TORE: Token Reduction for Efficient Human Mesh Recovery with Transformer

In this paper, we introduce a set of effective TOken REduction (TORE) strategies for Transformer-based Human Mesh Recovery from monocular images. Current SOTA performance is achieved by Transformer-based structures. However, they suffer from high model complexity and computation cost caused by redundant tokens. We propose token reduction strategies based on two important aspects, i.e., the 3D geometry structure and 2D image feature, where we hierarchically recover the mesh geometry with priors from body structure and conduct token clustering to pass fewer but more discriminative image feature tokens to the Transformer. As a result, our method vastly reduces the number of tokens involved in high-complexity interactions in the Transformer, achieving competitive accuracy of shape recovery at a significantly reduced computational cost. We conduct extensive experiments across a wide range of benchmarks to validate the proposed method and further demonstrate the generalizability of our method on hand mesh recovery. Our code will be publicly available once the paper is published

A Python Code for Simulating Single Tactile Receptors and the Spiking Responses of Their Afferents

This work presents a pieces of Python code to rapidly simulate the spiking responses of large numbers of single cutaneous tactile afferents with millisecond precision. To simulate the spike responses of all the major types of cutaneous tactile afferents, we proposed an electromechanical circuit model, in which a two-channel filter was developed to characterize the mechanical selectivity of tactile receptors, and a spike synthesizer was designed to recreate the action potentials evoked in afferents. The parameters of this model were fitted using previous neurophysiological datasets. Several simulation examples were presented in this paper to reproduce action potentials, sensory adaptation, frequency characteristics and spiking timing for each afferent type. The results indicated that the simulated responses matched previous neurophysiological recordings well. The model allows for a real-time reproduction of the spiking responses of about 4,000 tactile units with a timing precision of <6 ms. The current work provides a valuable guidance to designing highly realistic tactile interfaces such as neuroprosthesis and haptic device

Risk of COVID-19 Transmission Aboard Aircraft: An Epidemiological Analysis Based on the National Health Information Platform.

OBJECTIVES This study aims to investigate the risk of COVID-19 transmission on aircraft. METHODS We obtained data on all international flights to Lanzhou, China, from June 1 to August 1, 2020, through the Gansu Province National Health Information Platform and the official website of the Gansu Provincial Center for Disease Control and Prevention. Statistical analysis was then performed. RESULTS Three international flights arrived in Lanzhou. The flights had a total of 700 passengers, of whom 405 (57.9%) were male and 80 (11.4%) were children below age fourteen. Twenty-seven (3.9%) passengers were confirmed to have COVID-19. Confirmed patients were primarily male (17, 65.4%) with a median age of 27.0 years. The majority of confirmed cases were seated in the middle rows of the economy class, or near public facility areas such as restrooms and galleys. The prevalence of COVID-19 did not differ between passengers sitting on window, aisle or middle seats. Compared with passengers sitting on the same row up to two rows behind a confirmed case, passengers seated in the two rows ahead a confirmed case were at a slightly higher risk of being infected. CONCLUSIONS COVID-19 may be transmitted during a passenger flight, although there is still no direct evidence

Dynamic prediction of traffic incident duration on urban expressways: a deep learning approach based on LSTM and MLP

Purpose – Efficient traffic incident management is needed to alleviate the negative impact of traffic incidents. Accurate and reliable estimation of traffic incident duration is of great importance for traffic incident management. Previous studies have proposed models for traffic incident duration prediction; however, most of these studies focus on the total duration and could not update prediction results in real-time. From a traveler’s perspective, the relevant factor is the residual duration of the impact of the traffic incident. Besides, few (if any) studies have used dynamic traffic flow parameters in the prediction models. This paper aims to propose a framework to fill these gaps. Design/methodology/approach – This paper proposes a framework based on the multi-layer perception (MLP) and long short-term memory (LSTM) model. The proposed methodology integrates traffic incident-related factors and real-time traffic flow parameters to predict the residual traffic incident duration. To validate the effectiveness of the framework, traffic incident data and traffic flow data from Shanghai Zhonghuan Expressway are used for modeling training and testing. Findings – Results show that the model with 30-min time window and taking both traffic volume and speed as inputs performed best. The area under the curve values exceed 0.85 and the prediction accuracies exceed 0.75. These indicators demonstrated that the model is appropriate for this study context. The model provides new insights into traffic incident duration prediction. Research limitations/implications – The incident samples applied by this study might not be enough and the variables are not abundant. The number of injuries and casualties, more detailed description of the incident location and other variables are expected to be used to characterize the traffic incident comprehensively. The framework needs to be further validated through a sufficiently large number of variables and locations. Practical implications – The framework can help reduce the impacts of incidents on the safety of efficiency of road traffic once implemented in intelligent transport system and traffic management systems in future practical applications. Originality/value – This study uses two artificial neural network methods, MLP and LSTM, to establish a framework aiming at providing accurate and time-efficient information on traffic incident duration in the future for transportation operators and travelers. This study will contribute to the deployment of emergency management and urban traffic navigation planning

Patients with IBD have a more cautious attitude towards COVID-19 vaccination

BackgroundTo understand the awareness of COVID-19 vaccine, the willingness to vaccinate and the influencing factors of willingness to vaccinate in inflammatory bowel disease (IBD) patients.MethodsThe online questionnaire was distributed to conduct a survey to analyze and evaluate the willingness, awareness and trust in vaccines of IBD patients. Bivariate analyses and logistic regression models were used to analysis influencing factors.ResultsWe sent the questionnaire to the WeChat group for patient management and 304 patients responded, out of which 16 respondents had to be excluded and 288 respondents were included for the analysis. Among them, 209 patients vaccinated with COVID-19 vaccine. Among the non-vaccinated 79 patients, the main reasons for their concerns were afraid of vaccination aggravating IBD and fear of adverse effects. Our results showed that IBD patients with long disease duration were more willing to receive COVID-19 vaccination (P<0.05). We also observed that a high perception of benefits and cues to action to receive the vaccine were the two most important constructs affecting a definite intention for COVID-19 vaccination (P<0.05).ConclusionsPatients with IBD have a more cautious attitude towards COVID-19 vaccination, which may lead to a higher rate of vaccine hesitancy. Further efforts should be made to protect patients with IBD from COVID-19 infections and achieve adequate vaccination coverage

CIRBP is a novel oncogene in human bladder cancer inducing expression of HIF-1 alpha

Cold-inducible RNA binding protein (CIRBP) has been reported to be associated with distinct tumorigenesis. In this study, we investigated the role of CIRBP in human bladder cancer (BCa), indicating that CIRBP is overexpressed in BCa tissues and cell lines to promote proliferation and migration. Moreover, CIRBP could induce expression of HIF-1 alpha via binding to the 3'-UTR of its mRNA to increase the mRNA stability in BCa cells. Furthermore, we demonstrated that PTGIS is a HIF-1 alpha targeted gene, a major regulator in hypoxic cancer progression by activating transcription of various oncogenes. Our results also suggested that overexpression of HIF-1 alpha may suppress the expression of PTGIS in BCa cells, by binding to HRE sequence at the promoter region of PTGIS. In addition, we found a strongly downregulation of PTGIS in BCa tissue and transcriptionally inhibited by HIF-1 alpha in BCa cells, which could be triggered by its DNA methylation. Further result suggested that knockdown of CIRBP could promote the expression of PTGIS, meanwhile knockdown of PTGIS could partially rescue CIRBP-deficiency induced inhibition of migration and proliferation in BCa cells. Taken together, our study indicated that CIRBP could be a novel oncogene in human bladder cancer inducing transcription of HIF-1 alpha, which could inhibit expression of methylated PTGIS