Multi-Context Interaction Network for Few-Shot Segmentation

Few-Shot Segmentation (FSS) is challenging for limited support images and large intra-class appearance discrepancies. Due to the huge difference between support and query samples, most existing approaches focus on extracting high-level representations of the same layers for support-query correlations but neglect the shift issue between different layers and scales. In this paper, we propose a Multi-Context Interaction Network (MCINet) to remedy this issue by fully exploiting and interacting with the multi-scale contextual information contained in the support-query pairs. Specifically, MCINet improves FSS from the perspectives of boosting the query representations by incorporating the low-level structural information from another query branch into the high-level semantic features, enhancing the support-query correlations by exploiting both the same-layer and adjacent-layer features, and refining the predicted results by a multi-scale mask prediction strategy, with which the different scale contents have bidirectionally interacted. Experiments on two benchmarks demonstrate that our approach reaches SOTA performances and outperforms the best competitors with many desirable advantages, especially on the challenging COCO dataset

A pipeline for improved QSAR analysis of peptides: physiochemical property parameter selection via BMSF, near-neighbor sample selection via semivariogram, and weighted SVR regression and prediction

In this paper, we present a pipeline to perform improved QSAR analysis of peptides. The modeling involves a double selection procedure that first performs feature selection and then conducts sample selection before the final regression analysis. Five hundred and thirty-one physicochemical property parameters of amino acids were used as descriptors to characterize the structure of peptides. These high-dimensional descriptors then go through a feature selection process given by the Binary Matrix Shuffling Filter (BMSF) to obtain a set of important low dimensional features. Each descriptor that passed the BMSF filtering also receives a weight defined through its contribution to reduce the estimation error. These selected features were served as the predictors for subsequent sample selection and modeling. Based on the weighted Euclidean distances between samples, a common range was determined with high-dimensional semivariogram and then used as a threshold to select the near-neighbor samples from the training set. For each sample to be predicted, the QSAR model was established using SVR with the weighted, selected features based on the exclusive set of near-neighbor training samples. Prediction was conducted for each test sample accordingly. The performances of this pipeline are tested with the QSAR analysis of angiotensin-converting enzyme (ACE) inhibitors and HLA-A*0201 data sets. Improved prediction accuracy was obtained in both applications. This pipeline can optimize the QSAR modeling from both the feature selection and sample selection perspectives. This leads to improved accuracy over single selection methods. We expect this pipeline to have extensive application prospect in the field of regression prediction

EMA-VIO: Deep Visual-Inertial Odometry with External Memory Attention

Accurate and robust localization is a fundamental need for mobile agents. Visual-inertial odometry (VIO) algorithms exploit the information from camera and inertial sensors to estimate position and translation. Recent deep learning based VIO models attract attentions as they provide pose information in a data-driven way, without the need of designing hand-crafted algorithms. Existing learning based VIO models rely on recurrent models to fuse multimodal data and process sensor signal, which are hard to train and not efficient enough. We propose a novel learning based VIO framework with external memory attention that effectively and efficiently combines visual and inertial features for states estimation. Our proposed model is able to estimate pose accurately and robustly, even in challenging scenarios, e.g., on overcast days and water-filled ground , which are difficult for traditional VIO algorithms to extract visual features. Experiments validate that it outperforms both traditional and learning based VIO baselines in different scenes.Comment: Accepted by IEEE Sensors Journa

Research on mechanical design of a multi-function finger rehabilitation robot

Training with robots for injured fingers has achieved the efficacy of treatment. However, most of finger rehabilitation robots just have bending/extending movement. This paper presents a new multi-function finger rehabilitation robot with a simple mechanical structure, which could help fingers and thumb realize bending/extending movement and stretch/adduction movement. The paper firstly analyzes the hand physiological movement mechanism, confirming the motion range of each finger’s joint. Based on the fingers movement rules, the robot driving structure has been developed, which includes thumb training module and fingers training module and frame. In order to prove the rationality of mechanism design, an experiment was conducted. The experiment proved that the mechanism can run smoothly, and its rope wheels also drive well without skidding phenomenon as well as its tension is appropriate

Effects of Gualou Guizhi Decoction Aqueous Extract on Axonal Regeneration in Organotypic Cortical Slice Culture after Oxygen-Glucose Deprivation

Gualou Guizhi decoction (GLGZD) is effective for the clinical treatment of limb spasms caused by ischemic stroke, but its underlying mechanism is unclear. Propidium iodide (PI) fluorescence staining, terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling (TUNEL), immunohistochemistry, western blot, and real-time qPCR were used to observe the axonal regeneration and neuroprotective effects of GLGZD aqueous extract on organotypic cortical slices exposed to oxygen-glucose deprivation (OGD) and further elucidate the potential mechanisms. Compared with the OGD group, the GLGZD aqueous extract decreased the red PI fluorescence intensity; inhibited neuronal apoptosis; improved the growth of slice axons; upregulated the protein expression of tau and growth-associated protein-43; and decreased protein and mRNA expression of neurite outgrowth inhibitor protein-A (Nogo-A), Nogo receptor 1 (NgR1), ras homolog gene family A (RhoA), rho-associated coiled-coil-containing protein kinase (ROCK), and phosphorylation of collapsin response mediator protein 2 (CRMP2). Our study found that GLGZD had a strong neuroprotective effect on brain slices after OGD injury. GLGZD plays a vital role in promoting axonal remodeling and functional remodeling, which may be related to regulation of the expression of Nogo-A and its receptor NgR1, near the injured axons, inhibition of the Rho-ROCK pathway, and reduction of CRMP2 phosphorylation

Selective Catalytic Dehydrogenative Oxidation of Bio-Polyols to Lactic Acid

The global demand for lactic acid (LA) is increasing due to its successful application as monomer for the manufacture of bioplastics. Although N-heterocyclic carbene (NHC) iridium complexes are promising molecular catalysts for LA synthesis, their instabilities have hindered their utilization especially in commercial applications. Here, we report that a porous self-supported NHC-iridium coordination polymer can efficiently prevent the clusterization of corresponding NHC-Ir molecules and can function as a solid molecular recyclable catalyst for dehydrogenation of bio-polyols to form LA with excellent activity (97 %) and selectivity (>99 %). A turnover number of up to 5700 could be achieved in a single batch, due to the synergistic participation of the Ba2+ and hydroxide ions, as well as the blockage of unwanted pathways by adding methanol. Our findings demonstrate a potential route for the industrial production of LA from cheap and abundant bio-polyols, including sorbitol

Energy-Economical Heuristically Based Control of Compass Gait Walking on Stochastically Varying Terrain

Investigation uses simulation to explore the inherent tradeoffs ofcontrolling high-speed and highly robust walking robots while minimizing energy consumption. Using a novel controller which optimizes robustness, energy economy, and speed of a simulated robot on rough terrain, the user can adjust their priorities between these three outcome measures and systematically generate a performance curveassessing the tradeoffs associated with these metrics