Aria-NeRF: Multimodal Egocentric View Synthesis

We seek to accelerate research in developing rich, multimodal scene models trained from egocentric data, based on differentiable volumetric ray-tracing inspired by Neural Radiance Fields (NeRFs). The construction of a NeRF-like model from an egocentric image sequence plays a pivotal role in understanding human behavior and holds diverse applications within the realms of VR/AR. Such egocentric NeRF-like models may be used as realistic simulations, contributing significantly to the advancement of intelligent agents capable of executing tasks in the real-world. The future of egocentric view synthesis may lead to novel environment representations going beyond today's NeRFs by augmenting visual data with multimodal sensors such as IMU for egomotion tracking, audio sensors to capture surface texture and human language context, and eye-gaze trackers to infer human attention patterns in the scene. To support and facilitate the development and evaluation of egocentric multimodal scene modeling, we present a comprehensive multimodal egocentric video dataset. This dataset offers a comprehensive collection of sensory data, featuring RGB images, eye-tracking camera footage, audio recordings from a microphone, atmospheric pressure readings from a barometer, positional coordinates from GPS, connectivity details from Wi-Fi and Bluetooth, and information from dual-frequency IMU datasets (1kHz and 800Hz) paired with a magnetometer. The dataset was collected with the Meta Aria Glasses wearable device platform. The diverse data modalities and the real-world context captured within this dataset serve as a robust foundation for furthering our understanding of human behavior and enabling more immersive and intelligent experiences in the realms of VR, AR, and robotics

DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation

Graph Neural Network (GNN) based recommender systems have been attracting more and more attention in recent years due to their excellent performance in accuracy. Representing user-item interactions as a bipartite graph, a GNN model generates user and item representations by aggregating embeddings of their neighbors. However, such an aggregation procedure often accumulates information purely based on the graph structure, overlooking the redundancy of the aggregated neighbors and resulting in poor diversity of the recommended list. In this paper, we propose diversifying GNN-based recommender systems by directly improving the embedding generation procedure. Particularly, we utilize the following three modules: submodular neighbor selection to find a subset of diverse neighbors to aggregate for each GNN node, layer attention to assign attention weights for each layer, and loss reweighting to focus on the learning of items belonging to long-tail categories. Blending the three modules into GNN, we present DGRec(Diversified GNN-based Recommender System) for diversified recommendation. Experiments on real-world datasets demonstrate that the proposed method can achieve the best diversity while keeping the accuracy comparable to state-of-the-art GNN-based recommender systems.Comment: 9 pages, WSDM 202

ATGL promotes the proliferation of hepatocellular carcinoma cells via the p‐AKT signaling pathway

Abnormal metabolism, including abnormal lipid metabolism, is a hallmark of cancer cells. Some studies have demonstrated that the lipogenic pathway might promote the development of hepatocellular carcinoma (HCC). However, the role of adipose triglyceride lipase (ATGL) in hepatocellular carcinoma cells has not been elucidated. We evaluated the function of ATGL in hepatocellular carcinoma using methyl azazolyl blue and migration assay through overexpression of ATGL in HepG2 cells. Quantitative reverse‐transcription polymerase chain reaction and Western blot analyses were used to assess the mechanisms of ATGL in hepatocellular carcinoma. In the current study, we first constructed and transiently transfected ATGL into hepatocellular carcinoma cells. Secondly, we found that ATGL promoted the proliferation of hepatoma cell lines via upregulating the phosphorylation of AKT, but did not affect the metastatic ability of HCC cells. Moreover, the p‐AKT inhibitor significantly eliminated the effect of ATGL on the proliferation of hepatoma carcinoma cells. Taken together, our results indicated that ATGL promotes hepatocellular carcinoma cells proliferation through upregulation of the AKT signaling pathway

Simulations of BEAVRS Benchmark Cycle 2 Depletion with MCS/CTF Coupling System

The quarter-core simulation of BEAVRS Cycle 2 depletion benchmark has been conducted using the MCS/CTF coupling system. MCS/CTF is a cycle-wise Picard iteration based inner-coupling code system, which couples sub-channel T/H (thermal/hydraulic) code CTF as a T/H solver in Monte Carlo neutron transport code MCS. This coupling code system has been previously applied in the BEAVRS benchmark Cycle 1 full-core simulation. The Cycle 2 depletion has been performed with T/H feedback based on the spent fuel materials composition pre-generated by the Cycle 1 depletion simulation using refueling capability of MCS code. Meanwhile, the MCS internal one-dimension T/H solver (MCS/TH1D) has been also applied in the simulation as the reference. In this paper, an analysis of the detailed criticality boron concentration and the axially integrated assembly-wise detector signals will be presented and compared with measured data based on the real operating physical conditions. Moreover, the MCS/CTF simulated results for neutronics and T/H parameters will be also compared to MCS/TH1D to figure out their difference, which proves the practical application of MCS into the BEAVRS benchmark two-cycle depletion simulations. (C) 2019 Korean Nuclear Society, Published by Elsevier Korea LLC

Tetris-inspired detector with neural network for radiation mapping

In recent years, radiation mapping has attracted widespread research attention and increased public concerns on environmental monitoring. In terms of both materials and their configurations, radiation detectors have been developed to locate the directions and positions of the radiation sources. In this process, algorithm is essential in converting detector signals to radiation source information. However, due to the complex mechanisms of radiation-matter interaction and the current limitation of data collection, high-performance, low-cost radiation mapping is still challenging. Here we present a computational framework using Tetris-inspired detector pixels and machine learning for radiation mapping. Using inter-pixel padding to increase the contrast between pixels and neural network to analyze the detector readings, a detector with as few as four pixels can achieve high-resolution directional mapping. By further imposing Maximum a Posteriori (MAP) with a moving detector, further radiation position localization is achieved. Non-square, Tetris-shaped detector can further improve performance beyond the conventional grid-shaped detector. Our framework offers a new avenue for high quality radiation mapping with least number of detector pixels possible, and is anticipated to be capable to deploy for real-world radiation detection with moderate validation.Comment: 29 pages, 20 figures. Ryotaro Okabe and Shangjie Xue contributed equally to this wor

The Immungenicity and Cross-Neutralizing Activity of Enterovirus 71 Vaccine Candidate Strains

This study aimed to evaluate enterovirus 71 (EV-A71) vaccine candidate strains, including their genotypes, immunogenicity and cross-neutralization capacity. From clinical samples, EV-A71 strains were separated by using Vero cells. Six strains were chosen for vaccine candidates, and the sequences were analyzed. To detect the immunogenicity of the strains, we used them to immunize NIH mice at 0 and 14 days. Cytopathic effects (CPE) were examined to determine the EV-A71 neutralizing antibody (NTAb) titer 14 d after the first and second inoculations. To evaluate the cross-neutralizing capacity of the EV-A71 vaccine candidate strains, we tested serum immunized mice with ten EV-A71 genotype strains. Six EV-A71 vaccine candidate strains were identified, all belonging to sub-genotype C4, the prevalent genotype in China. The sequence similarity of the VP1 regions of the six candidate vaccine strains and three approved inactivated vaccines was 97.58%–97.77%, and the VP1 amino acid similarity was 98.65%–99.33%. Experiments were performed to evaluate the immunogenicity and cross-neutralizing activity of the EV-A71 vaccine candidate strains. The strains had good immunogenicity 14 d after two immunizations, inducing an NTAb titer ranging from 1:94 to 1:346. The NTAb seroconversion rates 14 d after one immunization were above 80% (except HB0007), and significantly increased immunogenicity of EV-A71 strains was observed post-inoculation. Furthermore, our candidate vaccine strains had broad cross-neutralizing activity after challenge with ten sub-genotypes of EV-A71. The highest NTAb titer/lowest NTAb titer ratios of sera against EV-A71 sub-genotypes were 8.0 (JS0002), 8.0 (JS0005), 21.3 (HB0005), 21.3 (HB0007), 10.7 (HB0040) and 8.0 (GD0002), respectively. Our EV-A71 strains had good immunogenicity and cross-neutralization activity, and have the potential to serve as vaccine strains for multivalent hand, foot and mouth disease vaccines

Open X-Embodiment:Robotic learning datasets and RT-X models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io

Fuel Performance Coupling of FRAPCON within MCS

[No abstract available