Miracle-dz/UniFAR-FTUs huggingface dataset:
Transgene Regulation Using the Tetracycline-Inducible TetR-KRAB System after AAV-Mediated Gene Transfer in Rodents and Nonhuman Primates

Numerous studies have demonstrated the efficacy of the Adeno-Associated Virus (AAV)-based gene delivery platform in vivo. The control of transgene expression in many protocols is highly desirable for therapeutic applications and/or safety reasons. To date, the tetracycline and the rapamycin dependent regulatory systems have been the most widely evaluated. While the long-term regulation of the transgene has been obtained in rodent models, the translation of these studies to larger animals, especially to nonhuman primates (NHP), has often resulted in an immune response against the recombinant regulator protein involved in transgene expression regulation. These immune responses were dependent on the target tissue and vector delivery route. Here, using AAV vectors, we evaluated a doxycyclin-inducible system in rodents and macaques in which the TetR protein is fused to the human Krüppel associated box (KRAB) protein. We demonstrated long term gene regulation efficiency in rodents after subretinal and intramuscular administration of AAV5 and AAV1 vectors, respectively. However, as previously described for other chimeric transactivators, the TetR-KRAB-based system failed to achieve long term regulation in the macaque after intramuscular vector delivery because of the development of an immune response. Thus, immunity against the chimeric transactivator TetR-KRAB emerged as the primary limitation for the clinical translation of the system when targeting the skeletal muscle, as previously described for other regulatory proteins. New developments in the field of chimeric drug-sensitive transactivators with the potential to not trigger the host immune system are still needed.
Managing Diversity Through Triple-Loop Learning

The purpose of this article is to conceptualize the meaning and explore the potential for triple-loop learning in the context of diversity management. Three different paradigms of diversity management, namely, discrimination and fairness, access and legitimacy, and learning and effectiveness, will be explored. The authors argue that whereas single-loop learning can be applied during the transition from the discrimination and fairness paradigm to the access and legitimacy paradigm, double-loop learning is required when transforming from the access and legitimacy paradigm to the learning and effectiveness paradigm. The authors assert that triple-loop learning can produce transformation, in individual and organizational capacity for curiosity, compassion, and courage, which goes beyond the cognitive dimensions of double-loop learning. Finally, the authors discuss and suggest Collaborative Developmental Action Inquiry (CDAI), as a method that generates and sustains this existential shift for individual and organizational transformation through ongoing single-, double-, and triple-loop learning.
Position Control of Electric Clutch Actuator Using a Triple-Step Nonlinear Method

For a novel electric clutch actuator, a nonlinear feedforward-feedback control scheme is proposed to improve the performance of the position tracking control. The design procedure is formalized as a triple-step deduction, and the derived controller consists of three parts: steady-state-like control; feedforward control based on reference dynamics; and state-dependent feedback control. The structure of the proposed nonlinear controller is concise and is also comparable to those widely used in modern automotive control. Finally, the designed controller is evaluated through simulations and experimental tests, which show that the proposed controller satisfied the control requirement. Comparison with proportional-integral-derivative control is given as well.
Temporal Feature Integration for Music Genre Classification

Temporal feature integration is the process of combining all the feature vectors in a time window into a single feature vector in order to capture the relevant temporal information in the window. The mean and variance along the temporal dimension are often used for temporal feature integration, but they capture neither the temporal dynamics nor dependencies among the individual feature dimensions. Here, a multivariate autoregressive feature model is proposed to solve this problem for music genre classification. This model gives two different feature sets, the diagonal autoregressive (DAR) and multivariate autoregressive (MAR) features which are compared against the baseline mean-variance as well as two other temporal feature integration techniques. Reproducibility in performance ranking of temporal feature integration methods were demonstrated using two data sets with five and eleven music genres, and by using four different classification schemes. The methods were further compared to human performance. The proposed MAR features perform better than the other features at the cost of increased computational complexity.
Toward Wide-Area Camera Localization for Mixed Reality

This paper describes a work in progress towards the implementation of a complete system that provides tourists with relevant visual information related to cultural heritage sites. Thanks to the diffusion of high-end mobile devices and the recent improvement in computer vision research on 3D Structure and Motion reconstruction, it is now possible to develop mobile mixed reality applications that can interact with spots of historical interest in the city. In particular we present an accurate localization of the mobile device that leverages on a pre-computed 3D structure to obtain image-model correspondences. Preliminary experiments with a calibrated camera – indoor and outdoor – demonstrate sufﬁcient accuracy to support mixed reality.
Reverse Subdivision for Optimizing Visibility Tests

Certain applications require knowledge of whether two entities are visible to each other over a terrain, determined using a line-of-sight computation. Several fast algorithms exist for terrain line-of-sight computations. However, performing numerous line-of-sight computations, particularly over a large terrain data set, can be highly resource-intensive (in run time and/or memory). Methods from the field of terrain simplification can be used to reduce the resource impact of the visibility algorithms. To take advantage of the especially fast algorithms that exist for regular terrain models, we introduce regularity-preserving terrain simplification methods based on reverse subdivision, including a novel reverse subdivision algorithm designed to maximize visibility test accuracy, and compared the resulting visibility test output to several terrain simplification methods. Additionally, the positions of the entities after simplification can have a significant impact on the visibility test results. Hence, we have experimented with different functions that change the positions of the test points in an attempt to maximize visibility test accuracy after simplification.
A preferred learning based adaptive differential evolution algorithm for large scale optimization

With the help of the cooperative co-evolution, differential evolution (DE) has been applied successfully from low-dimensional problems to large scale optimization. In this paper, we propose a preferred learning cooperative coevolution DE algorithm (LDECC-DG) which focuses on the basic optimizer for large scale optimization using cooperative coevolution. The proposed LDECC-DG builds on the differential evolution with cooperative coevolution and differential grouping (DECC-DG) algorithm which possesses an accurate grouping method and an effective basic optimizer method for large scale optimization. A novel DE algorithm called preferred learning based adaptive DE (LDE) is designed as a basic optimization algorithm for large scale problems and the control parameters in LDE are selected according to the self-adaptive strategy which corresponds to the preferred learning strategy. We show that how the LDE can improve the performance of Cooperative Co-evolution framework on account of its effectiveness. In order to evaluate the performance of LDECC-DG for large-scale global optimization, we carried out numerous computational studies on the CEC 2010 benchmark functions. The results show advantages of the LDECC-DG in both solution quality and convergence rate compared to other algorithms.
Nonconvex Regularization for Image Segmentation

We propose a new method for image segmentation based on a variational regularization algorithm for image denoising. We modify the Rudin-Osher-Fatemi (ROF) model in [1] by minimizing the p L -norm of the gradient, where 0 > p is very small. The result is that we better preserve edges, while flattening regions away from the edges. This results in an automatic segmentation of the image into several regions, which does not require any prior knowledge about the number of those regions, or their intensity levels.
Development of a ground-based Synthetic aperture radar system for highly repeatable measurements (Space, Aeronautical and navigational electronics: 第9回地下電磁計測ワークショップ)

We introduce the development of a robust ground-based synthetic aperture radar (GB-SAR) system capable of obtaining highly accurate SAR images of surface targets repeatedly by dedicated efforts for accurate motion control and innovative system design for stable RF acquisition. A brief introduction of the GB-SAR system and some test images will be followed by the accuracy assessment of the system performance for the future radar-interferometric applications to natural or anthropogenic surface/sub-surface targets.
A computational and empirical investigation of prioritization during multiple approach and avoidance goal pursuit

III ACKNOWLEDGEMENTS . V PREAMBLE . X PAPERS INCLUDED IN THIS THESIS . XI STATEMENT OF CANDIDATE CONTRIBUTION TO INCLUDED PAPERS . XII CHAPTER 1. GENERAL INTRODUCTION . 1 1.1 INTEGRATING DISPARATE THEORIES TO UNDERSTAND GOAL PRIORITIZATION . 2 1.1.1 Control Theory . 3 1.1.2 The Multiple-Goal Pursuit Model . 4 1.1.3 Decision Field Theory . 4 1.1. 4 Prospect Theory . 5 1.2 OVERVIEW OF COMPUTATIONAL AND NORMATIVE MODELING METHODOLOGY . 5 1.2.1 Computational Models . 6 1.2.2 Normative Models . 7 1.3 OVERVIEW OF THESIS . 8 CHAPTER 2. DEVELOPING A COMPUTATIONAL MODEL OF AVOIDANCE GOAL PURSUIT: COMPARING POSITIVE VERSUS NEGATIVE FEEDBACK CONTROL SYSTEMS . 11 2.1 FOREWORD . 12 2.2 ABSTRACT . 13 2.3 INTRODUCTION . 14 2.3. 1 Highlighting the Importance of a Computational Model of Avoidance Goal Pursuit . 16 2.3. 2 Theoretical Foundations: Carver and Scheier (1998) . 19 2.4 IMPLEMENTING CARVER AND SCHEIER’S (1998) THEORY AS A COMPUTATIONAL MODEL . 24 2.4. 1 Vancouver et al.’s (2010) Model of Approach Goal Pursuit . 24 2.4. 2 Two Alternative Models of Avoidance Goal Pursuit . 27 2.5 SIMULATING THE POSITIVE AND NEGATIVE FEEDBACK MODELS . 32
NOVA: A Log-structured File System for Hybrid Volatile/Non-volatile Main Memories

Fast non-volatile memories (NVMs) will soon appear on the processor memory bus alongside DRAM. The resulting hybrid memory systems will provide software with sub-microsecond, high-bandwidth access to persistent data, but managing, accessing, and maintaining consistency for data stored in NVM raises a host of challenges. Existing file systems built for spinning or solid-state disks introduce software overheads that would obscure the performance that NVMs should provide, but proposed file systems for NVMs either incur similar overheads or fail to provide the strong consistency guarantees that applications require. We present NOVA, a file system designed to maximize performance on hybrid memory systems while providing strong consistency guarantees. NOVA adapts conventional log-structured file system techniques to exploit the fast random access that NVMs provide. In particular, it maintains separate logs for each inode to improve concurrency, and stores file data outside the log to minimize log size and reduce garbage collection costs. NOVA's logs provide metadata, data, and mmap atomicity and focus on simplicity and reliability, keeping complex metadata structures in DRAM to accelerate lookup operations. Experimental results show that in write-intensive workloads, NOVA provides 22% to 216× throughput improvement compared to state-of-the-art file systems, and 3.1× to 13.5× improvement compared to file systems that provide equally strong data consistency guarantees.
Usability challenges in mobile web

Although the literature on mobile web usability is growing rapidly, there have not been any systematic and methodical efforts to review it. Here, a systematic literature review is presented on primary studies related to mobile web usability. A total of 35 papers are identified through search engines, which is then pruned to 19 papers directly related to our research. Various challenges for mobile web usability are revealed to have been encountered by users and developers. This paper identifies the issues investigated in the literature on mobile web usability, classifies them into categories on the basis of developer (or server) issues and user (or client) issues, and explains the current usability testing practices employed in mobile web.
A novel human computer interface based on hand gesture recognition using computer vision techniques

In daily life, human beings communicate with each other and use broad range of gestures in the process of interaction. Apart of the interpersonal communication, many hours are spent in the interaction with the electronic devices. In the last decade, new classes of devices for accessing information have emerged along with increased connectivity. In parallel to the proliferation of these devices, new interaction styles have been explored. The objective of this paper is to provide a gesture based interface for controlling applications like media player using computer vision techniques. The human computer interface application consists of a central computational module which uses the Principal Component Analysis for gesture images and finds the feature vectors of the gesture and save it into a XML file. The Recognition of the gesture is done by K Nearest Neighbour algorithm. The Training Images are made by cropping the hand gesture from static background by detecting the hand motion using Lucas Kanade Pyramidical Optical Flow algorithm. This hand gesture recognition technique will not only replace the use of mouse to control the media player but also provide different gesture commands which will be useful in controlling the application.
A hierarchical approach to generating power law Internet-like topologies

Hierarchical Internet-like topology generators reflect the hierarchical structure of the Internet, but they neglect the power law property of Internet topologies. On the other hand, common power law topology generators obey the power law, but they do not provide support for different types of nodes. In this paper, we propose a new algorithm for creating Internet-like network topologies. We introduce a simple classification among different types of nodes in the topology generation process. We evaluate the topology produced by our algorithm by means of standard measures, such as the power law degree distribution coefficient, the clustering coefficient and the average path length. Our preliminary results show that the proposed algorithm generates Internet-like network topologies that provide realistic environment for simulation, testing and analyzes of network protocols.
Ergenler İçin Okulda Öznel İyi Oluş Ölçeği’nin Türkçe’ye Uyarlanması: Geçerlik ve Güvenirlik Çalışması

Bu calismanin amaci, Ergenler Icin Okulda Oznel Iyi Olus Olcegi’nin Turkce’ye uyarlanmasi ve psikometrik ozelliklerinin incelenmesidir. Arastirma, 2014-2015 egitim-ogretim yilinda 5-8. siniflarda egitim goren toplam 336 ogrencinin katilimi ile gerceklestirilmistir. Orneklemin 182’si (%54.2) kiz, 154’u ise (%45.8) erkek ogrencilerden olusmustur. Katilimcilarin yaslari 10 ile 15 (= 12.36, ss = 1.11) arasinda degismistir. Ergenler Icin Okulda Oznel Iyi Olus Olcegi’nin yapi gecerligini incelemek icin Dogrulayici Faktor Analizi (DFA) uygulanmistir. Turkce’ye uyarlanan olcegin olcut-bagintili gecerligini incelemek icin Pozitif ve Negatif Duygu Olcegi, Yasam Doyumu Olcegi ve Cocuk ve Ergenler Icin Okula Baglanma Olcegi kullanilmistir. Olcegin guvenirlik calismasinda, Cronbach alfa katsayisi ve test-tekrar test guvenirligi hesaplanmistir. Arastirma bulgulari, olcegin iki faktorlu orijinal yapisinin hedef orneklemde de dogrulandigini gostermistir. Hedef olcek ve olcut olcme araclari arasinda orta duzeyde anlamli korelasyonlar hesaplanmistir. Guvenirlik calismasinda, Cronbach alfa katsayisi Okul Doyumu boyutunda . 93; olcegin butununde ise . 87 olarak belirlenmistir. Test-tekrar test guvenirlik katsayisi olcegin butunu, Okul Doyumu boyutu ve Okulda Duygu boyutu icin sirasiyla . 73, . 59 ve . 62 olarak hesaplanmistir. Sonuc olarak, tum bu bulgular Ergenler Icin Okulda Oznel Iyi Olus Olcegi’nin ulkemizde arastirmacilar ve uygulayicilar tarafindan kullanilabilecek gecerli ve guvenilir bir olcme araci oldugunu gostermistir. Anahtar Kelimeler : Okulda oznel iyi olus, ergenler, gecerlik, guvenirlik.
A preliminary study on the knowledge-based delineation of anatomical structures for whole body PET-CT studies

PET-CT imaging has shown its superiority in the clinical management of cancer. The markedly increased amount of imaging data have given rise to the development of computer-aided diagnosis (CAD) to aid the clinician in the interpretation of large volumes of data. The delineation of anatomical structures is one of the major components of CAD. Currently, the majority of segmentation methods are focused on the segmentation of organs and tissues using high-contrast anatomical images such as high-dose CT with injected contrast agent. However, typically low-dose CT protocol without the use of contrast agent are used in PET-CT studies, which leads to low-contrast CT images with a relatively high level of noise. This study investigated the potential of using information extracted from the co-registered PET-CT data in the segmentation of anatomical structures. A preliminary knowledge-based system was developed to process eight clinical PET-CT studies for lung cancer. The results of qualitative and quantitative analysis demonstrate the efficiency of incorporating the information derived from co-registered structural and functional images in the segmentation of anatomical structures for whole body PET-CT studies. It also implies that the methods relying on the HU value, like thresholding, are incapable of accurately delineating those organs suffering from high-level noise with unclear boundary. Further investigation using advanced technologies are warranted to achieve accurate segmentation for PET-CT imaging.
StarSum: A Simple Star Graph for Multi-document Summarization

Graph-based approaches for multi-document summarization have been widely used to extract top sentences for a summary. Traditionally, the documents' cluster is modeled as a graph of the cluster's sentences only which might limit the ability of recognizing topically discriminative sentences in regard to other clusters. In this paper, we propose StarSum a star bipartite graph which models sentences and their topic signature phrases. The approach ensures sentence similarity and content importance from the graph structure. We extract sentences in an approach that guarantees diversity and coverage which are crucial for multi-document summarization. Regardless of the simplicity of the approach in ranking, a DUC experiment shows the effectiveness of StarSum compared to different baselines.
A Threads-Only MPI Implementation for the Development of Parallel Programs

In this paper, we present a threads-only implementation of MPI, called TOMPI, that allows eecient development of parallel programs on a workstation. The communication and context-switching overhead is reduced signiicantly compared to existing MPI implementations , by the use of threads and shared memory in place of UNIX processes and often sockets. Results demonstrate the scalability of TOMPI in comparison to two MPICH devices. We provide a C/C++ preprocessor to modify the semantics of global variables to appear as if each thread has its own address space. This makes TOMPI a true MPI implementation, that is, MPI programs can be run in TOMPI without modiication.
Transition to Quorum Sensing in an Agrobacterium Population: A Stochastic Model

Understanding of the intracellular molecular machinery that is responsible for the complex collective behavior of multicellular populations is an exigent problem of modern biology. Quorum sensing, which allows bacteria to activate genetic programs cooperatively, provides an instructive and tractable example illuminating the causal relationships between the molecular organization of gene networks and the complex phenotypes they control. In this work we—to our knowledge for the first time—present a detailed model of the population-wide transition to quorum sensing using the example of Agrobacterium tumefaciens. We construct a model describing the Ti plasmid quorum-sensing gene network and demonstrate that it behaves as an “on–off” gene expression switch that is robust to molecular noise and that activates the plasmid conjugation program in response to the increase in autoinducer concentration. This intracellular model is then incorporated into an agent-based stochastic population model that also describes bacterial motion, cell division, and chemical communication. Simulating the transition to quorum sensing in a liquid medium and biofilm, we explain the experimentally observed gradual manifestation of the quorum-sensing phenotype by showing that the transition of individual model cells into the “on” state is spread stochastically over a broad range of autoinducer concentrations. At the same time, the population-averaged values of critical autoinducer concentration and the threshold population density are shown to be robust to variability between individual cells, predictable and specific to particular growth conditions. Our modeling approach connects intracellular and population scales of the quorum-sensing phenomenon and provides plausible answers to the long-standing questions regarding the ecological and evolutionary significance of the phenomenon. Thus, we demonstrate that the transition to quorum sensing requires a much higher threshold cell density in liquid medium than in biofilm, and on this basis we hypothesize that in Agrobacterium quorum sensing serves as the detector of biofilm formation.
Adaptive Weighted Demosaicing for Single Sensor Camera Images

In order to reduce the hardware size and cost, many digital still cameras (DSC) use a single sensor array equipped with a color filter array (CFA) to capture any one of the three primary color components, R (Red), G (Green), or B (Blue), on each pixel location. This single band is used to generate the true RGB image. Among the various suggested CFAs, the Bayer CFA pattern is the most prevalent one, where G pixels occupy half of all, and R and B pixels share the others. Demosaicing is a process of interpolating the missed colors, which aims to reconstruct the missing colors as close as possible by keeping the computational complexity is very low. In this paper a novel adaptive edge preserving, edge directed inter-plane weighted interpolating technique is proposed for color reproduction from Bayer mosaic images. The proposed interpolation algorithm aims to effectively estimate the missing green component in the edge and texture regions with red and blue planes to be estimated based on the interpolated values of the green plane. The algorithm uses a gradient test to effectively interpolate though the edge direction. Variance of color differences is also used as a supplementary criterion to determine the interpolation direction. Adaptive weight values are derived for each and every non green pixel locations depending on the classification as sharpness and smoothness. Experimental results show that the proposed method performs much better than other latest techniques in terms of PSNR.