Miracle-dz/UniFAR-FTUs huggingface dataset:
Specification Analysis of Affine Term Structure Models

In this paper, we explore the features of affine term structure models that are empirically important for explaining the joint distribution of yields on short and long-term interest rate swaps. We begin by showing that the family of N-factor affine models can be classified into N+1 non-nested sub-families of models. For each sub-family, we derive a maximal model with the property that every admissible member of this family is equivalent to or a nested special case of our maximal model. Second, using our classification scheme and maximal models, we show that many of the three-factor models in the literature impose potentially strong over-identifying restrictions on the joint distribution of short- and long-term rates. Third, we compute simulated method-of-moments estimates for several members of one of the four branches of three-factor models, and test the over-identifying restrictions implied by these models. We conclude that many of the extant affine models in the literature fail to describe important features of the distribution of long- and short- term rates. The source of the model misspecification is shown to be overly strong restrictions on the correlations among the state variables. Relaxing these restrictions leads to a model that passes several goodness-of-fit tests over our sample period.
Median Fusion of Bayesian Depth Maps for Automatic Model Creation

A method for dense reconstruction of a three-dimensional scene from a video sequence is presented. Together with an automatic calibration algorithm it forms a system for automatic dense reconstruction from uncalibrated video sequences. First, depth maps are calculated in a Bayesian framework, guided by hypotheses from several sources, including correlation based stereo, the Delaunay triangulation of visible features and automatically derived planes. The depth maps are then fused together into a consistent model with a novel approach herein referred to as ‘median fusion’. The idea behind median fusion is to assign the depth of all pixels in a number of output depth maps in such a manner that for any surface point, the number of input depth maps that indicate a greater depth is in balance with the number of input depth maps that indicate a smaller depth. Positive results are presented for a variety of real world sequences and the generality, scalability and robustness of the approach is demonstrated. The method can be used by itself or as an initialisation method for bundle adjustment or PDE-driven surface evolution.
Identification of Mouse Serum miRNA Endogenous References by Global Gene Expression Profiles

MicroRNAs (miRNAs) are recently discovered small non-coding RNAs and can serve as serum biomarkers for disease diagnosis and prognoses. Lack of reliable serum miRNA endogenous references for normalization in miRNA gene expression makes single miRNA assays inaccurate. Using TaqMan® real-time PCR miRNA arrays with a global gene expression normalization strategy, we have analyzed serum miRNA expression profiles of 20 female mice of NOD/ShiLtJ (n = 8), NOR/LtJ (n = 6), and C57BL/6J (n = 6) at different ages and disease conditions. We identified five miRNAs, miR-146a, miR-16, miR-195, miR-30e and miR-744, to be stably expressed in all strains, which could serve as mouse serum miRNA endogenous references for single assay experiments.
Graduate overeducation as a sheepskin effect: evidence from Northern Ireland

This article examines the nature of graduate overeducation amongst a group of applicants to a graduate conversion programme. It was found that while a substantial proportion of earnings differentials were associated with a mismatch between individual skill levels and job requirements, wage gaps were still likely to occur should such mismatches be eliminated. The evidence suggests that graduate wage levels are heavily related to sheepskin effects associated with the attainment of jobs with graduate level entry requirements. These sheepskin effects suggest that the process of job categorization is arbitrary in nature, with stated job requirements somewhat independent of actual skill requirements. The analysis suggests that graduate overeduction is better understood within the context of both skill and categorization mismatches as opposed to skill matches alone.
“Manning Up” to be a Good Father: Hybrid Fatherhood, Masculinity, and U.S. Responsible Fatherhood Policy

Drawing on theories of masculinities, I analyze how a U.S. government funded “responsible fatherhood” program utilized a political discourse of hybrid masculinity to shape disadvantaged men’s ideas of successful fathering. Using data from three sources that uniquely traces how this discourse traveled from policy to program implementation—including analysis of the curriculum, in-depth interviews with 10 staff, and in-depth interviews and focus groups with 64 participating fathers—I theorize hybrid fatherhood. As a discourse of paternal involvement that incorporates stereotypically feminine styles such as emotional expressiveness, hybrid fatherhood discursively reconfigures patriarchy by drawing distinctions between mothering and fathering and dominant and subordinate forms of masculinity as they relate to men’s parenting. I analyze how the promotion of hybrid fatherhood for poor men of color legitimates and sustains gender, race, and class inequalities through U.S. welfare policy.
Chambolle's Projection Algorithm for Total Variation Denoising

Denoising is the problem of removing the inherent noise from an image. The standard noise model is additive white Gaussian noise, where the observed image f is related to the underlying true image u by the degradation model f = u + �, andis supposed to be at each pixel inde- pendently and identically distributed as a zero-mean Gaussian random variable. Since this is an ill-posed problem, Rudin, Osher and Fatemi introduced the total variation as a regularizing term. It has proved to be quite efficient for regularizing images without smoothing the bound- aries of the objects. This paper focuses on the simple description of the theory and on the implementation of Cham- bolle's projection algorithm for minimizing the total variation of a grayscale image. Further- more, we adapt the algorithm to the vectorial total variation for color images. The implementa- tion is described in detail and its parameters are analyzed and varied to come up with a reliable implementation.
Knowledge Transfer in Inter-Firm Collaborations and Productive Efficiency: A Contingency Approach

This paper examines the performance implications of knowledge transfers across the organizational sub-units of a service franchise chain. Unlike previous studies in the field of inter-organizational learning, we directly address and measure the employment of knowledge transfer mechanisms involved in the learning process: regular communication, personal acquaintances, and meetings. Most importantly, we analyze the contribution of specific factors to inter-organizational learning by testing the conditions under which knowledge transfers have desired effects on performance. Drawing on the concept of absorptive capacity, we hypothesize that franchisees will achieve higher efficiency advantages from inter-organizational knowledge transfers if they (1) occupy central network positions, (2) build long enduring relationships with the franchise system, and (3) hold high amounts of tacit knowledge that is idiosyncratic to local markets. The data provides preliminary support for most of the hypotheses. Implications for research and practice are derived. (141 words)
Detecting Performance Anomalies in Global Applications

Understanding real, large distributed systems can be as difficult and important as building them. Complex modern applications that span geographic and organizational boundaries confound performance analysis in challenging new ways. These systems clearly demand new analytic methods, but we are wary of approaches that suffer from the same problems as the systems themselves (e.g., complexity and opacity). This paper shows how to obtain valuable insight into the performance of globally-distributed applications without abstruse techniques or detailed application knowledge: Simple queueing-theoretic observations together with standard optimization methods yield remarkably accurate performance models. The models can be used for performance anomaly detection, i.e., distinguishing performance faults from mere overload. This distinction can in turn suggest both performance debugging tools and remedial measures. Extensive empirical results from three production systems serving real customers--two of which are globally distributed and span administrative domains-- demonstrate that our method yields accurate performance models of diverse applications. Our method furthermore flagged as anomalous an episode of a real performance bug in one of the three systems.
On impact-oriented automatic resolution of pervasive context inconsistency

Context-awareness is a capability that allows applications in pervasive computing to adapt themselves continuously to changing contexts of their environments. However, contexts from physical environments may be inconsistent. It affects the correctness of these applications. Existing resolution strategies for context inconsistency have diverse adverse impacts on the context-awareness of applications, such as feeding different amounts of contexts to the applications. In this paper, we examine the impacts of inconsistency resolution and study the extent to which their effects on context-awareness can be reduced. We conduct simulation experiments of two pervasive computing applications. The experimental results show that existing inconsistency resolution strategies adversely affect the context-awareness of applications. This motivates the importance of deploying an impact-oriented approach to respect context-awareness in inconsistency resolution.
The Use of Radiation Therapy in the Management of Selected Patients with Atypical Lipomas

Background and Objectives. Atypical lipomas are uncommon, slow-growing benign tumors. While surgery has been the primary treatment modality, we have managed some patients with radiation (RT) as a component of the treatment and have reported their outcomes in this study. Methods. A retrospective review of all cases of extremity and trunk atypical lipomas in The Sarcoma Database at the study institution was conducted. Results. Thirteen patients were identified. All patients underwent surgical resection at initial presentation and received pre- or postoperative radiation for subtotal resection (n = 2), local recurrence (n = 8), or progressive disease (n = 3). The median total radiation dose was 50 Gy. Median followup was 65.1 months. All patients treated with RT remained free of disease at the last followup. No grade 3 or higher late toxicity from radiation was observed. No cases of tumor dedifferentiation occurred. Conclusion. For recurrent or residual atypical lipomas, a combination of reexcision and RT can provide long-term local control with acceptable morbidity. For recurrent tumors, pre-op RT of 50 Gy appears to be an effective and well-tolerated management approach.
Towards a Robust Spatio-Temporal Interest Point Detection for Human Action Recognition

Spatio-temporal salient features are widely being usedfor compact representation of objects and motions in video,especially for event and action recognition. The existingfeature extraction methods have two main problems: First,they work in batch mode and mostly use Gaussian (linear)scale-space filtering for multi-scale feature extraction. This linear filtering causes the blurring of the edges and salient motions which should be preserved for robust eature extraction.Second, the environmental motion and ego disturbances(e.g., camera shake) are not usually differentiated.These problems result in the detection of false features nomatter which saliency criteria is used. To address theseproblems, we developed a non-linear (scale-space) filteringapproach which prevents both spatial and temporal dislocations.This model can provide a non-linear counterpart ofthe Laplacian of Gaussian to form the conceptual structuremaps from which multi-scale spatio-temporal salient features are extracted. Preliminary evaluation shows promising result with false detection being removed.
Serious subjects: A test of the seriousness technique to increase participant motivation in political science experiments

A central concern for experimental studies is participant motivation, which is crucial for internal validity. When participants are not committed to the task, internal validity diminishes because responses might not be authentic. This study introduces and tests the seriousness technique as a method for increasing participant investment in political science experiments that use student samples. The seriousness technique aims at creating a sense of responsibility by telling students that their participation is important because science needs quality data. Results from a computer-assisted foreign policy decision-making experiment show that the seriousness technique increased the degree of information participants access during the foreign policy simulation and the time they spent on the study. These findings suggest that political scientists who use student samples in their experiments can nurture serious subjects by employing the seriousness technique. It is argued that the results should be of interest not only to experimentalists but also to all scholars who use human subjects, including survey researchers, in their research.
Dynamic Analysis of the Java Virtual Machine Method Invocation Architecture

Platform independent dynamic analysis has been shown to be an important technique for performance analysis and workload characterization of programs that run on the Java Virtual Machine. In this paper we explore how this methodology can me used to study method invocation. We identify differences in program behaviour and propose a metric to predict dynamic compilation efficiency. Key-Words: Java Virtual Machine, method invocation, performance analysis, workload characterization
Model-based design of transverse wall oscillations for turbulent drag reduction

Abstract Over the last two decades, both experiments and simulations have demonstrated that transverse wall oscillations with properly selected amplitude and frequency can reduce turbulent drag by as much as $40\hspace{0.167em} \% $ . In this paper, we develop a model-based approach for designing oscillations that suppress turbulence in a channel flow. We utilize eddy-viscosity-enhanced linearization of the turbulent flow with control in conjunction with turbulence modelling to determine skin-friction drag in a simulation-free manner. The Boussinesq eddy viscosity hypothesis is used to quantify the effect of fluctuations on the mean velocity in flow subject to control. In contrast to the traditional approach that relies on numerical simulations, we determine the turbulent viscosity from the second-order statistics of the linearized model driven by white-in-time stochastic forcing. The spatial power spectrum of the forcing is selected to ensure that the linearized model for uncontrolled flow reproduces the turbulent energy spectrum. The resulting correction to the turbulent mean velocity induced by small-amplitude wall movements is then used to identify the optimal frequency of drag-reducing oscillations. In addition, the control net efficiency and the turbulent flow structures that we obtain agree well with the results of numerical simulations and experiments. This demonstrates the predictive power of our model-based approach to controlling turbulent flows and is expected to pave the way for successful flow control at higher Reynolds numbers than currently possible.
Efficient Content Locating in Dynamic Peer-to-Peer Networks

The Peer-to-Peer (P2P) computing model has recently been recognized as a more natural and flexible approach to sharing resources. However, a fundamental issue, content locating (or content search) in P2P-based applications has not yet been successfully resolved. This thesis documents the design and implementation of a novel architecture, which combines the advantages of cluster infrastructure and Super-Peer overlay to address the scalability, robustness and efficiency issues in existing unstructured P2P systems. The first component of our architecture is called the TBCP model, which constructs a set of interconnected clusters, where each cluster forms a bounded-depth tree, and each peer node acts as a tree leaf. The duties of maintaining cluster topologies and providing search services are separated and re-balanced to address heterogeneity. The COOL model, the second component of our architecture, then constructs light-weight interconnected overlay networks, by following simple classification and mapping rules. Peers are connected in both vertical direction (within the cluster domain) and horizontal direction (within the Super-Peer overlay domain). We also propose two search algorithms, known as cluster search and overlay search, which works seamlessly with our new models to provide efficient and low-cost content locating services. Our experiments and analysis prove that our new architecture achieves significantly better scalability and efficiency than a basic unstructured model.
Incorporating Anthropogenic Influences into Fire Probability Models: Effects of Human Activity and Climate Change on Fire Activity in California

The costly interactions between humans and wildfires throughout California demonstrate the need to understand the relationships between them, especially in the face of a changing climate and expanding human communities. Although a number of statistical and process-based wildfire models exist for California, there is enormous uncertainty about the location and number of future fires, with previously published estimates of increases ranging from nine to fifty-three percent by the end of the century. Our goal is to assess the role of climate and anthropogenic influences on the state’s fire regimes from 1975 to 2050. We develop an empirical model that integrates estimates of biophysical indicators relevant to plant communities and anthropogenic influences at each forecast time step. Historically, we find that anthropogenic influences account for up to fifty percent of explanatory power in the model. We also find that the total area burned is likely to increase, with burned area expected to increase by 2.2 and 5.0 percent by 2050 under climatic bookends (PCM and GFDL climate models, respectively). Our two climate models show considerable agreement, but due to potential shifts in rainfall patterns, substantial uncertainty remains for the semiarid inland deserts and coastal areas of the south. Given the strength of human-related variables in some regions, however, it is clear that comprehensive projections of future fire activity should include both anthropogenic and biophysical influences. Previous findings of substantially increased numbers of fires and burned area for California may be tied to omitted variable bias from the exclusion of human influences. The omission of anthropogenic variables in our model would overstate the importance of climatic ones by at least 24%. As such, the failure to include anthropogenic effects in many models likely overstates the response of wildfire to climatic change.
Numerical bifurcation analysis of double +1 multiplier in ℤ3-symmetric maps

ABSTRACT We consider a resonance 1:1 bifurcation with -symmetry in a discrete-time dynamical system. We employ standard normalization and center manifold reduction techniques. With this, we obtain the normal form up to cubic order and also explicit formulas for the critical normal form coefficients. We provide an implementation of our algorithm that can be used in the numerical bifurcation toolbox MATCONTM. We illustrate our analysis with a Cournot triopoly model from economics.
Using X-ray observations to explore the binary interaction in Eta Carinae

We study the usage of the X-ray light curve, column density towards the hard X-ray source, and emission measure (density square times volume), of the massive binary system η Carinae to determine the orientation of its semimajor axis. The source of the hard X-ray emission is the shocked secondary wind. We argue that, by itself, the observed X-ray flux cannot teach us much about the orientation of the semimajor axis. Minor adjustment of some unknown parameters of the binary system allows to fit the X-ray light curve with almost any inclination angle and orientation. The column density and X-ray emission measure, on the other hand, impose strong constrains on the orientation. We improve our previous calculations and show that the column density is more compatible with an orientation where for most of the time the secondary – the hotter, less massive star – is behind the primary star. The secondary comes closer to the observer only for a short time near periastron passage. The 10-week X-ray deep minimum, which results from a large decrease in the emission measure, implies that the regular secondary wind is substantially suppressed during that period. This suppression is most likely resulted by accretion of mass from the dense wind of the primary luminous blue variable star. The accretion from the equatorial plane might lead to the formation of a polar outflow. We suggest that the polar outflow contributes to the soft X-ray emission during the X-ray minimum; the other source is the shocked secondary wind in the tail. The conclusion that accretion occurs at each periastron passage, every five and a half years, implies that accretion had occurred at a much higher rate during the Great Eruption of η Car in the 19th century. This has far reaching implications for major eruptions of luminous blue variable stars.
"Doctors Do Too Little Technology": A Longitudinal Field Study of an Electronic Healthcare System Implementation

With the strong ongoing push toward investment in and deployment of electronic healthcare (e-healthcare) systems, understanding the factors that drive the use of such systems and the consequences of using such systems is of scientific and practical significance. Elaborate training in new e-healthcare systems is not a luxury that is typically available to healthcare professionals---i.e., doctors, paraprofessionals (e.g., nurses) and administrative personnel---because of the 24 × 7 nature and criticality of operations of healthcare organizations, especially hospitals, thus making peer interactions and support a key driver of or barrier to such e-healthcare system use. Against this backdrop, using social networks as a theoretical lens, this paper presents a nomological network related to e-healthcare system use. A longitudinal study of an e-healthcare system implementation, with data gathered from doctors, paraprofessionals, administrative personnel, patients, and usage logs lent support to the hypotheses that: (1) ingroup and outgroup ties to doctors negatively affect use in all user groups; (2) ingroup and outgroup ties to paraprofessionals and administrative personnel positively affect use in both those groups, but have no effect on doctors' use; and (3) use contributes positively to patient satisfaction mediated by healthcare quality variables---i.e., technical quality, communication, interpersonal interactions, and time spent. This work contributes to the theory and practice related to the success of e-healthcare system use in particular, and information systems in general.