Miracle-dz/UniFAR-FTUs huggingface dataset:
Object substitution without reentry

G. Francis and F. Hermens (2002) used computer simulations to claim that many current models of metacontrast masking can account for the findings of V. Di Lollo, J. T. Enns, and R. A. Rensink (2000). They also claimed that notions of reentrant processing are not necessary because all of V. Di Lollo et al. 's data can be explained by feed-forward models. The authors show that G. Francis and F. Hermens's claims are vitiated by inappropriate modeling of attention and by ignoring important aspects of V. Di Lollo et al. 's results.
Identification of Barriers to Stroke Awareness and Risk Factor Management Unique to Hispanics

Barriers to risk factor control may differ by race/ethnicity. The goal of this study was to identify barriers to stroke awareness and risk factor management unique to Hispanics as compared to non-Hispanic whites (NHWs). We performed a prospective study of stroke patients from an academic Stroke Center in Arizona and surveyed members of the general community. Questionnaires included: the Duke Social Support Index (DSSI), the Multidimensional Health Locus of Control (MHLC) Scale, a stroke barriers questionnaire, and a Stroke Awareness Test. Of 145 stroke patients surveyed (72 Hispanic; 73 NHW), Hispanics scored lower on the Stroke Awareness Test compared to NHWs (72.5% vs. 79.1%, p = 0.029). Hispanic stroke patients also reported greater barriers related to medical knowledge, medication adherence, and healthcare access (p < 0.05 for all). Hispanics scored higher on the “powerful others” sub-scale (11.3 vs. 10, p < 0.05) of the MHLC. Of 177 members of the general public surveyed, Hispanics had lower stroke awareness compared to NHWs and tended to have lower awareness than Hispanic stroke patients. These results suggest that Hispanic stroke patients perceive less control over their health, experience more healthcare barriers, and demonstrate lower rates of stroke literacy. Interventions for stroke prevention and education in Hispanics should address these racial/ethnic differences in stroke awareness and barriers to risk factor control.
Structural Diversity of a Novel LTR Retrotransposon, RTPOSON, in the Genus Oryza

Retrotransposons with long terminal repeats (LTRs) are the most abundant transposable elements in plant genomes. A novel LTR retrotransposon named RTPOSON primarily occurs in the genus Oryza and in several species of the Poaceae family. RTPOSON has been identified in the Ty1-copia group of retrotransposons because two of its open reading frames encode an uncharacterized protein and UBN2_2 and zinc knuckle, respectively. More than 700 RTPOSONs were identified in Oryza genomes; 127 RTPOSONs with LTRs and gag-pol elements were classified into three subgroups. The subgroup RTPOSON_sub3 had the smallest DNA size and 97% (32/33) of RTPOSON elements from Oryza punctata are classified in this group. The insertion time of these RTPOSONs varied and their proliferation occurred within the last 8 Mya, with two bursting periods within the last 1.5-5.0 Mya. A total of 37 different orthologous insertions of RTPOSONs, with different nested transposable elements and gene fragments, were identified by comparing the genomes of ssp. japonica cv. Nipponbare and ssp. indica cv. 93-11. A part of intact RTPOSON elements was evolved independently after the divergence of indica and japonica. In addition, intact RTPOSONs and homologous fragments were preferentially retained or integrated in genic regions. This novel LTR retrotransposon, RTPOSON, might have an impact on genome evolution, genic innovation, and genetic variation.
Predicting Corporate Financial Distress Based on Fuzzy Support Vector Machine

In this paper, the fuzzy support vector machine (FSVM) was used to predict the corporate financial distress. First of all, a proper membership model was also proposed to fuzzy all the training data of positive/negative class. Secondly, the outliers were detected by the proposed outlier detection method (ODM). The ODM was a hybrid method based on the fuzzy c-means (FCM) algorithm cascaded with an unsupervised neural network, called self-organizing map (SOM). Finally, FSVM was applied to demonstration research of corporate financial distress prediction, and experimental results indicated that the proposed FSVM actually reduced the effect of outliers and yield higher classification rate than SVM did.
Chinese Tagging Based on Maximum Entropy Model

In the Fourth SIGHAN Bakeoff, we took part in the closed tracks of the word segmentation, part of speech (POS) tagging and named entity recognition (NER) tasks. Particularly, we evaluated our word segmentation model on all the corpora, namely Academia Sinica (CKIP), City University of Hong Kong (CITYU), University of Colorado (CTB), State Language Commission of P.R. C. (NCC) and Shanxi University (SXU). For POS tagging and NER tasks, our models were evaluated on CITYU corpus only. Our models for the evaulation are based on the maximum entropy approach, we concentrated on the word segmentation task for the bakeoff and our best official results on all the corpora for this task are 0.9083 F-score on CITYU, 0.8985 on CKIP, 0.9077 on CTB, 0.8995 on NCC and 0.9146 on SXU.
Low power FPGA design—a re-engineering approach

In this paper, technology mapping algorithmsfor minimizing power consumption in FPGA design are studied.The technology mapping problem for power minimizationhas been shown to be NP-complete.Furthermore, thereare other important objectives, such as the number of PLBs(Programmable Logic Blocks), the number of levels and soon, that should also be optimized simultaneously.We proposea transformational approach in which we start with amapping solution which optimizes certain objective(s) (e.g., the number of PLBs.)The mapping solution is then transformedto reduce the power consumption while keeping thenumber of PLBs fixed.Our algorithm explores the possibilitiesof transforming the functionality of the PLBs so thatthe switching densities of the output edges of the PLBs willbe reduced, leading to a reduction in total power consumption.Our transformational approach can also be viewed as are-engineering approach in which power reduction is achievedthrough re-routing after the PLBs have been placed, utilizingeffectively the capability of a PLB to realize any booleanfunction of up to k variables.
Parallelization of particle swarm optimization using message passing interfaces (MPIs)

Motivated by the growing demand of accuracy and low computational time in optimizing functions in various fields of engineering, an approach has been presented using the technique of parallel computing. The parallelization has been carried out on one of the simplest and flexible optimization algorithms, namely the particle swarm optimization (PSO) algorithm. PSO is a stochastic population global optimizer and the initial population may be provided with random values and later convergence may be achieved. The use of message passing interfaces (MPIs) for the parallelization of the asynchronous version of PSO is proposed. In this approach, initial population has been divided between the processors chosen at run time. Numerical values obtained using above approach are at last compared for standard test functions.
D G ] 2 9 A ug 2 00 0 Hausdorff Convergence and Universal Covers ∗

We prove that if Y is the Gromov-Hausdorff limit of a sequence of compact manifolds, M i , with a uniform lower bound on Ricci curvature and a uniform upper bound on diameter, then Y has a universal cover. We then show that, for i sufficiently large, the fundamental group of Mi has a surjective homeomorphism onto the group of deck transforms of Y . Finally, in the non-collapsed case where the Mi have an additional uniform lower bound on volume, we prove that the kernels of these surjective maps are finite with a uniform bound on their cardinality. A number of theorems are also proven concerning the limits of covering spaces and their deck transforms when the Mi are only assumed to be compact length spaces with a uniform upper bound on diameter.
A Unique Governance Learning Experience

Abstract This paper reports on some of the findings of a 2008–2009 graduate study conducted as a shared organizational learning experience for the Grant MacEwan College (now MacEwan University1) Board of Governors to learn about a vital board governance responsibility—presidential search. Through a facilitated, qualitative action research exercise, participants engaged in a four-stage progressive learning experience to create a body of knowledge about presidential search experiences and to develop strategies for transferring this knowledge when membership changes. The study examined how, through the application of knowledge management theory, a board can learn and share knowledge. This learning experience contributed to the creation of a comprehensive board succession plan for the MacEwan Board of Governors and, in 2010–2011, this plan was used to guide the institution’s search for its fourth president. Resume Cet article presente quelques-unes des conclusions d’une recherche menee en 2008-2009 aupres du Conseil des Gouverneurs du Grant MacEwan College (MacEwan2). Organisee sous la forme d’une experience d’apprentissage organisationnelle, l’etude visait a permettre aux membres du Conseil de s’approprier une des responsabilites cruciales des conseils en matiere de gouvernance : l’embauche d’un president. Au fil d’une recherche-action qualitative et encadree, les membres du Conseil ont ete amenes a s’engager dans un processus d’apprentissage progressif en quatre etapes leur permettant de mutualiser leurs connaissances et leurs experiences au sujet de l’embauche d’un president, et ainsi de creer des strategies pour transferer ce corps de connaissances et d’experiences au fil des changements dans la composition du Conseil. L’etude a ainsi pu examiner la facon dont un conseil peut apprendre et partager les savoirs en mettant en pratique la theorie de la gestion des connaissances. Cette experience unique a mene a la realisation d’un plan de transition exhaustif pour le Conseil et, en 2010-2011, a egalement soutenu le processus d’embauche du quatrieme president de MacEwan.
Three Cases with Visual Hallucinations following Combined Ocular and Occipital Damage

Charles Bonnet syndrome is an underrecognized disease that involves visual hallucinations in visually impaired patients. We present the cases of three patients who experienced complex visual hallucinations following various pathomechanisms. In two cases, diagnosis showed coexistence of occipital lobe damage with ocular damage, while in the third case it showed occipital lobe damage with retrobulbar optic neuritis. Theories of pathogenesis and the neuroanatomical basis of complex visual hallucinations are discussed and supported by literature review.
Dynamic Resource Management for ContinuousMedia Tra c over ATM NetworksRose

Real-time continuous media traac, such as digital video and audio, is expected to comprise a large percentage of the network load on future high speed packet switch networks such as ATM. A major feature which distinguishes high speed networks from traditional slower speed networks is the large amount of data the network must manage. For eecient network usage, traac control mechanisms are essential. Currently, most mechanisms for traac control (such as ow control) have centered on the support of Available Bit Rate (ABR), i.e., non real-time, traac. With regard to ATM, for ABR traac, two major types of schemes which have been proposed are rate-control and credit-control schemes. Neither of these schemes are directly applicable to Real-time Variable Bit Rate (VBR) traac such as continuous media traac. Traac control for continuous media traac is an inherently diicult problem due to the time-sensitive nature of the traac and its unpredictable burstiness. In this study, we present a scheme which controls traf-c by dynamically allocating/de-allocating resources among competing VCs based upon their real-time requirements. This scheme incorporates a form of rate-control, real-time burst-level scheduling and link-link ow control. We show analytically potential performance improvements of our rate-control scheme and present a scheme for buuer dimensioning. We also present simulation results of our schemes and discuss the tradeoos inherent in maintaining high network utilization and statistically guaranteeing many users' Quality of Service.
Coordination and Tailorability Issues in the design of a Generic Large Scale Groupware Platform

Tailorabilit y or adaptabilit y is commonly accepted as a fundamental property of modern and flexible CSCW systems. In order to match the differences in organizational structures, these systems have strong requirements in terms of dynamic reconfiguration and adaptation. This work is concerned with the design and implementation of a tailorable generic distributed system platform for large scale CSCW. We argue in favor of an approach for the tailorabilit y support, focusing different perspectives and emphasis according to different support levels. This approach can help in a better understanding of different structuring levels involved in the support of adaptabilit y. We discuss and ilustrate how these different perspectives are present in the proposed integrated framework and we exemplify the usage of a component model (JavaBeans) as a candidate to build software components for groupware, dealing with the different recognized structuring levels of the tailorability support.
Auditory analysis and of vowel-like sounds

The results of traditional masking experiments do not fit in with the effect of "two-tone inhibition" revealed by single-nerve-fibre studies. This is considered a serious objection against the interpretation of a masking pattern as reflecting the auditory spectrum of a masker. The results of a different masking paradigm, based on the pulsation threshold, do reveal an effect similar to "two-tone inhibition. " Therefore, such pulsation-threshold patterns might reflect more accurately the auditory spectrum of the masker. Pulsation-threshold patterns of three vowel-like sounds were determined. These were compared with the spectral patterns of the "vowels" after analysis with a 1/3 octave-band filter with running centre frequency. It appeared that the pulsation-threshold patterns are sharper than the 1/3 octave-band spectra and, in some cases. even more pronounced than the original vowel spectra. This has interesting implications with regard to vowel-perception theories, such as theories on "formant extraction. "
AN EFFICIENT GENETIC ALGORITHM PARADIGM FOR DISCRETE OPTIMISATION OF PIPELINE NETWORKS

Engineering and science disciplines make use of genetic algorithms. A number of genetic-based search paradigms have been developed and applied to different problems. A growing demand for algorithms to solve new problems and a never-ending process of designing algorithms strongly suggests the need for more efficient and more robust geneticbased optimisation techniques. Ideally these algorithms should make few assumptions regarding the objective functions and use as little domain knowledge as possible. In this paper, different genetic-based search paradigms including the standard GA, the messy GA and the fast messy GA are compared for the discrete optimisation of pipeline networks.
Comparative Music Similarity Modelling Using Transfer Learning Across User Groups

We introduce a new application of transfer learning for training and comparing music similarity models based on relative user data: The proposed Relative Information-The-oretic Metric Learning (RITML) algorithm adapts a Mahalanobis distance using an iterative application of the ITML algorithm, thereby extending it to relative similarity data. RITML supports transfer learning by training models with respect to a given template model that can provide prior information for regularisation. With this feature we use information from larger datasets to build better models for more speciﬁc datasets, such as user groups from different cultures or of different age. We then evaluate what model parameters, in this case acoustic features, are relevant for the speciﬁc models when compared to the general user data. We to this end introduce the new CASimIR dataset, the ﬁrst openly available relative similarity dataset with user attributes. With two age-related subsets, we show that transfer learning with RITML leads to better age-speciﬁc models. RITML here improves learning on small datasets. Using the larger MagnaTagATune dataset, we show that RITML performs as well as state-of-the-art algorithms in terms of general similarity estimation.
Classification of Agricultural Pests Using DWT and Back Propagation Neural Networks

Plant pest identification and detection is vital for food security, quality of life and a stable agricultural economy. Enormous agricultural yield is lost every year, due to rapid infestation by pests and according to experts, minimum 10% of crop yield to pigeon pea crop is lost due to pod borer (Helicoverpa armigera) pest attacks. Various methodologies were proposed earlier for identification and detection of agriculture pests. Mostly work was done for identification of whitefly pest on sticky traps in greenhouse environments and in real fields. We propose a new approach which exposes advance computing technology that has been developed to help the farmer to identify agricultural pests and take proper decision about preventive or control measure of it. Diagnosis of agricultural pests in the field is very critical and difficult. In our proposed work, we would be capturing images of pests from various crops like cotton, pigeon pea, chickpea, etc. Agricultural pest which need to be extracted available in foreground and hence from an image foreground need to be separated. For this saliency map based segmentation will be carried out. After segmentation, various features of segmented pests will be extracted. Feature vector includes energy is calculated with the help of discrete wavelet transform. These features will be stored in database with name of pest. With the help of back propagation neural networks, we would be classifying type of pest and give preventive and control measures to user. Keywords— Agricultural pests, back propagation neural networks, saliency map, discrete wavelet transform, pod borer.
Biology of Moderately Halophilic Aerobic Bacteria

SUMMARY The moderately halophilic heterotrophic aerobic bacteria form a diverse group of microorganisms. The property of halophilism is widespread within the bacterial domain. Bacterial halophiles are abundant in environments such as salt lakes, saline soils, and salted food products. Most species keep their intracellular ionic concentrations at low levels while synthesizing or accumulating organic solutes to provide osmotic equilibrium of the cytoplasm with the surrounding medium. Complex mechanisms of adjustment of the intracellular environments and the properties of the cytoplasmic membrane enable rapid adaptation to changes in the salt concentration of the environment. Approaches to the study of genetic processes have recently been developed for several moderate halophiles, opening the way toward an understanding of haloadaptation at the molecular level. The new information obtained is also expected to contribute to the development of novel biotechnological uses for these organisms.
Formalizing neural networks using graph transformations

In this paper a unifying framework for the formalization of different types of neural networks and the corresponding algorithms for computation and training is presented. The used graph transformation system offers a formalism to verify properties of the networks and their algorithms. In addition the presented methodology can be used as a tool to visualize and design different types of networks along with all required algorithms. An algorithm that adapts network parameters using standard gradient descent as well as parts of a constructive, topology-changing algorithm for probabilistic neural networks are used to demonstrate the proposed formalism.
A Study on Apparent Age Estimation

Age estimation from facial images is an important problem in computer vision and pattern recognition. Typically the goal is to predict the chronological age of a person given his or her face picture. It is seldom to study a related problem, that is, how old does a person look like from? the face photo? It is called apparent age estimation. A key difference between apparent age estimation and the traditional age estimation is that the age labels are annotated by human assessors rather than the real chronological age. The challenge for apparent age estimation is that there are? not many face images available with annotated age labels. Further, the annotated age labels for each face photo may not be consistent among different assessors. We study the problem of apparent age estimation by addressing the issues from different aspects, such as how to utilize a large number of face images without apparent age labels to learn a face representation using the deep neural networks, how to tune the deep networks using a limited number of examples with apparent age labels, and how well the machine learning methods can perform to estimate apparent ages. The apparent age data is from the ChaLearn Looking At People (LAP) challenge 2015. Using the protocol and time frame given by the challenge competition, we have achieved an error of 0.294835 on the final evaluation, and our result has been ranked the 3rd place in this competition.