Miracle-dz/UniFAR-FTUs huggingface dataset:
Menzerath's law for the smallest grammars

The aim of this article is to develop a discussion of Menzerath’s law from the point of view of information theory. More precisely, we shall seek for links between the law and the recently abstracted mathematical problem of the smallest grammar (Kieffer & Yang 2000, Charikar et al. 2005). The Altmann-Menzerath law is a general statement about the natural language constructions which says: The longer a construction, the shorter are its constituents. (1) For example, the average number of phonemes in a syllable of a word having n syllables decreases with n (Menzerath 1928, Altmann 1980). Analogical regularity was observed for a number of other linguistic entities. Similarly to Zipf’s law, law (1) is not restricted to human language. It has also been observed in DNA and in the social behavior of animals (Altmann & Schwibbe 1989). Such omnipresence needs explanation. Possibly, Zipf’s and Menzerath’s laws may be given many good explanations depending on the context where they are found. Three simple models by Mandelbrot (1953), Simon (1955), and Miller (1957) inspired multiple explanations of Zipf’s law. Less work, however, was done on conceiving analogical models for Menzerath’s law (Köhler 1989). Despite some similarity of wording, law (1) has nothing to do with regression towards the mean (Galton 1886). A new idea for explaining Zipf’s and Menzerath’s laws comes with the concept of the smallest grammar for a text (Dębowski 2004d). Consider context-free grammars that generate exactly one string, such as
Improved Keyword and Keyphrase Extraction from Meeting Transcripts

: Many a times, the size of the meeting transcript will be so large that identifying the real content in it is quite difficult. Documents do not have keywords like meeting transcripts, so it is required to generate keywords automatically in the large amount of audio and video files. This job can be simplified by extracting the keywords and keyphrases from the meeting transcripts. This paper aims to extract keywords and keyphrases from meeting transcripts and also to add some additional features for improving the keyword and keyphrase extraction method. There are various methods that can be employed to extract keywords such as Support Vector Machines(SVM), Max Entropy Classifier (MaxEnt), Latent Dirichlet Approach(LDA), tf-idf method, N-gram (bigram, trigram, …) based approach to identify low frequency words. The quality of the keywords that are extracted can be improved based on sequential pattern mining.
Emergence of Orientation Selectivity in the Mammalian Visual Pathway

Orientation selectivity is a property of mammalian primary visual cortex (V1) neurons, yet its emergence along the visual pathway varies across species. In carnivores and primates, elongated receptive fields first appear in V1, whereas in lagomorphs such receptive fields emerge earlier, in the retina. Here we examine the mouse visual pathway and reveal the existence of orientation selectivity in lateral geniculate nucleus (LGN) relay cells. Cortical inactivation does not reduce this orientation selectivity, indicating that cortical feedback is not its source. Orientation selectivity is similar for LGN relay cells spiking and subthreshold input to V1 neurons, suggesting that cortical orientation selectivity is inherited from the LGN in mouse. In contrast, orientation selectivity of cat LGN relay cells is small relative to subthreshold inputs onto V1 simple cells. Together, these differences show that although orientation selectivity exists in visual neurons of both rodents and carnivores, its emergence along the visual pathway, and thus its underlying neuronal circuitry, is fundamentally different.
Development and matching of binocular orientation preference in mouse V1

Eye-specific thalamic inputs converge in the primary visual cortex (V1) and form the basis of binocular vision. For normal binocular perceptions, such as depth and stereopsis, binocularly matched orientation preference between the two eyes is required. A critical period of binocular matching of orientation preference in mice during normal development is reported in literature. Using a reaction diffusion model we present the development of RF and orientation selectivity in mouse V1 and investigate the binocular orientation preference matching during the critical period. At the onset of the critical period the preferred orientations of the modeled cells are mostly mismatched in the two eyes and the mismatch decreases and reaches levels reported in juvenile mouse by the end of the critical period. At the end of critical period 39% of cells in binocular zone in our model cortex is orientation selective. In literature around 40% cortical cells are reported as orientation selective in mouse V1. The starting and the closing time for critical period determine the orientation preference alignment between the two eyes and orientation tuning in cortical cells. The absence of near neighbor interaction among cortical cells during the development of thalamo-cortical wiring causes a salt and pepper organization in the orientation preference map in mice. It also results in much lower % of orientation selective cells in mice as compared to ferrets and cats having organized orientation maps with pinwheels.
One-year results of coronary revascularization in diabetic patients with multivessel coronary artery disease.Sirolimus stent vs. coronary artery bypass surgery and bare metal stent: insights from ARTS-II and ARTS-I.

BACKGROUND ARTS-II was designed to evaluate the sirolimus-eluting stent (SES) versus ARTS-I. The objective of this analysis is to assess the safety and efficacy of the SES in diabetic patients with multivessel disease (MVD) versus both arms of ARTS-I. METHODS AND RESULTS The ARTS studies included 367 diabetic patients (ARTS-II: 159; ARTS-I-CABG: 96; ARTS-I-PCI: 112). Baseline characteristics showed a more diseased patient population in the ARTS-II study: 50.3% with 3VD vs. 35.4% (ARTS-I-CABG) and 30.8% (ARTS-I-PCI) (p=0.003). Treated or anastomosed lesions were 3.2+/-1.2 (ARTS-II), 2.8+/-0.8 (ARTS-I-CABG) and 2.5+/-1.1 (ARTS-I-PCI). At 30 days there was a significant difference in MACCE between ARTS-II (4.4%) and ARTS-I-PCI (12.5%) (p=0.02). At 1-year, the death rate was 2.5% (ARTS-II) vs. 3.1% (ARTS-I-CABG) and 6.3% (ARTS-I-PCI) without significant differences. Myocardial infarction rate was 0.6% (ARTS-II) vs. 2.1% (ARTS-I-CABG; p=0.56) and 6.3% (ARTS-I-PCI; p=0.01). The need for repeat revascularization was 12.6% (ARTS-II) vs. 4.2% (ARTS-I-CABG; p=0.027) and 22.3% (ARTS-I-PCI; p= 0.046). MACCE-free survival was 84.3% (ARTS-II) vs. 85.4% (ARTS-I-CABG; p=0.86) and 63.4% (ARTS-I-PCI; p<0.001). Also at 1 year, the overall MACCE rate in patients with diabetes was significantly higher than in nondiabetic patients, 15.7% vs. 8.5%, respectively [RR 1.85, 95%CI (1.16,2.97), p=0.015), due to a higher incidence of death and need for repeat revascularization, 2.5% vs. 0.4 and 12.5% vs. 5.6% in diabetes vs. nondiabetes groups, respectively. CONCLUSION Despite more extensive and treated disease, the overall MACCE-free survival in diabetic patients at 1 year in ARTS-II is similar to ARTS-I-CABG.
Audio Elements Based Auditory Scene Segmentation

Auditory scene segmentation is an important step in the process of high-level semantic inference from audio data streams, and in particular, a prerequisite for auditory scene categorization. In this paper, we analyze the limits of previous works on auditory scene segmentation, and then propose a novel method that, conceptually, is inspired by the ideas used in text and video scene segmentation, and is based on an analysis of audio elements and key audio elements, which can be seen as equivalents to the words and keywords in a text document, respectively. Experiments performed on 1.5 hours of audio data indicate that the proposed approach is promising
Automatically Expanding the Synonym Set of SNOMED CT using Wikipedia

Clinical terminologies and ontologies are often used in natural language processing/understanding tasks as a method for semantically tagging text. One ontology commonly used for this task is SNOMED CT. Natural language is rich and varied: many different combinations of words may be used to express the same idea. It is therefore essential that ontologies and terminologies have a rich set of synonyms. One source of synonyms is Wikipedia. We examine methods for aligning concepts in SNOMED CT with articles in Wikipedia so that newly-found synonyms may be added to SNOMED CT. Our experiments show promising results and provide guidance to researchers who wish to use Wikipedia for similar tasks.
Designing the Creative Child: Playthings and Places in Midcentury America

Designing the Creative Child: Playthings and Places in Midcentury America Amy F. Ogata Minneapolis: University of Minnesota, 2013. Introduction, images, notes, bibliography, index. 229 pp. $34.95 cloth. ISBN: 9780816679614In Designing the Creative Child: Playthings and Places in Midcentury America, Amy F. Ogata shows how a cultural preoccupation with childhood creativity leftits mark on American material life. While the idea of the child as naturally creative first emerged in the eighteenth century and grew steadily during the nineteenth, it was only in the twentieth century that it took root across America. The belief that children were naturally creative, and that their creative sensibilities could be further nurtured and expanded by exposing them to stimulating environments and objects spread rapidly during the 1950s, 1960s, and 1970s, particularly among middle- and upper-class Americans.Ogata explains that childhood creativity became a subject of pervasive concern because of social tensions related to America's Cold War with the Soviet Union. During that era, many social commentators investigated the effects of child rearing on political culture. For instance, anthropologist Margaret Mead suggested that Soviet children were trained to accept ideology without question, whereas American boys and girls had greater liberty of thought. Mead was not alone, and many experts believed that the key to winning the Cold War was to ensure that the rising generation was flexible, innovative, and open to exploration. The creative child also offered a comforting alternative to sociologist William Whyte's "Organization Man," the embodiment of conformity and conventional living. Child-rearing experts proposed that with proper training and encouragement, youth might develop into something other than the herd-like creatures of David Riesman's "lonely crowd. " And, some hoped, creative children, who could find new ways to amuse themselves and who were trained to think independently, might be able to resist the seductive lure of American mass culture that played incessantly on their television screens.Ogata's book is well researched, well written, and beautifully illustrated-and truly innovative in its depiction of how a generation of toy designers, architects, and museum curators gave shape to their faith in youthful creativity. Whether it was the flexible, bendable Gumby, the simple and unadorned wooden toys of Playskool, the school designs of Saarinen and other midcentury architects, the playrooms and spaces enshrined in many newly built houses, or the children's art carnivals put on by the Museum of Modern Art, the goal of fostering youthful creativity was embodied in an array of objects, buildings, and installations that still exist today. Often we take for granted these designs, giving scant consideration to their pedagogical and psychological goals, yet undergirding them were larger hopes for the development of an intellectually agile and inventive citizenry. …
The Value of a " Free " Customer Sunil

Central to a firm’s growth and marketing policy is the revenue and profit potential of its customer assets. As a result, there has been a recent proliferation of work regarding customer lifetime value. However, extant research in this area is silent regarding how to assess the profitability of customers in a networked setting. In such settings, the presence of one type of customer can affect the value of another. Examples of such settings include job agencies (whose customers include both job seekers and listers), realtors (whose clients include home sellers and purchasers), and auction houses (whose customers include buyers and sellers). Customers such as buyers of an auction house pay no fees to the firm making their value difficult to compute. Yet these customers generate value to the firm because their presence attracts fee-paying sellers. In this paper we consider the value of a customer in these types of networked setting. We compute the value of customers by developing a joint model of buyer and seller growth. This growth comes from three sources — marketing actions (price and advertising), direct network effects (e.g., buyer to buyer effects), and indirect network effects (e.g., buyer to seller effects). Using this growth model we concurrently solve the firm’s problem of choosing optimal pricing and advertising subject to constraints on customer growth. By relaxing constraints on growth by one customer, we can then impute their lifetime value to the firm. We apply our model to data from an auction house. Our results show that there are strong direct and indirect network effects present in our data. We find that in the most recent period buyers have a value of about $550 and the sellers have a value of around $500. We also find that our approach leads to estimates of firm value that are more accurate than models that fail to consider network effects. Finally, price and advertising elasticities are low (-0.16 and 0.006) and decrease over time as network effects become increasingly important.
Real deployment of consensus algorithm on self-organized wireless sensor networks

Reaching consensus on a self-organized wireless sensor networks through totally decentralized algorithms is a topic that has attracted considerable attention. The average consensus method is the most popular algorithm used in this kind of applications. The main advantage of these approaches is that the network does not involve a fusion center to organize nodes. Using a realistic environment to check the behavior of this scheme is the major objective of this work. Moreover, this paper contributes to answer and confirm some results which are approved by theoretical works.
Interactive Focus and Context Visualization for Augmented Reality

In this article we present interactive focus and context (F+C) visualizations for augmented reality (AR) applications. We demonstrate how F+C visualizations are used to affect the user's perception of hidden objects by presenting contextual information in the area of augmentation. We carefully overlay synthetic data on top of the real world imagery by taking into account the information that is about to be occluded. Furthermore, we present operations to control the amount of augmented information. Additionally, we developed an interaction tool, based on the magic lens technique, which allows for interactive separation of focus from context. We integrated our work into a rendering framework developed on top of the Studierstube augmented reality system. We finally show examples to demonstrate how our work benefits AR.
Assessing the Effect of Treatment Duration on the Association between Anti-Diabetic Medication and Cancer Risk

Most studies that have evaluated the association between anti-diabetic medication and cancer risk have suffered from methodological drawbacks. To avoid time-related biases, we evaluated the effect of treatment duration on the cancer risk among naive users of anti-diabetic medication as compared to non-users. In addition, we addressed the influence of common risk factors such as smoking and BMI. The study population comprised 23,394 participants of FINRISK surveys. Data on cancer and anti-diabetic medication were linked with the study cohorts. We applied Lexis tabulation to the data and analyzed split records by using Poisson regression. Changes in cancer incidence in relation to treatment duration were examined by modeling the rate ratio (RR). After a median follow-up of 9 years, 53 cancer cases among users of anti-diabetic medication and 1,028 among non-users were diagnosed. No significant difference in cancer risk between users and non-users was observed after adjustment. The RR for all medication regardless of its duration was 1.01 [95% CI 0.75–1.33], and 1.37 [0.94–1.94] for period of 1–4 years. The results were similar for metformin, sulfonylurea, and insulin. This study demonstrates that evaluation of the variation in cancer risk in relation to treatment duration is of particular importance for enhancing the accuracy of conclusions on the link between exposure to anti-diabetic medication and cancer risk.
Improving music mood classification using lyrics, audio and social tags

The affective aspect of music (popularly known as music mood) is a newly emerging metadata type and access point to music information, but it has not been well studied in information science. There has yet to be developed a suitable set of mood categories that can reflect the reality of music listening and can be well adopted in the Music Information Retrieval (MIR) community. As music repositories have grown to an unprecedentedly large scale, people call for automatic tools for music classification and recommendation. However, there have been only a few music mood classification systems with suboptimal performances, and most of them are solely based on the audio content of the music. Lyric text and social tags are resources independent of and complementary to audio content but have yet to be fully exploited. This dissertation research takes up these problems and aims to 1) summarize fundamental insights in music psychology that can help information scientists interpret music mood; 2) identify mood categories that are frequently used by real-world music listeners, through an empirical investigation of real-life social tags applied to music; 3) advance the technology in automatic music mood classification by a thorough investigation on lyric text analysis and the combination of lyrics and audio. Using linguistic resources and human expertise, 36 mood categories were identified from the most popular social tags collected from last.fm, a major Western music tagging site. A ground truth dataset of 5,296 songs in 18 mood categories were built with mood labels given by a number of real-life users. Both commonly used text features and advanced linguistic features were investigated, as well as different feature representation models and feature combinations. The best performing lyric feature sets were then compared to a leading audio-based system. In combining lyric and audio sources, both methods of feature iii concatenation and late fusion (linear interpolation) of classifiers were examined and compared. Finally, system performances on various numbers of training examples and different audio lengths were compared. The results indicate: 1) social tags can help identify mood categories suitable for a real world music listening environment; 2) the most useful lyric features are linguistic features combined with text stylistic features; 3) lyric features outperform audio features in terms of averaged accuracy across all considered mood categories; 4) systems combining lyrics and audio outperform audio-only and lyric-only systems; 5) combining lyrics and audio can reduce the requirement on training data size, …
EGL 2 U : Tractable Inference on Large Scale Credal Networks

Credal networks [1, 2] generalize Bayesian networks [3] by associating with variables (closed convex) sets of conditional probability mass functions, i.e., credal sets1, in place of precise conditional probability distributions. Credal networks are models of imprecise probabilities [4], which allow the capturing of incompleteness and imprecision of human knowledge and beliefs [1]. Credal networks can also be regarded, in an informal way, as collections of Bayesian networks sharing identical graphical structures2 [2]. A number of queries can be performed on credal nets, e.g. computing lower and upper expectations [6], drawing qualitative conclusions [11], classifications [12, 13], etc. In this paper, we consider the problem of computing lower and upper (marginal or posterior) probabilities, which is referred to as inference in the rest of this report. The computational complexity of exact inference on general credal networks is known to be NPPP-complete [10], which makes approximate inference the only applicable choice for complex credal nets. There are quite a few inference algorithms in the literature. One class of algorithms comes from an optimization point of view, where the lower and upper probabilities are treated as target functions subject to constraints embodied in the credal networks. In particular, there are algorithms explicitly turn credal nets into optimization problems and solve them using standard solvers, e.g., algorithms using multilinear programming [23], integer programming [24], etc. There are also algorithms performing the optimization by searching over
Use of albumin creatinine ratio and urine albumin concentration as a screening test for albuminuria in an Indo-Asian population.

BACKGROUND Albuminuria (>30 mg/day) based on 24 h urine albumin excretion is one of the criteria for chronic kidney disease (CKD) and a predictor of cardiovascular disease (CVD). Differences in urine albumin concentration and creatinine excretion rates between Indo-Asians and other populations may require different threshold values for detection of albuminuria. We compared the use of spot urine albumin concentration and urine albumin to creatinine excretion ratio for detection of albuminuria in this population. METHODS A total of 577 subjects aged >or=40 years, 54% of whom were women, were recruited from the general population in Karachi, Pakistan. Albumin concentration (mg/l) and albumin to creatinine ratio (mg/g of creatinine) were determined in a spot morning urine sample, and albuminuria (30 mg/day or greater) measured in a 24 h urine collected on the subsequent day. RESULTS The median (25-75 percentile) of urine albumin excretion was 4.8 (3.6-10.3) mg/day: 5.4 (3.7-12.5) mg/day in men and 4.5 (3.8-8.9) mg/day in women. The overall prevalence (95% CI) of albuminuria was 11.8% (7.2-12.0%): 14.8% in men and 9.2% in women (P = 0.04). The areas under the receiver operator characteristic (ROC) curves for urine albumin concentration were 0.86 (0.82-0.90) and 0.88 (0.84-0.92), respectively, in women and men. The areas under the ROC curves for albumin to creatinine ratio were 0.86 (0.82-0.89) and 0.90 (0.86-0.93), respectively, in women and men. For urine albumin concentration, the sensitivity and specificity were 37 and 97%, respectively, in women and 69 and 94%, respectively, in men at the conventionally recommended value of 2 mg/dl. The discriminator value of urine albumin concentration identified in the analysis was 0.5 mg/dl in women (sensitivity of 87% and specificity of 75%) and 1.7 mg/dl in men (sensitivity of 74% and specificity of 93%). For the albumin to creatinine ratio, the sensitivity and specificity were 46 and 95%, respectively, in women and 60 and 97%, respectively, in men at cut-off value of 30 mg/g. CONCLUSION Both urine albumin concentration and albumin to creatinine ratio are acceptable tests for population screening for albuminuria in Indo-Asians. While sensitivities may be suboptimal, particularly in women, lowering the existing thresholds would compromise specificity. Those who screen positive need evaluation and management of CKD and prevention of CVD.