Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning twig and path queries

We investigate the problem of learning XML queries, path queries and twig queries, from examples given by the user. A learning algorithm takes on the input a set of XML documents with nodes annotated by the user and returns a query that selects the nodes in a manner consistent with the annotation. We study two learning settings that differ with the types of annotations. In the first setting the user may only indicate required nodes that the query must select (i.e., positive examples). In the second, more general, setting, the user may also indicate forbidden nodes that the query must not select (i.e., negative examples). The query may or may not select any node with no annotation. We formalize what it means for a class of queries to be learnable. One requirement is the existence of a learning algorithm that is sound i.e., always returning a query consistent with the examples given by the user. Furthermore, the learning algorithm should be complete i.e., able to produce every query with sufficiently rich examples. Other requirements involve tractability of the learning algorithm and its robustness to nonessential examples. We identify practical classes of Boolean and unary, path and twig queries that are learnable from positive examples. We also show that adding negative examples to the picture renders learning unfeasible.
Analysis and Design of New Single-to-Balanced Multicoupled Line Bandpass Filters Using Low-Temperature Co-Fired Ceramic Technology

This paper presents the design procedure and performance of a new single-to-balanced multicoupled line bandpass filter structure. The proposed filter is composed of a multicoupled line of electric length much shorter than lambda/8 along with shunt capacitors loaded at suitable positions. By a proper design of ground terminations for the multicoupled line, the proposed filter is simultaneously equipped with the functionality of a bandpass filter, a balun, and an impedance transformer. The bandpass characteristic can be easily developed to higher order for better selectivity. The graph-transformation method for coupled-line analysis is adopted to make the design procedure efficient and intuitive. To validate the design procedure and feasibility of proposed filter for mobile applications, two design examples with different filter order, impedance transformation ratio, fractional bandwidth and center frequency have been implemented in chip type by using the low temperature co-fired ceramic technology. The second-order design is realized in a chip size of 2012, while the third-order one is realized in a chip size of 2612. Moreover, an additional transmission zero in the upper stopband can be achieved and controlled flexibly by adjusting the outer printed circuit board layout with minimum effect on passband performance. Fabrication and measurement of these designs show that compact sizes and good agreements between measured and simulated results can be obtained, which demonstrate their suitability in modern mobile communication applications.
Neuro emotional literacy program: Does teaching the function of affect and affect regulation strategies improve affect management and well-being?

Although research on Emotion Regulation (ER) is developing at a rapid rate, much of it lacks a clear theoretical framework and most focuses on a narrow set of ER strategies. This work presents the details of a pilot project, the Neuro Emotional Literacy Program (NELP), designed for parents and based on the Somatic Appraisal Model of Affect (SAMA). This 6-week parent program used two self-report questionnaires, the Positive Negative Affect Schedule Short-Form (PANAS-SF) and the newly created Personal Affect Regulation Capacity Inventory (PARCI) to collect pre- and post-workshop data. Pilot project data analysis indicates that parents’ knowledge of the function and actuation of brain-body affect and expansion of their practice to include several ER strategies helps improve emotion management and promotes positive affect, as measured by PANAS-SF and PARCI.
Hospital nurse staffing and patient mortality, nurse burnout, and job dissatisfaction.

CONTEXT The worsening hospital nurse shortage and recent California legislation mandating minimum hospital patient-to-nurse ratios demand an understanding of how nurse staffing levels affect patient outcomes and nurse retention in hospital practice. OBJECTIVE To determine the association between the patient-to-nurse ratio and patient mortality, failure-to-rescue (deaths following complications) among surgical patients, and factors related to nurse retention. DESIGN, SETTING, AND PARTICIPANTS Cross-sectional analyses of linked data from 10 184 staff nurses surveyed, 232 342 general, orthopedic, and vascular surgery patients discharged from the hospital between April 1, 1998, and November 30, 1999, and administrative data from 168 nonfederal adult general hospitals in Pennsylvania. MAIN OUTCOME MEASURES Risk-adjusted patient mortality and failure-to-rescue within 30 days of admission, and nurse-reported job dissatisfaction and job-related burnout. RESULTS After adjusting for patient and hospital characteristics (size, teaching status, and technology), each additional patient per nurse was associated with a 7% (odds ratio [OR], 1.07; 95% confidence interval [CI], 1.03-1.12) increase in the likelihood of dying within 30 days of admission and a 7% (OR, 1.07; 95% CI, 1.02-1.11) increase in the odds of failure-to-rescue. After adjusting for nurse and hospital characteristics, each additional patient per nurse was associated with a 23% (OR, 1.23; 95% CI, 1.13-1.34) increase in the odds of burnout and a 15% (OR, 1.15; 95% CI, 1.07-1.25) increase in the odds of job dissatisfaction. CONCLUSIONS In hospitals with high patient-to-nurse ratios, surgical patients experience higher risk-adjusted 30-day mortality and failure-to-rescue rates, and nurses are more likely to experience burnout and job dissatisfaction.
Differentially generated neural network classifiers are efficient

Differential learning for statistical pattern classification is based on the classification figure-of-merit (CFM) objective function. It is proved that differential learning is asymptotically efficient, guaranteeing the best generalization allowed by the choice of hypothesis class as the training sample size grows large, while requiring the least classifier complexity necessary for Bayesian (i.e., minimum probability-of-error) discrimination. Differential learning almost always guarantees the best generalization allowed by the choice of hypothesis class for small training sample sizes. <<ETX>>
Image Registration Based on Convex Hull RANSAC-like

In this paper, a improved algorithm based on convex hull vertices is proposed. After extracting feature points by Harris corner detector, for searching the same regions of reference images, correlation method is used to match these points firstly, which gains two initial pairing sets. Then convex hull vertices of the pairing sets are used to establish the transformation parameters that can match most points of the two pairing sets, which use a convex hull RANSAC-like procedure. Lots of experiments demonstrate the accuracy, efficiency and robust of this approach.
Software engineering for multicore systems: an experience report

The emergence of inexpensive parallel computers powered by multicore chips combined with stagnating clock rates raises new challenges for software engineering. As future performance improvements will not come "for free" from increased clock rates, performance critical applications will need to be parallelized. However, little is known about the engineering principles for parallel general-purpose applications. This paper presents an experience report with four diverse case studies on multicore software development for general-purpose applications. They were programmed in different languages and benchmarked on several multicore computers. Empirical findings include: 1) Multicore computers deliver: Real speedups are achievable, albeit with significant programming effort and speedups that are typically lower than the number of cores employed; 2) Massive refactoring of sequential programs is required, sometimes at several levels. Special tools for parallelization refactorings appear to be an important area of research; 3) Autotuning is indispensable, as manually tuning thread assignment, number of pipeline stages, size of data partitions and other parameters is difficult and error prone; 4) Architectures that encompass several parallel components are poorly understood. Tuneable architectural patterns with parallelism at several levels need to be discovered.
Investigating School Leadership Practice: A Distributed Perspective

actions, and interactions of school leadership as they unfold together in the daily life of schools. The research program involves in-depth observations and interviews with formal and informal leaders and classroom teachers as well as a social network analysis in schools in the Chicago metropolitan area. We outline the distributed framework below, beginning with a brief review of the theoretical underpinnings for this work—distributed cognition and activity theory—which we then use to re-approach the subject of leadership practice. Next we develop our distributed theory of leadership around four ideas: leadership tasks and functions, task enactment, social distribution of task enactment, and situational distribution of task enactment. Our central argument is that school leadership is best understood as a distributed practice, stretched over the school’s social and situational contexts.
Computerizing Trail Making Test for long-term cognitive self-assessment

Purpose This paper aims to adapt and computerize the Trail Making Test (TMT) to support long-term self-assessment of cognitive abilities. Design/methodology/approach The authors propose a divide-and-combine (DAC) approach for generating different instances of TMT that can be used in repeated assessments with nearly no discernible practice effects. In the DAC approach, partial trails are generated separately in different layers and then combined to form a complete TMT trail. Findings The proposed approach was implemented in a computerized test application called iTMT. A pilot study was conducted to evaluate iTMT. The results show that the instances of TMT generated by the DAC approach had an adequate level of difficulty. iTMT also achieved a stronger construct validity, higher test–retest reliability and significantly reduced practice effects than existing computerized tests. Originality/value The preliminary results suggest that iTMT is suitable for long-term monitoring of cognitive abilities. By supporting self-assessment, iTMT also can help to crowdsource the assessment processes, which need to be administered by healthcare professionals conventionally, to the patients themselves.
Netman: a learning network traffic controller

One of the goals of Machine Learning is the production of software that can improve itself. Such software can learn from experience and adapt to changing situations and requirements. In addition, such software can refine its knowledge-base, perhaps leading to a level of expertise beyond that of human experts. This paper describes NETMAN, a knowledge-based program that uses a machine learning technique, Knowledge-based Learning, in the domain of Network Traffic Control. NETMAN's task is to maximize call completion in a circuit-switched telecommunications network. NETMAN learns from its own experiences and by observing the actions of other agents. NETMAN is one of the components of ILS (Integrated Learning System), which contains implementations of several learning paradigms working together to improve problem-solving performance. NETMAN combines two machine learning paradigms: Explanation-Based Learning and Empirical Learning.
Proper Training

In deformable model segmentation, the geometric training process plays a crucial role in providing shape statistical priors and appearance statistics that are used as likelihoods. Also, the geometric training process plays a crucial role in providing shape probability distributions in methods finding significant differences between classes. The quality of the training seriously affects the final results of segmentation or of significant difference finding between classes. However, the lack of shape priors in the training stage itself makes it difficult to enforce shape legality, i.e., making the model free of local self-intersection or creases. Shape legality not only yields proper shape statistics but also increases the consistency of parameterization of the object volume and thus proper appearance statistics. In this paper we propose a method incorporating explicit legality constraints in training process. The method is mathematically sound and has proved in practice to lead to shape probability distributions over only proper objects and most importantly to better segmentation results.
An Integrative Neurological Model for Basic Observable Human Behavior

The scientific method uncovers information from the natural world in small increments. This spurs the design of models to explain how the pieces fit together and to identify future targets of research. This is especially the case in psychology, where visualizing concepts is an advantageous practice. One all too common criticism of cognitive and behavioral models in psychology is the lack of a biological basis. This paper aims to alleviate part of this issue by integrating currently understood biological and neurological mechanisms that drive psychological phenomena into a predictive and descriptive model for basic human behavior. To accomplish this task, this paper explores numerous scientific reviews and studies regarding sensory perception, emotion, learning, and memory. This paper also features original research about decision making. Creating this model is a necessary first step for targeting possible future research and clinical practices related to human behavior.
Reduced Height (Rht) Alleles Affect Wheat Grain Quality

The effects of dwarfing alleles (reduced height, Rht) in near isogenic lines on wheat grain quality are characterised in field experiments and related to effects on crop height, grain yield and GA-sensitivity. Alleles included those that conferred GA-insensitivity (Rht-B1b, Rht-B1c, Rht-D1b, Rht-D1c) as well as those that retained GA-sensitivity (rht(tall), Rht8, Rht8 + Ppd-D1a, Rht12). Full characterisation was facilitated by including factors with which the effects of Rht alleles are known to interact for grain yield (i.e. system, [conventional or organic]; tillage intensity [plough-based, minimum or zero]; nitrogen fertilizer level [0–450 kg N/ha]; and genetic backgrounds varying in height [cvs Maris Huntsman, Maris Widgeon, and Mercia]. Allele effects on mean grain weight and grain specific weight were positively associated with final crop height: dwarfing reduced these quality criteria irrespective of crop management or GA-sensitivity. In all but two experiments the effects of dwarfing alleles on grain nitrogen and sulphur concentrations were closely and negatively related to effects on grain yield, e.g. a quadratic relationship between grain yield and crop height manipulated by the GA-insensitive alleles was mirrored by quadratic relationships for nitrogen and sulphur concentrations: the highest yields and most dilute concentrations occurred around 80cm. In one of the two exceptional experiments the GA-insensitive Rht-B1b and Rht-B1c significantly (P<0.05) reduced grain nitrogen concentration in the absence of an effect on yield, and in the remaining experiment the GA-sensitive Rht8 significantly reduced both grain yield and grain nitrogen concentration simultaneously. When Rht alleles diluted grain nitrogen concentration, N:S ratios and SDS-sedimentation volumes were often improved. Hagberg falling number (HFN) was negatively related to crop height but benefits from dwarfing were only seen for GA-insensitive alleles. For HFN, therefore, there was the strongest evidence for a direct pleiotropic effect of GA-insensitivity, rather than an effect consequential to yield and/or height.
Behavior-based botnet detection in parallel

Botnet has become one major Internet security issue in recent years. Although signature-based solutions are accurate, it is not possible to detect bot variants in real-time. In this paper, we propose behavior-based botnet detection in parallel BBDP. BBDP adopts a fuzzy pattern recognition approach to detect bots. It detects a bot based on anomaly behavior in domain name service DNS queries and transmission control protocol TCP requests. With the design objectives of being efficient and accurate, a bot is detected using the proposed five-stage process, including: i traffic reduction, which shrinks an input trace by deleting unnecessary packets; ii feature extraction, which extracts features from a shrunk trace; iii data partitioning, which divides features into smaller pieces; iv DNS detection phase, which detects bots based on DNS features; and v TCP detection phase, which detects bots based on TCP features. The detection phases, which consume approximately 90% of the total detection time, can be dispatched to multiple servers in parallel and make detection in real-time. The large scale experiments with the Windows Azure cloud service show that BBDP achieves a high true positive rate 95%+ and a low false positive rate ~3%. Meanwhile, experiments also show that the performance of BBDP can scale up linearly with the number of servers used to detect bots. Copyright © 2013 John Wiley & Sons, Ltd.
Experience in Bedside Placement, Clinical Validity, and Cost-Efficacy of a Self-Propelled Nasojejunal Feeding Tube

Background: The procedures needed to insert nasojejunal tubes (NJTs) are often invasive or uncomfortable for the patient and require hospital resources. The objectives of this study were to describe our experience in inserting a self-propelling NJT with distal pigtail end and evaluate clinical validity and cost efficacy of this enteral nutrition (EN) approach compared with parenteral nutrition (PN). Materials and Methods: Prospective study from July 2009 to December 2010, including hospitalized noncritical patients who required short-term jejunal EN. The tubes were inserted at bedside, using intravenous erythromycin as a prokinetic drug. Positioning was considered correct when the distal end was beyond the ligament of Treitz. Migration failure was considered when the tube was not positioned into the jejunum within 48 hours postinsertion. Results: Fifty-six insertions were recorded in 47 patients, most frequently in severe acute pancreatitis (69.6%). The migration rates at 18 and 48 hours postinsertion were 73.2% and 82.1%, respectively. There was migration failure in 8.9% of cases, and 8.9% were classified null (the tube was no longer in the gastrointestinal tract at 18 hours). There were no reported or observed complications. The mean duration of the EN was 12 ± 10.8 days. Five different types of EN formula were used. The total study cost was 53.9% lower compared with using PN in all patients. Conclusions: Our study demonstrated that bedside insertion of a self-propelling NJT is a safe, cost-effective, and successful technique for postpyloric enteral feeding in at least 73% of the patients, and only 18% of patients could eventually need other placement techniques. It can avoid the need for more aggressive or expensive placement techniques or even PN if we cannot achieve enteral access.
Review of 20 years of range sensor development

This paper quickly reviews 20 years of development in the field of 3D laser imaging. An overview of 3D digitizing techniques is presented with an emphasis on some of the numerous commercial techniques and systems currently available. This paper covers some of the most important methods that have been developed during the years, both at NRC and elsewhere, with a focus on commercial systems that are good representation of the key technologies that survived the test of the years.
ECME Thresholding Methods for Sparse Signal Reconstruction

We propose a probabilistic framework for interpreting and developing hard thresholding sparse signal reconstruction methods and present several new algorithms based on this framework. The measurements follow an underdetermined linear model, where the regression-coefficient vector is the sum of an unknown deterministic sparse signal component and a zero-mean white Gaussian component with an unknown variance. We first derive an expectation-conditional maximization either (ECME) iteration that guarantees convergence to a local maximum of the likelihood function of the unknown parameters for a given signal sparsity level. To analyze the reconstruction accuracy, we introduce the minimum sparse subspace quotient (SSQ), a more flexible measure of the sampling operator than the well-established restricted isometry property (RIP). We prove that, if the minimum SSQ is sufficiently large, ECME achieves perfect or near-optimal recovery of sparse or approximately sparse signals, respectively. We also propose a double overrelaxation (DORE) thresholding scheme for accelerating the ECME iteration. If the signal sparsity level is unknown, we introduce an unconstrained sparsity selection (USS) criterion for its selection and show that, under certain conditions, applying this criterion is equivalent to finding the sparsest solution of the underlying underdetermined linear system. Finally, we present our automatic double overrelaxation (ADORE) thresholding method that utilizes the USS criterion to select the signal sparsity level. We apply the proposed schemes to reconstruct sparse and approximately sparse signals from tomographic projections and compressive samples.
Phase Lock Loop Used in Carrier Frequency and in Data Symbols

This work study the PLL (Phase Lock Loop) applied to systems of carrier frequency and data symbols. In each system, we consider four PLL types, namely the analog, hybrid, combinational and sequential.In the synchronizers for carrier frequency, the four PLL (Pll-cf) synchronizes directly with the input carrier.In the synchronizers for data symbols, the four PLL (f+Pll-ds) sysnchronizes with the data fundamental harmonic, selected by a previous tuning filter.The objective is to study the various synchronizers and evaluate their output jitter UIRMS (Unit Interval Root Mean Square) versus input SNR (Signal to Noise Ratio).
Self-Dual Manifolds and Mirror Symmetry for the Quintic Threefold

For all $m > 0$ we build a two-dimensional family of smooth manifolds of real dimension $3m + 2$ and use it to interpolate between the anticanonical family in the complex projective space of dimension $m + 1$ and its mirror dual. The main tool is the notion of self-dual manifold.
A Law and Finance Analysis of Initial Public Offerings

Do families keep control of their firms because they, operating in an environment with weak protection of minority shareholders, fear being exploited by management after the IPO? Or is ownership concentration due to the value families attach to control? We find a positive relation between use of security designs that separate votes from capital and frequency of family-controlled firms in Sweden and other countries. It is not caused by differences in legal regimes or in minority protection. Since control blocks are never sold piecemeal to preserve control value, ownership remains highly concentrated. Family-controlled firms trade at a discount because of the misallocation of control rights to heirs who make inefficient decisions, not because of extraction of pecuniary benefits.