The association Between Short-Term Emotion Dynamics and Cigarette Dependence: a Comprehensive Examination of Dynamic Measures

BACKGROUND: The association between short-term emotion dynamics and long-term psychopathology has been well established in the psychology literature. Yet, dynamic measures for inertia and instability of negative and positive affect have not been studied in terms of their association with cigarette dependence. This study builds an important bridge between the psychology and substance use literatures by introducing these novel measures and conducting a comprehensive examination of such association with intervention implications. METHODS: This study conducted secondary analysis on the data from a community sample of 136 dual users (e-cigarette + cigarette) and 101 exclusive smokers who completed both the two-week ecological momentary assessment (EMA) and cigarette dependence assessments in a recent study. RESULTS: Among dual users, a higher average level of negative affect, lower inertia of negative affect (i.e., less sustained negative affect), and higher instability of positive affect (i.e., greater magnitude of changes in positive affect) were associated with higher cigarette dependence. The patterns of associations among exclusive smokers were, however, different. Higher inertia of negative affect, lower instability of positive affect, and higher variability of negative affect were associated with higher dependence. CONCLUSIONS: The results illustrate the importance of examining not only negative affect but also positive affect in order to fully understand the association between emotion dynamics and cigarette dependence. The different patterns of association between emotion dynamics and cigarette dependence across the two groups of cigarette users also call for future research that is designed to compare cigarettes and e-cigarettes in terms of their effects on emotion regulation

Deep Learning in Single-Cell Analysis

Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high-dimensional, sparse, heterogeneous, and have complicated dependency structures, making analyses using conventional machine learning approaches challenging and impractical. In tackling these challenges, deep learning often demonstrates superior performance compared to traditional machine learning methods. In this work, we give a comprehensive survey on deep learning in single-cell analysis. We first introduce background on single-cell technologies and their development, as well as fundamental concepts of deep learning including the most popular deep architectures. We present an overview of the single-cell analytic pipeline pursued in research applications while noting divergences due to data sources or specific applications. We then review seven popular tasks spanning through different stages of the single-cell analysis pipeline, including multimodal integration, imputation, clustering, spatial domain identification, cell-type deconvolution, cell segmentation, and cell-type annotation. Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages. Deep learning tools and benchmark datasets are also summarized for each task. Finally, we discuss the future directions and the most recent challenges. This survey will serve as a reference for biologists and computer scientists, encouraging collaborations.Comment: 77 pages, 11 figures, 15 tables, deep learning, single-cell analysi

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

One-Shot Simple Pattern Detection without Pre-Training and Gradient-Based Strategy

One-shot object detection has been a highly demanded yet challenging task since the early age of convolutional neural networks (CNNs). For some newly started projects, a handy network that can learn the target’s pattern using a single picture and automatically decide its architecture is needed. To specifically address a scenario in which a single or multiple targets are standing in relatively stable circumstances with hardly any training data, where the rough location of the target is required, we propose a one-shot simple target detection model that focuses on two main tasks: (1) deciding if the target is in the testing image, and (2) if yes, outputting the target’s location in the image. This model requires no pre-training and decides its architecture automatically; therefore, it could be applied to a newly started target detection project with unconventionally simple targets and few training examples. We also propose an architecture with a non-training parameter-gaining strategy and correlation coefficient-based feedforward and activation functions, as well as easy interpretability, which might provide a perspective on studies in neural networks. We tested this design on the data we collected in our project, the Brown–Yosemite dataset and part of the Mnist dataset. It successfully returned the target area in our project and obtained an IOU of up to 87.04%, reached 80.28% accuracy on the Brown–Yosemite dataset with disposable networks, and obtained an accuracy of up to 89.4% on part of the Mnist dataset in the detection task

Remote-carbonyl-directed sequential Heck/isomerization/C(sp2)–H arylation of alkenes for modular synthesis of stereodefined tetrasubstituted olefins

Abstract Modular and regio-/stereoselective syntheses of all-carbon tetrasubstituted olefins from simple alkene materials remain a challenging project. Here, we demonstrate that a remote-carbonyl-directed palladium-catalyzed Heck/isomerization/C(sp2)–H arylation sequence enables unactivated 1,1-disubstituted alkenes to undergo stereoselective terminal diarylation with aryl iodides, thus offering a concise approach to construct stereodefined tetrasubstituted olefins in generally good yields under mild conditions; diverse carbonyl groups are allowed to act as directing groups, and various aryl groups can be introduced at the desired position simply by changing aryl iodides. The stereocontrol of the protocol stems from the compatibility between the E/Z isomerization and the alkenyl C(sp2)–H arylation, where the vicinal group-directed C(sp2)–H arylation of the Z-type intermediate product thermodynamically drives the reversible E to Z isomerization. Besides, the carbonyl group not only promotes the Pd-catalyzed sequential transformations of unactivated alkenes by weak coordination, but also avoids byproducts caused by other possible β-H elimination

Computer-aided Design for the Route of the Test Waveguides

With the rapid development of China's space industry, digitization and intelligent is the tendency of the future. The applications of the waveguide are gradually widespread. During the thermal test phase, the routes of the test waveguides are similar for each spacecraft. Although the waveguides are highly standardized, so far it needs engineers to design the particular route of the test waveguidess, then map the engineering drawing for every test. In order to efficiently design the route of waveguide, it needs to design an application to help the engineers. With the help of the MFC(Microsoft Foundation Classes) and the pro/toolkit, it is easily to do the modeling and simulation. After automatic design the particular the route of the waveguide, the API of AutoCAD type library is used to help to modify the engineer drawing. Engineers can supervise every step of this application, and easily to modify the key parameters

The Impact of Urbanization on Mesoscale Convective Systems in the Yangtze River Delta Region of China: Insights Gained From Observations and Modeling

Urbanization is an important factor that may influence the formation and development of clouds and precipitation. In this study, we focus on studying the influence of urbanization on mesoscale convective systems (MCS) over the Yangtze River Delta region in China under different synoptic conditions using a combination of radiosonde, meteorological station, and satellite observations. It demonstrates that synoptic forcing can be used to distinguish the effect of land cover and land use on MCS. When the synoptic-scale forcing is weak, the urban heat island (UHI) is the main factor affecting the vertical development of clouds. The UHI decreases atmospheric stability and enhances horizontal convergence, invigorating clouds over and downwind of cities. On the other hand, when strong synoptic-scale forcing is present, buildings in cities cause clouds to bifurcate upwind of cities, moving around them, primarily through their dynamic effects. The heights of cloud tops in central and downwind parts of cities thus drop. Using the Weather Research and Forecasting model simulations of different atmospheric forcings also demonstrate similar patterns around major urban areas. The joint analyses of observations and model simulations provide new insights into the net effects of urbanization on cloud systems.https://doi.org/10.1029/2022JD03770

Fine-Grained Recognition of Surface Targets with Limited Data

Recognition of surface targets has a vital influence on the development of military and civilian applications such as maritime rescue patrols, illegal-vessel screening, and maritime operation monitoring. However, owing to the interference of visual similarity and environmental variations and the lack of high-quality datasets, accurate recognition of surface targets has always been a challenging task. In this paper, we introduce a multi-attention residual model based on deep learning methods, in which channel and spatial attention modules are applied for feature fusion. In addition, we use transfer learning to improve the feature expression capabilities of the model under conditions of limited data. A function based on metric learning is adopted to increase the distance between different classes. Finally, a dataset with eight types of surface targets is established. Comparative experiments on our self-built dataset show that the proposed method focuses more on discriminative regions, avoiding problems like gradient disappearance, and achieves better classification results than B-CNN, RA-CNN, MAMC, and MA-CNN, DFL-CNN

AFFPN: Attention Fusion Feature Pyramid Network for Small Infrared Target Detection

The detection of small infrared targets lacking texture and shape information in the presence of complex background clutter is a challenge that has attracted considerable research attention in recent years. Typical deep learning-based target detection methods are designed with deeper network structures, which may lose targets in the deeper layers and cannot directly be used for small infrared target detection. Therefore, we designed the attention fusion feature pyramid network (AFFPN) specifically for small infrared target detection. Specifically, it consists of feature extraction and feature fusion modules. In the feature extraction stage, the global contextual prior information of small targets is first considered in the deep layer of the network using the atrous spatial pyramid pooling module. Subsequently, the spatial location and semantic information features of small infrared targets in the shallow and deep layers are adaptively enhanced by the designed attention fusion module to improve the feature representation capability of the network for targets. Finally, high-performance detection is achieved through the multilayer feature fusion mechanism. Moreover, we performed a comprehensive ablation study to evaluate the effectiveness of each component. The results demonstrate that the proposed method performs better than state-of-the-art methods on a publicly available dataset. Furthermore, AFFPN was deployed on an NVIDIA Jetson AGX Xavier development board and achieved real-time target detection, further advancing practical research and applications in the field of unmanned aerial vehicle infrared search and tracking