Higher superconducting transition temperature by breaking the universal pressure relation

By investigating the bulk superconducting state via dc magnetization measurements, we have discovered a common resurgence of the superconductive transition temperatures (Tcs) of the monolayer Bi2Sr2CuO6+{\delta} (Bi2201) and bilayer Bi2Sr2CaCu2O8+{\delta} (Bi2212) to beyond the maximum Tcs (Tc-maxs) predicted by the universal relation between Tc and doping (p) or pressure (P) at higher pressures. The Tc of under-doped Bi2201 initially increases from 9.6 K at ambient to a peak at ~ 23 K at ~ 26 GPa and then drops as expected from the universal Tc-P relation. However, at pressures above ~ 40 GPa, Tc rises rapidly without any sign of saturation up to ~ 30 K at ~ 51 GPa. Similarly, the Tc for the slightly overdoped Bi2212 increases after passing a broad valley between 20-36 GPa and reaches ~ 90 K without any sign of saturation at ~ 56 GPa. We have therefore attributed this Tc-resurgence to a possible pressure-induced electronic transition in the cuprate compounds due to a charge transfer between the Cu 3d_(x^2-y^2 ) and the O 2p bands projected from a hybrid bonding state, leading to an increase of the density of states at the Fermi level, in agreement with our density functional theory calculations. Similar Tc-P behavior has also been reported in the trilayer Br2Sr2Ca2Cu3O10+{\delta} (Bi2223). These observations suggest that higher Tcs than those previously reported for the layered cuprate high temperature superconductors can be achieved by breaking away from the universal Tc-P relation through the application of higher pressures.Comment: 13 pages, including 5 figure

Effects of Individual Differences on Measurements’ Drowsiness-Detection Performance

Individual differences (IDs) may reduce the detection-accuracy of drowsiness-driving by influencing measurements’ drowsiness-detection performance (MDDP). The purpose of this paper is to propose a model that can quantify the effects of IDs on MDDP and find measurements with less impact by IDs to build drowsiness-detection models. Through field experiments, drivers’ naturalistic driving data and subjective-drowsiness levels were collected, and drowsiness-related measurements were calculated using the double-layer sliding time window. In the model, MDDP was represented by |Z-statistics| of the Wilcoxon-test. First, the individual driver’s measurements were analysed by Wilcoxon-test. Next, drivers were combined in pairs, measurements of paired-driver combinations were analysed by Wilcoxon-test, and measurement’s IDs of paired-driver combinations were calculated. Finally, linear regression was used to fit the measurements’ IDs and changes of MDDP that equalled the individual driver’s |Z-statistics| minus the paired-driver combination’s |Z-statistics|, and the slope’s absolute value (|k|) indicated the effects of ID on the MDDP. As a result, |k| of the mean of the percentage of eyelid closure (MPECL) is the lowest (4.95), which illustrates MPECL is the least affected by IDs. The results contribute to the measurement selection of drowsiness-detection models considering IDs

P3OP^{3}O: Transferring Visual Representations for Reinforcement Learning via Prompting

It is important for deep reinforcement learning (DRL) algorithms to transfer their learned policies to new environments that have different visual inputs. In this paper, we introduce Prompt based Proximal Policy Optimization ($P^{3}O$), a three-stage DRL algorithm that transfers visual representations from a target to a source environment by applying prompting. The process of $P^{3}O$ consists of three stages: pre-training, prompting, and predicting. In particular, we specify a prompt-transformer for representation conversion and propose a two-step training process to train the prompt-transformer for the target environment, while the rest of the DRL pipeline remains unchanged. We implement $P^{3}O$ and evaluate it on the OpenAI CarRacing video game. The experimental results show that $P^{3}O$ outperforms the state-of-the-art visual transferring schemes. In particular, $P^{3}O$ allows the learned policies to perform well in environments with different visual inputs, which is much more effective than retraining the policies in these environments.Comment: This paper has been accepted to be presented at the upcoming IEEE International Conference on Multimedia & Expo (ICME) in 202

SegViT: Semantic Segmentation with Plain Vision Transformers

We explore the capability of plain Vision Transformers (ViTs) for semantic segmentation and propose the SegVit. Previous ViT-based segmentation networks usually learn a pixel-level representation from the output of the ViT. Differently, we make use of the fundamental component -- attention mechanism, to generate masks for semantic segmentation. Specifically, we propose the Attention-to-Mask (ATM) module, in which the similarity maps between a set of learnable class tokens and the spatial feature maps are transferred to the segmentation masks. Experiments show that our proposed SegVit using the ATM module outperforms its counterparts using the plain ViT backbone on the ADE20K dataset and achieves new state-of-the-art performance on COCO-Stuff-10K and PASCAL-Context datasets. Furthermore, to reduce the computational cost of the ViT backbone, we propose query-based down-sampling (QD) and query-based up-sampling (QU) to build a Shrunk structure. With the proposed Shrunk structure, the model can save up to $40\%$ computations while maintaining competitive performance.Comment: 9 Pages, NeurIPS 202

Cumulative live birth rates and birth outcomes after IVF/ICSI treatment cycles in young POSEIDON patients: A real-world study

ObjectiveThe aim of this study was to describe the cumulative live birth rates (CLBRs) of young women with or without low prognosis according to the POSEIDON criteria after IVF/ICSI cycles and to investigate whether the diagnosis of low prognosis increases the risk of abnormal birth outcomes.DesignRetrospective study.SettingA single reproductive medicine center.PopulationFrom January 2016 to October 2020, there were 17,893 patients (<35 years) involved. After screening, 4,105 women were included in POSEIDON group 1, 1,375 women were included in POSEIDON group 3, and 11,876 women were defined as non-POSEIDON.Intervention(s)Baseline serum AMH level was measured on the D2–D3 of menstrual cycle before IVF/ICSI treatment.Main outcome measure(s)Cumulative live birth rate (CLBR), birth outcomes.Result(s)After four stimulation cycles, the CLBRs in POSEIDON group 1, POSEIDON group 3, and non-POSEIDON group reached 67.9% (95% CI, 66.5%–69.3%), 51.9% (95% CI, 49.2%–54.5%), and 79.6% (95% CI, 78.9%–80.3%), respectively. There was no difference in gestational age, preterm delivery, cesarean delivery, and low birth weight infants between the three groups, but macrosomia was significantly higher in non-POSEIDON group, after adjusting for maternal age and BMI.Conclusion(s)The POSEIDON group shows lower CLBRs than the non-POSEIDON group in young women, while the risk of abnormal birth outcomes in the POSEIDON group will not increase

Novel nickel foam with multiple microchannels as combustion reaction support for the self-heating methanol steam reforming microreactor

To improve hydrogen production performance of self-heating methanol steam reforming (MSR) microreactor, novel nickel foam with multiple microchannels was proposed as combustion reaction support. A wall temperature comparison of the methanol combustion microreactors with nickel foam catalyst support and particles catalyst support in the combustion reaction process was performed. According to the numerical simulation result of combustion reaction of nickel foam, the shape and size of multiple microchannels of nickel foam were determined. The laser processing was then used to fabricate the multiple microchannels of nickel foam. The experimental results show that the methanol combustion microreactor with nickel foam loaded with Pt catalyst exhibits similar wall temperature distribution with the methanol combustion microreactor with Pt/γ-Al2O3 particles reaction support. Compared with the nickel foam without a microchannel, the maximum temperature difference (ΔTmax) and the maximum temperature of nickel foam with multiple microchannels were decreased, respectively, by 57.8% and 33.8 °C when 1.1 mL/min methanol flow rate was used. Hydrogen production performance of the self-heating MSR microreactor using the nickel foam with multiple microchannels increased by about 21% when 430 °C reforming temperature and 4 mL/h methanol–water mixture flow rate were performed