Miracle-dz/UniFAR-FTUs huggingface dataset:
DISC: Disambiguating homonyms using graph structural clustering

Author name ambiguity degrades information retrieval, database integration, search results and, more importantly, correct attributions in bibliographic databases. Some unresolved issues include how to ascertain the actual number of authors, how to improve the performance and how to make the method more effective in terms of representative clustering metrics (average cluster purity, average author purity, K-metric, pairwise precision, pairwise recall, pairwise-F1, cluster precision, cluster recall and cluster-F1). It is a non-trivial task to disambiguate authors using only the implicit bibliographic information. An effective method ‘DISC’ is proposed that uses graph community detection algorithm, feature vectors and graph operations to disambiguate homonyms. The citation data set is pre-processed and ambiguous author blocks are formed. A co-authors graph is constructed using authors and their co-author’s relationships. A graph structural clustering ‘gSkeletonClu’ is applied to identify hubs, outliers and clusters of nodes in a co-author’s graph. Homonyms are resolved by splitting these clusters of nodes across the hub if their feature vector similarity is less than a predefined threshold. DISC utilises only co-authors and titles that are available in almost all bibliographic databases. With little modifications, DISC can also be used for entity disambiguation. To validate the DISC performance, experiments are performed on two Arnetminer data sets and compared with five previous unsupervised methods. Despite using limited bibliographic metadata, DISC achieves on average K-metric, pairwise-F1, and cluster-F1 of 92%, 84% and 74%, respectively, using Arnetminer-S and 86%, 80% and 57%, respectively, using Arnetminer-L. About 77.5% and 73.2% clusters are within the range (ground truth clusters ± 3) in Arnetminer-S and Arnetminer-L, respectively.
Metabolic risk factors in young adults infected with HIV since childhood compared with the general population

Aim Metabolic risk factors are poorly documented for the first generation of young adults who have lived with HIV since childhood. We compared their metabolic profile with that of adults of same age from the general population. Methods We conducted a cross-sectional analysis of data from two populations: (1) COVERTE (ANRS-CO19), a French national cohort of 18 to 30-year-old patients HIV-infected since childhood, and (2) ENNS, a national cross-sectional population-based household survey on nutrition. Body mass index (BMI), blood pressure, waist circumference, fasting glucose, triglycerides, and HDL-, LDL- and total cholesterol were measured in both studies. Direct standardization on overweight and education level and logistic regression were used to compare the prevalence of metabolic abnormalities between the two populations. Results Data from 268 patients from COVERTE and 245 subjects from ENNS were analyzed. Tobacco use was similar in both groups. HIV-infected patients had increased mean waist-to-hip ratio and triglycerides to HDL-cholesterol ratio and decreased mean HDL-cholesterol as compared to their counterparts from the general population in both genders. In HIV-infected patients, metabolic syndrome was identified in 13.2% of men (95% confidence interval [CI]: 7.1–19.2) and 10.4% (95% CI: 5.4–15.3) of women versus 10.6% (95%CI: 1.5–19.7) and 1.7% (95%CI: 0–4.1) in subjects from the general population, respectively. Conclusion Young adults infected with HIV since childhood had a higher prevalence of dyslipidemia and metabolically detrimental fat distribution than adults of same age of the general population, supporting close monitoring for cardiometabolic diseases.
IQSEC2 mutation update and review of the female‐specific phenotype spectrum including intellectual disability and epilepsy

The IQSEC2‐ related disorders represent a spectrum of X‐chromosome phenotypes with intellectual disability (ID) as the cardinal feature. Here, we review the increasing number of reported families and isolated cases have been reported with a variety of different pathogenic variants. The spectrum of clinical features is expanding with early‐onset seizures as a frequent comorbidity in both affected male and female patients. There is a growing number of female patients with de novo loss‐of‐function variants in IQSEC2 have a more severe phenotype than the heterozygous state would predict, particularly if IQSEC2 is thought to escape X‐inactivation. Interestingly, these findings highlight that the classical understanding of X‐linked inheritance does not readily explain the emergence of these affected females, warranting further investigations into the underlying mechanisms.
Reasoning with time and data abstractions

In this thesis, we address the problem of verifying the functional correctness of concurrent programs, with emphasis on fine-grained concurrent data structures. Reasoning about such programs is challenging since data can be concurrently accessed by multiple threads: the reasoning must account for the interference between threads, which is often subtle. To reason about interference, concurrent operations should either be at distinct times or on distinct data. We present TaDA, a sound program logic for verifying clients and implementations that use abstract specifications that incorporate both abstract atomicity—the abstraction that operations take effect at a single, discrete instant in time—and abstract disjointness—the abstraction that operations act on distinct data resources. Our key contribution is the introduction of atomic triples, which offer an expressive approach for specifying program modules. We also present Total-TaDA, a sound extension of TaDA with which we can verify total correctness of concurrent programs, i.e. that such programs both produce the correct result and terminate. With Total-TaDA, we can specify constraints on a thread’s concurrent environment that are necessary to guarantee termination. This allows us to verify total correctness for nonblocking algorithms and express lockand wait-freedom. More generally, the abstract specifications can express that one operation cannot impede the progress of another, a new non-blocking property that we call non-impedance. Finally, we describe how to extend TaDA for proving abstract atomicity for data structures that make use of helping—where one thread is performing an abstract operation on behalf of another—and speculation—where an abstract operation is determined by future behaviour.
Gender classification based on isolated facial features and foggy faces using jointly trained deep convolutional neural network

Abstract. Gender classification, a two-class problem (male or female), has been the subject of extensive research recently and gained a lot of attention due to its varied set of applications. The proposed work relies on individual facial features to train a convolutional neural network (CNN) for gender classification. In contrast with previously reported results that assume the facial features are independent, we consider the facial features as correlated features by training a single CNN that jointly learns from all facial features. In terms of accuracy, our results either outperform, or are on par with, other gender classification techniques applied to three different datasets namely specs on faces, groups, and face recognition technology. In terms of performance, the proposed CNN has significantly fewer parameters as compared with other techniques reported in the literature. Our learnable parameters are fewer than those required in techniques reported in recent work, which enables them to make the network less sensitive to over-fitting and easier to train than techniques that use different CNNs for each facial feature as reported in the literature.
A New Dataset for Source Identification of High Dynamic Range Images

Digital source identification is one of the most important problems in the field of multimedia forensics. While Standard Dynamic Range (SDR) images are commonly analyzed, High Dynamic Range (HDR) images are a less common research subject, which leaves space for further analysis. In this paper, we present a novel database of HDR and SDR images captured in different conditions, including various capturing motions, scenes and devices. As a possible application of this dataset, the performance of the well-known reference pattern noise-based source identification algorithm was tested on both kinds of images. Results have shown difficulties in source identification conducted on HDR images, due to their complexity and wider dynamic range. It is concluded that capturing conditions and devices themselves can have an impact on source identification, thus leaving space for more research in this field.
Geodesic distance framework for contour-based consistent stereo image segmentation

Abstract. This study concentrates on consistent object contour extraction method for stereo image segmentation after the object regions in the left image have been obtained. By taking advantage of the epipolar geometry, our approach introduces an energy optimization framework that incorporates both the stereo correspondence term and patch-based object contour probability term. The contour map is generated by integrating the terms of stereo correspondence and patch-based object contour probability; then, the optimal contours are obtained using geodesic distance technology. The core of the proposed method is to build upon an energy optimization framework with two key contributions: first, it incorporates the patch-based object contour probability term that introduces two search strategies to efficiently find the joint nearest neighbor patch pairs for the stereo image pair. The patch-based object contour probability term provides consistent and reliable priors for the contour extraction. Second, previous methods encounter missing pixels in the extracted contour in the occluded regions. Our approach overcomes this limit by introducing the geodesic distance technology to search the optimal contours. Experimental evaluation on Middlebury dataset and Adobe open dataset indicates that the results of our stereo image segmentation are comparable with or of higher quality than state-of-the-art methods.
Anomaly detection in star light curves using hierarchical Gaussian processes

Here we examine astronomical time-series called light-curve data, which represent the brightness of celestial objects over a period of time. We focus specifically on the task of finding anomalies in three sets of light-curves of periodic variable stars. We employ a hierarchical Gaussian process to create a general and stable model of time series for anomaly detection, and apply this approach to the light curve problem. Hierarchical Gaussian processes require only a few additional parameters than Gaussian processes and incur negligible additional computational complexity. Additionally, the additional parameters are objectively optimised in a principled probabilistic framework. Experimentally, our approach outperforms several baselines and highlights several anomalous light curves in the datasets investigated.
Multi-Physics-based FEM Analysis for Post-voiding Analysis of Electromigration Failure Effects

In this paper, we propose a new multi-physics finite element method (FEM) based analysis method for void growth simulation of confined copper interconnects. This new method for the first time considers three important physics simultaneously in the EM failure process and their time-varying interactions: the hydrostatic stress in the confined interconnect wire, the current density and Joule heating induced temperature. As a result, we end up with solving a set of coupled partial differential equations which consist of the stress diffusion equation (Korhonen's equation), the phase field equation (for modeling void boundary move), the Laplace equation for current density and the heat diffusion equation for Joule heating and wire temperature. In the new method, we show that each of the physics will have different physical domains and differential boundary conditions, and how such coupled multi-physics transient analysis was carried out based on FEM and different time scales are properly handled. Experiment results show that by considering all three coupled physics - the stress, current density, and temperature - and their transient behaviors, the proposed FEM EM solver can predict the unique transient wire resistance change pattern for copper interconnect wires, which were well observed by the published experiment data. We also show that the simulated void growth speed is less conservative than recently proposed compact EM model.
Augmenting communication technologies with non-primary sensory modalities

Humans combine their senses to enhance the world around them. While computers have evolved to reflect these sensory demands, only the primary senses of vision and audition (and to an extent, touch) are used in modern communication. This thesis investigated how additional information, such as emotion and navigational assistance, might be communicated using technology-based implementations of sensory displays that output the non-primary modalities of smell, vibrotactile touch, and thermo-touch. This thesis explored using a portable atomiser sprayer to deliver emotional information via smell to mobile phone users, a ring-shaped device worn on the finger to display emotional information using vibration and colours, and an array of thermoelectric coolers worn on the arm to create temperature sensations. Additionally, this thesis explored two methods of signalling temperature using the thermal implementation, and finally, used it in a controlled study to augment the perceived emotion of text messages using temperature. There were challenges with using some of these implementations to display information. Smells produced with the scent technology were ambiguous and highly cognitive, and poor delivery to the user produced undesirable cross-adaption effects when smells lingered and mixed in the environment. The device used to communicate vibrotactile and colour lighting cues neutralized emotions in text messages. Furthermore, temperature pattern discrimination using the thermal implementation was difficult due to non-linear interaction effects that occurred on the skin’s surface, as well as latency resulting from the thermal neurological pathway and the technology used to heat and cool the skin. However, the thermal implementation enabled more accurate user discrimination between thermal signals than what a single stimulator design provided. Furthermore, the utility of continuous thermal feedback, in the context of spatial navigation, was demonstrated, which improved user performance compared to when the user was not presented with any thermal information. Finally, temperature was demonstrated to elicit arousal reactions across subjects using the thermal implementation, and could augment the arousal of text messages, especially when the content of the message was strongly neutral. However, no similar statistical significance was observed with valence, demonstrating the complex implications of using thermal cues to convey emotional information.
Spiral: splitters for identifiers in source code files

Spiral is a Python package that implements numerous algorithms for splitting identifiers. Identifier splitting (also known as identifier name tokenization) is the task of partioning program identifiers such as readUTF8stream into component tokens: [read, UTF8, stream]. The need for splitting identifiers arises in a variety of contexts, including natural language processing (NLP) methods applied to source code analysis and program comprehension.
Developing Practicum Module of Particle Dynamics Based on Scientific Methods to Improve Students' Science Process Skills

The existence of basic competence skills in the current national curriculum requires learners to have skilled expertise in the field of physics, which one of them is students’ science process skills. Students' science process skills become students’ guide in understanding physics concepts. The availability of resources and guidance in conducting practicum is very small, so teachers have difficulties when they want to conduct practicum in learning activities because of the lack of resources available. Therefore, we need a scientifically based practicum module to increase students’ science process skills. This development research using 4-D method (Define, Design, Develop, and Disseminate). In the development of this module, the researchers only come to the third stage of the four stages. Validation results from experts get an average score of 81% interpretation which means that the module is included in the criteria worthy of use in learning even with the notes of experts to revise some of the components present in this module so that this module complies with existing standards. The results of the analysis of student responses to the practicum module obtained a total average of 80% with the category is "strong. " This shows almost all the students gave a positive response to the scientific-based practicum module. The development of scientific-based practicum modules can improve science process skills of the students, with the average score of students’ science process skills before using a scientific-based practicum module of 2.54 and the average score of students’ science process skills after using a scientific-based practicum module of 3.20.
Integration of Local and Global Support Vector Machines to Improve Urban Growth Modelling

The use of local information for the classification and modelling of spatial variables has increased with the application of statistical and machine learning algorithms, such as support vector machines (SVMs). This study presents a new local SVM (LSVM) model that was developed to model the probability of urban development and simulate urban growth in a subregion in the southwestern suburb of the Tehran metropolitan area, Iran, for the periods of 1992–1996 and 1996–2002. Based on the focal training sample, the model was calibrated using the cross-validation method, and the optimal bandwidth was determined. The results were compared with those of a nonlinear global SVM (GSVM) model that was calibrated based on the ten-fold cross-validation method. This study then evaluated an integrated SVM model (LGSVM) obtained based on a weighted combination of the local and global urban development probabilities. A comparison of the probability maps showed a higher accuracy for the LGSVM than for either the LSVM or GSVM model. To assess the performance of the LSVM, GSVM and LGSVM models in the simulation of urban growth, probability maps were employed as the transition rules for urban cellular automata. The results show that a trade-off between local and global SVM models can enhance the performance of urban growth modelling.
Risk Factors for Invasive Fungal Infection in Children and Adolescents With Hematologic and Malignant Diseases: A 10-year Analysis in a Single Institute in Japan

Infection, especially invasive fungal infection (IFI), is an important complication of chemotherapy and stem cell transplantation. It is also a well-known risk factor in pediatric hematologic malignancy, acute myelogenous leukemia, recurrent disease and allogeneic stem cell transplantation. We previously revealed that a diagnosis of acute myelogenous leukemia, recurrent disease and >10 years of age were risk factors for IFI in patients with pediatric hematologic malignancies. We examined and compared the incidence, risk factors and mortality rate from IFI between 276 patients from 2007 to 2016 and patients in our past report. The cumulative incidence of IFI was 10.5%; this comprised cases of probable and possible IFI at rates of 5.1% and 5.4%, respectively. Univariate analysis showed that age >9 years at admission, recurrent disease and acute myelogenous leukemia diagnosis were risk factors for IFI. Similar to the results of the previous study, multivariate analysis showed that each of these 3 variables was an independent predictor of IFI. The survival rate was lower in patients with IFI than in those without IFI (38.8% versus 69.9%; P < 0.001). However, IFI was a direct cause of death in only 2 patients. Although 11 patients received stem cell transplantation after IFI treatment, only 2 patients have survived, and the other 9 patients died of other complications.
Novel Method for Virtual Restoration of Cultural Relics with Complex Geometric Structure Based on Multiscale Spatial Geometry

Because of the age of relics and the lack of historical data, the geometric forms of missing parts can only be judged by the subjective experience of repair personnel, which leads to varying restoration effects when the geometric structure of the complex relic is reconstructed. Therefore, virtual repair effects cannot fully reflect the historical appearance of cultural relics. In order to solve this problem, this paper presents a virtual restoration method based on the multiscale spatial geometric features of cultural relics in the case of complex construction where the geometric shape of the damaged area is unknown, using the Dazu Thousand-Hand Bodhisattva statue in China as an example. In this study, the global geometric features of the three-dimensional (3D) model are analyzed in space to determine the geometric shape of the damaged parts of cultural relics. The local geometric features are represented by skeleton lines based on regression analysis, and a geometric size prediction model of the defective parts is established, which is used to calculate the geometric dimensions of the missing parts. Finally, 3D surface reconstruction technology is used to quantitate virtual restoration of the defective parts. This method not only provides a new idea for the virtual restoration of artifacts with complex geometric structure, but also may play a vital role in the protection of cultural relics.
Twenty‐first‐century climate change impacts on marine animal biomass and ecosystem structure across ocean basins

Climate change effects on marine ecosystems include impacts on primary production, ocean temperature, species distributions, and abundance at local to global scales. These changes will significantly alter marine ecosystem structure and function with associated socio‐economic impacts on ecosystem services, marine fisheries, and fishery‐dependent societies. Yet how these changes may play out among ocean basins over the 21st century remains unclear, with most projections coming from single ecosystem models that do not adequately capture the range of model uncertainty. We address this by using six marine ecosystem models within the Fisheries and Marine Ecosystem Model Intercomparison Project (Fish‐MIP) to analyze responses of marine animal biomass in all major ocean basins to contrasting climate change scenarios. Under a high emissions scenario (RCP8.5), total marine animal biomass declined by an ensemble mean of 15%–30% (±12%–17%) in the North and South Atlantic and Pacific, and the Indian Ocean by 2100, whereas polar ocean basins experienced a 20%–80% (±35%–200%) increase. Uncertainty and model disagreement were greatest in the Arctic and smallest in the South Pacific Ocean. Projected changes were reduced under a low (RCP2.6) emissions scenario. Under RCP2.6 and RCP8.5, biomass projections were highly correlated with changes in net primary production and negatively correlated with projected sea surface temperature increases across all ocean basins except the polar oceans. Ecosystem structure was projected to shift as animal biomass concentrated in different size‐classes across ocean basins and emissions scenarios. We highlight that climate change mitigation measures could moderate the impacts on marine animal biomass by reducing biomass declines in the Pacific, Atlantic, and Indian Ocean basins. The range of individual model projections emphasizes the importance of using an ensemble approach in assessing uncertainty of future change.