Search

Vision-and-language navigation (VLN) is a crucial but challenging cross-modal navigation task. One powerful technique to enhance the generalization performance in VLN is the use of an independent speaker model to provide pseudo instructions for data augmentation. However, current speaker models based on Long-Short Term Memory (LSTM) lack the ability to attend to features relevant at different locations and time steps. To address this, we propose a novel progress-aware spatio-temporal transformer speaker (PASTS) model that uses the transformer as the core of the network. PASTS uses a spatio-temporal encoder to fuse panoramic representations and encode intermediate connections through steps. Besides, to avoid the misalignment problem that could result in incorrect supervision, a speaker progress monitor (SPM) is proposed to enable the model to estimate the progress of instruction generation and facilitate more fine-grained caption results. Additionally, a multifeature dropout (MFD) strategy is introduced to alleviate overfitting. The proposed PASTS is flexible to be combined with existing VLN models. The experimental results demonstrate that PASTS outperforms all existing speaker models and successfully improves the performance of previous VLN models, achieving state-of-the-art performance on the standard Room-to-Room (R2R) dataset., 15 pages, 11 figures

Vision-and-Language Navigation (VLN) is a realistic but challenging task that requires an agent to locate the target region using verbal and visual cues. While significant advancements have been achieved recently, there are still two broad limitations: (1) The explicit information mining for significant guiding semantics concealed in both vision and language is still under-explored; (2) The previously structured map method provides the average historical appearance of visited nodes, while it ignores distinctive contributions of various images and potent information retention in the reasoning process. This work proposes a dual semantic-aware recurrent global-adaptive network (DSRG) to address the above problems. First, DSRG proposes an instruction-guidance linguistic module (IGL) and an appearance-semantics visual module (ASV) for boosting vision and language semantic learning respectively. For the memory mechanism, a global adaptive aggregation module (GAA) is devised for explicit panoramic observation fusion, and a recurrent memory fusion module (RMF) is introduced to supply implicit temporal hidden states. Extensive experimental results on the R2R and REVERIE datasets demonstrate that our method achieves better performance than existing methods. Code is available at https://github.com/CrystalSixone/DSRG., Accepted by IJCAI 2023

Surface normal holds significant importance in visual environmental perception, serving as a source of rich geometric information. However, the state-of-the-art (SoTA) surface normal estimators (SNEs) generally suffer from an unsatisfactory trade-off between efficiency and accuracy. To resolve this dilemma, this paper first presents a superfast depth-to-normal translator (D2NT), which can directly translate depth images into surface normal maps without calculating 3D coordinates. We then propose a discontinuity-aware gradient (DAG) filter, which adaptively generates gradient convolution kernels to improve depth gradient estimation. Finally, we propose a surface normal refinement module that can easily be integrated into any depth-to-normal SNEs, substantially improving the surface normal estimation accuracy. Our proposed algorithm demonstrates the best accuracy among all other existing real-time SNEs and achieves the SoTA trade-off between efficiency and accuracy., Accepted to ICRA 2023. The source code, demo video, and supplement are publicly available at mias.group/D2NT

It is seen that there is enormous potential to leverage powerful deep learning methods in the emerging field of urban digital twins. It is particularly in the area of intelligent road inspection where there is currently limited research and data available. To facilitate progress in this field, we have developed a well-labeled road pothole dataset named Urban Digital Twins Intelligent Road Inspection (UDTIRI) dataset. We hope this dataset will enable the use of powerful deep learning methods in urban road inspection, providing algorithms with a more comprehensive understanding of the scene and maximizing their potential. Our dataset comprises 1000 images of potholes, captured in various scenarios with different lighting and humidity conditions. Our intention is to employ this dataset for object detection, semantic segmentation, and instance segmentation tasks. Our team has devoted significant effort to conducting a detailed statistical analysis, and benchmarking a selection of representative algorithms from recent years. We also provide a multi-task platform for researchers to fully exploit the performance of various algorithms with the support of UDTIRI dataset., Database webpage: https://www.udtiri.com/, Kaggle webpage: https://www.kaggle.com/datasets/jiahangli617/udtiri

Vision-and-Language Navigation (VLN) aims to develop intelligent agents to navigate in unseen environments only through language and vision supervision. In the recently proposed continuous settings (continuous VLN), the agent must act in a free 3D space and faces tougher challenges like real-time execution, complex instruction understanding, and long action sequence prediction. For a better performance in continuous VLN, we design a multi-level instruction understanding procedure and propose a novel model, Multi-Level Attention Network (MLANet). The first step of MLANet is to generate sub-instructions efficiently. We design a Fast Sub-instruction Algorithm (FSA) to segment the raw instruction into sub-instructions and generate a new sub-instruction dataset named ``FSASub". FSA is annotation-free and faster than the current method by 70 times, thus fitting the real-time requirement in continuous VLN. To solve the complex instruction understanding problem, MLANet needs a global perception of the instruction and observations. We propose a Multi-Level Attention (MLA) module to fuse vision, low-level semantics, and high-level semantics, which produce features containing a dynamic and global comprehension of the task. MLA also mitigates the adverse effects of noise words, thus ensuring a robust understanding of the instruction. To correctly predict actions in long trajectories, MLANet needs to focus on what sub-instruction is being executed every step. We propose a Peak Attention Loss (PAL) to improve the flexible and adaptive selection of the current sub-instruction. PAL benefits the navigation agent by concentrating its attention on the local information, thus helping the agent predict the most appropriate actions. We train and test MLANet in the standard benchmark. Experiment results show MLANet outperforms baselines by a significant margin.

We propose a meta-ability decoupling (MAD) paradigm, which brings together various object navigation methods in an architecture system, allowing them to mutually enhance each other and evolve together. Based on the MAD paradigm, we design a multiple thinking (MT) model that leverages distinct thinking to abstract various meta-abilities. Our method decouples meta-abilities from three aspects: input, encoding, and reward while employing the multiple thinking collaboration (MTC) module to promote mutual cooperation between thinking. MAD introduces a novel qualitative and quantitative interpretability system for object navigation. Through extensive experiments on AI2-Thor and RoboTHOR, we demonstrate that our method outperforms state-of-the-art (SOTA) methods on both typical and zero-shot object navigation tasks., Comment: 17 pages

6D object pose estimation for texture-less objects from RGB images remains challenging, especially in occlusion scenarios. Instead of localizing sparse keypoints by regressing their image coordinates or heatmaps, which are sensitive to occlusion, we introduce GeoPose, a novel reconstruction guided pose estimation pipeline that predicts dense correspondences and leverages geometric consistency effectively. We first design a dense reconstruction network (ReconNet) to reconstruct pixel-wise object coordinates in normalization space. Dense 2D-3D correspondences are generated intuitively by our explicit pa-rameterization for 3D object models, which dismisses keypoint selection efforts. These 2D-3D correspondences are then utilized to estimate 6D poses by the PnP algorithm with RANSAC iterations. Furthermore, a novel Cycle Loss is proposed to provide 3D prior supervision, which significantly correlates with the pose estimation task by guiding geometric consistency between reconstruction (pixel to 3D) and reprojection (3D to pixel). In addition, a training data augmentation method is proposed to handle the insufficiency of 6D datasets, the acquisition of which is error-prone and time-consuming. Extensive experiments demonstrate that, compared with existing RGB-based methods, our GeoPose can achieve state-of-the-art (SOTA) 6D pose estimation performance on the LINEMOD, Occlusion LINEMOD and T-LESS datasets.

Objective To use in vitro experiments to verify the changes of proliferation, senescence and apoptosis of hepatocellular carcinoma cells after inhibiting the expression of NEK7, and to explore the related molecular mechanism. Methods Western blot and RT-PCR were used to detect the expression of NEK7 in hepatocellular carcinoma cells and THLE-2 cells. A viral vector was designed to inhibit the expression of NEK7 based on the gene sequence. After hepatocellular carcinoma cells were transfected, we observed the changes of proliferation activity, cell senescence, cell apoptosis and cell cycle in vitro. Western blot was used to detect the expression of cell cycle-related factors. Results Compared with THLE-2 cells, NEK7 was highly expressed in hepatocellular carcinoma cells. After inhibiting the expression of NEK7 with shRNA, the proliferation of hepatocellular carcinoma cells was inhibited, the proportions of cell senescence and apoptosis were increased, meanwhile, the cell number in stage S and G2/M was significantly reduced, the cell cycle progression was blocked, the expression levels of C-myc, c-Fos, cyclin D1 and cyclin E were inhibited, P16 and P27 expression were increased, and CDK2, CDK4 and CDK6 expression were not significantly changed. Conclusion After inhibiting the expression of NEK7, the proliferation ability of hepatocellular carcinoma cells is reduced, cell senescence is promoted and apoptosis is induced; meanwhile, the cell cycle progress is blocked.

Graph kernels are conventional methods for computing graph similarities. However, most of the R-convolution graph kernels face two challenges: 1) They cannot compare graphs at multiple different scales, and 2) they do not consider the distributions of substructures when computing the kernel matrix. These two challenges limit their performances. To mitigate the two challenges, we propose a novel graph kernel called the Multi-scale Path-pattern Graph kernel (MPG), at the heart of which is the multi-scale path-pattern node feature map. Each element of the path-pattern node feature map is the number of occurrences of a path-pattern around a node. A path-pattern is constructed by the concatenation of all the node labels in a path of a truncated BFS tree rooted at each node. Since the path-pattern node feature map can only compare graphs at local scales, we incorporate into it the multiple different scales of the graph structure, which are captured by the truncated BFS trees of different depth. We use the Wasserstein distance to compute the similarity between the multi-scale path-pattern node feature maps of two graphs, considering the distributions of path-patterns. We empirically validate MPG on various benchmark graph datasets and demonstrate that it achieves state-of-the-art performance., 15 pages

This paper aims to solve the problem of estimating the 6D pose of an object under occlusion using RGB-D images. Most existing methods typically use the information of color and depth images separately to make predictions, which limits their performances in the presence of occlusion. Instead, we propose a pipeline to effectively fuse color and depth information and perform region-level pose estimation. Our method first uses a CNN to extract the color features, and then we obtain the fusion features by combining the color features into the point cloud. Unlike existing methods, the fusion features are in the form of point sets instead of feature maps. We further use a PointNet++-like network to process the fusion features, obtaining several region-level features. Each region-level feature can predict a pose with confidence. The pose with the highest confidence is chosen as the final output. Experiments show that the proposed method outperforms the state-of-the-art methods on both the LINEMOD and Occlusion LINEMOD datasets, indicating that the proposed pipeline can obtain accurate pose estimation results and is robust to occlusion.

In product inspection scenarios, the objects have different characteristics of textures and different potential anomalies. Still, traditional methods cannot precisely segment structural anomalies and achieve optimal performance on all object categories. This article introduces a flexible multilevel image reconstruction (MLIR) framework for the unsupervised anomaly segmentation task. The MLIR integrates multiple generators with different downsampling ratios to serve as multiple reconstruction levels and leverage an adjustable perceptual similarity measurement to obtain the featurewise reconstruction errors, overcoming the challenge of detecting multiscale anomalies. Leveraging embeddings with different receptive fields for reconstruction, generators perform differently on the same target. Therefore, we depend on the reconstruction uncertainty to propose an adaptive attention-level transition (ALT) strategy that optimizes the detection performance automatically according to the target’s category. ALT adjusts the weights of both reconstruction levels and feature measurement scales simultaneously to utilize features at a consistent level for reconstruction and anomaly detection (AD). This strategy remarkably improves the system’s adaptivity for different applications. Moreover, we propose reducing the signal-to-noise ratio (SNR) of the images by adding pepper noise so that the reconstruction process is framed as an image denoising task, which improves feature extraction and image restoration robustness. We conduct extensive experiments on the MVTec AD dataset and demonstrate that the proposed method performs satisfactorily to different categories and achieves the state-of-the-art (SOTA) performance.

For biped robots, the ability to maintain balance under external forces is an essential requirement. Inspired by human beings’ behaviors to resist external forces, a compliant-resistant balance-control method is proposed to keep the biped robot balance subjected to an external force. A model-free trajectory generator is designed based on the central pattern generator (CPG) to generate compliant-resistant human-like behavior. The CPG pattern generator generates the desired pulse signal utilizing Matsuoka’s CPG. The signal modulator applies the defined signal to the robot’s center of mass (CoM) to generate the workspace trajectory when standing on double feet. Moreover, when standing on single foot, the output signal of the CPG will directly act on the hip joint of the robot to generate the joint space trajectory. Furthermore, the motion engine calculates the workspace trajectory into joint sequence values. The proposed control strategy can generate defined pulse signals to realize compliant-resistant balance control for biped robots. The control strategy proposed in this paper is verified in the NAO simulation environment.

SUMMARYSelf-localization in highly dynamic environments is still a challenging problem for humanoid robots with limited computation resource. In this paper, we propose a dual-channel unscented particle filter (DC-UPF)-based localization method to address it. A key novelty of this approach is that it employs a dual-channel switch mechanism in measurement updating procedure of particle filter, solving for sparse vision feature in motion, and it leverages data from a camera, a walking odometer, and an inertial measurement unit. Extensive experiments with an NAO robot demonstrate that DC-UPF outperforms UPF and Monte–Carlo localization with regard to accuracy.

Cherry tomato (Solanum Lycopersicum) is popular with consumers over the world due to its special flavor. Soluble solids content (SSC) and firmness are two key metrics for evaluating the product qualities. In this work, we develop non-destructive testing techniques for SSC and fruit firmness based on hyperspectral images and a corresponding deep learning regression model. Hyperspectral reflectance images of over 200 tomato fruits are derived with spectrum ranging from 400 to 1000 nm. The acquired hyperspectral images are corrected and the spectral information is extracted. A novel one-dimensional(1D) convolutional ResNet (Con1dResNet) based regression model is prosed and compared with the state of art techniques. Experimental results show that, with a relatively large number of samples our technique is 26.4\% better than state of art technique for SSC and 33.7\% for firmness. The results of this study indicate the application potential of hyperspectral imaging technique in the SSC and firmness detection, which provides a new option for non-destructive testing of cherry tomato fruit quality in the future.

Correspondence matching is a fundamental problem in computer vision and robotics applications. Solving correspondence matching problems using neural networks has been on the rise recently. Rotation-equivariance and scale-equivariance are both critical in correspondence matching applications. Classical correspondence matching approaches are designed to withstand scaling and rotation transformations. However, the features extracted using convolutional neural networks (CNNs) are only translation-equivariant to a certain extent. Recently, researchers have strived to improve the rotation-equivariance of CNNs based on group theories. Sim(2) is the group of similarity transformations in the 2D plane. This paper presents a specialized dataset dedicated to evaluating sim(2)-equivariant correspondence matching algorithms. We compare the performance of 16 state-of-the-art (SoTA) correspondence matching approaches. The experimental results demonstrate the importance of group equivariant algorithms for correspondence matching on various sim(2) transformation conditions. Since the subpixel accuracy achieved by CNN-based correspondence matching approaches is unsatisfactory, this specific area requires more attention in future works. Our dataset is publicly available at: mias.group/SIM2E., Comment: ECCV2022 Workshop Paper

"Search for" or "Navigate to"? When finding an object, the two choices always come up in our subconscious mind. Before seeing the target, we search for the target based on experience. After seeing the target, we remember the target location and navigate to. However, recently methods in object navigation field almost only consider using object association to enhance "search for" phase while neglect the importance of "navigate to" phase. Therefore, this paper proposes the dual adaptive thinking (DAT) method to flexibly adjust the different thinking strategies at different navigation stages. Dual thinking includes search thinking with the object association ability and navigation thinking with the target location ability. To make the navigation thinking more effective, we design the target-oriented memory graph (TOMG) to store historical target information and the target-aware multi-scale aggregator (TAMSA) to encode the relative target position. We assess our methods on the AI2-Thor dataset. Compared with the state-of-the-art (SOTA) method, our method reports 10.8%, 21.5% and 15.7% increase in success rate (SR), success weighted by path length (SPL) and success weighted by navigation efficiency (SNE), respectively., Comment: 12 pages, ready for AAAI2023

Prime editing is a universal and very promising precise genome editing technology. However, optimization of prime editor (PE) from different aspects remains vital for its use as a routine tool in plant basic research and crop molecular breeding. In this report, we tested MS2-based prime editor (MS2PE). We fused the M-MLV reverse transcriptase (RT) gene variant to the MS2 RNA binding protein gene, MCP, and allowed the MCP-RT fusion gene to co-express with the SpCas9 nickase gene, SpCas9H840A, and various engineered pegRNAs harboring MS2 RNA (MS2pegR). Compared with control PEs, MS2PEs significantly enhanced editing efficiency at four of six targets in rice protoplasts, and achieved 1.2~10.1-fold increase in editing efficiency at five of six targets in transgenic rice lines. Furthermore, we tested total 22 different MS2pegR scaffolds, 3 RT variants or genes, 2 MCP variants, and various combinations of the Cas9 nickase, RT, and MCP modules. Our results demonstrated a new strategy for more efficient prime editing and provide a platform for further directed evolution of PEs.

Human-vehicle cooperative driving has become the critical technology of autonomous driving, which reduces the workload of human drivers. However, the complex and uncertain road environments bring great challenges to the visual perception of cooperative systems. And the perception characteristics of autonomous driving differ from manual driving a lot. To enhance the visual perception capability of human-vehicle cooperative driving, this paper proposed a cooperative visual perception model. 506 images of complex road and traffic scenarios were collected as the data source. Then this paper improved the object detection algorithm of autonomous vehicles. The mean perception accuracy of traffic elements reached 75.52%. By the image fusion method, the gaze points of human drivers were fused with vehicles' monitoring screens. Results revealed that cooperative visual perception could reflect the riskiest zone and predict the trajectory of conflict objects more precisely. The findings can be applied in improving the visual perception algorithms and providing accurate data for planning and control.

Bipedal walking is the basis for the humanoid robot to execute all the work. This paper proposed an online learning upper body posture control strategy for humanoid walking. The proposed posture controller is based on real-time adjustment of the hip joint, and keeps upper body posture upright stabilizing the robot. A backpropagation neural network (BPNN) controller is trained using the dataset from different PID controllers. A policy gradient reinforcement learning method is proposed to adjust the network parameters online according to the sensor feedback in different environments. Experiments on a NAO humanoid robot show the effectiveness of the proposed methods.

Developing a friendly and efficient obstacle avoidance algorithm for mobile robot in dynamic environments is challenging in the scenarios where robot plans its paths without observing other obstacles' intents. Recent works have shown the power of deep reinforcement learning techniques to learn collision-free policies. However, many of them ignore the interactions between obstacles, which may cause unnatural trajectory. Meanwhile, the performance of these methods is not well in simulation environment. Combined with the interactions of obstacles, we propose a mobile robot obstacle avoidance algorithm based on deep reinforcement learning in dynamic environments. Firstly, we not only consider the direct impact of obstacles on robots, but also take the interaction between obstacles into account using angel pedestrian grid. Therefore, robot can extract more abundant environmental prior information. Subsequently, the temporal characteristics of obstacles are extracted through attention mechanism, then we can obtain the joint impact of obstacles on the obstacle avoidance strategy. Finally, the robot obtains the control output through value-based reinforcement learning. The feasibility and effectiveness of our proposed algorithm in dynamic environments is verified through simulation experiments.

Toxoplasma gondii is an obligatory intracellular parasite that critically depends on active invasion and egress from infected host cells to complete its propagation cycle. T. gondii rhoptry proteins (TgROPs) are virulent factors associated with host cell invasion, growth. In this study, we analyzed the functions of ROP9 in the process of T. gondii infection. The TgROP9 knockout RH strain (RH△ROP9) and its recovery strain (RH-ReROP9) were constructed using the CRISPR/Cas9 system. The invasion, proliferation, and egress efficiency of the RH△ROP9 strain were evaluated and their pathogenicity to mice was analyzed. Compared with RH wild-type (RH-WT) and RH-ReROP9 strains, the invasion percentage of RH△ROP9 to Vero cells was reduced by about 28.0% (p< 0.01) at 1.5 h, and the relative proliferation percentage was decreased by about 35.0% (p< 0.01) after infection with 102 or 103 parasites. In addition, the RH△ROP9 strain also showed prolonged egress time from host cells. The survival time of the mice (12.6 ± 1.6 or 10.1 ± 1.1 days) were delayed (p < 0.001) after infection with either 200 or 1000 RH△ROP9 parasites. These evidences suggested that ROP9 facilitated T. gondii infection in vitro and in vivo.

Additional file 3: Table S2. Primer sequences used for donor construction and the detection of hDHFR gene identification.

Additional file 4: Table S3. Primer sequences used for Southern blot.

Additional file 8: Table S7. Gene expression of five differentially expressed genes at six time points.

Additional file 5: Table S4. Overview of the RNA-Seq data.

Additional file 2: Table S1. Primer sequences used for sgRNA synthesis.

Additional file 6: Table S5. The annotation of novel transcripts/genes.

In this paper, we are interested in the unbalanced assignment problem with constraints to agents. The modified Hungarian algorithm with dummy tasks and agents are most common methods to solve the unbalanced problem. However, it is impractical in the real scenarios sometimes since some tasks are unassigned actually using the above method. Instead, we propose a graph based twin cost matrices method with improved ant colony optimization algorithm to solve the assignment problem elegantly and uniformly. We start from the generation of the twin cost matrices, and modify the ant colony algorithm to be capable to assign tasks to agents with different numbers. Random mutation is conducted for the stable pheromones to help the ant colony keep diversity during the operation. Experiments demonstrates that using our proposed method to solve the unbalanced assignment problem can stably obtain better results than previous methods.

We propose a optimized lidar odometry and mapping method based on loam algorithm for pose estimation of ground vehicles.Loam algorithm proposed by Zhang Ji has achieved excellent results in 6-DOF lidar motion analysis. However, for ground vehicles, the main motions are the translational motion in the ground and the rotation of Yaw angle.The ground points have no contribution to the analysis of such motions and even cause the error.Based on this,this paper has done additional processing for ground points to get the better results for ground vehicles.In this paper,laser points are segmented to omit some outliesrs and two kinds of features of line and surface are extracted.Similar to the loam algorithm, the 6-DOF laser motion is solved for line, surface features and ground points with Levenberg-Marquardt optimization method. The pose-graph optimization algorithm is used to optimize the loop-closure, which solves the the drift problem of loam algorithm with a long time. On the Kitti data set, we get the very good result.

Research on kart racing games provides an opportunity to develop real autonomous driving controllers using unsupervised learning methods and therefore worth studying. In this paper we bring forward a feasible end-to-end control technique using a novel DQN architecture. Recurrence is introduced to eliminate the effect caused by the partially observable environment. A weighting layer is created to strengthen the impact differences of the lengthy episode. A modified relay mechanism is applied to solve the problem of sparsity and speed up learning. The method is tested and verified under the Mario Kart64 environment. With the screen pixels being the only input during both training and testing period, a direct speed control signal being the output of the system, this method makes the agent perform out similar learning behaviours just like a human player. The resulting agent is able to recognize the environment features well and could act out advanced behaviours which are undiscovered in previous methods.

Toxoplasmosis is a worldwide zoonosis caused by the protozoan parasite Toxoplasma gondii, an obligate intracellular parasite. Currently, no viable vaccine or effective drug strategies exist to prevent and control toxoplasmosis. T. gondii microneme protein 3 (MIC3), rhoptry protein 9 (ROP9), and surface antigen 2 (SAG2) are related to active invasion of the parasite. Hence, we constructed recombinant adenoviruses expressing TgMIC3, TgSAG2, or TgROP9 and evaluated the recombinant adenoviruses as potential vaccines against acute T. gondii infection in BALB/c mice. Mice immunized with the recombinant adenoviruses were measured the antibody levels, percentages of lymphocytes and actived T lymphocytes, cytokine productions, and the survival rates and time to evaluate the protective efficacy. Results showed that immunization with the bivalent or trivalent recombiant adenoviruses could strongly stimulate humoral and cellular immune responses in mice, resulting in effective immune protections against lethal challenge with the tachyzoites of T. gondii RH. These results indicated that the divalent and trivalent adenoviruses, especially Ad-ROP9-MIC3-EGFP, may be promising vaccine candidates against acute T. gondii infection.

Extramammary Paget’s Disease (EMPD), also called extramammary eczematoid carcinoma, is classified as primary and secondary EMPD based on the pathogenic mechanism. The origin of the Paget cells of primary EMPD is still controversial. Apocrine or eccrine gland cells, such as Bartholin’s gland, and intraepidermal pluripotent keratinocyte stem cells have been considered possible originating cells of primary EMPD. Secondary EMPD usually originates from the malignancies of rectum, cervix, and urinary bladder. We report a case of a 58-year-old woman suffering from intermittent vaginal bleeding for half a year, who was diagnosed as EMPD by vaginal cytology.

Invasion of erythrocytes by merozoites is required in the life cycle of malarial parasites. Proteins derived from the invasive merozoites are essential ligands for erythrocyte recognition and penetration., Invasion of erythrocytes by merozoites is required in the life cycle of malarial parasites. Proteins derived from the invasive merozoites are essential ligands for erythrocyte recognition and penetration. In this study, we report a novel protein that possesses a Trx domain-like structure of the thioredoxin family and is expressed on the surface of merozoites of the malaria parasite Plasmodium falciparum. This protein, namely, PfTrx-mero protein, displayed a mutated sequence character at the Trx domain, but with a specific binding activity to human erythrocytes. Specific antibodies to the protein inhibited merozoite invasion into human erythrocytes. Immunization with a homologous protein of Plasmodium berghei strain ANKA also showed significant protection against lethal infection in mice. These results suggested that the novel PfTrx-like-mero protein expressed on the surface of merozoites is an important ligand participating in erythrocyte invasion and a potential vaccine candidate.

Novel view synthesis aims to synthesize new images from different viewpoints of given images. Most of previous works focus on generating novel views of certain objects with a fixed background. However, for some applications, such as virtual reality or robotic manipulations, large changes in background may occur due to the egomotion of the camera. Generated images of a large-scale environment from novel views may be distorted if the structure of the environment is not considered. In this work, we propose a novel fully convolutional network, that can take advantage of the structural information explicitly by incorporating the inverse depth features. The inverse depth features are obtained from CNNs trained with sparse labeled depth values. This framework can easily fuse multiple images from different viewpoints. To fill the missing textures in the generated image, adversarial loss is applied, which can also improve the overall image quality. Our method is evaluated on the KITTI dataset. The results show that our method can generate novel views of large-scale scene without distortion. The effectiveness of our approach is demonstrated through qualitative and quantitative evaluation.

Blockchain is the basic technology of bitcoin. With the value appreciation and stable operation of bitcoin, blockchain is attracting more and more attention in many areas. Blockchain has the characteristics of decentralization, stability, security, and non-modifiability. It has the potential to change the network architecture. The consensus algorithm plays a crucial role in maintaining the safety and efficiency of blockchain. Using a right algorithm may bring a significant increase to the performance of blockchain application. In this paper, we reviewed the basic principles and characteristics of the consensus algorithms and analyzed the performance and application scenarios of different consensus mechanisms. We also gave a technical guidance of selecting a suitable consensus algorithm and summarized the limitations and future development of blockchain technology.

This paper presents a novel control method of extended virtual constraint to mimic human movement for a three-link planar robot with moving torso. Inspired by two supine yoga movements, the dynamic model of bipedal walker is modified accordingly, which enlarges application of generalized planar biped robot and provides a more stable and robust walking gait. Due to the continuity of kinematics and discreteness of impact, the walking motion is regarded as a hybrid system, whose zero dynamics determines the stable state of robot. Hence, a within-stride feedback controller is designed based on input-output linearization and extension of virtual constraint. Moreover, poincare return map is adopted to analyze the stability of walking gait. The simulation results demonstrate the validity of proposed control law, leading to asymptotically stable walking with modified planar biped robot.

The zero moment point (ZMP) control method is usually used in stable biped walking. A novel preview control of ZMP is proposed in the paper. First, the dynamics of a biped robot is modeled as a running cart on the table which gives a convenient representation to treat ZMP. Second, the walking pattern generation process is describes in details and a presented preview controller is introduced to reduce zero steady-state error significantly. Third, several patterns in the omnidirectional walking including support-foot and swing-foot are discussed respectively. In order to demonstrate the effectiveness of this approach, the proposed approach is validated via simulations and experiments on a NAO humanoid robot. The webots simulation results verify the feasibility of the proposed control scheme in the stable and robust walking.

Background The simian malaria parasite, Plasmodium knowlesi, can cause severe and fatal disease in humans yet it is rarely included in routine public health reporting systems for malaria and its geographical range is largely unknown. Because malaria caused by P. knowlesi is a truly neglected tropical disease, there are substantial obstacles to defining the geographical extent and risk of this disease. Information is required on the occurrence of human cases in different locations, on which non-human primates host this parasite and on which vectors are able to transmit it to humans. We undertook a systematic review and ranked the existing evidence, at a subnational spatial scale, to investigate the potential geographical range of the parasite reservoir capable of infecting humans. Methodology/Principal Findings After reviewing the published literature we identified potential host and vector species and ranked these based on how informative they are for the presence of an infectious parasite reservoir, based on current evidence. We collated spatial data on parasite occurrence and the ranges of the identified host and vector species. The ranked spatial data allowed us to assign an evidence score to 475 subnational areas in 19 countries and we present the results on a map of the Southeast and South Asia region. Conclusions/Significance We have ranked subnational areas within the potential disease range according to evidence for presence of a disease risk to humans, providing geographical evidence to support decisions on prevention, management and prophylaxis. This work also highlights the unknown risk status of large parts of the region. Within this unknown category, our map identifies which areas have most evidence for the potential to support an infectious reservoir and are therefore a priority for further investigation. Furthermore we identify geographical areas where further investigation of putative host and vector species would be highly informative for the region-wide assessment., Author Summary Plasmodium knowlesi is a malaria parasite found in monkeys which can infect humans via mosquito bites. People infected with the P. knowlesi parasite can suffer severe disease and death yet this disease has often been misdiagnosed as a different malaria type and its geographical distribution is largely unknown. The lack of data on human infections in much of Southeast Asia means a simple map of reported cases would likely misrepresent the extent of the disease. Instead we evaluated and ranked a range of evidence types according to how informative they are about the presence of an infection risk to humans and we mapped this ranked information. This highlighted those geographical areas where new data on the monkey and mosquito species involved in the infection of humans would add most to our knowledge of the full range of factors involved in disease risk. The resulting map highlights known locations of the parasite, and areas where presence of the disease in humans is unknown but possible.

Imitation or programming by demonstration from humans is a kind of effective method for robots to learn human's behaviors. This paper proposes a task-prior algorithm to solve the physical kinematic constraints of a full-body humanoid robot on real-time imitation. Redundant kinematic system is built to track the trajectory of the wrist position as a primary task. In the meanwhile, joint velocity limits and self-collision avoidance are introduced into the null space as additional tasks. Experiments on both simulation and a full-body humanoid are designed to verify the validity of the final policy.With the proposed method, the full-body humanoid robot succeeds in imitating humans' movement with Kinect sensor and comparison between with and without considering the constraints are making to show the necessaries of this algorithm.

We undertook genetic and nongenetic approaches to investigate the relationship between telomere maintenance and osteoblast differentiation, as well as to uncover a possible link between a known mediator of cellular aging and senile bone loss. Using mouse models of disrupted telomere maintenance molecules, including mutants in the Werner helicase (Wrn(-/-) ), telomerase (Terc(-/-) ), and Wrn(-/-) Terc(-/-) double mutants predisposed to accelerated bone loss, we measured telomere dysfunction-induced foci (TIFs) and markers of osteoblast differentiation in mesenchymal progenitor cells (MPCs). We found that telomere maintenance is directly and significantly related to osteoblast differentiation, with dysfunctional telomeres associated with impaired differentiation independent of proliferation state. Telomere-mediated defects in osteoblast differentiation are associated with increased p53/p21 expression and concomitant reduction in RUNX2. Conversely, MPCs from p53(-/-) mice do not have substantial telomere dysfunction and spontaneously differentiate into osteoblasts. These results suggest that critical telomere dysfunction may be a prominent mechanism for age-related osteoporosis and limits MPC differentiation into bone-forming cells via the p53/p21 pathway.

The Chinese GNP in 1993 was 13.4%, against 12.7% in 1992 and this paper highlights the role of the construction industry in China and pinpoints those areas that will have new legislation following the introduction of the Construction Industry Development Act as well as issues dealing with reform and international practice.

At present, Kalman filtering is widely used in dynamic positioning. With continuous development of deep-sea exploration, the accuracy of Kalman filtering is affected by more complex oceanic condition. As erratic performance of Kalman filtering with fixed system noise matrix and measurement noise matrix in complex oceanic condition, a new filtering method called Kalman filtering based on real-time updating of noise matrix is put forward. The algorithm obtains real-time calculation results of the system noise and measurement noise which are devoted to revising variance of noise array. Compared with Kalman filtering, it is more accurate and more efficacious in improving the performance of dynamic positioning in complex oceanic condition proved by computer simulation.

The standard simulated algorithm has been applied into vehicle routing problem, and it has the common defects of slow convergence and easily being trapped into local minima. In this paper, a new stochastic approach called the simulated annealing genetic algorithm is proposed to solve stochastic vehicle routing problems and the solution is then compared with that from simulated algorithm. Results from case studies show that the proposed algorithm can avoid getting stuck in local minima and has better convergence property and find the optimal or near-optimal solution effectively as well as time and quickly convergence property. So, it is an efficient method for vehicle routing problem.

Ibis paper presents a kind of strategy of task division to process the discontinuous trajectory tracking. Using cubic spline with string parameter to fit the discontinuous trajectory result in: (a) the desired trajectory for macro manipulator is continuous, of which, the discontinuous parts will be tracked by the micro manipulator; and (b) macro-micro manipulator - the redundant system can be regarded as two non-redundant sub-systems. In trajectory planning, a computing algorithm for maximum velocity and maximum acceleration are given based upon trapezoidal velocity profile and torque constraints. Besides tracking the divided trajectory, micro manipulator compensates the trajectory errors caused by macro manipulator. Simulations and experiments of discontinuous trajectory tracking verify the effectiveness of our strategy.

A small manipulator is attached at the tip of a large manipulator, this kind of system can be regarded as a macro-micro manipulator system. This paper presents a macro-micro manipulator system, which uses laser in endpoint operating and can be applied to trajectory tracking. This system has low inertia and wide bandwidth. A multi-stage rotational transformer with thirty pairs of poles as joint angle measurement of macro manipulator and a kind of errors correction strategy is given, which can reduce the measurement errors caused by excitation power. High accuracy and low inertia capacitive position sensor is used for joint angle sensing of micro manipulator. Joint controllers are independent, high computation ability is guaranteed by parallel process network. Fast and reliable data exchange between parallel process network and joint controller is implemented by using peripheral high speed bus chip. Experiments on continuous trajectory and Chinese character tracking showed the effectiveness of the macro-micro manipulator control system.

Combining depth information and color image, D-RGB cameras provide a ready detection of human and associated 3D skeleton joints data, facilitating, if not revolutionizing, conventional image centric researches in, among others, computer vision, surveillance, and human activity analysis. Applicability of a D-RBG camera, however, is restricted by its limited range of frustum of depth in the range of 0.8 to 4 meters. Although a D-RGB camera network, constructed by deployment of several D-RGB cameras at various locations, could extend the range of coverage, it requires precise localization of the camera network: relative location and orientation of neighboring cameras. By introducing a skeleton-based viewpoint invariant transformation (SVIT), which derives the relative location and orientation of a detected humans upper torso to a D-RGB camera, this paper presents a reliable automatic localization technique without the need for additional instrument or human intervention. By respectively applying SVIT to two neighboring D-RGB cameras on a commonly observed skeleton, the respective relative position and orientation of the detected humans skeleton for these two cameras can be obtained before being combined to yield the relative position and orientation of these two cameras, thus solving the localization problem. Experiments have been conducted in which two Kinects are situated with bearing differences of about 45 degrees and 90 degrees; the coverage can be extended by up to 70% with the installment of an additional Kinect. The same localization technique can be applied repeatedly to a larger number of D-RGB cameras, thus extending the applicability of D-RGB cameras to camera networks in making human behavior analysis and context-aware service in a larger surveillance area.