Miracle-dz/UniFAR-FTUs huggingface dataset:
Starlings Can Categorize Symmetry Differences in Dot Displays

Fluctuating asymmetry is an estimate of developmental stability and, in some cases, the asymmetry of morphological traits can reflect aspects of individual fitness. As asymmetry can be a marker for fitness, it has been proposed that organisms could use morphological asymmetry as a direct visual cue during inter‐ and intraspecific encounters. Despite some experimental evidence to support this prediction, the perceptual abilities of animals to detect and respond to symmetry differences have been largely overlooked. Studying the ability of animals to perceive symmetry and factors that affect this ability are crucial to assessing whether fluctuating asymmetry could be used as a visual cue in nature. In this study, we investigated the ability of wild‐caught European starlings Sturnus vulgaris to learn to discriminate symmetry from asymmetry in random dot patterns through operant learning experiments. The birds did not possess a spontaneous preference for either symmetry or asymmetry. The birds learned a symmetry preference, although the learning process took longer than that previously reported for pigeons Columba livia and was more error prone. After being trained to discriminate symmetry differences in random dot patterns, birds successfully transferred their symmetry discrimination abilities to a set of novel stimuli that they had not previously seen. This indicates that starlings can form a mental categorization of visual stimuli on the basis of a somewhat generalized symmetry phenomenon. We discuss these findings in relation to the probability that birds use fluctuating asymmetry as a visual cue.
Mechanisms of Self-Sustained Oscillatory States in Hierarchical Modular Networks with Mixtures of Electrophysiological Cell Types

In a network with a mixture of different electrophysiological types of neurons linked by excitatory and inhibitory connections, temporal evolution leads through repeated epochs of intensive global activity separated by intervals with low activity level. This behavior mimics “up” and “down” states, experimentally observed in cortical tissues in absence of external stimuli. We interpret global dynamical features in terms of individual dynamics of the neurons. In particular, we observe that the crucial role both in interruption and in resumption of global activity is played by distributions of the membrane recovery variable within the network. We also demonstrate that the behavior of neurons is more influenced by their presynaptic environment in the network than by their formal types, assigned in accordance with their response to constant current.
Multifunctional Nanofibers towards Active Biomedical Therapeutics

One-dimensional (1-D) nanostructures have attracted enormous research interest due to their unique physicochemical properties and wide application potential. These 1-D nanofibers are being increasingly applied to biomedical fields owing to their high surface area-to-volume ratio, high porosity, and the ease of tuning their structures, functionalities, and properties. Many biomedical nanofiber reviews have focused on tissue engineering and drug delivery applications but have very rarely discussed their use as wound dressings. However, nanofibers have enormous potential as wound dressings and other clinical applications that could have wide impacts on the treatment of wounds. Herein, the authors review the main fabrication methods of nanofibers as well as requirements, strategies, and recent applications of nanofibers, and provide perspectives of the challenges and opportunities that face multifunctional nanofibers for active therapeutic applications.
Acoustic scene classification using convolutional neural networks

Acoustic scene classiﬁcation (ASC) aims to distinguish between different acoustic environments and is a technology which can be used by smart devices for contextualization and personalization. Standard algorithms exploit hand-crafted features which are unlikely to offer the best potential for reliable classiﬁcation. This paper reports the ﬁrst application of convolutional neural networks (CNNs) to ASC, an approach which learns discriminant features automatically from spectral representations of raw acoustic data. A principal inﬂuence on performance comes from the speciﬁc convolutional ﬁlters which can be adjusted to capture different spectro-temporal, recurrent acoustic structure. The proposed CNN approach is shown to outperform a Gaussian mixture model baseline for the DCASE 2016 database even though training data is sparse.
Attention-Based Convolutional Neural Network for Semantic Relation Extraction

Nowadays, neural networks play an important role in the task of relation classification. In this paper, we propose a novel attention-based convolutional neural network architecture for this task. Our model makes full use of word embedding, part-of-speech tag embedding and position embedding information. Word level attention mechanism is able to better determine which parts of the sentence are most influential with respect to the two entities of interest. This architecture enables learning some important features from task-specific labeled data, forgoing the need for external knowledge such as explicit dependency structures. Experiments on the SemEval-2010 Task 8 benchmark dataset show that our model achieves better performances than several state-of-the-art neural network models and can achieve a competitive performance just with minimal feature engineering.
An event-based language for dynamic binary translation frameworks

Dynamic binary translation (DBT) frameworks such as DynamoRIO [1] or Granary apply just-in-time rewriting techniques to allow pervasive instrumentation of a target program, for applications such as instruction-level profiling or watchpoints. This is a powerful approach, but analysis tools based on DBT frameworks are difficult to develop. Client modules written using a DBT framework must specify the instrumentation to perform on each basic block of the target program, and make use of explicit synchronization when aggregating data from multiple threads of a program. This can result in hundreds of lines of code for even simple analysis tools.
Spatial Structures of the Environment and of Dispersal Impact Species Distribution in Competitive Metacommunities

The correspondence between species distribution and the environment depends on species’ ability to track favorable environmental conditions (via dispersal) and to maintain competitive hierarchy against the constant influx of migrants (mass effect) and demographic stochasticity (ecological drift). Here we report a simulation study of the influence of landscape structure on species distribution. We consider lottery competition for space in a spatially heterogeneous environment, where the landscape is represented as a network of localities connected by dispersal. We quantified the contribution of neutrality and species sorting to their spatial distribution. We found that neutrality increases and the strength of species-sorting decreases with the centrality of a community in the landscape when the average dispersal among communities is low, whereas the opposite was found at elevated dispersal. We also found that the strength of species-sorting increases with environmental heterogeneity. Our results illustrate that spatial structure of the environment and of dispersal must be taken into account for understanding species distribution. We stress the importance of spatial geographic structure on the relative importance of niche vs. neutral processes in controlling community dynamics.
HMM-based Pronunciation Dictionary Generation

In this paper, we discuss automatically generating a phonetic pronunciation from an orthographic spelling of words. The letter-sequence to phoneme-sequence mapping is useful in a variety of contexts, including text-to-speech applications, automatic spelling correction, and generating a pronunciation lexicon for a new training dataset which contains out-of-vocabulary words. A system based on hidden Markov models is described, and is then used to generate pronunciations for outof-vocabulary the words and word fragments in the Fisher conversational telephone speech corpus. The Fisher phonetic pronunciations are analyzed to show that for conversational speech and a typical phonetic dictionary, a large amount of lexical ambiguity remains even when the word boundaries and phonetic transcriptions are known.
Interoperable and adaptive fuzzy services for ambient intelligence applications

In Ambient Intelligence (AmI) vision, people should be able to seamlessly and unobtrusively use and configure the intelligent devices and systems in their ubiquitous computing environments without being cognitively and physically overloaded. In other words, the user should not have to program each device or connect them together to achieve the required functionality. However, although it is possible for a human operator to specify an active space configuration explicitly, the size, sophistication, and dynamic requirements of modern living environment demand that they have autonomous intelligence satisfying the needs of inhabitants without human intervention. This work presents a proposal for AmI fuzzy computing that exploits multiagent systems and fuzzy theory to realize a long-life learning strategy able to generate context-aware-based fuzzy services and actualize them through abstraction techniques in order to maximize the users' comfort and hardware interoperability level. Experimental results show that proposed approach is capable of anticipating user's requirements by automatically generating the most suitable collection of interoperable fuzzy services.
The lung diseases diagnosis software: Influenza and Tuberculosis case studies in the cloud computing environment

This research paper describes study of the lung diseases diagnosis software with Influenza and Tuberculosis as the cases study. Influenza is a highly contagious infection of the respiratory tract. In Indonesia, numerous number of Avian-Influenza (H5N1) cases occurred in rural area, such as cases in Garut, Jawa Barat in 2006 which started in Cikelet district causing 14 infected-cases and 5 death-cases and has put Garut in an emergency situation until 2010. The H5N1 detection in rural area requires fast yet accurate diagnosis result to enable immediate curative actions. The most accurate result is provided by RT-PCR (Reverse-Transcription Polymerase-Chain-Reaction), which is high investment, and usually locates in urban area. Another diagnostic tool, rapid diagnostic test, is most common tool but gives inaccurate result because of its low sensitivity and specificity. In case of Tuberculosis, Indonesia ranks number 3, after India and China, as a country with most number of infected-people. Symptoms are chronic-cough with blood-tinged-sputum, fever, night-sweats and weight-loss. Tuberculosis is diagnosed by identifying Mycobacterium-tuberculosis organism in clinical sample. When this is not possible, probable diagnosis may be made using imaging X-rays or scans, tuberculin skin test and/or Interferon-Gamma-Release-Assay (IGRA). The main problem with tuberculosis diagnosis is difficulty in culturing this slow-growing organism in laboratory, which may take 4 to 12 weeks for blood or sputum-culture. The requirement is to create a first-hand diagnostic tool which allows paramedic mobility to rural areas for Influenza/Tuberculosis diagnostic purposes. The study first introduces lung auscultation method, then analysis and preliminary design of lung diseases diagnosis software. To enable each Lung Health for Public Council (Balai Besar Kesehatan Paru Masyarakat) and Health Service Office (Dinas Kesehatan) within province/region/country as data owner to maintain its own data, and to provide the data confidentiality as well, the cloud computing environment is then applied.
Supplementary Material for “ Compact Hyperplane Hashing with Bilinear Functions ”

Theorem 2 in the main paper introduces the asymptotic complexity of our proposed randomized bilinear hashing scheme BH-Hash. For self-contained consideration, here we provide the proof, which is basically following the technique previously used in the proof of Theorem 1 in (Gionis et al., 1999). Recall that in Theorem 1 of the main paper, we have shown that the proposed BH-Hash is (r, r(1 + ǫ), p1, p2)-sensitive. Particularly, we show that p1 = 1 2 − 2r π2 and p2 = 1 2 − 2r(1+ǫ) π2 for BH-Hash.
The Nonlinear PCA Learning Rule and Signal Separation - Mathematical Analysis

It has been veriied experimentally that nonlinear versions of the PCA network learning rules for the weights of a neural layer produce neurons that have signal separation capabilities. One of the learning rules earlier proposed by the author is studied here mathematically to analyze why and how the algorithm works in this application. It is shown that for input vectors whose density is symmetrical around the origin and has equal variances for each element, the weight matrix obtained as the asymptotic solution of the nonlinear PCA learning rule is in some cases a rotation of the input vector to statistically independent directions. This explains why it can be used for image and speech signal separation. Suucient conditions are formulated, depending on the nonlinear neuron activation function and on the probability densities of the original signal components. It is shown that a sigmoidal nonlinearity as the activation function is feasible for at sub-Gaussian densities of the original signals, while polynomial activation functions are feasible for sharp super-Gaussian densities. An example with uniform densities and a hyperbolic tangent activation function is analyzed in depth both by mathematical analysis and by simulations on the learning rule. Aapo Hyvrinen for helpful comments on the preliminary version of the manuscript.
Adult-onset Still's disease with atypical cutaneous manifestations

Abstract The diagnosis of adult-onset Still's disease (AOSD) can be very difficult. There are no specific tests available, and diagnosis is usually based on a symptom complex and the well-described typical evanescent rash seen in the majority of patients. However, in recent years, other atypical cutaneous manifestations of AOSD have been reported. These atypical skin eruptions often present in addition to the typical evanescent rash but may also be the only skin manifestation, resulting in delayed diagnosis because of under-recognition. In this study, we present 3 new cases of AOSD with atypical cutaneous manifestations diagnosed during a 30-year period in our department and review 78 additional cases previously reported (PubMed 1990–2016). These 81 patients form the basis of the present analysis. The overall prevalence of atypical cutaneous manifestations in our AOSD population was 14%. These manifestations may appear at any time over the course of the disease, and usually occur in patients who have persistent and severe disease, with a considerable frequency of clinical complications (23%), including serositis, myopericarditis, lung involvement, abdominal pain, neurologic involvement, and reactive hemophagocytic syndrome. The most representative and frequent lesion among the nonclassical skin rashes is the development of persistent pruritic papules and/or plaques. Interestingly, these lesions show a distinctive histological pattern. Other, less frequently observed lesions include urticaria and urticaria-like eruptions, generalized or widespread non-pruritic persistent erythema, vesiculopustular eruptions, a widespread peau d’orange appearance of the skin, and edema of the eyelids mimicking dermatomyositis without any accompanying skin lesion. The great majority of these patients required medium or high doses of glucocorticoids (including intravenous methylprednisolone pulse therapy in some cases) and, in nearly 40%, a more potent or maintenance immunotherapy with immunosuppressant drugs and/or biologic agents (mainly anakinra or tocilizumab) to control or manage symptoms because of a polycyclic or chronic course. The development of atypical cutaneous manifestations seems to be associated with a potentially worse prognosis, with a mortality rate reaching 8% primarily because of infectious complications related to immunosuppressive therapy. In conclusion, the appearance of atypical cutaneous manifestations is not uncommon in AOSD. Recognition of this clinical variant is crucial for the early diagnosis of AOSD, as it might imply persistent disease activity and the need for more aggressive treatment.
A Survey of Peer-to-Peer Networks

The limitations of client/server systems become evident in large scale distributed environments. In such systems individual resources are concentrated on one or a small number of nodes and in order to provide access with acceptable response times sophisticated load balancing and fault-tolerance algorithms have to be applied. Also limitation on the network bandwidth adds the bottleneck problem. These problems have motivated researchers to come up with approaches to distribute processing loads and network bandwidth among all nodes participating in a distributed system. Peer-to-Peer systems offer an alternative to traditional client/server systems that solve bottleneck problems but need complex algorithms. This paper provides an overview on different p2p architectures and compares them with each other regarding some issues such as scalability, fault tolerance and manageability. P2P systems constitute highly dynamic networks of peers with complex topologies that create an overlay network. P2P applications need sophisticated discovery mechanisms to enable peers to find, identify and communicate with other peers. We discuss the discovery mechanisms in p2p systems which are based on the topology of the network and study the potential challenges in p2p networks such as reliability, security and adaptability.
MRI denoising using pixel-selection-based non-local means

The non-local means algorithm has been proven to allow a good image restoration performance of MR images. However, this method utilizes all pixels of the images, some of which might be useless for noise removal. In this paper, an improved pixel-selection-based non-local means algorithm which avoids the usage of useless pixels is supplied. The principle of pixel selection is based on the similarity between neighborhoods of the median value and mean absolute error which are insensitive to noise. The denoising performance is evaluated through quantitative methods (peak signal-to-noise ratio and mean structure similarity) and qualitative methods (a visual comparison of image residuals and denoised images). From the results over synthetic and real images we can conclude that the pixels-selection-based non-local means algorithm is of overall satisfaction for MR image denoising.
Identification and assessment of risks associated with ERP post-implementation in China

Purpose – The purpose of this paper is to identify, assess and explore potential risks that Chinese companies may encounter when using, maintaining and enhancing their enterprise resource planning (ERP) systems in the post‐implementation phase.Design/methodology/approach – The study adopts a deductive research design based on a cross‐sectional questionnaire survey. This survey is preceded by a political, economic, social and technological analysis and a set of strength, weakness, opportunity and threat analyses, from which the researchers refine the research context and select state‐owned enterprises (SOEs) in the electronic and telecommunications industry in Guangdong province as target companies to carry out the research. The questionnaire design is based on a theoretical risk ontology drawn from a critical literature review process. The questionnaire is sent to 118 selected Chinese SOEs, from which 42 (84 questionnaires) valid and usable responses are received and analysed.Findings – The findings ident.
Distance Metric Learning and Support Vector Machines for Classification of Mass Spectrometry Proteomics Data

Mass spectrometry becomes the most widely used measurement in proteomics research. High dimensionality of features and small dataset are two major limitations restrict the accuracy of classification in mass spectrum data analysis. To improve the data mining result, two major issues need to be highlighted, which are feature extraction and feature selection. The quality of the feature set determines the reliability of the prediction of disease status. A well-known approach is to detect peak values and apply support vector machine recursive feature elimination (SVMRFE) to choose feature sets for classification. In this article, we successfully apply a distance metric learning to classify proteomics mass spectrometry data. Experimental results show that distance metric learning can successfully be applied to the classification of proteomics data and the results are comparable to the best results by applying SVM to the feature sets chosen with the use of SVMRFE.
CpG methylation analysis at the putative differential methylated region of Xist gene in goats (Capra hircus) somatic cells.

________________________________________________________________________________________ PRUSTY, B. R. , D. KUMAR, T. K. PALAI, S. C. JENA, M. K. BEDEKAR, S. SINGH, B. C. SARKHEL: CpG methylation analysis at the putative differential methylated region of the Xist gene in goat (Capra hircus) somatic cells. Vet. arhiv 84, 365-376, 2014. ABSTRACT X chromosome inactivation is a complex epigenetic process that ensures equal expression of most Xlinked genes in both sexes. Xist (X-chromosome inactive specifi c transcript) is a crucial development related gene that initiates X chromosome inactivation in mammalian females during embryogenesis. DNA methylation is a major chromatin modifi cation involved in transcriptional repression of genes, regulating X chromosome inactivation, genomic imprinting, and cell or tissue specifi c gene expression and in many other processes during embryo development. Appropriate DNA methylation of cytosine residues at CpG dinucleotide within the differential methylated region of many imprinted genes is required for normal post implantation development of a mammalian embryo. An aberrant pattern of DNA methylation because of incomplete or anomalous nuclear reprogramming of somatic cells is a reason for poor success rate of somatic cell nuclear transfer in farm animals. In the present study, a putative differential methylated region was identifi ed for fi rst time in goats (Capra hircus) followed by study of the DNA methylation status of this region, using bisulfi te sequencing analysis taking fi broblast and cumulus cells from goats. The putative differential methylated region contains 10 CpG sites with a mean methylation level of 92% and 94% for fi broblast and cumulus cells respectively. Phylogenetic analysis of this region revealed goats and sheep positioned in a single cluster distinct from others studied. This study will be a defi nite help in future research on molecular events responsible for complete nuclear reprogramming of goat somatic cells in SCNT.
Inverted Slot-Mode Slow-Wave Structures for Traveling-Wave Tubes

The dispersion and impedance characteristics of an inverted slot-mode (ISM) slow-wave structure computed by three different techniques, i.e., an analytical model based on a periodic quasi-TEM approach, an equivalent-circuit model, and 3-D electromagnetic simulation are obtained and compared. The comparison was carried out for three different slot-mode structures at S-, C-, and X-bands. The approach was also validated with experimental measurements on a practical X-band ISM traveling-wave tube. The design of ferruleless ISM slow-wave structures, both in circular and rectangular formats, has also been proposed and the predicted dispersion characteristics for these two geometries are compared with 3-D simulation and cold-test measurements. The impedance characteristics for all three designs are also compared.