Miracle-dz/UniFAR-FTUs huggingface dataset:
Men’s Intentions to Recommend Professional Help-Seeking to Their Partners in the Postpartum Period: the Direct and Indirect Effects of Gender-Role Conflict

Women’s partners may act as facilitators of professional help-seeking for mental health problems in the postpartum period. This study aimed to examine the sociodemographic and clinical correlates of men’s intentions to recommend professional help-seeking to their partners if they display postpartum mood and anxiety disorders and to explore the relationship between gender-role conflict and the intention to recommend help-seeking. A cross-sectional study included 214 adult men in a heterosexual relationship with a partner within the reproductive age. Men presented a high intention to recommend professional help to their partners. All dimensions of gender-role conflict were directly associated with the intention to recommend professional help-seeking (p < 0.05). High levels of gender-role conflict (dimensions success, power and competition, and restricted emotionality) were found to lead to increased levels of stigma and lower levels of intention to seek professional help, which, in turn, translated into lower intention to recommend help-seeking. These results emphasize the importance of developing universal awareness-raising and education campaigns directed at men aiming to reduce levels of gender-role conflict and stigma, and normalize the use of mental health services, to increase men’s intentions to recommend professional help-seeking to their partners.
Novel species of Calonectria isolated from soil near Eucalyptus plantations in southern China

ABSTRACT Calonectria species are soil-borne and widely distributed in tropical and subtropical regions around the world. The species in this genus include many important plant pathogens that cause serious diseases to economically important crops and forest trees. Previous research results indicated that the leaf blight and cutting rot caused by Calonectria species caused big losses to the Eucalyptus industry in southern China, and large number of Calonectria species identified in China have been collected from soil in Eucalyptus plantations. In this study, Calonectria samples were isolated from soils close to Eucalyptus plantations in Guangdong Province, southern China. These isolates were identified by DNA sequence analyses based on the calmodulin (cmdA), histone H3 (his3), translation elongation factor 1-alpha (tef1), and β-tubulin (tub2) gene regions, and combined with morphological characteristics. One novel species of Calonectria was identified and described, named Calonectria xianrensis, which resides in the Prolate Group. Results in this study suggest that more species of Calonectria may be distributed in soils in China.
Sense of belonging to the community in continuing care retirement communities and adult day care centers: The role of the social network.

AIM This study examined the contribution of the social network to one's sense of belonging to the community (SCB) in two different long-term care settings: Continuing care retirement communities (CCRCs) and adult day care centers (ADCCs). METHODS Overall, 245 respondents participated in both waves of the study that were spaced about 1 year apart. RESULTS Results show that constraint in the social network (e.g., being socially invested in a single group of interconnected ties) is particularly detrimental for CCRC residents, but not for ADCC participants. Betweenness, defined as the number of shortest paths that pass through the focal person, was not significantly related to SCB. In addition, the size of the egocentric network, obtained through a name generator of a list of all potentially meaningful individuals identified by respondents, was directly correlated with SCB at follow-up. CONCLUSIONS The findings are discussed in light of differences between the two long-term care settings and the different social network indicators examined in this study.
NHS health trainers: a review of emerging evaluation evidence

Recent years have seen a change in focus in UK public health policies towards an emphasis on individual lifestyle choices. As part of this shift, NHS health trainers were introduced in disadvantaged communities in England, to provide peer support to people ‘at risk’ of developing lifestyle-related health problems and to help them to self manage their behaviour. Concerns have been expressed, however, about the strength of the evidence supporting the initiative. This article outlines a number of gaps between the theory and rhetoric underpinning the NHS health trainer initiative, and the reality in practice. This article critiques the evaluation evidence, questions the assumption that engaging lay people in health promotion activities in place of health professionals is necessarily a preferable option, identifies inconsistencies in the evidence supporting individually based health improvement initiatives, and suggests that interventions which target deprived areas but neglect the social determinants of health may be limited in their effectiveness.
The Effects of Physicians’ Financial Incentives on the Effectiveness of Taiwan’s Outpatient Drug Copayment Policy

This study examines how physicians’ financial incentives affect the effectiveness of the outpatient drug copayment policy in Taiwan under National Health Insurance (NHI). Our sample of 1,301,599 patients aged less than 65 years was drawn from 21 hospitals for the study. We use analysis of variance to better understand possible reasons for changes in drug costs. Regression analysis is invoked to investigate the effect of physicians’ financial incentives on drug utilization after the implementation of the drug copayment policy. The results indicate that total drug costs increase after the implementation of the co-payment policy because of increases in average drug price per prescription and drug utilization. In addition, the results indicate that hospitals with physician fee programs that include drug revenue have longer duration per prescription and higher drug items per prescription. From Taiwan’s experience, we infer that physicians’ financial incentives could have an impact on the effectiveness of policies aimed at containing drug costs. Results obtained from this study should help health service researchers and health policy officials design similar drug policies for their own countries.
TC-GAN: Triangle Cycle-Consistent GANs for Face Frontalization with Facial Features Preserved

Face frontalization has always been an important field. Recently, with the introduction of generative adversarial networks (GANs), face frontalization has achieved remarkable success. A critical challenge during face frontalization is to ensure the features of the original profile image are retained. Even though some state-of-the-art methods can preserve identity features while rotating the face to the frontal view, they still have difficulty preserving facial expression features. Therefore, we propose the novel triangle cycle-consistent generative adversarial networks for the face frontalization task, termed TC-GAN. Our networks contain two generators and one discriminator. One of the generators generates the frontal contour, and the other generates the facial features. They work together to generate a photo-realistic frontal view of the face. We also introduce cycle-consistent loss to retain feature information effectively. To validate the advantages of TC-GAN, we apply it to the face frontalization task on two datasets. The experimental results demonstrate that our method can perform large-pose face frontalization while preserving the facial features (both identity and expression). To the best of our knowledge, TC-GAN outperforms the state-of-the-art methods in the preservation of facial identity and expression features during face frontalization.
Fine Granular Multilevel Parallel Algorithm for HEVC Decoding Based on Tilera-GX36 Multi-core Platform

This paper presents a fine granular multilevel parallel algorithm for HEVC decoding, which is focus on two modules: the pixel reconstruction and the in-loop filter (ILF). Firstly, in the pixel reconstruction module, the problem of unbalanced load among decoding threads may occur in parallel decoding algorithm based on CTU rows, like wavefront parallel processing (WPP), because there may be big differences in the processing complexity of CTUs within various CTU rows. In order to solve this issue, we propose a fine granular parallel algorithm based on WPP. Then, a fast fusion ILF algorithm has been presented in the ILF module, which realizes the deep coupling between DBF and SAO by a new CTU-like process unit. Furthermore, the introduction of pipeline parallel technology makes a latency reduction between the two modules. We implement our method on Tilera-GX36 multi-core platform, each task executed by a thread bound to a separate core, which makes full use of the parallel computing performance. The experimental results show that the speedup of our proposed algorithms are 1.2, 1.13 and 1.8 times higher than that of those previous schemes respectively.
Recognizing the Style of Visual Arts via Adaptive Cross-layer Correlation

Visual arts consist of various art forms, \eg painting, sculpture, architecture,~\etc, which not only enrich our lives but also involve works related to aesthetics, history, and culture. Different eras have different artistic appeals, and the art also has characteristics of each era in terms of expression and spiritual pursuit, in which style is an important attribute to describe visual arts. In order to recognize the style of visual arts more effectively, we present an end-to-end trainable architecture to learn a deep style representation. The main component of our architecture, adaptive cross-layer correlation, is inspired by the Gram matrix based correlation calculation. Our proposed method can adaptively weight features in different spatial locations based on their intrinsic similarity. Extensive experiments on three datasets demonstrate the superiority of the proposed method over several state-of-the-art methods.
POINet: Pose-Guided Ovonic Insight Network for Multi-Person Pose Tracking

Multi-person pose tracking aims to jointly estimate and track multi-person keypoints in the unconstrained videos. The most popular solution to this task follows the tracking-by-detection strategy that relies on human detection and data association. While human detection has been boosted by deep learning, existing works mainly exploit several separated stages with hand-crafted metrics to realize data association, leading to great uncertainty and feeble adaption in complex scenes. To handle these problems, we propose an end-to-end pose-guided ovonic insight network (POINet) for the data association in multi-person pose tracking, which jointly learns feature extraction, similarity estimation, and identity assignment. Specifically, we design a pose-guided representation network to integrate pose information into hierarchical convolutional features, generating a pose-aligned person representation for person, which helps handle partial occlusions. Moreover, we propose an ovonic insight network to adaptively encode the cross-frame identity transformation, which can cope with the tough tracking cases of person leaving and entering the scene. In general, the proposed POINet provides a new insight to realize multi-person pose tracking in an end-to-end fashion. Extensive experiments conducted on the PoseTrack benchmark demonstrate that our POINet outperforms the state-of-the-art methods.
Comp-GAN: Compositional Generative Adversarial Network in Synthesizing and Recognizing Facial Expression

Facial expression is important in understanding our social interaction. Thus the ability to recognize facial expression enables the novel multimedia applications. With the advance of recent deep architectures, research on facial expression recognition has achieved great progress. However, these models are still suffering from the problems of lacking sufficient and diverse high quality training faces, vulnerability to the facial variations, and recognizing a limited number of basic types of emotions. To tackle these problems, this paper proposes a novel end-to-end Compositional Generative Adversarial Network (Comp-GAN) that is able to synthesize new face images with specified poses and desired facial expressions; and such synthesized images can be further utilized to help train a robust and generalized expression recognition model. Essentially, Comp-GAN can dynamically change the expression and pose of faces according to the input images while keeping the identity information. Specifically, the generator has two major components: one for generating images with desired expression and the other for changing the pose of faces. Furthermore, a face reconstruction learning process is applied to re-generate the input image and constrains the generator for preserving the key information such as facial identity. For the first time, various one/zero-shot facial expression recognition tasks have been created. We conduct extensive experiments to show that the images generated by Comp-GAN are helpful to improve the performance of one/zero-shot facial expression recognition.
MUCH: Mutual Coupling Enhancement of Scene Recognition and Dense Captioning

Due to the abstraction of scenes, comprehensive scene understanding requires semantic modeling in both global and local aspects. Scene recognition is usually researched from a global point of view, while dense captioning is typically studied for local regions. Previous works separately research on the modeling of scene recognition and dense captioning. In contrast, we propose a joint learning framework that benefits from the mutual coupling of scene recognition and dense captioning models. Generally, these two tasks are coupled through two steps, 1) fusing the supervision by considering the contexts between scene labels and local captions, and 2) jointly optimizing semantically symmetric LSTM models. Particularly, in order to balance bias between dense captioning and scene recognition, a scene adaptive non-maximum suppression (NMS) method is proposed to emphasize the scene related regions in region proposal procedure, and a region-wise and category-wise weighted pooling method is proposed to avoid over attention on particular regions in local to global pooling procedure. For the model training and evaluation, scene labels are manually annotated for Visual Genome database. The experimental results on Visual Genome show the effectiveness of the proposed method. Moreover, the proposed method also can improve previous CNN based works on public scene databases, such as MIT67 and SUN397.
Dataset: LoRa Underground Farm Sensor Network

This paper describes a dataset of LoRa radio channel metrics, soil moisture and temperature measurements, weather and flux tower observations, from an underground sensor network on a farm site in Western Australia. The dataset comprises half hourly readings from 10 months of continuous monitoring over 10 radio channels (5 transmitters and 2 receivers) for 24 sensors. R-notebooks are provided to to facilitate re-use of this data by documenting collection metadata and exploratory data analyses. The full dataset and metadata is available from DOI 10.26182/5d8036e5c4ca7
Survey of Compressed Domain Video Summarization Techniques

Video summarization is the method of extracting key frames or clips from a video to generate a synopsis of the content of the video. Generally, video is compressed before storing or transmitting it in most of the practical applications. Traditional techniques require the videos to be decoded to summarize them, which is a tedious job. Instead, compressed domain video processing can be used for summarizing videos by partially decoding them. A classification and analysis of various summarization techniques are presented in this article with special focus on compressed domain techniques along with a discussion on machine-learning-based techniques that can be applied to summarize the videos.
Interactive Decision Making Method Based on Improved Mean-Variance Model for Portfolio

For the investors, how to allocate a certain amount of capital to a set of assets in order to get higher gain at the cost of lower risk is their first concern. With respect to the universality and significant influence of transactional costs in Chinese investment market, and the facts that most investors in China are risk aversive, an improved MV model, which involves transactional cost and risk-free asset, was established. Based on the model, an interactive decision process was designed to assist an investor in choosing his most satisfied investment plan from the effective frontier. The application of the model was verified by the actual data of Shanghai and Shenzhen A-share Main Board Market. The experimental results showed that an investor could use the improved portfolio MV model to choose the investment plan closest to his needs by the interactive decision process.
A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms

Sending data to the cloud for analysis was a prominent trend during the past decades, driving cloud computing as a dominant computing paradigm. However, the dramatically increasing number of devices and data traffic in the Internet-of-Things (IoT) era are posing significant burdens on the capacity-limited Internet and uncontrollable service delay. It becomes difficult to meet the delay-sensitive and context-aware service requirements of IoT applications by using cloud computing alone. Facing these challenges, computing paradigms are shifting from the centralized cloud computing to distributed edge computing. Several new computing paradigms, including Transparent Computing, Mobile Edge Computing, Fog Computing, and Cloudlet, have emerged to leverage the distributed resources at network edge to provide timely and context-aware services. By integrating end devices, edge servers, and cloud, they form a hierarchical IoT architecture, i.e., End-Edge-Cloud orchestrated architecture to improve the performance of IoT systems. This article presents a comprehensive survey of these emerging computing paradigms from the perspective of end-edge-cloud orchestration. Specifically, we first introduce and compare the architectures and characteristics of different computing paradigms. Then, a comprehensive survey is presented to discuss state-of-the-art research in terms of computation offloading, caching, security, and privacy. Finally, some potential research directions are envisioned for fostering continuous research efforts.
Occluded Facial Expression Recognition Enhanced through Privileged Information

In this paper, we propose a novel approach of occluded facial expression recognition under the help of non-occluded facial images. The non-occluded facial images are used as privileged information, which is only required during training, but not required during testing. Specifically, two deep neural networks are first trained from occluded and non-occluded facial images respectively. Then the non-occluded network is fixed and is used to guide the fine-tuning of the occluded network from both label space and feature space. Similarity constraint and loss inequality regularization are imposed to the label space to make the output of occluded network converge to that of the non-occluded network. Adversarial leaning is adopted to force the distribution of the learned features from occluded facial images to be close to that from non-occluded facial images. Furthermore, a decoder network is employed to reconstruct the non-occluded facial images from occluded features. Under the guidance of non-occluded facial images, the occluded network is expected to learn better features and classifier during training. Experiments on the benchmark databases with both synthesized and realistic occluded facial images demonstrate the superiority of the proposed method to state-of-the-art.
DADNet: Dilated-Attention-Deformable ConvNet for Crowd Counting

Most existing CNN-based methods for crowd counting always suffer from large scale variation in objects of interest, leading to density maps of low quality. In this paper, we propose a novel deep model called Dilated-Attention-Deformable ConvNet (DADNet), which consists of two schemes: multi-scale dilated attention and deformable convolutional DME (Density Map Estimation). The proposed model explores a scale-aware attention fusion with various dilation rates to capture different visual granularities of crowd regions of interest, and utilizes deformable convolutions to generate a high-quality density map. There are two merits as follows: (1) varying dilation rates can effectively identify discriminative regions by enlarging the receptive fields of convolutional kernels upon surrounding region cues, and (2) deformable CNN operations promote the accuracy of object localization in the density map by augmenting the spatial object location sampling with adaptive offsets and scalars. DADNet not only excels at capturing rich spatial context of salient and tiny regions of interest simultaneously, but also keeps a robustness to background noises, such as partially occluded objects. Extensive experiments on benchmark datasets verify that DADNet achieves the state-of-the-art performance. Visualization results of the multi-scale attention maps further validate the remarkable interpretability achieved by our solution.
Exquisitor: Breaking the Interaction Barrier for Exploration of 100 Million Images

In this demonstration, we present Exquisitor, a media explorer capable of learning user preferences in real-time during interactions with the 99.2 million images of YFCC100M. Exquisitor owes its efficiency to innovations in data representation, compression, and indexing. Exquisitor can complete each interaction round, including learning preferences and presenting the most relevant results, in less than 30 ms using only a single CPU core and modest RAM. In short, Exquisitor can bring large-scale interactive learning to standard desktops and laptops, and even high-end mobile devices.
Hanasu: Interactive Japanese Language M-Learning Application to Support Listening and Speaking Exercise

The Japanese language is not only one of the most favorite foreign languages in Indonesian higher education, but it is also considered a difficult foreign language to learn. This fact creates many mobile learning (m-learning) applications that were developed to help learners learn the Japanese language independently. Existing Japanese m-learning apps concentrate only on improving vocabulary, writing and reading skills. Although improving vocabulary and reading skills are important, practicing Japanese pronunciation is also crucial in fundamental Japanese learning. This study introduces the idea of utilizing the listening and speaking exercise in the M-learning implementation to provide learners with a new experience in term of Japanese language learning method. In addition, this paper also describes preliminary learning outcomes from a small-scale assessment of the learners when they are using the speaking input in m-learning. Evaluation result indicates that the listening and speaking method is feasible for future implementation of m-learning with promising learning outcome enhancement compared to the conventional learning method.