Miracle-dz/UniFAR-FTUs huggingface dataset:
Image-based network rendering system for large sized meshes

Recent advancement in sensing and software technologies enables us to obtain large scale, yet fine 3D mesh models of cultural assets. However, such large-scale models cannot be displayed interactively on consumer computers because of the performance limitation of the hardware. We propose an interactive rendering system for large scale 3D mesh models, stored on a remote machine through relatively small capacity of networks. Our system uses both model and image based rendering methods for efficient load balance between a server and clients. On the server, the 3D models are rendered by the model-based method using a hierarchical data structure with multi-resolution. On the client, it reconstructs an arbitrary view by using a novel image-based method, referred to as the Grid-Lumigraph, with blending image colors from sampling images received from the server. The resulting rendering system can efficiently render any image in real-time.
Assessment of 5 Hz and 10 Hz GPS units for measuring athlete movement demands

This study aimed to assess the validity and inter-unit reliability of 5 Hz and 10 Hz global positioning system (GPS) units and determine the differences between these units as measures of team sport athlete movement demands. A team sport simulation circuit was completed by eight trained male participants to examine the following movement demands: total distance covered (TD), peak speed and the distance covered, time spent and number of efforts performed at different speed zones. Additionally, repeated high intensity efforts, player load and exertion index were investigated. The analysis using paired samples t-test and percentage typical error of measurement (%TEM) revealed that 10 Hz GPS units provided a valid and reliable measure of TD (p>0.05, %TEM=1.3). The 5 Hz units also displayed reliable measures of TD (%TEM=1.2). It was apparent that as the speed of movement increased, the level of GPS error increased for both 5 Hz and 10 Hz units (%TEM: 0-14%). The updates in GPS firmware and increased sampling rates have further improved the validity and inter-unit reliability of GPS. Consequently, practitioners can more confidently rely on the data obtained from current GPS units during training and matches, however, caution is required when measuring high speed movements.
A New Grading System for Ototoxicity in Adults

Objective: This study aimed to propose an ototoxicity grading system sensitive to the effect of ototoxicity on specific daily life situations like speech intelligibility and the perception of ultra-high sounds and to test its feasibility compared to current criteria. Methods: Pure tone averages (PTAs) for speech perception (1-2-4 kHz) and ultra-high frequencies (8-10-12.5 kHz) were incorporated. Threshold shift and hearing level posttreatment were taken into account. Criteria were tested on head and neck cancer patients treated with (chemo-)radiotherapy ([C]RT) and compared with the Common Terminology Criteria for Adverse Events version 4 (CTCAEv4) and the American Speech-Language-Hearing Association criteria (ASHA). Results: Grades 1 and 2 were based on threshold shifts from baseline (in dB) and subjective complaints. Grades 3 and 4 were defined as treatment-induced hearing loss of ≥ 35 dB at PTA 1-2-4 kHz and ≥ 70 dB at PTA 1-2-4 kHz, respectively. In high-dose cisplatin CRT incidences by the new criteria, CTCAEv4 and ASHA were comparable (78%-88%). In RT and low-dose cisplatin CRT, incidences were 36% to 39% in the new criteria versus 22% to 53% in CTCAEv4 and ASHA. Conclusion: The new criteria show an increased sensitivity to ototoxicity compared to CTCAEv4 and ASHA and provide insight into the effect of hearing loss on certain daily life situations. The new grading system seems feasible for clinic and research purposes.
EEG feature selection using mutual information and support vector machine: A comparative analysis

The large number of methods for EEG feature extraction demands a good choice for EEG features for every task. This paper compares three subsets of features obtained by tracks extraction method, wavelet transform and fractional Fourier transform. Particularly, we compare the performance of each subset in classification tasks using support vector machines and then we select possible combination of features by feature selection methods based on forward-backward procedure and mutual information as relevance criteria. Results confirm that fractional Fourier transform coefficients present very good performance and also the possibility of using some combination of this features to improve the performance of the classifier. To reinforce the relevance of the study, we carry out 1000 independent runs using a bootstrap approach, and evaluate the statistical significance of the Fscore results using the Kruskal-Wallis test.
Comprehensive Analysis of Alternative Splicing in Digitalis purpurea by Strand-Specific RNA-Seq

Digitalis purpurea (D. purpurea) is one of the most important medicinal plants and is well known in the treatment of heart failure because of the cardiac glycosides that are its main active compounds. However, in the absence of strand specific sequencing information, the post-transcriptional mechanism of gene regulation in D. purpurea thus far remains unknown. In this study, a strand-specific RNA-Seq library was constructed and sequenced using Illumina HiSeq platforms to characterize the transcriptome of D. purpurea with a focus on alternative splicing (AS) events and the effect of AS on protein domains. De novo RNA-Seq assembly resulted in 48,475 genes. Based on the assembled transcripts, we reported a list of 3,265 AS genes, including 5,408 AS events in D. purpurea. Interestingly, both glycosyltransferases and monooxygenase, which were involved in the biosynthesis of cardiac glycosides, are regulated by AS. A total of 2,422 AS events occurred in coding regions, and 959 AS events were located in the regions of 882 unique protein domains, which could affect protein function. This D. purpurea transcriptome study substantially increased the expressed sequence resource and presented a better understanding of post-transcriptional regulation to further facilitate the medicinal applications of D. purpurea for human health.
Splice‐isoform specific immunolocalization of neuronal nitric oxide synthase in mouse and rat brain reveals that the PDZ‐complex‐building nNOSα β‐finger is largely exposed to antibodies

Knock out mice deficient for the splice‐isoform αα of neuronal nitric oxide synthase (nNOSαα) display residual nitric oxide synthase activity and immunosignal. To attribute this signal to the two minor neuronal nitric oxide synthase splice variants, ββ and γγ, we generated isoform‐specific anti‐peptide antibodies against the nNOSαα specific ββ‐finger motif involved in PDZ domain scaffolding and the nNOSββ specific N‐terminus. The nNOSαα ββ‐finger‐specific antibody clearly recognized the 160‐kDa band of recombinant nNOSαα on Western blots. Using immunocytochemistry, this antibody displayed, in rats and wild‐type mice, a labeling pattern similar to but not identical with that obtained using a commercial pan‐nNOS antibody. This similarity indicates that the majority of immunocytochemically detectable nNOS is not likely to be complexed with PDZ‐domain proteins via the ββ‐finger motif. This conclusion was confirmed by the inhibition of PSD‐95/nNOS interaction by the nNOSαα ββ‐finger antibody in pull‐down assays. By contrast, nNOSαα ββ‐finger labeling was clearly reduced in hippocampal and cortical neuropil areas enriched in NMDA receptor complex containing spine synapses. In nNOSαα knock out mice, nNOSαα was not detectable, whereas the pan‐nNOS antibody showed a distinct labeling of cell bodies throughout the brain, most likely reflecting ββ/γγ‐isoforms in these cells. The nNOSββ antibody clearly detected bacterial expressed nNOSββ fusion protein and nNOSββ in overexpressing HEK cells by Western blotting. Immunocytochemically, individual cell bodies in striatum, cerebral cortex, and in some brain stem nuclei were labeled in knock out but not in wild‐type mice, indicating an upregulation of nNOSββ in nNOSαα deficient animals. © 2007 Wiley Periodicals, Inc. Develop Neurobiol, 2007
An evaluation of multi-resolution search and storage in resource-constrained sensor networks - eScholarship

Wireless sensor networks enable dense sensing of the environment, offering unprecedented opportunities for observing the physical world. Centralized data collection and analysis adversely impact sensor node lifetime. Previous sensor network research has, therefore, focused on in network aggregation and query processing, but has done so for applications where the features of interest are known a priori. When features are not known a priori, as is the case with many scientific applications in dense sensor arrays, efficient support for multi-resolution storage and iterative, drill-down queries is essential. Our system demonstrates the use of in-network wavelet-based summarization and progressive aging of summaries in support of longterm querying in storage and communication-constrained networks. We evaluate the performance of our linux implementation and show that it achieves: (a) low communication overhead for multi-resolution summarization, (b) highly efficient drill-down search over such summaries, and (c) efficient use of network storage capacity through load-balancing and progressive aging of summaries.
Comparison of inversion recovery preparation schemes for lipid suppression in 1H MRSI of human brain

To reduce contamination from subcutaneous lipid regions in MR spectroscopic imaging (MRSI) of whole brain, lipid signals are often suppressed using T1 nulling methods. If a range of lipid T1 values is present, the suppression efficiency will be improved using multiple inversion recovery (MIR) preparation. This study compared single IR (SIR) and double IR (DIR) applied with a volumetric MRSI sequence at 1.5 T based on experimental measurement of lipid T1 and T2 relaxation rates. At short and medium echo times (TEs), an approximately 28–47% improvement in lipid suppression was achieved with DIR compared to SIR. However, it also led to a loss of 37–43% in signal‐to‐noise ratio (SNR) for metabolites. Thus, SIR appears to be the better choice for suppressing lipid signals and maintaining metabolite sensitivity. Magn Reson Med 49:903–908, 2003. © 2003 Wiley‐Liss, Inc.
Verification of soft error detection mechanism through fault injection on hardware emulation platform

In this paper we describe one of the verification activities performed on a dual core 32-bit System-on-Chip designed for Automotive Safety applications and the consequent implementation of a methodology to verify the functionality of one of the safety mechanisms of the device. The Safety standards recommend the usage of fault-injection techniques to give evidence of the failure robustness of the electronic devices designed for Functional Safety. In this case we verified the robustness of the SoC processing subsystem to the Single Event Upset through the usage of some hardware emulation platforms where the device RTL was mapped, properly instrumented to allow the modification of Flip-Flop status during application runtime, thus modeling the SEUs effects. The main novelty of our work is therefore the definition of a methodology to verify the robustness of a SoC to SEUs; additionally we show that the same methodology can be used also to perform thorough measurements of the SER masking effect on a System on Chip.
Tolerating File-System Mistakes with EnvyFS

We introduce EnvyFS, an N-version local file system designed to improve reliability in the face of file-system bugs. EnvyFS, implemented as a thin VFS-like layer near the top of the storage stack, replicates file-system metadata and data across existing and diverse commodity file systems (e.g., ext3, ReiserFS, JFS). It uses majority-consensus to operate correctly despite the sometimes faulty behavior of an underlying commodity child file system. Through experimentation, we show EnvyFS is robust to a wide range of failure scenarios, thus delivering on its promise of increased fault tolerance; however, performance and capacity overheads can be significant. To remedy this issue, we introduce SubSIST, a novel single-instance store designed to operate in an N-version environment. In the common case where all child file systems are working properly, SubSIST coalesces most blocks and thus greatly reduces time and space overheads. In the rare case where a child makes a mistake, SubSIST does not propagate the error to other children, and thus preserves the ability of EnvyFS to detect and recover from bugs that affect data reliability. Overall, EnvyFS and SubSIST combine to significantly improve reliability with only modest space and time overheads.
Assistive tagging: A survey of multimedia tagging with human-computer joint exploration

Along with the explosive growth of multimedia data, automatic multimedia tagging has attracted great interest of various research communities, such as computer vision, multimedia, and information retrieval. However, despite the great progress achieved in the past two decades, automatic tagging technologies still can hardly achieve satisfactory performance on real-world multimedia data that vary widely in genre, quality, and content. Meanwhile, the power of human intelligence has been fully demonstrated in the Web 2.0 era. If well motivated, Internet users are able to tag a large amount of multimedia data. Therefore, a set of new techniques has been developed by combining humans and computers for more accurate and efficient multimedia tagging, such as batch tagging, active tagging, tag recommendation, and tag refinement. These techniques are able to accomplish multimedia tagging by jointly exploring humans and computers in different ways. This article refers to them collectively as assistive tagging and conducts a comprehensive survey of existing research efforts on this theme. We first introduce the status of automatic tagging and manual tagging and then state why assistive tagging can be a good solution. We categorize existing assistive tagging techniques into three paradigms: (1) tagging with data selection & organization; (2) tag recommendation; and (3) tag processing. We introduce the research efforts on each paradigm and summarize the methodologies. We also provide a discussion on several future trends in this research direction.
Tool support for architecture analysis and design

The needs of software architectural design and analysis have led to a desire to create CASE tools to support the processes. Such a tool should help: to document an architecture; to reuse architectural artifacts; to aid in exploring architectural alternatives; and to support architectural metrics. This position paper first presents a set of requirements that an ideal tool for architectural design and analysis, and then presents a tool-called SAAMtool-that meets most, but not all, of these requirements. SAAMtool embodies both SAAM (Software Architecture Analysis Method) and an architectural description framework which describes architectural elements according to their static and temporal features. The tool serves several purposes. It supports and documents the results of architectural design and analysis efforts at varying degrees of resolution, it acts as a repository of both designs and design rationales in the form of scenarios, it applies metrics to architectures, and it visualizes architectures with respect to architectural metrics.
Deep neural networks for iris recognition system based on video: Stacked sparse auto encoders (SSAE) and bi-propagation neural network models

Iris recognition technique is now regarded among the most trustworthy biometrics tactics. This is basically ascribed to its extraordinary consistency in identifying individuals. Moreover, this technique is highly efficient because of iris’ distinctive characteristics and due to its ability to protect the iris against environmental and aging effects. The Problem statement of this work is that the study presented an effective Iris recognition mechanism that is dependent on video. In this paper, it includes the data created based on best frame selected from iris video, then iris segmentation, normalization and feature extraction based on Hough Transform approach, Daugman rubber sheet modal and 1D Log-Gabor filter respectively. Flowed this proposed iris matching was proposed on the basis of two Deep Neural Networks models separately: Stacked Sparse Auto Encoders (SSAE) and Bi-propagation Neural network Models. Results are executed experimentally on the MBGC v1 NIR Iris Video datasets from the National Institute for Standards and Technology (NIST). The results displayed that Bi-propagation was the most efficient training algorithm for the iris recognition system based on video, and Stacked Sparse Auto Encoders (SSAE) is faster than Bipropagation. Finally, that can be conclude an iris recognition system based on video that produces based on both separately models: Stacked Sparse Auto Encoders (SSAE) and Bi-propagation Neural network Models was achieves very low error rates was mean the both models are successfully for iris recognition system based on video.
A Petri net application for energy management in aeronautical networks

In this paper, the implementation of an Intelligent Load Power Management (I-LPM) strategy for an aeronautical electrical network using a Petri net based approach will be discussed. The control strategy for I-LPM implementation will be presented following a step-by-step approach. In details, a rigorous method for translating the requirements that describe the desired energy management logic in a formal Petri net will be discussed. Next, in order to verify the Petri net correctness in terms of qualitative properties, a number of reduction techniques steps will be applied, eventually deriving a straightforward net where some basic properties will be verified by direct inspection. Finally, simulation results will evidence how the first Petri net so far derived can be applied, as a consequence of the successful verification of its basic properties, as a supervisory control strategy for the I-LPM of a basic aeronautical electrical network, as well as the obtained advantages over a traditional energy management strategy.
Characterizing Functional Connectivity Differences in Aging Adults using Machine Learning on Resting State fMRI Data

The brain at rest consists of spatially distributed but functionally connected regions, called intrinsic connectivity networks (ICNs). Resting state functional magnetic resonance imaging (rs-fMRI) has emerged as a way to characterize brain networks without confounds associated with task fMRI such as task difficulty and performance. Here we applied a Support Vector Machine (SVM) linear classifier as well as a support vector machine regressor to rs-fMRI data in order to compare age-related differences in four of the major functional brain networks: the default, cingulo-opercular, fronto-parietal, and sensorimotor. A linear SVM classifier discriminated between young and old subjects with 84% accuracy (p-value < 1 × 10−7). A linear SVR age predictor performed reasonably well in continuous age prediction (R2 = 0.419, p-value < 1 × 10−8). These findings reveal that differences in intrinsic connectivity as measured with rs-fMRI exist between subjects, and that SVM methods are capable of detecting and utilizing these differences for classification and prediction.
Single Image Segmentation with Estimated Depth

Object segmentation is a fundamental problem in computer vision. Although many segmentation methods have been proposed, most of them still rely on the appearances of images (i.e., colors or textures) [1, 2, 3, 4, 6, 8]. Consequently, they have a difficulty in distinguishing an object from the background with a similar appearance to the object. To overcome this difficulty, we employ a depth map of an input image as an additional cue to the object segmentation. The main contribution of this work is to introduce a novel segmentation framework that utilizes the depth map combined with a color image to describe the features of objects and backgrounds, where the depth map is estimated from the color image. While a depth map has great potential for use in segmentation, finding a way of integrating two completely different physical quantities, namely the color and depth, has remained unclear. We introduce an integration of the color and depth likelihood on objectness and backgroundness, which simply and effectively extends a traditional segmentation framework based on the Markov random fields (MRF) [2]. By refining the likelihood with the depth information, our proposed method can suppress the incorrect detection of misleading backgrounds. A single image is expressed by K, where K includes color information C = {Cx ∈R}x∈Ω, and in our case, depth informationZ = {Zx ∈R}x∈Ω (x is a position in the image domain Ω ⊂ N2). Object segmentation is the problem of assigning the label A = {Ax}x∈Ω, which gives a label Ax = {0,1} to each pixel, where the labels 1 and 0 at x respectively correspond to the object and background. The statistical relationship between K and A can be described by an MRF, and the appropriate configuration of the labels can be derived by minimizing the following energy function E:
On the Convergence of Stochastic Iterative Dynamic Programming Algorithms

Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. These algorithms, including the TD() algorithm of Sutton (1988) and the Q-learning algorithm of Watkins (1989), can be motivated heuristically as approximations to dynamic programming (DP). In this paper we provide a rigorous proof of convergence of these DP-based learning algorithms by relating them to the powerful techniques of stochastic approximation theory via a new convergence theorem. The theorem establishes a general class of convergent algorithms to which both TD() and Q-learning belong.
Predicting the Potential Impact of Selective Retina Therapy: An Assessment of Current Evidence

ABSTRACT Purpose: To predict the potential impact of selective retina therapy (SRT), as a treatment for diabetic macular edema (DME) and central serous chorioretinopathy (CSC), on the Korean healthcare system. Methods: Using horizon-scanning activities, we identified and filtered promising research-phase health technologies not listed within the domestic insurance system. After a process of prioritization, we selected SRT and evaluated the current clinical evidence concerning this technique. Finally, we analyzed the potential impacts through the peer-review process. Results: A total of 10 studies were included in the review: one randomized controlled trial, two single-arm trials, two cohort studies, and five case series. None of these studies reported severe complications or side effects when SRT was performed on patients with DME and CSC. However, there was a lack of statistically significant evidence regarding the main effectiveness indices (retinal thickness, regeneration of retinal pigment epithelium, and damage to surrounding tissues). Similarly, the outcome of the expert consultation on the potential impact of SRT was that it was a promising and safe treatment for DME and CSC; however, the experts also confirmed that evaluating the effectiveness of SRT is not possible based on existing evidence. Conclusion: SRT is a promising treatment for DME and CSC with great potential for acceptability within the current healthcare market. However, welldesigned clinical trials should be conducted for to confirm its effectiveness.