Assessing the feasibility of online SSVEP decoding in human walking using a consumer EEG headset.

BackgroundBridging the gap between laboratory brain-computer interface (BCI) demonstrations and real-life applications has gained increasing attention nowadays in translational neuroscience. An urgent need is to explore the feasibility of using a low-cost, ease-of-use electroencephalogram (EEG) headset for monitoring individuals' EEG signals in their natural head/body positions and movements. This study aimed to assess the feasibility of using a consumer-level EEG headset to realize an online steady-state visual-evoked potential (SSVEP)-based BCI during human walking.MethodsThis study adopted a 14-channel Emotiv EEG headset to implement a four-target online SSVEP decoding system, and included treadmill walking at the speeds of 0.45, 0.89, and 1.34 meters per second (m/s) to initiate the walking locomotion. Seventeen participants were instructed to perform the online BCI tasks while standing or walking on the treadmill. To maintain a constant viewing distance to the visual targets, participants held the hand-grip of the treadmill during the experiment. Along with online BCI performance, the concurrent SSVEP signals were recorded for offline assessment.ResultsDespite walking-related attenuation of SSVEPs, the online BCI obtained an information transfer rate (ITR) over 12 bits/min during slow walking (below 0.89 m/s).ConclusionsSSVEP-based BCI systems are deployable to users in treadmill walking that mimics natural walking rather than in highly-controlled laboratory settings. This study considerably promotes the use of a consumer-level EEG headset towards the real-life BCI applications

Tree-Structured Neural Machine for Linguistics-Aware Sentence Generation

Different from other sequential data, sentences in natural language are structured by linguistic grammars. Previous generative conversational models with chain-structured decoder ignore this structure in human language and might generate plausible responses with less satisfactory relevance and fluency. In this study, we aim to incorporate the results from linguistic analysis into the process of sentence generation for high-quality conversation generation. Specifically, we use a dependency parser to transform each response sentence into a dependency tree and construct a training corpus of sentence-tree pairs. A tree-structured decoder is developed to learn the mapping from a sentence to its tree, where different types of hidden states are used to depict the local dependencies from an internal tree node to its children. For training acceleration, we propose a tree canonicalization method, which transforms trees into equivalent ternary trees. Then, with a proposed tree-structured search method, the model is able to generate the most probable responses in the form of dependency trees, which are finally flattened into sequences as the system output. Experimental results demonstrate that the proposed X2Tree framework outperforms baseline methods over 11.15% increase of acceptance ratio

Rethink left-behind experience: new categories and its relationship with aggression

Left-behind experience refers to the experience of children staying behind in their hometown under the care of only one parent or their relatives while one or both of their parents leave to work in other places. College students with left-behind experience showed higher aggression levels. To further explore the relationship between left-behind experience and aggression, the current study categorized left-behind experience using latent class analysis and explored its relationship with aggression. One thousand twenty-eight Chinese college students with left-behind experience were recruited, and their aggression levels were assessed. The results showed that there were four categories of left-behind experience: “starting from preschool, frequent contact” (35.5%), “less than 10 years in duration, limited contact” (27.0%), “starting from preschool, over 10 years in duration, limited contact” (10.9%), and “starting from school age, frequent contact” (26.6%). Overall, college students who reported frequent contact with their parents during the left-behind period showed lower levels of aggression than others did. Females were less aggressive than males in the “starting from preschool, frequent contact” left-behind situation, while males were less aggressive than females in the “starting from school age, frequent contact” situation. These findings indicate that frequent contact with leaving parents contributes to decreasing aggression of college students with left-behind experience. Meanwhile, gender is an important factor in this relationship

Microstructure Effects on the Water Oxidation Activity of Co3O4/ Porous Silica Nanocomposites

We investigate the effect of microstructuring on the water oxidation (oxygen evolution) activity of two types of Co3O4/porous silica composites: Co3O4/porous SiO2 core/shell nanoparticles with varying shell thicknesses and surface areas, and Co3O4/mesoporous silica nanocomposites with various surface functionalities. Catalytic tests in the presence of Ru(bpy)3 2+ as a photosensitizer and S2O8 2- as a sacrificial electron acceptor show that porous silica shells of up to -20 nm in thickness lead to increased water oxidation activity. We attribute this effect to either (1) a combination of an effective increase in catalyst active area or consequent higher local concentration of Ru(bpy)3 2+; (2) a decrease in the permittivity of the medium surrounding the catalyst surface and a consequent increase in the rate of charge transfer; or both. Functionalized Co3O4/mesoporous silica nanocomposites show lower water oxidation activity compared with the parent nonfunctionalized catalyst, likely because of partial pore blocking of the silica support upon surface grafting. A more thorough understanding of the effects of microstructure and permittivity on water oxidation ability will enable the construction of next generation catalysts possessing optimal configuration and better efficiency for water splitting

Los Angeles Metro Bus Data Analysis Using GPS Trajectory and Schedule Data (Demo Paper)

With the widespread installation of location-enabled devices on public transportation, public vehicles are generating massive amounts of trajectory data in real time. However, using these trajectory data for meaningful analysis requires careful considerations in storing, managing, processing, and visualizing the data. Using the location data of the Los Angeles Metro bus system, along with publicly available bus schedule data, we conduct a data processing and analyses study to measure the performance of the public transportation system in Los Angeles utilizing a number of metrics including travel-time reliability, on-time performance, bus bunching, and travel-time estimation. We demonstrate the visualization of the data analysis results through an interactive web-based application. The developed algorithms and system provide powerful tools to detect issues and improve the efficiency of public transportation systems.Comment: SIGSPATIAL'18, demo paper, 4 page

Crowdsourced quality assessment of enhanced underwater images: a pilot study.

Underwater image enhancement (UIE) is essential for a high-quality underwater optical imaging system. While a number of UIE algorithms have been proposed in recent years, there is little study on image quality assessment (IQA) of enhanced underwater images. In this paper, we conduct the first crowdsourced subjective IQA study on enhanced underwater images. We chose ten state-of-the-art UIE algorithms and applied them to yield enhanced images from an underwater image benchmark. Their latent quality scales were reconstructed from pair comparison. We demonstrate that the existing IQA metrics are not suitable for assessing the perceived quality of enhanced underwater images. In addition, the overall performance of 10 UIE algorithms on the benchmark is ranked by the newly proposed simulated pair comparison of the methods

[OIII] 5007A Emission Line Width as a Surrogate for stellar dispersion in Type 1 AGNs?

We present a study of the relation between the [OIII] 5007A emission line width (sigma_{[OIII]}) and stellar velocity dispersion (sigma_{*}), utilizing a sample of 740 type 1 active galactic nuclei (AGNs) with high-quality spectra at redshift z < 1.0. We find the broad correlation between the core component of [OIII] emission line width (sigma_{[OIII,core]}) and sigma_{*} with a scatter of 0.11~dex for the low redshift (z < 0.1) sample; for redshift (0.3 < z < 1.0) AGNs, the scatter is larger, being 0.16~dex. We also find that the Eddington ratio (L_{bol}/L_{Edd}) may play an important role in the discrepancies between sigma_{[OIII,core]} and sigma_{*}. As the L_{bol}/L_{Edd} increases, sigma_{[OIII,core]} tends to be larger than sigma_{*}. By classifying our local sample with different minor-to-major axis ratios, we find that sigma_{*} is larger than sigma_{[OIII,core]} for those edge-on spiral galaxies. In addition, we also find that the effects of outflow strength properties such as maximum outflow velocity (V_{max}) and the broader component of [OIII] emission line width and line shift (sigma_{[OIII,out]} and V_{[OIII,out]}) may play a major role in the discrepancies between sigma_{[OIII,core]} and sigma_{*}. The discrepancies between sigma_{[OIII,core]} and sigma_{*} are larger when V_{max}, V_{[OIII,out]}, and sigma_{[OIII,out]} increase. Our results show that the outflow strengths may have significant effects on the differences between narrow-line region gas and stellar kinematics in AGNs. We suggest that caution should be taken when using sigma_{[OIII,core]} as a surrogate for sigma_{*}. In addition, the substitute of sigma_{[OIII,core]} for sigma_{*} could be used only for low luminosity AGNs.Comment: 17 pages, Accepted for publication in Ap