Miracle-dz/UniFAR-FTUs huggingface dataset:
Moult, flight performance and wingbeat kinematics during take-off in European starlings Sturnus vulgaris

The effects of natural moult on avian flight performance have received relatively little attention, yet moult is an important part of the annual cycle. Quantification of flight costs will help to explain the diversity of moult patterns observed in avian taxa. Take-off from the ground requires a high power output from the flight muscles compared to other modes of flight, and is an important feature of foraging and predation escape. The present study was designed to quantify the effect of natural moult and new plumage on the take-off strategy, kinematics, and flight performance of European starlings Sturnus vulgaris. A high-speed (185 Hz) cine camera was used to film seven European starlings on three occasions: session 1, two weeks prior to the onset of moult; session 2, during mid-moult; and session 3, two weeks after full plumage had re-grown. From subsequent film analysis, we assessed take-off speed and angle, the energy gained per wingbeat, and wingtip kinematics. Take-off strategy (measured by angle and speed) altered through the course of the three experimental sessions, i.e. ascent angle decreased and take-off speed increased. Energy gained per wingbeat did not vary, suggesting there was no significant decrease in flight performance due to moult, but there was a significant improvement in take-off performance due to renewal of flight plumage. Wingbeat amplitude increased in association with moult and after flight plumage had been completely renewed. The European starlings incurred relatively minor flight costs due to moult, when comparing before-moult with during-moult take-off performance. The apparent absence of additional flight costs associated with moult may reflect a decreased mechanical performance of year-old feathers (which are replaced during the moult) and may also help to explain the relatively long duration of the moult in this species. This study also provides evidence of the benefits of plumage renewal, as take-off performance is improved after moult has been completed.
A Therapeutic Maneuver for Oropharyngeal Dysphagia in Patients with Parkinson’s Disease

OBJECTIVE This study investigates resources to provide better conditions for oropharyngeal swallowing for improvement in the quality of life of Parkinson’s disease patients. METHOD Three men and one woman with an average age of 70.25 years had been afflicted with Parkinson’s disease for an average of 9.25 years. The patients were submitted to a rehabilitation program for oropharyngeal dysphagia after a clinical evaluation of swallowing. The rehabilitation program consisted of daily sessions for two consecutive weeks during which a biofeedback resource adapted especially for this study was used. The patients were then reevaluated for swallowing ability at follow-up. RESULTS The patients presenting difficulties with swallowing water displayed no such problems after rehabilitation. Only one patient exhibited slow oral transit of food and other discrete oropharyngeal food remnants when swallowing a biscuit. The sample variance was used to analyze the pressure measurements, demonstrating a numerical similarity of the results obtained with the swallowing of saliva or of biscuits (VAR = 4.41). A statistical difference was observed between the swallowing of saliva and biscuits, showing a significant pressure increase at the end of the rehabilitation program (p < 0.001). CONCLUSION The effortful swallow maneuver reinforced by using biofeedback appears to be a therapeutic resource in the rehabilitation of oropharyngeal dysphagia in Parkinson’s disease patients.
Intact baseline performance and priming in amnesia: Reply to Ostergaard and Jernigan.

In their comment on the article by S. B. Hamann, L. R. Squire, and D. L. Schacter (1995), A. L. Ostergaard and T. L. Jernigan (1996) reaffirmed their position that baseline perceptual identification performance and priming are impaired in amnesia. They also suggested certain shortcomings in the experiments of Hamann et al., who found normal baseline performance and normal priming in amnesia across a wide range of performance accuracies. In reply, the authors of this article suggest that the position of Ostergaard and Jernigan rests on selective consideration of data, inaccurate assumptions concerning 1 patient's priming performance (A.B.), and debatable concerns about the masking stimuli, ceiling effects, and presentation time of study items that were used. In addition, the authors of the present article suggest that Ostergaard and Jernigan have based their own experimental work on a task and test method that may not be optimal for studying priming.
Wireless connectivity for health and sports monitoring: a review

This is a review of health and sports monitoring research that uses or could benefit from wireless connectivity. New, enabling wireless connectivity standards are evaluated for their suitability, and an assessment of current exploitation of these technologies is summarised. An example of the application is given, highlighting the capabilities of a network of wireless sensors. Issues of timing and power consumption in a battery-powered system are addressed to highlight the benefits networking can provide, and a suggestion of how monitoring different biometric signals might allow one to gain additional information about an athlete or patient is made.
A Hybrid and Secure Mechanism to Execute Parameter Survey Applications on Local and Public Cloud Resources

A parameter survey application (PSA) is a typical application running on high-performance computing (HPC) systems. A PSA consists of a lot of independent tasks with different input parameters that are executed in parallel on different CPU cores. Infrastructure-as-a-Service Cloud (IaaS Cloud) is expected to be used as an HPC infrastructure to run PSAs, and some reports have discussed hybrid execution mechanisms to utilize both local resources and IaaS Clouds. However, users still have security problems in running applications with confidential data on an IaaS Cloud. We propose a hybrid and secure execution mechanism to run PSAs utilizing both local computing resources with a batch scheduler and an IaaS Cloud. The proposed mechanism utilizes both local resources and IaaS Clouds to meet the deadline of user applications. We conducted experiments running a natural language processing application, which uses machine learning to detect abusive language on Internet bulletin board systems. The experimental results showed that the proposed mechanism effectively allocated resources and met the deadlines of the user application.
Violence against Women: An Exploration of the Physical and Mental Health Trends among Immigrant and Refugee Women in Canada

Violence against women is a serious health and social problem for women worldwide. Researchers have investigated the broad physical and mental health consequences of violence against women but few have focused on immigrant and refugee women. We assessed the history of violence and the impairment of physical and mental health among 60 women participants from the Iranian and Sri Lankan Tamil communities in Toronto, Canada. Our survey findings revealed that the participants had experienced various types of violence throughout their lifespan, with psychological abuse by a spouse/partner occurring most frequently in the past 12 months. Commonly reported types of abuse included insulting, criticizing, and intimidation by partner (psychological abuse); slapping, hitting, and shoving (physical abuse); and forced sexual intercourse and sexually degrading acts (sexual abuse) by a partner/spouse. We found that a substantial proportion of the participants also had experienced physical and mental health impairment, which could be a result of the various types of violence they had experienced throughout their lifespan. Research and practice implications are provided.
Deep Semantic Mapping for Cross-Modal Retrieval

Cross-Modal mapping plays an essential role in multimedia information retrieval systems. However, most of existing work paid much attention on learning mapping functions but neglected the exploration of high-level semantic representation of modalities. Inspired by recent success of deep learning, in this paper, deep CNN (convolutional neural networks) features and topic features are utilized as visual and textual semantic representation respectively. To investigate the highly non-linear semantic correlation between image and text, we propose a regularized deep neural network(RE-DNN) for semantic mapping across modalities. By imposing intra-modal regularization as supervised pre-training, we finally learn a joint model which captures both intra-modal and inter-modal relationships. Our approach is superior to previous work in follows: (1) it explores high-level semantic correlations, (2) it requires little prior knowledge for model training, (3) it is able to tackle modality missing problem. Extensive experiments on benchmark Wikipedia dataset show RE-DNN outperforms the state-of-the-art approaches in cross-modal retrieval.
Hidden Markov processes

An overview of statistical and information-theoretic aspects of hidden Markov processes (HMPs) is presented. An HMP is a discrete-time finite-state homogeneous Markov chain observed through a discrete-time memoryless invariant channel. In recent years, the work of Baum and Petrie (1966) on finite-state finite-alphabet HMPs was expanded to HMPs with finite as well as continuous state spaces and a general alphabet. In particular, statistical properties and ergodic theorems for relative entropy densities of HMPs were developed. Consistency and asymptotic normality of the maximum-likelihood (ML) parameter estimator were proved under some mild conditions. Similar results were established for switching autoregressive processes. These processes generalize HMPs. New algorithms were developed for estimating the state, parameter, and order of an HMP, for universal coding and classification of HMPs, and for universal decoding of hidden Markov channels. These and other related topics are reviewed.
Adaptive structure metrics for automated feedback provision in Java programming

Today's learning supporting systems for programming mostly rely on pre-coded feedback provision, such that their applicability is restricted to modelled tasks. In this contribution, we investigate the suitability of machine learning techniques to automate this process by means of a presentationm of similar solution strategies from a set of stored examples. To this end we apply structure metric learning methods in local and global alignment which can be used to compare Java programs. We demonstrate that automatically adapted metrics better identify the underlying programming strategy as compared to their default counterparts in a benchmark example from programming.
Scaling properties of neural networks for the prediction of time series

Sealing properties of neural networks, i.e. relations between the number of hidden units and the training or generalization error, recently have been investigated theoretically with encouraging results. In this paper we investigate experimentally, whether the theoretic results may be expected in practical applications. We investigate different neural network structures with varying number of hidden units for solving two time series prediction tasks. The results show a considerable difference of the scaling behavior of multilayer perceptrons and radial basis function networks.
Tensor Voting Guided Mesh Denoising

Mesh denoising is imperative for improving imperfect surfaces acquired by scanning devices. The main challenge is to faithfully retain geometric features and avoid introducing additional artifacts when removing noise. Unlike the existing mesh denoising techniques that focus only on either the first-order features or high-order differential properties, our approach exploits the synergy when facet normals and quadric surfaces are integrated to recover a piecewise smooth surface. In specific, we vote on surface normal tensors from robust statistics to guide the creation of consistent subneighborhoods subsequently used by moving least squares (MLS). This voting naturally leads to a conceptually simple way that gives a unified mesh-denoising framework for not only handling noise but also enabling the recovering of surfaces with both sharp and small-scale features. The effectiveness of our framework stems from: 1) the multiscale tensor voting that avoids the influence from noise; 2) the effective energy minimization strategy to searching the consistent subneighborhoods; and 3) the piecewise MLS that fully prevents the side effects from different subneighborhoods during surface fitting. Our framework is direct, practical, and easy to understand. Comparisons with the state-of-the-art methods demonstrate its outstanding performance on feature preservation and artifact suppression.
Evaluation of left ventricular ejection fraction as a measure of pump performance in patients with chronic mitral regurgitation

Left ventricular (LV) ejection fraction may not adequately detect a reduction in LV systolic performance resulting from chronic mitral regurgitation (MR), due to ventricular unloading into the low‐impedance left atrium. To determine whether LV ejection fraction sufficiently gauges myocardial function in MR, nine patients were studied using micromanometer‐measured LV pressures and biplane cineventriculography before and 1 year after mitral valve surgery. Six control patients were also studied. LV ejection fraction was normal in MR patients, despite an increase in LV end‐systolic volume index. LV end‐systolic pressure‐volume and stress‐volume ratios in MR patients were lower than in controls (P < 0.05 and P < 0.01), suggesting that LV systolic performance fell. One year after mitral valve surgery, LV ejection fraction decreased (P < 0.05) even though LV end‐systolic volume index (P < 0.05), pressure‐volume (P < 0.05), and stress‐volume ratios (P < 0.01) all improved. Thus, LV ejection fraction inadequately reflected LV systolic function in MR patients before and after mitral valve surgery. Cathet. Cardiovasc. Intervent. 49:290–296, 2000. © 2000 Wiley‐Liss, Inc.
THE QUANTUM-CLASSICAL TRANSITION: THE FATE OF THE COMPLEX STRUCTURE

According to Dirac, fundamental laws of Classical Mechanics should be recovered by means of an "appropriate limit" of Quantum Mechanics. In the same spirit it is reasonable to enquire about the fundamental geometric structures of Classical Mechanics which will survive the appropriate limit of Quantum Mechanics. This is the case for the symplectic structure. On the contrary, such geometric structures as the metric tensor and the complex structure, which are necessary for the formulation of the Quantum theory, may not survive the Classical limit, being not relevant in the Classical theory. Here we discuss the Classical limit of those geometric structures mainly in the Ehrenfest and Heisenberg pictures, i.e. at the level of observables rather than at the level of states. A brief discussion of the fate of the complex structure in the Quantum-Classical transition in the Schrodinger picture is also mentioned.
Dynamic Imaging of Individual Remyelination Profiles in Multiple Sclerosis

Quantitative in vivo imaging of myelin loss and repair in patients with multiple sclerosis (MS) is essential to understand the pathogenesis of the disease and to evaluate promyelinating therapies. Selectively binding myelin in the central nervous system white matter, Pittsburgh compound B ([11C]PiB) can be used as a positron emission tomography (PET) tracer to explore myelin dynamics in MS.
Evaluating the value-added benefits of using requirements reuse metrics in ERP projects

Measurement programs often go astray and fail to reach full success because of misconceptions and differences in expectations about the benefits to be realized as a result of a measurement exercise. This paper suggests how to plan and apply requirements reuse measurement in a business focussed way, by doing frequent benefits assessments. We describe an approach to analyzing, evaluating and tracking the reuse metrics usage patterns in Enterprise Resource Planning (ERP) projects and the benefits gained from integrating requirements reuse measurement practices in the ERP requirements elicitation- modelling- negotiation cycle. Relationships between requirements measurement activities and requirements engineering activities and deliverables are studied in the context of SAP R/ 3 implementation projects.
Seasonal snowpack characteristics influence soil temperature and water content at multiple scales in interior western U.S. mountain ecosystems

Mountain snowpacks directly and indirectly influence soil temperature (Tsoil) and soil water content (θ). Vegetation, soil organisms, and associated biogeochemical processes certainly respond to snowpack‐related variability in the soil biophysical environment, but there is currently a poor understanding of how snow‐soil interactions vary in time and across the mountain landscape. Using data from a network of automated snowpack monitoring stations in the interior western U.S., we quantified seasonal and landscape patterns in Tsoil and θ, and their dependence on snowpack characteristics over an eleven year period. Elevation gradients in Tsoil were absent beneath winter snowpacks, despite large gradients in air temperature (Tair). Winter Tsoil was warmer and less variable than Tair, but interannual and across‐site variations in Tsoil were likely large enough to impact biogeochemical processes. Winter θ varied between years and across sites, but during a given winter at a site it changed little between the start of snowpack accumulation and the initiation of spring snowmelt. Winter Tsoil and θ were both higher when early winter snow accumulation was greater. Summer θ was lower when summer Tair was high. Depending on the site and the year examined, summer θ was higher when there was greater summer precipitation, a larger snowpack, later snowpack melt, or a combination of these factors. We found that snowpack‐related variability in the soil environment was of sufficient magnitude to influence biogeochemical processes in snow‐dominated ecosystems.
Performance Comparison of the Neuron Routing Algorithm for Mobile Ad Hoc Networks

This paper proposes a strong research interest in a field related to bio­informatics and computational biology in Mobile Ad Hoc Networks (MANET). Basically, we develop an intelligent mobile agent called the NEURAL Agent, which improves the performance of the NEUron Routing ALgorithm (NEURAL). This autonomous agent coordinates the gathering of information by integrating, planning, scheduling, and making­decision procedures with other agents through different modules that operate asynchronously. In addition, a comparison study is carried out based on the performance of the Dynamic Source Routing (DSR), and the Ad­Hoc On­Demand Distance Vector Routing (AODV).
Trends and Developments in ICT Industry: New Challenges for Performance Analysts

In the next few years the landscape of the information and communications technology (ICT) industry will be subject to many changes. New and advanced ICT services will be brought to the market, offered over large and highly distributed infrastructures consisting of a heterogeneity of information systems and communication networks, each owned by different parties. The business of many companies will increasingly depend on the performance of their ICT systems. T he unbundling of the telecommunications world will become reality, the classical role models and value chains will be subject to change, and third party service providers will enter the market. Network and service providers will need to negotiate Service Level Agreements (SLA's) in order to offer high and predictable service levels to their customers in a cost-effective manner. In this paper we give an overview of trends and development in the ICT industry, and address new research challenges for performance analysts, both for practitioners and theorists.
Fiscal Transfers and Structural Reforms in the European Monetary Union

In a monetary union, fiscal transfers are an important policy tool to adjust to asymmetric shocks. However, fiscal transfers cannot substitute structural reforms especially when shocks are permanent. In this way, the design of fiscal transfer systems determine whether structural reforms or non-reforming is preferred by governments. Inter-regional transfers provide the lowest incentive for structural reforms. Inter-temporal transfers might promote structural reforms as long as debt cannot be accumulated. Therefore, I oppose an EU-tax budget, call for a strict application of the Stability and Growth Pact, and explain low reform activity in the EMU by interest rate convergence.
An Improved Association Rule Mining Technique for Xml Data Using Xquery and Apriori Algorithm

The usage of XML data in the World Wide Web and elsewhere as a standard for the exchange of data and to represent semi structured data tends to develop the various tools and techniques to perform various data mining operations on XML documents and XML repositories. In recent years, several encouraging methods have been identified and developed for mining XML data. In this paper, we present an improved framework for mining association rules from XML data using XQUERY and . NET based implementation of Apriori algorithm.
Challenges of CRISPR/Cas9 applications for long non-coding RNA genes

Abstract The CRISPR/Cas9 system provides a revolutionary genome editing tool for all areas of molecular biology. In long non-coding RNA (lncRNA) research, the Cas9 nuclease can delete lncRNA genes or introduce RNA-destabilizing elements into their locus. The nuclease-deficient dCas9 mutant retains its RNA-dependent DNA-binding activity and can modulate gene expression when fused to transcriptional repressor or activator domains. Here, we systematically analyze whether CRISPR approaches are suitable to target lncRNAs. Many lncRNAs are derived from bidirectional promoters or overlap with promoters or bodies of sense or antisense genes. In a genome-wide analysis, we find only 38% of 15929 lncRNA loci are safely amenable to CRISPR applications while almost two-thirds of lncRNA loci are at risk to inadvertently deregulate neighboring genes. CRISPR- but not siPOOL or Antisense Oligo (ASO)-mediated targeting of lncRNAs NOP14-AS1, LOC389641, MNX1-AS1 or HOTAIR also affects their respective neighboring genes. Frequently overlooked, the same restrictions may apply to mRNAs. For example, the tumor suppressor TP53 and its head-to-head neighbor WRAP53 are jointly affected by the same sgRNAs but not siPOOLs. Hence, despite the advantages of CRISPR/Cas9 to modulate expression bidirectionally and in cis, approaches based on ASOs or siPOOLs may be the better choice to target specifically the transcript from complex loci.
The Parent-Teacher Partnership: How to Work Together for Student Achievement

With the National PTA's Standard for School-Family-Community Partnership as a framework, this guide offers advice for resolving common points of contention between parents and teachers, such as the most productive use of a parent-teacher conference, the best at-home environment for doing homework, the helpfulness of parental rewards for classroom performance, and a teacher's role in supporting a student with an at-home crisis. This solution manual draws from real-world experiences of parents, teachers, and administrators to tackle issues of communication, parenting skills, classroom volunteering, and mutual respect.