Miracle-dz/UniFAR-FTUs huggingface dataset:
The Long and the Short of it: Evidence of Year-End Price Manipulation by Short Sellers

We identify a setting in which there is a predictable incentive for short sellers to manipulate prices, and we find patterns consistent with short sellers manipulating prices. Specifically, we find that stocks with high short interest experience abnormally low returns on the last trading day of the year. This effect is strongest among stocks that are easily manipulated and during the last hour of trading. Further, this effect reverses at the beginning of the year, consistent with the temporary nature of price manipulation. We show that hedge funds’ portfolios are closely related to market-wide short interest, suggesting that hedge funds, with their convex compensation structures, may generate the patterns we observe. In additional analysis, we find that larger price effects are associated with higher idiosyncratic volatility, offering a potential explanation for why temporary price effects are allowed to persist in the presence of rational arbitrageurs, but we find no evidence to suggest that extended non-trading-day holding periods play a role in the magnitude of the effects. Finally, we provide evidence of mutual funds and short sellers avoiding each other, and we show that downward pressure by short sellers is outweighed by upward pressure by buyers. In other words, since short sellers’ incentives are mirrored by buyers’ incentives in the opposite direction, our experiment provides evidence that short sellers manipulate prices in much the same way buyers do, and manipulation by short sellers is no stronger than manipulation by buyers.
Zooming In on Paid Search Ads - A Consumer-Level Model Calibrated on Aggregated Data

We develop a two-stage consumer-level model of paid search advertising response based on standard aggregated data provided to advertisers by major search engines such as Google or Bing. The proposed model uses behavioral primitives in accord with utility maximization and allows recovering parameters of the heterogeneity distribution in consumer preferences. The model is estimated on a novel paid search data set that includes information on the ad copy. To that end, we develop an original framework to analyze composition and design attributes of paid search ads. Our results allow us to correctly evaluate the effects of specific ad properties on ad performance, taking consumer heterogeneity into account. Another benefit of our approach is allowing recovery of preference correlation across the click-through and conversion stage. Based on the estimated correlation between price-and position-sensitivity, we propose a novel contextual targeting scheme in which a coupon is offered to a consumer depending on the position in which the paid search ad was displayed. Our analysis shows that total revenues from conversion can be increased using this targeting scheme while keeping cost constant.
The accidental experts : a study of FE teacher educators, their professional development needs and ways of supporting these

The aim of the research was to investigate the professional development and support needs of new and experienced teacher educators in the FE system in the South of England. This thesis explored the ways in which teacher educators were recruited, inducted and supported, in order to gain insights into how these aspects of practice could be strengthened or improved in future. The first chapter introduces the purpose and themes of the thesis and subsequent chapters describe the culture, policy and context of the FE system; the identity and role of teacher educators; the methodology used in the research; followed by data analysis and discussion of findings. Ten experienced teacher educators were interviewed and their comments analysed. This was followed by an online survey to 270 teacher educators, 70 of whom responded. A focus group was held with 15 teacher educators in order to elicit further perspectives and to help triangulate the data. A further link between interviews and survey was made when some of the views, which the interviewees held in common, were used as statements in the survey. The survey participants were then asked to what extent they agreed or disagreed with the interviewees’ statements. This thesis argues that teacher educators in FE are a little known group whose professional needs are not considered systematically and for whom there are no substantive professional standards - despite teacher educators’ central role in the education of new teachers. Certain essential characteristics are required in order to become a ‘good’ teacher educator, as well as opportunities for professional development, including scholarship and research. However, the learning cultures and work contexts of the FE system, which shape the identity and role of teacher educators, also make their work more challenging due to the differing - or absent - support that teacher educators receive.
Epidemiology of recurrent venous thrombosis

Venous thrombosis, including deep vein thrombosis and pulmonary embolism, is a common disease that frequently recurs. Recurrence can be prevented by anticoagulants, but this comes at the risk of bleeding. Therefore, assessment of the risk of recurrence is important to balance the risks and benefits of anticoagulant treatment. This review briefly outlines what is currently known about the epidemiology of recurrent venous thrombosis, and focuses in more detail on potential new risk factors for venous recurrence. The general implications of these findings in patient management are discussed.
Solving Parameter Identification Problem by Hybrid Particle Swarm Optimization

Ordinary differential equations have been a useful tool for describing the behavior of wide variety of dynamic physical systems. In this study, a method for solving parameter identification problem for ordinary second order differential equations using hybrid Nelder-Mead simplex search and particle swarm optimization (NM-PSO) approach is presented. Experiments using two case problems are presented and compared with the best known solutions reported in the literature. The comparison results demonstrate that NM-PSO produced better estimated results with respect to previous findings from particle swarm optimization and genetic algorithm.
Basic Fibroblast Growth Factor Confers a Less Malignant Phenotype in MDA-MB-231 Human Breast Cancer Cells 1

Basic fibroblast growth factor (FGF-2) expression is associated with a more differentiated phenotype, earlier stage of disease, and a better prognosis in breast cancer patients. To determine whether expression of FGF-2 can cause a less malignant phenotype, we engineered MDA-MB231 cells, a highly dedifferentiated, invasive breast cancer cell line, to express different isoforms of FGF-2. Cells expressed either cytoplasmic, nuclear, or a combination of both FGF-2 isoforms. Western blots of 2M NaCl washes and of conditioned medium demonstrated that these cells did not export FGF-2. Cells expressing FGF-2 had levels of fibroblast growth factor receptors equivalent with those of control cells. Transformation was assayed by anchorage-independent colony formation and tumor formation in athymic mice. All of the constructs expressing various FGF-2 isoforms had a 60–70% reduction in colony formation in soft agar, but only cells expressing theMr 18,000 FGF-2 isoform formed fewer and smaller tumors in mice. To determine potential mechanisms responsible for a less malignant phenotype, experiments measuring invasion in Matrigel, the secretion of matrix metalloprotease activity and migration in a modified Boyden chamber and in a patch wound motility assay were carried out. Cells expressing theMr 18,000 cytoplasmic FGF-2 moiety had a 45% decrease in invasion in Matrigel compared to vector-transfected controls. Cells expressingMr 18,000 FGF-2 had an increase inMr 97,000 and Mr 48,000 collagenase, demonstrating that the decreased invasive potential was not due to a down-regulation of gelatinolytic or caseinolytic matrix metalloproteinases. However, motility was decreased in both assays, primarily in cells expressingMr 18,000 FGF-2, whereas exogenous recombinant human FGF-2 had no effect. These studies demonstrate for the first time that FGF-2 expression can cause a less malignant phenotype in breast cancer cells, possibly as a result of decreased motility and invasion.
Analysis of Transition Events in Health Insurance Coverage. Washington, DC: Mathematica Policy Research

This report analyzes factors that contribute to people losing or gaining health care coverage, prioritizes future research areas, and offers findings to inform future health care reform policy. Over time, the number of people lacking health insurance coverage in the U.S. is determined by dynamic processes. Comparatively few of the uninsured remain in that state indefinitely, but those who gain coverage are offset by insured persons who lose coverage. Achieving a long-term reduction in the number uninsured will require reducing the rate at which people lose coverage and increasing the rate at which they (re)gain coverage.
A model for the estimation of storm losses and the identification of severe winter storms in Germany

Abstract. A storm loss model for Germany is presented. Input data to the model are the daily maximum gust wind speeds measured at stations distributed over the country. The individual daily peak gust values are scaled with the local climatological upper 2% quantile at each station. This scaling serves to take local conditions at the stations into account, and thus permits a simple spatial interpolation of the storm field. The next step is the computation of a loss index for each storm. It is based on the excess of (scaled) wind speed over the upper 2% quantile, and on population numbers in the individual districts within Germany, with the latter serving as a proxy for the spatial distribution of values that could be affected by a storm. Using wind speeds in excess of the percentile value also serves to take spatial heterogeneity of vulnerability against storms into account. The aggregated storm index gives an estimate of the severity of an individual storm. Finally, the relation between actual loss produced by a storm and the index is estimated using published annual insurance loss due to windstorm in Germany. Index values are accumulated for each year, and the relation to actual loss is computed. The average ratio for the whole reference period is eventually used. It is shown that the interannual variability of storm-related losses can be reproduced with a correlation coefficient of r = 0.96, and even individual storm damages can be estimated. Based on these evaluations we found that only 50 storms account for about 80% of insured storm losses between 1970 and 1997.
Embedded vision-based SLAM: A model-driven approach

This paper presents a model driven design approach for embedded heterogeneous systems. This method relies on the use of a Data-Flow model to describe an application structure and behavior. This model is then transformed through an iterative process to refine an execution model using hardware/software resources described by the user. This approach is composed of a set of tasks and their matching validation to ensure tracking of the implementation validity and performance along the design flow. This method is then used for the implementation of a full vision-based Simultaneous Localization And Mapping systems on an heterogeneous architecture. The SLAM algorithm aims at computing the position of a mobile and build a map of its surrounding using a set of sensors. In vision-based SLAM this position is estimated by tracking features along time in images acquired by a set of cameras. The algorithm relies on several compute-intensive tasks for both vision and estimation making this problem hard to solve on resources limited platform such as embedded systems.
Between Professional Dedication and Corporate Design: Exploring Forms of New Concept Development in Consultancies

Like any producer who seeks to renew their products, management consultancies need to generate research and develop efforts to keep their portfolio in tune with market demand, a process we refer to as "new concept development. " Innovation literature suggests that such a development process involves moving through specific activities and inevitably generates managerial tensions. However, a concept's lack of a material component and the specific characteristics of consultancy work brings notable consequences for the process of developing a repertoire to support the commercialization and implementation of a new concept. To get an understanding about the way such innovation processes take shape in consultancies, this study explores different forms of new concept development and their managerial problems.
Adaptor Molecule Crk Is Required for Sustained Phosphorylation of Grb2-Associated Binder 1 and Hepatocyte Growth Factor–Induced Cell Motility of Human Synovial Sarcoma Cell Lines

Activation of the c-Met receptor tyrosine kinase through its ligand, hepatocyte growth factor (HGF), promotes mitogenic, motogenic, and morphogenic cellular responses. Aberrant HGF/c-Met signaling has been strongly implicated in tumor cell invasion and metastasis. Both HGF and its receptor c-Met have been shown to be overexpressed in human synovial sarcoma, which often metastasizes to the lung; however, little is known about HGF-mediated biological effects in this sarcoma. Here, we provide evidence that Crk adaptor protein is required for the sustained phosphorylation of c-Met-docking protein Grb2-associated binder 1 (Gab1) in response to HGF, leading to the enhanced cell motility of human synovial sarcoma cell lines SYO-1, HS-SY-II, and Fuji. HGF stimulation induced the sustained phosphorylation on Y307 of Gab1 where Crk was recruited. Crk knockdown by RNA interference disturbed this HGF-induced tyrosine phosphorylation of Gab1. By mutational analysis, we identified that Src homology 2 domain of Crk is indispensable for the induction of the phosphorylation on multiple Tyr-X-X-Pro motifs containing Y307 in Gab1. HGF remarkably stimulated cell motility and scattering of synovial sarcoma cell lines, consistent with the prominent activation of Rac1, extreme filopodia formation, and membrane ruffling. Importantly, the elimination of Crk in these cells induced the disorganization of actin cytoskeleton and complete abolishment of HGF-mediated Rac1 activation and cell motility. Time-lapse microscopic analysis revealed the significant attenuation in scattering of Crk knockdown cells following HGF treatment. Furthermore, the depletion of Crk remarkably inhibited the tumor formation and its invasive growth in vivo. These results suggest that the sustained phosphorylation of Gab1 through Crk in response to HGF contributes to the prominent activation of Rac1 leading to enhanced cell motility, scattering, and cell invasion, which may support the crucial role of Crk in the aggressiveness of human synovial sarcoma. (Mol Cancer Res 2006;4(7):499–510)
Mixed membership analysis of genome-wide expression data

Learning latent expression themes that best express complex patterns in a sample is a central problem in data mining and scientific research. For example, in computational biology we seek a set of salient gene expression themes that explain a biological process, extracting them from a large pool of gene expression profiles. In this paper, we introduce probabilistic models to learn such latent themes in an unsupervised fashion. Our models capture contagion, i.e., dependence among multiple occurrences of the same feature, using a hierarchical Bayesian scheme. Contagion is a convenient analytical formalism to characterize semantic themes underlying observed feature patterns, such as biological context. We present model variants tailored to different properties of biological data, and we outline a general variational inference scheme for approximate posterior inference. We validate our methods on both simulated data and realistic high-throughput gene expression profiles via SAGE. Our results show improved predictions of gene functions over existing methods based on stronger independence assumptions, and demonstrate feasibility of a promising hierarchical Bayesian formalism for soft clustering and latent aspects analysis.
The Right Supramarginal Gyrus Is Important for Proprioception in Healthy and Stroke-Affected Participants: A Functional MRI Study

Human proprioception is essential for motor control, yet its central processing is still debated. Previous studies of passive movements and illusory vibration have reported inconsistent activation patterns related to proprioception, particularly in high-order sensorimotor cortices. We investigated brain activation specific to proprioception, its laterality, and changes following stroke. Twelve healthy and three stroke-affected individuals with proprioceptive deficits participated. Proprioception was assessed clinically with the Wrist Position Sense Test, and participants underwent functional magnetic resonance imaging scanning. An event-related study design was used, where each proprioceptive stimulus of passive wrist movement was followed by a motor response of mirror copying with the other wrist. Left (LWP) and right (RWP) wrist proprioception were tested separately. Laterality indices (LIs) were calculated for the main cortical regions activated during proprioception. We found proprioception-related brain activation in high-order sensorimotor cortices in healthy participants especially in the supramarginal gyrus (SMG LWP z = 4.51, RWP z = 4.24) and the dorsal premotor cortex (PMd LWP z = 4.10, RWP z = 3.93). Right hemispheric dominance was observed in the SMG (LI LWP mean 0.41, SD 0.22; RWP 0.29, SD 0.20), and to a lesser degree in the PMd (LI LWP 0.34, SD 0.17; RWP 0.13, SD 0.25). In stroke-affected participants, the main difference in proprioception-related brain activation was reduced laterality in the right SMG. Our findings indicate that the SMG and PMd play a key role in proprioception probably due to their role in spatial processing and motor control, respectively. The findings from stroke-affected individuals suggest that decreased right SMG function may be associated with decreased proprioception. We recommend that clinicians pay particular attention to the assessment and rehabilitation of proprioception following right hemispheric lesions.
Minimizing Queries for Active Labeling with Sequential Analysis

When building datasets for supervised machine learning problems, data is often labelled manually by human annotators. In domains like medical imaging, acquiring labels can be prohibitively expensive. Both active learning and crowdsourcing have emerged as ways to frugally label datasets. In active learning, there has been recent interest in algorithms that exploit the data’s structure to direct querying. When learning from crowds, one must balance the accuracy and cost of different oracles when gathering labels; weak oracles are assumed to be most accurate when labeling samples from label-homogeneous regions of space. In this thesis, we explore how the data’s structure can be leveraged for both of these techniques. The sequential probability ratio test (SPRT) provides the backbone for our contributions. Using the SPRT, we provide a cluster-based active learning algorithm to find a small, homogeneous partitioning of the data. We also use the SPRT to measure the confidence of a weak oracle’s label by analyzing its estimates on neighboring labels. The optimality of the SPRT allows the algorithms to inherently minimize the average number of queries required before their termination.
A model for managing intellectual capital to generate wealth : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Business, Massey University, Albany, New Zealand

In an increasingly competitive environment an organisation‟s intellectual capital is the key to its ability to generate wealth. The intangibility of intellectual capital makes it difficult to replicate and therefore it is a crucial differentiator in the business environment. The objective of the research was to develop and test a model for the managing of intellectual capital. An examination of the literature provided the foundation for developing a model to illustrate the various facets an organisation must consider when managing intellectual capital. The Intellectual Capital Management Model specifies that management of intellectual capital is derived from the corporate vision and strategy. Three sources of intellectual capital – human capital, internal capital and external capital – contribute to the outcomes essential to differentiate the organisation in the marketplace. Within each of the three sources of intellectual capital, aspects of intellectual capital management were identified and described, according to the research literature. A case study approach was used to assess the extent to which an organisation was managing its intellectual capital. Nine chief executives of the independent business units in a large New Zealand company were interviewed to understand why and how they managed the company‟s intellectual capital. Additionally, 18 employees were interviewed and 44 employees were surveyed in a questionnaire, to determine their views about issues relating to intellectual capital, especially sharing knowledge within the company. Findings indicated that although most of the aspects of the Model were present in the company, conscious management of intellectual capital was not occurring. Metrics was one characteristic frequently mentioned in the literature, but not evident in practice. Behavioural changes and socialisation were two characteristics that emerged strongly from the interviews, but were not widely addressed in the literature. From the perspective of the theoretical model greater attention should be given to behavioural changes and the importance of socialisation; and from the view of the practice model, management needs to address the issue of metrics.
Weblog Analysis for Predicting Correlations in Stock Price Evolutions

We use data extracted from many weblogs to identify the underlying relations of a set of companies in the Standard and Poor (S\&P) 500 index. We define a pairwise similarity measure for the companies based on the weblog articles and then apply a graph clustering procedure. We show that it is possible to capture some interesting relations between companies using this method. As an application of this clustering procedure we propose a cluster-based portfolio selection method which combines information from the weblog data and historical stock prices. Through simulation experiments, we show that our method performs better (in terms of risk measures) than cluster-based portfolio strategies based on company sectors or historical stock prices. This suggests that the methodology has the potential to identify groups of companies whose stock prices are more likely to be correlated in the future.