Miracle-dz/UniFAR-FTUs huggingface dataset:
SRNet: A 3D Scene Recognition Network using Static Graph and Dense Semantic Fusion

Point cloud based 3D scene recognition is fundamental to many real world applications such as Simultaneous Localization and Mapping (SLAM). However, most of existing methods do not take full advantage of the contextual semantic features of scenes. And their recognition abilities are severely affected by dynamic noise such as points of cars and pedestrians in the scene. To tackle these issues, we propose a new Scene Recognition Network, namely SRNet. In this model, to learn local features without being affected by dynamic noise, we propose Static Graph Convolution (SGC) module, which are then stacked as our backbone. Next, to further avoid dynamic noise, we introduce a Spatial Attention Module (SAM) to make the feature descriptor pay more attention to immovable local areas that are more relevant to our task. Finally, in order to make a more profound sense of the scene, we design a Dense Semantic Fusion (DSF) strategy to integrate multi‐level features during feature propagation, which helps the model deepen its understanding of the contextual semantics of scenes. By utilizing these designs, SRNet can map scenes to discriminative and generalizable feature vectors, which are then used for finding matching pairs. Experimental studies demonstrate that SRNet achieves new state‐of‐the‐art on scene recognition and shows good generalization ability to other point cloud based tasks.
StyleProp: Real‐time Example‐based Stylization of 3D Models

We present a novel approach to the real‐time non‐photorealistic rendering of 3D models in which a single hand‐drawn exemplar specifies its appearance. We employ guided patch‐based synthesis to achieve high visual quality as well as temporal coherence. However, unlike previous techniques that maintain consistency in one dimension (temporal domain), in our approach, multiple dimensions are taken into account to cover all degrees of freedom given by the available space of interactions (e.g., camera rotations). To enable interactive experience, we precalculate a sparse latent representation of the entire interaction space, which allows rendering of a stylized image in real‐time, even on a mobile device. To the best of our knowledge, the proposed system is the first that enables interactive example‐based stylization of 3D models with full temporal coherence in predefined interaction space.
Interactive Design and Preview of Colored Snapshots of Indoor Scenes

This paper presents an interactive system for quickly designing and previewing colored snapshots of indoor scenes. Different from high‐quality 3D indoor scene rendering, which often takes several minutes to render a moderately complicated scene under a specific color theme with high‐performance computing devices, our system aims at improving the effectiveness of color theme design of indoor scenes and employs an image colorization approach to efficiently obtain high‐resolution snapshots with editable colors. Given several pre‐rendered, multi‐layer, gray images of the same indoor scene snapshot, our system is designed to colorize and merge them into a single colored snapshot. Our system also assists users in assigning colors to certain objects/components and infers more harmonious colors for the unassigned objects based on pre‐collected priors to guide the colorization. The quickly generated snapshots of indoor scenes provide previews of interior design schemes with different color themes, making it easy to determine the personalized design of indoor scenes. To demonstrate the usability and effectiveness of this system, we present a series of experimental results on indoor scenes of different types, and compare our method with a state‐of‐the‐art method for indoor scene material and color suggestion and offline/online rendering software packages.
Advances in neural networks and potential for their application to steel metallurgy

This review provides a timely exploration of several novel neural network (NN) architectures and learning methods, following a concise overview of the fundamentals of NNs and some important associated challenges. There are many benefits to using NNs, including deep learning models, in scientific research and, by understanding novel techniques better suited to certain applications, this benefit can be maximised. Finally, a few developed and emerging alternative learning paradigms are surveyed for their potential benefit to future research. The reviewed literature and accompanying discussion are of generic value well beyond steel metallurgy, and there is much to be gained from assessing methods used in other areas of materials science and further afield in order to apply them to steel metallurgy.
A Robust Symbiotic Relationship Between the Ciliate Paramecium multimicronucleatum and the Bacterium Ca. Trichorickettsia Mobilis

Close reciprocal interactions in symbiotic systems have suggested the holobiont concept, in which the host and its microbiota are considered as a single entity. Ciliates are known for their ability to form symbiotic associations with prokaryotes. Relationships between the partners in such systems vary from mutualism to parasitism and differ significantly in their robustness. We assessed the viability of the ciliate Paramecium multimicronucleatum and its ability to maintain its intranuclear endosymbiont Ca. Trichorickettsia mobilis (Rickettsiaceae) after treatment with antibiotics characterized by different mode of action, such as ampicillin, streptomycin, chloramphenicol, tetracycline. The presence of endosymbionts in the host cell was determined by means of living cell observations made using differential interference contrast or fluorescence in situ hybridization with the species-specific oligonucleotide probe (FISH). Administration of antibiotics traditionally used in treatments of rickettsioses, tetracycline and chloramphenicol, depending on the concentration used and the ciliate strain treated, either caused death of both, infected and control cells, or did not affect the ability of the host to maintain the intranuclear endosymbiont. The surviving cells always manifested motile bacteria in the macronucleus. Streptomycin treatment never led to the loss of endosymbionts in any of the four infected strains, and nearly all ciliates remained viable. Ampicillin treatment never caused host cell death, but resulted in formation of filamentous and immobile oval bacterial forms. Under repeated ampicillin treatments, a part of endosymbionts was registered in the host cytoplasm, as evidenced both by FISH and transmission electron microscopy. Endosymbionts located in the host cytoplasm were enclosed in vacuoles, apparently, corresponding to autophagosomes. Nevertheless, the bacteria seemed to persist in this compartment and might cause relapse of the infection. Although the antibiotic sensitivity profile of Trichorickettsia seems to resemble that of other representatives of Rickettsiaceae, causative agents of severe diseases in humans, neither of the antibiotic treatments used in this study resulted in an aposymbiotic cell line, apparently, due to the protists’ sensitivity to tetracyclines, the drugs of preference in rickettsiosis treatment. The observed robustness of this symbiotic system makes it a good model for further elaboration of the holobiont concept.
LncRNA MAGI2-AS3 Overexpression Sensitizes Esophageal Cancer Cells to Irradiation Through Down-Regulation of HOXB7 via EZH2

Background Accumulating evidence has suggested that aberrant expression of long non-coding RNAs (lncRNAs) may contribute to cancer progression in association with radioresistance. The current study aimed to identify the potential role of lncRNA MAGI2-AS3 and the underlying mechanism in its regulation of the radio-sensitivity of esophageal cancer cells. Methods and Results Initially, we detected high expression of HOXB7 from microarray-based gene expression profiling of esophageal cancer. Then, we identified the interactions among MAGI2-AS3, HOXB7, and EZH2 by dual-luciferase reporter gene assay, RNA pull-down assay, RIP assay and ChIP assay. HOXB7 was highly-expressed, while MAGI2-AS3 was poorly-expressed in esophageal cancer tissues and cells. The effect of MAGI2-AS3 and HOXB7 on esophageal cancer cell proliferation and apoptosis as well as tumorigenicity of radioresistant cells was examined by gain- and loss-of-function experiments. Interestingly, MAGI2-AS3 down-regulated HOXB7 through interaction with EZH2, which promoted cell apoptosis and inhibited proliferation and radio-resistance. Besides, down-regulation of MAGI2-AS3 exerted a promoting effect on these malignant phenotypes. Conclusion Taken together, our results reveal the potential role of MAGI2-AS3 over-expression in controlling esophageal cancer resistance to radiotherapy by down-regulating HOXB7, this providing a candidate biomarker for resistance to radiotherapy.
Matrix Factorization for High Frequency Non Intrusive Load Monitoring: Definitions and Algorithms

Non Intrusive Load Monitoring has been introduced 30 years ago in order to monitor the electric consumption of specific equipments inside a building without the need of installing multiples sensors. During three decades, researchers and industrials have described the NILM problems according to the electric data available, the desired quantity to be monitored and the application it was used for. As a consequence of the multitude of choices, a lot of different formulations can be found in the literature. This diversity makes it difficult for researchers from general domains such as machine learning to tackle the NILM problem. In this paper we aim at defining the NILM problem as a Matrix Factorization task using high frequency measurements and also to review methods to solve this problem. We start by defining the general concepts driving the NILM problem and then show how to cast high frequency NILM into a Matrix Factorization problem. Once casted as a machine learning problem, we will review general purposes algorithms applicable to this problem such as Independent Component Analysis, Sparse Coding or Semi Non-negative Matrix Factorization and specific NILM methods such as BOLT and IVMF.
Environmental health risk relationships, responsibility, and sources of information among Vietnamese Americans in coastal Mississippi

Coastal communities along the U.S. Gulf of Mexico have been profoundly shaped by environmental health (EH) threats, reflecting the region’s history of natural and technological disasters, as well as ongoing issues of environmental degradation and pollution. The Vietnamese American community in the state of Mississippi is vulnerable to EH threats, but there is limited research on their risk perceptions. Understanding perceptions of risk and responsibility is important to manage EH risks and promote community resilience. This community-based participatory research study characterises Vietnamese American perceptions of EH risks, sources of information, and responsibility in coastal Mississippi. Five focus groups were conducted in 2016 and 2017 in Biloxi, Mississippi with Vietnamese Americans working in the seafood industry (n = 24) and mothers of children under the age of 18 (n = 25). Findings related to participants’ concerns with air quality, drinking water quality, and the 2010 Deepwater Horizon oil spill are interpreted using Boholm’s relational theory of risk. Unreliable access to healthcare and limited English proficiency constrain participants from acting on their environmental health risk relationships. A community-based organisation in Biloxi that works with Vietnamese Americans plays an important role in addressing risk in this population.
A virtual environment for learning computer coding using gamification and emotion recognition

ABSTRACT Emotions play an important role in students learning to master complex intellectual activities such as computer programing. Emotions such as confusion, boredom and frustration in the student are important factors in determining whether the student will master the exercise of learning to program in the short and long term. Motivation also plays an important role in learning a programing language. However, it is extremely difficult to motivate students who find themselves in a negative emotional state. We developed an advanced learning environment that detects and responds to student emotions by using machine learning techniques, and incorporates modern motivation strategies by using gamification methods. We conducted two experiments; one to evaluate the acceptance of the learning system and a second one to evaluate the academic performance. The results of the experiments show that intention to use is dependent on perceived enjoyment but not on perceived usefulness and attitude toward the system. Also, better results were obtained in a post-test from students who used the learning system, compared with those who didn’t use it.
LSM3, NDUFB3, and PTGS2 may be potential biomarkers for BRCA1-mutation positive breast cancer

Abstract Purpose: We aimed to find critical biomakers associated with BRCA1-mutation positive breast cancer. Methods: The data set E-MTAB-982 was downloaded from ArrayExpress database and the data were preprocessed using R package Oligo. Differential expression analysis between BRCA1-mutation positive breast cancer patients and BRCA1-mutation positive healthy subjects were performed using limma package. Then, gene set enrichment analysis was conducted. We constructed the network for BRCA1, its related differentially expressed genes (DEGs), and the enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. After that, survival analysis was performed based on the clinical data of breast cancer in TCGA database. Finally, box diagram for key genes was drawn. Results: The network showed that LSM3, NDUFB3, GNPDA2, and PTGS2 were BRCA1 related DEGs. Furthermore, LSM3 was mainly enriched in RNA degradation pathway and spliceosome pathway. PTGS2 was enriched in arachidonic acid metabolism and VEGF signaling pathway. Survival analysis indicated that high expression of LSM3 indicated a poor prognosis of BRCA1-mutant breast cancer. Besides, box diagram showed that LSM3 was down-regulated in BRCA1-mutation positive breast cancer patients compared with that in BRCA1-mutation positive healthy subjects. Conclusions: LSM3, NDUFB3, and PTGS2 may be biomarkers in BRCA1-mutant breast cancer, and high expression of LSM3 may indicate a poor prognosis of BRCA1-mutant breast cancer.
Analyzing Students' Behavior in Blended Learning Environment for Programming Education

Analyzing students' test scores and online learning behaviors in the blended learning environment of programming education can help computer educators understand the students' learning and programming process. Furthermore, it can help teachers provide personalized guidance to students. In this article, we first performed a time-series clustering algorithm on the 5 test scores of students online and offline, and obtained three distinct student types ("Excellent", "Moderate", "Poor"). To further study the behavior of students, a correlation analysis of the four behavior data obtained from online programming and test scores was conducted. Students' online time is negatively correlated with the test score. Then, a cluster analysis of the online behaviors data was conducted, and three different student types were also obtained ("Poor performance", "High-quality learning", "Learning hard"). Finally, the two clustering results were compared. In the recognition of middle-level students, their similarity was 10/15. Long-term online learning can achieve good and stable test scores. The consistency of student behavior qualitatively proves the rationality of our research. Besides, for students with large differences in the two clustering results, we provided a targeted analysis and gave teachers corresponding suggestions.
Neurally-Guided Texturing for Garment Line Drawings

Adding texture to a line drawing is an important process in the production of comics and illustrations. Garment drawings especially often have large deformations with self-occlusions, so deforming patterns is essential for representing realistic garment designs. However, it is currently done manually and requires a significant amount of effort by experts. A possible approach is to infer 3D surface geometry and then apply texture to all 3D surfaces, but it is difficult to represent deep creases using this approach. In this paper, we introduce a “neurally-guided” optimization system for automatically deforming and directly mapping 2D texture patterns to 2D line drawings, bypassing 3D geometry. First, we build a deep neural network to estimate local transformation matrices of texture patterns, called neural-guidance, from line drawings. Second, we build a 2D triangle mesh for the garment and deforms the mesh to obtain the texture coordinates by integrating the local transformations. Our algorithm is effective and easy to integrate into existing drawing systems. We provide several examples to demonstrate the efficiency of our proposed system over previous methods and illustrate the versatility of our method.
Semantic Search for Biomedical Texts using Predicate-Argument Structure

: In this position paper we argue that using semantic roles in addition to using biologically-oriented ontologies and databases (or knowledge bases) will further enhance the generation of RDF triples that can be collected from biomedical text. RDF triples have been used to enhance semantic search beyond the simple use of linguistically oriented additions such as synonyms. We wish to focus on drug-virus interactions.
Migrant and Non-Migrant Origin Pre-Service Teachers’ Beliefs about Multilingualism and Teaching in Multilingual Classrooms: Convergences and Divergences

Summary This article reports findings from an investigation into migrant and non-migrant origin pre-service teachers’ beliefs about multilingualism and the relationship between their linguistic trajectories as students and how they perceive themselves as future teachers. We analize the beliefs of around seventy pre-service teachers taking part in a university course, collected through an individual reflection tool based on a SWOT analysis (Strengths, Weaknesses, Opportunities, Threats) and a group discussion in which students had to discuss their ideas and create a collective document. In this article we focus on the discourse of four pre-service teachers, two of whom had migration backgrounds and two of whom who did not. Findings suggest that pre-service teachers’ life and learning experiences contribute to different emotions and feeling about language and teaching in a multilingual setting. They also influence the perception the pre-service teachers have of themselves as future teachers. Pre-service teachers regard the preparation received at the university negatively and express insecurities. Findings illustrate that migrant origin pre-service teachers feel more prepared in the sense they believe that their migration experience can help to understand newcoming students and be empathic with them.
Dynamic Scheduling and Optimal Reconfiguration of UPF Placement in 5G Networks

Multi-access Edge Computing (MEC) is a key technology in the road to 5G and beyond networks. Significant reductions in both latency and backhaul traffic can be achieved by placing server applications, and network functions at the network edge. However, this implies new challenges for their dynamic placement and management. In this paper, we tackle the problem of dynamic placement reconfiguration of 5G User Plane Functions (UPFs) in a MEC ecosystem to adapt to changes in user locations while ensuring QoS and network operator expenditures reduction. In this vein, an Integer Linear Programming (ILP) solution is proposed to determine the optimal UPF placement configuration (e.g., number of UPFs and user-UPF mapping) by considering several cost components along with service requirements. Moreover, a scheduling technique based on Optimal Stopping Theory (OST) is presented to decide the optimal reconfiguration time according to instantaneous values of latency violations and established QoS thresholds. Extensive simulation results demonstrate their effectiveness, achieving significant improvements in metrics such as number of re-computation events, reconfiguration costs, and number of latency violations over time.
Modeling Sport Events Legacy based on Olympic Games

: This article presents a conceptual model of sport events legacy based on the Olympic and Paralympic Games as a reference. The legacy of mega sport events has gained ever more importance during recent years for both academics and practitioners. The International Olympic Committee looked at the concept of legacy, as it is the best argument with which to illustrate the lasting benefits that are derived from the Olympic Games. The Legacy is the way to structure the capitalization of the benefits and lessons related to the organization of major sporting events. So, our motivation is to develop a tool of the sport events legacy. The aims of the article are first, to investigate the legacy sport events literature, second, to propose a Legacy Conceptual Model (LCM) and to transpose it to a database which will be support to analyse the changes related to legacy. This research target also has a practical implication. So, we investigate how the LCM helps to analyze the legacy left by a sport event using a case study.
Prediction Model of Microblog Retweeting Based on Naive Bayesian

In this paper, we take Sina microblog as the research object to explore the features that influence microblog retweeting, as well as predict retweeting behavior. On the basis of obtaining a large number of microblog retweeting records, three features including number of historical interactions, interest similarity between users and microblog and similarity of active time are taken into account. We explore their exact influence on users' retweeting behavior, and establish a prediction model based on Naive Bayesian. Experiment indicates that the prediction model could achieve higher prediction accuracy with fewer features, which enables us to predict microblog retweeting timely in the process of dynamic public opinion propagation.
FLE: A Fuzzy Logic Algorithm for Classification of Emotions in Literary Corpora

This paper presents an algorithm based on fuzzy logic, devised to identify emotions in corpora of literary texts, called Fuzzy Logic Emotions (FLE) classifier. This algorithm evaluates a sentence to define the class(es) of emotions to which it belongs. For this purpose, it considers three types of linguistic variables (verb, noun and adjective) with associated linguistic values used to qualify the emotion they express. A numerical value is computed for each of these terms within a sentence, based on its frequency and the inverse document frequency (TF-IDF). We have tested our FLE classifier with an evaluation protocol, using a literary corpus in Spanish specially structured for working with the automatic detection of emotions in text. We present encouraging performance results favoring our FLE classifier, when compared to other known algorithms established in the literature used for the detection of emotions in text.
A Cluster-based Framework for Predicting Large Scale Road-Network Constrained Trajectories

The increasing availability of vehicle trajectory and road network datasets is crucial for the development of novel trajectory data mining-based applications. For instance, we can design more efficient routing protocols by applying vehicle trajectory prediction. In this paper, we propose a new cluster-based framework to predict road-network constrained trajectories. The framework, designed to perform long-term predictions, combines several steps that use historical trajectory datasets to train prediction models. Experimental results show the framework's effectiveness and efficiency to predict trajectories with different characteristics in a new real-world, large-scale scenario. Besides that, the framework outperformed some other solutions found in the literature in terms of prediction accuracy and computational overhead.mmm;