Miracle-dz/UniFAR-FTUs huggingface dataset:
Моделирование Рейтингов Российских Банков [Modeling Russian Banks Ratings]

The paper presents econometric analysis of the of Russian banks ratings, and the experts’ opinion based on a survey, designed in a framework of the project. From the ratings and the survey we derived the factors that in the experts’ opinion and in the rating agencies’ opinion are most important for bank reliability. The models of the ratings and experts’ opinions were designed for real and virtual banks. We constructed the model ratings of the Russian banks. These models use only publicly available information and hence could be used as an early warning system of the current bank reliability.
Text classification using combined sparse representation classifiers and support vector machines

Text classification is an important task in managing huge repository of textual content prevailing in various domains. In this paper, we propose to use sparse representation classifier (SRC) and support vector machines (SVMs) based classifiers using frequency-based kernels for text classification. We consider term-frequency (TF) representation for a text document. The sparse representation of an example is obtained by using an overcomplete dictionary made up of TF vectors corresponding to all the training documents [1]. We propose to seed the dictionary using principal components of TF vector representation corresponding to training text documents. SVM-based text classifiers use linear kernel or Gaussian kernel on the TF vector representation of documents. TF representation being a non-negative, histogram representation, we propose to build SVM-based text classifiers using frequency-based kernels such as histogram intersection kernel, Chi-square (X2) kernel and Hellinger's kernel. It is observed that the examples misclassified by one classifier is correctly classified in another classifier. To take advantage of the various classifiers, we introduce an approach to combine classifiers to improve the performance of text classification. The effectiveness of all the proposed techniques for text classification is demonstrated on 20 Newsgroup Corpus.
Robust ego-motion estimation and 3D model refinement using depth based parallax model

We present an iterative algorithm for robustly estimating the ego-motion and refining and updating a coarse, noisy and partial depth map using a depth based parallax model and brightness derivatives extracted from an image pair. Given a coarse, noisy and partial depth map acquired by a range-finder or obtained from a Digital Elevation Map (DFM), we first estimate the ego-motion by combining a global ego-motion constraint and a local brightness constancy constraint. Using the estimated camera motion and the available depth map estimate, motion of the 3D points is compensated. We utilize the fact that the resulting surface parallax field is an epipolar field and knowing its direction from the previous motion estimates, estimate its magnitude and use it to refine the depth map estimate. Instead of assuming a smooth parallax field or locally smooth depth models, we locally model the parallax magnitude using the depth map, formulate the problem as a generalized eigen-value analysis and obtain better results. In addition, confidence measures for depth estimates are provided which can be used to remove regions with potentially incorrect (and outliers in) depth estimates for robustly estimating ego-motion in the next iteration. Results on both synthetic and real examples are presented.
Evaluation of colonoscopy in the diagnosis and treatment of rectal carcinoid tumors with diameter less than 1 cm in 21 patients

The aim of this study was to evaluate colonoscopy in the diagnosis and treatment of rectal carcinoid tumors with diameter <1 cm. Elevated lesions with normal mucosal appearance under colonoscopy were identified. Endoscopic ultrasound (EUS) was performed in 16 patients. Lesions diagnosed as rectal carcinoid tumors were resected by endoscopic mucosal resection (EMR). The diagnosis of specimens by EMR was confirmed by pathological examination. Immunohistochemical staining was undertaken and follow-up data were collected. Twenty-two lesions were found among the 21 cases. The majority of these were located within 10 cm of the anal opening. Twenty two cases with rectal carcinoids were diagnosed by EUS under colonoscopy and all cases were verified by pathological examination. The resection rate was 95.5% (21/22). Of the lesions, six were mucosal and 10 were submucosal. Immunohistochemistry was undertaken for carcinoid tumors. Histological patterns of rectal carcinoids revealed solid nests or trabecular patterns. Eleven cases were synaptophysin (SYN)-positive, 8 cases were neurone-specific enolase (NSE)-positive and 5 cases were chromogranin A (CgA)-positive. Colonoscopy combined with EUS is effective in the diagnosis and determination of small rectal carcinoids. Endoscopic treatment is effective for small-sized tumors. Pathology and immunohistochemistry remain the diagnostic gold standard.
Factors influencing demand in Major League Baseball: Steroid policy, discrimination, and uncertainty of outcome

The research presented in this dissertation represents three distinct types of issues influencing demand. Chapter II examines how a policy change affected revenues. The results show that there was a financial incentive to not enact and enforce a policy punishing users of performance enhancing drugs. Chapter III measures the change in demand for teams attributable to foreign-born players on the squad. The results show fans have changed their bias from antito pro-foreign players over the period from 1985-2005. Chapter IV questions how fans level of consumption is affected by the relative uncertainty of a game and season’s outcome. The findings are that fans prefer to attend games when their team is in a better position to make the playoffs, when the game itself is important to determining playoff qualification, and when their team’s standing is similar to that of the previous season. While each study represents a different subset of studies within the literature on sport economics, each is aimed specifically at learning more about fan preferences and measuring their impact on demand.
WCET-centric partial instruction cache locking

Caches play an important role in embedded systems by bridging the performance gap between high speed processors and slow memory. At the same time, caches introduce imprecision in Worst-case Execution Time (WCET) estimation due to unpredictable access latencies. Modern embedded processors often include cache locking mechanism for better timing predictability. As the cache contents are statically known, memory access latencies are predictable, leading to precise WCET estimate. Moreover, by carefully selecting the memory blocks to be locked, WCET estimate can be reduced compared to cache modeling without locking. Existing static instruction cache locking techniques strive to lock the entire cache to minimize the WCET. We observe that such aggressive locking mechanisms may have negative impact on the overall WCET as some memory blocks with predictable access behavior get excluded from the cache. We introduce a partial cache locking mechanism that has the flexibility to lock only a fraction of the cache. We judiciously select the memory blocks for locking through accurate cache modeling that determines the impact of the decision on the program WCET. Our synergistic cache modeling and locking mechanism achieves substantial reduction in WCET for a large number of embedded benchmark applications.
Acceptance of Vasectomy as a family planning method among married men in Karimnagar District, Andhra Pradesh

Background: Vasectomy (male sterilization) is one of the safest and most effective family planning methods and is one of the few contraceptive options available for men. Objective of the present study was to determine the acceptance of vasectomy as a family planning method in Karimnagar District and to analyze the factors influencing on it. Methods: It was a community based cross-sectional study done in Karimnagar District, Andhra Pradesh wherein 956 married men above 21 years of age were selected by using multi stage sampling method. After obtaining written informed consent, respondents were interviewed personally in their local language by using a pre-designed, pre-tested, semi structured proforma. Data entry was done using Microsoft Excel 2007 and analysis done using EPI INFO Version 7. Statistical tests applied were Chi-square test with significance level at 5%. Results: Out of the total 956 study population , two thirds were in the age group of 31-39 years; majority from Hindu religion and belonged to Backward classes. Regarding family planning practices, two thirds practiced permanent methods with 15.2% (145) vasectomy acceptance. Demographic characteristics revealed that age, type of family, duration of marital life had significant association with acceptance of vasectomy (p<0.05). Health workers were major sources of information and motivation for adopters to accept vasectomy. Permanent nature of the method, method being suggested by health worker, concern about health and well being of spouse were major reasons for accepting vasectomy. Conclusion: Vasectomy was accepted as a family planning method with benefits of les pain and short duration of procedure.
Co-Audicle: a Collaborative audio Programming Space

The Co-Audicle describes the next phase of the Audicle’s development. We extend the Audicle to create a collaborative, multi-user interaction space based around the ChucK language. The Co-Audicle operates either in client/server mode or as part of a peer-to-peer network. We also describe new graphical and GUI-building functionalities. We draw inspiration from both live interaction software, as well as online gaming environments.
A new segment-based stereo matching using graph cuts

In the paper, we formulate a new energy function followed by the use of graph cuts to refine the disparity map which takes segment as node. Firstly, the robust disparity plane fitting is modeled and the method of Singular Value Decomposition (SVD) is used to solve least square. In order to ensure reliable pixel sets for the segment, we filter out outliers through three main rules, namely; cross-checking, judging reliable area and measuring the distance between previous disparity to the computed disparity plane. Secondly, we apply improve hierarchical clustering algorithm to merge neighbor. Finally, the final disparity map is obtained. Experimental results demonstrate that our approach is effective in improving the state of the art.
Processes and Analysis of Vehicle Driving Data Based on Short Stroke Method

In order to build China's typical urban vehicle driving cycle, after collecting data with vehicle recorder, in this paper, short-stroke method use for data processing, thus obtain the kinematic sequence described by series eigenvalues. Then principal component analysis theory and cluster analysis theory was use to analyze the kinematics fragment. Through analysis and calculation for the characteristic value of kinematics segments, characteristic value variable was conducted a dimension reduction process, and the kinematics fragments are divided into three categories, on behalf of the low speed of the vehicle, medium speed and high speed working condition models respectively, laid the foundation for building the vehicle driving cycle. Copyright © 2014 IFSA Publishing, S. L.
Using Action Research to Improve Tier II Leadership Preparation: A Play-within-the-Play.

This paper recounts one instructor’s importing action research into teaching to improve a Tier II Professional Administrative Services Credential program at a public university in California. The state and local context is described, why and how the action research was conducted, and the lessons that emerged about what advanced students need, prefer, and value are included. The findings of this case are of interest to leadership preparation faculty in general, and specifically to those faculty redesigning credential programs to compete with the growing number of newly authorized non-university credential paths.
The Investment Climate and the Firm: Firm-Level Evidence from China

The importance of a country's"investment climate"for economic growth has recently received much attention. The authors address the general lack of appropriate data for measuring the investment climate and its effects. The authors use a new survey of 1,500 Chinese enterprises in five cities to more precisely define and measure components of the investment climate, highlight the importance of firm-level data for rigorous analysis of the investment climate, and investigate empirically the effects of this comprehensive set of measures on firm performance in China. Overall, their firm-level analysis reveals that the main determinants of firm performance in China are international integration, entry and exit, labor market issues, technology use, and access to external finance.
Volatility, Growth and Labour Elasticity

We study the relationship between growth and variability in a DSGE model with nominal rigidities and growth driven by learning-by-doing. We show that this relationship may be positive or negative depending on the impulse source of fluctuations A key role is also played by the Frisch elasticity of labour supply and by institutional features of the labour market. Our general findings are that monetary shocks volatility will generally have a negative effect on growth, while the opposite tends to be true for fiscal and productivity shocks. These findings are somehow consistent with the existing empirical evidence: data show, in fact, a somewhat ambiguous relationship between output growth and real variability, but a generally negative relationship between output growth and nominal variability.
Multilevel local search algorithms for modularity clustering

Modularity is a widely used quality measure for graph clusterings. Its exact maximization is NP-hard and prohibitively expensive for large graphs. Popular heuristics first perform a coarsening phase, where local search starting from singleton clusters is used to compute a preliminary clustering, and then optionally a refinement phase, where this clustering is improved by moving vertices between clusters. As a generalization, multilevel heuristics coarsen in several stages, and refine by moving entire clusters from each of these stages, not only individual vertices. This article organizes existing and new single-level and multilevel heuristics into a coherent design space, and compares them experimentally with respect to their effectiveness (achieved modularity) and runtime. For coarsening by iterated cluster joining, it turns out that the most widely used criterion for joining clusters (modularity increase) is outperformed by other simple criteria, that a recent multistep algorithm [Schuetz and Caflisch 2008] is no improvement over simple single-step coarsening for these criteria, and that the recent multilevel coarsening by iterated vertex moving [Blondel et al. 2008] is somewhat faster but slightly less effective (with refinement). The new multilevel refinement is significantly more effective than the conventional single-level refinement or no refinement, in reasonable runtime. A comparison with published benchmark results and algorithm implementations shows that multilevel local search heuristics, despite their relative simplicity, are competitive with the best algorithms in the literature.
Discussions Among the Poor: Exploring Poverty Dynamics with Focus Groups in Bangladesh

Findings from 116 focus group discussions are presented, which took place in eleven districts in Bangladesh in mid-2006. It forms the first part of three phases of research in an integrated qualitative and quantitative study into poverty dynamics currently being undertaken by the author and partners from the CPRC, IFPRI and DATA Bangladesh. The central purpose of the focus group discussions was to inform subsequent phases of the research by exploring reasons perceived by participants for decline or improvement in peopleâ€™s well-being in their communities, and the hindrances to improvement for the chronically poor [CPRC WP No. 84].
Group Formation in a Public Good Experiment On the Dynamics of Social Ties Structures

Economic behavior often takes place in groups of small numbers of people interacting with each other (like work teams, neighborhoods, social networks, etc.). Characteristic of such interaction is the development of (affective) interpersonal relationships, or social ties. According to sociologists, the embeddedness of economic behavior in networks of social ties has a profound impact on economic performance. Although, in economics, there is a growing awareness of the significance of social factors, the study of social dynamics is still in its infancy. In this paper we investigate experimentally the development of social ties structures, and thereby the formation of informal groups, through economic interaction in a public good environment. It turns out that complicated dynamics arise from individual differences in social value orientation and (affective) response patterns. Our findings point at the importance of leadership in controlling and channeling emotions and sentiments to build effective groups, like teams or communities. This leads to a different perspective on management than the one provided by standard agency theory.
Spatial pooling of heterogeneous features for image applications

The Bag-of-Features (BoF) model has played an important role for image representation in many multimedia applications. It has been extensively applied to many tasks including image classification, image retrieval, scene understanding, and so on. Despite the advantages of this model such as simplicity, efficiency and generality, there are also notable drawbacks for this model, including poor power of semantic expression of local descriptors, and lack of robust structures upon single visual words. To overcome these problems, various techniques have been proposed, such as multiple descriptors, spatial context modeling and interest region detection. Though they have been proven to improve the BoF model to some extent, there still lacks a coherent scheme to integrate each individual module. To address the problems above, we propose a novel framework with spatial pooling of heterogeneous features. Our framework differs from the traditional Bag-of-Features model on three aspects. First, we propose a new scheme for combining texture and edge based local features together at the descriptor extraction level. Next, we build geometric visual phrases to model spatial context upon heterogeneous features for mid-level representation of images. Finally, based on a smoothed edgemap, a simple and effective spatial weighting scheme is performed on our mid-level image representation. We test our integrated framework on several benchmark datasets for image classification and retrieval applications. The extensive results show the superior performance of our algorithm over state-of-the-art methods.
Exploration of the DSM-5’s Autism Spectrum Disorder Severity Level Specifier and Prediction of Autism Severity

ABSTRACT With the publication of DSM-5, clinical assessment of Autism Spectrum Disorder (ASD) has begun to follow a new dimensional framework which includes new severity specifiers. Little research has explored these severity ratings in comparison to other previously established severity indicators (e.g. ADOS-2 calibrated severity score). The current study compared parent and teacher ratings using the BASC-2 for 43 children and adolescents diagnosed with DSM-5 ASD, to contribute novel information to the BASC literature and to explore the new DSM-5 severity ratings. Linear regression analyses were conducted to determine the extent to which the DSM-5 Social Communication and Restrictive/Repetitive Behavior Severity Ratings (clinician-rated) predicted autism severity based on ADOS-2 calibrated severity scores. Furthermore, linear regressions were conducted to explore whether teacher ratings on the BASC-2 enhance parent ratings. Implications of these preliminary results for the assessment of children and adolescents with ASD are suggested.
A Matter of Contrast: Yellow Flower Colour Constrains Style Length in Crocus species

Most flowers display distinct colour patterns comprising two different areas. The peripheral large-area component of floral colour patterns attracts flower visitors from some distance and the central small-area component guides flower visitors towards landing sites. Whereas the peripheral colour is largely variable among species, the central colour, produced mostly by anthers and pollen or pollen mimicking floral guides, is predominantly yellow and UV-absorbing. This holds also for yellow flowers that regularly display a UV bull’s eye pattern. Here we show that yellow-flowering Crocus species are a noticeable exception, since yellow-flowering Crocus species–being entirely UV-absorbing–exhibit low colour contrast between yellow reproductive organs and yellow tepals. The elongated yellow or orange-yellow style of Crocus flowers is a stamen-mimicking structure promoting cross-pollination by facilitating flower visitors’ contact with the apical stigma before the flower visitors are touching the anthers. Since Crocus species possess either yellow, violet or white tepals, the colour contrast between the stamen-mimicking style and the tepals varies among species. In this study comprising 106 Crocus species, it was tested whether the style length of Crocus flowers is dependent on the corolla colour. The results show that members of the genus Crocus with yellow tepals have evolved independently up to twelve times in the genus Crocus and that yellow-flowering Crocus species possess shorter styles as compared to violet- and white-flowering ones. The manipulation of flower visitors by anther-mimicking elongated styles in Crocus flowers is discussed.
Text mining: intermediate forms on knowledge representation

In this paper we review the main intermediate forms proposed in text mining, and we brie∞y study some fuzzy counterparts. The concept of intermediate form applies to any knowledge representation employed to represent in a structured way the semantic content of a text corpus. Intermediate forms play a central role in the text mining process since it is necessary to transform plain text into a form in order to apply mining techniques. Since the semantics of text use to be imprecise, the use of fuzzy intermediate forms seems to be a natural solution in many cases. We discuss about fuzzy intermediate forms and the corresponding fuzzy text mining techniques that may be applicable on them.
Fudan University at DUC 2006

This paper describes our work in query-based multi-document summarization task in DUC2006. We present the system overview, focusing on the newly developed techniques, including a new method of sentence similarity calculation, and application of anaphoric resolution to improve the readability of the summary. Evaluation results from NIST are also given and analyzed.
A Speaker Identification System Using MFCC Features with VQ Technique

The performance of speaker identification systems has improved due to recent advances in speech processing techniques but there is still need of improvement in term of text-independent speaker identification and suitable modelling techniques for voice feature vectors. It becomes difficult for person to recognize a voice when an uncontrollable noise adds in to it. In this paper, feature vectors from speech are extracted by using Mel-Frequency Cepstral Coefficients and Vector Quantization technique is implemented through Linde-Buzo-Gray algorithm. Two purposeful speech databases with added noise, recorded at sampling frequencies 8000 Hz and 11025 Hz, are used to check the accuracy of the developed speaker identification system in non-ideal conditions. An analysis is also provided by performing different experiments on the databases that number of vectors in VQ codebook and sampling frequency influence the identification accuracy significantly.