Latent Embeddings for Collective Activity Recognition

Rather than simply recognizing the action of a person individually, collective activity recognition aims to find out what a group of people is acting in a collective scene. Previ- ous state-of-the-art methods using hand-crafted potentials in conventional graphical model which can only define a limited range of relations. Thus, the complex structural de- pendencies among individuals involved in a collective sce- nario cannot be fully modeled. In this paper, we overcome these limitations by embedding latent variables into feature space and learning the feature mapping functions in a deep learning framework. The embeddings of latent variables build a global relation containing person-group interac- tions and richer contextual information by jointly modeling broader range of individuals. Besides, we assemble atten- tion mechanism during embedding for achieving more com- pact representations. We evaluate our method on three col- lective activity datasets, where we contribute a much larger dataset in this work. The proposed model has achieved clearly better performance as compared to the state-of-the- art methods in our experiments.Comment: 6pages, accepted by IEEE-AVSS201

Wavelet Denoising of Flight Flutter Testing Data for Improvement of Parameter Identification

AbstractThe accuracy of modal parameter estimation plays a crucial role in flutter boundary prediction. A new wavelet denoising method is introduced for flight flutter testing data, which can improve the estimation of frequency domain identification algorithms. In this method, the testing data is first preprocessed with a gradient inverse weighted filter to initially lower the noise. The redundant wavelet transform is then used to decompose the signal into several levels. A “clean” input is recovered from the noisy data by level dependent thresholding approach, and the noise of output is reduced by a modified spatially selective noise filtration technique. The advantage of the wavelet denoising is illustrated by means of simulated and real data

Research on Cloud Enterprise Resource Integration and Scheduling Technology Based on Mixed Set Programming

With the development of Industry 4.0 and intelligent manufacturing, aiming at the incompatibility of heterogeneous manufacturing resource interfaces and the low efficiency of collaborative scheduling of manufacturing resources among enterprises，we proposed the resource integration and scheduling strategy among enterprises based on Mixed Set Programming [1]. By using the metadata and ontology modeling methods, we were able to realize a standardized model description of manufacturing resources. At last, an enterprise application case was discussed to verify the resources integration and scheduling strategy based on Mixed Set Programming is effective to optimize and improve the efficiency of the collaborative scheduling of resources among enterprises. The resources integration and scheduling strategy based on Mixed Set Programming could be applied to promote the optimal allocation of manufacturing resources

Independent tuning of electronic properties and induced ferromagnetism in topological insulators with heterostructure approach

The quantum anomalous Hall effect (QAHE) has been recently demonstrated in Cr- and V-doped three-dimensional topological insulators (TIs) at temperatures below 100 mK. In those materials, the spins of unfilled d-electrons in the transition metal dopants are exchange coupled to develop a long-range ferromagnetic order, which is essential for realizing QAHE. However, the addition of random dopants does not only introduce excess charge carriers that require readjusting the Bi/Sb ratio, but also unavoidably introduces paramagnetic spins that can adversely affect the chiral edge transport in QAHE. In this work, we show a heterostructure approach to independently tune the electronic and magnetic properties of the topological surface states in (BixSb1-x)2Te3 without resorting to random doping of transition metal elements. In heterostructures consisting of a thin (BixSb1-x)2Te3 TI film and yttrium iron garnet (YIG), a high Curie temperature (~ 550 K) magnetic insulator, we find that the TI surface in contact with YIG becomes ferromagnetic via proximity coupling which is revealed by the anomalous Hall effect (AHE). The Curie temperature of the magnetized TI surface ranges from 20 to 150 K but is uncorrelated with the Bi fraction x in (BixSb1-x)2Te3. In contrast, as x is varied, the AHE resistivity scales with the longitudinal resistivity. In this approach, we decouple the electronic properties from the induced ferromagnetism in TI. The independent optimization provides a pathway for realizing QAHE at higher temperatures, which is important for novel spintronic device applications.Comment: Accepted by Nano Letter