Miracle-dz/UniFAR-FTUs huggingface dataset:
Angiotensin-converting enzyme in the human heart. Effect of the deletion/insertion polymorphism.

BACKGROUND An insertion (I)/deletion (D) polymorphism of the angiotensin-converting enzyme (ACE) gene has been associated with differences in the plasma levels of ACE as well as with myocardial infarction, cardiomyopathy, left ventricular hypertrophy, and coronary artery disease. METHODS AND RESULTS We determined the cardiac ACE activity and the ACE genotype in 71 subjects who died of noncardiac disorders. Cardiac ACE activity was significantly higher (P < .01) in subjects with the ACE DD genotype (12.7 +/- 1.9 mU/g wet wt) compared with subjects with the ID (8.7 +/- 0.8 mU/g) and the II (9.1 +/- 1.0 mU/g) genotypes. This difference was independent of sex, age, and the time required for tissue collection. CONCLUSIONS Cardiac ACE activity is highest in subjects with the DD genotype. Elevated cardiac ACE activity in these subjects may result in increased cardiac angiotensin II levels, and this may be a mechanism underlying the reported association between the ACE deletion polymorphism and the increased risk for several cardiovascular disorders.
Content availability, pollution and poisoning in file sharing peer-to-peer networks

Copyright holders have been investigating technological solutions to prevent distribution of copyrighted materials in peer-to-peer file sharing networks. A particularly popular technique consists in "poisoning" a specific item (movie, song, or software title) by injecting a massive number of decoys into the peer-to-peer network, to reduce the availability of the targeted item. In addition to poisoning, pollution, that is, the accidental injection of unusable copies of files in the network, also decreases content availability. In this paper, we attempt to provide a first step toward understanding the differences between pollution and poisoning, and their respective impact on content availability in peer-to-peer file sharing networks. To that effect, we conduct a measurement study of content availability in the four most popular peer-to-peer file sharing networks, in the absence of poisoning, and then simulate different poisoning strategies on the measured data to evaluate their potential impact. We exhibit a strong correlation between content availability and topological properties of the underlying peer-to-peer network, and show that the injection of a small number of decoys can seriously impact the users' perception of content availability.
Illusory correlation and attitudebased vested interest

The present studies test whether having a ‘vested interest’ in a particular outcome affects perceived covariation. Vested interest was defined as a function of whether Dutch university students were in favour or against the implementation of a threatening policy (receiving lectures in English as opposed to the native Dutch). In both studies subjects were told that this policy would be tried out at either their own or another comparable university, and that the university chosen would be the one with the greatest proportion of support for the plan. In Study I subjects (n = 151) were presented with statements expressing pro or contra attitudes and arguments to the policy. These were ostensibly derived from students at both universities but university affiliation was not indicated. In Study 2 (n = 114) similar information was provided but the statements were attributed to students from the two universities, such that there was an equal proportion of oppositionlsupport for the plan at both universities. We hypothesized that illusory correlations would reflect the vested interest of attitude such that students opposing the policy would overestimate the proportion of opponents to supporters at their own university compared to those in favour of the policy. The results of both studies supported our hypothesis and they also revealed attitude to be a more important predictor of illusory correlation than perceived personal consequences for themselves. The prediction that illusory correlations would be weaker in Study 2 than in Study I, because it provides less scope for bias, was not supported, The implications of these findings are discussed.
Maximum Entropy Discrimination Poisson Regression for Software Reliability Modeling

Reliably predicting software defects is one of the most significant tasks in software engineering. Two of the major components of modern software reliability modeling approaches are: 1) extraction of salient features for software system representation, based on appropriately designed software metrics and 2) development of intricate regression models for count data, to allow effective software reliability data modeling and prediction. Surprisingly, research in the latter frontier of count data regression modeling has been rather limited. More specifically, a lack of simple and efficient algorithms for posterior computation has made the Bayesian approaches appear unattractive, and thus underdeveloped in the context of software reliability modeling. In this paper, we try to address these issues by introducing a novel Bayesian regression model for count data, based on the concept of max-margin data modeling, effected in the context of a fully Bayesian model treatment with simple and efficient posterior distribution updates. Our novel approach yields a more discriminative learning technique, making more effective use of our training data during model inference. In addition, it allows of better handling uncertainty in the modeled data, which can be a significant problem when the training data are limited. We derive elegant inference algorithms for our model under the mean-field paradigm and exhibit its effectiveness using the publicly available benchmark data sets.
Music in Our Lives: Rethinking Musical Ability, Development and Identity

1. Frames of reference and the origins of the study 2. Initial music learning and practice 3. Early progress in music performance 4. 'For how long can you expect a child to blow into a French horn? ' 5. From childhood participation to adulthood involvement and experiences 6. Anthony and Alistair: a smooth progression to talent? 7. Lily and Bryan: exits and entrances in musical lives 8. A family dynamic 9. Tristram: Speaking about my own musical life, development and identity 10. Sarah: Speaking about my own musical life, development and identity 11. Music in our lives: A developmental explanation 12. Musical transactions, the power of expression and the self-regulation of musical development
Understanding Secondary Content Practices for Television Viewing

Secondary content experiences related to television viewing have been a frequent topic of study in the TVX community. While many new interfaces have been created and studied in the small scale, we are not aware of any larger quantitative work to study current practices now that many secondary content experiences are publicly available. We conducted a survey with a broad sample of the American population to explore current secondary content use. We report on our findings, including that 80% of these experiences occur before or after viewing the primary content, and not as simultaneous experiences, and that social posting about television content remains quite low, even for one's favorite show. We conclude with implications for the design new secondary content systems based on our findings.
Syllable-based pitch encoding for low bit rate speech coding with recognition/synthesis architecture

Current HMM-based low bit rate speech coding systems work with phonetic vocoders. Pitch contour coding (on frame or phoneme level) is usually fairly orthogonal to other speech coding parameters. We make an assumption in our work that the speech signal contains supra-segmental cues. Hence, we present encoding of the pitch on the syllable level, used in the framework of a recognition/synthesis speech coder with phonetic vocoder. The results imply that high accuracy pitch contour reconstruction with negligible speech quality degradation is possible. The proposed pitch encoding technique operates on 30 - 35 bits per second.
Experiences with a cooperative design process in developing a telecooperation system for collaborative document production

This paper describes a cooperative design approach and the experiences collected when applying this approach for developing and introducing a groupware system for the support of collaborative document processing in a geographically distributed ministerial environment. Methods were developed for the elicitation and analysis of explicit and tacit user requirements in work practice. The combination of these methods facilitated a mutual learning process between users and designers. These methods include interviews, laboratory workshops, user and designer workshops, as well as mediation by user advocates who explore users' needs during regular site visits. A portrait of the various methods is given, followed by a description of the experiences with the various types of interactions between the users and design team. This is accomplished with a report from system usage and implications these design methods had on system design.
Ieee Transactions on Signal Processing the Augmented Complex Kernel Lms

—Recently, a unified framework for adaptive kernel based signal processing of complex data was presented by the authors, which, besides offering techniques to map the input data to complex Reproducing Kernel Hilbert Spaces, developed a suitable Wirtinger-like Calculus for general Hilbert Spaces. In this short paper, the extended Wirtinger's calculus is adopted to derive complex kernel-based widely-linear estimation filters suitable for applications involving non-circular data. Furthermore, we illuminate several important characteristics of the widely linear filters. We show that, although in many cases the gains from adopting widely linear estimation filters, as alternatives to ordinary linear ones, are rudimentary, for the case of kernel based widely linear filters significant performance improvements can be obtained.
Neuroscience and Biobehavioral Reviews into the Groove: Can Rhythm Influence Parkinson's Disease?

Previous research has noted that music can improve gait in several pathological conditions, including Parkinson's disease, Huntington's disease and stroke. Current research into auditory-motor interactions and the neural bases of musical rhythm perception has provided important insights for developing potential movement therapies. Specifically, neuroimaging studies show that rhythm perception activates structures within key motor networks, such as premotor and supplementary motor areas, basal ganglia and the cerebellum – many of which are compromised to varying degrees in Parkinson's disease. It thus seems likely that automatic engagement of motor areas during rhythm perception may be the connecting link between music and motor improvements in Parkinson's disease. This review seeks to describe the link, address core questions about its underlying mechanisms, and examine whether it can be utilized as a compensatory mechanism.
Parsers as language models for statistical machine translation

Most work in syntax-based machine translation has been in translation modeling, but there are many reasons why we may instead want to focus on the language model. We experiment with parsers as language models for machine translation in a simple translation model. This approach demands much more of the language models, allowing us to isolate their strengths and weaknesses. We find that unmodified parsers do not improve BLEU scores over ngram language models, and provide an analysis of their strengths and weaknesses.
Brief Announcement: Benchmarking Concurrent Priority Queues:

A number of concurrent, relaxed priority queues have recently been proposed and implemented. Results are commonly reported for a throughput benchmark that uses a uniform distribution of keys from a large integer range, a balanced mixture of operations, and mostly for single systems. We have conducted more extensive benchmarking of three recent, relaxed priority queues on four different types of systems with different key ranges and distributions. While we can show superior throughput and scalability for our own k-LSM priority queue for the uniform key distribution, the picture changes drastically for other distributions, both with respect to achieved throughput and relative merit of the priority queues. The throughput benchmark alone is thus not sufficient to characterize the performance of concurrent priority queues. Our priority queue, benchmark code and full set of results are publicly available to foster comparison.
Effective Keyword Search in Graphs

In a node-labeled graph, keyword search finds subtrees of the graph whose nodes contain all of the query keywords. This provides a way to query graph databases that neither requires mastery of a query language such as SPARQL, nor a deep knowledge of the database schema. Previous work ranks answer trees using combinations of structural and content-based metrics, such as path lengths between keywords or relevance of the labels in the answer tree to the query keywords. We propose two new ways to rank keyword search results over graphs. The first takes node importance into account while the second is a bi-objective optimization of edge weights and node importance. Since both of these problems are NP-hard, we propose greedy algorithms to solve them, and experimentally verify their effectiveness and efficiency on a real dataset.
Learning from the ubiquitous language: an empirical analysis of emoji usage of smartphone users

Emojis have been widely used to simplify emotional expression and enrich user experience. As an interesting practice of ubiquitous computing, emojis are adopted by Internet users from many different countries, on many devices (particularly popular on smartphones), and in many applications. The "ubiquitous" usage of emojis enables us to study and compare user behaviors and preferences across countries and cultures. We present an analysis on how smartphone users use emojis based on a very large data set collected from a popular emoji keyboard. The data set contains a complete month of emoji usage of 3.88 million active users from 212 countries and regions. We demonstrate that the categories and frequencies of emojis used by these users provide rich signals for the identification and the understanding of cultural differences of smartphone users. Users from different countries present significantly different preferences on emojis, which complies with the well-known Hofstede's cultural dimensions model.
The Relative Importance and Interrelations between Behavior Parameters for Robots' Mood Expression

Bodily expression of affect is crucial to human robot interaction. Our work aims at designing bodily expression of mood that does not interrupt ongoing functional behaviors. We propose a behavior model containing specific (pose and motion) parameters that characterize the behavior. Parameter modulation provides behavior variations through which affective behavioral cues can be integrated into behaviors. To investigate our model and parameter set, we applied our model to two concrete behaviors (waving and pointing) on a NAO robot, and conducted a user study in which participants (N=24) were asked to design such variations corresponding with positive, neutral, and negative moods. Preliminary results indicated that most parameters varied significantly with the mood variable. The results also suggest that the relative importance may be different between parameters, and parameters are probably interrelated. This paper presents the analysis of these aspects. The results show that the spatial extent parameters (hand-height and amplitude), the head vertical position, and the temporal parameter (motion-speed) are the most important parameters. Moreover, multiple parameters were found to be interrelated. These parameters should be modulated in combination to provide particular affective cues. These results suggest that a designer should focus on the design of the important behavior parameters and utilize the parameter combinations when designing mood expression.
Timing Measurement Platform for Arbitrary Black-Box Circuits Based on Transition Probability

The key aspects of a good on-chip timing measurement platform are high measurement resolution, accuracy, and low area overhead. A measurement method based on transition probability (TP) has shown promising characteristics in all these areas. In this paper, the TP measurement method is examined through simulation to understand its apparent effectiveness and accuracy in measuring complex circuits. Timing uncertainties and logic glitch activities are considered in detail, and the effect of varying input vectors' probability distributions is analyzed to enable further accuracy improvements. Using a field-programmable gate array, the method is implemented and demonstrated as a modular on-chip test platform for testing complex arbitrary circuits. Practical circuits found in typical modular designs, including fixed/floating-point arithmetic and filter circuits, are chosen to evaluate the test platform. The resolution of the timing measurements ranges from 0.3 to 8.0 ps, and the measurement errors against reference measurements are found to be within 3.6%. The test platform can be applied to VLSI designs with minor area overhead, and provides designers with precise and accurate physical timing information of circuits.
Acute Acalculous Cholecystitis in Patients With Clear Cell Renal Cell Carcinoma Treated With Sunitinib: Report of Two Cases

Although sunitinib is associated with a variety of adverse events, cases of sunitinib-related acute cholecystitis have rarely been reported. We herein report two cases of sunitinib-related acute acalculous cholecystitis in patients with clear cell renal cell carcinoma. In both cases, the gallbladder was surgically removed because it was difficult to improve the patient’s condition with the cessation of sunitinib and non-surgical treatment only. Attention must be paid to the possibility of sunitinib-related acute cholecystitis, which, although uncommon, can be life-threatening.
Optimizing frame structure with real-time computation for interactive multiview video streaming

Interactive multiview video streaming (IMVS) is an application where a network client requests from server a single video view at a time but can periodically switch to other views as the video is played back uninterrupted. Existing IMVS algorithms output pre-computed frame structures that facilitate permissible view-switching while minimizing the expected transmission rate given a storage constraint. In this paper, we use real-time computation (available at a remote powerful server or media cloud) to assist the pre-computed frame structure to satisfy users' view-switch requests. In particular, we first propose a new frame type called uni-merge frame that is computed in real-time for view-switching from one single view to one target view with low transmission rate and reasonable computation cost. Then, to enable permissible view-switches to a particular target picture, we find the optimal combination of pre-computed frames and real-time computed frames-one that minimizes streaming rate subject to both storage and real-time computation constraints-using a greedy combinatorial algorithm. Experimental results show that with real-time computation, the expected streaming rate of the IMVS system can be further decreased by 50% compared to pre-encoded frame structures without real-time computation.
A Population-Based Case Control Study of Potential Risk Factors for IBD

BACKGROUND:We aimed to pursue potential etiological clues to Crohn's disease (CD) and ulcerative colitis (UC) through a population-based case control survey study.METHODS:Cases with CD (n = 364) and UC (n = 217), ages 18–50 yr were drawn from the population-based University of Manitoba IBD Research Registry. Potential control subjects were drawn from the population-based Manitoba Health Registry by age, gender, and geographic residence matching to the cases (n = 433). Subjects were administered a multiitem questionnaire.RESULTS:By univariate analysis, some of the variables predictive of CD included lower likelihood of living on a farm, of having drunk unpasteurized milk or having eaten pork, and UC patients were less likely to have drunk unpasteurized milk and to have eaten pork. On multivariate analysis, variables significantly associated with CD were being Jewish (OR = 4.32, 95% CI 1.10–16.9), having a first degree relative with IBD (OR = 3.07, 95% CI 1.73–5.46), ever having smoked (OR = 1.54, 95% CI, 1.06–2.25), living longer with a smoker (OR = 1.03, 95% CI, 1.01–1.04). Being a first generation Canadian (OR = 0.33, 95% CI, 0.17–0.62), having pet cats before age 5 (OR = 0.66, 95% CI, 0.46–0.96) and having larger families (OR = 0.87, 95% CI, 0.79–0.96) were protective against CD. For UC being Jewish (OR = 7.46, 95% CI, 2.33–23.89), having a relative with IBD (OR = 2.23, 95% CI, 1.27–3.9), and ever smoking (OR = 1.62, 95% CI, 1.14–2.32) were predictive.CONCLUSION:This study reinforced the increased risk associated with family history, being Jewish, and smoking history, however, a number of significant associations with CD and UC on univariate and multivariate analysis may support the “hygiene hypothesis” and warrant further exploration in prospective studies.
Can syndromic surveillance data detect local outbreaks of communicable disease? A model using a historical cryptosporidiosis outbreak

SUMMARY A national UK surveillance system currently uses data from a health helpline (NHS Direct) in an attempt to provide early warning of a bio-terrorist attack, or an outbreak caused by a more common infection. To test this syndromic surveillance system we superimposed data from a historical outbreak of cryptosporidiosis onto a statistical model of NHS Direct call data. We modelled whether calls about diarrhoea (a proxy for cryptosporidiosis) exceeded a statistical threshold, thus alerting the surveillance team to the outbreak. On the date that the public health team were first notified of the outbreak our model predicted a 4% chance of detection when we assumed that one-twentieth of cryptosporidiosis cases telephoned the helpline. This rose to a 72% chance when we assumed nine-tenths of cases telephoned. The NHS Direct surveillance system is currently unlikely to detect an event similar to the cryptosporidiosis outbreak used here and may be most suited to detecting more widespread rises in syndromes in the community, as previously demonstrated. However, the expected rise in NHS Direct call rates, should improve early warning of outbreaks using call data.