PGformer: Proxy-Bridged Game Transformer for Multi-Person Extremely Interactive Motion Prediction

Multi-person motion prediction is a challenging task, especially for real-world scenarios of densely interacted persons. Most previous works have been devoted to studying the case of weak interactions (e.g., hand-shaking), which typically forecast each human pose in isolation. In this paper, we focus on motion prediction for multiple persons with extreme collaborations and attempt to explore the relationships between the highly interactive persons' motion trajectories. Specifically, a novel cross-query attention (XQA) module is proposed to bilaterally learn the cross-dependencies between the two pose sequences tailored for this situation. Additionally, we introduce and build a proxy entity to bridge the involved persons, which cooperates with our proposed XQA module and subtly controls the bidirectional information flows, acting as a motion intermediary. We then adapt these designs to a Transformer-based architecture and devise a simple yet effective end-to-end framework called proxy-bridged game Transformer (PGformer) for multi-person interactive motion prediction. The effectiveness of our method has been evaluated on the challenging ExPI dataset, which involves highly interactive actions. We show that our PGformer consistently outperforms the state-of-the-art methods in both short- and long-term predictions by a large margin. Besides, our approach can also be compatible with the weakly interacted CMU-Mocap and MuPoTS-3D datasets and achieve encouraging results. Our code will become publicly available upon acceptance

HPV Infection in Esophageal Squamous Cell Carcinoma and Its Relationship to the Prognosis of Patients in Northern China

Purpose. Human papillomavirus (HPV) as a risk factor for esophageal squamous cell carcinoma (ESCC) has previously been studied, but importance of HPV status in ESCC for prognosis is less clear. Methods. A total of 105 specimens with ESCC were tested by in situ hybridization for HPV 16/18 and immunohistochemistry for p16 expression. The 5-year overall survival (OS) and progression-free survival were calculated in relation to these markers and the Cox proportional hazards model was used to determine the hazard ratio (HR) of variables in univariate and multivariate analysis. Results. HPV was detected in 27.6% (29) of the 105 patients with ESCC, and all positive cases were HPV-16. Twenty-five (86.2%) of the 29 HPV-positive tumors were stained positive for p16. HPV infected patients had better 5-year rates of OS (65.9% versus 43.4% among patients with HPV-negative tumors; P = 0.002 by the log-rank test) and had a 63% reduction in the risk of death (adjusted HR = 0.37, 95% CI = 0.16 to 0.82, and P = 0.01). Conclusions. HPV infection may be one of many factors contributing to the development of ESCC and tumor HPV status is an independent prognostic factor for survival among patients with ESCC

Interfacial Chemical Effects of Amorphous Zinc Oxide/Graphene

Research on the preparation and performance of graphene composite materials has become a hotspot due to the excellent electrical and mechanical properties of graphene. Among such composite materials, zinc oxide/graphene (ZnO/graphene) composite films are an active research topic. Therefore, in this study, we used the vacuum thermal evaporation technique at different evaporation voltages to fabricate an amorphous ZnO/graphene composite film on a flexible polyethylene terephthalate (PET). The amorphous ZnO/graphene composite film inherited the great transparency of the graphene within the visible spectrum. Moreover, its electrical properties were better than those of pure ZnO but less than those of graphene, which is not consistent with the original theoretical research (wherein the performance of the composite films was better than that of ZnO film and slightly lower than that of graphene). For example, the bulk free charge carrier concentrations of the composite films (0.13, 1.36, and 0.47 × 1018 cm−3 corresponding to composite films with thicknesses of 40, 75, and 160 nm) were remarkably lower than that of the bare graphene (964 × 1018 cm−3) and better than that of the ZnO (0.10 × 1018 cm−3). The underlying mechanism for the abnormal electrical performance was further demonstrated by X-ray photoelectron spectroscopy (XPS) detection and first-principles calculations. The analysis found that chemical bonds were formed between the oxide (O) of amorphous ZnO and the carbon (C) of graphene and that the transfer of the π electrons was restricted by C=O and C-O-C bonds. Given the above, this study further clarifies the mechanism affecting the photoelectric properties of amorphous composite films

Fluorine ion induced phase evolution of tin-based perovskite thin films: structure and properties

To study the effect of fluorine ions on the phase transformation of a tin-based perovskite, CsSnI3 x(F)x films were deposited by using thermal vacuum evaporation from a mixed powder of SnI2, SnF2 and CsI, followed by rapid vacuum annealing. The color evolution, structure, and properties of CsSnI3 xFx films aged in air were observed and analyzed. The results showed that the colors of the films changed from black to yellow, and finally presented as black again over time; the unstable B-g-CsSnI3 xFx phase transformed into the Y-CsSnI3 xFx phase, which is then recombined into the Cs2SnI6 xFx phase with the generation of SnO2 in air. Fluorine dopant inhibited the oxidation process. The postponement of the phase transformation is due to the stronger bonds between F and Sn than that between I and Sn. The color changing process of the CsSnI3 xFx films slowed that the hole concentrations increased and the resistivities decreased with the increase of the F dopant ratio. With the addition of SnF2, light harvesting within the visible light region was significantly enhanced. Comparison of the optical and electrical properties of the fresh annealed CsSnI3 xFx films showed that the band gaps of the aged films widened, the hole concentrations kept the same order, the hole mobilities reduced and therefore, the resistivities increased. The double layer Cs2SnI6 xFx phase also showed ‘p’ type semi-conductor properties, which might be due to the incomplete transition of Sn2+ to Sn4+, i.e. Sn2+ provides holes as the acceptor

Prediction of multiglandular parathyroid disease in primary hyperparathyroidism using ultrasound and clinical features

BackgroundIdentification of multigland disease (MGD) in primary hyperparathyroidism (PHPT) patients is essential for minimally invasive surgical decision-making.ObjectiveTo develop a nomogram based on US findings and clinical factors to predict MGD in PHPT patients.Materials and MethodsPatients with PHPT who underwent surgery between March 2021 and January 2022 were consecutively enrolled. Biochemical and clinicopathologic data were recorded. US images were analyzed to extract US features. Logistic regression analyses were used to identify the risk factors for MGD. The nomogram was constructed based on the factors. Nomogram performance was evaluated by area under the receiver operating characteristic curve (AUC), calibration curve, the Hosmer–Lemeshow test, and decision curve analysis.ResultsA total of 102 PHPT patients were included. 82 (80.4%) had the single-gland disease (SGD) and 20 (19.6%) had MGD. Using multivariate analysis, the MGD was positively correlated with age (OR = 1.033, 96%CI = 0.985-1.092), PTH level (OR = 1.001, 95% CI = 1.000–1.002), MEN-1 (OR = 29.730, 95% CI = 3.089-836.785), US size (OR = 1.198, 95% CI = 0.647–2.088) and US texture (cystic-solid) (OR = 5.357, 95% CI = 0.499–62.912). And negatively correlated with gender (OR = 0.985, 95% CI = 0.190–4.047), calcium level (OR = 0.453, 95% CI = 0.070–2.448), and symptoms(yes) (OR = 0.935, 95%CI = 0.257–3.365). The nomogram showed good discrimination with an AUC of 0.77 (0.68-0.85) and good agreement for predicting MGD in PHPT patients. And 65 points was recommended as a cut-off value with a specificity of 0.94 and a sensitivity of 0.50.ConclusionUS provided useful features for evaluating MGD. Combining the US and clinical features in a nomogram showed good diagnostic performance for predicting MGD

Learning-based Nonlinear Model Predictive Control

© 2017 This paper presents stabilizing Model Predictive Controllers (MPC) in which prediction models are inferred from experimental data of the inputs and outputs of the plant. Using a nonparametric machine learning technique called LACKI, the estimated (possibly nonlinear) model function together with an estimation of Holder constant is provided. Based on these, a number of predictive controllers with stability guaranteed by design are proposed. Firstly, the case when the prediction model is estimated offline is considered and robust stability and recursive feasibility is ensured by using tightened constraints in the optimisation problem. This controller has been extended to the more interesting and complex case: the online learning of the model, where the new data collected from feedback is added to enhance the prediction model. An on-line learning MPC based on a double sequence of predictions is proposed.Spanish MINECO Grant PRX15-00300 and projects DPI2013-48243-C2-2-R and DPI2016-76493-C3-1-R. UK Engineering and Physical Research Council, grant no.EP/J012300/1

A Random Forest Model for Daily PM2.5 Personal Exposure Assessment for a Chinese Cohort

Errors in air pollution exposure assessment are often considered as a major limitation in epidemiological studies. However, it is difficult to obtain accurate personal level exposure on cohort populations due to the often prohibitive expense. Personal exposure estimation models are used in lieu of direct personal exposure measures but still suffer from issues of availability and accuracy. We aim to establish a personal PM2.5 exposure assessment model for a cohort population and assess its performance by applying our model on cohort subjects. We analyzed data from representative sites selected from the subclinical outcomes of polluted air in China (SCOPA-China) cohort study and established a random forest model for estimating daily PM2.5 personal exposure. We also applied the model among subjects recruited in the project mentioned above within the same area and study period to estimate the reliability of the model. The established model showed a good fit with an R2 of 0.81. The model application results showed similar patterns with empirically measured data. Our pilot study provided a validated and feasible modeling approach for assessing daily personal PM2.5 exposure for large cohort populations. The promising model framework can improve PM2.5 exposure assessment accuracy for future environmental health studies of large populations