Miracle-dz/UniFAR-FTUs huggingface dataset:
Pain modulatory phenotypes differentiate subgroups with different clinical and experimental pain sensitivity

Abstract Pain biomarkers are warranted for individualized pain management. Based on different pain modulatory phenotypes, the objectives of this study were to explore the existence of subgroups within patients with nonmalignant chronic pain and to investigate differences in clinical pain and pain hypersensitivity between subgroups. Cuff algometry was performed on lower legs in 400 patients with chronic pain to assess pressure pain threshold, pressure pain tolerance, temporal summation of pain (TSP: increase in pain scores to 10 repeated stimulations), and conditioned pain modulation (CPM: increase in cuff pressure pain threshold during cuff pain conditioning on the contralateral leg). Heat detection and heat pain thresholds at clinical painful and nonpainful body areas were assessed. Based on TSP and CPM, 4 distinct groups were formed: group 1 (n = 85) had impaired CPM and facilitated TSP; group 2 (n = 148) had impaired CPM and normal TSP; group 3 (n = 45) had normal CPM and facilitated TSP; and group 4 (n = 122) had normal CPM and normal TSP. Group 1 showed more pain regions than the other 3 groups (P < 0.001), indicating that impaired CPM and facilitated TSP play an important role in widespread pain. Groups 1 and 2 compared with group 4 had lower heat pain threshold at nonpainful areas and lower cuff pressure pain tolerance (P < 0.02), indicating that CPM plays a role for widespread hyperalgesia. Moreover, group 1 demonstrated higher clinical pain scores than group 4 (P < 0.05). Although not different between subgroups, patients were profiled on demographics, disability, pain catastrophizing, and fear of movement. Future research should investigate interventions tailored towards these subgroups.
Preparation of Polymer Nanofiber and Its Application

Today in the field of nanotechnology, polymer nanofiber have become a prominent area of research. They are widely used in wound healing, tissue engineering. Drug loaded biopolymer based nanofiber is used in wound healing. They are also used in making scaffold for tissue engineering. Electrospinning is used for generating polymer nanofiber. In this article, preparation of polymer nanofiber and application of this nanofiber is described.
Selecting Data for English-to-Czech Machine Translation

We provide a few insights on data selection for machine translation. We evaluate the quality of the new CzEng 1.0, a parallel data source used in WMT12. We describe a simple technique for reducing out-of-vocabulary rate after phrase extraction. We discuss the benefits of tuning towards multiple reference translations for English-Czech language pair. We introduce a novel approach to data selection by full-text indexing and search: we select sentences similar to the test set from a large monolingual corpus and explore several options of incorporating them in a machine translation system. We show that this method can improve translation quality. Finally, we describe our submitted system CU-TAMCH-BOJ.
Spi Connuent Vandermonde Matrices Using Sylvester's Structures Connuent Vandermonde Matrices Using Sylvester's Structures

In this paper we rst show that a con uent Vandermonde matrix may be viewed as composed of some rows of a certain block Vandermonde matrix As a result we derive a Sylvester s structure for this class of matrices that ap pears as a natural generalization of the straightforward one known for usual Vandermonde matrices Then we present some applications as an illustration of the established structure For example we show how con uent Vandermonde and Hankel matrices are linked with each other and also we describe an O n algorithm for solving con uent Vandermonde least squares minimizations prob lems
Wafer Map Failure Pattern Recognition and Similarity Ranking for Large-Scale Data Sets

Wafer maps can exhibit specific failure patterns that provide crucial details for assisting engineers in identifying the cause of wafer pattern failures. Conventional approaches of wafer map failure pattern recognition (WMFPR) and wafer map similarity ranking (WMSR) generally involve applying raw wafer map data (i.e., without performing feature extraction). However, because increasingly more sensor data are analyzed during semiconductor fabrication, currently used approaches can be inadequate in processing large-scale data sets. Therefore, a set of novel rotation- and scale-invariant features is proposed for obtaining a reduced representation of wafer maps. Such features are crucial when employing WMFPR and WMSR to analyze large-scale data sets. To validate the performance of the proposed system, the world's largest publicly accessible data set of wafer maps was built, comprising 811 457 real-world wafer maps. The experimental results show that the proposed features and overall system can process large-scale data sets effectively and efficiently, thereby meeting the requirements of current semiconductor fabrication.
Towards Building A Domain Agnostic Natural Language Interface to Real-World Relational Databases

In this paper we present Surukam-NLI — a novel system of building a natural language interface to databases, which composes the earlier work on using linguistic syntax trees for parsing natural language queries with, the latest advances in natural language processing such as distributed language embedding models for semantic mapping of the natural language and the database schema. We will be evaluating the performance of our system on a sample online transaction processing (OLTP) database called as Adventure-WorksDB and show that we achieve partial domain independence by handling queries about three different scenarios — Human Resources, Sales & Marketing and Product scenarios. Since there is no baseline for query performance on OLTP databases, we report f-measure statistics on an internally curated query dataset.
Enhancing electric load forecasting of ARIMA and ANN using adaptive Fourier series

Since the early 20th century, Electricity is a necessity and essential to modern life. Producing and delivering electricity to 7.4 billion people is one of the most complex challenges in this decade. It is essential to forecast the energy load at different intervals. Several methods have been developed to carry out those predictions. Examples of such methods are ARIMA, regression and artificial Neural Network (ANN). This work investigates the usage of adaptive Fourier series in enhancing the prediction accuracy of ARIMA and ANN models. Those models along with their enhanced ones are applied to forecasting hourly Peak load data of The Electric Reliability Council of Texas (ERCOT). The experimental results demonstrate that using the adaptive Fourier series enhances the prediction power of the ARIMA and ANN models. The RMSE, MAE and MAPE are enhanced by increasing the number of harmonics required for a specific seasonal component.
Transition path sampling and forward flux sampling. Applications to biological systems

The last decade has seen a rapid growth in the number of simulation methods and applications dealing with the sampling of transition pathways of rare nanoscale events. Such studies are crucial, for example, for understanding the mechanism and kinetics of conformational transitions and enzymatic events associated with the function of biomolecules. In this review, a broad account of transition path sampling approaches is provided, starting from the general concepts, progressing to the specific principles that underlie some of the most important methods, and eventually singling out the so-called forward flux sampling method for a more detailed description. This is done because forward flux sampling, despite its appealing simplicity and potential efficiency, has thus far received limited attention from practitioners. While path sampling methods have a widespread application to many types of rare transitional events, here only recent applications involving biomolecules are reviewed, including isomerization, protein folding, and enzyme catalysis.
Incentive mechanism for proximity-based Mobile Crowd Service systems

We investigate emerging proximity-based Mobile Crowd Service or pMCS systems, in which services are provided and consumed by users carrying smart mobile devices (e.g., smartphones) and in proximity of each other (e.g., within Bluetooth range). Due to limited resources on smartphones, it is crucial to provide a mechanism to incentivize users' participation and ensure fair trading in a pMCS system. In this paper, we design a multi-market dynamic double auction mechanism for a pMCS system, referred to as MobiAuc, and we show that it is truthful, feasible, individual-rational, no-deficit, and computationally efficient. The novelty and significance of MobiAuc is that it addresses and solves the fair trading problem in a multi-market dynamic double auction setting which naturally occurs in a mobile wireless environment. We demonstrate its efficiency via simulations based on generated user patterns (stochastic arrivals and random market clustering of users) and real-world traces. Our preliminary implementation of MobiAuc and experiments on Android platform have demonstrated the feasibility of MobiAuc mechanism in practice.
Perspectives to the classification of information interactions: the Cool and Belkin faceted classification scheme under scrutiny

The faceted classification system of information interactions proposed by Cool and Belkin is discussed in the light of two case studies. The two examples use the classification scheme as a structural research instrument in two different phases of investigation: in framing the collection of data and in the analysis of already existing data. The discussion is focussed on usability and the issues related to the classification scheme. We conclude that the proposed scheme is a workable instrument for studying complex information interactions with different kinds of research designs. The either premisory or post-collection use of the classification scheme was recognised to have effects on the classification outcome. The principal issues of using the scheme seemed to relate to the frequent overlap of the facets and the tendency of the classifications to be highly dependent on the perspectives of the study.
The Identification of Two Head Smut Resistance-Related QTL in Maize by the Joint Approach of Linkage Mapping and Association Analysis

Head smut, caused by the fungus Sphacelotheca reiliana (Kühn) Clint, is a devastating threat to maize production. In this study, QTL mapping of head smut resistance was performed using a recombinant inbred line (RIL) population from a cross between a resistant line “QI319” and a susceptible line “Huangzaosi” (HZS) with a genetic map constructed from genotyping-by-sequencing (GBS) data and composed of 1638 bin markers. Two head smut resistance QTL were identified, located on Chromosome 2 (q2.09HR) and Chromosome 5 (q5.03HR), q2.09HR is co-localized with a previously reported QTL for head smut resistance, and the effect of q5.03HR has been validated in backcross populations. It was also observed that pyramiding the resistant alleles of both QTL enhanced the level of resistance to head smut. A genome-wide association study (GWAS) using 277 diverse inbred lines was processed to validate the mapped QTL and to identify additional head smut resistance associations. A total of 58 associated SNPs were detected, which were distributed in 31 independent regions. SNPs with significant association to head smut resistance were detected within the q2.09HR and q5.03HR regions, confirming the linkage mapping results. It was also observed that both additive and epistastic effects determine the genetic architecture of head smut resistance in maize. As shown in this study, the combined strategy of linkage mapping and association analysis is a powerful approach in QTL dissection for disease resistance in maize.
H-, N- and Kras cooperatively regulate lymphatic vessel growth by modulating VEGFR3 expression in lymphatic endothelial cells in mice

Mammalian Ras, which is encoded by three independent genes, has been thought to be a versatile component of intracellular signalling. However, when, where and how Ras signalling plays essential roles in development and whether the three Ras genes have overlapping functions in particular cells remain unclear. Here, we show that the three Ras proteins dose-dependently regulate lymphatic vessel growth in mice. We find that lymphatic vessel hypoplasia is a common phenotype in Ras compound knockout mice and that overexpressed normal Ras in an endothelial cell lineage selectively causes lymphatic vessel hyperplasia in vivo. Overexpression of normal Ras in lymphatic endothelial cells leads to sustained MAPK activation, cellular viability and enhanced endothelial network formation under serum-depleted culture conditions in vitro, and knockdown of endogenous Ras in lymphatic endothelial cells impairs cell proliferation, MAPK activation, cell migration and endothelial network formation. Ras overexpression and knockdown result in up- and downregulation of vascular endothelial growth factor receptor (VEGFR) 3 expression, respectively, in lymphatic endothelial cells in vitro. The close link between Ras and VEGFR3 in vitro is consistent with the result that Ras knockout and transgenic alleles are genetic modifiers in lymphatic vessel hypoplasia caused by Vegfr3 haploinsufficiency. Our findings demonstrate a cooperative function of the three Ras proteins in normal development, and also provide a novel aspect of VEGFR3 signalling modulated by Ras in lymphangiogenesis.
A Reporter Screen in a Human Haploid Cell Line Identifies CYLD as a Constitutive Inhibitor of NF-κB

The development of forward genetic screens in human haploid cells has the potential to transform our understanding of the genetic basis of cellular processes unique to man. So far, this approach has been limited mostly to the identification of genes that mediate cell death in response to a lethal agent, likely due to the ease with which this phenotype can be observed. Here, we perform the first reporter screen in the near-haploid KBM7 cell line to identify constitutive inhibitors of NF-κB. CYLD was the only currently known negative regulator of NF-κB to be identified, thus uniquely distinguishing this gene. Also identified were three genes with no previous known connection to NF-κB. Our results demonstrate that reporter screens in haploid human cells can be applied to investigate the many complex signaling pathways that converge upon transcription factors.
Accurate localization of cortical convolutions in MR brain images

Analysis of brain images often requires accurate localization of cortical convolutions. Although magnetic resonance (MR) brain images offer sufficient resolution for identifying convolutions in theory, the nature of tomographic imaging prevents clear definition of convolutions in individual slices. Existing methods for solving this problem rely on heuristic adaptation of brain atlases created from a small number of individuals. These methods do not usually provide high accuracy because of large biological variations among individuals. The authors propose to localize convolutions by linking realistic visualizations of the cortical surface with the original image volume. They have developed a system so that a user can quickly localize key convolutions in several visualizations of an entire brain surface. Because of the links between the visualizations and the original volume, these convolutions are simultaneously localized in the original image slices. In the process of the authors' development, they have implemented a fast and easy method for visualizing cortical surfaces in MR images, thereby making their scheme usable in practical applications.
Computing Geographical Scopes of Web Resources

Many information resources on the web are relevant primarily to limited geographical communities. For instance, web sites containing information on restaurants, theaters, and apartment rentals are relevant primarily to web users in geographical proximity to these locations. In contrast, other information resources are relevant to a broader geographical community. For instance, an on-line newspaper may be relevant to users across the United States. Unfortunately, current web search engines largely ignore the geographical scope of web resources. In this paper, we introduce techniques for automatically computing the geographical scope of web resources, based on the textual content of the resources, as well as on the geographical distribution of hyperlinks to them. We report an extensive experimental evaluation of our strategies using real web data. Finally, we describe a geographicallyaware search engine that we have built to showcase our techniques.
The fractal characteristic of porous coatings prepared with ground calcium carbonate

To enhance the print quality of coated paper, the methodology of adjusting the porous structure of the coating is frequently employed to obtain particular imbibition characteristics for print liquids. Due to the complex and random pore structure of coating materials, it is not plausible to use Euclidean geometry to describe the stochastic morphology of the pore surface. Therefore, we use the non-Euclidean fractal geometry to characterise the irregular internal pore surface and offer some evidences for the fractal nature of coating materials. To exemplify, we adopt an extensively used mineral coating pigment, ground calcium carbonate (GCC), to analyse the structural characteristics based on the fractal method. The fractal dimensions of these materials accordingly are calculated by the classical fractal Frenkel-Halsey-Hill (FHH) model on the basis of isothermal nitrogen sorption measurement. The fractal feature of GCC coatings is represented by the fractal dimensions, showing excellent correlation with the observed structure data (R 2 = 0.900~0.993). In addition, the relationship between spontaneous liquid imbibition into GCC coatings and their fractal dimensions is also discussed to support this model further. Thus, in principle, it is possible to adjust the porous structural parameters and internal surface morphology via fractal dimension to perform specific functions.
Efficient multiple virtual view generation based on reduced depth stereo image for advanced autostereoscopic displays

Recent development of autostereoscopic displays demands the synthesis of more virtual views in wider baseline, an inevitable trend for future 3DTV systems. However, current standard file formats, e.g. multi-view coding (MVC) and image plus depth, encounter challenging problems to meet the request. Therefore, we propose a new file format, called reduced depth stereo image (RDSI), which saves the color and depth images of the left view and the disoccluded regions in the right view. Based on RDSI, rendering virtual images from parallel viewpoints along the baseline can be simplified as view interpolation that is not only very efficient but also consistent in disocclusion regions. Through experiments on both simulated and real data, we demonstrate the superior performance with several quantitative assessments for the adopted file format and the rendering algorithm. The results suggest RDSI as a better choice to meet the demands for the online synthesis of many virtual views in wide baseline for advanced autostereoscopic displays.
A Formal Analysis of Fraud in Banking Sector

With the proliferation of CBS (Core Banking Service) into various digital channels, the Indian banking sector has witnessed an alarming spectre of fraud. Information asymmetry amongst different agents is the root cause of fraud. In this paper, we propose a logical framework involving multiple agents of different personality types. The agents have different trust quotients for co-agents and information coming from various trust-deficit channels connecting them. A procedural framework based on third party independent authentication on agents' activities is proposed. We also present a case study on customer reimbursement patterns in automobile loan segment. We show how a robust credit appraisal model, coupled with strict monitoring of loan reimbursement and loan recovery strategies achieve reduction of NPA (Non-Performing Assets) of the bank to an acceptable level.
Dutch Vocabulary of Adult L 2 Learners

Acknowledgement First of all, I would like to thank all the Latin American, Spanish and Dutch subjects that participated in this study. Secondly, I would like to thank Paola Escudero for giving me the opportunity to use data of her longitudinal project and for supervising me during the first stages of writing this thesis. Paul Boersma, thanks for taking over Paola's role and advising me in the final stages of writing my thesis. Finally, of course, I would like to thank my family and friends for supporting me, special thanks to my father who helped me analyzing the data.
QoS functionality in software defined network

Quality of Service (QoS) in networking is a way of managing the network resources effectively. QoS has been widely used in traditional network and can also be implemented in Software Defined Network (SDN). Software Defined Network is a new network paradigm that decouples the control plane from the data plane in the network and thus create a network that is scalable, dynamic and easily manageable. QoS in SDN can be used to control the network bandwidth, latency and throughput. This paper presents the QoS functionality in SDN and uses OpenFlow protocol, OpenvSwitch and QoS application built on top Ryu controller. It also shows the use of tools such as iperf in testing the throughput and sflow in visualizing and monitoring the network at large.
WAVELET-BASED THE CHARACTER RECOGNITION IN MAP

The automatic recognition, including the character, annotation, contour line and so on, is very important in many fields. Here, we focus on the character recognition of the scanned map, because the digitizing map is an important resource data for GIS and automatic recognition character of scanning map is still a key problem, which needs to be solved seriously. In our investigation, which is about automatic recognizing characters in scanning map utilizing zero crossings of wavelet transform, we firstly transformed the gray map scanned to the binary image, which is pre-processed (noise filter, thinning, etc). The closed-contours in the binary image are constructed using morphology operators and the chain codes of the closed-contour are traced. Then, the closed-contours related to the characters are separated from the binary image by the special threshold. The three features (x, y, and tangential angle in the closed-contour of characters ) are extracted, which is of great benefit to change 2D image to 1D signals. The feature signal related to x, y, and tangential angle is decomposed respectively by the discrete dyadic wavelet in 2 scale, and the zero-crossings of the wavelet transform are extracted, because the zero-crossings represent sharp variation points of the feature signals. The dynamic time warping is used as the similarity measure for the purpose of realizing the feature match and character recognition.
Discovering Key Phrase in Acoustic Audio Data

We present a method to automatically discover key phrase from audio recordings in this paper. Chroma features are firstly selected to represent melodic information and a modified similarity matrix based on this feature is then established for repeated pattern searching. We also run a pruning process to discard invalid phrase pairs and a novel boundary correction algorithm is put to make the phrases more precisely. Final key phrase is determined by both its occurrence frequency and energy information. Key phrases of nearly 200 pop songs are extracted using our proposed methodand 5 person's evaluations to the experiment results show the effectiveness of our method.