Miracle-dz/UniFAR-FTUs huggingface dataset:
Confidence-based Simple Graph Convolutional Networks for Face Clustering

Face clustering is an effective method for taking advantage of unlabeled face data. Recent studies use graph convolutional networks (GCNs) to learn feature embeddings from the neighborhood information between face images. However, most of the face clustering methods require numerous overlapping subgraphs to characterize the local structure around the nodes, which causes significant redundancy. Moreover, the nonlinearity of the GCN itself increases the calculation complexity, which further reduces the model’s training efficiency. In this study, we propose a lightweight clustering framework, the confidence-based simple graph convolutional network (CSGCN), for face clustering, which achieves more accurate clustering results and significantly improves the efficiency of GCN-based face clustering. Specifically, CSGCN does not construct any subgraphs but convolves the entire graph as a whole and also removes the nonlinearity of the convolution in the graph convolution module, which further reduces the computational complexity. Subsequently, an effective new confidence score is constructed to better characterize the embedded features and to ensure that the subsequent clustering still maintains a high accuracy rate under the aforementioned model simplification. In addition, while most of the existing GCN-based methods are actually supervised, we construct an unsupervised confidence to make it more suitable for clustering tasks. Extensive experiments with MS-Celeb-1M, YouTube-Faces and DeepFashion datasets show that our method not only improves the clustering accuracy but also significantly reduces the execution time, whether in supervised or unsupervised models.
Willingness to pay for sustainable drainage systems in a highly urbanised city: a contingent valuation study in Hong Kong

Sustainable drainage systems (SuDS) reduce surface runoff and pollution from developed areas using various techniques such as green roofs and porous pavements. These systems are becoming popular worldwide, except in highly urbanised cities. This study used contingent valuation to investigate city dwellers’ perceptions of SuDS and their willingness to pay (WTP). Face‐to‐face interviews were conducted with 600 respondents in Hong Kong. In general, the participants’ understanding of stormwater management was not strong, although they supported SuDS. The mean and the median WTP were 8.8 and 15.0%, respectively, of the current government spending on stormwater management for public places, and 1.8 and 2.5%, respectively, of the property price for private properties. These WTP were lower than those reported in the previous overseas studies. Respondents who were relatively younger, better educated, had higher income, and came from a previously flooded district had higher WTP. These results facilitated strategic SuDS implementation in highly urbanised cities.
Dual-Population Co-Evolution Multi-Objective Optimization Algorithm and Its Application: Power Allocation Optimization of Mobile Base Stations

In the multi-objective optimization algorithm, the parameter strategy has a huge impact on the performance of the algorithm, and it is difficult to set a set of parameters with excellent distribution and convergence performance in the actual optimization process. Based on the MOEA/D algorithm framework, this paper construct an improved dual-population co-evolution MOEA/D algorithm by adopt the idea of dual-population co-evolution. The simulation test of the benchmark functions shows that the proposed dual-population co-evolution MOEA/D algorithm have significant improvements in IGD and HV indicators compare with three other comparison algorithms. Finally, the application of the LTE base station power allocation model also verifies the effectiveness of the proposed algorithm.
Estimating Skeletal Maturity by Segmented Linear Modeling of Key AP Knee Radiographic Parameters

Background: The recently described Modified Fels knee system allows for accurate skeletal maturity estimation using a single anteroposterior knee radiograph but requires evaluation of 7 parameters. A faster method may have clinical utility in the outpatient setting. Methods: Seven anteroposterior knee radiographic parameters associated with 90% of the final height (an enhanced skeletal maturity standard compared with peak height velocity) were analyzed in 78 children. Segmented linear regression and generalized estimating equation analyses were used to identify the subsets of parameters most important for accurate skeletal maturity estimation for different patient demographics and parameter scores. This process produced abbreviated skeletal maturity systems, which include fewer parameters and are quicker to use. The accuracy of the resulting abbreviated skeletal maturity systems was evaluated and compared with the full 7-parameter Modified Fels knee system and with the Greulich and Pyle (GP) left-hand bone age. Results: A total of 326 left knee radiographs from 41 girls (range, 7 to 15 y) and 37 boys (range, 9 to 17 y) were included. Models generated by segmented regression and generalized estimating equation analysis required fewer parameters (range, 1 to 5 parameters) than the full Modified Fels knee system (7 parameters). Skeletal age estimates produced by segmented regression models were more accurate than GP (P<0.05) and not significantly different from the full Modified Fels system (P>0.05). The percentage of outlier estimations (estimations >1 y off from actual skeletal age) made by segmented regression models was not significantly different from GP (P>0.05) or the Modified Fels knee system (P>0.05). Conclusion: An abbreviated version of the Modified Fels knee system estimates skeletal maturity more accurately than the GP system with just 2 to 3 radiographic knee parameters. Clinical Relevance: The abbreviated Modified Fels knee system may allow for rapid skeletal age estimation (~30 s) appropriate for routine outpatient practice.
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The corrosive sulfur compounds present in the mineral oil causes the formation of dibenzyl disulfide (DBDS) complex and it can also erode the on-load tap changing selector contacts leading to generation of silver sulfide (AgS2). The present work reports the effect of DBDS and Ag2S concentrations in mineral oil on thermally aged pressboard material. The results demonstrate that surface discharge inception voltage (SDIV) is higher for the aged pressboard specimen. The higher concentrations of DBDS showed a reduction of more than 50% in the magnitude of SDIV compared to its effect with the addition of Ag2S. The surface potential analysis indicates a higher initial potential for the aged specimens with an increased half lifetime. In addition, only deep traps observed on the aged specimens with a slight right shift in its trap energy level. The mechanical strength of aged pressboard specimens with DBDS and Ag2S is understood using tensile testing and simultaneous analysis is captured using Digital Image Correlation (DIC) technique to understand its strain percentage at different areas on the pressboard material during its elongation. The laser induced breakdown spectroscopy (LIBS) analysis is performed to identify the elements responsible for thermal ageing such as carbon, copper and sulfur on the surface of pressboard material. The plasma temperature calculated from sulfur peaks is higher for DBDS aged specimen compared to AgS2 and a linear correlation is observed on the LIBS intensity with ageing duration. Further, the Principal Component Analysis (PCA) is used for its classification on the degradation of pressboard due to the diffusion of both copper and silver sulfide from the mineral oil. The two principal components (PC1 and PC2) provided a higher variance of 99.8% with a clear classification observed between the unaged and aged pressboard specimens due to the addition of both DBDS and Ag2S in mineral oil. INDEX TERMS thermal ageing, sulfur, surface discharge, fluorescence, trap density, DIC, strain, elongation, LIBS, plasma temperature, PCA
Associations between Dairy Herds’ Qualitative Behavior and Aspects of Herd Health, Stockperson and Farm Factors—A Cross-Sectional Exploration

Simple Summary Cows are sentient beings whose welfare depends on their physical health and how they perceive their living conditions. One scientific approach to this emotional realm is the method of qualitative behavior assessment (QBA) which relies on the animals’ body language expressing their affective state. In the present study we were interested whether important health impairments of dairy cows—udder infections and metabolic imbalances—and external aspects including housing, management, and human-animal contact might be associated with the qualitative behavior of the herds. For this purpose, QBA was carried out on 25 dairy farms. Farm data were recorded via interviews and observations, and health data retrieved from official milk recording reports. We found no associations between the included herd health indicators and QBA. However, herds on farms with deep bedded cubicles or straw yards expressed a more positive emotional state compared to farms with raised cubicles. Additionally, on farms with more voluntary stockperson-cow contacts and restraint of cows during main feeding times, possibly reducing competition, more positive QBA scores were found. These results underline that improved housing conditions and more intensive animal care are related to the emotional aspect of animal welfare. Abstract The affective state is an integrated aspect of farm animal welfare, which is understood as the animals’ perception of their living environment and of their internal biological functioning. The aim of this cross-sectional study was to explore animal-internal and external factors potentially influencing dairy cows’ affective state. For this purpose, qualitative behavior assessments (QBA) describing the animals’ body language were applied at herd level on 25 dairy farms. By means of principal component analysis (PCA), scores of PC1 (QBAscores) were determined for further analyses. From monthly milk recordings (MR) one year retrospectively, prevalences of udder and metabolic health impairments were calculated. Factors of housing, management, and human-animal contact were recorded via interviews and observations. A multivariable regression was calculated following a univariable preselection of factors. No associations were found between MR indicators and QBAscores. However, more positive QBAscores were associated with bedded cubicles or straw yards compared to raised cubicles, increased voluntary stockperson contact with the cows, and fixation of cows during main feeding times, the latter contributing to the explanatory model, but not being significant. These results underline the importance of lying comfort, positive human-animal relationship and reduction of competition during feeding for the well-being of dairy cows.
Prostaglandin E2 receptors as therapeutic targets in renal fibrosis

Prostaglandin E2 (PGE2), a lipid mediator produced by the cyclooxygenase enzyme system, is the main prostaglandin in the kidney. PGE2 is involved in various physiological and pathophysiological processes in the kidney, including renal hemodynamics, water and salt balance, and renal fibrosis—a key pathological feature of progressive kidney diseases. PGE2 functions by binding to four G-protein-coupled EP receptors (EP1 to EP4), which stimulate different intracellular signaling pathways. The intrarenal distribution of the four EP receptors as well as the different downstream signaling pathways associated with each receptor give rise to the distinct functional consequence of activating each receptor subtype. This review summarizes the current data on the renal expression of the four EP receptors and delineates the role of each receptor in renal fibrosis.
Microfinance Credit Prediction Using Machine Learning Techniques

: This research article will delve into the prediction of a defaulter in a microfinance credit industry using advanced machine learning techniques. The payment delinquency for a borrower is predicted using logistic regression, support vector classifier, random forest classifier, k-nearest neighbour and artificial neural network. The dataset is taken from an Indian microfinance company which contains an 18 feature set for this prediction derived from 4064 individuals living in different regions of India. The logistic regressor based classification technique performed slightly better than other techniques, reporting an accuracy of 87.94 %. The accuracy score of support vector classifier, random forest classifier and artificial neural network and K-nearest neighbours are 87.69 %, 87.82 %, 87.45 % and 87.69 % respectively. The models are further evaluated for their performance using confusion matrices, as well as receiver operating characteristic (ROC) curves and area under curve (AUC) values.
Examination of fake news from a viral perspective: an interplay of emotions, resonance, and sentiments

Purpose The purpose of this paper is to examine the factors that significantly affect the prediction of fake news from the virality theory perspective. The paper looks at a mix of emotion-driven content, sentimental resonance, topic modeling and linguistic features of news articles to predict the probability of fake news. Design/methodology/approach A data set of over 12,000 articles was chosen to develop a model for fake news detection. Machine learning algorithms and natural language processing techniques were used to handle big data with efficiency. Lexicon-based emotion analysis provided eight kinds of emotions used in the article text. The cluster of topics was extracted using topic modeling (five topics), while sentiment analysis provided the resonance between the title and the text. Linguistic features were added to the coding outcomes to develop a logistic regression predictive model for testing the significant variables. Other machine learning algorithms were also executed and compared. Findings The results revealed that positive emotions in a text lower the probability of news being fake. It was also found that sensational content like illegal activities and crime-related content were associated with fake news. The news title and the text exhibiting similar sentiments were found to be having lower chances of being fake. News titles with more words and content with fewer words were found to impact fake news detection significantly. Practical implications Several systems and social media platforms today are trying to implement fake news detection methods to filter the content. This research provides exciting parameters from a viral theory perspective that could help develop automated fake news detectors. Originality/value While several studies have explored fake news detection, this study uses a new perspective on viral theory. It also introduces new parameters like sentimental resonance that could help predict fake news. This study deals with an extensive data set and uses advanced natural language processing to automate the coding techniques in developing the prediction model.
Sequence Coverage Visualizer: A web application for protein sequence coverage 3D visualization

Protein structure is connected with its function and interaction and plays an extremely important role in protein characterization. As one of the most important analytical methods for protein characterization, Proteomics is widely used to determine protein composition, quantitation, interaction, and even structures. However, due to the gap between identified proteins by proteomics and available 3D structures, it was very challenging, if not impossible, to visualize proteomics results in 3D and further explore the structural aspects of proteomics experiments. Recently, two groups of researchers from DeepMind and Baker lab have independently published protein structure prediction tools that can help us obtain predicted protein structures for the whole human proteome. Although there is still debate on the validity of some of the predicted structures, it is no doubt that these represent the most accurate predictions to date. More importantly, this enabled us to visualize the majority of human proteins for the first time. To help other researchers best utilize these protein structure predictions, we present the Sequence Coverage Visualizer (SCV), http://scv.lab. gy, a web application for protein sequence coverage 3D visualization. Here we showed a few possible usages of the SCV, including the labeling of post-translational modifications and isotope labeling experiments. These results highlight the usefulness of such 3D visualization for proteomics experiments and how SCV can turn a regular result list into structural insights. Furthermore, when used together with limited proteolysis, we demonstrated that SCV can help validate and compare different protein structures, including predicted ones and existing PDB entries. By performing limited proteolysis on native proteins at various time points, SCV can visualize the progress of the digestion. This time-series data further allowed us to compare the predicted structure and existing PDB entries. Although not deterministic, these comparisons could be used to refine current predictions further and represent an important step towards a complete and correct protein structure database. Overall, SCV is a convenient and powerful tool for visualizing proteomics results.
Debt maturity structure and investment decisions: Evidence of listed companies on Vietnam’s stock market

Abstract The article analyzes the impact of debt maturity structure and other factors on investment decisions of enterprises listed on the Vietnam’s stock market from 2010 to 2019. Data used in this study are acquired from the financial statements of 558 enterprises listed on both the Ho Chi Minh and Hanoi stock exchanges in the period 2010–2019, from the FiinPro database. S-GMM regression method is utilised to analyze the influence of debt maturity structure and other factors on investment decisions of listed companies. The analysis results shows that the debt maturity structure has a positive impact on the investment decision of the enterprises. Besides, the profitability of total assets and the fixed assets turnover also have a positive impact on the investment decision of the enterprise, while liquidity and cash flow have a negative impact on investment decisions. Based on empirical results, the study proposes some recommendations to help the managers of listed companies to build a reasonable debt maturity structure in the direction of using more long-term debts to increase investment efficiency. At the same time, the study recommends some policy implications for the Government in managing macroeconomic policies in order to create favourable conditions for businesses to access many long-term capital sources.
MEK inhibition enhances presentation of targetable MHC-I tumor antigens in mutant melanomas

Combining multiple therapeutic strategies in NRAS/BRAF mutant melanoma – namely MEK/BRAF kinase inhibitors, immune checkpoint inhibitors, and targeted immunotherapies – may offer an improved survival benefit by overcoming limitations associated with any individual therapy. Still, optimal combination, order, and timing of administration remains under investigation. Here, we measure how MEK inhibition (MEKi) alters anti-tumor immunity by utilizing quantitative immunopeptidomics to profile changes in the peptide MHC (pMHC) repertoire. These data reveal a collection of tumor antigens whose presentation levels are selectively augmented following therapy, including several epitopes present at over 1000 copies-per-cell. We leveraged the tunable abundance of MEKi-modulated antigens by targeting 4 epitopes with pMHC-specific T cell engagers and antibody drug conjugates, enhancing cell killing in tumor cells following MEK inhibition. These results highlight drug treatment as a means to enhance immunotherapy efficacy by targeting specific upregulated pMHCs and provide a methodological framework for identifying, quantifying, and therapeutically targeting additional epitopes of interest. SIGNIFICANCE Kinase inhibitor treatment in NRAS/BRAF mutant melanoma can sensitize tumors to immunotherapy, in part through an increase in average surface presentation of peptide MHC molecules. Here, we demonstrate that MEK inhibition selectively boosts epitope abundance of select tumor-associated antigens in vitro and in vivo, enhancing targeted immunotherapy efficacy against these treatment-modulated epitopes.
Phenotypic Heterogeneity among GBA p.R202X Carriers in Lewy Body Spectrum Disorders

We describe the clinical and neuropathologic features of patients with Lewy body spectrum disorder (LBSD) carrying a nonsense variant, c.604C>T; p.R202X, in the glucocerebrosidase 1 (GBA) gene. While this GBA variant is causative for Gaucher’s disease, the pathogenic role of this mutation in LBSD is unclear. Detailed neuropathologic evaluation was performed for one index case and a structured literature review of other GBA p.R202X carriers was conducted. Through the systematic literature search, we identified three additional reported subjects carrying the same GBA mutation, including one Parkinson’s disease (PD) patient with early disease onset, one case with neuropathologically-verified LBSD, and one unaffected relative of a Gaucher’s disease patient. Among the affected subjects carrying the GBA p.R202X, all males were diagnosed with Lewy body dementia, while the two females presented as PD. The clinical penetrance of GBA p.R202X in LBSD patients and families argues strongly for a pathogenic role for this variant, although presenting with a striking phenotypic heterogeneity of clinical and pathological features.
Influence Maximization in Complex Networks by Using Evolutionary Deep Reinforcement Learning

Influence maximization (IM) in complex networks tries to activate a small subset of seed nodes that could maximize the propagation of influence. The studies on IM have attracted much attention due to their wide applications such as item recommendation, viral marketing, information propagation and disease immunization. Existing works mainly model the IM problem as a discrete optimization problem, and use either approximate or meta-heuristic algorithms to address this problem. However, these works are hard to find a good tradeoff between effectiveness and efficiency due to the NP-hard and large-scale network properties of the IM problem. In this article, we propose an evolutionary deep reinforcement learning algorithm (called EDRL-IM) for IM in complex networks. First, EDRL-IM models the IM problem as a continuous weight parameter optimization of deep Q network (DQN). Then, it combines an evolutionary algorithm (EA) and a deep reinforcement learning algorithm (DRL) to evolve the DQN. The EA simultaneously evolves a population of individuals, and each of which represents a possible DQN and returns a solution to the IM problem through a dynamic markov node selection strategy, while the DRL integrates all information and network-specific knowledge of DQNs to accelerate their evolution. Systematic experiments on both benchmark and real-world networks show the superiority of EDRL-IM over the state-of-the-art IM methods in finding seed nodes.