Self-Supervised Deep Visual Odometry with Online Adaptation

Self-supervised VO methods have shown great success in jointly estimating camera pose and depth from videos. However, like most data-driven methods, existing VO networks suffer from a notable decrease in performance when confronted with scenes different from the training data, which makes them unsuitable for practical applications. In this paper, we propose an online meta-learning algorithm to enable VO networks to continuously adapt to new environments in a self-supervised manner. The proposed method utilizes convolutional long short-term memory (convLSTM) to aggregate rich spatial-temporal information in the past. The network is able to memorize and learn from its past experience for better estimation and fast adaptation to the current frame. When running VO in the open world, in order to deal with the changing environment, we propose an online feature alignment method by aligning feature distributions at different time. Our VO network is able to seamlessly adapt to different environments. Extensive experiments on unseen outdoor scenes, virtual to real world and outdoor to indoor environments demonstrate that our method consistently outperforms state-of-the-art self-supervised VO baselines considerably.Comment: Accepted by CVPR 2020 ora

ASSIST: Interactive Scene Nodes for Scalable and Realistic Indoor Simulation

We present ASSIST, an object-wise neural radiance field as a panoptic representation for compositional and realistic simulation. Central to our approach is a novel scene node data structure that stores the information of each object in a unified fashion, allowing online interaction in both intra- and cross-scene settings. By incorporating a differentiable neural network along with the associated bounding box and semantic features, the proposed structure guarantees user-friendly interaction on independent objects to scale up novel view simulation. Objects in the scene can be queried, added, duplicated, deleted, transformed, or swapped simply through mouse/keyboard controls or language instructions. Experiments demonstrate the efficacy of the proposed method, where scaled realistic simulation can be achieved through interactive editing and compositional rendering, with color images, depth images, and panoptic segmentation masks generated in a 3D consistent manner

Environmental Perception Q-Learning to Prolong the Lifetime of Poultry Farm Monitoring Networks

The reduction of the effects of heat-stress phenomena on poultry health and energy conservation of poultry farm monitoring networks are highly related problems. To address these problems, we propose environmental perception Q-learning (EPQL) to prolong the lifetime of poultry farm monitoring networks. EPQL consists of an environmental-perception module and Q-learning. According to the temperature and humidity model of heat stress, an environmental-perception module determines the transmission rate, while Q-learning adjusts the transmission rate according to the success rate of packet transmission and the remaining energy. In real-world tests, our poultry farm monitoring networks used only about 8% of energy in a month. The real-time information of these monitoring networks was available on smartphones. In laboratory tests, compared with CSMA/CA (23.67 days), S-MAC (109.37 days), and T-MAC (252.79 days) under real systems with 2000 mAh battery, the battery-life performance of EPQL (436.48 days) was better. Moreover, EPQL reduces the packet loss rate by about 60% while simultaneously decreasing the average delay by about 20%. Generally, based on the framework of EPQL, the implemented temperature and humidity model of heat stress for poultry could be replaced by other models to extend its applicability range

Boosting the Photocatalytic Ability of TiO₂ Nanosheet Arrays for MicroRNA-155 Photoelectrochemical Biosensing by Titanium Carbide MXene Quantum Dots

The electrodes of two-dimensional (2D) titanium dioxide (TiO2) nanosheet arrays were successfully fabricated for microRNA-155 detection. The (001) highly active crystal face was exposed to catalyze signaling molecules ascorbic acid (AA). Zero-dimensional (0D) titanium carbide quantum dots (Ti3C2Tx QDs) were modified to the electrode as co-catalysts and reduced the recombination rate of the charge carriers. Spectroscopic methods were used to determine the band structure of TiO2 and Ti3C2Tx QDs, showing that a type Ⅱ heterojunction was built between TiO2 and Ti3C2Tx QDs. Benefiting the advantages of materials, the sensing platform achieved excellent detection performance with a wide liner range, from 0.1 pM to 10 nM, and a low limit of detection of 25 fM (S/N = 3)

Analysis of epidemiology characteristics of norovirus among diarrheal outpatients in 27 provinces in China, 2009-2013

Objective: to investigate the epidemiology characteristics of norovirus among diarrheal outpatients in China.Methods: diarrhea cases were monitored at emergency/outpatient departments at 173 hospitals in 27 provinces of China,with clinical and epidemiological data,and fecal specimens collected and sent to 58 network-laboratories to detect norovirus by RT-PCR method,and to analyze the positive rate of norovirus in various regions,population and time during 2009-2013.Results: 11.6? of the 34 031 diarrheal cases under surveillance were found with norovirus.Age group of 6-23 month-old children and that of people over 45 years old were found with the highest positive percentage,13.7? and 12.4? respectively.Positive percentage of norovirus peaks in autumn and winter in a year; it peaks in mid-temperate zones (10.7?) and warm-temperate zones (11.6?) in winter.It peaks in sub-tropical zones in autumn (14.3?).The most prevalent genogroups detected were norovirus G ?,accounting for 89.9? of identified strains.Conclusion: norovirus affects all ages and was most prevalent in children and the elderly among diarrhea outpatients.Norovirus' positive percentage showed strong seasonal pattern,and peaks at different times of a year in different climate zones of China.Since no effective preventive measures existed,further study on norovirus epidemiology and intervention strategies should be conducted in futur

Large-area tungsten disulfide for ultrafast photonics

Two-dimensional (2D) layered transition metal dichalcogenides (TMDs) have attracted significant interest in various optoelectronic applications due to their excellent nonlinear optical properties. One of the most important applications of TMDs is to be employed as an extraordinary optical modulation material (e.g., the saturable absorber (SA)) in ultrafast photonics. The main challenge arises while embedding TMDs into fiber laser systems to generate ultrafast pulse trains and thus constraints their practical applications. Herein, few-layered WS2 with a large-area was directly transferred on the facet of the pigtail and acted as a SA for erbium-doped fiber laser (EDFL) systems. In our study, WS2 SA exhibited remarkable nonlinear optical properties (e.g., modulation depth of 15.1% and saturable intensity of 157.6 MW cm(-2)) and was used for ultrafast pulse generation. The soliton pulses with remarkable performances (e.g., ultrashort pulse duration of 1.49 ps, high stability of 71.8 dB, and large pulse average output power of 62.5 mW) could be obtained in a telecommunication band. To the best of our knowledge, the average output power of the mode-locked pulse trains is the highest by employing TMD materials in fiber laser systems. These results indicate that atomically large-area WS2 could be used as excellent optical modulation materials in ultrafast photonics.Peer reviewe