OMG: How Much Should I Pay Bob in Truthful Online Mobile Crowdsourced Sensing?

Mobile crowdsourced sensing (MCS) is a new paradigm which takes advantage of the pervasive smartphones to efficiently collect data, enabling numerous novel applications. To achieve good service quality for a MCS application, incentive mechanisms are necessary to attract more user participation. Most of existing mechanisms apply only for the offline scenario where all users' information are known a priori. On the contrary, we focus on a more real scenario where users arrive one by one online in a random order. We model the problem as an online auction in which the users submit their private types to the crowdsourcer over time, and the crowdsourcer aims to select a subset of users before a specified deadline for maximizing the total value of the services provided by selected users under a budget constraint. We design two online mechanisms, OMZ and OMG, satisfying the computational efficiency, individual rationality, budget feasibility, truthfulness, consumer sovereignty and constant competitiveness under the zero arrival-departure interval case and a more general case, respectively. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our online mechanisms.Comment: 14 pages, 8 figure

Tether-cutting Reconnection between Two Solar Filaments Triggering Outflows and a Coronal Mass Ejection

Triggering mechanisms of solar eruptions have long been a challenge. A few previous case studies have indicated that preceding gentle filament merging via magnetic reconnection may launch following intense eruption, according with the tether-cutting (TC) model. However, detailed process of TC reconnection between filaments has not been exhibited yet. In this work, we report the high resolution observations from the Interface Region Imaging Spectrometer (IRIS) of TC reconnection between two sheared filaments in NOAA active region 12146. The TC reconnection commenced since 15:35 UT on 2014 August 29 and triggered an eruptive GOES C4.3-class flare 8 minutes later. An associated coronal mass ejection appeared in the field of view of SOHO/LASCO C2 about 40 minutes later. Thanks to the high spatial resolution of IRIS data, bright plasma outflows generated by the TC reconnection are clearly observed, which moved along the subarcsecond fine-scale flux tube structures in the erupting filament. Based on the imaging and spectral observations, the mean plane-of-sky and line-of-sight velocities of the TC reconnection outflows are separately measured to be 79 and 86 km/s, which derives an average real speed of 120 km/s. In addition, it is found that spectral features, such as peak intensities, Doppler shifts, and line widths in the TC reconnection region evidently enhanced compared with those in the nearby region just before the flare.Comment: Accepted for publication in ApJLette

PVSS: A Progressive Vehicle Search System for Video Surveillance Networks

This paper is focused on the task of searching for a specific vehicle that appeared in the surveillance networks. Existing methods usually assume the vehicle images are well cropped from the surveillance videos, then use visual attributes, like colors and types, or license plate numbers to match the target vehicle in the image set. However, a complete vehicle search system should consider the problems of vehicle detection, representation, indexing, storage, matching, and so on. Besides, attribute-based search cannot accurately find the same vehicle due to intra-instance changes in different cameras and the extremely uncertain environment. Moreover, the license plates may be misrecognized in surveillance scenes due to the low resolution and noise. In this paper, a Progressive Vehicle Search System, named as PVSS, is designed to solve the above problems. PVSS is constituted of three modules: the crawler, the indexer, and the searcher. The vehicle crawler aims to detect and track vehicles in surveillance videos and transfer the captured vehicle images, metadata and contextual information to the server or cloud. Then multi-grained attributes, such as the visual features and license plate fingerprints, are extracted and indexed by the vehicle indexer. At last, a query triplet with an input vehicle image, the time range, and the spatial scope is taken as the input by the vehicle searcher. The target vehicle will be searched in the database by a progressive process. Extensive experiments on the public dataset from a real surveillance network validate the effectiveness of the PVSS

Two Successive Type II Radio Bursts Associated with B-class Flares and Slow CMEs

From 2018 Oct 12 to 13, three successive solar eruptions (E1--E3) with B-class flares and poor white light coronal mass ejections (CMEs) occurred from the same active region NOAA AR 12724. Interestingly, the first two eruptions are associated with Type II radio bursts but the third is not. Using the soft X-ray flux data, radio dynamic spectra and dual perspective EUV intensity images, we comparatively investigate the three events. Our results show that their relevant flares are weak (B2.1, B7.9 and B2.3) and short-lived (13, 9 and 14 minutes). The main eruption directions of E1 and E2 are along $\sim$45$^\circ$ north of their radial directions, while E3 primarily propagated along the radial direction. In the EUV channels, the early speeds of the first two CMEs have apparent speeds of $\sim$320 km s$^{-1}$ and $\sim$380 km s$^{-1}$, which could exceed their respective local Alfv$\acute{e}$n speeds of $\sim$300 km s$^{-1}$ and $\sim$350 km s$^{-1}$. However, the CME in the third eruption possesses a much lower speed of $\sim$160 km s$^{-1}$. These results suggest that the observed Type II radio bursts in the eruptions E1 and E2 are likely triggered by their associated CMEs and the direction of eruption and the ambient plasma and magnetic environments may take an important place in producing Type II radio burst or shock as well.Comment: 9 figures and 1 tabl

KTAN: Knowledge Transfer Adversarial Network

To reduce the large computation and storage cost of a deep convolutional neural network, the knowledge distillation based methods have pioneered to transfer the generalization ability of a large (teacher) deep network to a light-weight (student) network. However, these methods mostly focus on transferring the probability distribution of the softmax layer in a teacher network and thus neglect the intermediate representations. In this paper, we propose a knowledge transfer adversarial network to better train a student network. Our technique holistically considers both intermediate representations and probability distributions of a teacher network. To transfer the knowledge of intermediate representations, we set high-level teacher feature maps as a target, toward which the student feature maps are trained. Specifically, we arrange a Teacher-to-Student layer for enabling our framework suitable for various student structures. The intermediate representation helps the student network better understand the transferred generalization as compared to the probability distribution only. Furthermore, we infuse an adversarial learning process by employing a discriminator network, which can fully exploit the spatial correlation of feature maps in training a student network. The experimental results demonstrate that the proposed method can significantly improve the performance of a student network on both image classification and object detection tasks.Comment: 8 pages, 2 figure

Generalized Zero-Shot Learning for Action Recognition with Web-Scale Video Data

Action recognition in surveillance video makes our life safer by detecting the criminal events or predicting violent emergencies. However, efficient action recognition is not free of difficulty. First, there are so many action classes in daily life that we cannot pre-define all possible action classes beforehand. Moreover, it is very hard to collect real-word videos for certain particular actions such as steal and street fight due to legal restrictions and privacy protection. These challenges make existing data-driven recognition methods insufficient to attain desired performance. Zero-shot learning is potential to be applied to solve these issues since it can perform classification without positive example. Nevertheless, current zero-shot learning algorithms have been studied under the unreasonable setting where seen classes are absent during the testing phase. Motivated by this, we study the task of action recognition in surveillance video under a more realistic \emph{generalized zero-shot setting}, where testing data contains both seen and unseen classes. To our best knowledge, this is the first work to study video action recognition under the generalized zero-shot setting. We firstly perform extensive empirical studies on several existing zero-shot leaning approaches under this new setting on a web-scale video data. Our experimental results demonstrate that, under the generalize setting, typical zero-shot learning methods are no longer effective for the dataset we applied. Then, we propose a method for action recognition by deploying generalized zero-shot learning, which transfers the knowledge of web video to detect the anomalous actions in surveillance videos. To verify the effectiveness of our proposed method, we further construct a new surveillance video dataset consisting of nine action classes related to the public safety situation

Multi-Granularity Reasoning for Social Relation Recognition from Images

Discovering social relations in images can make machines better interpret the behavior of human beings. However, automatically recognizing social relations in images is a challenging task due to the significant gap between the domains of visual content and social relation. Existing studies separately process various features such as faces expressions, body appearance, and contextual objects, thus they cannot comprehensively capture the multi-granularity semantics, such as scenes, regional cues of persons, and interactions among persons and objects. To bridge the domain gap, we propose a Multi-Granularity Reasoning framework for social relation recognition from images. The global knowledge and mid-level details are learned from the whole scene and the regions of persons and objects, respectively. Most importantly, we explore the fine-granularity pose keypoints of persons to discover the interactions among persons and objects. Specifically, the pose-guided Person-Object Graph and Person-Pose Graph are proposed to model the actions from persons to object and the interactions between paired persons, respectively. Based on the graphs, social relation reasoning is performed by graph convolutional networks. Finally, the global features and reasoned knowledge are integrated as a comprehensive representation for social relation recognition. Extensive experiments on two public datasets show the effectiveness of the proposed framework

Untwisting and Disintegration of a Solar Filament Associated with Photospheric Flux Cancellation

Using the high-resolution observations from New Vacuum Solar Telescope (NVST) jointly with the Solar Dynamics Observatory data, we investigate two successive confined eruptions (Erup1 and Erup2) of a small filament in a decaying active region on 2017 November 10. During the process of Erup1, the overlying magnetic arcade is observed to inflate with the rising filament at beginning and then stop the ongoing of the explosion. In the hot EUV channel, a coronal sigmoidal structure appears during the first eruption and fade away after the second one. The untwisting rotation and disintegration of the filament in Erup2 are clearly revealed by the NVST H_alpha intensity data, hinting at a pre-existing twisted configuration of the filament. By tracking two rotating features in the filament, the average rotational angular velocity of the unwinding filament is found to be ~10.5 degree/min. A total twist of ~1.3 pi is estimated to be stored in the filament before the eruption, which is far below the criteria for kink instability. In the course of several hours prior to the event, some photospheric flux activities, including the flux convergence and cancellation, are detected around the northern end of the filament, where some small-scale EUV brightenings are also captured. Moreover, strongly-sheared transverse fields are found in the cancelling magnetic features from the vector magnetograms. Our observational results support the flux cancellation model, in which the interaction between the converging and sheared opposite-polarity fluxes destabilizes the filament and triggers the ensuing ejection.Comment: Accepted to be published in the Ap

Generalized Lottery Trees: Budget-Consistent Incentive Tree Mechanisms for Crowdsourcing

Incentive mechanism design has aroused extensive attention for crowdsourcing applications in recent years. Most research assumes that participants are already in the system and aware of the existence of crowdsourcing tasks. Whereas in real life scenarios without this assumption, it is a more effective way to leverage incentive tree mechanisms that incentivize both users' direct contributions and solicitations to other users. Although some such mechanisms have been investigated, we are the first to propose budget-consistent incentive tree mechanisms, called generalized lottrees, which require the total payout to all participants to be consistent with the announced budget, while guaranteeing several other desirable properties including continuing contribution incentive, continuing solicitation incentive, value proportional to contribution, unprofitable solicitor bypassing, and unprofitable sybil attack. Moreover, we present three types of generalized lottree mechanisms, 1-Pachira, K-Pachira, and Sharing-Pachira, which support more diversified requirements. A solid theoretical guidance to the mechanism selection is provided as well based on the Cumulative Prospect Theory. Both extensive simulations and realistic experiments with 82 users have been conducted to confirm our theoretical analysis.Comment: 14 pages, 22 figure

Language Guided Networks for Cross-modal Moment Retrieval

We address the challenging task of cross-modal moment retrieval, which aims to localize a temporal segment from an untrimmed video described by a natural language query. It poses great challenges over the proper semantic alignment between vision and linguistic domains. Existing methods independently extract the features of videos and sentences and purely utilize the sentence embedding in the multi-modal fusion stage, which do not make full use of the potential of language. In this paper, we present Language Guided Networks (LGN), a new framework that leverages the sentence embedding to guide the whole process of moment retrieval. In the first feature extraction stage, we propose to jointly learn visual and language features to capture the powerful visual information which can cover the complex semantics in the sentence query. Specifically, the early modulation unit is designed to modulate the visual feature extractor's feature maps by a linguistic embedding. Then we adopt a multi-modal fusion module in the second fusion stage. Finally, to get a precise localizer, the sentence information is utilized to guide the process of predicting temporal positions. Specifically, the late guidance module is developed to linearly transform the output of localization networks via the channel attention mechanism. The experimental results on two popular datasets demonstrate the superior performance of our proposed method on moment retrieval (improving by 5.8\% in terms of [email protected] on Charades-STA and 5.2\% on TACoS). The source code for the complete system will be publicly available