The advancement of deep convolutional neural networks (DCNNs) has driven significant improvement in the accuracy recognition systems for many computer vision tasks. However, their practical applications are often restricted resource-constrained environments. In this paper, we introduce projection (PCNNs) with a discrete back propagation via (DBPP) to improve performance binarized (BNNs). contributions our paper include: 1) first time, function is exploited efficiently solve problem, which...

The rapidly decreasing computation and memory cost has recently driven the success of many applications in field deep learning. Practical learning resource-limited hardware, such as embedded devices smart phones, however, remain challenging. For binary convolutional networks, reason lies degraded representation caused by binarizing full-precision filters. To address this problem, we propose new circulant filters (CiFs) a convolution (CBConv) to enhance capacity binarized features via our...

Deep convolutional neural networks (DCNNs) have dominated the recent developments in computer vision through making various record-breaking models. However, it is still a great challenge to achieve powerful DCNNs resource-limited environments, such as on embedded devices and smart phones. Researchers realized that 1-bit CNNs can be one feasible solution resolve issue; however, they are baffled by inferior performance compared full-precision DCNNs. In this paper, we propose novel approach,...

Current Image Captioning (IC) methods predict textual words sequentially based on the input visual information from feature extractor and partially generated sentence information. However, for most cases, may dominate target word prediction due to insufficiency of information, making descriptions irrelevant content given image. In this paper, we propose a Dual Information Flow Network (DIFNet <sup xmlns:mml="http://www.w3.org/1998/Math/MathML"...

The neoliberalization of housing policy and financialization have brought unequal impacts on outcomes. Drawing eight waves census data, this study uncovers the changing mechanism stratification in selected Canadian metropolitan areas from 1981 to 2016, a period when Canada transitioned welfare regime neoliberal regime. This reveals entrenched inequality strengthened income effect determining access affordable era. Housing has significantly contributed intensified accessing housing. Access is...

Although there has been a longstanding curiosity about the socio-political consequences of China’s remarkable urban–rural divide, we have yet to understand divide’s possible influence on mental health. Using data from 2016 wave China Labor-force Dynamics Survey (CLDS), find that depressive symptoms both rural–urban migrants and rural residents are significantly higher than those urban residents. Consistent with fundamental-causes-of-disease stress-exposure perspectives, results zero-inflated...

Abstract To investigate temporal patterns, sociodemographic gradients, and structural breaks in adolescent marijuana use the United States from 1991 to 2018, we used hierarchical age-period-cohort logistic regression models distinguish effects of among 8th, 10th, 12th graders 28 waves Monitoring Future survey (1991–2018). Structural period were further detected via a dynamic-programing–based method. Net other effects, found clear age-related increase probability (10.46%, 23.17%, 31.19% for...

Topic modeling has been an important field in natural language processing (NLP) and recently witnessed great methodological advances. Yet, the development of topic is still, if not increasingly, challenged by two critical issues. First, despite intense efforts toward nonparametric/post-training methods, search for optimal number topics K remains a fundamental question warrants input from domain experts. Second, with more sophisticated models, now ironically treated as black box it becomes...

The advancement of deep convolutional neural networks (DCNNs) has driven significant improvement in the accuracy recognition systems for many computer vision tasks. However, their practical applications are often restricted resource-constrained environments. In this paper, we introduce projection (PCNNs) with a discrete back propagation via (DBPP) to improve performance binarized (BNNs). contributions our paper include: 1) first time, function is exploited efficiently solve problem, which...

Abstract Methodological advances in demographic research, especially age‐period‐cohort (APC) analysis, primarily focus on developing new models yet often fail to consider practical concerns empirical analysis. We propose a mixed approach that integrates multiple data imputation and structural change analysis time series so scholars can (i) construct pseudo age groups based more coarsely grouped (ii) identify temporal anomalies. This is illustrated using waves of Canadian Population Census...

Bipolar disorder (BD) has become a serious mental problem in the group of adolescents. The main symptoms BD are depression, (hypo)mania, anxiety, and functional disabilities. two subtypes different. is that probability misdiagnosis high, which may cause non-serious to exacerbate. methods researching atypical emotional development include measuring level mood instability examining deficit facial emotion family factors. patients highly connected with high inappropriate self-sacrifice from...

Deep convolutional neural networks (DCNNs) have dominated the recent developments in computer vision through making various record-breaking models. However, it is still a great challenge to achieve powerful DCNNs resource-limited environments, such as on embedded devices and smart phones. Researchers realized that 1-bit CNNs can be one feasible solution resolve issue; however, they are baffled by inferior performance compared full-precision DCNNs. In this paper, we propose novel approach,...

The rapidly decreasing computation and memory cost has recently driven the success of many applications in field deep learning. Practical learning resource-limited hardware, such as embedded devices smart phones, however, remain challenging. For binary convolutional networks, reason lies degraded representation caused by binarizing full-precision filters. To address this problem, we propose new circulant filters (CiFs) a convolution (CBConv) to enhance capacity binarized features via our...