DSCom: A Data-Driven Self-Adaptive Community-Based Framework for Influence Maximization in Social Networks

Influence maximization aims to find a subset of seeds that maximize the influence spread under a given budget. In this paper, we mainly address the data-driven version of this problem, where the diffusion model is not given but needs to be inferred from the history cascades. Several previous works have addressed this topic in a statistical way and provided efficient algorithms with theoretical guarantee. However, in their settings, though the diffusion parameters are inferred, they still need users to preset the diffusion model, which can be an intractable problem in real-world practices. In this paper, we reformulate the problem on the attributed network and leverage the node attributes to estimate the closeness between the connected nodes. Specifically, we propose a machine learning-based framework, named DSCom, to address this problem in a heuristic way. Under this framework, we first infer the users' relationship from the diffusion dataset through attention mechanism and then leverage spectral clustering to overcome the influence overlap problem in the lack of exact diffusion formula. Compared to the previous theoretical works, we carefully designed empirical experiments with parameterized diffusion models based on real-world social networks, which prove the efficiency and effectiveness of our algorithm

Incorporating Probing Signals into Multimodal Machine Translation via Visual Question-Answering Pairs

This paper presents an in-depth study of multimodal machine translation (MMT), examining the prevailing understanding that MMT systems exhibit decreased sensitivity to visual information when text inputs are complete. Instead, we attribute this phenomenon to insufficient cross-modal interaction, rather than image information redundancy. A novel approach is proposed to generate parallel Visual Question-Answering (VQA) style pairs from the source text, fostering more robust cross-modal interaction. Using Large Language Models (LLMs), we explicitly model the probing signal in MMT to convert it into VQA-style data to create the Multi30K-VQA dataset. An MMT-VQA multitask learning framework is introduced to incorporate explicit probing signals from the dataset into the MMT training process. Experimental results on two widely-used benchmarks demonstrate the effectiveness of this novel approach. Our code and data would be available at: \url{https://github.com/libeineu/MMT-VQA}.Comment: Findings of EMNLP202

Positive delay? The influence of perceived stress on active procrastination

Purpose: Although it is widely accepted that procrastination is counterproductive, active procrastination may be considered a constructive coping strategy in situations where work-related stress is high. Drawing upon the conservation of resource theory and the ego depletion theory, the article suggests that active procrastination can be influenced by perceived stress, mediated by ego depletion, and potentially moderated by the Big Five personality traits. Design/methodology/approach: Using hierarchical regression analysis, Hayes Process Macros, and the general path analytic framework, our hypotheses were investigated. The sample was made up of 651 Chinese civil servants. Findings/results: According to the results, ego depletion fully mediated the positive connection between perceived stress and active procrastination. Furthermore, extroversion, conscientiousness, and openness negatively moderate the link between perceived stress and ego depletion as well as mediating effect. While neuroticism exhibited a positive moderating effect. Practical implications: The findings can serve as references for civil servants and public organisations to address stress and create a more relaxed work environment. Recognising active procrastination as a potential coping strategy can help to reframe the perception of procrastination and guide organisations in supporting their employees’ wellbeing. Originality/value: This study extends comprehension of active procrastination in stressful situations and highlights the potential positive coping consequences of stress attributes. By exploring the mechanisms involved, the study sheds light on how perceived stress can influence active procrastination, with ego depletion serving as a mediating factor, which helps to explain how individuals may experience reduced self-control and subsequently engage in active procrastination as a coping strategy

Fermented Grains from Different Layers of Cellar in the First and Second Rounds of Fermentation of Maotai-Flavor Baijiu: Analysis of microbial community structure and Acid Composition as Well as Correlation between Them

In this study, the microbial community structure in fermented grains from the upper middle and lower layers of the cellar in the first and second rounds of fermentation of Maotai-flavor was analyzed by high-throughput sequencing, and the acid composition by ultra-high performance liquid chromatography (UPLC). Moreover, the correlation between them was investigated. The results showed that the total acid content of fermented grains was greater in the second round than the first round of fermentation. The radial distribution patterns of seven acidic compounds in different times and spaces of the cellar were different. The contents of lactic acid and acetic acid accounted for a large proportion in the two rounds of fermentation. For both rounds, the absolutely dominant bacterial genus was Limosilactobacillus, and the dominant fungal genera were Saccharomyces and Candida. As the fermentation time extended, there was a significant difference in the microbial community structure. The contents of total and individual acids were positively correlated with the relative abundance of Limosilactactacillacus, Schizosaccharomyces, Zygosaccharomyces, Candida and Kazachstania, and negatively correlated with the relative abundance of Lactobacillus, Saccharomyces, Paecilomyces and Torulaspora. This study provides a theoretical basis for further elucidating the fermentation mechanism of Maotai-flavor Baijiu cellar

Impact of exercise training on gut microbiome imbalance in obese individuals: a study based on Mendelian randomization analysis

Objective: The aim of this study was to investigate the relationship between exercise and gut Microbiome and to assess its possible causality.Methods: Using Mendelian randomization (MR) research methods, we collected genetic data from different populations, including genetic variants associated with relative abundance or presence of microbial taxa as instrumental variables. At the same time, we extracted results related to obesity and gut Microbiome from existing relevant studies and used inverse variance weighting (IVW), weighted median, and MR-Egger regression to assess the causal relationship between obesity and gut Microbiome. We plotted forest plots and scatter plots of the association between obesity and gut Microbiome.Results: Gut Microbiome was positively associated with obesity, and four bacterial genera (Akkermansia, RuminococcaceaeUCG011, Holdemania, and Intestinimonas) were associated with obesity according to inverse variance-weighted estimation in at least one MR method. Inverse variance weighted estimation showed that obesity was associated with obesity in Akkermansia (OR = 0.810, 95% CI 0.608–1.079, p = 0.04), RuminococcaceaeUCG011 (OR = 1.238, 95% CI 0. 511–2.999, p = 0.04), Holdemania Intestinimonas (OR = 1.214, 95% CI 1.002–1.470, p = 0.03), and Intestinimonas (OR = 0.747, 95% CI 0.514–1.086, p = 0.01) had a relevant effect. Obesity decreased the abundance of Akkermansia, Intestinimonas microbiome and increased the abundance of RuminococcaceaeUCG011, Holdemania microbiome.Conclusion: The results of this study, conducted using a two-sample Mendelian randomization method, suggest a causal relationship between obesity and intestinal microbiome. Obesity decreased the abundance of Akkermansia, Intestinimonas microbiome and increased the abundance of RuminococcaceaeUCG011, Holdemania microbiome. More randomized controlled trials are necessary to elucidate the protective effects of exercise on gut Microbiome and its unique protective mechanisms

Artificial Intelligence-Enabled ECG Algorithm Based on Improved Residual Network for Wearable ECG

Heart disease is the leading cause of death for men and women globally. The residual network (ResNet) evolution of electrocardiogram (ECG) technology has contributed to our understanding of cardiac physiology. We propose an artificial intelligence-enabled ECG algorithm based on an improved ResNet for a wearable ECG. The system hardware consists of a wearable ECG with conductive fabric electrodes, a wireless ECG acquisition module, a mobile terminal App, and a cloud diagnostic platform. The algorithm adopted in this study is based on an improved ResNet for the rapid classification of different types of arrhythmia. First, we visualize ECG data and convert one-dimensional ECG signals into two-dimensional images using Gramian angular fields. Then, we improve the ResNet-50 network model, add multistage shortcut branches to the network, and optimize the residual block. The ReLu activation function is replaced by a scaled exponential linear units (SELUs) activation function to improve the expression ability of the model. Finally, the images are input into the improved ResNet network for classification. The average recognition rate of this classification algorithm against seven types of arrhythmia signals (atrial fibrillation, atrial premature beat, ventricular premature beat, normal beat, ventricular tachycardia, atrial tachycardia, and sinus bradycardia) is 98.3%

Fully Photonic Integrated Wearable Optical Interrogator

Wearable technology constitutes a pioneering and leading innovation and a market development platform worldwide for technologies worn close to the body. Wearable optical fiber sensors have the most value for advanced multiparameter sensing in digital health monitoring systems. We demonstrated the first example of a fully integrated optical interrogator. By integrating all the optical components on a silicon photonic chip, we realized a stable, miniaturized and low-cost optical interrogator for the continuous, dynamic, and long-term acquisition of human physiological signals. The interrogator was integrated in a wristband, enabling the detection of body temperature and heart sounds. Our study paves the way for the development of watch-sized integrated wearable optical interrogators with potential applications in health monitoring and can be directly exploited for the customized design of ultraminiaturized optical interrogator systems.H.L. acknowledges the support from the Tianjin Talent Special Support Program. J.D.P.G. acknowledges the support from the Serra Hunter Program, the ICREA Academia Program, and the Tianjin Distinguished University Professor Program. This work was supported by the National Natural Science Foundation of China (no. 61675154), the Tianjin Key Research and Development Program (no. 19YFZCSY00180), the Tianjin Major Project for Civil-Military Integration of Science and Technology (no. 18ZXJMTG00260), the Tianjin Science and Technology Program (no. 20YDTPJC01380), and the Tianjin Municipal Special Foundation for Key Cultivation of China (no. XB202007)

KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction

In this paper, we propose KnowCoder, a Large Language Model (LLM) to conduct Universal Information Extraction (UIE) via code generation. KnowCoder aims to develop a kind of unified schema representation that LLMs can easily understand and an effective learning framework that encourages LLMs to follow schemas and extract structured knowledge accurately. To achieve these, KnowCoder introduces a code-style schema representation method to uniformly transform different schemas into Python classes, with which complex schema information, such as constraints among tasks in UIE, can be captured in an LLM-friendly manner. We further construct a code-style schema library covering over $\textbf{30,000}$ types of knowledge, which is the largest one for UIE, to the best of our knowledge. To ease the learning process of LLMs, KnowCoder contains a two-phase learning framework that enhances its schema understanding ability via code pretraining and its schema following ability via instruction tuning. After code pretraining on around $1.5$B automatically constructed data, KnowCoder already attains remarkable generalization ability and achieves relative improvements by \textbf{49.8%} F1, compared to LLaMA2, under the few-shot setting. After instruction tuning, KnowCoder further exhibits strong generalization ability on unseen schemas and achieves up to \textbf{12.5%} and \textbf{21.9%}, compared to sota baselines, under the zero-shot setting and the low resource setting, respectively. Additionally, based on our unified schema representations, various human-annotated datasets can simultaneously be utilized to refine KnowCoder, which achieves significant improvements up to \textbf{7.5%} under the supervised setting

Biomechanical and histological changes associated with riboflavin ultraviolet-A-induced CXL with different irradiances in young human corneal stroma.

Keratoconus (KC) is a degenerative condition affecting the cornea, characterized by progressive thinning and bulging, which can ultimately result in serious visual impairment. The onset and progression of KC are closely tied to the gradual weakening of the cornea's biomechanical properties. KC progression can be prevented with corneal cross-linking (CXL), but this treatment has shortcomings, and evaluating its tissue stiffening effect is important for determining its efficacy. In this field, the shortage of human corneas has made it necessary for most previous studies to rely on animal corneas, which have different microstructure and may be affected differently from human corneas. In this research, we have used the lenticules obtained through small incision lenticule extraction (SMILE) surgeries as a source of human tissue to assess CXL. And to further improve the results' reliability, we used inflation testing, personalized finite element modeling, numerical optimization and histology microstructure analysis. These methods enabled determining the biomechanical and histological effects of CXL protocols involving different irradiation intensities of 3, 9, 18, and 30 mW/cm2, all delivering the same total energy dose of 5.4 J/cm2. The results showed that the CXL effect did not vary significantly with protocols using 3-18 mW/cm2 irradiance, but there was a significant efficacy drop with 30 mW/cm2 irradiance. This study validated the updated algorithm and provided guidance for corneal lenticule reuse and the effects of different CXL protocols on the biomechanical properties of the human corneal stroma

Potential candidates for liver resection in liver-confined advanced HCC: a Chinese multicenter observational study

BackgroundAdvanced hepatocellular carcinoma (HCC) is characterized as symptomatic tumors [performance status (PS) score of 1-2], vascular invasion and extrahepatic spread, but patients with PS1 alone may be eliminated from this stage. Although liver resection is used for liver-confined HCC, its role in patients with PS1 alone remains controversial. Therefore, we aimed to explore its application in such patients and identify potential candidates.MethodsEligible liver-confined HCC patients undergoing liver resection were retrospectively screened in 15 Chinese tertiary hospitals, with limited tumor burden, liver function and PS scores. Cox-regression survival analysis was used to investigate the prognostic factors and develop a risk-scoring system, according to which patients were substratified using fitting curves and the predictive values of PS were explored in each stratification.ResultsFrom January 2010 to October 2021, 1535 consecutive patients were selected. In the whole cohort, PS, AFP, tumor size and albumin were correlated with survival (adjusted P<0.05), based on which risk scores of every patient were calculated and ranged from 0 to 18. Fitting curve analysis demonstrated that the prognostic abilities of PS varied with risk scores and that the patients should be divided into three risk stratifications. Importantly, in the low-risk stratification, PS lost its prognostic value, and patients with PS1 alone achieved a satisfactory 5-year survival rate of 78.0%, which was comparable with that PS0 patients (84.6%).ConclusionSelected patients with PS1 alone and an ideal baseline condition may benefit from liver resection and may migrate forward to BCLC stage A