Miracle-dz/UniFAR-FTUs huggingface dataset:
CGL Computational Geometric Learning Geometric Inference for Probability Measures

Data often comes in the form of a point cloud sampled from an unknown compact subset of Euclidean space. The general goal of geometric inference is then to recover geometric and topological features (e.g., Betti numbers, normals) of this subset from the approximating point cloud data. It appears that the study of distance functions allows to address many of these questions successfully. However, one of the main limitations of this framework is that it does not cope well with outliers nor with background noise. In this paper, we show how to extend the framework of distance functions to overcome this problem. Replacing compact subsets by measures, we introduce a notion of distance function to a probability distribution in R. These functions share many properties with classical distance functions, which make them suitable for inference purposes. In particular, by considering appropriate level sets of these distance functions, we show that it is possible to reconstruct offsets of sampled shapes with topological guarantees even in the presence of outliers. Moreover, in settings where empirical measures are considered these functions can be easily evaluated, making them of particular practical interest. AMS classification numbers: 62G05, 62-07, 28A33
Lisbon: Evaluating TurboSemanticParser on Multiple Languages and Out-of-Domain Data

As part of the SemEval-2015 shared task on Broad-Coverage Semantic Dependency Parsing, we evaluate the performace of our last year’s system (TurboSemanticParser) on multiple languages and out-of-domain data. Our system is characterized by a feature-rich linear model, that includes scores for first and second-order dependencies (arcs, siblings, grandparents and co-parents). For decoding this second-order model, we solve a linear relaxation of that problem using alternating directions dual decomposition (AD 3 ). The experiments have shown that, even though the parser’s performance in Chinese and Czech attains around 80% (not too far from English performance), domain shift is a serious issue, suggesting domain adaptation as an interesting avenue for future research.
Recommendation Scheme via Improved Iteratively Collaborative Filtering Algorithm with Neighborhood Scale Research

Collaborative filtering algorithm is a traditional algorithm for recommendation system, and user rating (user-based collaborative filtering algorithm) is the primitive method of collaborative filtering algorithm. In this paper, with respect to the sparse data, the strategy of both user-based and item-based collaborative filtering algorithm is proposed and a three-steps updating algorithm is used, along with an iterative method to form a consistent recommendation scheme. Finally, the neighborhood scale is studied.
Characterizing and Modulating Brain Circuitry through Transcranial Magnetic Stimulation Combined with Electroencephalography

The concurrent combination of transcranial magnetic stimulation (TMS) with electroencephalography (TMS-EEG) is a powerful technology for characterizing and modulating brain networks across developmental, behavioral, and disease states. Given the global initiatives in mapping the human brain, recognition of the utility of this technique is growing across neuroscience disciplines. Importantly, TMS-EEG offers translational biomarkers that can be applied in health and disease, across the lifespan, and in humans and animals, bridging the gap between animal models and human studies. However, to utilize the full potential of TMS-EEG methodology, standardization of TMS-EEG study protocols is needed. In this article, we review the principles of TMS-EEG methodology, factors impacting TMS-EEG outcome measures, and the techniques for preventing and correcting artifacts in TMS-EEG data. To promote the standardization of this technique, we provide comprehensive guides for designing TMS-EEG studies and conducting TMS-EEG experiments. We conclude by reviewing the application of TMS-EEG in basic, cognitive and clinical neurosciences, and evaluate the potential of this emerging technology in brain research.
A Framework for Cross-Platform Mobile Web Applications Using HTML5

In recent years the use of smart mobile devices has become an integral part of everyday life leading to the expansion of applications development for the various mobile platforms. Each of these platforms requires separate software development process, which subsequently increases dramatically the corresponding effort. With the emergence of HTML5 these issues can be addressed efficiently since application development is allowed in a cross-platform manner. An additional important benefit is that users can access the application immediately without any need for installation. In this paper, we investigate the potentials of mobile application development with web technologies and we present a development framework that utilizes the most important state-of-art web technologies for the support of mobile devices. This framework can be used for the implementation of mobile web applications and also for the investigation and experimentation on the main features that HTML5 offers for this specific type of devices.
Parameter estimation for von mises-fisher mixture model via Gaussian distribution

Directional statistics deal with direction, such as angles and phases. A well-known distribution in directional statistics is von Mises-Fisher (vMF) distribution, which is Gaussian distribution on a unit hypersphere. For a vMF mixture model, a maximum likelihood estimator and a variational Bayes estimator have already been derived. However, an iterative algorithm for finding the maximum likelihood estimator may accumulate approximation error. Besides, the variational Bayes estimator cannot estimate some parameters. This paper derives an estimator of the parameters in the vMF mixture model via the Gaussian distribution to solve these problems. We focus on the fact that the vMF distribution is derived from the Gaussian distribution. At first, we apply the estimation for the Gaussian mixture model to observed samples. Then, we convert the estimated Gaussian mixture distribution to a vMF mixture distribution. Experimental results support the analysis.
Reflection and the distance language learner

This research examines the role of critical reflection in learning theories and the relationship between Kolb's learning cycle and notions of the 'good' language learner, the deep approach to learning and autonomous learning in the context of adult, part-time, distance language learning. This group of learners is under-represented in the research literature. The research takes an exploratory-interpretative approach. Open University Language students had been invited by their tutors to use materials based on Kolb's learning cycle, designed to encourage critical reflection in order to enhance learning. In-depth interviews explored the experience of users and non-users. Course materials were examined for evidence of encouragement and support for critical reflection and autonomy. The research aimed to establish what OU language learners do to develop productive and receptive language skills and the extent to which they demonstrate capacities of critical reflection and autonomy. It examined the extent to which these capacities were developed via course materials and assignments and the impact of the style and pace of study. It considered whether these capacities could be enhanced by the project materials, as well as the influence of tutors' expectations and approaches. The majority of interviewees exhibited considerable functional activity except in writing skills. They demonstrated characteristics of the 'good' language learner, elements of a deep approach and features of autonomous learning. This contrasted with a surface approach to coursework and assignments, brought about by excessive workload and the controlled, anxiety-provoking nature of assessment. Analysis of assignments also suggested they were likely to evoke a surface approach. Course materials advocated reflection, self-assessment and self-evaluation, but did not support this via teaching or practice and offered few decision-making opportunities.Students exercised the capacity for critical reflection and autonomy to varying degrees. Those who had used the proj ect materials appeared more likely to make decisions about their learning, and set specific goals. The project materials were judged a straightforward framework for reflection. Tutors were positive about the materials but appeared to give little attention to critical reflection. Their concerns about time constraints and student workload may have confirmed student perceptions and discouraged use of the project materials. The research suggests broadening the notion of the 'good' language learner. It proposes more explicit development of learning strategies and the capacity for critical reflection within course materials and tutorials, and giving more attention to the nature and impact of assessment in order to foster 'active', deep, autonomous learning.
TRECVID 2004 Search and Feature Extraction Task by NUS PRIS

This paper describes the details of our systems for feature extraction and search tasks of TRECVID-2004. For feature extraction, we emphasize the use of visual auto-concept annotation technique, with the fusion of text and specialized detectors, to induce concepts in videos. For the search task, our emphasis is two-fold. First we employ query-specific models, and second, we employ multi-modality features, including text, annotated visual concepts, OCR output, shot classes and specialized detectors to perform the search. Our search pipeline is similar to that employed in text-based definition question-answering approaches. We first perform query analysis to categorize the query into the categories of: {PERSON, SPORTS, FINANCE, WEATHER, DISASTER and GENERAL}. From these categories, we induce a number of constraints on the search process, including: (a) the type of multi-modality features to use or emphasize; (b) the key concept terms in text query to use; and (c) the video classes, such as sports or anchor person etc to use or exclude in the search results. The results on 60 hours of test video from TRECVID 2004 evaluation demonstrate that our approaches are effective.
Comparison of crowd simulation for building evacuation and an alternative approach

This paper presents an overview of crowd simulation models, their limitations, and an alternative agent- based approch. First we introduce several methods and then we focus on two widely used and validated simulation tools that use grid-based models. We discus the artifacts that these models introduce regarding the way they treat the space and the implication that this has in the movement of the agents during the simulation. We also describe the limitations that current commercial software tools have in terms of simulating human psychology and physiology. The paper discusses an agent-based alternative approach developed to overcome these limitations. The model allows for the simulation of human movement that can provide results more closely describing behavior of real people during an emergency situation. Flow rates, densities and speeds emerge in our model from the physical interactions between people instead of being predefined.
CAN EMOTIONAL BALANCE TRAINING REDUCE STRESS AND ANXIETY, AND PROMOTE TRAIT MINDFULNESS AMONG COLLEGE STUDENTS? - A BRAZILIAN INVESTIGATION

Stress in the academic environment can lead to negative consequences in concentration, memory, and problem solving, which affect student retention and academic success. The purpose of this study was to evaluate the impact of an emotional balance training in the levels of anxiety, depression and mindfulness in college students. Method: A pretest/posttest design with 59 undergraduate and graduate female students (treatment group (TG): n = 27; control group (CG): n = 32) in a Brazilian public university was performed between October and December 2016. TG completed an 8-week program with the Cultivating Emotional Balance (CEB) based course and Hatha Yoga exercises. All participants completed self-report questionnaires at baseline and after 8 weeks (Beck Anxiety Inventory (BAI), Beck Depression Inventory (BDI), Lipp Stress Symptoms Inventory for adults (LSSI), and the Mindful Attention Awareness Scale (MAAS)). Results: Anxiety (t(26) = 2.57; p = . 016) and depression scores (t(26)= 3.63, p = . 001) were significantly reduced at the posttest. Results for MAAS (t(26) = -1.663; p = . 108) was not significant in TG. Conclusions: The CEB based course (CEB short version) associated to Hatha Yoga exercises were able to reduce the anxiety and depressive symptoms in college students.
Paper Bagging ensemble based on fuzzy c-means

Based on fuzzy clustering, a new ensemble method of Bagging F-Bagging is proposed in this paper. Firstly the training data are clustered using fuzzy clustering, and then according to the matrix, dividing the training samples into subset intersect, at last each subset of the data are trained, and proper weighted method is used to base learners. As each subset contains different categories and different training data, thus the members of the classifier are diverse. The number of subsets determines the number of the base learners. Experimental results show that this approach can achieve good results.
YfiBNR Mediates Cyclic di-GMP Dependent Small Colony Variant Formation and Persistence in Pseudomonas aeruginosa

During long-term cystic fibrosis lung infections, Pseudomonas aeruginosa undergoes genetic adaptation resulting in progressively increased persistence and the generation of adaptive colony morphotypes. This includes small colony variants (SCVs), auto-aggregative, hyper-adherent cells whose appearance correlates with poor lung function and persistence of infection. The SCV morphotype is strongly linked to elevated levels of cyclic-di-GMP, a ubiquitous bacterial second messenger that regulates the transition between motile and sessile, cooperative lifestyles. A genetic screen in PA01 for SCV-related loci identified the yfiBNR operon, encoding a tripartite signaling module that regulates c-di-GMP levels in P. aeruginosa. Subsequent analysis determined that YfiN is a membrane-integral diguanylate cyclase whose activity is tightly controlled by YfiR, a small periplasmic protein, and the OmpA/Pal-like outer-membrane lipoprotein YfiB. Exopolysaccharide synthesis was identified as the principal downstream target for YfiBNR, with increased production of Pel and Psl exopolysaccharides responsible for many characteristic SCV behaviors. An yfi-dependent SCV was isolated from the sputum of a CF patient. Consequently, the effect of the SCV morphology on persistence of infection was analyzed in vitro and in vivo using the YfiN-mediated SCV as a representative strain. The SCV strain exhibited strong, exopolysaccharide-dependent resistance to nematode scavenging and macrophage phagocytosis. Furthermore, the SCV strain effectively persisted over many weeks in mouse infection models, despite exhibiting a marked fitness disadvantage in vitro. Exposure to sub-inhibitory concentrations of antibiotics significantly decreased both the number of suppressors arising, and the relative fitness disadvantage of the SCV mutant in vitro, suggesting that the SCV persistence phenotype may play a more important role during antimicrobial chemotherapy. This study establishes YfiBNR as an important player in P. aeruginosa persistence, and implicates a central role for c-di-GMP, and by extension the SCV phenotype in chronic infections.
Association Pattern Based Prediction for Moving Objects

This paper addresses the prediction for moving objects when the motions of the objects having some associations. In the real world, the moving objects often have some associations with other objects. Unfortunately, existing techniques for moving objects prediction are designed for single independent object, and ignore the associations between the moving objects. This paper firstly defines an association pattern based prediction model, which uses the association patterns of the moving objects to predict the trajectory of the moving objects, and then presents the Association Pattern based Prediction algorithm (APP algorithm) for moving objects. Theoretical analyses results show that, the APP algorithm has good efficiency for predicting moving objects.
Signal Compensation for Analog Rotor Position Errors due to Nonideal Sinusoidal Encoder Signals

This paper proposes a compensation algorithm for the analog rotor position errors caused by nonideal sinusoidal encoder output signals including offset and gain errors. In order to achieve a much higher resolution, position sensors such as resolvers or incremental encoders can be replaced by sinusoidal encoders. In practice, however, the periodic ripples related to the analog rotor position are generated by the offset and gain errors between the sine and cosine output signals of sinusoidal encoders. In this paper, the effects of offset and gain errors are easily analyzed by applying the concept of a rotating coordinate system based on the dq transformation method. The synchronous d-axis signal component is used directly to detect the amplitude of the offset and gain errors for the proposed compensator. As a result, the offset and gain errors can be well corrected by three integrators located on the synchronous d-axis component. In addition, the proposed algorithm does not require any additional hardware and can be easily implemented by a simple integral operation. The effectiveness of the proposed algorithm is verified through several experimental results.
Link Dynamics and Protocol Design in a Multihop Mobile Environment

A multihop ad hoc network with mobile elements is considered. A model is created and analytic expressions derived to characterize the statistics for link lifetime, new link interarrival time, link breakage interarrival time, and link change interarrival time. Applications of these expressions to protocol design are discussed. As an example application, the link model is used to find an optimal balance between reactive and proactive routing strategies. It is shown that, when control traffic generated through proactive route updating is matched to link lifetimes, control traffic is significantly reduced while the goodput and delay benefits of the proactive approach are retained
A Rev-Independent gag/pol Eliminates Detectable psi-gag Recombination in Lentiviral Vectors

Abstract Lentiviral vectors (LVs) are being developed for clinical use in humans for applications including gene therapy and immunotherapy. A safety concern for use of LVs in humans is the generation of replication-competent lentivirus (RCL), which may arise due to recombination between the split genomes of third-generation LVs. Although no RCL has been detected to date, design optimizations that minimize recombination events between split genome vectors would provide an added safety benefit that may further reduce the risk of RCL formation. Here we describe design elements introduced to the gag/pol plasmid with the intention of eliminating psi-gag recombination between the vector genome and gag/pol. These design changes, consisting of codon optimization of the gag/pol sequence and the deletion of the Rev-responsive element, abrogate the requirement for Rev in expression of Gag protein, thus the resulting gag/pol construct being Rev independent (RI gag/pol). We show that generating vector using the RI gag/pol construct has no effect on particle production or transduction titers. The RI and wild-type gag/pol vectors function equivalently as antigen-specific immunotherapy, potently inducing antigen-specific CD8 T cells that protect against challenge with vaccinia virus. Most importantly, the designed RI gag/pol eliminated detectable psi-gag recombination. Interestingly, we detected recombination between the vector genome and gag/pol from regions without sequence homology. Our findings imply that although unpredictable recombination events may still occur, the RI gag/pol design is sufficient to prevent psi-gag recombination.
Interactions between the Ionic Liquid and the ZrO2 Support in Supported Ionic Liquid Membranes for CO2 Separation

This work reports the interaction study of two supported ionic liquid membranes (SILMs) based on 1-butyl-3-methylimidazolium hexafluorophosphate ([C4mim][PF6]) and 1-butyl-3-methylimidazolium tetrafluoroborate ([C4mim][BF4]), which were impregnated into porous zirconia supports with 20 nm average pore diameters. The interaction of ionic liquid-support observed from diffuse reflectance (DR), Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy and energy dispersive spectroscopy (SEM/EDS) is reported. The IR spectrum in the 600 to 4000 cm−1 range showed a specific interaction of the ionic liquid with the support. The N2 and CO2 permeances in the SILMs with [C4mim][BF4] were 8.7 × 10−8 mol·s−1·m−2·Pa−1 and 9.6 × 10−7 mol·s−1·m−2·Pa−1, respectively. The separation factor through the ionic liquid in the membrane as a function of temperature showed that the SILMs studied here can be used for CO2 separation at low temperatures.
Design of Synchronous Reluctance Motors With Multiobjective Optimization Algorithms

The automatic design of synchronous reluctance (SyR) machines is considered in this paper by means of finite-element analysis and multiobjective optimization algorithms (MOOAs). The research focuses on the design of the rotor geometry, which is the key aspect of the SyR machine design. In particular, the performance of three popular MOOAs is analyzed and compared in terms of quality of the final design and computational time. A procedure to minimize the computational burden of the optimized design process is introduced and applied to a three-layer and a five layer rotor. Two prototypes experimentally demonstrate the feasibility of the design procedure.
New spaces of new speaker profiles: Exploring language ideologies in transnational multilingual families

Abstract This article looks at Spanish-Estonian speaking families and their language ideologies in relation to language use in the family setting—how parents decide to use languages among themselves and with their children. Family members choose different languages for different purposes when they talk to one another. In our study, parents draw on their knowledge of the ‘one parent–one language’ strategy but also translanguage for different reasons, constructing new patterns of bilingual modes. In the article, we examine parents’ attitudes towards language maintenance, transmission, and use with their children. We incorporate the lens of ‘new speaker’ research to analyse the empirical data collected in Tallinn households among Spanish-Estonian speaking families so as to contribute to a better understanding of family language policy, planning, and management, highlighting how macro-level sociolinguistic expectations and norms might be elaborated on the micro level in everyday social interactions. (Family language policy, language ideology, new speakers, Estonian, Spanish)*