Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring How Lead Users Develop Radical Innovation: Opportunity Recognition and Exploitation in the Field of Medical Equipment Technology

In this paper, we explore how lead users develop radical innovations outside of manufacturing firms. We analyze the transition from a very early stage of activities to the ultimate commercialization of these innovations. The focus in this context is on the initiatives undertaken by lead users in order to transform their radically new solutions into functional prototypes, and then, to convince established manufacturers of the technological and market potential of their innovations. Using a comparative case study design in the high-tech field of medical equipment technology, we find that individual lead users as surgeons launch entrepreneurial activities and bridge periods in which established medical equipment manufacturers would not risk investing in radical innovations. By doing so, lead users create the conditions usually provided in manufacturer-initiated lead user projects, including the supply of diversified knowledge, the development and coordination of a network for further development of the innovation, and initial tests of technical or market feasibility. Our findings have implications for manufacturing firms that wish to design radical innovation projects with (individual) lead users and for research on new forms of lead user cooperation and integration.
On the Structural Complexity of Natural Language Sentences

The objective of this paper is to formalize the intnition about the complexity of syntactic structures. We propose a definition of structural complexity such that sentences ranked by our definition as more complex are generally more difficult for humans to process. We justify the definition by showing how it is able to account for several seemingly unrelated phenomena in natural languages.
Long Synthetic Peptides for the Production of Vaccines and Drugs: A Technological Platform Coming of Age

Improved methods for producing long synthetic peptides, together with bioinformatics approaches, offer promise for the development of clinically relevant products. Practical Applications of Peptide Production Biochemists, of course, cannot compete with ribosomes when it comes to speed. Whereas a bacterial (Escherichia coli) cell needs only a second or so to link 40 amino acids into a polypeptide chain, an automated peptide synthesizer requires a day or more to complete this task. The slower speed of the synthetic approach, however, is offset by several key advantages: the ability to include non-natural amino acids, if desired; the capacity to incorporate modifications in the backbone to confer resistance to proteases; and the increased safety and purity of synthetic peptides relative to proteins that are isolated from a pathogen of interest or produced from an expression vector, for example. Such benefits mean that synthetic peptides offer potential clinical advantages in the development of vaccines and drugs. Improvements in peptide synthesis technology that allow the production of long synthetic peptides (over 100 amino acids in length), together with bioinformatic techniques in which sequenced genomes are analyzed to determine appropriate peptides to synthesize, should both aid the synthetic peptide approach to clinical problems. Corradin and colleagues briefly review the history of synthetic peptide synthesis and discuss recent efforts to produce clinical products—including vaccines to prevent malaria, cancer, and allergies—based on long synthetic peptides. Long synthetic peptides (LSPs) have a variety of important clinical uses as synthetic vaccines and drugs. Techniques for peptide synthesis were revolutionized in the 1960s and 1980s, after which efficient techniques for purification and characterization of the product were developed. These improved techniques allowed the stepwise synthesis of increasingly longer products at a faster rate, greater purity, and lower cost for clinical use. A synthetic peptide approach, coupled with bioinformatics analysis of genomes, can tremendously expand the search for clinically relevant products. In this Review, we discuss efforts to develop a malaria vaccine from LSPs, among other clinically directed work.
Stuck Around the Stadium? An Approach to Identify Road Segments Affected by Planned Special Events

The recent availability of large amount of mobility data has fostered many research efforts to improve mobility prediction. Lots of these studies are focused on learning the impact of influencing factors on traffic, such as rush hour or accidents. Nevertheless, only very few have investigated the impact of Planned Special Events (PSEs), such as concerts, soccer games, etc., despite their well-known influence on traffic. In this paper we present an automatic solution to model the impact of PSEs on traffic around the venue of the events. In particular, we answer the question of "which road segments are affected by PSEs? " by identifying which roads show an event specific behavior that can identify the happening of a PSE reliably. For that, we propose a solution based on an Artificial Neural Network (ANN) classifier that is trained on traffic data on event and non-event days for each road. The proposed approach has been evaluated on two different venues in Germany with a leave-one-out cross-validation performed on all the soccer matches played in those locations during the season 2013/14 of the German First League. Results show that the approach can reliably identify road segments affected by PSEs, with an F-Measure up to 0.97.
Social Network Analysis Within The ICMB Community: Co-Authorship Networks

Founded in Athens during 2002, ICMB developed to the major international research conference on mobile business with a significant number of researchers and authors contributing state of the art scientific papers in academia. In this paper we examine the state of the ICMB co-authorship network from 2002 to 2013 by applying Social Network Analysis techniques and measures. Our analysis is based on a network model generated by data gathered from papers featured in the aforementioned conferences. Our analysis consists of metrics such as clustering analysis, degree, betweenness centrality measures as well as network component related properties. These measures aim to answer a wide range of questions about collaboration patterns, such as the numbers of papers submitted, coauthorships, and showcase how patterns of collaboration emerge between larger scale, tightly connected node formations of the co-authorship network.
Context and Action in the Transformation of Strategy Scholarship

We revisit Andrew Pettigrew's seminal ‘Context and action in the transformation of the firm’, first published in this journal in November 1987. In doing so, we not only review Pettigrew's contributions, but contextualize these with respect to adjoining strategy scholarship. Our discussion concludes with some novel, and fairly speculative, ways forward, designed to advance strategy scholarship. Working with the ideas contained in Pettigrew's article and drawing on insights from institutional entrepreneurship, we make a case for the processualization of strategy content research. This allows us to add the issue of effectiveness of strategy practices to current strategy‐as‐practice research while arguing for a further humanization of strategic management.
Implicit Social Network Construction and Expert User Determination in Web Portals

Especially within the context of the ongoing Web 2.0 hype, social software has recently gained more and more momentum. Social software is software that focuses on the interaction and collaboration between users being part of social networks. So far, most of these networks are constructed explicitly by users themselves. On the other hand Web Portals give users a central point of access to relevant information. They provide users with a highly collaborative environment, but, until now, required the users that interact and collaborate to know each other. As part of this work we present a system additionally able to construct social networks implicitly by observing users behavior in Web Portals. We aim to use the derived social networks to enhance interaction and collaboration within the community.
"It's Hard at the Top but It's a Whole Lot Easier than Being at the Bottom": The Role of Privilege in Understanding Disparities in Aotearoa/New Zealand

Objective: An initial exploration of the available conceptualizations of privilege in Aotearoa/New Zealand with the goal of developing a more in-depth qualitative study, incorporating kaupapa Maori and public health framings.Design: Interviews were conducted with a purposive sample of key informants that span the academic, community development, health service provision, and activist sectors. Participants were selected from multiple networks for their expressed interest and insight into the concept of privilege and its importance to analyses of social justice.Results: Six key themes emerged that include the invisibility of privilege; the weighting of different variables; individual vs group privilege; class; the active aspects of privilege; and links between privilege and Pakeha culture.Conclusion: Participants' constructions of privilege emphasize the multifaceted complexity and discursive ambiguities of the ways in which the concept is utilized within our political economy to account for disparity and covertly reproduce the status quo of Pakeha advantage.
Parallel Deep Neural Network Training for Big Data on Blue Gene/Q

Deep Neural Networks (DNNs) have recently been shown to significantly outperform existing machine learning techniques in several pattern recognition tasks. DNNs are the state-of-the-art models used in image recognition, object detection, classification and tracking, and speech and language processing applications. The biggest drawback to DNNs has been the enormous cost in computation and time taken to train the parameters of the networks - often a tenfold increase relative to conventional technologies. Such training time costs can be mitigated by the application of parallel computing algorithms and architectures. However, these algorithms often run into difficulties because of the cost of inter-processor communication bottlenecks. In this paper, we describe how to enable Parallel Deep Neural Network Training on the IBM Blue Gene/Q (BG/Q) computer system. Specifically, we explore DNN training using the data parallel Hessian-free 2nd order optimization algorithm. Such an algorithm is particularly well-suited to parallelization across a large set of loosely coupled processors. BG/Q, with its excellent inter-processor communication characteristics, is an ideal match for this type of algorithm. The paper discusses how issues regarding programming model and data-dependent imbalances are addressed. Results on large-scale speech tasks show that the performance on BG/Q scales linearly up to 4096 processes with no loss in accuracy. This allows us to train neural networks using billions of training examples in a few hours.
SOPC Co-design Platform for UWB System in Wireless Sensor Network Context

This paper presents our approach of the radio interface problematic for wireless sensor network. We introduce the WSN context and constraints associated. We propose an IR-UWB solution and illustrate why it could be a viable solution for WSN. A high level modelling and simulation platform for IR-UWB radio interface is proposed on Matlab. It allows us to determine according to BER versus Eb/N0 criteria and the WSN constraints what kind of design is more adequate. Moreover, a co-design co-simulation platform Matlab VHDL is proposed here. Using this platform we designed IR-UWB transceiver having reconfigurable capabilities, such as data rate reconfiguration, time hopping code, spectrum occupation and radio range reconfiguration.
Cognitive presence as a unified concept of virtual reality effectiveness

We propose a synthesis of two current major approaches to understanding the effectiveness of VR systems. We propose cognitive presence as a single, unified concept of presence which includes previous ideas of presence in a cognitive psychological framework, and propose a methodology for measuring it. We then critically examine the notion of cognitive presence, as a way of conceptualizing HCI quality in virtual environments.
Formation of Linear Amplicons with Inverted Duplications in Leishmania Requires the MRE11 Nuclease

Extrachromosomal DNA amplification is frequent in the protozoan parasite Leishmania selected for drug resistance. The extrachromosomal amplified DNA is either circular or linear, and is formed at the level of direct or inverted homologous repeated sequences that abound in the Leishmania genome. The RAD51 recombinase plays an important role in circular amplicons formation, but the mechanism by which linear amplicons are formed is unknown. We hypothesized that the Leishmania infantum DNA repair protein MRE11 is required for linear amplicons following rearrangements at the level of inverted repeats. The purified LiMRE11 protein showed both DNA binding and exonuclease activities. Inactivation of the LiMRE11 gene led to parasites with enhanced sensitivity to DNA damaging agents. The MRE11−/− parasites had a reduced capacity to form linear amplicons after drug selection, and the reintroduction of an MRE11 allele led to parasites regaining their capacity to generate linear amplicons, but only when MRE11 had an active nuclease activity. These results highlight a novel MRE11-dependent pathway used by Leishmania to amplify portions of its genome to respond to a changing environment.
Speech reinforcement based on partial masking effect

Perceived quality of the speech signal deteriorates significantly in the presence of ambient noise. In this paper, based on the analysis that the partial masking effect is a main source of the quality degradation when interfering signals are present, we propose a novel approach to enhance the perceived quality of speech signal when the ambient noise cannot be directly controlled by reinforcing it so that it can be heard more clearly. To find a suitable reinforcement rule, the loudness perception model proposed by Moore et al. [1] is adopted with the consideration on the prevention of the hearing damage. Experimental results show that the perceived quality and intelligibility can be enhanced under various noise environments.
Growth‐dependent bacterial susceptibility to ribosome‐targeting antibiotics

Bacterial growth environment strongly influences the efficacy of antibiotic treatment, with slow growth often being associated with decreased susceptibility. Yet in many cases, the connection between antibiotic susceptibility and pathogen physiology remains unclear. We show that for ribosome‐targeting antibiotics acting on Escherichia coli, a complex interplay exists between physiology and antibiotic action; for some antibiotics within this class, faster growth indeed increases susceptibility, but for other antibiotics, the opposite is true. Remarkably, these observations can be explained by a simple mathematical model that combines drug transport and binding with physiological constraints. Our model reveals that growth‐dependent susceptibility is controlled by a single parameter characterizing the ‘reversibility’ of ribosome‐targeting antibiotic transport and binding. This parameter provides a spectrum classification of antibiotic growth‐dependent efficacy that appears to correspond at its extremes to existing binary classification schemes. In these limits, the model predicts universal, parameter‐free limiting forms for growth inhibition curves. The model also leads to non‐trivial predictions for the drug susceptibility of a translation mutant strain of E. coli, which we verify experimentally. Drug action and bacterial metabolism are mechanistically complex; nevertheless, this study illustrates how coarse‐grained models can be used to integrate pathogen physiology into drug design and treatment strategies.
Involvement of the host DNA-repair enzyme TDP2 in formation of the covalently closed circular DNA persistence reservoir of hepatitis B viruses

Significance Chronic hepatitis B virus (HBV) infection puts >250 million humans at risk for developing liver cirrhosis and liver cancer. Current therapies are not curative because they do not target HBV´s persistence reservoir, the plasmid-like covalently closed circular DNA (cccDNA). RNA production from cccDNA initiates the generation of progeny virus via protein-primed reverse transcription, yielding viral polymerase-linked relaxed-circular DNA (RC-DNA). Its conversion, upon infection, into cccDNA requires multiple poorly understood steps, including polymerase removal. We found that the host enzyme tyrosyl-DNA-phosphodiesterase 2 (TDP2), important for repair of cellular protein–DNA adducts, performed this step in vitro and that TDP2 depletion impaired the conversion of RC-DNA to cccDNA in cells. These data establish a functional link between HBV and cellular DNA repair and pave the way for targeting HBV persistence. Hepatitis B virus (HBV), the causative agent of chronic hepatitis B and prototypic hepadnavirus, is a small DNA virus that replicates by protein-primed reverse transcription. The product is a 3-kb relaxed circular DNA (RC-DNA) in which one strand is linked to the viral polymerase (P protein) through a tyrosyl–DNA phosphodiester bond. Upon infection, the incoming RC-DNA is converted into covalently closed circular (ccc) DNA, which serves as a viral persistence reservoir that is refractory to current anti-HBV treatments. The mechanism of cccDNA formation is unknown, but the release of P protein is one mandatory step. Structural similarities between RC-DNA and cellular topoisomerase–DNA adducts and their known repair by tyrosyl-DNA-phosphodiesterase (TDP) 1 or TDP2 suggested that HBV may usurp these enzymes for its own purpose. Here we demonstrate that human and chicken TDP2, but only the yeast ortholog of TDP1, can specifically cleave the Tyr–DNA bond in virus-adapted model substrates and release P protein from authentic HBV and duck HBV (DHBV) RC-DNA in vitro, without prior proteolysis of the large P proteins. Consistent with TPD2’s having a physiological role in cccDNA formation, RNAi-mediated TDP2 depletion in human cells significantly slowed the conversion of RC-DNA to cccDNA. Ectopic TDP2 expression in the same cells restored faster conversion kinetics. These data strongly suggest that TDP2 is a first, although likely not the only, host DNA-repair factor involved in HBV cccDNA biogenesis. In addition to establishing a functional link between hepadnaviruses and DNA repair, our results open new prospects for directly targeting HBV persistence.
Adapting the DySER Architecture with DSP Blocks as an Overlay for the Xilinx Zynq

Coarse-grained overlay architectures have been shown to be effective when paired with general purpose processors, offering software-like programmability, fast compilation, and improved design productivity. These architectures enable general purpose hardware accelerators, allowing hardware design at a higher level of abstraction, but at the cost of area and performance overheads. This paper examines the DySER overlay architecture as a hardware accelerator paired with a general purpose processor in a hybrid FPGA such as the Xilinx Zynq. We evaluate the DySER architecture mapped on the Xilinx Zynq and show that it suffers from a significant area and performance overhead. We then propose an improved functional unit architecture using the flexibility of the DSP48E1 primitive which results in a 2.5 times frequency improvement and 25% area reduction compared to the original functional unit architecture. We demonstrate that this improvement results in the routing architecture becoming the bottleneck in performance.
Image Encryption Using Chaotic Maps: A Survey

As the exchange of data over the open networks and Internet is rapidly growing, security of the data becomes a major concern. One possible solution to this problem is to encrypt the data. The data can be text, image, audio, video etc. In today's world most of the multimedia applications involve images. Earlier image encryption techniques like AES,DES,RSA etc. exhibit low levels of security and also weak anti attack ability. This problem was overcome by using chaos based cryptography. The chaotic systems are very sensitive to initial conditions and control parameters which make them suitable for image encryption. Many works have been done in the field of chaos based image encryption. In this survey paper an attempt has been made to review the aspects and approaches of the design used for image encryption.
Element level semantic matching using WordNet

We think of Match as an operator which takes two graph-like structures and produces a mapping between semantically related nodes. The matching process is essentially divided into two steps: element level and structure level. Element level matchers consider only labels of nodes, while structure level matchers start from this information to consider the full graph. In this paper we present and evaluate, on the large scale real world dataset, twelve new element level semantic matchers. The matchers exploit WordNet as a background knowledge source, and return semantic relations (e.g., equivalence, more general) between concepts rather than similarity coefficients between labels in the [0, 1] range. The twelve element level semantic matchers are evaluated against seven state of the art matching systems. The results of the matchers are found comparable with the results of the matching systems.
Ergodic Convergence of a Stochastic Proximal Point Algorithm

The purpose of this paper is to establish the almost sure weak ergodic convergence of a sequence of iterates $(x_n)$ given by $x_{n+1} = (I+\lambda_n A(\xi_{n+1},\,.\,))^{-1}(x_n)$ where $(A(s,\,.\,):s\in E)$ is a collection of maximal monotone operators on a separable Hilbert space, $(\xi_n)$ is an independent identically distributed sequence of random variables on $E$ and $(\lambda_n)$ is a positive sequence in $\ell^2\backslash \ell^1$. The weighted averaged sequence of iterates is shown to converge weakly to a zero (assumed to exist) of the Aumann expectation ${\mathbb E}(A(\xi_1,\,.\,))$ under the assumption that the latter is maximal. We consider applications to stochastic optimization problems of the form $\min {\mathbb E}(f(\xi_1,x))$ w.r. t. $x\in \bigcap_{i=1}^m X_i$ where $f$ is a normal convex integrand and $(X_i)$ is a collection of closed convex sets. In this case, the iterations are closely related to a stochastic proximal algorithm recently proposed by Wang and Bertsekas.
Optimization Problems over Noncompact Semialgebraic Sets

In this talk, we will introduce some recent progress in dealing with optimization problems over noncompact semialgebraic sets. We will start with the problem of optimizing a parametric linear function over a noncompact real algebraic variety. Then we will introduce how to compute the semidefinite representation or approximation of the convex hull of a noncompact semialgebraic set. Finally, we will show how to characterize the lifts of noncompact convex sets by the cone factorizations of properly defined slack operators.