Punishment diminishes the benefits of network reciprocity in social dilemma experiments

Network reciprocity has been widely advertised in theoretical studies as one of the basic cooperation-promoting mechanisms, but experimental evidence favoring this type of reciprocity was published only recently. When organized in an unchanging network of social contacts, human subjects cooperate provided the following strict condition is satisfied: The benefit of cooperation must outweigh the total cost of cooperating with all neighbors. In an attempt to relax this condition, we perform social dilemma experiments wherein network reciprocity is aided with another theoretically hypothesized cooperation-promoting mechanism—costly punishment. The results reveal how networks promote and stabilize cooperation. This stabilizing effect is stronger in a smaller-size neighborhood, as expected from theory and experiments. Contrary to expectations, punishment diminishes the benefits of network reciprocity by lowering assortment, payoff per round, and award for cooperative behavior. This diminishing effect is stronger in a larger-size neighborhood. An immediate implication is that the psychological effects of enduring punishment override the rational response anticipated in quantitative models of cooperation in networks.We thank J. H. Lee for useful discussions. M.J. and Z.W. were, respectively, supported by the Research Grant Program of Inamori Foundation and the Chinese Young 1000 Talents Plan. B.P. received support from the Slovenian Research Agency (ARRS) and the Croatian Science Foundation through Projects J5-8236 and 5349, respectively. S.H. thanks the Israel-Italian collaborative project Network Cyber Security (NECST), Israel Science Foundation, Office of Naval Research (ONR), Japan Science Foundation, and the US-Israel Binational Science Foundation and the US National Science Foundation (BSF-NSF) for financial support. The Boston University Center for Polymer Studies is supported by NSF Grants PHY-1505000, CMMI-1125290, and CHE-1213217, by Defense Threat Reduction Agency (DTRA) Grant HDTRA1-14-1-0017, and by Department of Energy (DOE) Contract DE-AC07-05Id14517. (Inamori Foundation; Chinese Young 1000 Talents Plan; J5-8236 - Slovenian Research Agency (ARRS); 5349 - Croatian Science Foundation; Israel-Italian collaborative project Network Cyber Security (NECST); Israel Science Foundation; Office of Naval Research (ONR); Japan Science Foundation; US-Israel Binational Science Foundation; US National Science Foundation (BSF-NSF); PHY-1505000 - NSF; CMMI-1125290 - NSF; CHE-1213217 - NSF; HDTRA1-14-1-0017 - Defense Threat Reduction Agency (DTRA); DE-AC07-05Id14517 - Department of Energy (DOE))Published versio

ControlCom: Controllable Image Composition using Diffusion Model

Image composition targets at synthesizing a realistic composite image from a pair of foreground and background images. Recently, generative composition methods are built on large pretrained diffusion models to generate composite images, considering their great potential in image generation. However, they suffer from lack of controllability on foreground attributes and poor preservation of foreground identity. To address these challenges, we propose a controllable image composition method that unifies four tasks in one diffusion model: image blending, image harmonization, view synthesis, and generative composition. Meanwhile, we design a self-supervised training framework coupled with a tailored pipeline of training data preparation. Moreover, we propose a local enhancement module to enhance the foreground details in the diffusion model, improving the foreground fidelity of composite images. The proposed method is evaluated on both public benchmark and real-world data, which demonstrates that our method can generate more faithful and controllable composite images than existing approaches. The code and model will be available at https://github.com/bcmi/ControlCom-Image-Composition

College students' cyberloafing and the sense of meaning of life: The mediating role of state anxiety and the moderating role of psychological flexibility

BackgroundWith the gradual penetration of network media into various fields of people's life, the relationship between network behavior and the sense of meaning of life is bound to be closer and closer. The purpose of this study is to explore the mediating role of state anxiety between cyber loafing and the sense of meaning of life, and the moderating role of psychological flexibility in this mediating relationship.MethodologyWith 964 undergraduates recruited as subjects three-wave-time-lagged quantitative research design was conducted in China. All participants were required to complete a self-reported electronic questionnaire. Then, the mediating mechanism and moderating effect were explored with utilization of SPSS25.0.ResultsThe results showed that cyberloafing had significant negative correlation with the sense of meaning of life. Our analysis testing the mediating effect showed that state anxiety partially mediated the relationship between cyberloafing and the sense of meaning of life (indirect effect = −0.05, p < 0.01,), while the mediating effect was 31.25% of the total effect. Our analysis testing the moderating effect showed that psychological flexibility significantly moderated the relationship between cyberloafing and state anxiety (interaction effect = −0.26, p < 0.01). And our analysis testing the moderated mediating effect showed that psychological flexibility played a moderating role in the mediating effect of state anxiety.ConclusionBased on the findings of this study, college students' cyberloafing negatively affects their sense of meaning of life. Therefore, appropriate measures should be taken to supervise and restrict college students' Internet use and provide them with corresponding guidance; certain psychological adjustment measures should also be taken when necessary to help college students with low psychological flexibility in reducing their state anxiety and improving their sense of meaning of life

DiffUTE: Universal Text Editing Diffusion Model

Diffusion model based language-guided image editing has achieved great success recently. However, existing state-of-the-art diffusion models struggle with rendering correct text and text style during generation. To tackle this problem, we propose a universal self-supervised text editing diffusion model (DiffUTE), which aims to replace or modify words in the source image with another one while maintaining its realistic appearance. Specifically, we build our model on a diffusion model and carefully modify the network structure to enable the model for drawing multilingual characters with the help of glyph and position information. Moreover, we design a self-supervised learning framework to leverage large amounts of web data to improve the representation ability of the model. Experimental results show that our method achieves an impressive performance and enables controllable editing on in-the-wild images with high fidelity. Our code will be avaliable in \url{https://github.com/chenhaoxing/DiffUTE}

Cortisol dysregulation in anxiety infertile women and the influence on IVF treatment outcome

IntroductionDysregulation of the stress-regulatory hormone cortisol is associated with anxiety, but its potential impact on infertile women and in vitro fertilization (IVF) treatment remains unclear. This prospective cross-sectional study aimed at evaluating the dysregulation of cortisol and its correlation to anxiety in infertile women. The influence of stress on IVF outcomes was also investigated.MethodsA point-of-care test was used for the measurement of morning serum cortisol in 110 infertile women and 112 age-matching healthy individuals. A Self-Rating Anxiety Scale (SAS) was used for the anxiety assessment of infertile women, and 109 of them underwent IVF treatment starting with the GnRH-antagonist protocol. If clinical pregnancy was not achieved, more IVF cycles were conducted with adjusted protocols until the patients got pregnant or gave up.ResultsHigher morning serum cortisol level was identified for infertile patients, especially for the elder. Women with no anxiety showed significant differences in cortisol levels, monthly income, and BMI compared with those with severe anxiety. A strong correlation was found between the morning cortisol level and the SAS score. When the cutoff value is 22.25 μg/dL, cortisol concentration could predict the onset of anxiety with high accuracy (95.45%) among infertile women. After IVF treatments, women with high SAS scores (>50) or cortisol levels (>22.25 μg/dL) demonstrated a lower rate of pregnancy (8.0%-10.3%) and more IVF cycles, although the impact of anxiety was not affirmative.ConclusionHypersecretion of cortisol related to anxiety was prevalent among infertile women, but the influence of anxiety on multi-cycle IVF treatment was not affirmative due to the complicated treatment procedures. This study suggested that the assessment of psychological disorders and stress hormone dysregulation should not be overlooked. An anxiety questionnaire and rapid cortisol test might be included in the treatment protocol to provide better medical care

Intelligent identification of logging cuttings based on deep learning

In oil and gas exploration, rock sample identification is a basic and important work. At present, the methods of rock sample identification mainly include gravity and magnetism, well logging, earthquake, remote sensing, electromagnetism, geochemistry, hand sample, and thin section analysis. Most of these methods for lithology identification are based on manual identification methods, which require a certain professional background and rich identification experience. Based on the deep learning method, this paper constructs the intelligent recognition model of cuttings lithology in logging work, which can realize the automatic recognition of rock images. The pre-training model is VGG16, and the parameters in the pre-training model are frozen and fine-tuned by the method of transfer learning, and finally the rock image identification based on vgg16 is realized. The accuracy of the trained model was 98% in the training set and 86% in the validation set. The experimental results show that the deep learning model based on transfer learning and vgg16 proposed in this paper has strong applicability to cuttings data and can distinguish rock types well. The intelligent lithology identification method proposed in this paper has good generalization performance and can be used for rapid intelligent identification of rock lithology in geology, well logging, power fault inspection, water conservancy and other projects

Directed Network Disassembly Method Based on Non-Backtracking Matrix

Network disassembly refers to the removal of the minimum set of nodes to split the network into disconnected sub-part to achieve effective control of the network. However, most of the existing work only focuses on the disassembly of undirected networks, and there are few studies on directed networks, because when the edges in the network are directed, the application of the existing methods will lead to a higher cost of disassembly. Aiming at fixing the problem, an effective edge module disassembly method based on a non-backtracking matrix is proposed. This method combines the edge module spectrum partition and directed network disassembly problem to find the minimum set of key points connecting different edge modules for removal. This method is applied to large-scale artificial and real networks to verify its effectiveness. Multiple experimental results show that the proposed method has great advantages in disassembly accuracy and computational efficiency