Miracle-dz/UniFAR-FTUs huggingface dataset:
DiVE: Diversifying View Recommendation for Visual Data Exploration

To support effective data exploration, there has been a growing interest in developing solutions that can automatically recommend data visualizations that reveal interesting and useful data-driven insights. In such solutions, a large number of possible data visualization views are generated and ranked according to some metric of importance (e.g., a deviation-based metric), then the top-k most important views are recommended. However, one drawback of that approach is that it often recommends similar views, leaving the data analyst with a limited amount of gained insights. To address that limitation, in this work we posit that employing diversification techniques in the process of view recommendation allows eliminating that redundancy and provides a good and concise coverage of the possible insights to be discovered. To that end, we propose a hybrid objective utility function, which captures both the importance, as well as the diversity of the insights revealed by the recommended views. While in principle, traditional diversification methods (e.g., Greedy Construction) provide plausible solutions under our proposed utility function, they suffer from a significantly high query processing cost. In particular, directly applying such methods leads to a "process-first-diversify-next" approach, in which all possible data visualization are generated first via executing a large number of aggregate queries. To address that challenge, we propose an integrated scheme called DiVE, which efficiently selects the top-k recommended view based on our hybrid utility function. DiVE leverages the properties of both the importance and diversity metrics to prune a large number of query executions without compromising the quality of recommendations. Our experimental evaluation on real datasets shows the performance gains provided by DiVE.
VIVID: Virtual Environment for Visual Deep Learning

Due to the advances in deep reinforcement learning and the demand of large training data, virtual-to-real learning has gained lots of attention from computer vision community recently. As state-of-the-art 3D engines can generate photo-realistic images suitable for training deep neural networks, researchers have been gradually applied 3D virtual environment to learn different tasks including autonomous driving, collision avoidance, and image segmentation, to name a few. Although there are already many open-source simulation environments readily available, most of them either provide small scenes or have limited interactions with objects in the environment. To facilitate visual recognition learning, we present a new Virtual Environment for Visual Deep Learning (VIVID), which offers large-scale diversified indoor and outdoor scenes. Moreover, VIVID leverages the advanced human skeleton system, which enables us to simulate numerous complex human actions. VIVID has a wide range of applications and can be used for learning indoor navigation, action recognition, event detection, etc. We also release several deep learning examples in Python to demonstrate the capabilities and advantages of our system.
Learning Analytics Case Study in Blended Learning Scenarios

In this paper we present a case study that provides a learning analytics tool to 53 course rooms on the learning platform in a higher education institution in Germany. The case study objective was to observe in which ways the teaching staff would use the learning analytics module while performing other teaching activities within the course room on the learning platform. We collected raw data from three different sources to examine the hypothesis of the case study in a quasi-experimental setting. The analyzed results showed that over the course of the study, in 40 courses the teaching staff used the learning analytics module at least on one occasion; in 25 courses the teaching stuff used the learning analytics module more than five times; and in 36 courses, the teaching staff has used the learning analytics module on multiple occasions within the same session while conducting teaching activities on the learning platform. As part of the data collection strategy for the case study, we conducted a two-part anonymous survey to collect qualitative feedback about the features and the user interface of the analytics prototype. The results of the survey revealed that the teaching staff used the analytics prototype mainly to observe the learning resources in their respective courses and be aware about the student behavior in their courses on the learning platform. Overall, we concluded that Learning Analytics should be an integral part of the provisioned e-learning services in higher educational institutions; the place for delivering Learning Analytics is the virtual course rooms on the learning platform; and that the teaching staff (if provided) would use it on regular basis in short while doing their day-to-day teaching activities on the learning platform.
Predictors of Physical Activity in Middle Childhood. A Fixed-Effects Regression Approach

Background: Moderate-to-vigorous physical activity (MVPA) has a range of health benefits across the life span. Although many putative determinants of children's MVPA have been identified, their causal status is uncertain due to difficulties in adjusting for potential confounders. Objective: To inform promotion of children's MVPA we therefore aimed to examine well-known child-, family- and contextual predictors of MVPA in middle childhood, by applying a fixed effects regression approach, which rules out the influence of all unmeasured time-invariant confounders. Methods: Two birth cohorts of children living in the city of Trondheim, Norway were invited to participate (82.0% consented). The participants were followed-up biennially from age 6 to 10 years (n = 800) between 2009 and 2014. MVPA in children was recorded by accelerometers and child-, family- and contextual factors were obtained through interviews and questionnaires. Predictors included (i) child-level factors: the child's time outdoors, organized sports participation, athletic self-concept, self-reported screen time and objectively measured sedentariness; (ii) family factors: self-reported parental MVPA and actively transporting the child to school; and (iii) contextual factors: parental socio-economic status (SES), access to playgrounds and ballparks, traffic safety, and having a garden. A three-wave prospective study was conducted with a hybrid fixed effects regressions analysis adjusting for all time-invariant confounders to examine predictors of MVPA. Results: Children evidenced increased MVPA when they spent more time outside, spent less time being sedentary and when the family had a garden and lived in a traffic-safe area. Conclusion: Adjusting for measured time-varying and all unmeasured time-invariant confounders renders many previously identified child and family factors without impact on MVPA in children. However, several contextual factors may promote MVPA in middle childhood, and efforts to facilitate children being outside in environments that promote physical activity (e.g., being outside, in gardens, or otherwise traffic safe areas) may prove important.
User-Oriented Energy-Saving Offloading for Wireless Virtualization Aided Mobile Edge Computing

Mobile1 edge computing (MEC) is a promising solution to reduce latency and improve user experience. Moreover, by appropriately offloading computation intensive tasks to MEC node other than user equipment (UE), the energy consumption of computing is transferred to MEC node, so that the lifetime of UE can be prolonged. However, offloading will increase additional delay and energy consumption. To address this issue, the task offloading is formulated as an optimization problem to minimize energy consumption of UE, as well as maintain the latency constraints. The convexity of the problem is theoretically proved. On the basis of that, a heuristic algorithm is proposed to reduce the complexity. Simulation results show that the proposed algorithm is effective to reduce energy consumption of the user.
Trypanosoma cruzi Neurotrophic Factor Facilitates Cardiac Repair in a Mouse Model of Chronic Chagas Disease

Most patients acutely infected with Trypanosoma cruzi undergo short-term structural and functional cardiac alterations that heal without sequelae. By contrast, in patients whose disease progresses to chronic infection, irreversible degenerative chronic Chagas cardiomyopathy (CCC) may develop. To account for the contrast between cardiac regeneration in high-parasitism acute infection and progressive cardiomyopathy in low-parasitism CCC, we hypothesized that T. cruzi expresses repair factors that directly facilitate cardiac regeneration. We investigated, as one such repair factor, the T. cruzi parasite-derived neurotrophic factor (PDNF), known to trigger survival of cardiac myocytes and fibroblasts and upregulate chemokine chemokine C-C motif ligand 2, which promotes migration of regenerative cardiac progenitor cells (CPCs). Using in vivo and in vitro models of Chagas disease, we tested whether T. cruzi PDNF promotes cardiac repair. Quantitative PCR and flow cytometry of heart tissue revealed that stem-cell antigen-1 (Sca-1+) CPCs expand in acute infection in parallel to parasitism. Recombinant PDNF induced survival and expansion of ex vivo CPCs, and intravenous administration of PDNF into naïve mice upregulated mRNA of cardiac stem-cell marker Sca-1. Furthermore, in CCC mice, a 3-week intravenous administration of PDNF protocol induced CPC expansion and reversed left ventricular T-cell accumulation and cardiac remodeling including fibrosis. Compared with CCC vehicle-treated mice, which developed severe atrioventricular block, PDNF-treated mice exhibited reduced frequency and severity of conduction abnormalities. Our findings are in support of the novel concept that T. cruzi uses PDNF to promote mutually beneficial cardiac repair in Chagas disease. This could indicate a possible path to prevention or treatment of CCC.
Multiperspective Graph-Theoretic Similarity Measure

Determining the similarity between two objects is pertinent to many applications. When the basis for similarity is a set of object-to-object relationships, it is natural to rely on graph-theoretic measures. One seminal technique for measuring the structural-context similarity between a pair of graph vertices is SimRank, whose underlying intuition is that two objects are similar if they are connected by similar objects. However, by design, SimRank as well as its variants capture only a single view or perspective of similarity. Meanwhile, in many real-world scenarios, there emerge multiple perspectives of similarity, i.e., two objects may be similar from one perspective, but dissimilar from another. For instance, human subjects may generate varied, yet valid, clusterings of objects. In this work, we propose a graph-theoretic similarity measure that is natively multiperspective. In our approach, the observed object-to-object relationships due to various perspectives are integrated into a unified graph-based representation, stylised as a hypergraph to retain the distinct perspectives. We then introduce a novel model for learning and reflecting diverse similarity perceptions given the hypergraph, yielding the similarity score between any pair of objects from any perspective. In addition to proposing an algorithm for computing the similarity scores, we also provide theoretical guarantees on the convergence of the algorithm. Experiments on public datasets show that the proposed model deals better with multiperspectivity than the baselines.
Delays induce an exponential memory gap for rendezvous in trees

The aim of rendezvous in a graph is meeting of two mobile agents at some node of an unknown anonymous connected graph. The two identical agents start from arbitrary nodes in the graph and move from node to node with the goal of meeting. In this paper, we focus on rendezvous in trees, and, analogously to the efforts that have been made for solving the exploration problem with compact automata, we study the size of memory of mobile agents that permits to solve the rendezvous problem deterministically. We first show that if the delay between the starting times of the agents is arbitrary, then the lower bound on memory required for rendezvous is Ω(log n) bits, even for the line of length n. This lower bound meets a previously known upper bound of O(log n) bits for rendezvous in arbitrary trees of size at most n. Our main result is a proof that the amount of memory needed for rendezvous with simultaneous start depends essentially on the number L of leaves of the tree, and is exponentially less impacted by the number n of nodes. Indeed, we present two identical agents with O(log L + log log n) bits of memory that solve the rendezvous problem in all trees with at most n nodes and at most L leaves. Hence, for the class of trees with polylogarithmically many leaves, there is an exponential gap in minimum memory size needed for rendezvous between the scenario with arbitrary delay and the scenario with delay zero. Moreover, we show that our upper bound is optimal by proving that Ω(log L + log log n) bits of memory is required for rendezvous, even in the class of trees with degrees bounded by 3.
Finding a Dense Subgraph with Sparse Cut

Community detection is one of the fundamental tasks in graph mining, which has many real-world applications in diverse domains. In this study, we propose an optimization model for finding a community that is densely connected internally but sparsely connected to the rest of the graph. The model extends the densest subgraph problem, in which we maximize the density while minimizing the average cut size. We first show that our proposed model can be solved efficiently. Then we design two polynomial-time exact algorithms based on linear programming and a maximum flow algorithm, respectively. Moreover, to deal with larger-sized graphs in practice, we present a scalable greedy algorithm that runs in almost linear time with theoretical performance guarantee of the output. In addition, as our model is closely related to a quality function called the modularity density, we show that our algorithms can also be used to find global community structure in a graph. With thorough experiments using well-known real-world graphs, we demonstrate that our algorithms are highly effective in finding a suitable community in a graph. For example, for web-Google, our algorithm finds a solution with more than 99.1% density and less than 3.1% cut size, compared with a solution obtained by a baseline algorithm for the densest subgraph problem.
Estimating Clickthrough Bias in the Cascade Model

Recently, there has been considerable interest in the use of historical logged user interaction data—queries and clicks—for evaluation of search systems in the context of counterfactual analysis [8,10]. Recent approaches attempt to de-bias the historical log data by conducting randomization experiments and modeling the bias in user behavior. Thus far, the focus has been on addressing bias that arises due to the position of the document being clicked (position-bias) or sparsity of clicks on certain query-document pairs (selection-bias). However, there is another source of bias that could arise: the bias due to the context in which a document was presented to the user. The propensity of the user clicking on a document depends not only on its position but also on many other contextual factors. In this work, we show that the existing counterfactual estimators fail to capture one type of bias, specifically, the effect on click-through rates due to the relevance of documents ranked above. Further, we propose a modification to the existing estimator that takes into account this bias. We rely on full result randomization that allows us to control for the click context at various ranks; we demonstrate the effectiveness of our methods in evaluating retrieval system through experiments on a simulation setup that is designed to cover a wide variety of scenarios.
Identification and characterization of novel mutations implicated in congenital fibrinogen disorders

Essentials Fibrinogen Disorders are characterized by variable expressivity. Patients with fibrinogen disorders can present with bleeding, thrombosis, or both. As previously reported, genotype‐phenotype correlations are difficult to establish. Molecular modeling may help to further understand the effects of mutations on the mature fibrinogen protein.
It takes a dimer to tango: Oligomeric small heat shock proteins dissociate to capture substrate

Small heat-shock proteins (sHsps) are ubiquitous molecular chaperones, and sHsp mutations or altered expression are linked to multiple human disease states. sHsp monomers assemble into large oligomers with dimeric substructure, and the dynamics of sHsp oligomers has led to major questions about the form that captures substrate, a critical aspect of their mechanism of action. We show here that substructural dimers of two plant dodecameric sHsps, Ta16.9 and homologous Ps18.1, are functional units in the initial encounter with unfolding substrate. We introduced inter-polypeptide disulfide bonds at the two dodecameric interfaces, dimeric and nondimeric, to restrict how their assemblies can dissociate. When disulfide-bonded at the nondimeric interface, mutants of Ta16.9 and Ps18.1 (TaCT-ACD and PsCT-ACD) were inactive but, when reduced, had WT-like chaperone activity, demonstrating that dissociation at nondimeric interfaces is essential for sHsp activity. Moreover, the size of the TaCT-ACD and PsCT-ACD covalent unit defined a new tetrahedral geometry for these sHsps, different from that observed in the Ta16.9 X-ray structure. Importantly, oxidized Tadimer (disulfide bonded at the dimeric interface) exhibited greatly enhanced ability to protect substrate, indicating that strengthening the dimeric interface increases chaperone efficiency. Temperature-induced size and secondary structure changes revealed that folded sHsp dimers interact with substrate and that dimer stability affects chaperone efficiency. These results yield a model in which sHsp dimers capture substrate before assembly into larger, heterogeneous sHsp–substrate complexes for substrate refolding or degradation, and suggest that tuning the strength of the dimer interface can be used to engineer sHsp chaperone efficiency.
MEgo2Vec: Embedding Matched Ego Networks for User Alignment Across Social Networks

Aligning users across multiple heterogeneous social networks is a fundamental issue in many data mining applications. Methods that incorporate user attributes and network structure have received much attention. However, most of them suffer from error propagation or the noise from diverse neighbors in the network. To effectively model the influence from neighbors, we propose a graph neural network to directly represent the ego networks of two users to be aligned into an embedding, based on which we predict the alignment label. Three major mechanisms in the model are designed to unitedly represent different attributes, distinguish different neighbors and capture the structure information of the ego networks respectively. Systematically, we evaluate the proposed model on a number of academia and social networking datasets with collected alignment labels. Experimental results show that the proposed model achieves significantly better performance than the state-of-the-art comparison methods (+3.12-30.57% in terms of F1 score).
Deep Spectrum Feature Representations for Speech Emotion Recognition

Automatically detecting emotional state in human speech, which plays an effective role in areas of human machine interactions, has been a difficult task for machine learning algorithms. Previous work for emotion recognition have mostly focused on the extraction of carefully hand-crafted and tailored features. Recently, spectrogram representations of emotion speech have achieved competitive performance for automatic speech emotion recognition. In this work we propose a method to tackle the problem of deep features, herein denoted as deep spectrum features, extraction from the spectrogram by leveraging Attention-based Bidirectional Long Short-Term Memory Recurrent Neural Networks with fully convolutional networks. The learned deep spectrum features are then fed into a deep neural network (DNN) to predict the final emotion. The proposed model is then evaluated on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) dataset to validate its effectiveness. Promising results indicate that our deep spectrum representations extracted from the proposed model perform the best, 65.2% for weighted accuracy and 68.0% for unweighted accuracy when compared to other existing methods. We then compare the performance of our deep spectrum features with two standard acoustic feature representations for speech-based emotion recognition. When combined with a support vector classifier, the performance of the deep feature representations extracted are comparable with the conventional features. Moreover, we also investigate the impact of different frequency resolutions of the input spectrogram on the performance of the system.
Categorizing factors of adherence to parenteral treatment in growth hormone deficiencies and hemophilia: What should be the targets for future research?

Adherence to treatment regimens in growth hormone dysregulations and hemophilia is related to better outcome and fewer complications over time. Subcutaneous growth hormone injection and intravenous blood factor replacement therapies are parenteral treatments with a comparable regimen calling for similar behavioral processes. Although we have lists of possible factors influencing adherence in these conditions, the evidence is scattered. The objective of this study was to systematically review empirical studies linking factors of adherence with measures of adherence. To categorize the factors, we used a taxonomy from the diabetes literature. We used four major electronic databases to identify articles. We synthesized 27 articles dated 2011–2017 corresponding to inclusion criteria. Results showed a consistent proportion of 20%–25% participants with adherent issues. Strong arguments pointed to the transition to self-care in pediatrics as a vulnerability period (7/27 reports). We found the domains of individual factors (<30% reports), relational factors (<13%), health care (<30%), to be understudied in comparison with that of demographic or clinical context (>74%), and practical issues (>37%). The results suggest that future research should focus on modifiable factors of adherence, with appropriate measurement and intervention strategies. One central methodological limitation of reviewed reports was the lack of longitudinal designs, and the quasi absence of behavioral trial targeting modifiable factors of adherence. A new research agenda should be set in these rare diseases as higher adherence should translate into improved outcome and better quality of life for patients and their families.
[18F]FDG-PET for evaluating pediatric Rosai–Dorfman disease

Nicholas S. Whipple, Laura L. Marion, David M. Dansie, Andrea T. Murina, Gail C. Megason, Vani Vijayakumar, and Betty L. Herrington Division of Hematology/Oncology, Department of Pediatrics, University of Utah and Primary Children’s Hospital, Salt Lake City, Utah, USA; Physicians and Surgeons Clinic, Columbus, Mississippi, USA; Department of Radiology, Primary Children’s Hospital, Salt Lake City, Utah, USA; Department of Dermatology, Tulane University School of Medicine, New Orleans, Louisiana, USA; Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Mississippi Medical Center, Jackson, Mississippi, USA; Department of Radiology, University of Mississippi Medical Center, Jackson, Mississippi, USA