Learning discriminative features for human motion understanding

Human motion understanding has attracted considerable interest in recent research for its applications to video surveillance, content-based search and healthcare. With different capturing methods, human motion can be recorded in various forms (e.g. skeletal data, video, image, etc.). Compared to the 2D video and image, skeletal data recorded by motion capture device contains full 3D movement information. To begin with, we first look into a gait motion analysis problem based on 3D skeletal data. We propose an automatic framework for identifying musculoskeletal and neurological disorders among older people based on 3D skeletal motion data. In this framework, a feature selection strategy and two new gait features are proposed to choose an optimal feature set from the input features to optimise classification accuracy. Due to self-occlusion caused by single shooting angle, 2D video and image are not able to record full 3D geometric information. Therefore, viewpoint variation dramatically affects the performance on lots of 2D based applications (e.g. arbitrary view action recognition and image-based 3D human shape reconstruction). Leveraging view-invariance from the 3D model is a popular idea to improve the performance on 2D computer vision problems. Therefore, in the second contribution, we adopt 3D models built with computer graphics technology to assist in solving the problem of arbitrary view action recognition. As a solution, a new transfer dictionary learning framework that utilises computer graphics technologies to synthesise realistic 2D and 3D training videos is proposed, which can project a real-world 2D video into a view-invariant sparse representation. In the third contribution, 3D models are utilised to build an end-to-end 3D human shape reconstruction system, which can recover the 3D human shape from a single image without any prior parametric model. In contrast to most existing methods that calculate 3D joint locations, the method proposed in this thesis can produce a richer and more useful point cloud based representation. Synthesised high-quality 2D images and dense 3D point clouds are used to train a CNN-based encoder and 3D regression module. It can be concluded that the methods introduced in this thesis try to explore human motion understanding from 3D to 2D. We investigate how to compensate for the lack of full geometric information in 2D based applications with view-invariance learnt from 3D models

Prior-less 3D Human Shape Reconstruction with an Earth Mover’s Distance Informed CNN

We propose a novel end-to-end deep learning framework, capable of 3D human shape reconstruction from a 2D image without the need of a 3D prior parametric model. We employ a “prior-less” representation of the human shape using unordered point clouds. Due to the lack of prior information, comparing the generated and ground truth point clouds to evaluate the reconstruction error is challenging. We solve this problem by proposing an Earth Mover’s Distance (EMD) function to find the optimal mapping between point clouds. Our experimental results show that we are able to obtain a visually accurate estimation of the 3D human shape from a single 2D image, with some inaccuracy for heavily occluded parts

Arbitrary view action recognition via transfer dictionary learning on synthetic training data

Human action recognition is an important problem in robotic vision. Traditional recognition algorithms usually require the knowledge of view angle, which is not always available in robotic applications such as active vision. In this paper, we propose a new framework to recognize actions with arbitrary views. A main feature of our algorithm is that view-invariance is learned from synthetic 2D and 3D training data using transfer dictionary learning. This guarantees the availability of training data, and removes the hassle of obtaining real world video in specific viewing angles. The result of the process is a dictionary that can project real world 2D video into a view-invariant sparse representation. This facilitates the training of a view-invariant classifier. Experimental results on the IXMAS and N-UCLA datasets show significant improvements over existing algorithms

A Historical Sedimentary Record of Mercury in a Shallow Eutrophic Lake: Impacts of Human Activities and Climate Change

Mercury and its derivatives are hazardous environmental pollutants and could affect the aquatic ecosystems and human health by biomagnification. Lake sediments can provide important historical information regarding changes in pollution levels and thus trace anthropogenic or natural influences. This research investigates the 100-year history of mercury (Hg) deposition in sediments from Chao Lake, a shallow eutrophic lake in China. The results indicate that the Hg deposition history can be separated into three stages (pre-1960s, 1960s–1980s, and post-1980s) over the last 100 years. Before the 1960s, Hg concentrations in the sediment cores varied little and had no spatial difference. Since the 1960s, the concentration of Hg began to increase gradually, and showed a higher concentration of contamination in the western half of the lake region than in the eastern half of the lake region due to all kinds of centralized human-input sources. The influences of anthropogenic factors and hydrological change are revealed by analyzing correlations between Hg and heavy metals (Fe, Co, Cr, Cu, Mn, Pb, and Zn), stable carbon and nitrogen isotopes (δ13C and δ15N), nutrients, particle sizes, and meteorological factors. The results show that Hg pollution intensified after the 1960s, mainly due to hydrological change, rapid regional development and urbanization, and the proliferation of anthropogenic Hg sources. Furthermore, the temperature, wind speed, and evaporation are found to interactively influence the environmental behaviors and environmental fate of Hg

Investigation of Multi-Stage Evaporation and Wave Multiplicity of Two-Phase Rotating Detonation Waves Fueled by Ethanol

In this study, a numerical investigation based on the Eulerian-Lagrangian model is conducted to explore a rotating detonation engine (RDE) fueled by liquid ethanol. The focus is on examining the characteristic phenomena of the two-phase rotating detonation wave (RDW) caused by droplet evaporation and varying inlet conditions. To enhance the evaporation of liquid fuel, pre-heated air is used, and both liquid and pre-vaporized ethanol are simultaneously injected. The distribution of ethanol droplets reveals an initial concentration near the injection surface and accumulation in the fuel-refill zone. Here, liquid droplets gradually evaporate after absorbing latent heat from the surrounding gas. The subsequent interactions between the evaporating droplets and the RDW vary with the droplet size. For droplets with diameters of $d_0$ = 5-15 $\mu$m, after being swept by the RDW, a secondary evaporation process occurs, leading to an enlargement of the width of the reaction zone. However, the chemical reactions still predominantly take place in close proximity to the detonation front. As further increases, droplet evaporation persists in the post-detonation expansion zone over a long distance until the remaining droplets are fully evaporated and eventually burned by the hot products. The study also analyzes the extinction of rotating detonations and the emergence of new detonation waves resulting from local explosions and consequent shock collisions. It is demonstrated that variations in the diameter of injected droplets and inlet temperature can lead to different operating modes with varying numbers of RDWs

MUI-TARE: Multi-Agent Cooperative Exploration with Unknown Initial Position

Multi-agent exploration of a bounded 3D environment with unknown initial positions of agents is a challenging problem. It requires quickly exploring the environments as well as robustly merging the sub-maps built by the agents. We take the view that the existing approaches are either aggressive or conservative: Aggressive strategies merge two sub-maps built by different agents together when overlap is detected, which can lead to incorrect merging due to the false-positive detection of the overlap and is thus not robust. Conservative strategies direct one agent to revisit an excessive amount of the historical trajectory of another agent for verification before merging, which can lower the exploration efficiency due to the repeated exploration of the same space. To intelligently balance the robustness of sub-map merging and exploration efficiency, we develop a new approach for lidar-based multi-agent exploration, which can direct one agent to repeat another agent's trajectory in an \emph{adaptive} manner based on the quality indicator of the sub-map merging process. Additionally, our approach extends the recent single-agent hierarchical exploration strategy to multiple agents in a \emph{cooperative} manner by planning for agents with merged sub-maps together to further improve exploration efficiency. Our experiments show that our approach is up to 50\% more efficient than the baselines on average while merging sub-maps robustly.Comment: 8 pages, 8 figures, Submitted to IEEE RA

The Contribution of Geomagnetic Activity to Polar Ozone Changes in the Upper Atmosphere

Energetic particle precipitation (EPP) has significant impacts on ozone depletion in the polar middle atmosphere during geomagnetic activity. It is well known that solar ultraviolet (UV) radiation plays an important role in ozone generation. Therefore, it is interesting to compare the contributions of EPP and solar UV to ozone changes in the polar upper atmosphere. In this article, we use the annual average Ap index to denote the annual-mean magnitude of the geomagnetic activity, which is closely correlated with the EPP flux, and the annual average F10.7 index to denote the annual-mean magnitude of the solar radiation, which is somewhat related to the solar UV. We adopt the 5° zonal annual-mean ozone profile dataset to study the statistical characters between the ozone dataset and the Ap, F10.7 indices. Multiple regression analysis shows that the contributions of geomagnetic activity are not negligible and are of a similar order of magnitude as the solar UV radiation in the polar upper atmosphere (above 10 hPa). The results also show that high-speed solar-wind-stream-induced and coronal-mass-ejection-driven geomagnetic activity is of the same order of magnitude. There are interhemispheric differences according to our multiple regression analysis. We discuss the possible causes of these differences

Variation of endosymbiont and citrus tristeza virus (CTV) titers in the Huanglongbing insect vector, Diaphorina citri, on CTV-infected plants

“Candidatus Liberibacter asiaticus” (CLas) is a notorious agent that causes Citrus Huanglongbing (HLB), which is transmitted by Diaphorina citri (D. citri). We recently found that the acquisition and transmission of CLas by D. citri was facilitated by Citrus tristeza virus (CTV), a widely distributed virus in the field. In this study, we further studied whether different CTV strains manipulate the host preference of D. citri, and whether endosymbionts variation is related to CTV strains in D. citri. The results showed that the non-viruliferous D. citri preferred to select the shoots infected with CTV, without strain differences was observed in the selection. However, the viruliferous D. citri prefered to select the mixed strain that is similar to the field’s. Furthermore, D. citri effectively acquired the CTV within 2–12 h depending on the strains of the virus. The persistence period of CTV in D. citri was longer than 24 days, without reduction of the CTV titers being observed. These results provide a foundation for understanding the transmission mode of D. citri on CTV. During the process of CTV acquisition and persistence, the titers of main endosymbionts in D. citri showed similar variation trend, but their relative titers were different at different time points. The titers of the “Candidatus Profftella armatura” and CTV tended to be positively correlated, and the titers of Wolbachia and “Candidatus Carsonella ruddii” were mostly negatively related with titers of CT31. These results showed the relationship among D. citri, endosymbionts, and CTV and provided useful information for further research on the interactions between D. citri and CLas, which may benefit the development of approaches for the prevention of CLas transmission and control of citrus HLB

Broadband nonlinear modulation of incoherent light using a transparent optoelectronic neuron array

Nonlinear optical processing of ambient natural light is highly desired in computational imaging and sensing applications. A strong optical nonlinear response that can work under weak broadband incoherent light is essential for this purpose. Here we introduce an optoelectronic nonlinear filter array that can address this emerging need. By merging 2D transparent phototransistors (TPTs) with liquid crystal (LC) modulators, we create an optoelectronic neuron array that allows self-amplitude modulation of spatially incoherent light, achieving a large nonlinear contrast over a broad spectrum at orders-of-magnitude lower intensity than what is achievable in most optical nonlinear materials. For a proof-of-concept demonstration, we fabricated a 10,000-pixel array of optoelectronic neurons, each serving as a nonlinear filter, and experimentally demonstrated an intelligent imaging system that uses the nonlinear response to instantly reduce input glares while retaining the weaker-intensity objects within the field of view of a cellphone camera. This intelligent glare-reduction capability is important for various imaging applications, including autonomous driving, machine vision, and security cameras. Beyond imaging and sensing, this optoelectronic neuron array, with its rapid nonlinear modulation for processing incoherent broadband light, might also find applications in optical computing, where nonlinear activation functions that can work under ambient light conditions are highly sought.Comment: 20 Pages, 5 Figure

The anti-biofilm effect of silver-nanoparticle-decorated quercetin nanoparticles on a multi-drug resistant Escherichia coli strain isolated from a dairy cow with mastitis

Background Escherichia coli is an important opportunistic pathogen that could cause inflammation of the udder in dairy cows resulting in reduced milk production and changes in milk composition and quality, and even death of dairy cows. Therefore, mastitis is the main health issue which leads to major economic losses on dairy farms. Antibiotics are routinely used for the treatment of bovine mastitis. The ability to form biofilm increases the antibiotic resistance of E. coli. Nanoparticles (NPs), a nanosized, safe, and highly cost-effective antibacterial agent, are potential biomedical tools. Given their antibacterial activities, silver nanoparticles (Ag NPs) have a broad range of applications. Methods In this study, we performed antibacterial activity assays, biofilm formation assays, scanning electron microscopy (SEM) experiments, and real-time reverse transcription PCR (RT-PCR) experiments to investigate the antibacterial and anti-biofilm effect of quercetin, Ag NPs, and Silver-nanoparticle-decorated quercetin nanoparticles (QA NPs) in E. coli strain ECDCM1. Results In this study, QA NPs, a composite material combining Ag NPs and the plant-derived drug component quercetin, exhibited stronger antibacterial and anti-biofilm properties in a multi-drug resistant E. coli strain isolated from a dairy cow with mastitis, compared to Ag NPs and Qe. Discussion This study provides evidence that QA NPs possess high antibacterial and anti-biofilm activities. They proved to be more effective than Ag NPs and Qe against the biofilm formation of a multi-drug resistant E. coli isolated from cows with mastitis. This suggests that QA NPs might be used as a potential antimicrobial agent in the treatment of bovine mastitis caused by E. coli