Social status, masculinity, and testosterone in young men

Among other things, sexual selection theory posits that in species with limited male parental investment, males should evolve biological mechanisms to competitively dominate or otherwise compete with one another. It is unclear whether such proposed mechanisms would influence status hierarchies in small human groups. We report the results of a study investigating possible effects of prenatal (index-to-ring finger length ratio or 2D:4D) and salivary testosterone, and masculine identification, on status among 71 male college students living in nine residential groups. The results indicate no role of either prenatal or salivary testosterone in maintaining status hierarchies in these groups. A possible explanation is that peer-assessed status in these groups was arrived at cooperatively rather than through dominance contests.     

Longer is better

The longer something is thought to exist, the better it is evaluated. In Study 1, participants preferred an existing university requirement over an alternative; this pattern was more pronounced when the existing requirement was said to be in place for a longer period of time. In Study 2, participants rated acupuncture more favorably as a function of how old the practice was described. Aesthetic judgments of art (Study 3) and nature (Study 4) were also positively affected by time in existence, as were gustatory evaluations of an edible consumer good (Study 5). Features of the research designs argue against mere exposure, loss aversion, and rational inference as explanations for these findings. Instead, time in existence seems to operate as a heuristic; longer means better.     

Company matters: Goal-related social capital in the transition to working life

Using longitudinal data on 343 young adults, the present study investigated the social ties involved in young adults’ work-related goals, how these ties change during transition to working life, and whether social ties contribute to success in dealing with the transition. The results showed that goal-relevant social ties reflected changes in the young adults’ developmental context. Furthermore, social ties that included a person with high socioeconomic status and weaker social ties contributed to employment success, whereas social ties containing one’s supervisor were associated with quality of employment. The results also showed that goal-related social hindrance increased young adults’ intentions to quit and decreased job satisfaction.     

Guanxi networks and members' effectiveness in Chinese work teams: Mediating effects of trust networks

The present study examined 33 teams with a total of 206 team members (excluding team leaders) to explore the effects of intrateam guanxi and trust networks on individual effectiveness in Taiwan. Guanxi networks included non-job, departmental and past team networks, while trust networks included affective and cognitive trust networks. Results showed that: (i) guanxi networks could better explain members' effectiveness compared with relational demography, and different guanxi networks had different effects; (ii) the more central an individual's position was in the cognitive trust network, the higher the individual's effectiveness; (iii) the past team guanxi network of a team member displayed a positive effect on the centrality of cognitive trust network, while non-job and departmental guanxi networks showed negative effects; and (iv) the effects of members' guanxi networks on individual effectiveness were mediated by the centrality of trust networks.     

万拉法新对抑郁症注意网络功能的影响

目的 研究抑郁症的注意网络状况及抑制神经递质再摄取的药物万拉法新对其的影响,探索神经递质与注意网络的关系。方法 对32例应用万拉法新治疗的抑郁症患者治疗前及疗后6周进行注意网络测定和临床症状评分。结果 与正常组比较,抑郁组的警觉网络和执行控制网络效率差异有显著性,定向网络效率差别无显著性。警觉网络效率与HAMD评分的睡眠障碍因子分呈负相关。治疗后,抑郁组的警觉效率显著增加,其改善与睡眠障碍的好转呈正相关。执行控制网络效率,平均反应时和临床症状评分均较治疗前显著降低,定向网络效率无变化。结论 抑郁症患者存在警觉和执行控制注意网络的异常,万拉法新可选择性地改善警觉和执行控制注意网络的异常而不影响其定向网络     

The power of powerless speech: The effects of speech style and task interdependence on status conferral

Two studies examine the effects of speech styles and task interdependence on status conferral judgments. In both studies, participants were exposed to an individual who used either a powerful or powerless speech style in a low or high task interdependence group, and made judgments about the amount of status to confer to the individual. When task interdependence was low, participants conferred more status to powerful speakers, whereas when interdependence was high, participants conferred more status to powerless speakers. Furthermore, Study 2 demonstrated that speech styles influenced trait inferences about the speaker (agency and communality), but these traits were weighted differently in status conferral judgments across groups. These findings provide insight into both the relationship between observed behaviors and status positions and the decision process underlying status conferral judgments.     

An improved learning algorithm for random neural networks based on particle swarm optimization and input-to-output sensitivity

Extreme learning machine (ELM) for random single-hidden-layer feedforward neural networks (RSLFN) has been widely applied in many fields in the past ten years because of its fast learning speed and good generalization performance. But because traditional ELM randomly selects the input weights and hidden biases, it typically requires high number of hidden neurons and thus decreases its convergence performance. It is necessary to select optimal input weights and hidden biases to improve the convergence performance of the traditional ELM. Generally, the single-hidden-layer feedforward neural networks (SLFN) with low input-to-output sensitivity will cause good robustness of the network, which may further lead into good generalization performance. Moreover, particle swarm optimization (PSO) has no complicated evolutionary operators and fewer parameters need to adjust, and is easy to implement. In this study, an improved ELM based on PSO and input-to-output sensitivity information is proposed to improve RSLFN’s convergence performance. In the improved ELM, PSO encoding the input to output sensitivity information of the SLFN is used to optimize the input weights and hidden biases. The improved ELM could obtain better generalization performance as well as improve the conditioning of the SLFN by decreasing the input-to-output sensitivity of the network. Finally, experiment results on the regression and classification problems verify the improved performance the proposed ELM.     

A performance comparison between shallow and deeper neural networks supervised classification of tomosynthesis breast lesions images

Computer Aided Decision (CAD) systems, based on 3D tomosynthesis imaging, could support radiologists in classifying different kinds of breast lesions and then improve the diagnosis of breast cancer (BC) with a lower X-ray dose than in Computer Tomography (CT) systems.In previous work, several Convolutional Neural Network (CNN) architectures were evaluated to discriminate four different classes of lesions considering high-resolution images automatically segmented: (a) irregular opacity lesions, (b) regular opacity lesions, (c) stellar opacity lesions and (d) no-lesions. In this paper, instead, we use the same previously extracted relevant Regions of Interest (ROIs) containing the lesions, but we propose and evaluate two different approaches to better discriminate among the four classes.In this work, we evaluate and compare the performance of two different frameworks both considering supervised classifiers topologies. The first framework is feature-based, and consider morphological and textural hand-crafted features, extracted from each ROI, as input to optimised Artificial Neural Network (ANN) classifiers. The second framework, instead, considers non-neural classifiers based on automatically computed features evaluating the classification performance extracting several sets of features using different Convolutional Neural Network models.Final results show that the second framework, based on features computed automatically by CNN architectures performs better than the first approach, in terms of accuracy, specificity, and sensitivity.     

Three-channel convolutional neural networks for vegetable leaf disease recognition

The color information of diseased leaf is the main basis for leaf based plant disease recognition. To make use of color information, a novel three-channel convolutional neural networks (TCCNN) model is constructed by combining three color components for vegetable leaf disease recognition. In the model, each channel of TCCNN is fed by one of three color components of RGB diseased leaf image, the convolutional feature in each CNN is learned and transmitted to the next convolutional layer and pooling layer in turn, then the features are fused through a fully connected fusion layer to get a deep-level disease recognition feature vector. Finally, a softmax layer makes use of the feature vector to classify the input images into the predefined classes. The proposed method can automatically learn the representative features from the complex diseased leaf images, and effectively recognize vegetable diseases. The experimental results validate that the proposed method outperforms the state-of-the-art methods of the vegetable leaf disease recognition.