Miracle-dz/UniFAR-FTUs huggingface dataset:
Posterior Transpedicular Dynamic Stabilization versus Total Disc Replacement in the Treatment of Lumbar Painful Degenerative Disc Disease: A Comparison of Clinical Results

Study Design. Prospective clinical study. Objective. This study compares the clinical results of anterior lumbar total disc replacement and posterior transpedicular dynamic stabilization in the treatment of degenerative disc disease. Summary and Background Data. Over the last two decades, both techniques have emerged as alternative treatment options to fusion surgery. Methods. This study was conducted between 2004 and 2010 with a total of 50 patients (25 in each group). The mean age of the patients in total disc prosthesis group was 37,32 years. The mean age of the patients in posterior dynamic transpedicular stabilization was 43,08. Clinical (VAS and Oswestry) and radiological evaluations (lumbar lordosis and segmental lordosis angles) of the patients were carried out prior to the operation and 3, 12, and 24 months after the operation. We compared the average duration of surgery, blood loss during the surgery and the length of hospital stay of both groups. Results. Both techniques offered significant improvements in clinical parameters. There was no significant change in radiologic evaluations after the surgery for both techniques. Conclusion. Both dynamic systems provided spine stability. However, the posterior dynamic system had a slight advantage over anterior disc prosthesis because of its convenient application and fewer possible complications.
Roles for Endothelial Cells in Dengue Virus Infection

Dengue viruses cause two severe diseases that alter vascular fluid barrier functions, dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS). The endothelium is the primary fluid barrier of the vasculature and ultimately the effects of dengue virus infection that cause capillary leakage impact endothelial cell (EC) barrier functions. The ability of dengue virus to infect the endothelium provides a direct means for dengue to alter capillary permeability, permit virus replication, and induce responses that recruit immune cells to the endothelium. Recent studies focused on dengue virus infection of primary ECs have demonstrated that ECs are efficiently infected, rapidly produce viral progeny, and elicit immune enhancing cytokine responses that may contribute to pathogenesis. Furthermore, infected ECs have also been implicated in enhancing viremia and immunopathogenesis within murine dengue disease models. Thus dengue-infected ECs have the potential to directly contribute to immune enhancement, capillary permeability, viremia, and immune targeting of the endothelium. These effects implicate responses of the infected endothelium in dengue pathogenesis and rationalize therapeutic targeting of the endothelium and EC responses as a means of reducing the severity of dengue virus disease.
Multiclass epileptic seizure classification using time-frequency analysis of EEG signals

Seizure is a transient abnormal behavior of neurons within one or several neural networks, which limits the patients physical and mental activities. Since conventional time or frequency domain analysis is found inadequate to describe the characteristics of a non-stationary signal, such as electroen-cephalography (EEG), in this paper, we propose to transform the EEG data using twelve Cohen class kernel functions in order to facilitate the time-frequency analysis. The transformed data thus obtained is exploited to formulate a feature vector consists of modular energy and modular entropy that can better model the time-frequency behavior of the EEG data. The feature vector is fed to an Artificial Neural Network (ANN) classifier in order to classify epileptic seizure data originating from different parts and state of the brain. A number of simulations is carried out using a benchmark EEG dataset. It is shown that the proposed method is capable of producing greater accuracy in comparison to that obtained by using a state-of-the-art method of epileptic seizure classification using the same EEG dataset and classifier.
Concurrent tri-band GaN HEMT power amplifier using resonators in both input and output matching networks

This paper presents a novel method by using resonators in both input and output matching networks to design a tri-band GaN HEMT power amplifier. Two parallel resonators in series as one frequency selection element are used for each operation frequency. By applying this frequency selection element in both input and output matching networks constructed with microstrip line, tri-band matching network is realized. With our proposed frequency selection element, we can use the conventional L-type structure to design matching network for three frequencies so that the design analysis procedure is easier. We also propose a new simplified output matching network by using bias line to match the output impedance to reduce the number of resonators. To demonstrate our method, we fabricate a tri-band power amplifier that can work at 1 GHz, 1.5 GHz, and 2.5 GHz concurrently. Experimental results show that the output power is 39.8 dBm, 40.8 dBm, and 39.2 dBm with 56.4%, 58.3%, and 43.4% power added efficiency (PAE) at 1 GHz, 1.5 GHz and 2.5 GHz, respectively.
An efficient image-based telepresence system for videoconferencing

In this paper, we describe the view representation and reconstruction module for an image-based telepresence system, based on a viewpoint-adaptation scheme and the image-based rendering technique. For real-time three-dimensional synthesis, a parallel version of our multistep view reconstruction algorithm is realized on dedicated hardware based on Trimedia digital signal processors. A new scalable representation, named middle view stereo representation (MVSR), is further constructed to be able to adapt to the bandwidth requirement of the broadcasting network. Experiments show the promising performance of the parallel realization of the multistep view reconstruction method and the power of the MVSR representation.
Approximation of phase and envelope distributions of Gaussian random amplitude-modulated signals

In this paper the probability density functions (pdf) for the phase and the envelope of an amplitude-modulated (AM) signal, where the modulation is modeled as a random Gaussian function, in the presence of narrowband Gaussian noise are calculated. An exact, closed-form expression is obtained for the phase pdf, while approximate results, accurate for small and large carrier-to-noise ratios (CNR), are derived for the envelope pdf.
Restructuring the problem of Global Warming Mitigation : Climate Protection vs . Economic Growth as a false Trade off

10 The project asks how to derive and then promote a balanced portfolio of climate policies and technologies currently under discussion as efficient means to avoid (‘mitigate’), or at least limit, global warming. Thereby, the agenda responds to the emerging consensus within the climate research community that anthropogenic greenhouse gas emissions – such as generated during combustion – would induce global warming with 15 serious potential consequences. The ongoing climate debate about how much global warming should be mitigated serves as a starting point. While ‘climate environmentalists’ opt for strict emission reductions, influential economists proposed – at least at the beginning of the project – that the requested reductions would severely hamper the world economy. The ‘metamethod’ of the project disentangles the deterministic and normative 20 arguments of the debate. Within that setting, the methodology of the project displays two main characteristics. Firstly, robust deterministic knowledge about the climate system and the energy technology sector is captured by mechanistic models. This ‘deterministic branch’ would allow us to represent the effects of greenhouse gas emissions on global warming as well as the effects of investment decisions on the competitive advantage of 25 renewable sources over greenhouse gas emitting technologies. Secondly, the project distills the present line of normative settings involved in the climate debate – what impacts of global warming on the one hand, and of strict emission reduction targets on the other hand, are acceptable. A further normative issue is how to decide, under present-day uncertainties that modulate our knowledge, about the causal links from potential political actions to their 30 impacts. The approach of the project is to search for climate policies that would observe the minimum requests of each of the two major disputing ‘camps’ and to thereby maximise the chance for societal consensus. The minimum request of the ‘environmentalists’ is to guarantee that global warming shall not transgress 2°C. The minimum request of economists is that welfare loss due to climate protection should be somewhat below 1%. As 35 a primary result, the project has qualified the systems dynamics, enabling the identification of investment paths that are likely to observe the minimum requests of both parties. Key transdisciplinary challenges of the ongoing project are as follows: distilling the major epistemic (knowledge on systems dynamics) and major normative arguments, and alleged disagreements, as the two are intimately entangled within economic theory; finally, keeping 40 track of assumptions and desires of major players to ensure that the project’s stylised solutions will in fact catalyse a societal consensus.
HiCrawl: A Hidden Web Crawler for Medical Domain

The Hidden Web refers to a huge portion of the WWW that holds numerous freely accessible Web databases, hidden behind search form interfaces which can only be accessed through dynamic web pages that are generated in response to the user queries issued at the search form interface. Thus, the core challenge to implement any crawler for the Hidden Web is to routinely surpass these search form interfaces by automatically generating & issuing queries that help discover these dynamic Web pages. The paper provides a novel approach to guide the crawler in choosing the right query term to be submitted to any search form interface that has been designed to accept keywords or terms as input to it. The system is based on the use of classification hierarchies that might have either been manually or automatically constructed. And for the purposes of illustration, we have considered the search form interfaces in the 'Medical' domain, it being one of the most popular domains used by the researchers and the use of a manually generated top-down classification hierarchy in the same domain.
Crop Classification in Southern Sweden with Multitemporal ERS-2 SAR Data

Remote sensing techniques have become an important tool for resource management, with successful mapping and monitoring projects demonstrated for a wide variety of applications. In the last decade there has been an escalation in the use of remote sensing data to identify different crops and evaluate crop production [1]. Remotely-sensed data recorded at periods throughout the growing season can be used to produce an accurate crop classification. Multispectral data have been widely used [2] and good results have been achieved. But spectral data are sensitive to weather condition, especially cloud cover. There is no guarantee for data availability, especially during critical development stages of crops. High resolution synthetic aperture radar systems (SAR) overcome the problem of cloud cover since their ability to record data is independent of the prevailing atmospheric conditions. Much research using ERS radar data has been done in the last years [3] [4]. Techniques of multitemporal ERS radar data, different filters and integration with spectral data were used.
EM segmentation algorithm for colour image retrieval

Paper presents a method for object recognition that uses whole images of abstract regions, rather than single regions for classification. A key part of our approach is that we do not need to know where in each image the objects lie. We only utilize the fact that objects exist in an image, not where they are located. We have designed a procedure that learns multivariate models for object classes based on the attributes of abstract regions from multiple segmentations of colour images. The objective of this algorithm is to produce a distribution for each of the object classes being learned.
Explaining Price Heterogeneity through Leisure Participation

The purpose of this article is to analyze the effect of distinct participation frequencies of leisure activities at and away from home on the consumer price sensitivity. The idea is that the individual price sensitivity is influenced, at the moment of choosing a destination, by the motivation that drives people to search for destinations that allow them to do specific leisure activities; these motivations condition the influence of the residual culture (through which people exhibit at destination a similar behavior to their home environment) and tourist culture (by which at the destination they change the patterns of activities they get involved in at their place of origin). For this purpose, we measure and identify individual price sensitivities – individual by individual –. The empirical application is carried out on a sample of 2,127 individuals, and the operative formalization used to estimate individual sensitivities to price follows a Random-Coefficient Logit Model; and to detect the way these sensitivities relate to the different participation frequencies in leisure activities, a regression procedure is employed. The results show that the residual and tourist cultures have an influence on price sensitivity, in such a way that different price sensitivities exist per participation-frequency scenario and per type of leisure activity.
Student Bullying: Overview of Research, Federal Initiatives, and Legal Issues

This report begins with working definitions of bullying and cyber-bullying that were developed by a federal interagency working group. It provides a brief overview of research on the prevalence and impact of bullying, and it reviews research on what can be done to reduce student bullying. It discusses the responsibility of schools regarding bullying behavior; and it reviews the status of state laws that have been adopted to address student bullying. In addition, it discusses federal interagency initiatives intended to prevent bullying, and includes an appendix that summarizes many additional federal initiatives that permit some funding to be used for antibullying efforts. Finally, it discusses the legal issues and recent case law that define the parameters schools must consider in developing anti-bullying policies.
The effects of discourse and local organizing practices on disabled academics identities

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Management Turnover and Under-Investment in R&D: An Agency Theory Explanation for Under-Investment in Research and Development in Some Corporations

This paper provides an agency theory explanation for the apparent managerial myopia that is present in many corporations in the United States. The underlying premise of this research is that corporate managers may not really be short-sighted, but are in fact acting in what they perceive as their personal long-term best interests, rather than taking actions aimed at maximizing shareholders' wealth. Corporate managers may recognize, based on the experience of their predecessors, that their expected longevity with their current employers is limited. Therefore, they are likely to be reluctant to undertake any activities that will be costly to the firm in the short run, and can be expected to enrich the company only after a long period of time, when they may no longer be with their employer to share the rewards. This research focuses on RD Undcr-investment; RD CLO; Technology rich industries. Section 1 - Introduction The objective of this paper is to propose that agency theory may explain the apparent myopia that may he manifested in underinvestment in research and development in the United States, particularly in technology-intensive industries. The hypothesis under investigation is that managerial turnover may be one of the causes of this apparent myopia. To test this hypothesis, we conducted a number of econometric tests, such as measuring the correlation between the average annual rate of top management turnover and R&D expenditures as a percentage of gross revenue. Under-investment in R&D may he one of the causes of the sluggish economic growth that the United States experienced in the 1980s, relative to other industrialized nations, and the resulting trade deficit. Therefore, from the standpoint of international economics as well as corporate finance, it is important to inquire into the causes of this under-investment and general short-term orientation. We look to agency theory, and specifically to management turnover, for a possible explanation. It is noteworthy that the market appears to appropriately reward investment in R&D, especially in technology-intensive growth industries in which R&D expenditures have resulted in profitable new products in the recent past. …
Spectrum: A hybrid nanophotonic—electric on-chip network

On many-core chip designs, short, often-multicast, latency-critical messsages, used extensively in high-level coherence and synchronization protocols, often become the bottleneck of parallel performance scaling. This paper presents Spectrum, a hybrid nanophotonic-electric on-chip network that optimizes both throughput and latency. Spectrum's novel planar nanophotonic subnetwork broadcasts latency-critical messages through a wavelength-division multiplexed (WDM) two-dimensional waveguide. Spectrum's throughput-optimized packet-switching electrical subnetwork handles high bandwidth traffic. Overall, Spectrum delivers an almost ideal CMOS-compatible interconnection network for many-core systems.
Serotonin and Parkinson's disease: On movement, mood, and madness

An appreciation of the multiple roles that serotonin (5‐HT) may play in Parkinson's disease (PD) has increased in recent years. Early pathological studies in PD demonstrated nonselective reductions of 5‐HT in brain tissue but little correlation to comorbidities such as dyskinesia and mood disturbance. This, combined with treatment failures using serotonergic drugs in comparison to levodopa, meant the field was largely neglected until recently. The multitude of subtypes of 5‐HT receptors in the brain and an increased understanding of the potential function 5‐HT may play in modulating other neurotransmitter systems, including dopamine, GABA, and glutamate, have meant an expansion in efforts to develop potential serotonergic drugs for both motor and nonmotor symptoms in PD. However, several unanswered questions remain, and future studies need to focus on correlating changes in 5‐HT neurotransmission in both pathological and in vivo imaging studies with a full clinical phenotype. © 2009 Movement Disorder Society
Personality prediction based on intonation stylization

This study's aim is to predict speaker personality from intonation patterns in spoken dialogs. Intonation patterns were extracted by a parametric superpositional stylization approach that allows for pattern description on a parametric as well as on a categorical level. Based on features derived from these representations we trained support vector machines and fitted generalized linear regression models to predict speaker personality with respect to the four dimensions acting, extroversion, other-directedness, and sensitivity. The personality classification accuracies ranged from 79 to 91%.
Interactive Visual Histories for Vector Graphics

Presentation and graphics software enables users to experiment with variations of illustrations. They can revisit recent editing operations using the ubiquitous undo command, but they are limited to sequential exploration. We propose a new interaction metaphor and visualization for operation history. While editing, a user can access a history mode in which actions are denoted by graphical depictions appearing on top of the document. Our work is inspired by the visual language of film storyboards and assembly instructions. Our storyboard provides an interactive visual history, summarizing the editing of a document or a selected object. Each view is composed of action depictions representing the user’s editing actions and enables the user to consider the operation history in context rather than in a disconnected list view. This metaphor provides instant access to any past action and we demonstrate that this is an intuitive interface to a selective undo mechanism.
Nearest Neighbor algorithms in High Dimensions

In this report, the nearest neighbor search problem is considered. It is fundamentally important in several areas of computer science, including machine learning, pattern recognition and data mining. The most natural category of application for this type of problem is similarity search. For instance, consider a huge database, containing either, text documents, audio files or image files. A nearest neighbor search on such a database could be to find all pairs of closest items. This appears for example in copyright violation detection and duplicate image detection. Many of these applications involve very large data sets (e.g. billions of web pages, or billions of pictures on the web, or hundreds of thousands of pictures on a personal computer). Moreover, the dimensionality of the items is very large. Further, many other problems can be reduced to a nearest neighbor problem, such as MST, matching and clustering. In this report, a brief overview of the problem is described together with some algorithmic approaches to solving the problem, and one area of application, namely, duplicate image detection using search by example.
BrainCAT - a tool for automated and combined functional magnetic resonance imaging and diffusion tensor imaging brain connectivity analysis

Multimodal neuroimaging studies have recently become a trend in the neuroimaging field and are certainly a standard for the future. Brain connectivity studies combining functional activation patterns using resting-state or task-related functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) tractography have growing popularity. However, there is a scarcity of solutions to perform optimized, intuitive, and consistent multimodal fMRI/DTI studies. Here we propose a new tool, brain connectivity analysis tool (BrainCAT), for an automated and standard multimodal analysis of combined fMRI/DTI data, using freely available tools. With a friendly graphical user interface, BrainCAT aims to make data processing easier and faster, implementing a fully automated data processing pipeline and minimizing the need for user intervention, which hopefully will expand the use of combined fMRI/DTI studies. Its validity was tested in an aging study of the default mode network (DMN) white matter connectivity. The results evidenced the cingulum bundle as the structural connector of the precuneus/posterior cingulate cortex and the medial frontal cortex, regions of the DMN. Moreover, mean fractional anisotropy (FA) values along the cingulum extracted with BrainCAT showed a strong correlation with FA values from the manual selection of the same bundle. Taken together, these results provide evidence that BrainCAT is suitable for these analyses.