Miracle-dz/UniFAR-FTUs huggingface dataset:
Fuzzy c-auto regression models

Fuzzy c-auto regression models (FCARM) combine clustering with time series prediction. Given a set of time series, FCARM finds clusters of time series with similar dynamics. More specifically, FCARM finds a partition matrix that quantifies to which degree each time series is associated with each prediction model, and the parameters of the (linear) auto regression models for each cluster. FCARM can thus be used for two different purposes: (i) the automatic identification of clusters of time series with similar dynamics and (ii) the forecast of a large number of time series using only a small number of generic forecast models, leading to higher data efficiency and lower model validation and maintenance effort. We illustrate the application of FCARM to sales forecasts for products that can be clustered into groups with similar sales dynamics.
Automated Crash Notification Algorithms: Evaluation of In-Vehicle Principal Direction of Force (PDOF) Estimation Algorithms

Automated Crash Notification (ACN) algorithms utilize telemetric data from vehicles involved in collisions to notify the appropriate emergency services with the aim to elicit the appropriate medical response. One vital piece of telemetric data is the Principal Direction of Force (PDOF) of the collision, which can be determined from data stored in the Event Data Recorder (EDR). This study examined PDOF estimation algorithms using EDRs recovered from staged crash tests and real-world collisions. In crash tests, PDOF estimated by the measurements from the EDR were comparable to laboratory-grade instrumentation. In 4 side impact crash tests, the average PDOF measured by crash test instrumentation and computed from the EDR differed by 7% on average. In some proposed ACN systems, real-world collisions, PDOF is used as a surrogate for damage side of the crash (i.e. front, side, rear) which may not be appropriate in many crashes. In 91 EDRs recovered from real-world collisions, oblique PDOF estimates near 45° could be interpreted as either frontal and side damage; 18 cases of 91 would have been misclassified as damage side using PDOF as a surrogate, which would have resulted in an incorrect assessment of injury risk. For example, using a previously developed ACN algorithm that predicts risk of serious injury in a crash, misclassifying the damage side as a frontal collision instead of a side collision would result in a risk prediction of 9% instead of 38%.
Bradyrhizobium japonicum senses iron through the status of haem to regulate iron homeostasis and metabolism

The Irr protein from the bacterium Bradyrhizobium japonicum is expressed under iron limitation to mediate iron control of haem biosynthesis. The regulatory input to Irr is the status of haem and its precursors iron and protoporphyrin at the site of haem synthesis. Here, we show that Irr controls the expression of iron transport genes and many other iron‐regulated genes not directly involved in haem synthesis. Irr is both a positive and negative effector of gene expression, and in at least some cases the control is direct. Loss of normal iron responsiveness of those genes in an irr mutant, as well as a lower total cellular iron content, suggests that Irr is required for the correct perception of the cellular iron status. Degradation of Irr in iron replete cells requires haem. Accordingly, control of Irr‐regulated genes by iron was aberrant in a haem‐defective strain, and iron replete mutant cells behave as if they are iron‐limited. In addition, the haem mutant had an abnormally high cellular iron content. The findings indicate that B. japonicum senses iron via the status of haem biosynthesis in an Irr‐dependent manner to regulate iron homeostasis and metabolism.
Knowledge and periconceptional use of folic acid for the prevention of neural tube defects in ethnic communities in the United Kingdom: Systematic review and meta-analysis

BACKGROUND: It is widely accepted that periconceptional supplementation with folic acid can prevent a significant proportion of neural tube defects (NTDs). The present study evaluated how folic acid knowledge and periconceptional use for NTD prevention varies by ethnicity in the United Kingdom (U.K.). METHODS: A literature search was conducted to identify studies that included assessment of folic acid knowledge or use in U.K. women of different ethnicities. Only research and referenced sources published after 1991, the year of the landmark Medical Research Council’s Vitamin Study, were included. A meta-analysis was performed of studies that assessed preconceptional folic acid use in Caucasians and non-Caucasians. RESULTS: Five studies met the inclusion criteria for assessment of knowledge and/or use of folic acid supplements in U.K. women including non-Caucasians. The available evidence indicates that South Asians specifically have less knowledge and lower periconceptional use of folic acid than Caucasians; one study found that West Indian and African women also had lower folic acid uptake. A synthesis of results from three of the studies, in a meta-analysis, shows that Caucasians are almost three times more likely to take folic acid before conception than non-Caucasians. CONCLUSION: From the limited evidence available, U.K. women of non-Caucasian ethnicity appear to have less knowledge and a lower uptake of folic acid supplementation than Caucasians during the periconceptional period. Implementing targeted, innovative education campaigns together with a mandatory fortification policy, including the fortification of ethnic minority foods, will be required for maximum prevention of folic acid–preventable NTDs across different ethnic groups. Birth Defects Research (Part A) 97:444–451, 2013. © 2013 Wiley Periodicals, Inc.
Proteomic analysis reveals GIT1 as a novel mTOR complex component critical for mediating astrocyte survival

Smithson and Gutmann reveal that mTOR complex molecular composition varies in different somatic tissues. In astrocytes and neural stem cells, they identified G-protein-coupled receptor kinase interactor protein 1 (GIT1) as a novel mTOR-binding protein, creating a unique mTOR complex lacking Raptor and Rictor. GIT1 binding to mTOR was essential for mTOR-mediated astrocyte survival.
The link between intergenerational earnings mobility and inequality of opportunity : Application to France Arnaud LEFRANC

We analyze equality of opportunity for earnings acquisition in France between 1973 and 1993 conditional on the father earnings in the earnings distribution using two waves of the French data set FQP. First, using stochastic dominance tools, we find that inequality of opportunity has remained stable when conditioning on the earnings level of the father, while it has diminished when conditioning on his rank in the earnings distribution. The former result is explained by the stable intergenerational earnings elasticity. The latter by the decreasing wage inequality in the previous generation. Then, we decompose the evolution of inequality of opportunity using the mean logarithmic deviation and the results of regressions of descendants’earnings on their parents earnings. It is shown that the main reason beneath the reduction of inequality of opportunity lies in the decrease of earnings inequality taking place in the eighties. JEL Codes: D1, D3, J3
Unexplained Early Neurological Deterioration After Intravenous Thrombolysis: Incidence, Predictors, and Associated Factors

Background and Purpose— Early neurological deterioration (END) after anterior circulation stroke is a serious clinical event strongly associated with poor outcome. Regarding specifically END occurring within 24 hours of intravenous recombinant tissue-type plasminogen activator, apart from definite causes such as symptomatic intracranial hemorrhage and malignant edema whose incidence, predictors, and clinical management are well established, little is known about END without clear mechanism (ENDunexplained). Methods— We analyzed 309 consecutive patients thrombolysed intravenously ⩽4.5 hours from onset of anterior circulation stroke. ENDunexplained was defined as a ≥4-point deterioration on 24-hour National Institutes of Health Stroke Scale, without definite mechanism on concomitant imaging. ENDunexplained and no-END patients were compared for pretreatment clinical and imaging (including magnetic resonance diffusion and diffusion/perfusion mismatch volumes) data and 24-hour post-treatment clinical (including blood pressure and glycemic changes) and imaging (24-hour recanalization) data, using univariate logistic regression. Exploratory multivariate analysis was also performed after variable reduction, with bootstrap analysis for internal validation. Results— Among 33 END patients, 23 (7% of whole sample) had ENDunexplained. ENDunexplained was associated with poor 3-month outcome (P<0.01). In univariate analysis, admission predictors of ENDunexplained included no prior use of antiplatelets (P=0.02), lower National Institutes of Health Stroke Scale score (P<0.01), higher glycemia (P=0.03), larger mismatch volume (P=0.03), and proximal occlusion (P=0.01), with consistent results from the multivariate analysis. Among factors recorded during the first 24 hours, only no recanalization was associated with ENDunexplained in multivariate analysis (P=0.02). Conclusions— ENDunexplained affected 7% of patients and accounted for most cases of END. Several predictors and associated factors were identified, with important implications regarding underlying mechanisms and potential prevention of this ominous event.
New Algorithm for Building Ontology from Existing Rules: A Case Study

From the fact that ontologies can help in making sense of huge amount of content, this paper proposes a case study for building ontology via set of rules generated by rule-based learning system. The proposed algorithm utilises the extracted and representative rules generated from the original dataset in developing ontology elements. The proposed algorithm is applied to a well known dataset in the breast cancer domain. The results are encouraging and support the potential role that this approach can play in providing a suitable starting point for ontology development.
Air Quality, Climate, and Policy: A Case Study of Ozone Pollution in Tucson, Arizona

This article addresses the need to better understand the complex interactions between climate, human activities, vegetation responses, and surface ozone so that more informed air-quality policy recommendations can be made. The impacts of intraseasonal climate variations on ozone levels in Tucson, Arizona from April through September of 1995 to 1998 are determined by relating variations in ozone levels to variations in atmospheric conditions and emissions of ozone's precursor chemicals, volatile organic compounds (VOCs) and nitrogen oxides (NOx), and by determining month-specific atmospheric conditions that are conducive to elevated ozone levels. Results show that the transport of ozone and its precursor chemicals within the Tucson area causes the highest ozone levels to be measured at a downwind monitor. The highest ozone levels occur in August, due in part to the presence of the North American monsoon. Atmospheric conditions conducive to elevated ozone concentrations differ substantially between the arid foresummer (May and June) and the core monsoon months ( July and August). Transport of pollution from Phoenix may have a substantial impact on elevated ozone concentrations during April, May, and June, while El Paso/Ciudad Juarez –derived pollution may contribute significantly to elevated ozone concentrations in August and September. Two broad policy implications derive from this work. Regional pollutant transport, both within the U.S. and between the U.S. and Mexico, is a potential issue that needs to be examined more intensively in future studies. In addition, spatiotemporal variations in sensitivities of ozone production require the adoption of both NOx and VOC control measures to reduce ozone levels in the Tucson area.
What do you mean? Arguing for meaning

Building ontologies has been proven to be a complex issue in part because a community must commit to the conceptualization that the ontology represents. The community members must align their concepts and co-create. Arguing about a useful conceptualization is therefore an essential part of the process of designing an ontology. Logicians have developed formal argumentation theories, but have not combined formal argumentation with conceptualization. Rather, while conceptualization should play an important role in any argumentation theoretical approach, argumentation theories focus on arguments based on propositional logic and argument structures, which are not sufficient for arguing about domain conceptualization, which requires a more fine-grained logical analysis. In this paper we will explain why conceptualization plays an important role within argumentation and why argumentation support tools, especially if they use Natural Language Processing (NLP), can help in creating domain ontologies.
A method for 3D reconstruction of piecewise planar objects from single panoramic images

We present an approach for 3D reconstruction of objects from a single panoramic image. Obviously, constraints on the 3D structure are needed to perform this task. Our approach is based on user-provided coplanarity, perpendicularity and parallelism constraints. The method is described in detail for the case of a parabolic mirror-based omnidirectional sensor and results are provided.
Automating Platform Selection for MapReduce Processing in the Cloud

Cloud computing enables a user to quickly provision any desirable size Hadoop cluster and then pay for the time these resources were used. With the same budget, a user can rent a larger amount of resources and process its scale-out application in a shorter time, or rent a smaller size cluster but pay a for longer processing time. Moreover, there is a variety of different types of VM instances in the Cloud (e.g., small, medium, or large EC2 instances). The capacity differences of the offered VMs are reflected in VM's pricing. Therefore, again for the same price a user can get a variety of "similar capacity" Hadoop clusters based on different VM instance types. We observe that performance of MapReduce applications may vary significantly on different platforms. This makes a selection of the best cost/performance platform for a given workload a non-trivial problem, especially when it contains multiple jobs with different platform preferences. In this work1, we design a framework for solving the following problem: given a completion time target for a set of MapReduce jobs, determine a homogeneous or heterogeneous Hadoop cluster configuration (i.e., the number, types of VMs, and the job schedule) for processing these jobs within a given deadline while minimizing the rented infrastructure cost. We generalize the proposed framework to take into account possible node failures and degraded performance goals. Our evaluation study with Amazon EC2 platform reveals that for different workload mixes, an optimized platform choice may result in 45-68% cost savings for achieving the same performance objectives when using different (but seemingly equivalent) choices. Moreover, depending on a workload the heterogeneous solution may outperform the homogeneous cluster solution by 26-42%. We analyze and discuss possible causes for observed performance differences of MapReduce processing on the Amazon EC2 platforms.
Asymmetric Stereoscopic Video Encoding Algorithm Based on Joint Compensation Prediction

Stereoscopic video bring people more actual 3D scene feeling, while it also makes bitrates increase largely. To reduce bitrates is highly emphasized in stereoscopic video encoding. In the current stereo video coding scheme, the two views channels have the same spatial resolution. In order to raise the coding efficiency, an asymmetric stereoscopic video encoding algorithm of based on joint Compensation Prediction is proposed in this paper, using different spatial resolution of the two view channels. And in encoding the lower images phase, a low complexity prediction mode is proposed. Experimental result indicates that this proposed algorithm is more efficient than JMVM algorithm while the coding complexity is also dropped
Textural-Spectral Feature-Based Species Classification of Mangroves in Mai Po Nature Reserve from Worldview-3 Imagery

The identification of species within an ecosystem plays a key role in formulating an inventory for use in the development of conservation management plans. The classification of mangrove species typically involves intensive field surveys, whereas remote sensing techniques represent a cost-efficient means of mapping and monitoring mangrove forests at large scales. However, the coarse spectral resolution of remote sensing technology has up until recently restricted the ability to identify individual species. The more recent development of very high-resolution spatial optical remote sensing sensors and techniques has thus provided new opportunities for the accurate mapping of species within mangrove forests over large areas. When dealing with the complex problems associated with discriminating among species, classifier performance could be enhanced through the adoption of more intrinsic features; such as textural and differential spectral features. This study explored the effectiveness of textural and differential spectral features in mapping mangrove inter-species obtained from WorldView-3 high-spatial-resolution imagery for mangrove species in Hong Kong. Due to the different arrangement of leaves, the branch density, and the average height and size of plants, we found that the differential spectral features could aid in reducing inner-species variability and increasing intra-species separation. Using a combination of textural and differential spectral features thus represents a promising tool for discriminating among mangrove species. Experimental results suggest that combining these features can greatly improve mapping accuracy, thereby providing more reliable mapping results.
On empirical tradeoffs in large scale hierarchical classification

While multi-class categorization of documents has been of research interest for over a decade, relatively fewer approaches have been proposed for large scale taxonomies in which the number of classes range from hundreds of thousand as in Directory Mozilla to over a million in Wikipedia. As a result of ever increasing number of text documents and images from various sources, there is an immense need for automatic classification of documents in such large hierarchies. In this paper, we analyze the tradeoffs between the important characteristics of different classifiers employed in the top down fashion. The properties for relative comparison of these classifiers include, (i) accuracy on test instance, (ii) training time (iii) size of the model and (iv) test time required for prediction. Our analysis is motivated by the well known error bounds from learning theory, which is also further reinforced by the empirical observations on the publicly available data from the Large Scale Hierarchical Text Classification Challenge. We show that by exploiting the data heterogenity across the large scale hierarchies, one can build an overall classification system which is approximately 4 times faster for prediction, 3 times faster to train, while sacrificing only 1% point in accuracy.
Both cultured and freshly isolated adipose tissue-derived stem cells enhance cardiac function after acute myocardial infarction.

AIMS We assessed whether freshly isolated human adipose tissue-derived cells (fhADCs) or cultured human adipose tissue-derived stem cells (hASCs) have beneficial effects on cardiac function after myocardial infarction (MI), whether the injected cells can survive long term, and whether their effects result from direct differentiation or paracrine mechanisms. METHODS AND RESULTS Myocardial infarction was experimentally induced in severe combined immunodeficient mice, and either fhADCs, cultured hASCs, or phosphate-buffered saline was injected into the peri-infarct region. Myocardial function improved significantly in mice treated with hASCs or fhADCs 4 weeks after MI. Immunofluorescence revealed that grafted hASCs and fhADCs underwent cardiomyogenic differentiation pathway, as indicated by expression of connexin 43 and troponin I in a fusion-independent manner. Some of the injected cells integrated with host cardiomyocytes through connexin 43, and others were incorporated into newly formed vessels. Human adipose tissue-derived stem cells survived in injured hearts up to 4 months, as detected by luciferase-based bioluminescence imaging. Vascular density was significantly increased, and fewer apoptotic cells were present in the peri-infarct region of cell-injected mice. CONCLUSION This is the first study to systematically compare the effects of fhADCs and hASCs on myocardial regeneration. Both cell types engraft into infarcted myocardium, survive, and improve myocardial function, suggesting that fhADCs, like hASCs, are a promising alternative cell source for myocardial repair after MI.
Dysfunctional Attitudes and Cognitive Schemas in Bipolar Manic and Unipolar Depressed Outpatients: Implications for Cognitively Based Psychotherapeutics

Dysfunctional thought patterns are presumed to underlie cognitive biases in mood disorder patients. However, few studies have compared dysfunctional thought patterns in bipolar manic and unipolar depressed patients. Cognitive schemas and dysfunctional attitudes were evaluated using the cognitive checklist for mania and Dysfunctional Attitudes Scale (DAS) in 34 bipolar manic, 35 unipolar depressed, and 29 nonpsychiatric control subjects. Unipolar depressed subjects had significantly higher total DAS scores and subfactor scores as compared with nonpsychiatric controls, whereas bipolar patients had intermediate scores between both groups. Significant correlations emerged between cognitive checklist for mania total and subcomponent scores and the DAS (total, performance subfactor, and approval subfactor scales) for the bipolar, but not the unipolar or nonpsychiatric control groups. Core beliefs among bipolar patients appear negativistic during manic phases, potentially reflecting an overcompensation for depression. The findings support clinical approaches targeting depressive cognitions regardless of the presence of manic symptoms.
Mining HTML5 code: a view to how humans use emerging web standards

Currently, Web quality research term mainly refers to the quality of content of web pages. There are some research efforts on Javascript code and CSS code quality metrics, but none for measuring metrics of HTML5 code and estimating its quality and quantity. Towards this direction the aim of this work is to determine what characteristics of HTML5 code can be measured, to specify concrete metrics to measure those characteristics, to implement a tool for calculating them for a small set of representative web pages and finally evaluate experimentally the results based on statistical analysis and data mining techniques.