Miracle-dz/UniFAR-FTUs huggingface dataset:
Successful Development and Commercialization of Technological Innovation: Insights Based on Strategy Type

How can market leaders avoid the innovator’s dilemma and continually develop disruptive innovations to retain their leadership position? We argue that the capability to successfully develop and commercialize one type of disruptive innovation—technological innovation—is based on the interaction between a firm’s strategic orientation (Prospector, Analyzer, Defender) and (1) its selection of target market; and (2) the way it implements its market orientation. The insights offered by this framework assist in predicting whether a firm’s strategic orientation enhances or thwarts its ability to successfully commercialize disruptive innovations and also suggests the development of critical, yet contradictory, skill sets in order to remain successful over time. How can industry leaders reinvent themselves by developing and successfully commercializing disruptive innovations that challenge their existing business models? Known as the innovator’s dilemma, Christensen (1997) argued that market leaders have difficulty diverting resources from the development of sustaining innovations, which address known customer needs in established markets, to the development of disruptive innovations, which often underperform established products in mainstream markets but offer benefits some emerging customers value. Christensen’s (1997) initial research focused primarily on technological innovations, broadly defined as those that introduce a different set of features, performance, and price attributes relative to existing products and technologies. In other words, technological innovations create new products based on new underlying technological underpinnings. Over time, further developments improve the new technology’s performance on the attributes mainstream customers do value, to a level where the new technology begins to cannibalize the existing technology. This progression reflects the classic S-shaped curve prevalent in the
SpikeProp: backpropagation for networks of spiking neurons

For a network of spiking neurons with reasonable postsynaptic potentials, we derive a supervised learning rule akin to traditional error-back-propagation, SpikeProp and show how to overcome the discontinuities introduced by thresholding. Using this learning algorithm, we demonstrate how networks of spiking neurons with biologically plausible time-constants can perform complex non-linear classi cation in fast temporal coding just as well as rate-coded networks. When comparing the (implicit) number of neurons required for the respective encodings, it is empirically demonstrated that temporal coding potentially requires signi cantly less neurons.
The dual imperatives of action research

Action research (AR) is not without its critics, and those who reject some of the paradigmatic assumptions embodied in AR maintain that AR is little more than consultancy, that it is impossible to establish causal relationships, that it is difficult to generalize from AR studies, that there is a risk of researcher bias, and that generally speaking, it lacks some of the key qualities that are normally associated with rigorous research. The authors are sensitive to such criticisms, for although they are committed action researchers, they have elsewhere voiced their concerns about the quality of AR practice in the field of information systems. The authors argue that part of the issue concerns the way in which we currently conceptualize AR. In this article, the argument for a deeper and more reflective analysis of the meaning and full implications of AR is developed, culminating in a model of AR being developed that explicitly includes both a problem solving interest cycle and a research interest cycle. Important implications of this new model are articulated, with examples to illustrate these points being drawn from a real‐life AR study.
Development of New Smart Materials and Spinning Systems Inspired by Natural Silks and Their Applications

Silks produced by spiders and silkworms are charming natural biological materials with highly optimized hierarchical structures and outstanding physicomechanical properties. The superior performance of silks relies on the integration of a unique protein sequence, a distinctive spinning process, and complex hierarchical structures. Silks have been prepared to form a variety of morphologies and are widely used in diverse applications, for example, in the textile industry, as drug delivery vehicles, and as tissue engineering scaffolds. This review presents an overview of the organization of natural silks, in which chemical and physical functions are optimized, as well as a range of new materials inspired by the desire to mimic natural silk structure and synthesis.
The Correlation between the Wavelet Base Properties and Image Compression

When wavelet transform is applied to image compression, the chosen wavelet base affects the efficiency of coding and the quality of the reconstructed image, because the property parameters of different wavelet bases are varied, it is very important to research the correlation between the wavelet base properties and image compression. Chosen wavelet bases of one family for the experiments are convenient for analyzing contrastively, and the results have high reliability, and Daubechies wavelet bases with the properties of compactly supported, orthogonality, regularity, vanishing moment are widely used, then the paper chooses Daubechies wavelet bases as the research object, analyzes the correlation between the wavelet base properties and the image compression. The experiment results present the principles of wavelet base choice in image compression.
Super-fast parallel eigenface implementation on GPU for face recognition

Eigenface is one of the most common appearance based approaches for face recognition. Eigenfaces are the principal components which represent the training faces. Using Principal Component Analysis, each face is represented by very few parameters called weight vectors or feature vectors. While this makes testing process easy, it also includes cumbersome process of generating eigenspace and projecting every training image onto it to extract weight vectors. This approach works well with small set of images. As number of images to train increases, time taken for generating eigenspace and weight vectors also increases rapidly and it will not be feasible to recognize face in big data or perform real time video analysis. In this paper, we propose a super-fast parallel solution which harnesses the power of GPU and utilizes benefits of the thousands of cores to compute accurate match in fraction of second. We have implemented Parallel Eigenface, the first complete super-fast Parallel Eigenface implementation for face recognition, using CUDA on NVIDIA K20 GPU. Focus of the research has been to gain maximum performance by implementing highly optimized kernels for complete approach and utilizing available fastest library functions. We have used dataset of different size for training and noted very high increase in speedup. We are able to achieve highest 460X speed up for weight vectors generation of 1000 training images. We also get 73X speedup for overall training process on the same dataset. Speedup tends to increase with respect to training data, proving the scalability of solution. Results prove that our parallel implementation is best fit for various video analytics applications and real time face recognition. It also shows strong promise for excessive use of GPUs in face recognition systems.
Regularized Deconvolution-Based Approaches for Estimating Room Occupancies

We address the problem of estimating the number of people in a room using information available in standard HVAC systems. We propose an estimation scheme based on two phases. In the first phase, we assume the availability of pilot data and identify a model for the dynamic relations occurring between occupancy levels, CO2 concentration and room temperature. In the second phase, we make use of the identified model to formulate the occupancy estimation task as a deconvolution problem. In particular, we aim at obtaining an estimated occupancy pattern by trading off between adherence to the current measurements and regularity of the pattern. To achieve this goal, we employ a special instance of the so-called fused lasso estimator, which promotes piecewise constant estimates by including an ℓ1 norm-dependent term in the associated cost function. We extend the proposed estimator to include different sources of information, such as actuation of the ventilation system and door opening/closing events. We also provide conditions under which the occupancy estimator provides correct estimates within a guaranteed probability. We test the estimator running experiments on a real testbed, in order to compare it with other occupancy estimation techniques and assess the value of having additional information sources. Note to Practitioners - Home automation systems benefit from automatic recognition of human presence in the built environment. Since dedicated hardware is costly, it may be preferable to detect occupancy with software-based systems which do not require the installation of additional devices. The object of this study is the reconstruction of occupancy patterns in a room using measurements of concentration, temperature, fresh air inflow, and door opening/closing events. All these signals are information sources often available in HVAC systems of modern buildings and homes. We assess the value of such information sources in terms of their relevance in detecting occupancy in small and medium-sized rooms. The proposed estimation scheme is composed of two distinct phases. The first is a training phase where the goal is to derive a mathematical model relating the number of occupants with the concentration. It is required to record the actual occupants in the room for a time period spanning few days, a task that can be performed either with manual logging or with temporary dedicated hardware counting systems. In a second phase, we use the derived model to design an online software which collects measurements of the environmental signals and provides the number of people currently in the room. The estimated occupancy levels can then be employed to enhance the efficiency of the HVAC system of the building. We notice that, in modern residential buildings composed by structurally equal flats, the training phase can be run in one flat only, since the obtained model will be reasonably valid for the other flats.
Parallel programming using shared objects and broadcasting

The two major design approaches taken to build distributed and parallel computer systems, multiprocessing and multicomputing, are discussed. A model that combines the best properties of both multiprocessor and multicomputer systems, easy-to-build hardware, and a conceptually simple programming model is presented. Using this model, a programmer defines and invokes operations on shared objects, the runtime system handles reads and writes on these objects, and the reliable broadcast layer implements indivisible updates to objects using the sequencing protocol. The resulting system is easy to program, easy to build, and has acceptable performance on problems with a moderate grain size in which reads are much more common than writes. Orca, a procedural language whose sequential constructs are roughly similar to languages like C or Modula 2 but which also supports parallel processes and shared objects and has been used to develop applications for the prototype system, is described. <<ETX>>
THE IEA 10-YEAR TREND STUDY OF READING LITERACY : A MULTIVARIATE REANALYSIS

In the paper the data from the 9 countries participating in the IEA 10-Year Trend Study of reading literacy was reanalyzed using multivariate methods. In the first step a three-factor latent-variable model was fitted to the scores from the 15 textblocks included in the reading test. One factor was a reading comprehension factor, another was interpreted as a speed factor, and the third factor reflected performance on documents texts. In the next step differences in latent variable means were investigated for the countries. This was done in two different ways: through multiple-group modelling and through computing means on estimated factor scores. The results obtained in these analyses were compared with one another, and with the estimated IRT scores on narrative, expository and documents test types. The most important finding was that the speed factor identified in the latent variable model affected the estimates of the changes in level of reading comprehension over time, as well as the estimated differences between countries. Differences also were noted in the results obtained for the speed factor when using estimated factor scores and results obtained using estimated latent variable means.
Inter-Coder Agreement in One-to-Many Classification: Fuzzy Kappa

Content analysis involves classification of textual, visual, or audio data. The inter-coder agreement is estimated by making two or more coders to classify the same data units, with subsequent comparison of their results. The existing methods of agreement estimation, e.g., Cohen’s kappa, require that coders place each unit of content into one and only one category (one-to-one coding) from the pre-established set of categories. However, in certain data domains (e.g., maps, photographs, databases of texts and images), this requirement seems overly restrictive. The restriction could be lifted, provided that there is a measure to calculate the inter-coder agreement in the one-to-many protocol. Building on the existing approaches to one-to-many coding in geography and biomedicine, such measure, fuzzy kappa, which is an extension of Cohen’s kappa, is proposed. It is argued that the measure is especially compatible with data from certain domains, when holistic reasoning of human coders is utilized in order to describe the data and access the meaning of communication.
ReportTechnical 54 Faster than Pairing and Fibonacci Heaps : Rank-Relaxed Weak Queues

A run-relaxed weak queue by Elmasry et al. (2005) is a priority queue data structure with insert and decrease-key in O(1) as well as delete and delete-min in O(log n) worst-case time. One further advantage is the small space consumption of 3n+O(log n) pointers. In this paper we propose rank-relaxed weak queues, reducing the number of rank violations nodes for each level to a constant, while providing amortized constant time for decrease-key. Compared to runrelaxed weak queues, the new structure additionally gains one pointer per node. An empirical evaluation shows that the implementation can outperform Fibonacci and pairing heaps in practice even on rather simple data types.
Ultraviolet Absorption Lines from High-Velocity Gas in the Vela Supernova Remnant: New Insights from Space Telescope Imaging Spectrograph Echelle Observations of HD 72089

The star HD 72089 is located behind the Vela supernova remnant and shows a complex array of high- and low-velocity interstellar absorption features arising from shocked clouds. A spectrum of this star was recorded over the wavelength range 1196.4-1397.2 Å at a resolving power of λ/Δλ = 110,000 and a signal-to-noise ratio of 32 by the Space Telescope Imaging Spectrograph on the Hubble Space Telescope. We have identified seven narrow components of C I and have measured their relative populations in excited fine-structure levels. Broader features at heliocentric velocities ranging from -70 to 130 km s-1 are seen in C II, N I, O I, Si II, S II, and Ni II. In the high-velocity components, the unusually low abundances of N I and O I, relative to S II and Si II, suggest that these elements may be preferentially ionized to higher stages by radiation from hot gas immediately behind the shock fronts.
Common error hierarchical NLMS algorithm

Two-stage common error hierarchical normalized least-mean-square (NLMS) algorithm is presented in the context of network echo cancellers and sparse systems. The suggested adaptive filter structure is generic, uses a common error feedback for both stages, and is applicable with any type of error minimization technique. The simulation results show that the two-stage method exploits the sparseness of the system better than the proportionate NLMS (PNLMS) while keeping the initial convergence rate intact and improving the steady state convergence time significantly.
A mixture of Gaussians Hidden Markov Model for failure diagnostic and prognostic

This paper deals with a data-driven diagnostic and prognostic method based on a Mixture of Gaussians Hidden Markov Model. The prognostic process of the proposed method is made in two steps. In the first step, which is performed off-line, the monitoring data provided by sensors are processed to extract features, which are then used to learn different models that capture the time evolution of the degradation and therefore of the system's health state. In the second step, performed on-line, the learned models are exploited to do failure diagnostic and prognostic by estimating the asset's current health state, its remaining useful life and the associated confidence degree. The proposed method is tested on a benchmark data related to several bearings and simulation results are given at the end of the paper.
Analysis of loop boundaries using different local structure assignment methods

Loops connect regular secondary structures. In many instances, they are known to play important biological roles. Analysis and prediction of loop conformations depend directly on the definition of repetitive structures. Nonetheless, the secondary structure assignment methods (SSAMs) often lead to divergent assignments. In this study, we analyzed, both structure and sequence point of views, how the divergence between different SSAMs affect boundary definitions of loops connecting regular secondary structures. The analysis of SSAMs underlines that no clear consensus between the different SSAMs can be easily found. Because these latter greatly influence the loop boundary definitions, important variations are indeed observed, that is, capping positions are shifted between different SSAMs. On the other hand, our results show that the sequence information in these capping regions are more stable than expected, and, classical and equivalent sequence patterns were found for most of the SSAMs. This is, to our knowledge, the most exhaustive survey in this field as (i) various databank have been used leading to similar results without implication of protein redundancy and (ii) the first time various SSAMs have been used. This work hence gives new insights into the difficult question of assignment of repetitive structures and addresses the issue of loop boundaries definition. Although SSAMs give very different local structure assignments capping sequence patterns remain efficiently stable.
Intelligent Ziegler-Nichols-Based Fuzzy Controller Design for Mobile Satellite Antenna Tracking System with Parameter Variations Effect

This research applied both the traditional, Ziegler-Nichols-based and Ziegler-Nichols-based fuzzy control methods for mobile satellite antenna tracking system design. Firstly, the antenna tracking and the stabilization loops were roughly designed according to the traditional bandwidth and phase margin requirements. However, the performances would be degraded if the tacking loop gain is reduced due to parameter variations. On the other hand both Ziegler-Nichols-based PID-type and Ziegler-Nichols-based fuzzy controllers were also applied in the tracking loop for refinement. But we can find only the fuzzy controller were better for both low and high antenna tracking loop gains, and the tracking loop gain parameter variations effect can be reduced. Key-Words: Tracking Loop, Stabilization loop, PD-type Fuzzy Controller; PI Compensator
The impact of donor age on the results of renal transplantation.

BACKGROUND The use of elderly donors is becoming more frequent. An increase in the donor's age is associated with a greater incidence of delayed graft function (DGF), chronic allograft nephropathy (CAN) and worse graft survival. Poor renal graft function is a risk factor for cardiovascular (CV) complications and, finally, for mortality of the patients. METHODS A total of 3365 adult patients transplanted in 1990 (n = 824), 1994 (n = 1075) and 1998 (n = 1466) with a functioning graft after the first year were included. The impact of donor age on renal function, DGF, acute rejection and other clinical factors was evaluated according to two donor and recipient age categories: young (< 60 years old) and elderly (> or =60 years old). Additionally, donor age was categorized by decades for the analysis of patient and graft survival, acute rejection and CV mortality. RESULTS Donor mean age significantly increased during the three transplantation periods. A total of 478 out of 3365 donors were older than 60 years. Elderly donors showed an increased risk of DGF (38.9 vs 28.8%) and CAN (56.8 vs 46.2%). Mean serum creatinine at 3 and 12 months and proteinuria were significantly higher in the old donor group. Incidence and severity of acute rejection were similar in both groups. Graft and patient survival were significantly lower in the old donor group. Also, risk of mortality due to CV events was also significantly higher. A linear increase in risk of graft loss, patient death or CV mortality was observed when donor age was divided into 10 year increase subsets. CONCLUSIONS Donor age is a strong predictor of CAN and graft loss. Patient survival is also affected by donor age, particularly by a higher risk of CV mortality.
Predicting Network Response Times Using Social Information

Social networks and discussion boards have become a significant outlet where people communicate and express their opinion freely. Although the social networks themselves are usually well-provisioned, the participating users frequently point to external links to substantiate their discussions. Unfortunately, the sudden heavy traffic load imposed on the external, linked web sites causes them to become unresponsive leading to the â€œFlash Crowdsâ€ effect. In this paper, we quantify the prevalence of flash crowd events for a popular social discussion board (Digg). We measured the response times of 1289 unique popular websites. We were able to verify that 89% of the popular URLs suffered variations in their response times. By analyzing the content and structure of the social discussions, we were able to forecast accurately for 86% of the popular web sites within 5 minutes of their submission and 95% of the sites when more (5 hours) of social content became available. Our work indicates that we can effectively leverage social activity to forecast network events that will be otherwise infeasible to anticipate.
Extended Port Dual Band Planar Branch Line Coupler

Modern communication systems need various hybrids to enable digital data transmit via microwave bands. Thus several types of microwave quadrature hybrids have been reported for the realization of balanced circuits, matched attenuators, and phase shifter. The branch line coupler is one of the most popular hybrids for the convenience of design and implementation. But it offers limited bandwidth and requires a large circuit area. A Novel branch line coupler which can operate at two frequencies is presented in this paper. The proposed planar topology, which is different from the conventional one ,is analyzed and designed. To validate our idea quadrature hybrid coupler operated at 1.5/3.5 GHz are designed using microstrip technology. The simulated result show good agreement with theoretical analysis.