Questionnaire Data From the Revision of a Chinese Version of Free Will and Determinism Plus Scale

Funding statement: This work was supported by National Nature Science Foundations of China No. 31471001 to Kaiping Peng. All data, together with their codebooks and manipulation code, are available at osf.io/t2nsw/.Peer reviewedPublisher PD

Study of spatio-temporal modeling in video quality assessment

Video quality assessment (VQA) has received remarkable attention recently. Most of the popular VQA models employ recurrent neural networks (RNNs) to capture the temporal quality variation of videos. However, each long-term video sequence is commonly labeled with a single quality score, with which RNNs might not be able to learn long-term quality variation well. A natural question then arises: What’s the real role of RNNs in learning the visual quality of videos? Does it learn spatio-temporal representation as expected or just aggregating spatial features redundantly? In this study, we conduct a comprehensive study by training a family of VQA models with carefully designed frame sampling strategies and spatio-temporal fusion methods. Our extensive experiments on four publicly available in-the-wild video quality datasets lead to two main findings. First, the plausible spatio-temporal modeling module ( i.e ., RNNs) does not facilitate quality-aware spatio-temporal feature learning. Second, sparsely sampled video frames are capable of obtaining the competitive performance against using all video frames as the input. In other words, spatial features play a vital role in capturing video quality variation for VQA. To our best knowledge, this is the first work to explore the issue of spatio-temporal modeling in VQA

Dopant Segregation Boosting High‐Voltage Cyclability of Layered Cathode for Sodium Ion Batteries

As a widely used approach to modify a material’s bulk properties, doping can effectively improve electrochemical properties and structural stability of various cathodes for rechargeable batteries, which usually empirically favors a uniform distribution of dopants. It is reported that dopant aggregation effectively boosts the cyclability of a Mg‐doped P2‐type layered cathode (Na0.67Ni0.33Mn0.67O2). Experimental characterization and calculation consistently reveal that randomly distributed Mg dopants tend to segregate into the Na‐layer during high‐voltage cycling, leading to the formation of high‐density precipitates. Intriguingly, such Mg‐enriched precipitates, acting as 3D network pillars, can further enhance a material’s mechanical strength, suppress cracking, and consequently benefit cyclability. This work not only deepens the understanding on dopant evolution but also offers a conceptually new approach by utilizing precipitation strengthening design to counter cracking related degradation and improve high‐voltage cyclability of layered cathodes.Improved cyclability of Mg‐doped P2‐NMM layered cathode is mainly due to suppression of cracking. Randomly distributed Mg dopants tend to segregate into precipitates during high‐voltage cycling, which can further strengthen the layered cathode and suppress cracking, leading to superior cycling stability at elevated voltage. Dopant precipitate is a new design concept to improve layered cathode cyclability.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153093/1/adma201904816.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153093/2/adma201904816-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153093/3/adma201904816_am.pd

Multi-scale convolutional recurrent neural network for psychiatric disorder identification in resting-state EEG

BackgroundAccurate classification based on affordable objective neuroimaging biomarkers are important steps toward designing individualized treatment.MethodsIn this work, we investigated a deep learning classification model, multi-scale convolutional recurrent neural network (MCRNN), to explore psychiatric disorder-related biomarkers by leveraging the spatiotemporal information of resting-state EEG (rsEEG) using a multiple psychiatric disorder database containing 327 individuals diagnosed with schizophrenia, bipolar, major depressive disorders, and healthy controls. All subjects were mapped to a shared low-dimensional subspace for intuitively interpreting the inter-relationship and separation of psychiatric disorders.ResultsPsychiatric disorders were identified using rsEEG with high accuracy ranged from 78.6 to 91.3% in patient vs. controls two-class classification, and 68.2% in four-class classification. The control-to-schizophrenia trajectory interpretated by the model was consistent with the disease severity in clinical observation.ConclusionThe MsRNN demonstrated a capability in extracting discriminative rsEEG biomarkers for psychiatric disorder classification, indicating its potential to facilitate our understanding of psychiatric disorders and monitoring interventions