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Abstract Preferential attachment is an important mechanism in the structural evolution of complex networks. However, though resources on a network propagate and have an effect beyond a direct relationship, growth by preferential attachment based on indirectly propagated resources has not been systematically investigated. Here, we propose a mathematical model of an evolving network in which preference is proportional to a utility function reflecting direct utility from directly connected nodes and indirect utility from indirectly connected nodes beyond the directly connected nodes. Our analysis showed that preferential attachment involving indirect utility forms a converged and hierarchical structure, thereby significantly increasing the indirect utility across the entire network. Further, we found that the structures are formed by mutual growth between adjacent nodes, which promotes a scaling exponent of 1.5 between the number of indirect and direct links. Lastly, by examining several real networks, we found evidence of mutual growth, especially in social networks. Our findings demonstrate a growth mechanism emerging in evolving networks with preference for indirect utility, and provide a foundation for systematically investigating the role of preference for indirect utility in the structural and functional evolution of large-scale social networks.

Individuals develop interdependence through interactions. The process of physical coordination during face-to-face interactions facilitates relationship formation, emotional experiences, and emotional contagion. Workplaces inherently involve organic and continuous face-to-face interactions. Examining the interpersonal association between physical movement and emotional state among employees in the workplace is crucial for understanding an organization’s emotional dynamics. This study investigated the association between patterns of physical engagement during face-to-face interactions and depressive symptoms with those of interaction partners in a real-world workplace setting. The analysis utilized face-to-face interaction data from 449 employees across ten organizations, measured using wearable devices, along with a self-reported depression scale. The results showed that employees’ average physical engagement negatively correlated with the average depressive symptoms of their interaction partners, rather than their own. The association remained robust regardless of the number of relationships or workplace type. Furthermore, the depressive symptoms of individuals who had a significant influence on interpersonal relationships within organizations negatively correlated with their and their partner’s physical engagement. Our findings have important implications for the epidemiological understanding of organizational mental health in relation to face-to-face interactions among employees.

Abstract An unhealthy communication structure at a workplace can adversely affect the mental health of employees. However, little is known about the relationship between communication structures in the workplace and the mental health of employees. Here, we evaluated the face-to-face interaction network among employees (N = 449) in a variety of real-world working environments by using wearable devices and investigated the relationship between social network characteristics and depressive symptoms. We found that the cohesive interaction structure surrounding each individual was negatively correlated with depressive symptoms: a universal relationship regardless of occupation type. This correlation was evident at the group scale and was strongly related to active interactions with abundant body movement. Our findings provide a quantitative and collective perspective on taking a systematic approach to workplace depression, and they suggest that the mental health of employees needs to be addressed systematically, not only individually.

Synthetic training data has been extensively used to train Automatic Post-Editing (APE) models in many recent studies because the quantity of human-created data has been considered insufficient. However, the most widely used synthetic APE dataset, eSCAPE, overlooks respecting the minimal editing property of genuine data, and this defect may have been a limiting factor for the performance of APE models. This article suggests adapting back-translation to APE to constrain edit distance, while using stochastic sampling in decoding to maintain the diversity of outputs, to create a new synthetic APE dataset, RESHAPE. Our experiments show that (1) RESHAPE contains more samples resembling genuine APE data than eSCAPE does, and (2) using RESHAPE as new training data improves APE models’ performance substantially over using eSCAPE.

High efficiency video coding (HEVC) has been developed rapidly to support new generation display devices and their ultra high definition (UHD) with high dynamic range (HDR) and wide color gamut (WCG). To support HDR/WCG sequences on the HEVC standard, pre-/post-processing technique has been designed. After an HDR video is compressed, a reconstructed frame exhibits chromatic distortions that resemble color smearing. To remove this color artifact, we herein propose a block-level quantization parameter (QP) offset-control-based efficient compression algorithm for the HDR sequence. First, we extract the candidate coding units (CUs) with the annoying area to the human eye based on the just noticeable distortion (JND) model. Subsequently, the chromatic distorted blocks are verified by the activity function as the chromatic artifact is observed at the nearby strong edge. For the verified artifact blocks, we reassign the QPs for the Cb and Cr chroma components. Our experimental results show that the proposed method yields an average gain in BD-rate of 3.3% for U, and 3.4% for V with a negligible bitrate increase of 0.3% on average.

Identifying power-law scaling in real networks—indicative of preferential attachment—has proved controversial. Critics argue that measuring the temporal evolution of a network directly is better than measuring the degree distribution when looking for preferential attachment. However, many of the established methods do not account for any potential time dependence in the attachment kernels of growing networks, or methods assume that node degree is the key observable determining network evolution. In this paper, we argue that these assumptions may lead to misleading conclusions about the evolution of growing networks. We illustrate this by introducing a simple adaptation of the Barabási-Albert model, the “k2 model,” where new nodes attach to nodes in the existing network in proportion to the number of nodes one or two steps from the target node. The k2 model results in time dependent degree distributions and attachment kernels, despite initially appearing to grow as linear preferential attachment, and without the need to include explicit time dependence in key network parameters (such as the average out-degree). We show that similar effects are seen in several real world networks where constant network growth rules do not describe their evolution. This implies that measurements of specific degree distributions in real networks are likely to change over time.

Video data for the Internet traffic is increasing, and video data transmission is important for consideration of real-time process in the Internet of Things (IoT). Thus, in the IoT environment, video applications will be valuable approach in networks of smart sensor devices. High Efficiency Video Coding (HEVC) has been developed by the Joint Collaborative Team on Video Coding (JCT-VC) as a new generation video coding standard. Recently, HEVC includes range extensions (RExt), scalable coding extensions, and multiview extensions. HEVC RExt provides high resolution video with a high bit-depth and an abundance of color formats. In this paper, a fast intraprediction unit decision method is proposed to reduce the computational complexity of the HEVC RExt encoder. To design intramode decision algorithm, Local Binary Pattern (LBP) of the current prediction unit is used as texture feature. Experimental results show that the encoding complexity can be reduced by up to 12.35% on average in the AI-Main profile configuration with only a small bit-rate increment and a PSNR decrement, compared with HEVC test model (HM) 12.0-RExt4.0 reference software.

Objective This study aimed to assess the degree of interest in robot-assisted spine surgery (RASS) among residents and to investigate the learning curve for beginners performing robotic surgery. Methods We conducted a survey to assess awareness and interest in RASS among young neurosurgery residents. Subsequently, we offered a hands-on training program using a dummy to educate one resident. After completing the program, the trained resident performed spinal fusion surgery with robotic assistance under the supervision of a mentor. The clinical outcomes and learning curve associated with robotic surgery were then analyzed. Results Neurosurgical residents had limited opportunities to participate in spinal surgery during their training. Despite this, there was a significant interest in the emerging field of robotic surgery. A trained resident performed RASS under the supervision of a senior surgeon. A total of 166 screw insertions were attempted in 28 patients, with 2 screws failing due to skiving. According to the Gertzbein-Robbins classification, 85.54% of the screws were rated as grade A, 11.58% as grade B, 0.6% as grade C, and 1.2% as grade D. The clinical acceptance rate was approximately 96.99%, which is comparable to the results reported by senior experts and time per screw statistically significantly decreased as experience was gained. Conclusion RASS can be performed with high accuracy within a relatively short timeframe, if residents receive adequate training.