Miracle-dz/UniFAR-FTUs huggingface dataset:
Computer Environments for the Learning of Mathematics

Computer software for the learning of mathematics, as distinct from software for doing mathematics, needs to be designed to take account of the cognitive growth of the learner which may differ significantly from the logical structure of the formal subject. It is therefore of value to begin by considering cognitive aspects relevant to the use of computer technology before the main task of focusing on computer environments and their role in the learning of mathematics. The growth of (mathematical) knowledge The human brain is remarkable in its ability to store and retrieve complex information, but it is correspondingly limited in the quantity of independent pieces of data that may be manipulated in conscious short-term memory. To minimise the effects of these limitations, one method is to ‘chunk’ the data by using an appropriate representation which is more easily manipulable. For instance, standard decimal notation is a compact method of representing numerical quantities of any size, with corresponding routines for manipulation; algebraic notation can be used to formulate and manipulate certain types of data for problem-solving; graphical representations are appropriate for other tasks, such as representation of complex data in a single gestalt.
RITZ: a RealTime interactive tool for spatialization

In this paper we discuss the use of sound spatialization in western classical music and attempt to explain a potential reason why it is not found in current popular music. We argue that the barriers limiting the use of musical sound spatialization are nearly gone and attempt to facilitate the return by providing a computer interface for musical spatialization. The interface is described in detail and is briefly compared to the few currently available interfaces. The paper focuses on real-time sound spatialization and includes a discussion of real-time spatialization versus spatial choreography.
Closed trajectories on symmetric convex Hamiltonian energy surfaces

In this article, let $\Sigma\subset\R^{2n}$ be a compact convex Hamiltonian energy surface which is symmetric with respect to the origin. where $n\ge 2$. We prove that there exist at least two geometrically distinct symmetric closed trajectories of the Reeb vector field on $\Sg$.
Is East Asia's Economic Fate Chained to the West?1

Abstract This paper analyzes to what extent East Asia's integration into global value chains (GVCs) has made the region more vulnerable to business cycle shocks in the West. We identify different channels through which business cycle shocks in the West can propagate to East Asian countries via global value chains. We next demonstrate that standard measures of external dependence are biased since they do not adequately capture the effects of these various GVC channels. Finally, we apply our framework to gain new insights into the role of GVCs in triggering East Asia's trade collapse during the Global Recession of 2008–2009.
Empirical study of soft clustering approaches for large data

Mining valuable information and knowledge in the prevalent large data nowadays is crucial to gain competitive advantages for many parties. Clustering is an important technique for data analysis to find the natural distribution of the unlabelled data. Clustering algorithms need to store the entire data into memory for analysis become infeasible when the data set is too large to be stored. To solve this problem, some approaches were developed by different strategies. In this paper, we have studied five soft clustering algorithms for large data using a chunk style strategy and evaluated their performances for different kinds of data sets. Each approach handles large data by processing data chunk by chunk instead of handling the entire data set at a time. Experimental results which show the effectiveness of these approaches are presented and discussed. The recommendations for the usage of these approaches are also proposed.
Osmotic Stress Signaling and Osmoadaptation in Yeasts

SUMMARY The ability to adapt to altered availability of free water is a fundamental property of living cells. The principles underlying osmoadaptation are well conserved. The yeast Saccharomyces cerevisiae is an excellent model system with which to study the molecular biology and physiology of osmoadaptation. Upon a shift to high osmolarity, yeast cells rapidly stimulate a mitogen-activated protein (MAP) kinase cascade, the high-osmolarity glycerol (HOG) pathway, which orchestrates part of the transcriptional response. The dynamic operation of the HOG pathway has been well studied, and similar osmosensing pathways exist in other eukaryotes. Protein kinase A, which seems to mediate a response to diverse stress conditions, is also involved in the transcriptional response program. Expression changes after a shift to high osmolarity aim at adjusting metabolism and the production of cellular protectants. Accumulation of the osmolyte glycerol, which is also controlled by altering transmembrane glycerol transport, is of central importance. Upon a shift from high to low osmolarity, yeast cells stimulate a different MAP kinase cascade, the cell integrity pathway. The transcriptional program upon hypo-osmotic shock seems to aim at adjusting cell surface properties. Rapid export of glycerol is an important event in adaptation to low osmolarity. Osmoadaptation, adjustment of cell surface properties, and the control of cell morphogenesis, growth, and proliferation are highly coordinated processes. The Skn7p response regulator may be involved in coordinating these events. An integrated understanding of osmoadaptation requires not only knowledge of the function of many uncharacterized genes but also further insight into the time line of events, their interdependence, their dynamics, and their spatial organization as well as the importance of subtle effects.
Attitude estimation with accelerometers and gyros using fuzzy tuned Kalman filter

This paper introduces the attitude estimation method of humanoid robot using an extended Kalman filter with a fuzzy logic based tuning algorithm. A humanoid robot which uses inertial sensors such as gyros and accelerometers to calculate its attitude is considered. It is known that the attitude update using gyros are prone to diverge and hence the attitude error needs to be compensated using accelerometers. In this paper, a Kalman filter model with a modified state is presented and an adaptive algorithm is used to make the filter more robust regarding acceleration disturbances. If the accelerometer measures any disturbances caused by movement of the vehicle, the characteristics of the filter must be changed to ensure confidence of the outputs of the gyros. The performance of the proposed algorithm is shown by the experiments.
Role engineering using graph optimisation

Role engineering is one of the fundamental phases for migrating existing enterprises to Role Based Access Control. In organisations with a large number of users and permissions, this task can be time consuming and costly if a top down approach is used. Existing bottom up approaches are not sufficient in producing a comprehensive set of roles for hierarchical Role Based Access Control. In this research, we propose a predominately bottom up approach that uses Graph Optimisation to identify appropriate role hierarchies. Additional partial role specifications can be incorporated to produce a hybrid approach. Using rules that reduce administration requirements, roles and their hierarchies are automatically extracted from large numbers of permission assignments. The results of the Graph Optimisation approach are hierarchical Role Based Access Control infrastructures that offer improved access control administration for the system.
Neuropsychological function in nondemented carriers of presenilin-1 mutations

Background: Prospective and case-control studies have demonstrated that memory loss and executive dysfunction occur early in Alzheimer disease (AD). Objective: To investigate these observations by the study of persons at risk for autosomal dominant forms of AD. Methods: Neuropsychological and genetic tests were performed on 51 nondemented at-risk members of 10 Mexican families with two distinct presenilin-1 (PS1) mutations. Test scores were compared between PS1 mutation carriers (MCs; n = 30) and noncarriers (NCs; n = 21) by analyses of variance, co-varying for family and specific mutation. Regression analyses were performed, taking into account age relative to the median age at dementia diagnosis in the family (adjusted age), gender, Beck Depression Inventory (BDI) scores, education, and number of APOE ε4 alleles. Subjects were divided into age tertiles and scores compared within these groups. Composite scores for Verbal Memory, Executive Function/Working Memory, Language, and Visuospatial Function were created, and these scores compared between MCs and NCs. Results: MCs performed worse than NCs on the Mini-Mental State Examination, Trails Making Tests A and B, Delayed Recall of a 10-Word List, and Wechsler Adult Intelligence Scale WAIS Block Design. In multiple linear regression analyses, BDI score, gender, and number of APOE ε4 alleles did not consistently affect test scores. The differences seen between MCs and NCs were due to differences in the oldest tertile. MCs had lower Visuospatial and Executive Function/Working Memory but not Verbal Memory or Language composite scores. Conclusions: This study is consistent with findings in sporadic Alzheimer disease of early problems with memory, visuospatial function, and particularly with executive function in PS1 mutation carriers. Depression, gender, and presence of an APOE ε4 allele did not demonstrate large influences on neuropsychological performance.
Corrections to the statistical entropy of five dimensional black holes

We compute the statistical entropy of the three charge (D1-D5-p) five dimen- sional black hole to sub-leading order in a large charge expansion. We find an agreement with the macroscopic calculation of the Wald entropy in R 2 corrected supergravity theory. The two calculations have a overlapping regime of validity which is not the Cardy regime in the microscopic conformal field theory. We use this result to clarify the 4d-5d lift for black holes on Taub-NUT space. In particular, we compute sub-leading corrections to the formula S 4d = S 5d . In the microscopic analysis, this correction arises from excitations bound to the Taub-NUT space. In the macroscopic picture, the difference is accounted by a mechanism present in a higher derivative theory wherein the geometry of the Taub-NUT space absorbs some of the electric charge.
Bi-level Characterization of Manual-Setup Residential Non-Intrusive Demand Disaggregation using Enhanced Differential Evolution

Renewed interest in formalization of non-intrusiveload monitoring algorithms in recent years has been compounded by inroads into efficient optimization and global search strategies. This work attempts to characterize the manual-setup nonintrusive load disaggregation problem on a household scale using a bi-level strategy composed of an enhanced variant of differential evolution as the optimizing algorithm for a highly adaptive disaggregation in the lower level. Enhanced DifferentialEvolution (EDE) offers efficient convergence compared to itsparent stochastic global search strategy, differential evolution (DE); Coupled with generalized metrics to gauge the selectivity of search for optimal training attributes allows for a highly flexible dissolution of the overall demand profile into its constituent steady-state appliance signatures. Accuracy and efficiency is evaluated forthe proposed strategy.
Linearized and Turbo Belief Propagation

How can we tell when accounts are fake or real in a social network? And how can we tell which accounts belong to liberal, conservative or centrist users? Often, we can answer such questions and label nodes in a network based on the labels of their neighbors and appropriate assumptions of homophily ("birds of a feather flock together") or heterophily ("opposites attract"). One of the most widely used methods for this kind of inference is Belief Propagation (BP) which iteratively propagates the information from a few nodes with explicit labels throughout a network until convergence. One main problem with BP, however, is that there are no known exact guarantees of convergence in graphs with loops. This paper introduces Linearized Belief Propagation (LinBP), a linearization of BP that allows a closed-form solution via intuitive matrix equations and, thus, comes with convergence guarantees. It handles homophily, heterophily, and more general cases that arise in multi-class settings. Plus, it allows a compact implementation in SQL. The paper also introduces Single-pass Belief Propagation (SBP), a"localized"version of LinBP that propagates information across every edge at most once and for which the final class assignments depend only on the nearest labeled neighbors. In addition, SBP allows fast incremental updates in dynamic networks. Our runtime experiments show that LinBP and SBP are orders of magnitude faster than standard
Guillain-Barré syndrome following primary cytomegalovirus infection: a prospective cohort study.

BACKGROUND Little is known about the epidemiology and the prognostic factors of Guillain-Barré syndrome (GBS) following primary infection with cytomegalovirus (CMV-GBS). METHODS We prospectively followed up 506 patients with cases of GBS who were admitted to our center from 1996 through 2006. We diagnosed 63 (12.4%) CMV-GBS cases by immunoglobulin (Ig) M detection and IgG avidity. Plasma CMV DNA was detected at hospital admission. Patient subgroups were compared using Fisher's exact test and the Wilcoxon rank-sum test. Temporal variations were analyzed with time series methods. RESULTS Patients with CMV-GBS were mostly young (median age, 32 years; sex ratio, 0.85), but we also identified a subpopulation of patients consisting of women aged >50 years. Sensory defects (in 72% of cases) and facial palsy (49%) were frequent, and test results positive for CMV DNA in plasma at hospital admission (found in 62% of cases) tended to be associated with objective sensory defect (P=.052). The main factors associated with long-term neurological sequelae (21%) were older age (P<.001) and assisted ventilation during hospitalization (P=.005). The number of CMV-GBS cases decreased between 1996 and 2006 (P=.019) and displayed an annual periodicity between the months of July and October. The incidence of CMV-GBS was estimated to be between 0.6 and 2.2 cases per 1000 cases of primary CMV infection (versus 0.25 to 0.65 cases per 1000 cases of Campylobacter jejuni infection). CONCLUSIONS This study provides new insights about the epidemiology of CMV-GBS and shows that the risk of developing GBS is similar following primary CMV infection or C. jejuni infection. Our results also suggest a direct or indirect involvement of viral replication in the neuropathological processes of CMV-GBS.
Machine Learning Final Project : Handwritten Sanskrit Recognition using a Multi-class SVM with K-NN Guidance

We develop an optical character recognition (OCR) engine for handwritten Sanskrit using a two-stage classifier. Inside the standard OCR pipeline, we focus on the classification problem assuming characters have been preprocessed decently. One challenge we face is that the language of Sanskrit has about a hundred core characters where model driven methods, like Support Vector Machine (SVM), have to search in the exponentially growth of the combinatoric model space during training, while data driven methods, like k nearest neighbor (kNN), becomes costly in computation during testing. To address this challenge, we propose a two-stage multiclassifier, using non-parametric to reduce the model space to search, and parametric models to relieve computation burden with better generalization. In the first stage, we apply kNN to coarsely assign the test data into the possible group of k classes, and a multiclassifier of k classes to label the sample in the second stage. Our method is fully automatic, highly accurate, and computational efficiently.
A channel estimation technique for OFDM systems in dispersive time-varying channels

Coherent modulation is more appropriate than differential modulation for orthogonal frequency division multiplexing (OFDM) systems requiring high data rate and spectral efficiency. Channel estimation is therefore required as an integral part of the receiver design. An iterative maximum likelihood-based channel estimation algorithm for an OFDM system in dispersive time-varying channels is proposed. The multipath fading channel is modeled such that the channel state can be determined by estimating the unknown channel parameters. A second-order Taylor series expansion is adopted to simplify the channel estimation problem. The performance of the proposed algorithm is analyzed and numerical experiments are performed to confirm the theoretical analysis.
Role of p14ARF in Replicative and Induced Senescence of Human Fibroblasts

ABSTRACT Following a proliferative phase of variable duration, most normal somatic cells enter a growth arrest state known as replicative senescence. In addition to telomere shortening, a variety of environmental insults and signaling imbalances can elicit phenotypes closely resembling senescence. We used p53−/− and p21−/− human fibroblast cell strains constructed by gene targeting to investigate the involvement of the Arf-Mdm2-p53-p21 pathway in natural as well as premature senescence states. We propose that in cell types that upregulate p21 during replicative exhaustion, such as normal human fibroblasts, p53, p21, and Rb act sequentially and constitute the major pathway for establishing growth arrest and that the telomere-initiated signal enters this pathway at the level of p53. Our results also revealed a number of significant differences between human and rodent fibroblasts in the regulation of senescence pathways.
Real-time metric state estimation for modular vision-inertial systems

Single camera solutions - such as monocular visual odometry or mono SLAM approaches - found a wide echo in the community. All the monocular approaches, however, suffer from the lack of metric scale. In this paper, we present a solution to tackle this issue by adding an inertial sensor equipped with a three-axis accelerometer and gyroscope. In contrast to previous approaches, our solution is independent of the underlying vision algorithm which estimates the camera poses. As a direct consequence, the algorithm presented here operates at a constant computational complexity in real time. We treat the visual framework as a black box and thus the approach is modular and widely applicable to existing monocular solutions. It can be used with any pose estimation algorithm such as visual odometry, visual SLAM, monocular or stereo setups or even GPS solutions with gravity and compass attitude estimation. In this paper, we show the thorough development of the metric state estimation based on an Extended Kalman Filter. Furthermore, even though we treat the visual framework as a black box, we show how to detect failures and estimate drifts in it. We implement our solution on a monocular vision pose estimation framework and show the results both in simulation and on real data.
Psychometric properties of the Veterans Affairs Low-Vision Visual Functioning Questionnaire.

PURPOSE To describe psychometric properties of a self-report questionnaire, the Veterans Affairs (VA) Low-Vision Visual Functioning Questionnaire (LV VFQ-48), which was designed to measure the difficulty visually impaired persons have performing daily activities and to evaluate low-vision outcomes. METHODS The VA LV VFQ-48 was administered by telephone interview to subjects with visual acuity ranging from near normal to total blindness at five sites in the VA and private sector. Rasch analysis with the Andrich rating scale model was applied to difficulty ratings from 367 subjects, to evaluate measurement properties of the instrument. RESULTS High intercenter correlations for item measure estimates (intraclass correlation coefficient [ICC]=0.97) justified pooling the data from these sites. The person measure fit statistics (mean square residuals) confirm that the data fit the assumptions of the model. The item measure fit statistics indicate that responses to 19% of the items were confounded by factors other than visual ability. The separation reliabilities for pooled data (0.94 for persons and 0.98 for items) demonstrate that the estimated measures discriminate persons and items well along the visual ability dimension. ICCs for test-retest data (0.98 for items and 0.84 for persons) confirm temporal stability. Subjects used the rating categories in the same way at all five centers. Ratings of slight and moderate difficulty were used interchangeably, suggesting that the instrument could be modified to a 4-point scale including not difficult, slightly/moderately difficult, extremely difficult, and impossible. Fifty additional subjects were administered the questionnaire with a 4-point scale to confirm that the scale was used in the same way when there were four rather than five difficulty ratings. CONCLUSIONS The VA LV VFQ-48 is valid and reliable and has the range and precision necessary to measure visual ability of low-vision patients with moderate to severe vision loss across diverse clinical settings.
On the initiation of nearshore morphological rhythmicity

Nearshore rhythmicity is often initiated in the period just after a storm where the subtidal bar is turned alongshore uniform. The initiation time as well as the length scales of the created rhythmicity varies from one storm period to another. Here we show that the post-storm wave conditions are related to the initiation of the morphological rhythmicity. Narrow-banded and long wave period, both proxies for swell waves, are often found to be present prior to the initiation of rhythmicity. Furthermore, numerical model computations illustrate that swell waves induce significantly larger wave group induced velocities on the bar. These findings imply that the arrival of swell waves can initiate and stimulate the nearshore morphological rhythmicity
Combining imaging and clinical data in manifold learning: Distance-based and graph-based extensions of Laplacian Eigenmaps

Manifold learning techniques have been widely used to produce low-dimensional representations of patient brain magnetic resonance (MR) images. Diagnosis classifiers trained on these coordinates attempt to separate healthy, mild cognitive impairment and Alzheimer's disease patients. The performance of such classifiers can be improved by incorporating clinical data available in most large-scale clinical studies. However, the standard non-linear dimensionality reduction algorithms cannot be applied directly to imaging and clinical data. In this paper, we introduce a novel extension of Laplacian Eigenmaps that allow the computation of manifolds while combining imaging and clinical data. This method is a distance-based extension that suits better continuous clinical variables than the existing graph-based extension, which is suitable for clinical variables in finite discrete spaces. These methods were evaluated in terms of classification accuracy using 288 MR images and clinical data (ApoE genotypes, Aβ42 concentrations and mini-mental state exam (MMSE) cognitive scores) of patients enrolled in the Alzheimer's disease neuroimaging initiative (ADNI) study.