Miracle-dz/UniFAR-FTUs huggingface dataset:
Pacific Asia Conference on Information Systems ( PACIS ) July 2008 THE EFFECTS OF WEB PERSONALIZATION ON INFLUENCING USERS ' SWITCHING DECISIONS TO A NEW WEBSITE

Numerous firms are taking advantage of interactive technologies to personalize their interactions with users. They invest many resources in personalization to provide personalized services of higher quality in the hope of more business opportunities and higher customer satisfaction. Despite the proliferation of personalized web services worldwide, prior research on how personalization attracts users to switch to a personalized website is minimal. If this question is left unanswered, online firms do not know whether their investment can be justified. Our research examines how users’ web experience and their perceptions towards personalization influence their attitudes towards switching to two types of personalized websites, online shopping websites and search engines. A survey study with 192 Internet users was conducted. The findings show that personalization plays different roles of attracting users in the context of search engines and online shopping sites. Internet users want useful personalized services, but at the same time, they are concerned with the manner in which firms use their data for personalization. If the involvement of Internet users with their current site is high, then personalized services are not attractive enough to motivate them to migrate from one site to another.
Fast implementations of algebraic methods for three-dimensional reconstruction from cone-beam data

The prime motivation of this work is to devise techniques that make the algebraic reconstruction technique (ART) and related methods more efficient for routine clinical use, while not compromising their accuracy. Since most of the computational effort of ART is spent for projection/backprojection operations, the authors first seek to optimize the projection algorithm. Existing projection algorithms are surveyed and it is found that these algorithms either lack accuracy or speed, or are not suitable for cone-beam reconstruction. The authors hence devise a new and more accurate extension to the splatting algorithm, a well-known voxel-driven projection method. They also describe a new three-dimensional (3-D) ray-driven projector that is considerably faster than the voxel-driven projector and, at the same time, more accurate and perfectly suited for the demands of cone beam. The authors then devise caching schemes for both ART and simultaneous ART (SART), which minimize the number of redundant computations for projection and backprojection and, at the same time, are very memory conscious. They find that with caching, the cost for an ART projection/backprojection operation can be reduced to the equivalent cost of 1.12 projections. They also find that SART, due to its image-based volume correction scheme, is considerably harder to accelerate with caching. Implementations of the algorithms yield runtime ratios T/sub SART//T/sub ART/ between 1.5 and 1.15, depending on the amount of caching used.
Chiral Supersymmetric Gepner Model Orientifolds

We explicitly construct A-type orientifolds of supersymmetric Gepner models. In order to reduce the tadpole cancellation conditions to a treatable number we explicitly work out the generic form of the one-loop Klein bottle, annulus and Moebius strip amplitudes for simple current extensions of Gepner models. Equipped with these formulas, we discuss two examples in detail to provide evidence that in this setting certain features of the MSSM like unitary gauge groups with large enough rank, chirality and family replication can be achieved.
Time, change, and longitudinally emergent conditions: understanding and applying longitudinal growth modeling in sales research

Despite calls for increased use of longitudinal data in academic sales research, the overwhelming majority of published studies utilize cross-sectional designs. Yet, given the critical importance of understanding the evolution of performance and other important outcomes, sales management, as a discipline, is particularly well suited for analysis using methodological techniques that properly account for the role of time. The purpose of this manuscript is to advocate for the increased use of longitudinal growth modeling (LGM), a technique for analyzing longitudinal data that can be applied by researchers to generate knowledge that can elude cross-sectional designs, in research on selling and sales management. In so doing, this article reviews extant research utilizing this technique and demonstrates that performing this type of analysis is well within the capabilities of many sales researchers, in terms of both ease of application and having access to the data necessary to generate insights from this methodology. The article also provides some topical areas in sales research that are particularly amenable to analysis using LGM in an effort to encourage future research in this area.
Analysis of a physics teacher's pedagogical ‘micro-actions’ that support 17-year-olds’ learning of free body diagrams via a modelling approach

ABSTRACT Great teaching is characterised by the specific actions a teacher takes in the classroom to bring about learning. In the context of model-based teaching (MBT), teachers’ difficulty in working with students’ models that are not scientifically consistent is troubling. To address this problem, the aim of this study is to identify the pedagogical micro-actions to support the development of scientific models and modelling skills during the evaluation and modification stages of MBT. Taking the perspective of pedagogical content knowing (PCKg), it identifies these micro-actions as an in-situ, dynamic transformation of knowledges of content, pedagogy, student and environment context. Through a case study approach, a lesson conducted by an experienced high-school physics teacher was examined. Audio and video recordings of the lesson contributed to the data sources. Taking a grounded approach in the analysis, eight pedagogical micro-actions enacted by the teacher were identified, namely ‘clarification’, ‘evaluation’, ‘explanation’, ‘modification’, ‘exploration’, ‘referencing conventions’, ‘focusing’ and ‘meta-representing’. These micro-actions support students’ learning related to the conceptual, cognitive, discursive and epistemological aspects of modelling. From the micro-actions, we identify the aspects of knowledges of PCKg that teachers need in order to competently select and enact these micro-actions. The in-situ and dynamic transformation of these knowledges implies that professional development should also be situated in the context in which these micro-actions are meaningful.
Extensive Geographic Mosaicism in Avian Influenza Viruses from Gulls in the Northern Hemisphere

Due to limited interaction of migratory birds between Eurasia and America, two independent avian influenza virus (AIV) gene pools have evolved. There is evidence of low frequency reassortment between these regions, which has major implications in global AIV dynamics. Indeed, all currently circulating lineages of the PB1 and PA segments in North America are of Eurasian origin. Large-scale analyses of intercontinental reassortment have shown that viruses isolated from Charadriiformes (gulls, terns, and shorebirds) are the major contributor of these outsider events. To clarify the role of gulls in AIV dynamics, specifically in movement of genes between geographic regions, we have sequenced six gull AIV isolated in Alaska and analyzed these along with 142 other available gull virus sequences. Basic investigations of host species and the locations and times of isolation reveal biases in the available sequence information. Despite these biases, our analyses reveal a high frequency of geographic reassortment in gull viruses isolated in America. This intercontinental gene mixing is not found in the viruses isolated from gulls in Eurasia. This study demonstrates that gulls are important as vectors for geographically reassorted viruses, particularly in America, and that more surveillance effort should be placed on this group of birds.
Modeling superposition of many sources generating self similar traffic

This paper presents steps towards creating a general model for aggregated traffic streams. We show that fitting the mean, variance and Hurst parameter is insufficient to consistently model a long range dependent traffic stream. A fourth parameter, the "level of aggregation", is required. We also show that with increased aggregation, the behaviour of a traffic stream tends towards that of a fractal Gaussian process.
Gene Expression Profiles for Predicting Metastasis in Breast Cancer: A Cross-Study Comparison of Classification Methods

Machine learning has increasingly been used with microarray gene expression data and for the development of classifiers using a variety of methods. However, method comparisons in cross-study datasets are very scarce. This study compares the performance of seven classification methods and the effect of voting for predicting metastasis outcome in breast cancer patients, in three situations: within the same dataset or across datasets on similar or dissimilar microarray platforms. Combining classification results from seven classifiers into one voting decision performed significantly better during internal validation as well as external validation in similar microarray platforms than the underlying classification methods. When validating between different microarray platforms, random forest, another voting-based method, proved to be the best performing method. We conclude that voting based classifiers provided an advantage with respect to classifying metastasis outcome in breast cancer patients.
Adaptive decision feedback equalization for filter bank based multicarrier systems

In this work we present a per-subchannel nonlinear adaptive equalizer for a class of filter bank based multicarrier (FBMC) systems. We consider the class of exponentially modulated FBMCs with offset quadrature amplitude modulated (OQAM) input symbols. We present a fractionally spaced adaptive decision feedback equalizer (DFE) based on the least-mean-square (LMS) algorithm. The input of each equalizer comprises only the output of each subchannel. In the simulation results we show that, despite its increased computational complexity, the performance and higher bandwidth efficiency of OQAM FBMC systems makes them a competitive alternative to conventional multicarrier systems like cyclic prefix based orthogonal frequency division multiplexing (CP-OFDM).
Bridging the gap between applications and networks in data centers

Modern data centers host tens (if not hundreds) of thousands of servers and are used by companies such as Amazon, Google, and Microsoft to provide online services to millions of individuals distributed across the Internet. They use commodity hardware and their network infrastructure adopts principles evolved from enterprise and Internet networking. Applications use UDP datagrams or TCP sockets as the primary interface to other applications running inside the data center. This effectively isolates the network from the end-systems, which then have little control over how the network handles packets. Likewise, the network has limited visibility on the application logic. An application injects a packet with a destination address and the network just delivers the packet. Network and applications effectively treat each other as black-boxes. This strict separation between applications and networks (also referred to as dumb network) is a direct outcome of the so-called end-to-end argument [49] and has arguably been one of the main reasons why the Internet has been capable of evolving from a small research project to planetary scale, supporting a multitude of different hardware and network technologies as well as a slew of very diverse applications, and using networks owned by competing ISPs. Despite being so instrumental in the success of the Internet, this black-box design is also one of the root causes of inefficiencies in large-scale data centers. Given the little control and visibility over network resources, applications need to use low-level hacks, e.g., to extract network properties (e.g., using traceroute and IP addresses to infer the network topology) and to prioritize traffic (e.g., increasing the number of TCP flows used by an application to increase its bandwidth share). Further, a simple functionality like multicast or anycast routing is not available and developers must resort to application-level overlays. This, however, leads to inefficiencies as typically multiple logical links are mapped to the same physical link, significantly reducing application throughput. Even with perfect knowledge of the underlying topology, there is still the constraint that servers
Can deep brain stimulation of the nucleus basalis of Meynert improve thinking and memory problems in Lewy body dementias

The Lewy body dementias, Parkinson’s disease dementia and dementia with Lewy bodies, are two of the most common causes of dementia worldwide, and share both a common clinical phenotype and underlying pathology. Despite their growing economic and societal disease burden, there are currently only a small number of limited symptomatic therapies available, while modern approaches to develop disease modifying biologic agents have so far produced little tangible effect. There is growing recognition of the need to explore alternative treatment avenues, and the success of deep brain stimulation (DBS) in modulating aberrant neural network processing to relieve symptoms in other neuropsychiatric diseases raises the possibility that this might be achievable in Lewy body dementias. The nucleus basalis of Meynert (NBM) provides the major source of ascending cholinergic innervation to the cortex, and is proposed to be a key node in multiple distributed cognitive networks. The nucleus degenerates significantly in Lewy body dementias, which correlates closely with the severity of cognitive decline. It is therefore proposed that deep brain stimulation to the NBM may be able to modulate cholinergic transmission to cortex, and thereby impact directly upon dementia symptoms. In this thesis I will present preliminary evidence from two experimental clinical trials of deep brain stimulation to the NBM in Lewy body dementias. I will present data showing that this invasive neurosurgical procedure is both safe and well tolerated in patients with advanced dementia, and that low frequency stimulation may be associated with improvements in both memory functions and neuropsychiatric symptomatology. Furthermore, I will present results from the first direct electrophysiological recordings from human NBM in vivo, showing that activity in the nucleus may reflect levels of sustained attention. Finally, I evaluate the overall clinical impact of this novel therapeutic approach in Lewy body dementias, and discuss how our electrophysiological findings may relate to this, and how they contribute to our existing understanding of the physiological function of NBM.
Direct Evidence for Domain-Sensitive Functional Subregions in Human Entorhinal Cortex

The medial temporal lobes (MTL) are known to play a crucial role in memory processes. Anatomical findings from animal studies suggest partially segregated MTL pathways converge in the hippocampus, with a posterior stream including parahippocampal and medial lateral entorhinal cortex and an anterior stream including perirhinal and lateral entorhinal cortex. These streams may operate on spatial and nonspatial information, respectively. In humans, such a functional dissociation has been suggested between parahippocampal and perirhinal cortex. Data from rodents and nonhuman primates suggest a similar dissociation between medial and lateral entorhinal cortex, which are reciprocally connected to parahippocampal and perirhinal cortex, but evidence for functional subregions within entorhinal cortex in humans is lacking. We addressed this issue using high-resolution fMRI with improved spatial normalization. Volunteers (n = 28) performed a working memory paradigm involving the retrieval of spatial (scenes) and nonspatial (faces) information after distraction. A clear dissociation between MTL subcircuits emerged. A perirhinal–lateral entorhinal pathway was more involved in the retrieval of faces after distraction, whereas a parahippocampal–medial entorhinal pathway was more involved in the retrieval of scenes after distraction. A cluster in posterior hippocampus showed a deactivation for the retrieval of faces after distraction. Our data thus provide direct evidence for a functional specialization within human entorhinal cortex and thereby strongly support MTL models that emphasize the importance of partially segregated parallel processing streams.
Corporate Science, Innovation and Firm Value

It can be observed that many R&D performing firms produce scientific knowledge and discloseresearch outcomes in scientific journals. At the micro-level, prior work identified several potentialbenefits of such a strategy like superior access to informal information networks or the opportunity ofrecruiting the best PhD graduates. However, scientific research is costly and subject to considerableuncertainty with respect to the outcomes, and the disclosure may lead to spillover effects that decreasethe ability of firms to generate returns of their R&D investments. Overall, it remains unclear if andunder what conditions science-oriented strategies are beneficial for firms. We address this gap andexamine the impact of scientific activities on the firm’s market value using accounting data for USfirms from Compustat and matched patent and scientific publication data. We find evidence for apositive impact of scientific publication stocks on the firm value beyond the effects of R&D, patentstocks and patent quality.
An intermediate representation for speculative optimizations in a dynamic compiler

We present a compiler intermediate representation (IR) that allows dynamic speculative optimizations for high-level languages. The IR is graph-based and contains nodes fixed to control flow as well as floating nodes. Side-effecting nodes include a framestate that maps values back to the original program. Guard nodes dynamically check assumptions and, on failure, deoptimize to the interpreter that continues execution. Guards implicitly use the framestate and program position of the last side-effecting node. Therefore, they can be represented as freely floating nodes in the IR. Exception edges are modeled as explicit control flow and are subject to full optimization. We use profiling and deoptimization to speculatively reduce the number of such edges. The IR is the core of a just-in-time compiler that is integrated with the Java HotSpot VM. We evaluate the design decisions of the IR using major Java benchmark suites.
How the mass counts: An electrophysiological approach to the processing of lexical features

Nouns may refer to countable objects such as tables, or to mass entities such as rice. The mass/count distinction has been discussed in terms of both semantic and syntactic features encoded in the mental lexicon. Here we show that event-related potentials (ERPs) can reflect the processing of such lexical features, even in the absence of any feature-related violations. We demonstrate that count (vs mass) nouns elicit a frontal negativity which is independent of the N400 marker for conceptual-semantic processing, but resembles anterior negativities related to grammatical processing. This finding suggests that the brain differentiates between count and mass nouns primarily on a syntactic basis.
CHARACTERIZATION OF TRUCK-MOUNTED ATOMIZATION EQUIPMENT TYPICALLY USED IN VECTOR CONTROL1

ABSTRACT The control of medically important arthropod vectors of human and animal disease is a high priority for both public health and military officials. Because droplet size of pesticide spray material is a critical factor affecting vector control applications, the droplet-size spectra produced by 11 sprayers and 3 spray formulations were evaluated. Droplet-size spectra were measured by a laser diffraction instrument, a hot-wire system, and rotating slides. There were considerable differences in the droplet-size spectra produced by the different sprayers tested. The volume median diameter (DV0.5) for the water-based sprays ranged from 4.7 to 211 µm, depending on the sprayer, and the percent of spray volume contained in droplets less than 20 µm (%vol <20 µm) ranged between 0.5% and 98.9%. The DV0.5 measurements for the oil-based sprays ranged from 9.4 to 125.3 µm and the %vol <20 µm ranged between 2.4% and 97.9%. The correlations between the DV0.5 measured by the laser system (DV0.5-laser) and the mass median diameter, Sauter diameter, and DV0.5 measured by the AIMS probe were all significant. Generally, the slide DV0.5s were numerically similar to the DV0.5 from the laser system and the Sauter diameter from the Army Insecticide Measuring System probe. There was less consistent agreement between the % <32 µm values obtained from the slides and those from the other 2 samplers. The information presented can be used by applicators to select the sprayer that produces the droplet-size spectra needed for their particular application situation.
Mojo: A Dynamic Optimization System

Dynamic optimization systems have the flexibility to adapt program execution to changing scenarios and differing hardware configurations. Previous work has shown that this flexibility can be exploited for sizeable performance improvement. Yet, the work to date has been chiefly targeted towards running the SPEC benchmarks on scientific workstations. We contend that this technology is also important to the desktop computing environment where running large, complex commercial software applications is commonplace. In this paper, we describe work that has been accomplished over the past several months at Microsoft Research to design and develop a dynamic software optimization system called Mojo. In particular, we present implementation details for the x86 architecture -- Mojo's initial target. Additionally, we present our experience in supporting exception handling and multi- threaded applications on Windows 2000 along with preliminary performance measurements.
Assessing Actions and Feelings Related to Multidimensional Well-Being: Validation of the I COPPE Actions and Feelings Scales

of a dissertation at the University of Miami. Dissertation supervised by Dean Isaac Prilleltensky. No. of pages in text. (124) The purpose of this study was to validate two new scales on actions and feelings for well-being in key areas of life. The I COPPE Actions Scale and the I COPPE Feelings Scale measure Interpersonal, Community, Occupational, Physical, Psychological, and Economic (I COPPE) well-being. A total of 478 University of Miami employees provided online responses to the I COPPE Actions and Feelings Scales. Confirmatory factor analysis was used to examine the a priori hypothesized correlated six-factor structure for responses to the I COPPE Actions and Feelings Scales. Multiple group confirmatory factor analysis was used to evaluate measurement invariance across gender for the I COPPE Actions and Feelings Scales. There was strong empirical evidence to support the hypothesized factor structure of the I COPPE Actions Scale. While there was inadequate empirical evidence to support the hypothesized factor structure of the I COPPE Feelings Scale, a slightly modified model achieved acceptable fit. Partial strict invariance with equal factor variances and covariances was met across gender for the I COPPE Actions Scale; however, measurement invariance did not hold across gender for the I COPPE Feelings Scale. The ability of these scales to measure multidimensional well-being through actions and feelings in two separate but conceptually related measures is a potential contribution to the growing body of literature on well-being.