model compression technique of DNNs to reduce the storage and computation requirements and accelerate inference. An automatic hyperparameter determination process is necessary due to the large number of flexible hyperparameters. This work proposes AutoCompress, an automatic structured pruning framework with the following key performance improvements: (i) effectively incorporate the combination of structured pruning schemes in the automatic process; (ii) adopt the state-of-art ADMM-based structured weight pruning as the core algorithm, and propose an innovative additional purification step for further weight reduction without accuracy loss; and (iii) develop effective heuristic search method enhanced by experience-based guided search, replacing the prior deep reinforcement learning technique which has underlying incompatibility with the target pruning problem. Extensive experiments on CIFAR-10 and ImageNet datasets demonstrate that AutoCompress is the key to achieve ultra-high pruning rates on the number of weights and FLOPs that cannot be achieved before. As an example, AutoCompress outperforms the prior work on automatic model compression by up to 33x in pruning rate (120x reduction in the actual parameter count) under the same accuracy. Significant inference speedup has been observed from the AutoCompress framework on actual measurements on smartphone. We release all models of this work at anonymous link: http://bit.ly/2VZ63dS

ResNorm: Tackling Long-tailed Degree Distribution Issue in Graph Neural Networks via Normalization

Graph Neural Networks (GNNs) have attracted much attention due to their ability in learning representations from graph-structured data. Despite the successful applications of GNNs in many domains, the optimization of GNNs is less well studied, and the performance on node classification heavily suffers from the long-tailed node degree distribution. This paper focuses on improving the performance of GNNs via normalization. In detail, by studying the long-tailed distribution of node degrees in the graph, we propose a novel normalization method for GNNs, which is termed ResNorm (\textbf{Res}haping the long-tailed distribution into a normal-like distribution via \textbf{norm}alization). The $scale$ operation of ResNorm reshapes the node-wise standard deviation (NStd) distribution so as to improve the accuracy of tail nodes (\textit{i}.\textit{e}., low-degree nodes). We provide a theoretical interpretation and empirical evidence for understanding the mechanism of the above $scale$. In addition to the long-tailed distribution issue, over-smoothing is also a fundamental issue plaguing the community. To this end, we analyze the behavior of the standard shift and prove that the standard shift serves as a preconditioner on the weight matrix, increasing the risk of over-smoothing. With the over-smoothing issue in mind, we design a $shift$ operation for ResNorm that simulates the degree-specific parameter strategy in a low-cost manner. Extensive experiments have validated the effectiveness of ResNorm on several node classification benchmark datasets

Copper-catalyzed methylative difunctionalization of alkenes

Trifluoromethylative difunctionalization and hydrofunctionalization of unactivated alkenes have been developed into powerful synthetic methodologies. On the other hand, methylative difunctionalization of olefins remains an unexplored research field. We report in this paper the Cu-catalyzed alkoxy methylation, azido methylation of alkenes using dicumyl peroxide (DCP), and di-tert-butyl peroxide (DTBP) as methyl sources. Using functionalized alkenes bearing a tethered nucleophile (alcohol, carboxylic acid, and sulfonamide), methylative cycloetherification, lactonization, and cycloamination processes are subsequently developed for the construction of important heterocycles such as 2,2-disubstituted tetrahydrofurans, tetrahydropyrans, γ-lactones, and pyrrolidines with concurrent generation of a quaternary carbon center. The results of control experiments suggest that the 1,2-alkoxy methylation of alkenes goes through a radical-cation crossover mechanism, whereas the 1,2-azido methylation proceeds via a radical addition and Cu-mediated azide transfer process

Fiber optic coherent laser radar 3D vision system

This CLVS will provide a substantial advance in high speed computer vision performance to support robotic Environmental Management (EM) operations. This 3D system employs a compact fiber optic based scanner and operator at a 128 x 128 pixel frame at one frame per second with a range resolution of 1 mm over its 1.5 meter working range. Using acousto-optic deflectors, the scanner is completely randomly addressable. This can provide live 3D monitoring for situations where it is necessary to update once per second. This can be used for decontamination and decommissioning operations in which robotic systems are altering the scene such as in waste removal, surface scarafacing, or equipment disassembly and removal. The fiber- optic coherent laser radar based system is immune to variations in lighting, color, or surface shading, which have plagued the reliability of existing 3D vision systems, while providing substantially superior range resolution

Flexible online task assignment in real-time spatial data

The popularity of Online To Offline (O2O) service platforms has spurred the need for online task assignment in real-time spatial data, where streams of spatially distributed tasks and workers are matched in real time such that the total number of assigned pairs is maximized. Existing online task assignment models assume that each worker is either assigned a task immediately or waits for a subsequent task at a fixed location once she/he appears on the platform. Yet in practice a worker may actively move around rather than passively wait in place if no task is assigned. In this paper, we define a new problem Flexible Two-sided Online task Assignment (FTOA). FTOA aims to guide idle workers based on the prediction of tasks and workers so as to increase the total number of assigned worker-task pairs. To address the FTOA problem, we face two challenges: (i) How to generate guidance for idle workers based on the prediction of the spatiotemporal distribution of tasks and workers? (ii) How to leverage the guidance of workers’ movements to optimize the online task assignment? To this end, we propose a novel two-step framework, which integrates offline prediction and online task assignment. Specifically, we estimate the distributions of tasks and workers per time slot and per unit area, and design an online task assignment algorithm, Prediction-oriented Online task Assignment in Real-time spatial data (POLAR-OP). It yields a 0.47-competitive ratio, which is nearly twice better than that of the state-of-the-art. POLAR-OP also reduces the time complexity to process each newly-arrived task/worker to O(1). We validate the effectiveness and efficiency of our methods via extensive experiments on both synthetic datasets and real-world datasets from a large-scale taxi-calling platform.ISSN:2150-809