GaitStrip: Gait Recognition via Effective Strip-based Feature Representations and Multi-Level Framework

Many gait recognition methods first partition the human gait into N-parts and then combine them to establish part-based feature representations. Their gait recognition performance is often affected by partitioning strategies, which are empirically chosen in different datasets. However, we observe that strips as the basic component of parts are agnostic against different partitioning strategies. Motivated by this observation, we present a strip-based multi-level gait recognition network, named GaitStrip, to extract comprehensive gait information at different levels. To be specific, our high-level branch explores the context of gait sequences and our low-level one focuses on detailed posture changes. We introduce a novel StriP-Based feature extractor (SPB) to learn the strip-based feature representations by directly taking each strip of the human body as the basic unit. Moreover, we propose a novel multi-branch structure, called Enhanced Convolution Module (ECM), to extract different representations of gaits. ECM consists of the Spatial-Temporal feature extractor (ST), the Frame-Level feature extractor (FL) and SPB, and has two obvious advantages: First, each branch focuses on a specific representation, which can be used to improve the robustness of the network. Specifically, ST aims to extract spatial-temporal features of gait sequences, while FL is used to generate the feature representation of each frame. Second, the parameters of the ECM can be reduced in test by introducing a structural re-parameterization technique. Extensive experimental results demonstrate that our GaitStrip achieves state-of-the-art performance in both normal walking and complex conditions.Comment: Accepted to ACCV202

1

1H NMR-based metabolic profiling combined with multivariate data analysis was used to explore the metabolic phenotype of functional dyspepsia (FD) in stressed rats and evaluate the intervention effects of the Chinese medicine Weikangning (WKN). After a 7-day period of model establishment, a 14-day drug administration schedule was conducted in a WKN-treated group of rats, with the model and normal control groups serving as negative controls. Based on 1H NMR spectra of urine and serum from rats, PCA, PLS-DA, and OPLS-DA were performed to identify changing metabolic profiles. According to the key metabolites determined by OPLS-DA, alterations in energy metabolism, stress-related metabolism, and gut microbiota were found in FD model rats after stress stimulation, and these alterations were restored to normal after WKN administration. This study may provide new insights into the relationship between FD and psychological stress and assist in research into the metabolic mechanisms involved in Chinese medicine

Acoustoelectric brain imaging with different conductivities and acoustic distributions

Objective: Acoustoelectric brain imaging (AEBI) is a promising imaging method for mapping brain biological current densities with high spatiotemporal resolution. Currently, it is still challenging to achieve human AEBI with an unclear acoustoelectric (AE) signal response of medium characteristics, particularly in conductivity and acoustic distribution. This study introduces different conductivities and acoustic distributions into the AEBI experiment, and clarifies the response interaction between medium characteristics and AEBI performance to address these key challenges.Approach: AEBI with different conductivities is explored by the imaging experiment, potential measurement, and simulation on a pig’s fat, muscle, and brain tissue. AEBI with different acoustic distributions is evaluated on the imaging experiment and acoustic field measurement through a deep and surface transmitting model built on a human skullcap and pig brain tissue.Main results: The results show that conductivity is not only inversely proportional to the AE signal amplitude but also leads to a higher AEBI spatial resolution as it increases. In addition, the current source and sulcus can be located simultaneously with a strong AE signal intensity. The transcranial focal zone enlargement, pressure attenuation in the deep-transmitting model, and ultrasound echo enhancement in the surface-transmitting model cause a reduced spatial resolution, FFT-SNR, and timing correlation of AEBI. Under the comprehensive effect of conductivity and acoustics, AEBI with skull finally shows reduced imaging performance for both models compared with no-skull AEBI. On the contrary, the AE signal amplitude decreases in the deep-transmitting model and increases in the surface-transmitting model.Significance: This study reveals the response interaction between medium characteristics and AEBI performance, and makes an essential step toward developing AEBI as a practical neuroimaging technique

Adaptive Multi-Modal Ensemble Network for Video Memorability Prediction

Video memorability prediction aims to quantify the credibility of being remembered according to the video content, which provides significant value in advertising design, social media recommendation, and other applications. However, the main attributes that affect the memorability prediction have not been determined so that making the design of the prediction model more challenging. Therefore, in this study, we analyze and experimentally verify how to select the most impact factors to predict video memorability. Furthermore, we design a new framework, Adaptive Multi-modal Ensemble Network, based on the chosen vital impact factors to predict video memorability efficiently. Specifically, we first conduct three main impact factors that affect video memorability, i.e., temporal 3D information, spatial information and semantics derived from video, image and caption, respectively. Then, the Adaptive Multi-modal Ensemble Network integrates the three individual base learners (i.e., ResNet3D, Deep Random Forest and Multi-Layer Perception) into a weighted ensemble framework to score the video memorability. In addition, we also design an adaptive learning strategy to update the weights based on the importance of memorability, which is predicted by the base learners rather than assigning weights manually. Finally, the experiments on the public VideoMem dataset demonstrate that the proposed method provides competitive results and high efficiency for video memorability prediction

Study of the Predictive Mechanism with Big Data-Driven Lean Manufacturing and Six Sigma Methodology

Part 14: The New Digital Lean Manufacturing ParadigmInternational audienceIn order to achieve the sustainable development, the predictive mechanism with big data-driven Lean Manufacturing and Six Sigma methodology is proposed in this paper. The sustainable development for serious competition is often studied, however, the predictive mechanism with big data-driven Lean Manufacturing and Six Sigma methodology is seldom mentioned in publications. This paper reports the predictive mechanism from the perspective of big data-driven Lean Manufacturing. The key techniques including PLC communication, DMAIC roadmap, SPC technique and Hypothesis Testing are utilized to eliminate the waste and obtain continuous improvement. The demonstration of calculator production indicates the predictive mechanism can effectively eliminate the waste and improve the output by 60% with the sufficient capability of Cp > 1.33 and Cpk > 1

Towards the 4 V-class n-type organic lithium-ion positive electrode materials: the case of conjugated triflimides and cyanamides

Organic electrode materials have garnered a great deal of interest owing to their sustainability, cost-efficiency, and design flexibility metrics. Despite numerous endeavors to fine-tune their redox potential, the pool of organic positive electrode materials with a redox potential above 3 V versus Li+/Li0, and maintaining air stability in the Li-reservoir configuration remains limited. This study expands the chemical landscape of organic Li-ion positive electrode chemistries towards the 4 V-class through molecular design based on electron density depletion within the redox center via the mesomeric effect of electron-withdrawing groups (EWGs). This results in the development of novel families of conjugated triflimides and cyanamides as high-voltage electrode materials for organic lithium-ion batteries. These are found to exhibit ambient air stability and demonstrate reversible electrochemistry with redox potentials spanning the range of 3.1 V to 3.8 V (versus Li+/Li0), marking the highest reported values so far within the realm of n-type organic chemistries. Through comprehensive structural analysis and extensive electrochemical studies, we elucidate the relationship between the molecular structure and the ability to fine-tune the redox potential. These findings offer promising opportunities to customize the redox properties of organic electrodes, bridging the gap with their inorganic counterparts for application in sustainable and eco-friendly electrochemical energy storage devices

High performance Li-, Na-, and K-ion storage in electrically conducting coordination polymers

Coordination polymers (CPs) made of redox-active organic moieties and metal ions emerge as an important class of electroactive materials for battery applications. However, the design and synthesis of high voltage alkali-cation reservoir anionic CPs remains challenging, hindering their practical applications. Herein, we report a family of electrically conducting alkali-cation reservoir CPs with the general formula of A2-TM-PTtSA (wherein A = Li+, Na+, or K+; TM = Fe2+, Co2+, or Mn2+; and PTtSA = benzene-1,2,4,5-tetra-methylsulfonamide). The incorporation of transition metal centers not only enables intrinsic high electrical conductivity, but also shows an impressive redox potential increase of as high as 1 V as compared to A4-PTtSA analogues, resulting in a class of organometallic cathode materials with a high average redox potential of 2.95–3.25 V for Li-, Na- and K-ion batteries. A detailed structure – composition – physicochemical properties – performance correlation study is provided relying on experimental and computational analysis. The best performing candidate shows excellent rate capability (86% of the nominal capacity retained at 10C rate), remarkable cycling stability (96.5% after 1000 cycles), outstanding tolerance to low carbon content (5 wt%), high mass loading (50 mg cm−2), and extreme utilisation conditions of low earth orbit space environment tests. The significance of the disclosed alkali-ion reservoir cathodes is further emphasized by utilizing conventional Li-host graphite anode for full cell assembly, attaining a record voltage of 3 V in an organic cathode Li-ion proof-of-concept cell

Proteomics analysis of IBS-D with spleen and kidney yang deficiency

Objective: To investigate the molecular mechanism underlying the development of diarrhea-predominant irritable bowel syndrome (IBS-D) with spleen and kidney yang deficiency (SKYD) using a proteomics approach. Methods: Male Sprague–Dawley rats (n = 22) were divided into IBS-D (n = 12) and normal control (n = 10) groups. SKYD was then modeled in IBS-D rats by a combination of acetic acid enema, bondage, rectal dilation, tail stimulation, and Senna gavage. Colon tissue samples were subsequently collected and examined by Q Exactive mass spectrometry to identify differentially expressed proteins between the two groups. Results: The occurrence of SKYD/IBS-D was associated with ribosomal protein S23 (Rps23), protein phosphatase 2 catalytic subunit alpha (Pp2a), and growth factor receptor-bound protein 2 (Grb2), which are involved in the ribosome, neurotrophin signaling, and Janus kinase–signal transducer and activator of transcription (JAK–STAT) signaling pathways. Conclusion: These data suggest that SKYD/IBS-D pathophysiology likely involves inflammation, cell growth, apoptosis, stress granule formation, immune activation, loss of epithelial cell integrity, and visceral hypersensitivity