Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes in microflora in dental plaque from cancer patients undergoing chemotherapy and the relationship of these changes with mucositis: A pilot study

Background To assess changes in oral micro flora in dental plaque from cancer patients within 7 days of the first course of chemotherapy, and the relationship of the changes with mucositis. Material and Methods Thirty cancer patients, divided into a test group undergoing chemotherapy and a control group no undergoing chemotherapy, were enrolled in this pilot study. Oral micro flora were cultured from three samples of dental plaque at t0 (before chemotherapy), t1 (1 day after chemotherapy) and t2 (7 days after chemotherapy). Single and crossed descriptive analyses were used to establish prevalence, and the χ2 test was used to establish the statistical significance of the differences observed in distributions (significance level: P<0.05. Results In most patients (57%), oral micro flora consisted mainly of Gram-positive cocci, while the remaining 43% of the bacterial flora also had periodontal-pathogenic species. No Porphyromonas gingivalis appeared in the test group. Actinobacillus was the least frequently found bacterium among periodontal pathogens in the test group, while Fusobacterium nucleatum was the most frequently found. No significant differences were found in quantitative bacterial changes between t0, t1 and t2 in either the test or control groups, or between the two groups. According to World Health Organization scores, oral mucositis developed in 10 patients (66.6%) in the test group. Conclusions The results of this pilot study indicate that there were no changes in microflora in dental plaque in cancer patients within 7 days of the first course of chemotherapy. No correlations between oral mucositis and specific microorganisms were assessed. Key words: Oral microflora, dental plaque, cancer patients, chemotherapy.
An experience of applying architecture-based approach to build a component-based requirements management toolset

It has been advocated and shown that software architecture can be an effective tool to reduce development cost and increase the quality of the end product. Rigorous efforts invested in architectural design and evaluation activities results in increased comprehension of the system, better communication among stakeholders, effective project management, controlled evolution, and rapid development. This paper presents our experience of applying an architecture-based approach to develop an integrated requirements management toolset, PARSNIP (PARsed Natural language Input Processor). We also report the integration issues peculiar to our project, our approach to address them and rational for our architectural and integration decisions.
COMP Superscalar: Bringing GRID Superscalar and GCM Together

This paper presents the design, implementation and evaluation of COMP Superscalar, a new and componentised version of the GRID superscalar framework that enables the easy development of Grid-unaware applications. By means of a simple programming model, COMP Superscalar keeps the Grid as transparent as possible to the programmer. Moreover, the performance of the applications is optimized by exploiting their inherent concurrency when executing them on the Grid. The runtime of COMP Superscalar has been designed to follow the Grid Component Model (GCM) and is therefore formed by several components, each one encapsulating a given functionality identified in GRID superscalar.
SMALL POINTS ON SUBVARIETIES OF A TORUS

Let V be a subvariety of a torus deﬁned over the algebraic numbers. We give a qualitative and quantitative description of the set of points of V of height bounded by invariants associated to any variety containing V . Especially, we determine whether such a set is or is not dense in V . We then prove that these sets can always be written as the intersection of V with a ﬁnite union of translates of tori of which we control the sum of the degrees. As a consequence, we prove a conjecture by the ﬁrst author and David up to a logarithmic factor.
Optimizing memory bandwidth in OpenVX graph execution on embedded many-core accelerators

Computer vision and computational photography are hot applications areas for mobile and embedded computing platforms. As a consequence, many-core accelerators are being developed to efficiently execute highly-parallel image processing kernels. However, power and cost constraints impose hard limits on the main memory bandwidth available, and push for software optimizations which minimize the usage of large frame buffers to store the intermediate results of multi-kernel applications. In this work we propose a set of techniques, mainly based on graph analysis and image tiling, targeted to accelerate the execution on cluster-based many-core accelerators of image processing applications expressed as standard OpenVX graphs. We have developed a run-time framework which implements these techniques using a front-end compliant to the OpenVX standard, and based on an OpenCL extension that enables more explicit control and efficient reuse of on-chip memory and greatly reduces the recourse to off-chip memory for storing intermediate results. Experiments performed on the STHORM many-core accelerator prototype demonstrate that our approach leads to massive reductions of main memory related stall time even when the main memory bandwidth available to the accelerator is severely constrained.
A Dynamic Theory of Expertise and Occupational Boundaries in New Technology Implementation: Building on Barley's Study of CT Scanning

In this paper, we develop a theory to explain why the implementation of new technologies often disrupts occupational roles in ways that delay the expected benefits. To explore these disruptions, we construct a dynamic model grounded in ethnographic data from Barley's widely cited (1986) study of computed tomography (CT) as implemented in two hospitals. Using modeling, we formalize the recursive relationship between the activity of CT scanning and the types and accumulations of knowledge used by doctors and technologists. We find that a balance of expertise across occupational boundaries in operating the technology creates a pattern in which the benefits of the new technology are likely to be realized most rapidly. By operationalizing the dynamics between knowledge and social action, we specify more clearly the recursive relationship between structuring and structure. *
Universität Des Saarlandes Fachrichtung 6.1 – Mathematik on the Equivalence of Soft Wavelet Shrinkage, Total Variation Diffusion, Total Variation Regularization, and Sides on the Equivalence of Soft Wavelet Shrinkage, Total Variation Diffusion, Total Variation Regularization, and Sides

This preprint is a revised version of Preprint 26 (February 2003) from Series DFG–SPP 1114, issued by DFG-Schwerpunktprogramm 1114 " Mathematical methods for time series analysis and image processing " at the University of Bremen. Abstract Soft wavelet shrinkage, total variation (TV) diffusion, total variation regularization, and a dynamical system called SIDEs are four useful techniques for discontinuity preserving denoising of signals and images. In this paper we investigate under which circumstances these methods are equivalent in the 1-D case. First we prove that Haar wavelet shrinkage on a single scale is equivalent to a single step of space-discrete TV diffusion or regularization of two-pixel pairs. In the translationally invariant case we show that applying cycle spinning to Haar wavelet shrinkage on a single scale can be regarded as an absolutely stable explicit discretization of TV diffusion. We prove that space-discrete TV diffusion and TV regularization are identical, and that they are also equivalent to the SIDEs system when a specific force function is chosen. Afterwards we show that wavelet shrinkage on multiple scales can be regarded as a single step diffusion filtering or regularization of the Laplacian pyramid of the signal. We analyse possibilities to avoid Gibbs-like artifacts for multiscale Haar wavelet shrinkage by scaling the thesholds. Finally we present experiments where hybrid methods are designed that combine the advantages of wavelets and PDE / variational approaches. These methods are based on iterated shift-invariant wavelet shrinkage at multiple scales with scaled thresholds.
The A B Cs of Evidence-Based Practice for Teachers

teacher at Wyatt Elementary School. She completed 2 weeks of intensive professional development just before school started, learning about the materials to be used for the school’s new reading program for kindergarten and first grade. She then spent 2 days of professional development on writing strategies and another on inclusion strategies. While setting up her new classroom, she also found a bookshelf full of workbooks, reading materials, manipulatives, computer programs, and textbooks. Almost all of the materials she received in training and the programs on the shelf in her room are prominently labeled as “researchbased. ” Celeste is not sure which materials to use with her new students. Gerard has been teaching special education for 8 years at Jefferson High School and is in his second year as department chair. At the beginning of the school year, Gerard’s administration informed the faculty that using evidence-based practices was a priority, and teachers would need to develop a way to document this in lesson plans. As the special education department chair, it will be Gerard’s job to ensure that the practices he and his colleagues are using are “evidence-based. ” Gerard has always believed the methods he uses with his students are effective, as most of his students perform well on classroom tests and end-of-year standardized exams. Gerard is unsure how he will find research that supports the practices and programs he is using, let alone assist his colleagues in the special education department to do this as well.
Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors

Query optimizers rely on accurate estimations of the sizes of intermediate results. Wrong size estimations can lead to overly expensive execution plans. We first define the q-error to measure deviations of size estimates from actual sizes. The q-error enables the derivation of two important results: (1) We provide bounds such that if the q-error is smaller than this bound, the query optimizer constructs an optimal plan. (2) If the q-error is bounded by a number q, we show that the cost of the produced plan is at most a factor of q4 worse than the optimal plan. Motivated by these findings, we next show how to find the best approximation under the q-error. These techniques can then be used to build synopsis for size estimates. Finally, we give some experimental results where we apply the developed techniques.
Prophage Integrase Typing Is a Useful Indicator of Genomic Diversity in Salmonella enterica

Salmonella enterica is a bacterial species that is a major cause of illness in humans and food-producing animals. S. enterica exhibits considerable inter-serovar diversity, as evidenced by the large number of host adapted serovars that have been identified. The development of methods to assess genome diversity in S. enterica will help to further define the limits of diversity in this foodborne pathogen. Thus, we evaluated a PCR assay, which targets prophage integrase genes, as a rapid method to investigate S. enterica genome diversity. To evaluate the PCR prophage integrase assay, 49 isolates of S. enterica were selected, including 19 clinical isolates from clonal serovars (Enteritidis and Heidelberg) that commonly cause human illness, and 30 isolates from food-associated Salmonella serovars that rarely cause human illness. The number of integrase genes identified by the PCR assay was compared to the number of integrase genes within intact prophages identified by whole genome sequencing and phage finding program PHASTER. The PCR assay identified a total of 147 prophage integrase genes within the 49 S. enterica genomes (79 integrase genes in the food-associated Salmonella isolates, 50 integrase genes in S. Enteritidis, and 18 integrase genes in S. Heidelberg). In comparison, whole genome sequencing and PHASTER identified a total of 75 prophage integrase genes within 102 intact prophages in the 49 S. enterica genomes (44 integrase genes in the food-associated Salmonella isolates, 21 integrase genes in S. Enteritidis, and 9 integrase genes in S. Heidelberg). Collectively, both the PCR assay and PHASTER identified the presence of a large diversity of prophage integrase genes in the food-associated isolates compared to the clinical isolates, thus indicating a high degree of diversity in the food-associated isolates, and confirming the clonal nature of S. Enteritidis and S. Heidelberg. Moreover, PHASTER revealed a diversity of 29 different types of prophages and 23 different integrase genes within the food-associated isolates, but only identified four different phages and integrase genes within clonal isolates of S. Enteritidis and S. Heidelberg. These results demonstrate the potential usefulness of PCR based detection of prophage integrase genes as a rapid indicator of genome diversity in S. enterica.
Action Recognition with Shot Boundary Detection and Decoded iDT Features

We report our results on the THUMOS Challenge 2014 Ac- tion Recognition Task. Given an untrimmed video as input, our method recognizes its major action category in four main steps: (1) detect shot boundaries within the video; (2) decode pre-computed indices of iDTFs within each shot; (3) encode the recovered iDTFs into shot-wise fisher vectors and compute the shot-to-category classification scores; (4) sum- marize shot-wise classification scores into a video-wise classification score. We report results on four different strategies that are used for summa- rizing the video-wise classification scores.
NMDA receptor binding in focal epilepsies

Objective To demonstrate altered N-methyl-d-aspartate (NMDA) receptor availability in patients with focal epilepsies using positron emission tomography (PET) and [18F]GE-179, a ligand that selectively binds to the open NMDA receptor ion channel, which is thought to be overactive in epilepsy. Methods Eleven patients (median age 33 years, 6 males) with known frequent interictal epileptiform discharges had an [18F]GE-179 PET scan, in a cross-sectional study. MRI showed a focal lesion but discordant EEG changes in two, was non-localising with multifocal EEG abnormalities in two, and was normal in the remaining seven patients who all had multifocal EEG changes. Individual patient [18F]GE-179 volume-of-distribution (VT) images were compared between individual patients and a group of 10 healthy controls (47 years, 7 males) using Statistical Parametric Mapping. Results Individual analyses revealed a single cluster of focal VT increase in four patients; one with a single and one with multifocal MRI lesions, and two with normal MRIs. Post hoc analysis revealed that, relative to controls, patients not taking antidepressants had globally increased [18F]GE-179 VT (+28%; p<0.002), and the three patients taking an antidepressant drug had globally reduced [18F]GE-179 VT (−29%; p<0.002). There were no focal abnormalities common to the epilepsy group. Conclusions In patients with focal epilepsies, we detected primarily global increases of [18F]GE-179 VT consistent with increased NMDA channel activation, but reduced availability in those taking antidepressant drugs, consistent with a possible mode of action of this class of drugs. [18F]GE-179 PET showed focal accentuations of NMDA binding in 4 out of 11 patients, with difficult to localise and treat focal epilepsy.
Local Exome Sequences Facilitate Imputation of Less Common Variants and Increase Power of Genome Wide Association Studies

The analysis of less common variants in genome-wide association studies promises to elucidate complex trait genetics but is hampered by low power to reliably detect association. We show that addition of population-specific exome sequence data to global reference data allows more accurate imputation, particularly of less common SNPs (minor allele frequency 1–10%) in two very different European populations. The imputation improvement corresponds to an increase in effective sample size of 28–38%, for SNPs with a minor allele frequency in the range 1–3%.
Mining the Largest Dense N-vertexlet in a Fuzzy Scale-free Graph

The topology of many real-life networks, e.g., the world wide web, social networks, ecological networks, genetic networks, comply with scale-free models. In scale-free models, the vertices of the underlying graph follow a power-law degree distribution. Observably, the graphs corresponding to most of the real-life networks are fuzzy in nature. An important problem of knowledge engineering that has evolved in various real-life networks is to identify the largest group of similar vertices in such networks that are functionally associated. Here, the problem of finding the largest group or association of vertices that are dense (denoted as dense N-vertexlet) in a fuzzy scale-free graph is addressed. Density quantifies the degree of similarity within a group of vertices in a graph. The density of an N-vertexlet is defined in a novel way that ensures significant participation of all the vertices in the N-vertexlet. First, it is established that the problem is NP-complete in nature. An upper bound on the size of the largest dense N-vertexlet in a fuzzy graph, with respect to certain density threshold value, is then derived. Finally, an O(n2 log n), n being the number of vertices in the graph, heuristic graph mining algorithm that produces an approximate solution for the problem is presented. The efficacy of the method is shown by applying it on some artificial and biological networks.
Social discounting involves modulation of neural value signals by temporoparietal junction

Significance People often consider the well-being of others. However, they are more likely to be generous toward individuals they feel close to than to those they only meet sporadically. Using neuroimaging tools, we show that the decline in generosity across social distance is realized by the interplay of two brain structures—the ventromedial prefrontal cortex coding the relative appeal of a selfish or a generous option, and the temporoparietal junction modulating appeal signals of the generous outcome, depending on social distance between participant and beneficiary. Based on these findings, we developed a biologically plausible model explaining social discounting in particular, and prosocial behavior in general. Our study opens up new avenues to understand and tackle frictions arising in social networks. Most people are generous, but not toward everyone alike: generosity usually declines with social distance between individuals, a phenomenon called social discounting. Despite the pervasiveness of social discounting, social distance between actors has been surprisingly neglected in economic theory and neuroscientific research. We used functional magnetic resonance imaging (fMRI) to study the neural basis of this process to understand the neural underpinnings of social decision making. Participants chose between selfish and generous alternatives, yielding either a large reward for the participant alone, or smaller rewards for the participant and another individual at a particular social distance. We found that generous choices engaged the temporoparietal junction (TPJ). In particular, the TPJ activity was scaled to the social-distance–dependent conflict between selfish and generous motives during prosocial choice, consistent with ideas that the TPJ promotes generosity by facilitating overcoming egoism bias. Based on functional coupling data, we propose and provide evidence for a biologically plausible neural model according to which the TPJ supports social discounting by modulating basic neural value signals in the ventromedial prefrontal cortex to incorporate social-distance–dependent other-regarding preferences into an otherwise exclusively own-reward value representation.
Knowledge extraction from on-line open source bug tracking systems to predict bug-fixing time

1For large scale software systems, many bugs can be reported over a long period of time. For software quality assurance and software project management, it is important to assign adequate resources to resolve the reported bug. An important issue concerning assignment is the ability to predict bug-fixing time because it can help a project team better estimate software maintenance efforts and better manage software projects. In this paper, we propose a model that can predict the bug-fixing time using the text information extracted from Bugzilla, an on-line open source Bug Tracking System (BTS). We perform an empirical investigation for the bugs of Novell, OpenOffice and LiveCode, three open source projects using Bugzilla. Proposed model is based on historical data stored on the BTS. For each bug-report we build a classification model to predict the time of its resolution, as slow or fast. In this work we used, as classifier, Support Vector Machine (SVM) but different classifier can be easily used. Our model, differently from existing work reported in the literature, selects all and only the attributes useful for prediction and filters appropriately attributes for the test-set. Experimental results show the model is effective. In the future, we will use and compare other different classification method to select the best one for a specific data-set.
An efficient method for identifying the initial position of a PMSM with an incremental encoder

This letter describes an efficient method for identifying the initial position of a permanent magnet synchronous motor (PMSM) with an incremental encoder, even when a brake and/or a constant load torque is being applied. The authors' method is more efficient in the respects of accuracy and reliability than the conventional method using commutation signals.
Codes defined on graphs

Low-density parity-check codes, turbo codes, and indeed most practically decodable capacity-approaching error correcting codes can all be understood as codes defined on graphs. Graphs not only describe the codes, but, more important, they structure the operation of the sum-product decoding algorithm (or one of many possible variations), which can be used for iterative decoding. Such coding schemes have the potential to approach channel capacity, while maintaining reasonable decoding complexity. In this tutorial article we review factor graphs, which can be used to describe codes and the joint probability distributions that must be dealt with in decoding. We also review the sum-product algorithm, and show how this algorithm leads to iterative decoding algorithms for codes defined on graphs.
Superpixel segmentation based on multiple seed growth

The purpose of the superpixel algorithm is to find an over-segmented set of images. In this paper, we transform the superpixel segmentation problem into the problem of minimizing the cost function on the graph. We propose a new minimum spanning tree cost function based on graph theory. In order to achieve this cost function, we propose a greedy algorithm based on multiple seed growth. Compared to other superpixel segmentation algorithms, our proposed algorithm not only ensures superpixel segmentation quality but also has linear execution time and is easy to implement. Experiments on the BSD benchmark dataset show that superpixel segmentation algorithm in this paper is superior to the current most of the superpixel segmentation algorithm in Boundary recall and Under-segmentation error, and the algorithm is more efficient than the current most of the superpixel segmentation algorithm.