Miracle-dz/UniFAR-FTUs huggingface dataset:
An array based design for Real-Time Volume Rendering

This paper describes a new algorithm and hardware design for the generation of two dimensional images from volume data using the ray casting technique. The algorithm is part of an image generation system that is broken down into three subsystems. The first subsystem stores the input data in a buffered memory using a rearrangement of the original address value. The second subsystem reads data points from the buffered memory and shifts the data to computational elements in order to complete the viewing calculations for the image synthesis process. The final stage takes the results of the viewing calculations combined with the original input data to complete the surface rendering and pixel compositing to create the final image. This paper focusses on the second subsystem which consists of two, two dimensional arrays of processing elements. The first array performs a limited angle, single dimension rotation by shifting the data. The second array performs a two dimensional ray casting operation where viewing rays are assigned to each processing element. The first stage is outlined in this paper and the final rendering stages are the subject of previous work. The hardware design associated with these algorithms is described and tested. It is estimated that this architecture is capable of producing 384 × 384 pixel images at speeds of 15 frames per second for 2563 data sets. Real time generation of images of volume data is important in scientific applications of volume visualization and computer graphics applications which use volume graphics.
DUCHY: Double Cost Field Hybrid Link Estimation for Low-Power Wireless Sensor Networks

We present a novel link estimator for cost-based routing in low-power sensor networks. Our design is hybrid in two dierent ways: it leverages on both broadcast control trac and unicast data trac, and it exploits channel state information and delivery estimates. Broadcast control beacons and data trac are used to set up a double cost field: an outer field bootstrapped by depth estimates, and an inner field of link estimates. Both sets of estimates are based on control trac and corrected with feedback from the data plane. We benchmark our link estimation technique and evaluate its performance on Berkeley motes using a public testbed.
Dynamic transcriptomes during neural differentiation of human embryonic stem cells revealed by short, long, and paired-end sequencing

To examine the fundamental mechanisms governing neural differentiation, we analyzed the transcriptome changes that occur during the differentiation of hESCs into the neural lineage. Undifferentiated hESCs as well as cells at three stages of early neural differentiation—N1 (early initiation), N2 (neural progenitor), and N3 (early glial-like)—were analyzed using a combination of single read, paired-end read, and long read RNA sequencing. The results revealed enormous complexity in gene transcription and splicing dynamics during neural cell differentiation. We found previously unannotated transcripts and spliced isoforms specific for each stage of differentiation. Interestingly, splicing isoform diversity is highest in undifferentiated hESCs and decreases upon differentiation, a phenomenon we call isoform specialization. During neural differentiation, we observed differential expression of many types of genes, including those involved in key signaling pathways, and a large number of extracellular receptors exhibit stage-specific regulation. These results provide a valuable resource for studying neural differentiation and reveal insights into the mechanisms underlying in vitro neural differentiation of hESCs, such as neural fate specification, neural progenitor cell identity maintenance, and the transition from a predominantly neuronal state into one with increased gliogenic potential.
Unexpected Diversity of Chloroplast Noncoding RNAs as Revealed by Deep Sequencing of the Arabidopsis Transcriptome

Noncoding RNAs (ncRNA) are widely expressed in both prokaryotes and eukaryotes. Eukaryotic ncRNAs are commonly micro- and small-interfering RNAs (18–25 nt) involved in posttranscriptional gene silencing, whereas prokaryotic ncRNAs vary in size and are involved in various aspects of gene regulation. Given the prokaryotic origin of organelles, the presence of ncRNAs might be expected; however, the full spectrum of organellar ncRNAs has not been determined systematically. Here, strand-specific RNA-Seq analysis was used to identify 107 candidate ncRNAs from Arabidopsis thaliana chloroplasts, primarily encoded opposite protein-coding and tRNA genes. Forty-eight ncRNAs were shown to accumulate by RNA gel blot as discrete transcripts in wild-type (WT) plants and/or the pnp1-1 mutant, which lacks the chloroplast ribonuclease polynucleotide phosphorylase (cpPNPase). Ninety-eight percent of the ncRNAs detected by RNA gel blot had different transcript patterns between WT and pnp1-1, suggesting cpPNPase has a significant role in chloroplast ncRNA biogenesis and accumulation. Analysis of materials deficient for other major chloroplast ribonucleases, RNase R, RNase E, and RNase J, showed differential effects on ncRNA accumulation and/or form, suggesting specificity in RNase-ncRNA interactions. 5′ end mapping demonstrates that some ncRNAs are transcribed from dedicated promoters, whereas others result from transcriptional read-through. Finally, correlations between accumulation of some ncRNAs and the symmetrically transcribed sense RNA are consistent with a role in RNA stability. Overall, our data suggest that this extensive population of ncRNAs has the potential to underpin a previously underappreciated regulatory mode in the chloroplast.
Web application generator by image-oriented design

With the development of the information society, it has become necessary to release software early that satisfies users. Therefore, it has become important to develop software quickly so that users can try it, and give the developers feedback. Developers make the specifications of a program based on users' requirements, and then design, write, test and debug the program. Through this procedure, the program is completed. However, if we can directly use the image that the users have when we design a program, and if the program is completed when the design finishes, the procedure for software development will be made much simpler, and the time required to develop it will be reduced greatly. This paper presents an approach to brain-image oriented programming and describes the BioPro system that implements this method for Web applications. The brain-image oriented programming has the following features; (1) users can develop programs based on their image, (2) they can easily verify the completeness of the components that make up the program and the consistency of those relationships, and (3) they can easily confirm what they have developed, regardless of which stage of development they are currently at.
Context-specific information distribution using the Web 3L Model

This paper describes the use of social, semantic networks in business applications. Our approach - the Web 3L model - reinterprets the idea of the Web 3.0 as a 3-layer structure of networks. It combines relations from social and semantic networks in order to use them for context specific search and distribution of information. As an example the distribution of urgent requests in an internal knowledge management application is described. In addition, a social semantic search with a sophisticated user interface is shown.
Robust Modified Newton Algorithm for Adaptive Frequency Estimation

In this letter, we study the problem of adaptive retrieval of multiple sinusoids in white noise. The frequency estimation problem can be reformulated as an unconstrained optimization problem. From the proposed unconstrained cost function, a numerically robust and low complexity modified Newton algorithm is derived for tracking frequencies. A rigorous convergence analysis of the proposed algorithm by adopting Ljung's ordinary differential equation approach is presented. Simulation results show that the proposed adaptive frequency estimation algorithm has fast convergence and excellent tracking capability in nonstationary environment.
Topology-aware server selection method for dynamic parallel downloading

For distributed file transfers, parallel downloading is one of effective schemes to shorten a transmission time and/or to get more file transfer rate. In parallel downloading, single file is transferred by using multiple connections to different file servers. However, the effectiveness of parallel downloading is strongly dependent on the physical network topology, i.e., if some connections share the same bottleneck link, the throughput is not increased and the additional connection simply wastes the server resources. In this paper, we propose a server selection method for parallel downloading by taking the physical network topology into consideration. Our approach tries to minimize the total number of shared links between a client and downloading servers. Through simulation results, we show that our method can keep high throughput regardless the network topology and decrease the number of selected servers compared with random server selection.
Owner-Manager Preference , Management Development and the Growing Family Business : A Preliminary Investigation

Contrary to the assertion that the entrepreneur is instrumental in small business growth, the process of growth cannot be construed as automatic. Accordingly there is a need to examine the relationships between the personal characteristics, experience, knowledge, skills and motivations of the entrepreneur in determining the strategic direction of subsequent performance of the small firm. Interactions between business operations and family dynamics are important, including evolution of the business in terms of management development and growth. Personal aspirations can have a significant effect upon the systems, controls and management processes implemented in the family firm. The use of a single case study examining the development of a small family firm in South Wales has been utilised to illustrate how personal aspirations of the family determine the managerial capacity of the firm. The paper suggests that an extension to the dichotomy between growth-oriented or less growth-oriented businesses should be considered in which growth is ultimately superseded by the need for economic security. In these circumstances, it is argued that management development is constrained to the extent that management processes are simply introduced to ensure that the vision and aspirations of the family are continually secured. Moreover, this paper proposes that evidence of a firm's disengagement from the process of growth could be ascertained from the management practices adopted by the owner-management team.
A Generator of MVC-based Web Applications

This paper presents a generator of Spring MVC-based web applications. The generator was designed using Freemarker template and HibernateTools toolset. The generated applications are fully functional web applications based on three tiers architecture and MVC software pattern. The structure of Spring web applications is explained in brief, while designed templates and their role in the process of code generation are elaborated in more detail. The generator was designed to show a possibility for fast development of a custom generator as a solution for specific application requirements. Keywordsapplications generator; Spring; Freemarker; HibernateTools; MVC.
Sleep Pattern, Duration and Quality in Relation with Glycemic Control in People with Type 2 Diabetes Mellitus

Sleep disturbances have been shown to be associated with diabetes control, but the relation between planned wakings or napping with glycemic indices has not been evaluated yet. This study evaluated the relation between sleep quality, duration, and pattern, including daytime napping of people with diabetes and their glycemic control. A cross-sectional correlation research design was used for this study. We enrolled 118 people with type 2 diabetes receiving oral agents without major complications at the Shahid Bahonar Center, Kerman. The age, weight, height, serum HbA1c, as well as other glycemic indices and lipid profile were measured. BMI was also calculated. All participants were requested to fill in the Pittsburgh Sleep Quality Index (PSQI) questionnaire to evaluate their sleep quality. In addition, they were inquired about their sleep schedule during day and night. Pearson correlation and multiple regression analyses were conducted to examine the correlation between HbA1c and sleep pattern variables. The variables were also compared between participants with or without napping using t-test. All analyses were performed with the SPSS version 19 (SPSS, Chicago, IL, USA). The mean age was 58±11 years and mean HbA1c (%) was 7.8±11 (62±13 mmol/mol). Sleep duration and the number of sleep segments significantly predicted HbA1c (F (2,114)=5.232, P=0.007, R2=0.084). A one-hour increment in sleep duration was associated with a 0.174% (1.4 mmol/mol) decrement in HbA1c. PSQI score did not contribute to the regression model. Moreover, participants who napped (66%) had a lower HbA1c (7.6±1) compared to others (8.1±1.3) (P=0.04). We concluded that napping and segmented sleep are associated with a better glycemic control in type 2 diabetes and there is a linear correlation between sleep duration and better glycemic control.
Qualitatively different coding of symbolic and nonsymbolic numbers in the human brain

Are symbolic and nonsymbolic numbers coded differently in the brain? Neuronal data indicate that overlap in numerical tuning curves is a hallmark of the approximate, analogue nature of nonsymbolic number representation. Consequently, patterns of fMRI activity should be more correlated when the representational overlap between two numbers is relatively high. In bilateral intraparietal sulci (IPS), for nonsymbolic numbers, the pattern of voxelwise correlations between pairs of numbers mirrored the amount of overlap in their tuning curves under the assumption of approximate, analogue coding. In contrast, symbolic numbers showed a flat field of modest correlations more consistent with discrete, categorical representation (no systematic overlap between numbers). Directly correlating activity patterns for a given number across formats (e.g., the numeral “6” with six dots) showed no evidence of shared symbolic and nonsymbolic number‐specific representations. Overall (univariate) activity in bilateral IPS was well fit by the log of the number being processed for both nonsymbolic and symbolic numbers. IPS activity is thus sensitive to numerosity regardless of format; however, the nature in which symbolic and nonsymbolic numbers are encoded is fundamentally different. Hum Brain Mapp 36:475–488, 2015. © 2014 Wiley Periodicals, Inc.
Incremental learning and novelty detection of gestures using extreme value theory

The problems of data streaming, e.g. "infinite length" and "concept-drift", require incremental self-adapting classifiers. The performance of the classifier, however, is affected by false labels. Consequently, the classifier is required to detect outliers or samples belonging to unseen classes, i.e. novelties. We propose an incremental Mahalanobis distance based classifier using extreme value theory to detect novelties. Extreme value theory allows for the determination of a global constant threshold that does not change during the adaption of the classifier and thus does not need additional validation data and/or procedures. The results show high accuracy and high efficiency in linear and non-linear spaces with respect to recognition results and computation time.
Role of pregabalin in the treatment of generalized anxiety disorder

Generalized anxiety disorder (GAD) is a common, typically persistent, and disabling condition that is often not recognised, or treated in an evidence-based manner. Current pharmacological and psychological treatment approaches have a number of drawbacks, including a delay in onset of clinical effect, varying relative efficacy against psychological or somatic symptoms of anxiety, potentially troublesome adverse effects, and discontinuation symptoms on stopping treatment. Pregabalin is a structural analog of the inhibitory neurotransmitter gamma amino butyric acid (GABA) but is thought to exert its anxiolytic effects through binding in a state-dependent manner to the alpha-2-delta sub-unit of voltage-gated calcium channels in “over-excited” pre-synaptic neurones, reducing release of excitatory neurotransmitters such as glutamate and substance P. At fixed doses of 200 mg/day or greater, it has consistent proven efficacy in acute treatment of DSM-IV-defined GAD, with some evidence of an early onset of clinical effect, and of efficacy across psychological and somatic anxiety symptom clusters. A pregabalin dosage of 450 mg/day is efficacious in the prevention of relapse. There is at present no published direct comparison with an SSRI. The current known adverse effect profile and studies in healthy volunteers together suggest that pregabalin may have some tolerability advantages over benzodiazepines and venlafaxine, at least in short-term treatment.
Prediction of Protein-Protein Recognition Using Support Vector Machine Based on Feature Vectors

Analysis of protein-protein recognition is a popular issue recently, which plays a crucial role in regulation of biochemical pathways and signaling transmittal. A protein is recognized with the other protein by combining a transient complex, otherwise which will become a permanent complex. Therefore, understanding physico-chemical properties of the protein interface can offer important clues for biological processes and functions. In this paper, we propose prediction method for protein-protein recognition based on features extracted from the residues. Residues on binding sites of two contacting proteins in a complex are projected from 3D to 2D plane. In order to have the same direction, each 2D plane is rotated by an angle decided by principal component analysis (PCA) method. Then, the 2D plane is partitioned into a 5x5 grid. The feature vector is composed of the residues distribution of polarity, electricity, and hydrophobicity on the 2D plane. Support vector machine (SVM) is adopted for prediction. Experimental results show that the prediction achieves an accuracy rate of 80%.
On assortative and disassortative mixing in scale-free networks: The case of interbank credit networks

Networks constructed from credit relationships in the interbank market have been found to exhibit disassortative mixing together with a scale-free degree distribution, in contrast to most social networks that are assortative and not necessarily scale-free. This provokes the question whether generating mechanisms for scale-free networks have enough flexibility to generate both assortative and disassortative structures depending on their parametrization. Using Monte-Carlo simulations, we show that scale-free networks with a small tail exponent tend to be disassortative. However, the simulations indicate also that the level of disassortativity is sensitive to changes in the scaling exponent and the density. A given combination of disassortativity, scaling of the degree distribution, and density in an empirical data set, might be hard or impossible to obtain from any of the known generating mechanisms for scale-free networks.
Transmit Directions and Optimality of Beamforming in MIMO-MAC with Partial CSI at the Transmitters 1

We consider a multi-input multi-output (MIMO) multiple access channel (MAC) where the receiver has the perfect channel state information (CSI), while the transmitters have partial CSI, which is in the form of either the covariance matrix of the channel or the mean matrix of the channel. We show that the transmit directions of each user are the eigenvectors of the channel covariance and mean feedback matrices in covariance and mean feedback models, respectively. Then, we find the conditions under which beamforming is optimal for all users. We observe through simulations that the region where beamforming is optimal for all users gets larger with the addition of new users to the system.
Windfield: Learning Wind Meteorology with Handheld Haptic Robots

This article presents a learning activity and its user study involving the Cellula platform, a novel versatile robotic tool designed for education. In order to show the potential of Cel-lulo in the classroom as part of standard curricular activities, we designed a learning activity called Windfield that aims to teach the atmospheric formation mechanism of wind to early middle school children. The activity involves a didactic sequence, introducing the Cellulo robots as hot air balloons and enabling children to feel the wind force through haptic feedback. We present a user study, designed in the form of a real hour-long lesson, conducted with 24 children in 8 groups who had no prior knowledge in the subject. Collaborative metrics within groups and individual performances about the learning of key concepts were measured with only the hardware and software integrated in the platform in a completely automated manner. The results show that almost all participants showed learning of symmetric aspects of wind formation while about half showed learning of asymmetric vectorial aspects that are more complex.
Optimality in the design of overcomplete decompositions

We lay a philosophical framework for the design of overcomplete multidimensional signal decompositions based on the union of two or more orthonormal bases. By combining orthonormal bases in this way, tight (energy preserving) frames are automatically produced. The advantage of an overcomplete (tight) frame over a single orthonormal decomposition is that a signal is likely to have a more sparse representation among the overcomplete set than by using any single orthonormal basis. We discuss the question of the relationship between pairs of bases and the various criteria that can be used to measure the goodness of a particular pair of bases. A particular case considered is the dual-tree Hilbert-pair of wavelet bases. Several definitions of optimality are presented along with conjectures about the subjective characteristics of the ensembles where the optimality applies. We also consider relationships between sparseness and approximate representations.
Performance evaluation of the SX‐6 vector architecture for scientific computations

The growing gap between sustained and peak performance for scientific applications is a well‐known problem in high‐performance computing. The recent development of parallel vector systems offers the potential to reduce this gap for many computational science codes and deliver a substantial increase in computing capabilities. This paper examines the intranode performance of the NEC SX‐6 vector processor, and compares it against the cache‐based IBM Power3 and Power4 superscalar architectures, across a number of key scientific computing areas. First, we present the performance of a microbenchmark suite that examines many low‐level machine characteristics. Next, we study the behavior of the NAS Parallel Benchmarks. Finally, we evaluate the performance of several scientific computing codes. Overall results demonstrate that the SX‐6 achieves high performance on a large fraction of our application suite and often significantly outperforms the cache‐based architectures. However, certain classes of applications are not easily amenable to vectorization and would require extensive algorithm and implementation reengineering to utilize the SX‐6 effectively. Copyright © 2005 John Wiley & Sons, Ltd.