LSTM Pose Machines

We observed that recent state-of-the-art results on single image human pose estimation were achieved by multi-stage Convolution Neural Networks (CNN). Notwithstanding the superior performance on static images, the application of these models on videos is not only computationally intensive, it also suffers from performance degeneration and flicking. Such suboptimal results are mainly attributed to the inability of imposing sequential geometric consistency, handling severe image quality degradation (e.g. motion blur and occlusion) as well as the inability of capturing the temporal correlation among video frames. In this paper, we proposed a novel recurrent network to tackle these problems. We showed that if we were to impose the weight sharing scheme to the multi-stage CNN, it could be re-written as a Recurrent Neural Network (RNN). This property decouples the relationship among multiple network stages and results in significantly faster speed in invoking the network for videos. It also enables the adoption of Long Short-Term Memory (LSTM) units between video frames. We found such memory augmented RNN is very effective in imposing geometric consistency among frames. It also well handles input quality degradation in videos while successfully stabilizes the sequential outputs. The experiments showed that our approach significantly outperformed current state-of-the-art methods on two large-scale video pose estimation benchmarks. We also explored the memory cells inside the LSTM and provided insights on why such mechanism would benefit the prediction for video-based pose estimations.Comment: Poster in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 201

A Transformer-based deep neural network model for SSVEP classification

Steady-state visual evoked potential (SSVEP) is one of the most commonly used control signal in the brain-computer interface (BCI) systems. However, the conventional spatial filtering methods for SSVEP classification highly depend on the subject-specific calibration data. The need for the methods that can alleviate the demand for the calibration data become urgent. In recent years, developing the methods that can work in inter-subject classification scenario has become a promising new direction. As the popular deep learning model nowadays, Transformer has excellent performance and has been used in EEG signal classification tasks. Therefore, in this study, we propose a deep learning model for SSVEP classification based on Transformer structure in inter-subject classification scenario, termed as SSVEPformer, which is the first application of the transformer to the classification of SSVEP. Inspired by previous studies, the model adopts the frequency spectrum of SSVEP data as input, and explores the spectral and spatial domain information for classification. Furthermore, to fully utilize the harmonic information, an extended SSVEPformer based on the filter bank technology (FB-SSVEPformer) is proposed to further improve the classification performance. Experiments were conducted using two open datasets (Dataset 1: 10 subjects, 12-class task; Dataset 2: 35 subjects, 40-class task) in the inter-subject classification scenario. The experimental results show that the proposed models could achieve better results in terms of classification accuracy and information transfer rate, compared with other baseline methods. The proposed model validates the feasibility of deep learning models based on Transformer structure for SSVEP classification task, and could serve as a potential model to alleviate the calibration procedure in the practical application of SSVEP-based BCI systems

Developing the surgeon-machine interface: Using a novel instance-segmentation framework for intraoperative landmark labelling

Introduction: The utilisation of artificial intelligence (AI) augments intraoperative safety, surgical training, and patient outcomes. We introduce the term Surgeon-Machine Interface (SMI) to describe this innovative intersection between surgeons and machine inference. A custom deep computer vision (CV) architecture within a sparse labelling paradigm was developed, specifically tailored to conceptualise the SMI. This platform demonstrates the ability to perform instance segmentation on anatomical landmarks and tools from a single open spinal dural arteriovenous fistula (dAVF) surgery video dataset. Methods: Our custom deep convolutional neural network was based on SOLOv2 architecture for precise, instance-level segmentation of surgical video data. Test video consisted of 8520 frames, with sparse labelling of only 133 frames annotated for training. Accuracy and inference time, assessed using F1-score and mean Average Precision (mAP), were compared against current state-of-the-art architectures on a separate test set of 85 additionally annotated frames. Results: Our SMI demonstrated superior accuracy and computing speed compared to these frameworks. The F1-score and mAP achieved by our platform were 17% and 15.2% respectively, surpassing MaskRCNN (15.2%, 13.9%), YOLOv3 (5.4%, 11.9%), and SOLOv2 (3.1%, 10.4%). Considering detections that exceeded the Intersection over Union threshold of 50%, our platform achieved an impressive F1-score of 44.2% and mAP of 46.3%, outperforming MaskRCNN (41.3%, 43.5%), YOLOv3 (15%, 34.1%), and SOLOv2 (9%, 32.3%). Our platform demonstrated the fastest inference time (88ms), compared to MaskRCNN (90ms), SOLOV2 (100ms), and YOLOv3 (106ms). Finally, the minimal amount of training set demonstrated a good generalisation performance -our architecture successfully identified objects in a frame that were not included in the training or validation frames, indicating its ability to handle out-of-domain scenarios. Discussion: We present our development of an innovative intraoperative SMI to demonstrate the future promise of advanced CV in the surgical domain. Through successful implementation in a microscopic dAVF surgery, our framework demonstrates superior performance over current state-of-the-art segmentation architectures in intraoperative landmark guidance with high sample efficiency, representing the most advanced AI-enabled surgical inference platform to date. Our future goals include transfer learning paradigms for scaling to additional surgery types, addressing clinical and technical limitations for performing real-time decoding, and ultimate enablement of a real-time neurosurgical guidance platform.</p

A cross-sectional survey on mother-to-child transmission of HIV among the migrant population in Dongguan, China

IntroductionThe migrant population, consisting of individuals who relocate from rural to urban areas, faces unique challenges that heighten their vulnerability to HIV infection. These challenges stem from a combination of sociodemographic factors and limited access to healthcare services. Understanding the dynamics of HIV transmission within this population is crucial for the development of effective prevention strategies.MethodsTo investigate the factors contributing to HIV vulnerability among migrants, we conducted a cross-sectional study at Dongguan People's Hospital from January 1, 2018, to December 31, 2021. Our study focused on pregnant women living with HIV and their infants, with a particular emphasis on sociodemographic characteristics, HIV testing and treatment profiles, and neonatal clinical data. Data were systematically collected using standardized forms.ResultsAnalysis of data from 98 participants revealed noteworthy findings. No significant associations were observed between age, marital status, and educational background regarding HIV vulnerability. Similarly, factors such as the status of sexual partners, spousal therapy, and the number of children had no significant impact. However, our analysis highlighted the critical role of treatment strategies for HIV-positive women and the timing of antiretroviral therapy initiation for women with HIV, both of which were associated with HIV transmission (P &lt; 0.05). Additionally, factors such as feeding type, neonatal antiretroviral prophylaxis, and preventive treatment strategies showed significant associations, while the preventive treatment program for neonates demonstrated no significant impact.DiscussionThese findings provide valuable insights into the specific risk factors and barriers to HIV prevention faced by the migrant population in Dongguan. They underscore the importance of targeted interventions and policies aimed at curtailing mother-to-child HIV transmission. By addressing the unique challenges experienced by migrant mothers and their infants, this study contributes significantly to broader efforts in controlling the spread of HIV, ultimately enhancing the health outcomes and well-being of Dongguan's migrant population. Furthermore, our research introduces a distinctive perspective within the extensively examined domain of Prevention of Mother-to-Child Transmission (PMTCT) programs, focusing on the internally migrant Chinese population, an understudied demographic group in this context. This study, conducted in Dongguan, China, represents one of the pioneering investigations into pregnant women with HIV and their infants within this migrant community

Strong Electronic Interaction of Amorphous Fe2O3 Nanosheets with Single‐Atom Pt toward Enhanced Carbon Monoxide Oxidation

Platinum‐based catalysts are critical to several chemical processes, but their efficiency is not satisfying enough in some cases, because only the surface active‐site atoms participate in the reaction. Henceforth, catalysts with single‐atom dispersions are highly desirable to maximize their mass efficiency, but fabricating these structures using a controllable method is still challenging. Most previous studies have focused on crystalline materials. However, amorphous materials may have enhanced performance due to their distorted and isotropic nature with numerous defects. Here reported is the facile synthesis of an atomically dispersed catalyst that consists of single Pt atoms and amorphous Fe2O3 nanosheets. Rational control can regulate the morphology from single atom clusters to sub‐nanoparticles. Density functional theory calculations show the synergistic effect resulted from the strong binding and stabilization of single Pt atoms with the strong metal‐support interaction between the in situ locally anchored Pt atoms and Fe2O3 lead to a weak CO adsorption. Moreover, the distorted amorphous Fe2O3 with O vacancies is beneficial for the activation of O2, which further facilitates CO oxidation on nearby Pt sites or interface sites between Pt and Fe2O3, resulting in the extremely high performance for CO oxidation of the atomic catalyst.An atomically Pt dispersed catalyst on amorphous Fe2O3 nanosheets is developed. The size effect of Pt and phase effect of support are explored. The synergistic effect results from the strong metal‐support interactions between the single Pt atoms and the amorphous Fe2O3 structure supports lead to an enhanced CO oxidation performance.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151833/1/adfm201904278-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151833/2/adfm201904278.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151833/3/adfm201904278_am.pd

Understanding the Discrepancy between IRX and Balmer Decrement in Tracing Galaxy Dust Attenuation

We compare the infrared excess (IRX) and Balmer decrement (${\rm H\alpha/H\beta }$) as dust attenuation indicators in relation to other galaxy parameters using a sample of $\sim$32 000 local star-forming galaxies (SFGs) carefully selected from SDSS, GALEX and WISE. While at fixed ${\rm H\alpha/H\beta }$, IRX turns out to be independent on galaxy stellar mass, the Balmer decrement does show a strong mass dependence at fixed IRX. We find the discrepancy, parameterized by the color excess ratio $R_{\rm EBV} \equiv E(B-V)_{\rm IRX}/E(B-V)_{\rm H\alpha/H\beta }$, is not dependent on the gas-phase metallicity and axial ratio but on the specific star formation rate (SSFR) and galaxy size ($R_{\rm e}$) following $R_{\rm EBV}=0.79+0.15\log({\rm SSFR}/R_{\rm e}^{2})$. This finding reveals that the nebular attenuation as probed by the Balmer decrement becomes increasingly larger than the global (stellar) attenuation of SFGs with decreasing SSFR surface density. This can be understood in the context of an enhanced fraction of intermediate-age stellar populations that are less attenuated by dust than the HII region-traced young population, in conjunction with a decreasing dust opacity of the diffuse ISM when spreading over a larger spatial extent. Once the SSFR surface density of an SFG is known, the conversion between attenuation of nebular and stellar emission can be well estimated using our scaling relation.Comment: 9 pages, 5 figures, Accepted for publication in Ap

Preparation of fluorescence-encoded microspheres in a core-shell structure for suspension arrays

Fluorescence-encoded microspheres are widely used in the detection and analysis of biological molecules, especially in suspension arrays. Here, we report an efficient strategy for the preparation of fluorescence-encoded polystyrene microspheres with desirable optical and surface properties. The micron-sized, monodisperse polystyrene seed beads were first synthesized by dispersion polymerization. Then, dye molecules and carboxyl functional groups were copolymerized on the surface of the seed beads by forming a core-shell structure. Rhodamine 6G (R6G) was used as a model dye molecule to prepare the fluorescent beads, and the fluorescence intensity of the beads can be precisely controlled by adjusting the quantity of R6G. These fluorescent beads were characterized by environmental scanning electron microscopy, laser scanning confocal microscopy, and spectrofluorometry. The differences of the fluorescence spectra between fluorescent beads and R6G in solution were investigated. Twelve kinds of fluorescent beads encoded with different R6G fluorescence intensities were prepared, and they can be clearly distinguished on a conventional flow cytometer. Furthermore, the encoded beads are stable in water and resistant to photobleaching, which is crucial for their potential applications in diagnostic assays and imaging. Detection of human alpha fetoprotein antigen via a sandwich microsphere-based immunoassay yielded a detection limit of 80 pg mL(-1), demonstrating that the fluorescence-encoded microspheres synthesized herein are efficient in serving as the microcarriers in suspension arrays. As both the encoding and functionalizing procedures are made simultaneously, the newly designed technique is extremely simple and time-saving. Moreover, it could be readily applicable to the preparation of a wide size range of fluorescent particles made by polymerization.National Natural Science Foundation of China [20675070]; Program for New Century Excellent Talents in University [NCET-07-0729]; NFFTBS [J0630429]; Scientific Research Foundation ; State Education Ministr