DART: Distribution Aware Retinal Transform for Event-based Cameras

We introduce a generic visual descriptor, termed as distribution aware retinal transform (DART), that encodes the structural context using log-polar grids for event cameras. The DART descriptor is applied to four different problems, namely object classification, tracking, detection and feature matching: (1) The DART features are directly employed as local descriptors in a bag-of-features classification framework and testing is carried out on four standard event-based object datasets (N-MNIST, MNIST-DVS, CIFAR10-DVS, NCaltech-101). (2) Extending the classification system, tracking is demonstrated using two key novelties: (i) For overcoming the low-sample problem for the one-shot learning of a binary classifier, statistical bootstrapping is leveraged with online learning; (ii) To achieve tracker robustness, the scale and rotation equivariance property of the DART descriptors is exploited for the one-shot learning. (3) To solve the long-term object tracking problem, an object detector is designed using the principle of cluster majority voting. The detection scheme is then combined with the tracker to result in a high intersection-over-union score with augmented ground truth annotations on the publicly available event camera dataset. (4) Finally, the event context encoded by DART greatly simplifies the feature correspondence problem, especially for spatio-temporal slices far apart in time, which has not been explicitly tackled in the event-based vision domain.Comment: 12 pages, revision submitted to TPAMI in Nov 201

e-TLD: Event-based Framework for Dynamic Object Tracking

This paper presents a long-term object tracking framework with a moving event camera under general tracking conditions. A first of its kind for these revolutionary cameras, the tracking framework uses a discriminative representation for the object with online learning, and detects and re-tracks the object when it comes back into the field-of-view. One of the key novelties is the use of an event-based local sliding window technique that tracks reliably in scenes with cluttered and textured background. In addition, Bayesian bootstrapping is used to assist real-time processing and boost the discriminative power of the object representation. On the other hand, when the object re-enters the field-of-view of the camera, a data-driven, global sliding window detector locates the object for subsequent tracking. Extensive experiments demonstrate the ability of the proposed framework to track and detect arbitrary objects of various shapes and sizes, including dynamic objects such as a human. This is a significant improvement compared to earlier works that simply track objects as long as they are visible under simpler background settings. Using the ground truth locations for five different objects under three motion settings, namely translation, rotation and 6-DOF, quantitative measurement is reported for the event-based tracking framework with critical insights on various performance issues. Finally, real-time implementation in C++ highlights tracking ability under scale, rotation, view-point and occlusion scenarios in a lab setting.Comment: 11 pages, 10 figure

Nitrogen loss in vegetable field under the simulated rainfall experiments in Hebei, China

Agricultural non-point source pollution is one of the main factors contaminating the environment. However, the impact of rainfall on loss of non-point nitrogen is far from well understood. Based on the artificial rainfall simulation experiments to monitor the loss of dissolved nitrogen (DN) in surface runoff and interflow of vegetable field, this study analyzed the effects of rainfall intensity and fertilization scheme on nitrogen (N) loss. The results indicated that fertilizer usage is the main factor affecting the nitrogen loss in surface runoff, while runoff and rainfall intensity play important roles in interflow nitrogen loss. The proportion of DN lost through the surface runoff was more than 91%, and it decreased with increasing rainfall intensity. There was a clear linear trend (r2 > 0.96) between the amount of DN loss and runoff. Over 95% of DN was lost as nitrate nitrogen (NN), which was the major component of nitrogen loss. Compared with the conventional fertilization treatment (CF), the amount of nitrogen fertilizer applied in the optimized fertilization treatment (OF) decreased by 38.9%, and the loss of DN decreased by 28.4%, but root length, plant height and yield of pak choi increased by 6.3%, 2.7% and 5.6%, respectively. Our findings suggest that properly reducing the amount of nitrogen fertilizer can improve the utilization rate of nitrogen fertilizer but will not reduce the yield of pak choi. Controlling fertilizer usage and reducing runoff generation are important methods to reduce the DN loss in vegetable fields

Biomechanical and histological changes associated with riboflavin ultraviolet-A-induced CXL with different irradiances in young human corneal stroma.

Keratoconus (KC) is a degenerative condition affecting the cornea, characterized by progressive thinning and bulging, which can ultimately result in serious visual impairment. The onset and progression of KC are closely tied to the gradual weakening of the cornea's biomechanical properties. KC progression can be prevented with corneal cross-linking (CXL), but this treatment has shortcomings, and evaluating its tissue stiffening effect is important for determining its efficacy. In this field, the shortage of human corneas has made it necessary for most previous studies to rely on animal corneas, which have different microstructure and may be affected differently from human corneas. In this research, we have used the lenticules obtained through small incision lenticule extraction (SMILE) surgeries as a source of human tissue to assess CXL. And to further improve the results' reliability, we used inflation testing, personalized finite element modeling, numerical optimization and histology microstructure analysis. These methods enabled determining the biomechanical and histological effects of CXL protocols involving different irradiation intensities of 3, 9, 18, and 30 mW/cm2, all delivering the same total energy dose of 5.4 J/cm2. The results showed that the CXL effect did not vary significantly with protocols using 3-18 mW/cm2 irradiance, but there was a significant efficacy drop with 30 mW/cm2 irradiance. This study validated the updated algorithm and provided guidance for corneal lenticule reuse and the effects of different CXL protocols on the biomechanical properties of the human corneal stroma

Hippo Signaling Suppresses Cell Ploidy and Tumorigenesis through Skp2

大多数真核生物的体细胞是二倍体，即仅含有两组染色体，分别遗传自父本和母本。而一些特定组织如心脏、肝脏等就含有多倍体细胞，特别是肝脏组织含有较高比例的四、八倍体等多倍体细胞。肝脏是人体的重要解毒器官，同时酒精、肝炎病毒等毒性物质或毒性代谢物容易诱发肝细胞的基因突变，多倍体被认为有利于提供代偿性的正常基因来维持肝脏稳态。然而肝脏受损后，多倍体细胞将会受胁迫进行增殖，再生修复受损的肝组织。因此研究机体调控多倍体细胞产生及多倍体细胞进行细胞分裂的调控机理对于理解肝癌的发病机理和肝癌的治疗至关重要。Hippo信号通路在调节组织成体干细胞的分化和增殖，调控器官再生与尺寸大小中具有重要作用。深入研究发现, Hippo信号通路下游效应分子YAP通过AKT-SKP2信号促进二倍体细胞向多倍体转化及多倍体细胞的生长增殖。本项研究阐明了Hippo缺失及YAP激活促进多倍体细胞产生及增殖作为肝癌发生发展中的一个重要机制，为肝癌诊疗提供了新的策略。 周大旺，博士，厦门大学生命科学学院教授、副院长、国家杰出青年基金获得者。【Abstract】Polyploidy can lead to aneuploidy and tumorigenesis. Here, we report that the Hippo pathway effector Yap promotes the diploid-polyploid conversion and polyploid cell growth through the Akt-Skp2 axis. Yap strongly induces the acetyltransferase p300-mediated acetylation of the E3 ligase Skp2 via Akt signaling. Acetylated Skp2 is exclusively localized to the cytosol, which causes hyper-accumulation of the cyclin-dependent kinase inhibitor p27, leading to mitotic arrest and subsequently cell polyploidy. In addition, the pro-apoptotic factors FoxO1/3 are overly degraded by acetylated Skp2, resulting in polyploid cell division, genomic instability, and oncogenesis. importantly, the depletion or inactivation of Akt or Skp2 abrogated Hippo signal deficiency-induced liver tumorigenesis, indicating their epistatic interaction. Thus, we conclude that Hippo-Yap signaling suppresses cell polyploidy and oncogenesis through Skp2.该研究工作获得了国家自然科学基金委、国家重点基础研究发展计划(973)项目、青年千人计划和中央高校基本科研基金的资助。 The Yap (S127A) transgenic mice were kindly provided by Dr. Fernando Camargo from Harvard Medical School, Boston, MA. D.Z. and L.C. were supported by the National Natural Science Foundation of China (31625010,U1505224, and J1310027 to D.Z.; 81422018, U1405225, and 81372617 to L.C.; 81472229 to L.H.), the National Basic Research Program (973) of China (2015CB910502 to L.C.), the Fundamental Research Funds for the Central Universities of China-Xiamen University (20720140551 to L.C. and 2013121034 and 20720140537 to D.Z.)

Kinases Mst1 and Mst2 positively regulate phagocytic induction of reactive oxygen species and bactericidal activity

该研究成果揭示了吞噬性细胞内Hippo信号通路关键激酶Mst1和Mst2通过活化Rac家族蛋白来调节线粒体向吞噬小泡募集并释放ROS来清除病原体，这个生物学过程在天然免疫和宿主防御中发挥着重要作用。该成果解析了人的Mst1基因缺失或Rac2基因突变引发免疫缺陷综合症的致病机理，为研究人类感染性疾病提供了全新的视角。 该论文的主要工作由2012级博士生耿晶、2013级博士生孙秀峰以及王平、张世浩和王晓珍等学生共同承担，并与厦门市第一医院、台湾长庚大学、中国科学技术大学等单位合作完成，通讯作者为周大旺教授和陈兰芬教授。该研究工作获得了“青年千人计划”、国家自然科学基金委和科技部的资助。Mitochondria need to be juxtaposed to phagosomes for the synergistic production of ample reactive oxygen species (ROS) in phagocytes to kill pathogens. However, how phagosomes transmit signals to recruit mitochondria has remained unclear. Here we found that the kinases Mst1 and Mst2 functioned to control ROS production by regulating mitochondrial trafficking and mitochondrion-phagosome juxtaposition. Mst1 and Mst2 activated the GTPase Rac to promote Toll-like receptor (TLR)-triggered assembly of the TRAF6-ECSIT complex that is required for the recruitment of mitochondria to phagosomes. Inactive forms of Rac, including the human Rac2D57N mutant, disrupted the TRAF6-ECSIT complex by sequestering TRAF6 and substantially diminished ROS production and enhanced susceptibility to bacterial infection. Our findings demonstrate that the TLR-Mst1-Mst2-Rac signaling axis is critical for effective phagosome-mitochondrion function and bactericidal activity.Supported by the National Basic Research Program (973) of China (2015CB910502 to L.C.), China's 1000 Young Talents Program (D.Z. and L.C.), the 111 Projects (B12001 and B06016), the Fundamental Research Funds for the Central Universities of China-Xiamen University (CXB2014004 to J.Z.; 20720140551 to L.C.; and 2013121034 and 20720140537 to D.Z.), the National Natural Science Foundation of China (31270918, 81222030 and J1310027 to D.Z.; 81372617, 81422018 and U1405225 to L.C.; 81472229 to L.H.; and 81302529 to X.L.), the Natural Science Foundation of Fujian (2013J06011 to D.Z. and 2014D007 to X.L.), the US National Institutes of Health (RO1 CA136567 for J.A.) and institutional funds from Massachusetts General Hospital (for J.A.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Off-line evaluation of indoor positioning systems in different scenarios: the experiences from IPIN 2020 competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001

Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001.Peer reviewe

FIRN: A Novel Fish Individual Recognition Method with Accurate Detection and Attention Mechanism

Fish individual recognition technology is one of the key technologies to realize automated farming. Aiming at the deficiencies in the existing animal individual recognition technology, this paper proposes a method for individual recognition of underwater fish based on deep learning technology, which is divided into two parts: fish individual object detection and fish individual recognition. In the object detection part, the research has improved a new object detection for underwater fish based on the YOLOv4 algorithm, which changed the feature extraction network in YOLOv4 from CSP Darknet53 to Mobilenetv3 and changed the 3 × 3 convolution in the enhanced feature extraction network PANet to depthwise separable convolution. Compared with the original YOLOv4, the mean average precision is improved by 1.97%. For individual recognition, an algorithm called FIRN (Fish Individual Recognition Network) for individual recognition of underwater fish is proposed. The feature extraction network of the algorithm uses the improved ResNext50, and the loss function uses Arcface Loss. The CBAM attention module is introduced in the residual block of ResNext50, the max-pooling layer in the trunk is removed, and dilated convolution is introduced in the residual block, which increases the receptive field and improves the ability of feature extraction. Experiments show that the FIEN algorithm can enhance the compactness within a class while ensuring the separability between classes, and has a better recognition effect than other algorithms

FIRN: A Novel Fish Individual Recognition Method with Accurate Detection and Attention Mechanism

Fish individual recognition technology is one of the key technologies to realize automated farming. Aiming at the deficiencies in the existing animal individual recognition technology, this paper proposes a method for individual recognition of underwater fish based on deep learning technology, which is divided into two parts: fish individual object detection and fish individual recognition. In the object detection part, the research has improved a new object detection for underwater fish based on the YOLOv4 algorithm, which changed the feature extraction network in YOLOv4 from CSP Darknet53 to Mobilenetv3 and changed the 3 × 3 convolution in the enhanced feature extraction network PANet to depthwise separable convolution. Compared with the original YOLOv4, the mean average precision is improved by 1.97%. For individual recognition, an algorithm called FIRN (Fish Individual Recognition Network) for individual recognition of underwater fish is proposed. The feature extraction network of the algorithm uses the improved ResNext50, and the loss function uses Arcface Loss. The CBAM attention module is introduced in the residual block of ResNext50, the max-pooling layer in the trunk is removed, and dilated convolution is introduced in the residual block, which increases the receptive field and improves the ability of feature extraction. Experiments show that the FIEN algorithm can enhance the compactness within a class while ensuring the separability between classes, and has a better recognition effect than other algorithms