The CoFeNC@NC Catalyst with Numerous Surface Cracks Bidirectionally Catalyzes the Conversion of Polysulfides to Accelerate the Reaction Kinetics of Lithium-Sulfur Batteries.

More and more attention has been paid to lithium-sulfur (Li─S) batteries due to their high energy density and low cost. However, the intractable "shuttle effect" and the low conductivity of S and its reaction product, Li2 S, compromise battery performance. To address the inherent challenges, a hollow composite catalyst as a separator coating material is designed, in which CoFe alloy is embedded in a carbon skeleton (CoFeNC@NC). In the hybrid structure, the carbon layer can endow the batteries with high electrical conductivity, while the CoFe alloy can effectively bidirectionally catalyze the conversion between lithium polysulfides (LiPSs) and Li2 S, accelerating the reaction kinetics and reducing the dissolution of LiPSs. Furthermore, the distinctive hollow structure with a cracked surface can facilitate the exposure of a more accessible catalytically active site and enhance Li+ diffusion. Benefiting from the synergistic effects, Li─S batteries with a CoFeNC@NC catalyst achieve a high sulfur utilization (1250.8 mAh g-1 at 0.2 C), superior rate performance (756 mAh g-1 at 2 C), and excellent cycling stability (an ultralow capacity fading of 0.054% per cycle at 1 C for 1000 cycles). Even at a sulfur loading of 5.3 mg cm-2 , a high area capacity of 4.05 mAh cm-2 can still be achieved after 100 cycles, demonstrating its potential practicality.

SimpleTrack: Rethinking and Improving the JDE Approach for Multi-Object Tracking.

Joint detection and embedding (JDE) methods usually fuse the target motion information and appearance information as the data association matrix, which could fail when the target is briefly lost or blocked in multi-object tracking (MOT). In this paper, we aim to solve this problem by proposing a novel association matrix, the Embedding and GioU (EG) matrix, which combines the embedding cosine distance and GioU distance of objects. To improve the performance of data association, we develop a simple, effective, bottom-up fusion tracker for re-identity features, named SimpleTrack, and propose a new tracking strategy which can mitigate the loss of detection targets. To show the effectiveness of the proposed method, experiments are carried out using five different state-of-the-art JDE-based methods. The results show that by simply replacing the original association matrix with our EG matrix, we can achieve significant improvements in IDF1, HOTA and IDsw metrics, and increase the tracking speed of these methods by around 20%. In addition, our SimpleTrack has the best data association capability among the JDE-based methods, e.g., 61.6 HOTA and 76.3 IDF1, on the test set of MOT17 with 23 FPS running speed on a single GTX2080Ti GPU.

Hierarchically Hybrid Porous Co3O4@NiMoO4/CoMoO4 Heterostructures for High-Performance Electrochemical Energy Storage.

Hierarchical, ultrathin, and porous NiMoO4@CoMoO4 on Co3O4 hollow bones were successfully designed and synthesized by a hydrothermal route from the Co-precursor, followed by a KOH (potassium hydroxide) activation process. The hydrothermally synthesized Co3O4 nanowires act as the scaffold for anchoring the NiMoO4@CoMoO4 units but also show more compatibility with NiMoO4, leading to high conductivity in the heterojunction. The intriguing morphological features endow the hierarchical Co3O4@NiMoO4@CoMoO4 better electrochemical performance where the capacity of the Co3O4@NiMoO4@CoMoO4 heterojunction being 272 mA·h·g-1 at 1 A·g-1 can be achieved with a superior retention of 84.5% over 1000 cycles. The enhanced utilization of single/few NiMoO4@CoMoO4 shell layers on the Co3O4 core make it easy to accept extra electrons, enhancing the adsorption of OH- at the shell surface, which contribute to the high capacity. In our work, an asymmetric supercapacitor utilizing the optimized Co3O4@NiMoO4@CoMoO4 activated carbon (AC) as electrode materials was assembled, namely, Co3O4@NiMoO4@CoMoO4//AC device, yielding a maximum high energy density of 53.9 W·h·kg-1 at 1000 W·kg-1. It can retain 25.92 W·h·kg-1 even at 8100 W·kg-1, revealing its potential and viability for applications. The good power densities are ascribed to the porous feature from the robust architecture with recreated abundant mesopores on the composite, which assure improved conductivity and enhanced diffusion of OH- and also the electron transport. The work demonstrated here holds great promise for synthesizing other heterojunction materials M3O4@MMoO4@MMoO4 (M = Fe, Ni, Sn, etc).

A flame-retardant polyimide interlayer with polysulfide lithium traps and fast redox conversion towards safety and high sulfur utilization Li-S batteries.

In recent years and following the progress made in lithium-ion battery technology, substantial efforts have been devoted to developing practical lithium-sulfur (Li-S) batteries for next-generation commercial energy storage devices. The practical application of Li-S batteries is still limited by dramatically reduced capacities, cycling instabilities, and safety issues arising from flammable components. In this study, we designed and fabricated a flame-retardant, multifunctional interlayer which integrated electroconductive networks, lithium polysulfide (LiPS) traps and catalysts to significantly elevate the electrochemical performance and safety of pristine Li-S batteries. The LiPS adsorptive polymer polyimide (PI) constrains polysulfides to the cathode region and effectively suppresses the shuttle effect. Coralloid PI/multiwalled carbon nanotube (MCNT) compounds provide plentiful reaction sites for active materials. The catalytic Ni on the metal skeleton surface notably promotes Li+ diffusion, lowers the redox overpotential and accelerates LiPS conversion, which improves the redox kinetics associated with sulfur-related species and significantly elevates sulfur utilization. At different current densities of 0.2 C and 0.5 C, impressive initial discharge capacities of 1275.3 mA h g-1 and 1190.9 mA h g-1 are attainable respectively, with high capacity retentions of 80.3% and 78.6% over 600 cycles. Besides, the multifunctional interlayer can also act as a flame-retardant layer to promote the safety of Li-S batteries by inhibiting the spread of fire. This study provides a feasible and prospective strategy that adopts a multifunctional interlayer to develop Li-S batteries with higher capacities, longer cycling lives and safer working conditions.

High performance flexible asymmetric supercapacitor constructed by cobalt aluminum layered double hydroxide @ nickel cobalt layered double hydroxide heterostructure grown in-situ on carbon cloth.

HYPOTHESIS: Three-dimensional layered layered double hydroxide (LDH) nanostructure materials grow in-situ on excellent conductive and flexible carbon cloth (CC) substrate not only reduce the ability of binders in resisting ions transfer, but also make them to be quasi-vertically arranged well on substrates without aggregation. This would result in enough electroactive sites, to obtain superior electrochemical performance. EXPERIMENTS: A hierarchical CoAl-LDH@NiCo-LDH composite was prepared on a surface-modified carbon cloth by a simple two-step hydrothermal process. In this process, CoAl-LDH nanosheets (NSs)/CC acting as the inner core were wrapped up in NiCo-LDH nanoneedle arrays (NNAs) evenly. Also, a flexible quasi-solid-state supercapacitor device was constructed using CoAl-LDH@NiCo-LDH/CC and activated carbon (AC) as a positive electrode and a negative electrode, respectively. FINDINGS: The CoAl-LDH@NiCo-LDH/CC developed had an excellent specific capacitance (2633.6F/g at 1 A/g) with remarkable cyclic performance (92.5% retention of its incipient over 5000 cycles at 4 A/g). The flexible quasi-solid-state supercapacitor device CoAl-LDH@NiCo-LDH/CC//AC/CC yielded a splendid energy density of 57.8 Wh/kg at a power density of 0.81 kW/kg and a brilliant power density of 16.09 kW/kg at 38.0 Wh/kg in a broad potential window of 1.55 V. Furthermore, the exceptional cyclic stability and excellent flexibility of the device show it can be applied in flexible energy storage systems.

Well-designed sophisticated structure of sandwich-like CC@NiAl-LDH@GO@NiCo-LDH material with unique advantages for high performance and practicality hybrid quasi-solid-state supercapacitors.

A sandwich-like flexible architecture electrode material composed of NiAl-LDH nanoplates grown on carbon cloths (CC), coupled with GO interlayer and NiCo-LDH nanowire on the interlayer was successfully assembled via hydrothermal and chemical bath deposition (denoted as CC@NiAl-LDH@GO@NiCo-LDH). The promising combination of NiAl-LDH, graphene and NiCo-LDH forming a multilayer structure through electrostatic absorption and in-situ growth process which endow a high mass loading superiority and synergistic effect for supercapacitors. In addition, the interspace inside the sandwich-like architecture constructed by the graphene and the NiAl-LDH/ NiCo-LDH nano-flakes contribute to alleviate of the volume expansion during the cycling process and promote the diffusion rate of ions. The CC@NiAl-LDH@GO@NiCo-LDH material demonstrates excellent electrochemical performance which exhibit remarkable specific capacitance of 2359.8F·g-1 (14.2F·cm-2) at 1 A·g-1 (6 mA·cm-2) and outstanding capacitance retentions of 93.1% after 1500 cycles. Subsequently, the CC@NiAl-LDH@GO@NiCo-LDH material was used as cathode material to fabricate a hybrid quasi-solid-state supercapacitor that exhibits a high energy density of 52.0 Wh·kg-1 at 796.7 W·kg-1 and 38.4 Wh·kg-1 at 12015 W·kg-1, revealing its potential and viability for commercial applications. Furthermore, the hybrid quasi-solid-state supercapacitor can be applied under different extreme operating conditions such as bending, twisting, sour/alkali soaking, ice bathing, warm bathing, hammering and cutting conditions. It is predictable that the unique sandwich-like structure will be an extremely promising electrode material for high-performance supercapacitors.

Brain functional and effective connectivity based on electroencephalography recordings: A review.

Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional mechanism. Many recent studies on electroencephalography (EEG) have been focusing on modeling and estimating brain connectivity due to increasing evidence that it can help better understand various brain neurological conditions. However, there is a lack of a comprehensive updated review on studies of EEG-based brain connectivity, particularly on visualization options and associated machine learning applications, aiming to translate those techniques into useful clinical tools. This article reviews EEG-based functional and effective connectivity studies undertaken over the last few years, in terms of estimation, visualization, and applications associated with machine learning classifiers. Methods are explored and discussed from various dimensions, such as either linear or nonlinear, parametric or nonparametric, time-based, and frequency-based or time-frequency-based. Then it is followed by a novel review of brain connectivity visualization methods, grouped by Heat Map, data statistics, and Head Map, aiming to explore the variation of connectivity across different brain regions. Finally, the current challenges of related research and a roadmap for future related research are presented.

FSD-BRIEF: A Distorted BRIEF Descriptor for Fisheye Image Based on Spherical Perspective Model.

Fisheye images with a far larger Field of View (FOV) have severe radial distortion, with the result that the associated image feature matching process cannot achieve the best performance if the traditional feature descriptors are used. To address this challenge, this paper reports a novel distorted Binary Robust Independent Elementary Feature (BRIEF) descriptor for fisheye images based on a spherical perspective model. Firstly, the 3D gray centroid of feature points is designed, and the position and direction of the feature points on the spherical image are described by a constructed feature point attitude matrix. Then, based on the attitude matrix of feature points, the coordinate mapping relationship between the BRIEF descriptor template and the fisheye image is established to realize the computation associated with the distorted BRIEF descriptor. Four experiments are provided to test and verify the invariance and matching performance of the proposed descriptor for a fisheye image. The experimental results show that the proposed descriptor works well for distortion invariance and can significantly improve the matching performance in fisheye images.

Nonlinear Modeling of Cortical Responses to Mechanical Wrist Perturbations Using the NARMAX Method.

OBJECTIVE: Nonlinear modeling of cortical responses (EEG) to wrist perturbations allows for the quantification of cortical sensorimotor function in healthy and neurologically impaired individuals. A common model structure reflecting key characteristics shared across healthy individuals may provide a reference for future clinical studies investigating abnormal cortical responses associated with sensorimotor impairments. Thus, the goal of our study is to identify this common model structure and therefore to build a nonlinear dynamic model of cortical responses, using nonlinear autoregressive-moving-average model with exogenous inputs (NARMAX). METHODS: EEG was recorded from ten participants when receiving continuous wrist perturbations. A common model structure detection method was developed for identifying a common NARMAX model structure across all participants, with individualized parameter values. The results were compared to conventional subject-specific models. RESULTS: The proposed method achieved 93.91% variance accounted for (VAF) when implementing a one-step-ahead prediction and around 50% VAF for a k-step ahead prediction (k = 3), without a substantial drop of VAF as compare to subject-specific models. The estimated common structure suggests that the measured cortical response is a mixed outcome of the nonlinear transformation of external inputs and local neuronal interactions or inherent neuronal dynamics at the cortex. CONCLUSION: The proposed method well determined the common characteristics across subjects in the cortical responses to wrist perturbations. SIGNIFICANCE: It provides new insights into the human sensorimotor nervous system in response to somatosensory inputs and paves the way for future translational studies on assessments of sensorimotor impairments using our modeling approach.

LdsConv: Learned Depthwise Separable Convolutions by Group Pruning.

Standard convolutional filters usually capture unnecessary overlap of features resulting in a waste of computational cost. In this paper, we aim to solve this problem by proposing a novel Learned Depthwise Separable Convolution (LdsConv) operation that is smart but has a strong capacity for learning. It integrates the pruning technique into the design of convolutional filters, formulated as a generic convolutional unit that can be used as a direct replacement of convolutions without any adjustments of the architecture. To show the effectiveness of the proposed method, experiments are carried out using the state-of-the-art convolutional neural networks (CNNs), including ResNet, DenseNet, SE-ResNet and MobileNet, respectively. The results show that by simply replacing the original convolution with LdsConv in these CNNs, it can achieve a significantly improved accuracy while reducing computational cost. For the case of ResNet50, the FLOPs can be reduced by 40.9%, meanwhile the accuracy on the associated ImageNet increases.

Highly Stable Three-Dimensional Nickel-Cobalt Hydroxide Hierarchical Heterostructures Hybridized with Carbon Nanotubes for High-Performance Energy Storage Devices.

A three-dimensional (3D) composite consisting of nickel-cobalt (Ni-Co) dual hydroxide nanoneedles (NCDHNs) grown on a carbon nanotube (CNT) material, denoted as CNTs@NCDHNs, was designed using a facile one-step hydrothermal method. This composite was further fabricated into electrodes, which exhibited high rate capability and long cycle life. Comparative analysis of the electrochemical performance between 3D CNTs@NCDHNs electrodes and Ni-Co hydroxide electrodes revealed that the high rate capability and long cycle life of the CNTs@NCDHNs are due to a synergistic effect. The CNTs@NCDHNs exhibited a high specific capacitance of 1823 F g-1 at a current density of 1 A g-1, and more than 77.6% of the capacitance was retained at a charge-discharge rate of 20 A g-1. To evaluate the functional behavior of the CNTs@NCDHNs, quasi-solid-state cells using CNTs@NCDHNs as the positive electrode and rGO-Fe2O3 as the negative electrode were assembled and tested. These devices presented ultrafast charge-discharge rates of up to 20 A g-1 with high rate capabilities and excellent long-term cyclic stability. The corresponding quasi-solid-state device presented a high energy density of up to 54.6 Wh kg-1 at a power density of 1.13 kW kg-1 and an energy density of 35.8 Wh kg-1 at 12.4 kW kg-1 when a voltage in the range 0-1.6 V was applied. Moreover, the device exhibited optimal flexibility, stability, and safety under different extreme conditions.

A Parametric Time-Frequency Conditional Granger Causality Method Using Ultra-Regularized Orthogonal Least Squares and Multiwavelets for Dynamic Connectivity Analysis in EEGs.

OBJECTIVE: This study proposes a new parametric time-frequency conditional Granger causality (TF-CGC) method for high-precision connectivity analysis over time and frequency domain in multivariate coupling nonstationary systems, and applies it to source electroencephalogram (EEG) signals to reveal dynamic interaction patterns in oscillatory neocortical sensorimotor networks. METHODS: The Geweke's spectral measure is combined with the time-varying autoregressive with exogenous input (TVARX) modeling approach, which uses multiwavelet-based ultra-regularized orthogonal least squares (UROLS) algorithm, aided by adjustable prediction error sum of squares (APRESS), to obtain high-resolution time-varying CGC representations. The UROLS-APRESS algorithm, which adopts both the regularization technique and the ultra-least squares criterion to measure not only the signal themselves, but also the weak derivatives of them, is a novel powerful method in constructing time-varying models with good generalization performance, and can accurately track smooth and fast changing causalities. The generalized measurement based on CGC decomposition is able to eliminate indirect influences in multivariate systems. RESULTS: The proposed method is validated on two simulations, and then applied to source level motor imagery (MI) EEGs, where the predicted distributions are well recovered with high TF precision, and the detected connectivity patterns of MI-EEGs are physiologically interpretable and yield new insights into the dynamical organization of oscillatory cortical networks. CONCLUSION: Experimental results confirm the effectiveness of the TF-CGC method in tracking rapidly varying causalities of EEG-based oscillatory networks. SIGNIFICANCE: The novel TF-CGC method is expected to provide important information of neural mechanisms of perception and cognition.

Distractor-Aware Deep Regression for Visual Tracking.

In recent years, regression trackers have drawn increasing attention in the visual-object tracking community due to their favorable performance and easy implementation. The tracker algorithms directly learn mapping from dense samples around the target object to Gaussian-like soft labels. However, in many real applications, when applied to test data, the extreme imbalanced distribution of training samples usually hinders the robustness and accuracy of regression trackers. In this paper, we propose a novel effective distractor-aware loss function to balance this issue by highlighting the significant domain and by severely penalizing the pure background. In addition, we introduce a full differentiable hierarchy-normalized concatenation connection to exploit abstractions across multiple convolutional layers. Extensive experiments were conducted on five challenging benchmark-tracking datasets, that is, OTB-13, OTB-15, TC-128, UAV-123, and VOT17. The experimental results are promising and show that the proposed tracker performs much better than nearly all the compared state-of-the-art approaches.

A Pilot Study Investigating a Novel Non-Linear Measure of Eyes Open versus Eyes Closed EEG Synchronization in People with Alzheimer's Disease and Healthy Controls

BACKGROUND: The incidence of Alzheimer disease (AD) is increasing with the ageing population. The development of low cost non-invasive diagnostic aids for AD is a research priority. This pilot study investigated whether an approach based on a novel dynamic quantitative parametric EEG method could detect abnormalities in people with AD. METHODS: 20 patients with probable AD, 20 matched healthy controls (HC) and 4 patients with probable fronto temporal dementia (FTD) were included. All had detailed neuropsychology along with structural, resting state fMRI and EEG. EEG data were analyzed using the Error Reduction Ratio-causality (ERR-causality) test that can capture both linear and nonlinear interactions between different EEG recording areas. The 95% confidence intervals of EEG levels of bi-centroparietal synchronization were estimated for eyes open (EO) and eyes closed (EC) states. RESULTS: In the EC state, AD patients and HC had very similar levels of bi-centro parietal synchronization; but in the EO resting state, patients with AD had significantly higher levels of synchronization (AD = 0.44; interquartile range (IQR) 0.41 vs. HC = 0.15; IQR 0.17, p < 0.0001). The EO/EC synchronization ratio, a measure of the dynamic changes between the two states, also showed significant differences between these two groups (AD ratio 0.78 versus HC ratio 0.37 p < 0.0001). EO synchronization was also significantly different between AD and FTD (FTD = 0.075; IQR 0.03, p < 0.0001). However, the EO/EC ratio was not informative in the FTD group due to very low levels of synchronization in both states (EO and EC). CONCLUSION: In this pilot work, resting state quantitative EEG shows significant differences between healthy controls and patients with AD. This approach has the potential to develop into a useful non-invasive and economical diagnostic aid in AD.

The cortical focus in childhood absence epilepsy; evidence from nonlinear analysis of scalp EEG recordings.

OBJECTIVE: To determine the origin and dynamic characteristics of the generalised hyper-synchronous spike and wave (SW) discharges in childhood absence epilepsy (CAE). METHODS: We applied nonlinear methods, the error reduction ratio (ERR) causality test and cross-frequency analysis, with a nonlinear autoregressive exogenous (NARX) model, to electroencephalograms (EEGs) from CAE, selected with stringent electro-clinical criteria (17 cases, 42 absences). We analysed the pre-ictal and ictal strength of association between homologous and heterologous EEG derivations and estimated the direction of synchronisation and corresponding time lags. RESULTS: A frontal/fronto-central onset of the absences is detected in 13 of the 17 cases with the highest ictal strength of association between homologous frontal followed by centro-temporal and fronto-central areas. Delays consistently in excess of 4 ms occur at the very onset between these regions, swiftly followed by the emergence of "isochronous" (0-2 ms) synchronisation but dynamic time lag changes occur during SW discharges. CONCLUSIONS: In absences an initial cortico-cortical spread leads to dynamic lag changes to include periods of isochronous interhemispheric synchronisation, which we hypothesize is mediated by the thalamus. SIGNIFICANCE: Absences from CAE show ictal epileptic network dynamics remarkably similar to those observed in WAG/Rij rats which guided the formulation of the cortical focus theory.

Time-Varying System Identification Using an Ultra-Orthogonal Forward Regression and Multiwavelet Basis Functions With Applications to EEG.

A new parametric approach is proposed for nonlinear and nonstationary system identification based on a time-varying nonlinear autoregressive with exogenous input (TV-NARX) model. The TV coefficients of the TV-NARX model are expanded using multiwavelet basis functions, and the model is thus transformed into a time-invariant regression problem. An ultra-orthogonal forward regression (UOFR) algorithm aided by mutual information (MI) is designed to identify a parsimonious model structure and estimate the associated model parameters. The UOFR-MI algorithm, which uses not only the observed data themselves but also weak derivatives of the signals, is more powerful in model structure detection. The proposed approach combining the advantages of both the basis function expansion method and the UOFR-MI algorithm is proved to be capable of tracking the change of TV parameters effectively in both numerical simulations and the real EEG data.

Quantifying heterogeneous responses of fish community size structure using novel combined statistical techniques.

To understand changes in ecosystems, the appropriate scale at which to study them must be determined. Large marine ecosystems (LMEs) cover thousands of square kilometres and are a useful classification scheme for ecosystem monitoring and assessment. However, averaging across LMEs may obscure intricate dynamics within. The purpose of this study is to mathematically determine local and regional patterns of ecological change within an LME using empirical orthogonal functions (EOFs). After using EOFs to define regions with distinct patterns of change, a statistical model originating from control theory is applied (Nonlinear AutoRegressive Moving Average with eXogenous input - NARMAX) to assess potential drivers of change within these regions. We have selected spatial data sets (0.5° latitude × 1°longitude) of fish abundance from North Sea fisheries research surveys (spanning 1980-2008) as well as of temperature, oxygen, net primary production and a fishing pressure proxy, to which we apply the EOF and NARMAX methods. Two regions showed significant changes since 1980: the central North Sea displayed a decrease in community size structure which the NARMAX model suggested was linked to changes in fishing; and the Norwegian trench region displayed an increase in community size structure which, as indicated by NARMAX results, was primarily linked to changes in sea-bottom temperature. These regions were compared to an area of no change along the eastern Scottish coast where the model determined the community size structure was most strongly associated to net primary production. This study highlights the multifaceted effects of environmental change and fishing pressures in different regions of the North Sea. Furthermore, by highlighting this spatial heterogeneity in community size structure change, important local spatial dynamics are often overlooked when the North Sea is considered as a broad-scale, homogeneous ecosystem (as normally is the case within the political Marine Strategy Framework Directive).

A nonlinear generalization of spectral Granger causality.

Spectral measures of linear Granger causality have been widely applied to study the causal connectivity between time series data in neuroscience, biology, and economics. Traditional Granger causality measures are based on linear autoregressive with exogenous (ARX) inputs models of time series data, which cannot truly reveal nonlinear effects in the data especially in the frequency domain. In this study, it is shown that the classical Geweke's spectral causality measure can be explicitly linked with the output spectra of corresponding restricted and unrestricted time-domain models. The latter representation is then generalized to nonlinear bivariate signals and for the first time nonlinear causality analysis in the frequency domain. This is achieved by using the nonlinear ARX (NARX) modeling of signals, and decomposition of the recently defined output frequency response function which is related to the NARX model.