Miracle-dz/UniFAR-FTUs huggingface dataset:
Pinning Synchronization of Directed Networks With Switching Topologies: A Multiple Lyapunov Functions Approach

This paper studies the global pinning synchronization problem for a class of complex networks with switching directed topologies. The common assumption in the existing related literature that each possible network topology contains a directed spanning tree is removed in this paper. Using tools from M-matrix theory and stability analysis of the switched nonlinear systems, a new kind of network topology-dependent multiple Lyapunov functions is proposed for analyzing the synchronization behavior of the whole network. It is theoretically shown that the global pinning synchronization in switched complex networks can be ensured if some nodes are appropriately pinned and the coupling is carefully selected. Interesting issues of how many and which nodes should be pinned for possibly realizing global synchronization are further addressed. Finally, some numerical simulations on coupled neural networks are provided to verify the theoretical results.
Identification of combination therapy models using a neuro fuzzy identification scheme

In this paper a neuro fuzzy identification scheme is used in order to identify four different nonlinear biological systems that describe four combination therapy models respectively. Combination therapy is a significant field in modern medicine and it is very important to be able to develop a system-level understanding of such biological systems and provide information that will be useful to molecular biology and medicine. Apart from this, we have the opportunity to show the great capabilities of the scheme we used, in biological systems of great interest that show amplification, ultrasensitivity and feedback control. Our results illustrate the potency of this method.
Blur Kernel GT Camera Trajectory Time Tracked Camera Motion

Optical flow estimation is a difficult task given realworld video footage with camera and object blur. In this paper, we combine a 3D pose&position tracker with an RGB sensor allowing us to capture video footage together with 3D camera motion. We show that the additional camera motion information can be embedded into a hybrid optical flow framework by interleaving an iterative blind deconvolution and warping based minimization scheme. Such a hybrid framework significantly improves the accuracy of optical flow estimation in scenes with strong blur. Our approach yields improved overall performance against three state-ofthe-art baseline methods applied to our proposed ground truth sequences as well as in several other real-world cases.
DOES UNIVERSITY EDUCATION LEAD TO ADOPTION ?

The diffusion and adoption of systems development methods have been a subject for research and a topic for discussion for a number of years. One important factor often emphasized is education and training. However, very little research focusing at the role of formal university education and the dissemination of systems development methodologies has been carried out. This is the background for this study. The study investigates to which degree a specific methodology —Multiview—taught in-depth for more than 10 years at the Copenha-gen Business School, has been adopted by the students and the organizations they are working for. The main result is that very few examples of adoption were found. The paper investigates reasons for not adopting by using a grounded theory analysis.
Towards an energy-efficient framework for location-triggered mobile application

Numerous Location-Based Applications (LBAs) have been emerging in our smart phones over the past several years. Most of them are user-triggered LBAs which heavily depend on end users' manual operations and are easy to lose their accessibility and usability when fail to get instructions in time. In contrast, location-triggered applications usually bring better user experiences by performing actions automatically when reaching predefined locations. However, there are still some burdens of location-triggered mechanisms to be widely adopted. The most challenging one is the lack of a unified framework to define location-triggered data collection behaviors and corresponding system internal behaviors. In this paper, we propose a preliminary framework to manage location-specific actions associated with specific targets of interest. We explored both dynamic data collection scenarios and the mobile system internal reaction scenario. We also examine the battery draining problem caused by periodically duty-cycling power-intensive GPS sensor. A new application level energy-efficient location detection approach is proposed which intelligently duty-cycles GPS with other on-device sensors. The results of our real-world experiment demonstrate that mobile device could reduce the usage of powerintensive GPS location update operation by as much as 90% with this new approach in location-triggered application.
An Application of the Means-End Theory: Measurement of Delivery and Consumption of an Educational Service

I am submitting herewith a dissertation written by Melek Meral Anıtsal entitled “An Application of the Means-End Theory: Measurement of Delivery and Consumption of an Educational Service. ” I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, with a major in Business Administration.
Conditions for successful data assimilation

We show, using idealized models, that numerical data assimilation can be successful only if an effective dimension of the problem is not excessive. This effective dimension depends on the noise in the model and the data, and in physically reasonable problems, it can be moderate even when the number of variables is huge. We then analyze several data assimilation algorithms, including particle filters and variational methods. We show that well‐designed particle filters can solve most of those data assimilation problems that can be solved in principle and compare the conditions under which variational methods can succeed to the conditions required of particle filters. We also discuss the limitations of our analysis.
Efficient method for solving globally optimal solutions of weighted LP norm and L2 norm optimization problems

This paper extends the existing L1 norm separable surrogate functional (SSF) iterative shrinkage algorithm to approximate the objective function of a weighted Lp norm and L2 norm optimization problem by N one dimensional independent objective functions. However, as the weighted Lp norm and L2 norm optimization problem is nonconvex, there may be more than one locally optimal solution. Hence, it is difficult to find the globally optimal solution. To address this difficulty, this paper further characterizes the regions that the signs of the convexity of the objective function within the regions remain unchanged. Then, the optimal solution within each region and eventually the globally optimal solution of the original optimization problem are found.
Option Markets for Water in California: Effective Management of Water Supply Risk

In California, the tremendous spatial and temporal variation in precipitation suggests that flexible contractual arrangements, such as option contracts, would increase allocative efficiency of water. Under such arrangements, a water agency pays an option premium for the right to purchase water at some point in the future, if water conditions turn out to be dry. The premium represents the value of the flexibility gained by the buyer from postponing its decision whether to purchase water. In California, the seller of existing option arrangements is often an agricultural producer who can fallow land in the event that a water option is exercised. We seek to determine the value of transferring water supply risk from one party to another at key locations in California, given current water prices and the spatial and temporal distribution of water year types in the state. We utilize output from CALVIN, an economic-engineering optimization model of the California water system, to construct a mathematical programming model that runs historical hydrological conditions over the current configuration of the California water system. Our model allocates water between current-year agricultural and urban demand and future anticipated excess demand without knowledge of future water conditions. This limited foresight and the consequent storage levels results in a distribution of water’s imputed price at different locations throughout northern California. We utilize these distributions of water price to calculate annual option premia at key locations in California. Analysis performed to date covers northern California.
Knowledge engineering approach for power transformer asset management

The main objective of the utility company is to maximize the utilization of its existing assets during their life cycle meeting both engineering and financial requirements. However, the knowledge available in the utility is unstructured and often in tacit form. This paper presents a knowledge based system for the utility companies in order to make an optimal decision on power transformer with their utilization. CommonKADS methodology, a structured development for knowledge and expertise representation, is utilized for designing and developing knowledge based system. A case study of One MVA power transformer of Nepal Electricity Authority is presented. The results show that the reusable knowledge can be categorized, modelled and utilized within the utility company using the proposed methodologies. Moreover, the results depict that utility company can achieve both engineering and financial benefits from its utilization.
Furin Targeted Drug Delivery for Treatment of Rhabdomyosarcoma in a Mouse Model

Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in children. Improvement of treatment efficacy and decreased side effects through tumor-targeted drug delivery would be desirable. By panning with a phage-displayed cyclic random peptide library we selected a peptide with strong affinity for RMS in vitro and in vivo. The peptide minimal binding motif Arg-X-(Arg/Lys)(Arg/Lys) identified by alanine-scan, suggested the target receptor to be a proprotein convertase (PC). Expression profiling of all PCs in RMS biopsies and cell lines revealed consistent high expression levels for the membrane-bound furin and PC7. Direct binding of RMS-P3 peptide to furin was demonstrated by affinity chromatography and supported by activity and colocalization studies. Treatment of RMS in mice with doxorubicin coupled to the targeting peptide resulted in a two-fold increase in therapeutic efficacy compared to doxorubicin treatment alone. Our findings indicate surface-furin binding as novel mechanism for therapeutic cell penetration which needs to be further investigated. Furthermore, this work demonstrates that specific targeting of membrane-bound furin in tumors is possible for and suggests that RMS and other tumors might benefit from proprotein convertases targeted drug delivery.
The Global Accounting Experiment. Bruegel blueprint series, Volume 2, April 2007

[From the introduction]. This work starts with a brief summary of the historical developments which led to what we call the Global Accounting Experiment, ie the endorsement of IFRS (or of standards modeled almost entirely on IFRS) in a growing number of jurisdictions, thus conferring de facto international policymaking power on the entity that sets them, the International Accounting Standards Board (IASB). The question of why countries have been willing to abandon this power is the focus of the following section. We then turn to other questions of a more forward-looking nature. Can the IASB build legitimacy on a global scale? Can it avoid capture by specific national or sectoral interests? Can its global standards be effectively enforced in individual countries? Ultimately, how can the experiment succeed?
Towards Effectively Test Report Classification to Assist Crowdsourced Testing

Context: Automatic classification of crowdsourced test reports is important due to their tremendous sizes and large proportion of noises. Most existing approaches towards this problem focus on examining the performance of different machine learning or information retrieval techniques, and most are evaluated on open source dataset. However, our observation reveals that these approaches generate poor and unstable performances on real industrial crowdsourced testing data. We further analyze the deep reason and find that industrial data have significant local bias, which degrades existing approaches. Goal: We aim at designing an approach to overcome the local bias in industrial data and automatically classifying true fault from the large amounts of crowdsourced reports. Method: We propose a cluster-based classification approach, which first clusters similar reports together and then builds classifiers based on most similar clusters with ensemble method. Results: Evaluation is conducted on 15,095 test reports of 35 industrial projects from Chinese largest crowdsourced testing platform and results are promising, with 0.89 precision and 0.97 recall on average. In addition, our approach improves the existing baselines by 17% - 63% in average precision and 15% - 61% in average recall. Conclusions: Results imply that our approach can effectively discriminate true fault from large amounts of crowdsourced reports, which can reduce the effort required for manual inspection and facilitate project management in crowdsourced testing. To the best of our knowledge, this is the first work to address the test report classification problem in real industrial crowdsourced testing practice.
A Psychometric Study of the Index of Learning Styles©

A study was conducted on the Felder‐Soloman Index of Learning Styles© (ILS) to assess reliability, factor structure, and construct validity as well to determine whether changing its dichotomous response scale to a five‐option response scale would improve reliability and validity. Data collected in this study had internal consistency reliability ranging from 0.55 to 0.77 across the four learning style scales of the ILS. Factor analysis revealed that multiple factors were present within three of the learning style scales, which correspond to known aspects of the scale definitions. The factor analysis and direct feedback from students on whether they felt their scores accurately represented their learning preferences provide evidence of construct validity for the ILS. Changing the response scale improved reliability, but it did not change the factor structure substantially nor did it affect the strength of the evidence for construct validity based on student feedback.
Microstructural and Mechanical Properties of Alkali Activated Colombian Raw Materials

Microstructural and mechanical properties of alkali activated binders based on blends of Colombian granulated blast furnace slag (GBFS) and fly ash (FA) were investigated. The synthesis of alkali activated binders was conducted at 85 °C for 24 h with different slag/fly ash ratios (100:0, 80:20, 60:40, 40:60, 20:80, and 0:100). Mineralogical and microstructural characterization was carried out by means of X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM/EDX) and Nuclear magnetic resonance (NMR). Mechanical properties were evaluated through the compressive strength, modulus of elasticity and Poisson’s ratio. The results show that two different reaction products were detected in the slag/fly ash mixtures, a calcium silicate hydrate with Al in its structure (C-A-S-H gel) and a sodium aluminosilicate hydrate (N-A-S-H gel) with higher number of polymerized species and low content in Ca. It was found that with the increase of the amount of added slag, the amount of C-A-S-H gel increased and the amount of N-A-S-H gel decreased. The matrix was more dense and compact with almost absence of pores. The predominance of slag affected positively the compressive strength, Young’s modulus and Poisson’s ratio, with 80% slag and 20% fly ash concrete being the best mechanical performance blend.
Do You Need a New Product-Development Strategy?

OVERVIEW: Many firms rely on a single new-product development process for all projects. But designing new products for different business contexts requires that a firm deploy different new-product development processes. Products designed for stable and mature end-user markets require a process optimized for control and efficiency. In contrast, first-of-a-kind “breakthrough” products require a more emergent process that aims to discover whether there is any market to be served in the first place. Applying a uniform “best-practice” process to all development efforts ignores the major differences between these projects and may result in missed opportunities. This article describes a framework to address this problem, allowing a firm to better align the design of its development processes to the specific aims of individual projects. We illustrate this framework with examples from Hewlett-Packard, a large, diversified electronics firm that has successfully piloted this new approach across multiple business units.
The IOC Centres of Excellence bring prevention to Sports Medicine

The protection of an athlete’s health and preventing injuries and illnesses in sport are top priorities for the IOC and its Medical Commission. The IOC therefore partners with selected research centres around the world and supports research in the field of sports medicine. This has enabled the IOC to develop an international network of expert scientists and clinicians in sports injury and disease prevention research. The IOC wants to promote injury and disease prevention and the improvement of physical health of the athlete by: (1) establishing long-term research programmes on injury and disease prevention (including studies on basic epidemiology, risk factors, injury mechanisms and intervention), (2) fostering collaborative relationships with individuals, institutions and organisations to improve athletes’ health, (3) implementing and collaborating with applied, ongoing and novel research and development within the framework and long-term strategy of the IOC and (4) setting up knowledge translation mechanisms to share scientific research results with the field throughout the Olympic Movement and sports community and converting these results into concrete actions to protect the health of the athletes. In 2009, the IOC also identified four research centres that had an established track record in research, educational and clinical activities to achieve these ambitions: (1) the Australian Centre for Research into Injury in Sport and its Prevention (ACRISP), Australia; (2) the Sport Injury Prevention Research Centre (SIPRC), Canada; (3) the Clinical Sport and Exercise Medicine Research (CSEM), South Africa and (4) the Oslo Sports Trauma Research Center (OSTRC), Norway. This paper highlights the work carried out by these four IOC Centres of Excellence over the past 6 years and their contribution to the world of sports medicine.
Probabilistic range queries for uncertain trajectories on road networks

Trajectories representing the motion of moving objects are typically obtained via location sampling, e.g. using GPS or road-side sensors, at discrete time-instants. In-between consecutive samples, nothing is known about the whereabouts of a given moving object. Various models have been proposed (e.g., sheared cylinders; spacetime prisms) to represent the uncertainty of the moving objects both in unconstrained Euclidian space, as well as road networks. In this paper, we focus on representing the uncertainty of the objects moving along road networks as time-dependent probability distribution functions, assuming availability of a maximal speed on each road segment. For these settings, we introduce a novel indexing mechanism -- UTH (Uncertain Trajectories Hierarchy), based upon which efficient algorithms for processing spatio-temporal range queries are proposed. We also present experimental results that demonstrate the benefits of our proposed methodologies.
Collaborative Filtering Recommendation Algorithm Based on MDP Model

Collaborative filtering, which makes personalized predictions by learning the historical behaviors of users, is widely used in recommender systems. It makes the prediction and recommend by similarity of users, and it can handle the various work. But the traditional collaborative filtering ignores the connection of users and items. Affect the recommendation's results. To find similar users by measuring the customer relationship between the neighbors can improve the accuracy of prediction user interests'. Then it can improve the accuracy of the recommendation. So collaborative filtering recommendation algorithm based on MDP model is proposed. It can find the connection of users purchase and next purchase. So it can predict users next purchase. Then can recommend items to users. The test results shows the algorithm of this paper have more accuracy.
Extremist Funding, Centrist Voters, and Candidate Divergence

In an electoral framework of unidimensional two-candidate spatial competition with probabilistic voting, special interest groups present candidates with schedules that give the level of campaign contribution they will make for each feasible candidate policy location. Candidates, motivated by the desire both to hold office and to see their most preferred policy position enacted, spend contributions in an effort to "convince" voters of the merits of their announced position. In equilibrium candidates adopt policy positions which balance the centrifugal force generated by special interests against the centripetal force to converge to the position of the expected median voter. Analytical and numerical methods are used to characterize candidate reaction functions and equilibrium locational outcomes. Even purely office-oriented candidates may diverge and policy-oriented candidates may adopt positions more extreme than their own ideal points. Where any particular candidate locates depend on the interaction of several factors including the candidate's ideal policy, the relative utility she derives from policy realizations versus office-holding per se, her particular opponent's location, the nature of the special-interest contribution schedules, and the technology by which campaign spending affects voters' beliefs.
Runs on Money Market Mutual Funds

We study daily money market mutual fund flows at the individual share class level during the crisis of September 2008. The empirical approach that we apply to this fine granularity of data brings new insights into the investor and portfolio holding characteristics that are conducive to run-risk in cash-like asset pools, as well as providing evidence on the time-series dynamics of runs and the equilibria that develop. We propose two identification approaches to test predictions of recent theoretical models with strategic complementarities and incomplete information. First, we study dynamic interactions between investors with differing levels of sophistication within the same money fund, thus holding constant the quality of the underlying portfolio. Second, we employ a novel quantile regression methodology to identify relationships between observable characteristics and tail outcomes. Our results provide considerable support for the theoretical predictions, providing some of the strongest empirical evidence to date on run-like behavior within intermediated asset pools during the financial crisis.