Indoor Depth Completion with Boundary Consistency and Self-Attention

Depth estimation features are helpful for 3D recognition. Commodity-grade depth cameras are able to capture depth and color image in real-time. However, glossy, transparent or distant surface cannot be scanned properly by the sensor. As a result, enhancement and restoration from sensing depth is an important task. Depth completion aims at filling the holes that sensors fail to detect, which is still a complex task for machine to learn. Traditional hand-tuned methods have reached their limits, while neural network based methods tend to copy and interpolate the output from surrounding depth values. This leads to blurred boundaries, and structures of the depth map are lost. Consequently, our main work is to design an end-to-end network improving completion depth maps while maintaining edge clarity. We utilize self-attention mechanism, previously used in image inpainting fields, to extract more useful information in each layer of convolution so that the complete depth map is enhanced. In addition, we propose boundary consistency concept to enhance the depth map quality and structure. Experimental results validate the effectiveness of our self-attention and boundary consistency schema, which outperforms previous state-of-the-art depth completion work on Matterport3D dataset. Our code is publicly available at https://github.com/patrickwu2/Depth-CompletionComment: Accepted by ICCVW (RLQ) 201

Deep convolutional neural network classifier for travel patterns using binary sensors

The early detection of dementia is crucial in independent life style of elderly people. Main intention of this study is to propose device-free non-privacy invasive Deep Convolutional Neural Network classifier (DCNN) for Martino-Saltzman's (MS) travel patterns of elderly people living alone using open dataset collected by binary (passive infrared) sensors. Travel patterns are classified as direct, pacing, lapping, or random according to MS model. MS travel pattern is highly related with person's cognitive state, thus can be used to detect early stage of dementia. The dataset was collected by monitoring a cognitively normal elderly resident by wireless passive infrared sensors for 21 months. First, over 70000 travel episodes are extracted from the dataset and classified by MS travel pattern classifier algorithm for the ground truth. Later, 12000 episodes (3000 for each pattern) were randomly selected from the total episodes to compose training and testing dataset. Finally, DCNN performance was compared with three other classical machine-learning classifiers. The Random Forest and DCNN yielded the best classification accuracies of 94.48% and 97.84%, respectively. Thus, the proposed DCNN classifier can be used to infer dementia through travel pattern matching

Odorant Inhibition in Mosquito Olfaction.

How chemical signals are integrated at the peripheral sensory system of insects is still an enigma. Here we show that when coexpressed with Orco in Xenopus oocytes, an odorant receptor from the southern house mosquito, CquiOR32, generated inward (regular) currents when challenged with cyclohexanone and methyl salicylate, whereas eucalyptol and fenchone elicited inhibitory (upward) currents. Responses of CquiOR32-CquiOrco-expressing oocytes to odorants were reduced in a dose-dependent fashion by coapplication of inhibitors. This intrareceptor inhibition was also manifested in vivo in fruit flies expressing the mosquito receptor CquiOR32, as well in neurons on the antennae of the southern house mosquito. Likewise, an orthologue from the yellow fever mosquito, AaegOR71, showed intrareceptor inhibition in the Xenopus oocyte recording system and corresponding inhibition in antennal neurons. Inhibition was also manifested in mosquito behavior. Blood-seeking females were repelled by methyl salicylate, but repellence was significantly reduced when methyl salicylate was coapplied with eucalyptol

2D Barcode and Augmented Reality Supported English Learning System

This study aims to construct a 2D barcode and handheld augmented reality supported learning system called HELLO (Handheld English Language Learning Organization), to improve students ’ English level. The HELLO integrates the 2D barcodes, the Internet, augmented reality, mobile computing and database technologies. The proposed system consists of two subsystems: an English learning management system and a mobile learning tools system. A four-week pilot study and questionnaire survey were conducted in college to evaluate effects of proposed learning system and student learning attitudes. Furthermore, the evaluation results indicate that 2D barcodes and augmented reality technology are useful for English learning. 1

The nucleolar protein NIFK promotes cancer progression via CK1α/β-catenin in metastasis and Ki-67-dependent cell proliferation.

Nucleolar protein interacting with the FHA domain of pKi-67 (NIFK) is a Ki-67-interacting protein. However, its precise function in cancer remains largely uninvestigated. Here we show the clinical significance and metastatic mechanism of NIFK in lung cancer. NIFK expression is clinically associated with poor prognosis and metastasis. Furthermore, NIFK enhances Ki-67-dependent proliferation, and promotes migration, invasion in vitro and metastasis in vivo via downregulation of casein kinase 1α (CK1α), a suppressor of pro-metastatic TCF4/β-catenin signaling. Inversely, CK1α is upregulated upon NIFK knockdown. The silencing of CK1α expression in NIFK-silenced cells restores TCF4/β-catenin transcriptional activity, cell migration, and metastasis. Furthermore, RUNX1 is identified as a transcription factor of CSNK1A1 (CK1α) that is negatively regulated by NIFK. Our results demonstrate the prognostic value of NIFK, and suggest that NIFK is required for lung cancer progression via the RUNX1-dependent CK1α repression, which activates TCF4/β-catenin signaling in metastasis and the Ki-67-dependent regulation in cell proliferation

A hybrid switched reactive-based visual servo control of 5-DOF robot manipulators for pick-and-place tasks

[[notice]]補正完