Miracle-dz/UniFAR-FTUs huggingface dataset:
Political Competition and the Regulation of Foreign Direct Investment

Political Competition and the Regulation of Foreign Direct Investment by Sinziana Paulina Ruxandra Dorobantu Department of Political Science Duke University Date:______________________ Approved: ___________________________ Karen Remmer, Supervisor ___________________________ Witold Henisz ___________________________ Judith Kelley ___________________________ Herbert Kitschelt ___________________________ David Soskice ___________________________ Bennet Zelner An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Political Science in the Graduate School of Duke University 2010 Copyright by Sinziana Pulina Ruxandra Dorobantu 2010
Parallel magnetic resonance imaging

Parallel imaging has been the single biggest innovation in magnetic resonance imaging in the last decade. The use of multiple receiver coils to augment the time consuming Fourier encoding has reduced acquisition times significantly. This increase in speed comes at a time when other approaches to acquisition time reduction were reaching engineering and human limits. A brief summary of spatial encoding in MRI is followed by an introduction to the problem parallel imaging is designed to solve. There are a large number of parallel reconstruction algorithms; this article reviews a cross-section, SENSE, SMASH, g-SMASH and GRAPPA, selected to demonstrate the different approaches. Theoretical (the g-factor) and practical (coil design) limits to acquisition speed are reviewed. The practical implementation of parallel imaging is also discussed, in particular coil calibration. How to recognize potential failure modes and their associated artefacts are shown. Well-established applications including angiography, cardiac imaging and applications using echo planar imaging are reviewed and we discuss what makes a good application for parallel imaging. Finally, active research areas where parallel imaging is being used to improve data quality by repairing artefacted images are also reviewed.
Annotation Quality Checking and Its Implications for Design of Treebank ( in Building the Prague Czech-English Dependency Treebank )

This article presents the system for annotation quality checking, proposed and used during the building of the Czech part of the Prague Czech-English Dependency Treebank. At first, the treebank project is introduced, as well as its basic principles and annotation process. The second part of the article pursues in detail one of the important phases of the annotation process, namely how the correctness of the annotated data is automatically and continuously checked during the process. The system of annotation quality checking is demonstrated on several particular checking procedures concerning syntactical phenomena. We try to evaluate the contribution of the system not only to the quality of the data and annotation, but also to the corpus design, impact on annotation rules and the annotation process as a whole.
A New Method of Class Centriod Vectors Classification Based on the Feedback

It is a great challenge for information technology that how to organize and manage large amount of document data, and find users' interested information quickly and exactly. Text classification can achieve the goal of information distributaries and solve the problem of information disorder, and then it can offer the convenience to users to make decisions. Centroid classifier is one of the most efficient models. Firstly the paper expatiates on the disadvantage of traditional weight calculation method applied in text classification, and then a new method which uses feature selection evaluation function value as a factor to term frequency is proposed. This paper present an improved centroid classifier based on feedback. The main idea of the algorithm is using the misfit samples in the training set to modify the center vectors which are related with them. From test results, the algorithm proposed by the paper is valid.
A low complexity and a low latency bit parallel systolic multiplier over GF(2/sup m/) using an optimal normal basis of type II

Using the self duality of an optimal normal basis (ONB) of type II, we present a bit parallel systolic multiplier over GF(2/sup m/), which has a low hardware complexity and a low latency. We show that our multiplier has a latency m+1 and the basic cell of our circuit design needs 5 latches (flip-flops). On the other hand, most of other multipliers of the same type have latency 3m and the basic cell of each multiplier needs 7 latches. Comparing the gates areas in each basic cell, we find that the hardware complexity of our multiplier is 25 percent reduced from the multipliers with 7 latches.
Display Prospects on Vertical Landing

In the final approach of vertical landing, the pilot of the vehicle cannot check the target point directly. The monitor display should project the target point with a camera. However, by using only the monitor display, the pilot cannot perform optimal vertical landing, because of the lack of various numerical information and prospects on the vehicle motion. We suppose the several types of future vehicle that can perform vertical landing: Luna Landing Vehicle in CEV (crew exploration vehicle) project, SSTO (single stage to orbit) transportation system such as DC-X by scaled composites and a kind of RSTS (reusable space transportation system) by ISAS of JAXA, and VTOL (vertical take-off and landing) airplane. In order to assistance to the maneuver at vertical landing, display system should project the most optimal trajectory for landing and other candidates. Such prospective trajectories are displayed with the enhanced viewing projection composed by the numerical data as 3D computer graphics. This paper shows a prototype of such display system for vertical landing
Dual boundary element analysis of closed cracks

A two-dimensional boundary element method for analysis of closed or partially closed cracks under normal and frictional forces is developed. The single domain dual formulation is used. As a contact problem is non-linear due to the friction phenomena at the crack interface and also because of the boundary conditions which may change during the loading, it is formulated in an incremental and iterative fashion. The stress intensity factors are calculated with the J-integral method. Also crack growth is considered. Several benchmark cases have been analysed to verify the results given by the method. The stress intensity factors and crack paths calculated are similar to those given in the literature. ( 1997 by John Wiley & Sons, Ltd.
Multi-tier Elastic Computation Framework for Mobile Cloud Computing

Federating the portability and mobility of mobile devices with the computation capacity on desktop computers have been a widely discussed computation model for the next decade. However, the mobility of the mobile devices also introduces challenges on the federation. This work developed the elastic computation framework to tackle the aforementioned challenge. The elastic computation framework federates the computation resources on wearable devices, mobile devices, nearby computers, and remote computers into a pool of computation resources. Each resource in the pool is characterized by its network delay, expected response time, and computation capability. Each mobile device is also characterized by its mobility and computation workload requirements. The elastic computation framework assigns computation resources in the pool to meet the workload requirements on mobile devices. The framework consists of resource allocation component, task scheduling algorithm, and task dispatch middleware. In the experiment, we compare the developed scheduling algorithm with other known algorithms by simulation. The results show that the developed scheduling algorithm does not complete the most tasks though, the cost/performance of system resources is the best among all the algorithms. To be specific, the cost-performance of the developed algorithm can be at least two times better than that of compared algorithms.
Expression of Odorant Receptor Family, Type 2 OR in the Aquatic Olfactory Cavity of Amphibian Frog Xenopus tropicalis

Recent genome wide in silico analyses discovered a new family (type 2 or family H) of odorant receptors (ORs) in teleost fish and frogs. However, since there is no evidence of the expression of these novel OR genes in olfactory sensory neurons (OSN), it remains unknown if type 2 ORs (OR2) function as odorant receptors. In this study, we examined expression of OR2 genes in the frog Xenopus tropicalis. The overall gene expression pattern is highly complex and differs depending on the gene and developmental stage. RT-PCR analysis in larvae showed that all of the OR2η genes we identified were expressed in the peripheral olfactory system and some were detected in the brain and skin. Whole mount in situ hybridization of the larval olfactory cavity confirmed that at least two OR2η genes so far tested are expressed in the OSN. Because tadpoles are aquatic animals, OR2η genes are probably involved in aquatic olfaction. In adults, OR2η genes are expressed in the nose, brain, and testes to different degrees depending on the genes. OR2η expression in the olfactory system is restricted to the medium cavity, which participates in the detection of water-soluble odorants, suggesting that OR2ηs function as receptors for water-soluble odorants. Moreover, the fact that several OR2ηs are significantly expressed in non-olfactory organs suggests unknown roles in a range of biological processes other than putative odorant receptor functions.
New statistical goodness of fit techniques in noisy inhomogeneous inverse problems - With application to the recovering of the luminosity distribution of the Milky Way

The assumption that a parametric class of functions ts the data structure suciently well is common in tting curves and surfaces to regression data. One then derives a parameter estimate resulting from a least squares t, say, and in a second step various kinds of 2 goodness of t measures, to assess whether the deviation between data and estimated surface is due to random noise and not to systematic departures from the model. In this paper we show that commonly-used 2 -measures are invalid in regression models, particularly when inhomogeneous noise is present. Instead we present a bootstrap algorithm which is applicable in problems described by noisy versions of Fredholm integral equations of the rst kind. We apply the suggested method to the problem of recovering the luminosity density in the Milky Way from data of the DIRBE experiment on board the COBE satellite.
Clustering Based Compress Data Cube Algorithm

In this paper, we present a novel clustering based technique to build an approximate compressed data cube and using this compressed data cube to provide answers to queries directly. Results of experiments on the effectiveness of Compact Cube are presented, using both synthetic and real-world data. The results indicate that the proposed Compress Cube can efficiently reduce the cube size. Through comparing the algorithm with some other algorithms, we can conclude that the compact cube is more efficient than others.
Using virtual machines for collaborative research

Collaboration between research groups can be quite difficult. Differences in hardware, software, and operating environments can lead to differences in results or even errors. Overcoming these difficulties can take significant time, or even result in ending the collaboration. A solution is presented and applied to two collaborations between ABB and universities. This collaboration involves using virtual machines as a way to create and deploy environments to each collaborator. The results show that this virtual machine solution allows for significant time savings in experimental setup. Also, collaborators are able to quickly reproduce results and remotely investigate any issues uncovered. Finally, this solution allows experiments to be rerun at later points in time without the need to recreate the environment.
Semantic classification by covariance descriptors within a randomized forest

This paper investigates an approach to perform semantic classification in aerial imagery by compactly integrating multiple feature cues, like appearance and 3D height information. We therefore propose a novel technique to incorporate powerful covariance region descriptors into the decision nodes of a randomized forest framework efficiently. The concept of finding reliable binary splits is based on repeated random sampling of distributions that are specified by mean vectors and covariance matrices. The sampling strategy is related to Monte Carlo simulations and perfectly fits the learning strategy of randomized decision trees, while the covariance descriptors are exploited to perform a plausible feature cue integration. To show state-of-the-art performance, we first evaluate our proposed approach on the MSRC dataset including 21 object classes. Then, we illustrate how an additional integration of 3D information improves the classification accuracy in real world aerial images taken from Dallas, San Francisco, and Graz. In addition, we use the available camera data and 3D information to combine the overlapping per-image classifications into a large-scale semantic description map that is directly applicable to virtual or procedural 3D modeling of urban environments.
Efficient kernel feature extraction for massive data sets

Maximum margin discriminant analysis (MMDA) was proposed that uses the margin idea for feature extraction. It often outperforms traditional methods like kernel principal component analysis (KPCA) and kernel Fisher discriminant analysis (KFD). However, as in other kernel methods, its time complexity is cubic in the number of training points m, and is thus computationally inefficient on massive data sets. In this paper, we propose an (1+ε)2-approximation algorithm for obtaining the MMDA features by extending the core vector machines. The resultant time complexity is only linear in m, while its space complexity is independent of m. Extensive comparisons with the original MMDA, KPCA, and KFD on a number of large data sets show that the proposed feature extractor can improve classification accuracy, and is also faster than these kernel-based methods by more than an order of magnitude.
PCR Technology for Detection of Invasive Aspergillosis

The application of molecular technologies to aid diagnosis and management of infectious diseases has had a major impact and many assays are in routine use. Diagnosis of aspergillosis has lagged behind. Lack of standardization and limited commercial interest have meant that PCR was not included in consensus diagnostic criteria for invasive fungal disease. In the last ten years careful evaluation and validation by the Aspergillus European PCR initiative with the development of standardized extraction, amplification and detection protocols for various specimen types, has provided the opportunity for clinical utility to be investigated. PCR has the potential to not only exclude a diagnosis of invasive aspergillosis but in combination with antigen testing may offer an approach for the early diagnosis and treatment of invasive aspergillosis in high-risk populations, with the added benefit of detection of genetic markers associated with antifungal resistance.
On the Impact of Migration Parameters on DIMEP for Designing Combinational Circuits

This paper proposes an Island Model-based parallel linear genetic programming methodology: distributed multi expression programming (DMEP) to support the design of combinational logic circuits and investigates how the migration policy (the migration period, the number of migrants and the migration topology) affects the behavior of the evolutionary process in term of different statistics (computational effort, percentage of successful runs and average fitness) depending on the type and the size of the problems being solved. Two benchmark problems are considered: multiplier circuits and n-bit even parity circuits.
EMPIRICAL STUDY OF PARALLEL LRU SIMULATION ALGORITHMS

This paper reports on the performance of five parallel algorithms for simulating a fully associative cache operating under the LRU (Least-Recently-Used) replacement policy. Three of the algorithms are SIMD, and are implemented on the MasPar MP-2 architecture. Two other algorithms are parallelizations of an efficient serial algorithm on the Intel Paragon. One SIMD algorithm is quite simple, but its cost is linear in the cache size. The two other SIMD algorithm are more complex, but have costs that are independent of the cache size. Both the second and third SIMD algorithms compute all stack distances; the second SIMD algorithm is completely general, whereas the third SIMD algorithm presumes and takes advantage of bounds on the range of reference tags. Both MIMD algorithm implemented on the Paragon are general, and compute all stack distances; they differ in one step that may affect their respective scalability. We assess the strengths and weaknesses of these algorithms as a function of problem size and characteristics, and compare their performance on traces derived from execution of three SPEC benchmark programs.
Converting a set into a minimum-phase sequence

In this paper we discuss whether a given finite set of real or complex numbers can be ordered such that the derived sequence is minimum-phase. We characterize several types of sets that can be ordered in minimum-phase sequences. These sets do not span the sets of all real or complex sequences of finite length. To find the minimum-phase sequence from a given set may be computational expensive and we propose an alternative to reduce the number of operations using a list of appropriate permutations.
Multi-level performance-driven stylised facial animation

In this paper, our objective is to assist a graphical artist throughout the creation of stylised facial animations that exhibit characters with a hand-drawn look and resemble real life without mimicking it. To this end, we present a hybrid approach combining beneﬁts from performance-driven facial animation and user-controlled 2D modelling and animation techniques. Once we capture the movement and timing of facial components, ‘multi-level facial motion descriptors’ are extracted according to MPEG-4 characterisation. Within the animation system these motion descriptors are mapped onto a set of artist-drawn facial channels where the parameters for each channel control the degree of caricature applied to the movement allowing ﬂexible carica-turing from a single set of movement data.
Perceptual model based data embedding in medical images

In this work, we propose a perceptual model based technique for embedding patient information in a medical image. The distortions in the diagnostic information-content, which will arise due to data embedding, can be controlled using a Perceptual Quality Measure (PQM). A Perceptual Quality Distortion Measure (PQDM) has been defined to measure the visible distortions between the original image and the processed image. The PQDM increases with the increase in the quantity of data embedded into the image. However it has been shown that the PQDM has different values when the same quantity of data is embedded into different regions inside the image, whereas the peak signal to noise ratio (PSNR) is only dependent on the quantity of embedded-data and does not show any change when the same quantity of data is embedded at different regions in the image. Quantification of correlation between PQDM and the loss in diagnostic information in medical images could play the key role for further work in this research area.
Using palaeo-climate comparisons to constrain future projections in CMIP5

Abstract. We present a selection of methodologies for using the palaeo-climate model component of the Coupled Model Intercomparison Project (Phase 5) (CMIP5) to attempt to constrain future climate projections using the same models. The constraints arise from measures of skill in hindcasting palaeo-climate changes from the present over three periods: the Last Glacial Maximum (LGM) (21 000 yr before present, ka), the mid-Holocene (MH) (6 ka) and the Last Millennium (LM) (850–1850 CE). The skill measures may be used to validate robust patterns of climate change across scenarios or to distinguish between models that have differing outcomes in future scenarios. We find that the multi-model ensemble of palaeo-simulations is adequate for addressing at least some of these issues. For example, selected benchmarks for the LGM and MH are correlated to the rank of future projections of precipitation/temperature or sea ice extent to indicate that models that produce the best agreement with palaeo-climate information give demonstrably different future results than the rest of the models. We also explore cases where comparisons are strongly dependent on uncertain forcing time series or show important non-stationarity, making direct inferences for the future problematic. Overall, we demonstrate that there is a strong potential for the palaeo-climate simulations to help inform the future projections and urge all the modelling groups to complete this subset of the CMIP5 runs.
AdaptiveNav: Adaptive Discovery of Interesting and Surprising Nodes in Large Graphs

Visualization is a powerful paradigm for exploratory data analysis. Visualizing large graphs, however, often results in a meaningless hairball. In this paper, we propose a different approach that helps the user adaptively explore large million-node graphs from a local perspective. For nodes that the user investigates, we propose to only show the neighbors with the most subjectively interesting neighborhoods. We contribute novel ideas to measure this interestingness in terms of how surprising a neighborhood is given the background distribution, as well as how well it fits the nodes the user chose to explore. We are currently designing and developing AdaptiveNav, a fast and scalable method for visually exploring large graphs. By implementing our above ideas, it allows users to look into the forest through its trees.
Local Microcode Compaction Techniques

Microcode compaction is an essential tool for the compilation of high-level language microprograms into microinstructions with parallel microoperations. Although guaranteeing minimum execution time is an exponentially complex problem, recent research indicates that it is not difficult to obtain practical results. This paper, which assumes no prior knowledge of microprogramming on the part of the reader, surveys the approaches that have been developed for compacting microcode. A comprehensive terminology for the area is presented, as well as a general model of processor behavior suitable for comparing the algorithms. Execution examples and a discussion of strengths and weaknesses are given for each of the four classes of loc . al compaction algorithms: linear, critical path, branch and bound, and list scheduling. Local compaction, which applies to jump-free code, is fundamental to any compaction technique. The presentation emphasizes the conceptual distinction between data dependency and conflict analysis.
Being Less Active and Outnumbered? The Political Participation and Relative Pressure Potential of Young People in Europe

Does demographic ageing matter for the political participation process in Europe? Evidence from the European Social Survey shows that the political participation process in Europe is currently skewed in favor of middle-aged people who dominate in their pressure potential measured through their participation levels and demographic size over other age groups. Older people, the fastest growing age group, come in second on almost all dimensions of participation. Young people, unlikely to increase in numbers in the near future, have the lowest pressure potential due to their low participation rates and their small demographic weight. Since age groups differ in their political preferences, young people may be less able to convey information about their different preferences to and to exert pressure on political elites than other age groups.