Miracle-dz/UniFAR-FTUs huggingface dataset:
A conceptual model for integrating strategic supply management into the supply chain

In this paper, a strategic supply management reference model for supplier integration in the supply chain is presented. The model developed is based on the comparative case study of two relevant industries – automotive and aircraft – and may be suitable for other industries. The survey and the subsequent analysis of these industries have confirmed the strategic importance of supplies and the need for an integrated approach of supply management, both in terms of internal processes and of external suppliers. Additionally, the study has proved that there is not a unique model for supply management, valid for all industries.
Improve Intrusion Detection Using Decision Tree with Sampling

Intrusion detection system used to discover illegitimate and unnecessary behavior at accessing or manipulating computer systems. The present paper aims to improve accuracy Rate of intrusion detection using decision tree algorithm. Intrusion detection systems aim to identify attacks with a high detection rate and a low Error rate. In this paper we have supervised learning with preprocessing step for intrusion detection. I using the stratified Weighted sampling techniques to generate the samples from original dataset. These sampled applied on the proposed algorithm. The accuracy of proposed model is compared with existing results in order to verify the validity and accuracy of the proposed model. The results showed that the proposed approach gives better and robust representation of data. The experiments and evaluations of the proposed intrusion detection system are performed with the KDD Cup 99 dataset. The experimental results clearly show that the proposed system achieved higher Accuracy and Low Error in identifying whether the records are normal or attack one. Index Term: Intrusion Detection, Decision Tree, KDD Cup 99 dataset, Stratified Weighted Sampling, Data preprocessing and ID3.
Red light spotters: images-driven sound and rhythm landscape

Red Light Spotters is a multi-contextual interactive project which aims to create an open and real-time musical environment sharing relations with and extending a natural ambient landscape. It encompass artistic creation process embedding image tracking, artificial intelligence with inductive original tempo tracking and beat prediction algorithms. We end up with emergent rhythmic processing for ambiant musical creation, interpreted and performed by sound artists.
Using Semantic Roles for Coreference Resolution

In this paper, we systematically explore the use of semantic roles in coreference resolution. Here, the semantic roles are automatically determined using a state-of-the-art SRL system, ASSERT, and integrated into the resolution by combining them with detailed pronoun categories. In particular, the anaphoricity of the pronoun "it" in "it" sentence pattern is determined using its semantic role and applied in a filter process. Experimental results on the ACE 2003 corpus show that our method can significantly outperform the baseline system by 3.1% in F-measure.
TNF-Induced Death Signaling Triggers Alveolar Epithelial Dysfunction in Acute Lung Injury

The ability of the alveolar epithelium to prevent and resolve pulmonary edema is a crucial determinant of morbidity and mortality in acute lung injury (ALI). TNF has been implicated in ALI pathogenesis, but the precise mechanisms remain undetermined. We evaluated the role of TNF signaling in pulmonary edema formation in a clinically relevant mouse model of ALI induced by acid aspiration and investigated the effects of TNF p55 receptor deletion, caspase-8 inhibition, and alveolar macrophage depletion on alveolar epithelial function. We found that TNF plays a central role in the development of pulmonary edema in ALI through activation of p55-mediated death signaling, rather than through previously well-characterized p55-mediated proinflammatory signaling. Acid aspiration produced pulmonary edema with significant alveolar epithelial dysfunction, as determined by alveolar fluid clearance (AFC) and intra-alveolar levels of the receptor for advanced glycation end-products. The impairment of AFC was strongly correlated with lung caspase-8 activation, which was localized to type 1 alveolar epithelial cells by flow cytometric analysis. p55-deficient mice displayed markedly attenuated injury, with improved AFC and reduced caspase-8 activity but no differences in downstream cytokine/chemokine production and neutrophil recruitment. Caspase-8 inhibition significantly improved AFC and oxygenation, whereas depletion of alveolar macrophages attenuated epithelial dysfunction with reduced TNF production and caspase-8 activity. These results provide in vivo evidence for a novel role for TNF p55 receptor–mediated caspase-8 signaling, without substantial apoptotic cell death, in triggering alveolar epithelial dysfunction and determining the early pathophysiology of ALI. Blockade of TNF-induced death signaling may provide an effective early-phase strategy for ALI.
Combining attributes and Fisher vectors for efficient image retrieval

Attributes were recently shown to give excellent results for category recognition. In this paper, we demonstrate their performance in the context of image retrieval. First, we show that retrieving images of particular objects based on attribute vectors gives results comparable to the state of the art. Second, we demonstrate that combining attribute and Fisher vectors improves performance for retrieval of particular objects as well as categories. Third, we implement an efficient coding technique for compressing the combined descriptor to very small codes. Experimental results on the Holidays dataset show that our approach significantly outperforms the state of the art, even for a very compact representation of 16 bytes per image. Retrieving category images is evaluated on the “web-queries” dataset. We show that attribute features combined with Fisher vectors improve the performance and that combined image features can supplement text features.
Tools for an extended object modelling environment

MDA promises a high software return on investment by producing software that evolves easily and is more portable and interoperable. However, MDA has a high price, namely, the need for a unified modelling infrastructure and a rich range of tools to create, maintain and transform models. Without these, model-driven development will not be able to deliver on its promises. This paper presents a common modelling infrastructure plus a collection of tools that, operating on it, can be used to create, edit and interchange models at any abstraction level. By using these tools and the modelling infrastructure, model-driven development is more likely to deliver.
Stem Cell Implants for Cancer Therapy: TRAIL-Expressing Mesenchymal Stem Cells Target Cancer Cells In Situ

Purpose Tumor-specific delivery of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), an apoptosis-inducing peptide, at effective doses remains challenging. Herein we demonstrate the utility of a scaffold-based delivery system for sustained therapeutic cell release that capitalizes on the tumor-homing properties of mesenchymal stem cells (MSCs) and their ability to express genetically-introduced therapeutic genes. Methods Implants were formed from porous, biocompatible silk scaffolds seeded with full length TRAIL-expressing MSCs (FLT-MSCs). under a doxycycline inducible promoter. In vitro studies with FLT-MSCs demonstrated TRAIL expression and antitumor effects on breast cancer cells. Next, FLT-MSCs were administered to mice using three administration routes (mammary fat pad co-injections, tail vein injections, and subcutaneous implantation on scaffolds). Results In vitro cell-specific bioluminescent imaging measured tumor cell specific growth in the presence of stromal cells and demonstrated FLT-MSC inhibition of breast cancer growth. FLT-MSC implants successfully decreased bone and lung metastasis, whereas liver metastasis decreased only with tail vein and co-injection administration routes. Average tumor burden was decreased when doxycycline was used to induce TRAIL expression for co-injection and scaffold groups, as compared to controls with no induced TRAIL expression. Conclusion This implant-based therapeutic delivery system is an effective and completely novel method of anticancer therapy and holds great potential for clinical applications.
iWIRES: an analyze-and-edit approach to shape manipulation

Man-made objects are largely dominated by a few typical features that carry special characteristics and engineered meanings. State-of-the-art deformation tools fall short at preserving such characteristic features and global structure. We introduce iWIRES, a novel approach based on the argument that man-made models can be distilled using a few special 1D wires and their mutual relations. We hypothesize that maintaining the properties of such a small number of wires allows preserving the defining characteristics of the entire object. We introduce an analyze-and-edit approach, where prior to editing, we perform a light-weight analysis of the input shape to extract a descriptive set of wires. Analyzing the individual and mutual properties of the wires, and augmenting them with geometric attributes makes them intelligent and ready to be manipulated. Editing the object by modifying the intelligent wires leads to a powerful editing framework that retains the original design intent and object characteristics. We show numerous results of manipulation of man-made shapes using our editing technique.
Dynamic Causal Modeling of the Response to Frequency Deviants

This article describes the use of dynamic causal modeling to test hypotheses about the genesis of evoked responses. Specifically, we consider the mismatch negativity (MMN), a well-characterized response to deviant sounds and one of the most widely studied evoked responses. There have been several mechanistic accounts of how the MMN might arise. It has been suggested that the MMN results from a comparison between sensory input and a memory trace of previous input, although others have argued that local adaptation, due to stimulus repetition, is sufficient to explain the MMN. Thus the precise mechanisms underlying the generation of the MMN remain unclear. This study tests some biologically plausible spatiotemporal dipole models that rest on changes in extrinsic top-down connections (that enable comparison) and intrinsic changes (that model adaptation). Dynamic causal modeling suggested that responses to deviants are best explained by changes in effective connectivity both within and between cortical sources in a hierarchical network of distributed sources. Our model comparison suggests that both adaptation and memory comparison operate in concert to produce the early (N1 enhancement) and late (MMN) parts of the response to frequency deviants. We consider these mechanisms in the light of predictive coding and hierarchical inference in the brain.
Concerning the Applicability of Geometric Models to Similarity Data : The Interrelationship Between Similarity and Spatial Density

In a recent article, Tversky questioned the application of geometric models to similarity data and proposed an alternative set-theoretic approach. He suggested that geometric models are inappropriate because the similarity data may violate the metric assumptions underlying such models. In addition, he demonstrated that the stimulus context and the nature of the experimental task can affect the similarity relations. The present article suggests that a geometric approach may be compatible with these effects if the traditional multidimensional scaling model is augmented by the assumption that spatial density in the configuration has an effect on the similarity measure. A distance-density model is outlined that assumes that similarity is a function of both interpoint distance and the spatial density of other stimulus points in the surrounding region of the metric space. The proposed relationship between similarity and spatial density is supported by empirical evidence. The distance-density model is shown to be able to account for violations of the metric axioms and certain context and task effects. A number of other issues are discussed with respect to geometric and set-theoretic models of similarity.
Parameters Associated with Elevated Systolic Blood Pressure Levels inGreek Military Pilots

Background: Elevated blood pressure levels (BP) continue to comprise one of the most important public health problems worldwide. Hypertension is among the most serious risk factors that relates to myocardial infarction, stroke and kidney failure. The purpose of the study was to examine factors that may contribute to presentation of hypertension in military jet pilots. Methods: This is a cross-sectional study of 300 jet pilots of Greek Air-Force. Several data were re- trieved from medical files. Participants filled in a questionnaire about demographics, dietary habits and lifestyle factors. Arterial BP was also measured. Spearman's r was used for correlations. Also, multivariate linear regression analysis was performed. Results: All pilots presented systolic and diastolic BP within normal range (mean: 117.5 mmHg and 77.0 mmHg, respectively). 200 pilots presented optimal BP, 80 normal BP and 20 had marginally normal BP. Most pilots were exercising up to two times weekly (66.9%), however, some reported they didn't exercise at all (12%) and mentioned exer- cising for a mean period of 12.0 (±7.6) years, mainly (62.1%) outdoors with aerobic (52.4%), anaerobic exercises (5.6%) or both (42%). Factors that influenced systolic BP levels were body mass index, flight hours, HDL values and exercise in- tensity. Conclusion: Pilots' medical monitoring seems adequate since there weren't any abnormal BP values. Emphasis should be placed on the continuation of programs already in place with further refinement according to specific needs. Smoking cessation interventions are needed as well as promotion of measures that can effectively lead to long standing life-style changes and dietary modifications.
Rhetorical based Music-Inspired Optimization Algorithm : Harmony-TABU for Document Retrieval using Relevance Feedback Approach

Harmony search (HS) is a meta-heuristic algorithm mimicking the improvisation process of musicians. This paper arranges the basic structure of the HS algorithm and customizes the algorithm for clustering optimization problems. We propose novel clustering algorithm based on Harmony Search (HS) optimization method that deals with document clustering. By modeling Retrieval as an optimization problem, first, we propose a pure HS based clustering algorithm, then we hybridize Harmony clustering with TABU to achieve better retrieval. Experimental results reveal that the proposed algorithms can find better results when compared to HS and TABU methods and the quality of results is comparable. Then Relevance feedback mechanisms such as term feedback, cluster feedback and term-cluster feedback are used to further improve the retrieved results. Finally we propose abstract generation system based on rhetorical structure for generating abstract for the retrieved documents.
A parallel algorithm for state assignment of finite state machines

Optimization of huge sequential circuits has become unmanageable in CAD of VLSI due to enormous time and memory requirements. In this paper, we report a parallel algorithm for the state assignment problem for finite state machines. Our algorithm has three significant contributions: It is an asynchronous parallel algorithm portable across different MIMD machines. Time and memory requirements reduce by a factor of P (the number of processors), enabling it to handle large problem sizes which the sequential algorithm fails to handle. The quality of the results for multiprocessor runs remains comparable to the sequential algorithm on which it is based.
An inducible RNA interference system for the functional dissection of mouse embryogenesis

Functional analysis of multiple genes is key to understanding gene regulatory networks controlling embryonic development. We have developed an integrated vector system for inducible gene silencing by shRNAmir-mediated RNA interference in mouse embryos, as a fast method for dissecting mammalian gene function. For validation of the vector system, we generated mutant phenotypes for Brachyury, Foxa2 and Noto, transcription factors which play pivotal roles in embryonic development. Using a series of Brachyury shRNAmir vectors of various strengths we generated hypomorphic and loss of function phenotypes allowing the identification of Brachyury target genes involved in trunk development. We also demonstrate temporal control of gene silencing, thus bypassing early embryonic lethality. Importantly, off-target effects of shRNAmir expression were not detectable. Taken together, the system allows the dissection of gene function at unprecedented detail and speed, and provides tight control of the genetic background minimizing intrinsic variation.
AEROSOL MODEL SELECTION AND UNCERTAINTY MODELLING BY RJMCMC TE CHNIQUE

We apply Bayesian model selection techniques on GO- MOS inverse problem to obtain information on which type of aerosol model fits best the data and to show how the uncertainty of the aerosol model can be included in the error estimates. We use reversible jump Markov chain Monte Carlo (RJMCMC), which is a general extension to the Metropolis-Hastings MCMC algorithm that can ex- plore parameter space of varying/unknown dimensional- ity. This makes it suitable for the model determination problem. This work is related to Envisat AO NORM. Advances in computer resources and algorithms have made it possible to consider increasingly complicated models for data. In geophysical sciences the estimation of unknowns in large models are usually handled using linearizations and approximations that can have effect on the uncertainty estimates of the retrievals. Such things as the effect of the modelling error or the effect of the dis- cretization on the uncertainty of the results require care- ful analysis and are best studied with methods that do rely on approximations. Bayesian inference provides an uni- fied and natural framework to consider uncertainty in es- timated values and the model uncertainty. In many cases, classical approximative estimation methods can be seen as special cases of Bayesian analyses. In Bayesian inference, the uncertainty of the estimated value is the primary target of the investigation and when- ever computationally possible, the results of the analysis is the full multi dimensional posterior probability den- sity of the unknowns. Bayesian methods allow the study of many kinds of uncertainties, including uncertainty in the model itself. Prior information can be pooled from different sources and incorporated statistically correctly, and the correlation structure of the unknowns can be fully explored. For applying Bayesian inference to modelling we have the MCMC methods, which are computer inten- sive method to simulate possible values of the unknowns from its posterior probability distribution. For account on applying Bayesian MCMC methods in geophysical appli- cations see for example (1).
Association between Gender, Process of Care Measures, and Outcomes in ACS in India: Results from the Detection and Management of Coronary Heart Disease (DEMAT) Registry

Background Studies from high-income countries have shown that women receive less aggressive diagnostics and treatment than men in acute coronary syndromes (ACS), though their short-term mortality does not appear to differ from men. Data on gender differences in ACS presentation, management, and outcomes are sparse in India. Methods and Results The Detection and Management of Coronary Heart Disease (DEMAT) Registry collected data from 1,565 suspected ACS patients (334 women; 1,231 men) from ten tertiary care centers throughout India between 2007–2008. We evaluated gender differences in presentation, in-hospital and discharge management, and 30-day death and major adverse cardiovascular event (MACE; death, re-hospitalization, and cardiac arrest) rates. Women were less likely to present with STEMI than men (38% vs. 55%, p<0.001). Overall inpatient diagnostics and treatment patterns were similar between men and women after adjustment for potential confounders. Optimal discharge management with aspirin, clopidogrel, beta-blockers, and statin therapy was lower for women than men, (58% vs. 65%, p = 0.03), but these differences were attenuated after adjustment (OR = 0.86 (0.62, 1.19)). Neither the outcome of 30-day mortality (OR = 1.40 (0.62, 3.16)) nor MACE (OR = 1.00 (0.67, 1.48)) differed significantly between men and women after adjustment. Conclusions ACS in-hospital management, discharge management, and 30-day outcomes did not significantly differ between genders in the DEMAT registry, though consistently higher treatment rates and lower event rates in men compared to women were seen. These findings underscore the importance of further investigation of gender differences in cardiovascular care in India.
Differential Expression of Distinct Members of Rho Family GTP-Binding Proteins during Neuronal Development: Identification ofRac1B, a New Neural-Specific Member of the Family

Previous studies on small GTP-binding proteins of the Rho family have revealed their involvement in the organization of cell actin cytoskeleton. The function of these GTPases during vertebrate development is not known. With the aim of understanding the possible role of these proteins during neuronal development, we have cloned and sequenced five members expressed in developing chick neural retinal cells. We have identified four chicken genes, cRhoA, cRhoB, cRhoC, and cRac1A, homologous to known human genes, and a novel Rac gene,cRac1B. Analysis of the distribution of four of the identified transcripts in chicken embryos shows for the first time high levels of expression of Rho family genes in the vertebrate developing nervous system, with distinct patterns of distribution for the different transcripts. In particular, cRhoA andcRac1A gene expression appeared ubiquitous in the whole embryo, and the cRhoB transcript was more prominent in populations of neurons actively extending neurites, whereas the newly identified cRac1B gene was homogeneously expressed only in the developing nervous system. Temporal analysis of the expression of the five genes suggests a correlation with the morphogenetic events occurring within the developing retina and the retinotectal pathway. Expression of an epitope-tagged cRac1B in retinal neurons showed a diffuse distribution of the protein in the cell body and along neurites. Taken as a whole, our results suggest important roles for ubiquitous and neural-specific members of the Rho family in the acquisition of the mature neuronal phenotype.
COHORT PROFILE Cohort Profile : Andhra Pradesh Children and Parents Study ( APCAPS )

Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK, National Institute of Nutrition, Indian Council for Medical Research, Hyderabad, India, Public Health Foundation of India, New Delhi, India, School of Social and Community Medicine, University of Bristol, Bristol, UK, Department of Anthropology, University of Delhi, Delhi, India, South Asia Network for Chronic Disease, Public Health Foundation of India, New Delhi, India, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia and Centre for Chronic Disease Control, New Delhi, India