Miracle-dz/UniFAR-FTUs huggingface dataset:
Directional benefit in simulated classroom environments.

PURPOSE To examine speech recognition performance and subjective ratings for directional and omnidirectional microphone modes across a variety of simulated classroom environments. METHOD Speech recognition was measured in a group of 26 children age 10-17 years in up to 8 listening environments. RESULTS Significant directional benefit was found when the sound source(s) of interest was in front, and directional decrement was measured when the sound source of interest was behind the participants. Of considerable interest is that a directional decrement was observed in the absence of directional benefit when sources of interest were both in front of and behind the participants. In addition, limiting directional processing to the low frequencies eliminated both the directional deficit and the directional advantage. CONCLUSIONS Although these data support the use of directional hearing aids in some noisy school environments, they also suggest that use of the directional mode should be limited to situations in which all talkers of interest are located in the front hemisphere. These results highlight the importance of appropriate switching between microphone modes in the school-age population.
Respiratory rehabilitation: a physiotherapy approach to the control of asthma symptoms and anxiety

OBJECTIVES: The objectives of this study were to verify the degree of anxiety, respiratory distress, and health-related quality of life in a group of asthmatic patients who have experienced previous panic attacks. Additionally, we evaluated if a respiratory physiotherapy program (breathing retraining) improved both asthma and panic disorder symptoms, resulting in an improvement in the health-related quality of life of asthmatics. METHODS: Asthmatic individuals were assigned to a chest physiotherapy group that included a breathing retraining program held once a week for three months or a paired control group that included a Subtle Touch program. All patients were assessed using the Diagnostic and Statistical Manual of Mental Disorders IV, the Sheehan Anxiety Scale, the Quality of Life Questionnaire, and spirometry parameter measurements. RESULTS: Both groups had high marks for panic disorder and agoraphobia, which limited their quality of life. The Breathing Retraining Group program improved the clinical control of asthma, reduced panic symptoms and agoraphobia, decreased patient scores on the Sheehan Anxiety Scale, and improved their quality of life. Spirometry parameters were unchanged. CONCLUSION: Breathing retraining improves the clinical control of asthma and anxiety symptoms and the health-related quality of life in asthmatic patients.
Synthetic Aperture Radar Ship Detection Using Modifi Ed Gamma Fisher Metric

This article proposes a novel ship detection method for high-resolution SAR images. Our goal is to look at this question from a information geometry point of view. The method consists of two steps: construction of revised metric and Riemann structure, and extraction of targets. For the first step of the process, a revised metric is introduced on Gamma 2-manifold. We construct a special Riemannian structure by using the proposed metric. For the second step, the regions of interest (ROIs) are extracted out based on the Riemann structure. Experimental results of the detection method on SAR images show that the algorithm presented is effective.
(Sub-)Optimality of Treating Interference as Noise in the Cellular Uplink With Weak Interference

Despite the simplicity of the scheme of treating interference as noise (TIN), it was shown to be sum-capacity optimal in the Gaussian interference channel (IC) with very-weak (noisy) interference. In this paper, the two-user IC is altered by introducing an additional transmitter that wants to communicate with one of the receivers of the IC. The resulting network thus consists of a point-to-point channel interfering with a multiple access channel (MAC) and is denoted by PIMAC. The sum-capacity of the PIMAC is studied with main focus on the optimality of TIN. It turns out that TIN in its naive variant, where all transmitters are active and both receivers use TIN for decoding, is not the best choice for the PIMAC. In fact, a scheme that combines both time division multiple access and TIN (TDMA-TIN) strictly outperforms the naive-TIN scheme. Furthermore, it is shown that in some regimes, TDMA-TIN achieves the sum-capacity for the deterministic PIMAC and the sum-capacity within a constant gap for the Gaussian PIMAC. In addition, it is shown that, even for very-weak interference, there are some regimes where a combination of interference alignment with power control and TIN at the receiver side outperforms TDMA-TIN. As a consequence, on the one hand, TIN in a cellular uplink is approximately optimal in certain regimes. On the other hand, those regimes cannot be simply described by the strength of interference.
Enumeration of Respiring Pseudomonas spp. in Milk within 6 Hours by Fluorescence In Situ Hybridization following Formazan Reduction

ABSTRACT Respiring Pseudomonas spp. in milk were quantified within 6 h by fluorescence in situ hybridization (FISH) with vital staining. FISH with an oligonucleotide probe based on 16S rRNA sequences was used for the specific detection of Pseudomonas spp. at the single cell level. 5-Cyano-2,3-ditolyl tetrazolium chloride (CTC) was used to estimate bacterial respiratory activity. The numbers of respiring Pseudomonas cells as determined by FISH with CTC staining (CTC-FISH) were almost the same or higher than the numbers of CFU as determined by the conventional culture method.
Overview of Classification Subtask at NTCIR-5 Patent Retrieval Task

This paper describes Classification Subtask at NTCIR-5 Patent Retrieval Task. We perform two subtasks for patent classification using a multi-dimensional classification structure called “F-term (File Forming Term) classification system”. The first one is Theme Categorization Subtask, where each participant classifies a patent into technological fields called themes. The second one is F-term Categorization Subtask, where each participant classifies a patent, whose theme has been given, into multifaceted categories called F-terms. We overview the designs of these subtasks, the test collections produced, and the evaluation results.
Rushes video summarization by object and event understanding

This paper explores a variety of visual and audio analysis techniques in selecting the most representative video clips for rushes summarization at TRECVID 2007. These techniques include object detection, camera motion estimation, keypoint matching and tracking, audio classification and speech recognition. Our system is composed of two major steps. First, based on video structuring, we filter undesirable shots and minimize theinter-shot redundancy by repetitive shot detection. Second, a representability measure is proposed to model the presence of objects and four audio-visual events: motion activity of objects, camera motion, scene changes,and speech content, in a video clip. The video clips with the highest representability scores are selected for summarization. The evaluation at TRECVID shows that our experimental results are highly encouraging, where we rank first in EA (easy to understand), second in RE (little redundancy) and third in IN (inclusion of objects and events).
The Integration of Extrarational and Rational Learning Processes: Moving Towards the Whole Learner

The polemical debate between rationalists and nonrationalists need not occur. The paper briefly traces the evolution of information processing theory as it pertains to the emerging emphasis being placed on the psychological unconscious in classroom learning. Theory and practice are then synthesized, resulting in the development of a strategy which emphasizes extrarational learning processes (i.e., processes which occur at the unconscious level of the human mind) as well as a strategy which integrates rational and extrarational processes. It is felt that the self directed whole learner must and can be taught to make inner connections between these two levels of the human mind.
DYNAMICAL, SYMPLECTIC AND STOCHASTIC PERSPECTIVES ON GRADIENT-BASED OPTIMIZATION

Our topic is the relationship between dynamical systems and optimization. This is a venerable, vast area in mathematics, counting among its many historical threads the study of gradient flow and the variational perspective on mechanics. We aim to build some new connections in this general area, studying aspects of gradient-based optimization from a continuous-time, variational point of view. We go beyond classical gradient flow to focus on second-order dynamics, aiming to show the relevance of such dynamics to optimization algorithms that not only converge, but converge quickly. Although our focus is theoretical, it is important to motivate the work by considering the applied context from which it has emerged. Modern statistical data analysis often involves very large data sets and very large parameter spaces, so that computational efficiency is of paramount importance in practical applications. In such settings, the notion of efficiency is more stringent than that of classical computational complexity theory, where the distinction between polynomial complexity and exponential complexity has been a useful focus. In large-scale data analysis, algorithms need to be not merely polynomial, but linear, or nearly linear, in relevant problem parameters. Optimization theory has provided both practical and theoretical support for this endeavor. It has supplied computationally-efficient algorithms, as well as analysis tools that allow rates of convergence to be determined as explicit functions of problem parameters. The dictum of efficiency has led to a focus on algorithms that are based principally on gradients of objective functions, or on estimates of gradients, given that Hessians incur quadratic or cubic complexity in the dimension of the configuration space (Bottou, 2010; Nesterov, 2012). More broadly, the blending of inferential and computational ideas is one of the major intellectual trends of the current century—one currently referred to by terms such as “data science” and “machine learning. ” It is a trend that inspires the search for new mathematical concepts that allow computational and inferential desiderata to be studied jointly. For example, one would like to impose runtime budgets on data-analysis algorithms as a function of statistical
Individual, Organizational and Emotional Determinants of Teacher Self-Efficacy

Teachers’ self-efficacy has been related to teachers' behavior in the classroom and to teacher burnout. Only few studies have examined the ability to influence teachers' self-efficacy or predict it from different angles. The present research aimed to draw together and examine individual, organizational, and emotional variables that may predict teacher self-efficacy, and contribute to our ability to strengthen teachers’ beliefs about their capability. Results indicated that emotional self-efficacy and empathy were the strongest predictors of teacher self-efficacy and explained 36% of its variance. Individual characteristics were the second predictor of teacher self-efficacy and explained 11% of its variance. Organizational factors were the weakest predictor of teacher self-efficacy (1% of variance). These findings may contribute to the ongoing argument that the focus on teachers' emotions and emotional abilities must grow in order to better the overall educational milieu.
Inner Experience and Neuroscience: Merging Both Perspectives

The study of consciousness has advanced rapidly over the last two decades. And yet there is no clear path to creating models for a direct science of human experience or for integrating its insights with those of neuroscience, psychology, and philosophy. In Inner Experience and Neuroscience, Donald Price and James Barrell show how a science of human experience can be developed through a strategy that integrates experiential paradigms with methods from the natural sciences. They argue that the accuracy and results of both psychology and neuroscience would benefit from an experiential perspective and methods. Price and Barrell describe phenomenologically based methods for scientific research on human experience, as well as their philosophical underpinnings, and relate these to empirical results associated with such phenomena as pain and suffering, emotions, and volition. They argue that the methods of psychophysics are critical for integrating experiential and natural sciences, describe how qualitative and quantitative methods can be merged, and then apply this approach to the phenomena of pain, placebo responses, and background states of consciousness. In the course of their argument, they draw on empirical results that include qualitative studies, quantitative studies, and neuroimaging studies. Finally, they propose that the integration of experiential and natural science can extend efforts to understand such difficult issues as free will and complex negative emotions including jealousy and greed.
Web Mining: Opinion and Feedback Analysis for Educational Institutions

Big amount of data available in the forms of reviews, opinions, feedbacks, remarks, comments, observations, clarifications, and explanations that require a robust mechanism to store, retrieve, analyze, and management. In this paper, we are proposing system that provides review or summary of above mention text data available on web for an educational institute. Due to big data size above system can be extended as real time recommendation system for Big Data analysis as future enhancement.
Mining Tables from Large Scale HTML Texts

Table is a very common presentation scheme, but few papers touch on table extraction in text data mining. This paper focuses on mining tables from large-scale HTML texts. Table filtering, recognition, interpretation, and presentation are discussed. Heuristic rules and cell similarities are employed to identify tables. The F-measure of table recognition is 86.50%. We also propose an algorithm to capture attribute-value relationships among table cells. Finally, more structured data is extracted and presented.
Dynamic Memory Management for GPU-Based Training of Deep Neural Networks

Deep learning has been widely adopted for different applications of artificial intelligence - speech recognition, natural language processing, computer vision etc. The growing size of Deep Neural Networks (DNNs) has compelled the researchers to design memory efficient and performance optimal algorithms. Apart from algorithmic improvements, specialized hardware like Graphics Processing Units (GPUs) are being widely employed to accelerate the training and inference phases of deep networks. However, the limited GPU memory capacity limits the upper bound on the size of networks that can be offloaded to and trained using GPUs. vDNN addresses the GPU memory bottleneck issue and provides a solution which enables training of deep networks that are larger than GPU memory. In our work, we characterize and identify multiple bottlenecks with vDNN like delayed computation start, high pinned memory requirements and GPU memory fragmentation. We present vDNN++ which extends vDNN and resolves the identified issues. Our results show that the performance of vDNN++ is comparable or better (up to 60% relative improvement) than vDNN. We propose different heuristics and order for memory allocation, and empirically evaluate the extent of memory fragmentation with them. We are also able to reduce the pinned memory requirement by up to 60%.
Random degree–degree correlated networks

Correlations may affect propagation processes on complex networks. To analyze their effect, it is useful to build ensembles of networks constrained to have a given value of a structural measure, such as the degree–degree correlation r, but that are random in other respects and preserve the degree sequence. This can be done through Monte Carlo optimization procedures. Meanwhile, when tuning r, other network properties may concomitantly change. Thus, in this work we analyze, for the r-ensembles, the impact of r on properties such as transitivity, branching and characteristic path lengths, that are relevant when investigating spreading phenomena on these networks. The present analysis is performed for networks with degree distributions of two main types: either localized around a typical degree (with exponentially bounded asymptotic decay) or broadly distributed (with power-law decay). Size effects are also investigated.
Characterization of wheat / A egilops ventricosa introgression and addition lines with respect to the M v genome

Stable wheal-Aegilops introgression lines with 42 chromosomes (H-93). derived by repeated seifing from a cross ( Triticum turgidum x Aegilops ventricosa) x T. aestivum. nave been characterized using the following DNA probes and isozyme markers: (1) single or low-copy DNA fragments from Ae. ventricosa: (2) known cDNA probes corresponding to ai-thionin. monomeric x-amylase inhibitor. the CM3 subunit of tetramcric a-amylase inhibitor. and sucrose synthase from wheat: (3) anonymous cDNA probes from wheat that have been mapped by Sharp et al. (1989): (4) isozyme markers corresponding to aconitase. shikimate dehydrogenase. adenylate kinase. and endopeptidase. Meiotic metaphases of appropriate hybrids involving selected H-93 lines have been investigated by the Gicmsa C-banding technique. The substitution of whole chromosomes [(5A)5M: (4D)4M: i5D) 5\V: (7D) 7M] and chromosomal segments (1M: 3M: 5M: 7M) from the M genome of Aegilops ventricosa has been demonstrated. The distribution of selected markers among putative wheat-. -íe. ventricosa addition lines has also been investigated. The 7M addition has been characterized for the first time, while the identity of the previously reported 5M and 6M additions has been confirmed.
Effect of nonylphenol on semen quality and testosterone level in male breeders of Brown Tsaiya

Nonylphenol (NP), an estrogen like compound classified as an endocrine disrupter capably interfering hormonal system of numerous organisms, originates principally from the degradation of nonylphenol ethoxylates (NE) widely used in industrial, agricultural, commercial and household applications as detergents, emulsifiers, wetting and dispersing agents. Due to the extensive use of NE, NP may reach either sewage treatment fields or rivers and contaminate environments from many aspects. Water fowls mostly raised with water pools in which the water coming from either underground or rivers may be contaminated with NP and were consequently affected of their reproductive performances. Thus, the study was to investigate the effect of NP on reproductive performances in male breeders of Brown Tsaiya (MBBT) in Taiwan. Sixty MBBT at age of 50 wks were randomly assigned into five treatments, i.e., control, corn oil, and oral administration of either 1 (NP1), 10 (NP10) and 250 (NP250) mg NP dissolved in corn oil/kg body weight daily for 28 days. Semen was collected weekly for analyses of counts, viability, acrosome integrity, mitopotential integrity, total cytosolic calcium content and DNA condensation (sperm chromatin structure assay, SCSA) of sperms, and testosterone level. Fertility was determined with intermingled semen collected from each bird in the same treatment for artificial insemination. Results revealed that there were no significant differences among the treatments in counts, viability, acrosome integrity, mitopotential integrity and total cytosolic calcium content of the sperms as well as fertility. SCSA in NP10 or NP250 was, however, significantly higher than that in the other treatments (P<0.05). Testosterone level in NP1 was significantly lower than that in the other treatments (P<0.05). In conclusion, nonylphenol may affect semen quality and testosterone level in male breeders of Brown Tsaiya and consequently reduced their reproductive performance if the duration and/or the dosage of exposure are over the threshold which has not been fully and clearly elucidated so far.
AN ANALYSIS OF INVENTORY INVESTMENT IN SOUTH AFRICA

This paper describes the evolution of inventory investment in South Africa over the past two decades, and identifies the factors influencing inventory investment over this period. An econometric model of inventory investment in South Africa, based on the production smoothing approach, is constructed. The results of the model indicate that actual sales, production, unfilled orders, price levels, interest rates and expected sales have an influence on the evolution of inventory investment. These variables are directly or indirectly influenced by macroeconomic policy decisions and through their influence on inventory investment they also influence changes in gross domestic product. Therefore, prior information on the factors that influence inventory investment contributes to explaining changes in gross domestic product and may help to prepare more accurate short-term forecasts of overall economic activity. Production and sales are economic processes that continuously adjust to changes in demand and supply conditions, but it seldom happens that changes in the one would exactly meet changes in the other. The difference between demand and supply is reflected by the change in inventories. An increase in the level of inventories implies that a part of production was not sold. Conversely, sales exceed production when inventory levels decrease. Conventionally, inventory changes occur either as the outcome of planned or predetermined action among producers and consumers, or are the net result of exogenous shocks influencing the behaviour of producers and consumers (Kahn & McConnell 2002). Inventory investment contributes significantly to the changes in gross domestic expenditure and gross domestic product, not through its size, but as a result of its rate of change. Analysing the factors that influence investment in inventories should lead to a better understanding of the dynamics of the economic system. The purpose of this paper is to describe the evolution of inventory investment in the South African economy over the past two decades, and to identify the factors that had an influence on inventory investment over this period. Following this introduction, the composition, structure and cyclical nature of inventory investment in South Africa is discussed, followed by the methodology for estimating inventory investment at the
Limitation of body mass of herbivores-Allometry of food quality and of digestive aspects

The relation between body mass (BM) and digesta mean retention time (MRT) was in the focus of several studies in the last years. Because of the accepted linear scaling between gut capacity and BM in herbivorous mammals, and the fact that the energy intake of animals scales to BM to the power of 0.75 (BM) it was assumed that MRT scales with BM. Literature studies that tested this hypothesis produced differing results. This study was conducted with 8 ruminating species (n = 2 6) and 6 hindgut fermenting species/breeds (n = 2 6, warthog n = 1) of an average BM range of 60 4000 kg. All animals received a ration of 100% grass hay with ad libitum access. Dry matter intake was measured and MRT was estimated by the use of a fluid and a particle (<2 mm) marker. There was no significant scaling of MRTparticle with BM for hindgut fermenters (30.97 BM , p = 0.9120) and only a trend for ruminants (29.11 BM, p = 0.0730). Ruminants on average had a MRTparticle 1.61 fold longer than that of the hindgut fermenters. Whereas an exponent of 0.25 is reasonable from theoretical considerations on the scaling of MRT with BM much lower exponents were found in this and other studies. The energetic benefit of increasing MRT is by no means continuously increasing since the energy released from a given food unit via digestion is decreasing continuously over time. The low and non-significant scaling factors for both digestion types suggest that MRT is largely independent of BM or at least considerably less influenced than often reported, with a scaling exponent of not more than 0.1.