Miracle-dz/UniFAR-FTUs huggingface dataset:
A Clinical Prediction Formula for Apnea-Hypopnea Index

Objectives. There are many studies regarding unnecessary polysomnography (PSG) when obstructive sleep apnea syndrome (OSAS) is suspected. In order to reduce unnecessary PSG, this study aims to predict the apnea-hypopnea index (AHI) via simple clinical data for patients who complain of OSAS symptoms. Method. Demographic, anthropometric, physical examination and laboratory data of a total of 390 patients (290 men, average age 50 ± 11) who were subject to diagnostic PSG were obtained and evaluated retrospectively. The relationship between these data and the PSG results was analyzed. A multivariate linear regression analysis was performed step by step to identify independent AHI predictors. Results. Useful parameters were found in this analysis in terms of body mass index (BMI), waist circumference (WC), neck circumference (NC), oxygen saturation measured by pulse oximetry (SpO2), and tonsil size (TS) to predict the AHI. The formula derived from these parameters was the predicted AHI = (0.797 × BMI) + (2.286 × NC) − (1.272 × SpO2) + (5.114 × TS) + (0.314 × WC). Conclusion. This study showed a strong correlation between AHI score and indicators of obesity. This formula, in terms of predicting the AHI for patients who complain about snoring, witnessed apneas, and excessive daytime sleepiness, may be used to predict OSAS prior to PSG and prevent unnecessary PSG.
Application of wavelet transform in edge detection

Edge is the most important feature of an image. It contains much of information of an image. In addition, edge detection has a significant influence on the performance of image analysis and comprehension. In order to study the edge detection algorithm on the edge of the location and antinoise capability, we selected Log operator, Canny operator and wavelet transform edge detection algorithm to detect image edge by using Matlab software. Simulation results show that, three edge detection operators possessed different capacity in the edge location and suppress noise. Wavelet transform can provide the edge information of different scales. The experimental result shows that, since wavelet transform have a multi-scale feature and localization characteristic, it can accurately detect the edge points, In the meantime ,it can overcome preferably noise-sensitive problems which arise from Log operator and Canny operator .
Improving the Effectiveness of Collaborative Filtering on Anonymous Web Usage Data

Recommender systems based on collaborative filtering usually require real-time comparison of users’ ratings on objects. In the context of Web personalization, particularly at the early stages of a visitor’s interaction with the site (i.e., before registration or authentication), recommender systems must rely on anonymous clickstream data. The lack of explicit user ratings and the shear amount of data in such a setting poses serious challenges to standard collaborative filtering techniques in terms of scalability and performance. Offline clustering of users transactions can be used to improve the scalability of collaborative filtering, however, this is often at the cost of reduced recommendation accuracy. In this paper we study the impact of various preprocessing techniques applied to clickstream data, such as clustering, normalization, and significance filtering, on collaborative filtering. Our experimental results, performed on real usage data, indicate that with proper data preparation, the clustering-based approach to collaborative filtering can achieve dramatic improvements in terms of recommendation effectiveness, while maintaining the computational advantage over the direct approaches such as the k-NearestNeighbor technique.
When and How is Concord preferred? An Experimental approach

A longstanding debate asks whether negative polarity (1a.NPI) and negative concord (1b.NC) involve identical or distinct syntactic/semantic operations. Although French (1ab non-parallel structures should favor DN. B) DN readings should lead lengthened choice time vs NC C) NC and DN preference should manifest characteristically distinct intonation contours. The paper reports on the first results of this experiment. (1)a. Personne ne fait quoique ce soit. b. Personne ne fait rien. (2)a ¬∃x ∃y do(x,y) ‘No one does anything’. b. ¬∃x,¬∃y do(x,y) ‘No one does nothing’. (3)a. (NOx (NOy do(x,y))) b.NO do(x,y)
Traffic matrix tracking using Kalman filters

In this work we develop a new approach to monitoring origin-destination flows in a large network. We start by building a state space model for OD flows that is rich enough to fully capture temporal and spatial correlations. We apply a Kalman filter to our linear dynamic system that can be used for both estimation and prediction of traffic matrices. We call our system a traffic matrix tracker due to its lightweight mechanism for temporal updates that enables tracking traffic matrix dynamics at small time scales. Our Kalman filter approach allows us to go beyond traffic matrix estimation in that our single system can also carry out traffic prediction and yield confidence bounds on the estimates, the predictions and the residual error processes. We show that these elements provide key functionalities needed by monitoring systems of the future for carrying out anomaly detection. Using real data collected from a Tier-1 ISP, we validate our model, illustrate that it can achieve low errors, and that our method is adaptive on both short and long timescales.
School district support to schools: voices and perspectives of school principals in a province in South Africa*

Abstract The role of education districts is central to the success of schools as they are mandated to work collaboratively with school principals, giving guidance and professional support. In this paper, we investigate school principals’ perspectives regarding the nature of support provided by their districts to effectively conduct their work and how that support can be enhanced. Using semi-structured interviews, 18 principals from five districts were interviewed. Findings show school principals’ dissatisfaction with their districts’ low levels of support caused by the lack of district support on provision of resources, lack of consultation in key decisions involving their schools; district officials’ lack of visibility in schools and responsiveness to change. We conclude that the success of principals and their schools partly depends on the nature of support received from their districts, and argue for improved collaboration between schools and the districts.
State of the art – teacher effectiveness and professional learning

One of the key findings from decades of educational effectiveness research is the importance of the classroom level as a predictor of pupil outcomes. In this review, we therefore look at synthesising our best evidence from research on effective teaching, and its corollary, teacher development. In the 1st section, we will look at key findings from 35 years of research on effective teaching using a process-product research that has led to the identification of a range of behaviours which are positively related to student achievement. A key limitation of this research, however, is its focus on basic skills in English and maths. Therefore, in the 2nd section we review research on “new learning” and teaching for metacognitive and thinking skills. While in these 2 sections we have discussed key findings from research on teaching, including emerging knowledge on metacognition, it is important to continue to take into account ongoing developments in theories of learning. In the 3rd section of this paper, we develop the argument that a major contributing factor to this situation is that “state-of-the-art” understandings about processes and conditions that promote student learning are typically not used to construct appropriate learning environments for their teachers.
Broadband dial-up user behavior identification and analysis

With the evolvement of the Internet and network services, broadband user behavior tends to diversify. It is a rising challenge for the network operators to understand network user behavior. In this paper, we present a general approach for identification and profiling user behavior model by using data mining technique to analyze user activity data. We present analysis of a data set comprised of the dial-up users' usage data in a typical metropolitan area network of a national broadband operator in China. The data recorded activity of over 250,000 unique user accounts. We identify several main behavior groups by using K-means algorithm, and further summarize behavior profile of each user group as main user behavior models. The results show that our approach can properly identify user behavior models. Our results provide a basis for network operators to understand dial-up user behavior, optimize network planning and adjust customer related policies accordingly.
Stuctural Analysis of Molecular Networks : AMES Mutagenicity

The characterization of chemical compounds based on their molecular graphs is an important task for identifying properties such as toxicity or mutagenicity. We used different groups of topological descriptors using the AMES mutagenicity data. Instead of optimizing the classification performance, the aim of this study is to perform a structural analysis of the underlying set of molecular graphs to gain better insights of the data set. The structural analysis identifies two groups of molecular networks. One group contains graphs with linear patterns (outliers), and the other group contains graphs that exhibit patterns of regular graphs (remainders). We show that the set of used topological descriptors chosen for this study cannot capture enough group-specific structural information within the remainders group to achieve the discrimination ability of the outliers group. Finally, this leads us to the conclusion that it is necessary to identify existing or develop new descriptors that capturing specific structural information to achieve better discrimination ability.
Feedback control of data aggregation in sensor networks

Sensor networks have recently emerged as a new paradigm for distributed sensing and actuation. This paper describes fundamental performance trade-offs in sensor networks and the utility of simple feedback control mechanisms for distributed performance optimization. A data communication and aggregation framework is presented that manipulates the degree of data aggregation to maintain specified acceptable latency bounds on data delivery while attempting to minimize energy consumption. An analytic model is constructed to describe the relationships between timeliness, energy, and the degree of aggregation, as well as to quantify constraints that stem from real-time requirements. Feedback control is used to adapt the degree of data aggregation dynamically in response to network load conditions while meeting application deadlines. The results illustrate the usefulness of feedback control in the sensor network domain.
Validation of questionnaire on the Spiritual Needs Assessment for Patients (SNAP) questionnaire in Brazilian Portuguese

Summary Objectives Spirituality is related to the care and the quality of life of cancer patients. Thus, it is very important to assess their needs. The objective of this study was the translation and cultural adjustment of the Spiritual Needs Assessment for Patients (SNAP) questionnaire to the Brazilian Portuguese language. Methodology The translation and cultural adjustment of the SNAP questionnaire involved six stages: backtranslation, revision of backtranslation, translation to the original language and adjustments, pre-test on ten patients, and test and retest with 30 patients after three weeks. Adult patients, with a solid tumour and literate with a minimum of four years schooling were included. For analysis and consistency we used the calculation of the Cronbach alpha coefficient and the Pearson linear correlation. Results The final questionnaire had some language and content adjustments compared to the original version in English. The correlation analysis of each item with the total score of the questionnaire showed coefficients above 0.99. The calculation of the Cronbach alpha coefficient was 0.9. The calculation of the Pearson linear correlation with the test and retest of the questionnaire was equal to 0.95. Conclusion The SNAP questionnaire translated into Brazilian Portuguese is adequately reliable and consistent. This instrument allows adequate access to spiritual needs and can help patient care.
Model checking guarded protocols

The parameterized model checking problem (PMCP) is to decide whether a temporal property holds for a uniform family of systems, C||U/sup n/, comprised of a control process, C, and finitely, but arbitrarily, many copies of a user process, U, executing concurrently with interleaving semantics. We delineate the decidability/undecidability boundary of the PMCP for all possible systems that arise by letting processes coordinate using different subsets of the following communication primitives: conjunctive Boolean guards, disjunctive Boolean guards, pairwise rendezvous, asynchronous rendezvous and broadcast actions. Our focus is on the following linear time properties: (p1) LTL/spl bsol/X formulae over C; (p2) LTL formulae over C; (p3) regular properties specified as regular automata; and (p4) /spl omega/-regular automata. We also establish a hierarchy based on the relative expressive power of the primitives by showing that disjunctive guards and pairwise rendezvous are equally expressive, in that we can reduce the PMCP for regular and /spl omega/-regular properties for systems with disjunctive guards and pairwise rendezvous are equally expressive, in that we can reduce the PMCP for regular and /spl omega/-regular properties for systems with disjunctive guards to ones with pairwise rendezvous and vise versa, but that each of asynchronous rendezvous and broadcasts is strictly more expressive than pairwise rendezvous (and disjunctive guards). Moreover, for systems with conjunctive guards, we give a simple characterization of the decidability/undecidability boundary of the PMCP by showing that allowing stuttering sensitive properties bridges the gap between decidability (for p1) and undecidability (for p2). A broad framework for modeling snoopy cache protocols is also presented for which the PMCP for p3 is decidable and that can model all snoopy cache protocols given by Culler and Emerson (1988) thereby overcoming the undecidability results.
Peer Commentary on " Clinical Pragmatics : Expectations and Realizations "

Dr. Gallagher's thoughtful appraisal of the "pragmatics revolution" in the field of language intervention describes the influence of the increased focus on language use in clinical assessment and treatment of language impairment. As Gallagher notes, the last 10 years of emphasis on pragmatics has altered the way that language impairment is viewed and approached. However, as she also comments, the impact of research in pragmatic aspects of language has not been as pervasive as was expected. I would like to extend these comments by adding some speculations about why this might be the case.
Discovering influential nodes from trust network

The goal of viral marketing is that, by the virtue of mouth to mouth word spread, a small set of influential customers can influence more customers. Influence maximization (IM) task is used to discover such influential nodes (or customers) from a social network. Existing algorithms for IM adopt Greedy and Lazy forward optimization approaches which assume only positive influence among users and availability of influence probability, the probability that a user is influenced by another. In this work, we propose the T-GT model, which considers both positive (trust) and negative (distrust) influences in social trust networks. We first compute positive and negative influences by mining frequent patterns of actions performed by users. Then, a local search based algorithm called mineSeedLS for node add, exchange and delete operations, is proposed to discover influential nodes from trust networks. Experimental results shows that our approach outperforms Greedy based approach by about 35%.
How Caching Improves Efficiency and Result Completeness for Querying Linked Data

Link traversal based query execution is a novel query approach which enables applications that exploit the Web of Data to its full potential. This approach makes use of the characteristics of Linked Data: During query execution it traverses data links to discover data that may contribute to query results. Once retrieved from the Web, the data can be cached and reused for subsequent queries. We expect such a reuse to be beneficial for two reasons: First, it may improve query performance because it reduces the need to retrieve data multiple times; second, it may provide for additional query results, calculated based on cached data that would not be discoverable by a link traversal based execution alone. However, no systematic analysis exist that justifies the application of caching strategies based on these assumptions. In this paper we evaluate the potential of caching to improve efficiency and result completeness in link traversal based query execution systems. We conceptually analyze the potential benefit of keeping and reusing retrieved data. Furthermore, we verify the theoretical impact of caching by conducting a comprehensive experiment that is based on a real-world application scenario.
Hierarchical Alignment Decomposition Labels for Hiero Grammar Rules

Selecting a set of nonterminals for the synchronous CFGs underlying the hierarchical phrase-based models is usually done on the basis of a monolingual resource (like a syntactic parser). However, a standard bilingual resource like word alignments is itself rich with reordering patterns that, if clustered somehow, might provide labels of different (possibly complementary) nature to monolingual labels. In this paper we explore a first version of this idea based on a hierarchical decomposition of word alignments into recursive tree representations. We identify five clusters of alignment patterns in which the children of a node in a decomposition tree are found and employ these five as nonterminal labels for the Hiero productions. Although this is our first non-optimized instantiation of the idea, our experiments show competitive performance with the Hiero baseline, exemplifying certain merits of this novel approach.
Fitting a conic A-spline to contour image data

In this paper an algorithm for constructing contours from image data by means of a G conic A-spline is presented. A conic A-spline is a piecewise smooth chain of connected real quadratic algebraic curves meeting G at the junction points. Once the contour of the image data has been extracted, the algorithm computes the breakpoints of the conic A-spline, i.e the junction points for the conic curves make up the curve. Inflection points are also added to the set of junction points of the A-spline. Tangent lines at the junction points are computed using a weighted least square linear fit instead of the classical divided difference techniques. The conic A-spline interpolates the junction points along with the tangent directions and least-squares approximates the given data between junction points. We discuss and compare our experience with the approaches reported in the recent literature. Additionally, we propose some improvements.
Layer 2 Path Selection Protocol for Wireless Mesh Networks with Smart Antennas

In this thesis the possibilities of smart antenna systems in wireless mesh networks are examined. With respect to the individual smart antenna tradeoffs, a routing protocol (Modified HWMP, MHWMP) for IEEE 802.11s mesh networks is presented, that exploits the full range of benefits provided by smart antennas: MHWMP actively switches between the PHY-layer transmission/reception modes (multiplexing, beamforming and diversity) according to the wireless channel conditions. Spatial multiplexing and beamforming are used for unicast data transmissions, while antenna diversity is employed for efficient broadcasts. To adapt to the directional channel environment and to take full benefit of the PHY capabilities, a respective MAC scheme is employed. The presented protocol is tested in extensive simulation and the results are examined.
Classification of RNA Splice Junctions Based on Genomic Signals

Classification of splice junctions is a critical component in the identification of new RNA isoforms in genetic annotations. Current high-throughput experimental techniques are unable to reliably identify splice junctions with high confidence. We propose to use a support vector machine (SVM) based model to improve the specificity of such experiments. Compared to existing schemes, our model performs competitively despite its simplicity. Various parameters such as the length of the sequence feature vector and the distance between nucleotides were tuned to optimal values. Overall, it was shown that the SVM is a capable tool for identification of splice junctions based on their contextual genomic signals.
Quantifying Fire Behavior Versus Societal Benefits of Southern California Shrublands and Grasslands

Urban sprawl in southern California perpetually threatens native shrublands and grasslands, which intrinsically provide both biophysical and socioeconomic benefits to society. However, these vegetation types are simultaneously prone to high-intensity wildfires that lead to enormous damage to human interests. After the southern California firestorms of October 2003, new regulations were adopted that increased the mandatory vegetation clearance around structures in order to reduce fire risk, which may significantly impact the positive benefits that grasslands and shrublands provide. To address this apparent conflict, we investigated the tradeoffs between societal benefits derived from major shrubland, grassland, and woodland vegetation types in southern California versus the potential fire behavior associated with each vegetation type. Two state-of-the-art, geographic information system–based software packages were utilized in the analysis, which focused on San Diego County, California. For each of the most common grassland, shrubland, and woodland vegetation communities in the area, FARSITE was utilized to assess potential fire behavior under average and extreme weather conditions. The most extreme fire behavior was found in nonnative grasslands and scrub oak chaparral communities and least extreme in coast live oak (Quercus agrifolia) communities. Under Santa Ana wind conditions, simulated fires in almost all vegetation types burned over 3 km into a developed area in 1 h. CITYgreen was used to quantify air pollution removal, carbon sequestration, and stormwater retention for each of the vegetation types, but was found to be largely ineffective because it calculated no measurable benefits for any non-tree vegetation types. To ensure sustainable neighborhoods in the wildland–urban interface, diverse stakeholders must create collaborative management plans that simultaneously reduce fire risk and maximize societal benefits. keywords: chaparral, CITYgreen, FARSITE, fire behavior modeling, grassland, shrubland, southern California, wildland–urban interface. Citation: Dicus, C.A. , and M.A. Zimmerman. 2007. Quantifying fire behavior versus societal benefits of southern California shrublands and grasslands. Pages 214–220 in R.E. Masters and K.E. M. Galley (eds.). Proceedings of the 23rd Tall Timers Fire Ecology Conference: Fire in Grassland and Shrubland Ecosystems. Tall Timbers Research Station, Tallahassee, Florida, USA.