Efficient Fully Convolution Neural Network for Generating Pixel Wise Robotic Grasps With High Resolution Images

This paper presents an efficient neural network model to generate robotic grasps with high resolution images. The proposed model uses fully convolution neural network to generate robotic grasps for each pixel using 400 $\times$ 400 high resolution RGB-D images. It first down-sample the images to get features and then up-sample those features to the original size of the input as well as combines local and global features from different feature maps. Compared to other regression or classification methods for detecting robotic grasps, our method looks more like the segmentation methods which solves the problem through pixel-wise ways. We use Cornell Grasp Dataset to train and evaluate the model and get high accuracy about 94.42% for image-wise and 91.02% for object-wise and fast prediction time about 8ms. We also demonstrate that without training on the multiple objects dataset, our model can directly output robotic grasps candidates for different objects because of the pixel wise implementation.Comment: Submitted to ROBIO 201

Self-supervised Spatio-temporal Representation Learning for Videos by Predicting Motion and Appearance Statistics

We address the problem of video representation learning without human-annotated labels. While previous efforts address the problem by designing novel self-supervised tasks using video data, the learned features are merely on a frame-by-frame basis, which are not applicable to many video analytic tasks where spatio-temporal features are prevailing. In this paper we propose a novel self-supervised approach to learn spatio-temporal features for video representation. Inspired by the success of two-stream approaches in video classification, we propose to learn visual features by regressing both motion and appearance statistics along spatial and temporal dimensions, given only the input video data. Specifically, we extract statistical concepts (fast-motion region and the corresponding dominant direction, spatio-temporal color diversity, dominant color, etc.) from simple patterns in both spatial and temporal domains. Unlike prior puzzles that are even hard for humans to solve, the proposed approach is consistent with human inherent visual habits and therefore easy to answer. We conduct extensive experiments with C3D to validate the effectiveness of our proposed approach. The experiments show that our approach can significantly improve the performance of C3D when applied to video classification tasks. Code is available at https://github.com/laura-wang/video_repres_mas.Comment: CVPR 201

Adaptive Fine-tuning based Transfer Learning for the Identification of MGMT Promoter Methylation Status

Glioblastoma Multiforme (GBM) is an aggressive form of malignant brain tumor with a generally poor prognosis. Treatment usually includes a mix of surgical resection, radiation therapy, and akylating chemotherapy but, even with these intensive treatments, the 2-year survival rate is still very low. O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation has been shown to be a predictive bio-marker for resistance to chemotherapy, but it is invasive and time-consuming to determine the methylation status. Due to this, there has been effort to predict the MGMT methylation status through analyzing MRI scans using machine learning, which only requires pre-operative scans that are already part of standard-of-care for GBM patients. We developed a 3D SpotTune network with adaptive fine-tuning capability to improve the performance of conventional transfer learning in the identification of MGMT promoter methylation status. Using the pretrained weights of MedicalNet coupled with the SpotTune network, we compared its performance with two equivalent networks: one that is initialized with MedicalNet weights, but with no adaptive fine-tuning and one initialized with random weights. These three networks are trained and evaluated using the UPENN-GBM dataset, a public GBM dataset provided by the University of Pennsylvania. The SpotTune network showed better performance than the network with randomly initialized weights and the pre-trained MedicalNet with no adaptive fine-tuning. SpotTune enables transfer learning to be adaptive to individual patients, resulting in improved performance in predicting MGMT promoter methylation status in GBM using MRIs as compared to conventional transfer learning without adaptive fine-tuning.Comment: 18 pages, 4 figures. Preprin

The Sales Impact of Storytelling in Live Streaming E-Commerce

Live streaming e-commerce (LSE) has emerged as a popular third-party service for improving product sales. It persuades consumers through streamers’ storytelling or narratives, which encompass descriptions and depictions of their own product experiences. However, the sales impact of a story or narrative in LSEs has been overlooked in the literature. Extending the narrative transportation theory to the LSE context, we posit that the dual landscapes of narrative—the landscapes of action and the landscape of consciousness—can improve product sales through their influence on consumers’ imagination of story plotline and empathy for streamers’ product experiences. We also propose that the efficacy of the dual landscapes is contingent on streamers’ interaction response to consumer query. By collecting LSE data from the Taobao Live platform, we manually and algorithmically measured these variables and proposed to empirically examine their effects

Synthesis and characterisation of regioselective cellulose derivatives

The typical regioselective cellulose derivatives 3-mono-O-alkyl cellulose samples bearing two different ether moieties, namely methyl/ethyl, methyl/n-propyl, and ethyl/n-propyl were synthesized applying protecting group technique. The NMR spectra of the peracetylated products revealed the regioselectivity of the alkylation as well as the degree of substitution of both alkyl moieties. The number average degree of polymerization (DPn) calculated from size exclusion chromatography decreases from DPn 117 (starting cellulose, Avicel PH-101) to DPn range of 20-50 due to the multi-step synthesis. It could be demonstrated that the lower critical solution temperature (LCST) is influenced by the degree of substitution of both alkyl groups. For example, LCST values between 33 and 58°C were measured for aqueous solutions of 3-mono-O-ethyl/n-propyl cellulose. On the contrary, the solution behaviour of a mixture of 3-mono-O-ethyl- and 3-mono-O-propyl cellulose, e. g., was controlled by the derivative with the lowest LCST

Clinical significance of obstructive sleep apnea in patients with acute coronary syndrome in relation to diabetes status.

Objective: The prognostic significance of obstructive sleep apnea (OSA) in patients with acute coronary syndrome (ACS) according to diabetes mellitus (DM) status remains unclear. We aimed to elucidate the association of OSA with subsequent cardiovascular events in patients with ACS with or without DM. Research design and methods: In this prospective cohort study, consecutive eligible patients with ACS underwent cardiorespiratory polygraphy between June 2015 and May 2017. OSA was defined as an Apnea Hypopnea Index ≥15 events/hour. The primary end point was major adverse cardiovascular and cerebrovascular events (MACCEs), including cardiovascular death, myocardial infarction, stroke, ischemia-driven revascularization, or hospitalization for unstable angina or heart failure. Results: Among 804 patients, 248 (30.8%) had DM and 403 (50.1%) had OSA. OSA was associated with 2.5 times the risk of 1 year MACCE in patients with DM (22.3% vs 7.1% in the non-OSA group; adjusted HR (HR)=2.49, 95% CI 1.16 to 5.35, p=0.019), but not in patients without DM (8.5% vs 7.7% in the non-OSA group, adjusted HR=0.94, 95% CI 0.51 to 1.75, p=0.85). Patients with DM without OSA had a similar 1 year MACCE rate as patients without DM. The increased risk of events was predominately isolated to patients with OSA with baseline glucose or hemoglobin A1c levels above the median. Combined OSA and longer hypoxia duration (time with arterial oxygen saturation22 min) further increased the MACCE rate to 31.0% in patients with DM. Conclusions: OSA was associated with increased risk of 1 year MACCE following ACS in patients with DM, but not in non-DM patients. Further trials exploring the efficacy of OSA treatment in high-risk patients with ACS and DM are warranted

Visual Communication and Fashion Popularity Contagion in Social Networks

Fast fashion has emerged as a prevalent retail strategy shaping fashion popularity. However, due to the lack of historical records and the dynamics of fashion trends, existing demand prediction methods do not apply to new-season fast fashion sales forecasting. We draw on the Social Contagion Theory to conceptualize a sales prediction framework for fast fashion new releases. We posit that fashion popularity contagion comes from Source Contagion and Media Contagion, which refer to the inherent infectiousness of fashion posts and the popularity diffusion in social networks, respectively. We consider fashion posts as the contagion source that visually attracts social media users with images of fashion products. Graph Convolutional Network is developed to model the dynamic fashion contagion process in the topology structure of social networks. This theory-based deep learning method can incorporate the latest social media activities to offset the deficiency of historical fashion data in new seasons