Self-supervised Video Representation Learning by Pace Prediction

This paper addresses the problem of self-supervised video representation learning from a new perspective -- by video pace prediction. It stems from the observation that human visual system is sensitive to video pace, e.g., slow motion, a widely used technique in film making. Specifically, given a video played in natural pace, we randomly sample training clips in different paces and ask a neural network to identify the pace for each video clip. The assumption here is that the network can only succeed in such a pace reasoning task when it understands the underlying video content and learns representative spatio-temporal features. In addition, we further introduce contrastive learning to push the model towards discriminating different paces by maximizing the agreement on similar video content. To validate the effectiveness of the proposed method, we conduct extensive experiments on action recognition and video retrieval tasks with several alternative network architectures. Experimental evaluations show that our approach achieves state-of-the-art performance for self-supervised video representation learning across different network architectures and different benchmarks. The code and pre-trained models are available at https://github.com/laura-wang/video-pace.Comment: Correct some typos;Update some cocurent works accepted by ECCV 202

Tetra­aqua­bis[3-(3-pyrid­yl)-5-(4-pyrid­yl)-1,2,4-triazolido]nickel(II) dihydrate

In the title compound, [Ni(C12H8N5)2(H2O)4]·2H2O, the NiII atom is coordinated by the two N atoms [Ni—N = 2.094 (3) Å] and four O atoms [Ni—O = 2.063 (3)–2.083 (2) Å] in a distorted octa­hedral geometry. The mol­ecule is centrosymmetric and the NiII atom is located on an inversion center. Inter­molecular O—H⋯N and O—H⋯O hydrogen bonds link the complex into a three-dimensional supra­molecular framework

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

Self-supervised Video Representation Learning by Uncovering Spatio-temporal Statistics

This paper proposes a novel pretext task to address the self-supervised video representation learning problem. Specifically, given an unlabeled video clip, we compute a series of spatio-temporal statistical summaries, such as the spatial location and dominant direction of the largest motion, the spatial location and dominant color of the largest color diversity along the temporal axis, etc. Then a neural network is built and trained to yield the statistical summaries given the video frames as inputs. In order to alleviate the learning difficulty, we employ several spatial partitioning patterns to encode rough spatial locations instead of exact spatial Cartesian coordinates. Our approach is inspired by the observation that human visual system is sensitive to rapidly changing contents in the visual field, and only needs impressions about rough spatial locations to understand the visual contents. To validate the effectiveness of the proposed approach, we conduct extensive experiments with four 3D backbone networks, i.e., C3D, 3D-ResNet, R(2+1)D and S3D-G. The results show that our approach outperforms the existing approaches across these backbone networks on four downstream video analysis tasks including action recognition, video retrieval, dynamic scene recognition, and action similarity labeling. The source code is publicly available at: https://github.com/laura-wang/video_repres_sts.Comment: Accepted by TPAMI. An extension of our previous work at arXiv:1904.0359

A Preliminary Study of the Microbial Resources and Their Biological Activities of the East China Sea

East China Sea is one of the four sea areas in China, which possesses peculiar ecological environment and many kinds of living creatures, especially the microorganisms. We established the East China Sea microorganism library (during 2006–2010) for the first time, which stored about 30000 strains that covered most kinds of the species. In this paper, 395 pure strains of East China Sea microorganism library which belong to 33 different genera were mainly introduced. Sulfitobacter, Halomonas, Bacillus, Pseudoalteromonas, and Idiomarina were the most dominant species. On the large-scale biological activity screening of the 395 strains, 100 strains possess different biological activities based on different screening models, of which 11.4% strains have antibacterial activities, 15.9% have cytotoxicity activities, and 6.1% have antioxidation activities. Besides, the secondary metabolites of 6 strains with strong biological activities were studied systematically; diketopiperazines and macrocyclic lactones are the active secondary metabolites. The species and the biological activity of microorganisms diversity, the abundant structure type of the secondary metabolites, and their bioactivities all indicate that East China Sea is a potent marine microorganisms-derived developing resource for drug discovery

Description of a Sulfitobacter Strain and Its Extracellular Cyclodipeptides

A marine bacterium M44 was separated from 30 m deep seawater in the East China Sea (26° 28.3′ N 122° 29.0′ E) in 2006. 16S rDNA gene sequence comparison showed that the strain M44 was a member of the genus Sulfitobacter and highly similar to KMM 3554T. A series of experiments demonstrated that this strain M44 had many distinctive characteristics: its cells were gram-negative and mesophilic; its colonies were slightly yellowish, round, convex, and smooth; and it could grow at 10–28°C, pH 6.0–10.0, and in the presence of 0–12.5% (w/v) NaCl; the optimum growth conditions were 25°C and pH 7.0, and the optimum Na+ concentration was 2.5%. In addition, strain M44 contained 18 : 1 ω7c, 11 methyl 18 : 1 ω7c and 16 : 0 fatty acids as major fatty acids, and the genomic DNA G+C content was 58.04 mol%. According to our results of the secondary metabolites, six cyclodipeptides were isolated from the strain M44, which were Cyclo (Val-Leu), Cyclo (Phe-Val), Cyclo (Phe-Leu), Cyclo (Leu-Ile), Cyclo (Phe-Ile), and Cyclo (Trp-Pro). It is the first study of secondary metabolites isolated from this genus

In-Vitro Study on the Antibacterial and Antioxidant Activity of Four Commercial Essential Oils and In-Situ Evaluation of Their Effect on Quality Deterioration of Pacific White Shrimp (Litopenaeus vannamei) during Cold Storage

The antioxidant and antibacterial properties of four essential oils (oregano essential oil (OEO), tea tree essential oil (TTEO), wild orange essential oil (WOEO), and clove leaf essential oil (CLEO)) were determined. The in-vitro experiment indicated that CLEO had the highest total phenolic content and DPPH scavenging activity, and OEO displayed the highest antibacterial effect, so they were applied to maintain the quality of shrimp for further study. In-situ study, the total viable counts of shrimp were inhibited from 9.05 log CFU/g to 8.18 and 8.34 log CFU/g by 2% of OEO and CLEO treated alone on 10 d. The melanosis ratio was also retarded from 38.16% to 28.98% and 26.35% by the two essential oils. The inhibitory effects of OEO and CLEO on the increase of PPO activity, weight loss, and TCA-soluble peptides, and the decreasing tendency of whiteness, the contents of myofibrillar and sarcoplasmic proteins were also founded. The samples treated with 1% OEO + 1% CLEO had better quality than those treated alone. Therefore, the combination of OEO and CLEO had a synergistic effect, which displayed the highest efficiency to prevent the melanosis, bacterial growth, and protein hydrolysis of shrimp.Peer reviewe