Enhancement of quantum correlations for the system of cavity QED by applying bang-bang pulses

We propose a scheme of increasing quantum correlations for the cavity quantum electrodynamics system consisting of two noninteracting two-level atoms each locally interacting with its own quantized field mode by bang-bang pulses. We investigate the influence of the bang-bang pulses on the dynamics of quantum discord, entanglement, quantum mutual information and classical correlation between the two atoms. It is shown that the amount of quantum discord and entanglement of the two atoms can be improved by applying the bang-bang pulses.Comment: 6 pages, 5 figure

Effects on the pore structure and permeability change by coke deposition during crude oil in-situ combustion

In-situ combustion(ISC) is an enhanced oil recovery technique to exploit the unconventional crude oil resources with high recovery efficiency. Great amount of reaction heat is released in-place by burning the solid residue, so-called coke at the combustion front with the temperature higher than 400℃. Significant open ISC questions include the effect of coke formation on the pore structure and permeability. Coke deposition reduces the permeability and increases the permeability heterogeneities which will affect the oxygen transport in the formation, thereby influencing the oxygen participating reactions downstream. However, the existing empirical or semi-empirical relationships are still questionable to model the permeability change due to coke deposition. In the study, a high temperature and high pressure experimental apparatus was constructed to physically simulate the coke formation during the ISC processes. Please download the full abstract below

Protecting quantum correlations of two qubits in independent non-Markovian environments by bang-bang pulses

We investigate how to protect quantum correlations for two qubits each locally interacting with its own non-Markovian environment by making use of bang-bang pulses. It is shown that the quantum discord dynamics presents the phenomenon of sudden change for some certain initial states. We also find that the amount of quantum correlation between two qubits can be improved by applying a train of pulses and protected more effectively with shorter interval pulses or longer reservoir correlation time.Comment: 7 pages, 7 figure

Risk factors for high-altitude headache upon acute high-altitude exposure at 3700 m in young Chinese men: a cohort study.

BackgroundThis prospective and observational study aimed to identify demographic, physiological and psychological risk factors associated with high-altitude headache (HAH) upon acute high-altitude exposure.MethodsEight hundred fifty subjects ascended by plane to 3700 m above Chengdu (500 m) over a period of two hours. Structured Case Report Form (CRF) questionnaires were used to record demographic information, physiological examinations, psychological scale, and symptoms including headache and insomnia a week before ascending and within 24 hours after arrival at 3700 m. Binary logistic regression models were used to analyze the risk factors for HAH.ResultsThe incidence of HAH was 73.3%. Age (p =0.011), physical labor intensity (PLI) (p =0.044), primary headache history (p <0.001), insomnia (p <0.001), arterial oxygen saturation (SaO2) (p =0.001), heart rate (HR) (p =0.002), the Self-Rating Anxiety Scale (SAS) (p <0.001), and the Epworth Sleepiness Scale (ESS) (p <0.001) were significantly different between HAH and non-HAH groups. Logistic regression models identified primary headache history, insomnia, low SaO2, high HR and SAS as independent risk factors for HAH.ConclusionsInsomnia, primary headache history, low SaO2, high HR, and high SAS score are the risk factors for HAH. Our findings will provide novel avenues for the study, prevention and treatment of HAH

MPPNet: Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detection

Accurate and reliable 3D detection is vital for many applications including autonomous driving vehicles and service robots. In this paper, we present a flexible and high-performance 3D detection framework, named MPPNet, for 3D temporal object detection with point cloud sequences. We propose a novel three-hierarchy framework with proxy points for multi-frame feature encoding and interactions to achieve better detection. The three hierarchies conduct per-frame feature encoding, short-clip feature fusion, and whole-sequence feature aggregation, respectively. To enable processing long-sequence point clouds with reasonable computational resources, intra-group feature mixing and inter-group feature attention are proposed to form the second and third feature encoding hierarchies, which are recurrently applied for aggregating multi-frame trajectory features. The proxy points not only act as consistent object representations for each frame, but also serve as the courier to facilitate feature interaction between frames. The experiments on large Waymo Open dataset show that our approach outperforms state-of-the-art methods with large margins when applied to both short (e.g., 4-frame) and long (e.g., 16-frame) point cloud sequences. Code is available at https://github.com/open-mmlab/OpenPCDet.Comment: Accepted by ECCV 202

Transmissible ST3-IncHI2 Plasmids Are Predominant Carriers of Diverse Complex IS26-Class 1 Integron Arrangements in Multidrug-Resistant Salmonella

Diverse mobile genetic elements (MGEs) including plasmids, insertion sequences, and integrons play an important role in the occurrence and spread of multidrug resistance (MDR) in bacteria. It was found in previous studies that IS26 and class 1 integrons integrated on plasmids to speed the dissemination of antibiotic-resistance genes in Salmonella. It is aimed to figure out the patterns of specific genetic arrangements between IS26 and class 1 integrons located in plasmids in MDR Salmonella in this study. A total of 74 plasmid-harboring Salmonella isolates were screened for the presence of IS26 by PCR amplification, and 39 were IS26-positive. Among them, 37 isolates were resistant to at least one antibiotic. The thirty-seven antibiotic-resistant isolates were further involved in PCR detection of class 1 integrons and variable regions, and all were positive for class 1 integrons. Six IS26-class 1 integron arrangements with IS26 inserted into the upstream or downstream of class 1 integrons were characterized. Eight combinations of these IS26-class 1 integron arrangements were identified among 31 antibiotic-resistant isolates. Multidrug-resistance plasmids of the IncHI2 incompatibility group were dominant, which all belonged to ST3 by plasmid double locus sequence typing. These 21 IncHI2-positive isolates harbored six complex IS26-class 1 integron arrangement patterns. Conjugation assays and Southern blot hybridizations confirmed that conjugative multidrug-resistance IncHI2 plasmids harbored the different complex IS26-class 1 integron arrangements. The conjugation frequency of IncHI2 plasmids transferring alone was 10−5-10−6, reflecting that different complex IS26-class 1 integron arrangement patterns didn't significantly affect conjugation frequency (P > 0.05). These data suggested that class 1 integrons represent the hot spot for IS26 insertion, forming diverse MDR loci. And ST3-IncHI2 was the major plasmid lineage contributing to the horizontal transfer of composite IS26-class 1 integron MDR elements in Salmonella

GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-Image Pre-training

Cross-modal pre-training has shown impressive performance on a wide range of downstream tasks, benefiting from massive image-text pairs collected from the Internet. In practice, online data are growing constantly, highlighting the importance of the ability of pre-trained model to learn from data that is continuously growing. Existing works on cross-modal pre-training mainly focus on training a network with fixed architecture. However, it is impractical to limit the model capacity when considering the continuously growing nature of pre-training data in real-world applications. On the other hand, it is important to utilize the knowledge in the current model to obtain efficient training and better performance. To address the above issues, in this paper, we propose GrowCLIP, a data-driven automatic model growing algorithm for contrastive language-image pre-training with continuous image-text pairs as input. Specially, we adopt a dynamic growth space and seek out the optimal architecture at each growth step to adapt to online learning scenarios. And the shared encoder is proposed in our growth space to enhance the degree of cross-modal fusion. Besides, we explore the effect of growth in different dimensions, which could provide future references for the design of cross-modal model architecture. Finally, we employ parameter inheriting with momentum (PIM) to maintain the previous knowledge and address the issue of the local minimum dilemma. Compared with the existing methods, GrowCLIP improves 2.3% average top-1 accuracy on zero-shot image classification of 9 downstream tasks. As for zero-shot image retrieval, GrowCLIP can improve 1.2% for top-1 image-to-text recall on Flickr30K dataset.Comment: Accepted by ICCV202