Miracle-dz/UniFAR-FTUs huggingface dataset:
The Virtual Focus Group: A Modern Methodology for Facial Attractiveness Rating

Background: Traditional focus groups have been essential to facial aesthetics research. Although they are currently the criterion standard in acquiring facial attractiveness ratings, they retain many shortcomings. This study's objectives were twofold: to determine whether attractiveness scores obtained from a social network site correlate with those from the traditional focus group method; and to evaluate whether this methodology could be a superior tool in evaluating facial attractiveness. Methods: Forty facial portraits were rated for attractiveness scores using three different subject recruitment methods: traditional live focus groups (n = 123 raters), Internet-based rating (n = 857 raters), and a novel method using a combination of focus groups and a social network site (i.e., Facebook) (n = 1775 raters). Each facial image was scored on a 10-point Likert scale. Regression analysis compared each approach to the traditional method. Results: The methods varied in terms of data accrual time, rater demographics/ages, researcher's accessibility, necessity for subject incentives, researcher labor, and rater effort/accuracy. A strong correlation (0.922) existed between the online social network-based rating and focus group method. A minimum of 992 raters achieved stabilization of the attractiveness scores using social network–based rating. Conclusions: This study shows significant advantages to using a social network site–based method over both Internet-based rating and traditional focus groups for evaluating facial attractiveness. The main benefits include exponential increase in raters, minimized researcher time/labor, rater scores comparable to those of the focus group method, nonnecessity of rater monetary incentives, and selectable demographics/ages of raters. CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, II.
Approximate Max Cut

We had encountered in the previous lecture examples of using rounding techniques for approximating discrete optimization problems. So far, we had seen such techniques when the relaxed optimization problem is a linear program. Interestingly, it is currently known how to solve optimization problems that are considerably more general than linear programs. Specifically, one can solve convex programming. Here the feasible region is convex. How to solve such an optimization problems is outside the scope of this course. It is however natural to ask what can be done if one assumes that one can solve such general continuous optimization problems exactly. In the following, we show that (optimization problem) max cut can be relaxed into a weird continuous optimization problem. Furthermore, this semi-definite program can be solved exactly efficiently. Maybe more surprisingly, we can round this continuous solution and get an improved approximation.
Return-based style analysis with time-varying exposures

Abstract This paper focuses on the estimation of mutual fund styles by return-based style analysis. Often the investment style is assumed to be constant through time. Alternatively, time variation is sometimes implicitly accounted for by using rolling regressions when estimating the style exposures. The former assumption is often contradicted empirically, and the latter is inefficient due to its ad hoc chosen window size. Here, the Kalman filter is used to model time-varying exposures of mutual funds explicitly. This leads to a testable model and more efficient use of the data, which reduces the influence of spurious correlation between mutual fund returns and style indices. Several stylized examples indicate that more reliable style estimates can be obtained by modelling the style exposure as a random walk, and estimating the coefficients with the Kalman filter. The differences with traditional techniques are substantial in these stylized examples. The results from the empirical analyses indicate that the structural model estimated by the Kalman filter improves style predictions and influences results on performance measurement.
Advertising, Concentration, and Profitability: The Simultaneity Problem

This article examines the identification of a system of equations which seeks to explain industrial profitability, concentration, and advertising intensity. We specify a concentration equation which reflects a dynamic adjustment to a long-run level which depends on the nature of entry conditions. The specification of the concentration equation is seen to be critical to the identification of the profitability equation. We test the model against a sample of input-output table detailed industries and find that the resulting estimates suggest the importance of avoiding the omission of relevant explanatory variables.
Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making.
Drosophila C-Terminal Src Kinase Negatively Regulates Organ Growth and Cell Proliferation through Inhibition of the Src, Jun N-Terminal Kinase, and STAT Pathways

ABSTRACT Src family kinases regulate multiple cellular processes including proliferation and oncogenesis. C-terminal Src kinase (Csk) encodes a critical negative regulator of Src family kinases. We demonstrate that the Drosophila melanogaster Csk ortholog, dCsk, functions as a tumor suppressor: dCsk mutants display organ overgrowth and excess cellular proliferation. Genetic analysis indicates that the dCsk−/− overgrowth phenotype results from activation of Src, Jun kinase, and STAT signal transduction pathways. In particular, blockade of STAT function in dCsk mutants severely reduced Src-dependent overgrowth and activated apoptosis of mutant tissue. Our data provide in vivo evidence that Src activity requires JNK and STAT function.
Comparison of Selected Ambiguity Removal Enhancement Techniques for Quikscat Level 2b Processing Algorithm

In this memo, we compare several techniques for improving ambiguity removal algorithm used in QuikSCAT Level 2B (L2B) processing algorithm. We have developed a stand-alone module capable of testing any technique one by one or all the techniques sequentially to perform a systematic and automated comparison. Outputs from the SeaPAC L2B science processor, i.e., retrieved winds, produced from the simulated Level 0 (L0) test data are used as inputs to this analysis; the ambiguity removal test program just repeats the ambiguity removal processing on this data using the various techniques.
Planar region depth filling using edge detection with embedded confidence technique and Hough transform

This paper proposes a method to fill in the missing range information of a planar region in the depth map of an image obtained from a commercial stereo vision system. The Digiclops stereo vision system, a fast three-camera module range measurement device, is used to provide the initial depth image. The edge information of the reference image from the system is extracted by the embedded confidence edge detection technique. Hough transform (HT) is then applied to the binary edge image to extract the straight-line boundary edges and the planar region is determined by the intersections of the edges. A 3-D planar equation is then used to determine the missing depth information of the region. The range data in the world coordinate system is projected back into the image coordinate system using a pixel-to-pixel projection algorithm. Results demonstrate the accuracy of the method in filling the missing depth information in a corrupted region in a depth map.
Abstraction Based Domain Ontology Extraction for Idea Creation

Idea creation is a complicated process in which it requires plenty of knowledge as support for specific domain. Creativity becomes a more and more important feature for idea nowadays when information in various domains is on an explosion. Likewise, it raises the requirement for the amount of domain knowledge as background. Many previous traditional approaches to domain specific idea creation are performed by domain experts based on their personal knowledge storage and manually information research, which are considered to be time consuming, uncreative and out of date prone processes. As a creative knowledge combination activity, idea creation requires a great number of knowledge covers from research to industry in the application domains. The integration of domain ontology and idea creation is a trend in creative computing research area. The introduction of domain ontology based approach into idea creation can bridge the gap between knowledge collection and mental thought, and improve the efficiency and creativity of idea creation. In this paper, we propose an abstraction method to support one of the essential parts in this field - domain ontology extraction. Abstraction techniques are explored, classified, selected and integrated while elements of domain ontology are defined including concepts and relations. Also, a framework and approach is specified for apply the method into domain ontology extraction with designed abstraction rules to support its automation. A case study on idea creation scenario particularly is represented to validate the feasibility and reusability of our proposed method. Furthermore, the mapping rules for transformation from abstracted results to domain ontology are discussed as an initial idea and further work.
Developing Countries' Stake in the Doha Round

In this paper we discuss the various aspects of the Doha Round of Multilateral Trade Negotiations in the WTO that offer potential benefits for developing countries. We then use the Michigan Model of World Production and Trade to simulate the economic effects on the major trading countries/regions of the reductions in tariffs, subsidies in agriculture, and barriers in services that may be negotiated in the Doha Round, as well as a variety of regional free trade agreements (FTAs). We estimate that an assumed reduction of post-Uruguay Round tariffs and other barriers on agricultural and industrial products and services by 33 percent in the Doha Round would increase world welfare by $686.4 billion, with significant gains for all industrialized and developing countries/regions. Regional agreements such as an APEC FTA, an ASEAN Plus 3 FTA, and a Western Hemisphere FTA would increase global and member country welfare, but by much less than the Doha multilateral trade round. There would also be trade diversion and detrimental welfare effects on some nonmember countries for the FTAs analyzed. The welfare gains from multilateral trade liberalization are therefore considerably greater than the gains from preferential trading arrangements and more uniformly positive for all countries.
Unpacking Multi-valued Symbolic Features and Classes in Memory-Based Language Learning

In supervised machine-learning applications to natural language processing, tasks are typically formulated as classiication tasks mapping multi-valued features to multi-valued classes. Memory-based or instance-based learning algorithms are suited for such representations , but they are not restricted to them; both features and classes may be unpacked in binary values. We demonstrate in a matrix of empirical tests on a range of natural language learning tasks that when using k = 1 in the k ? N N classiier kernel, binary unpacking of features and classes tends to be harmful to generalization accuracy. Unpacking features and classes causes the kernel classiier to rely on smaller sets of nearest neighbors, which generally leads to more misclassiications; only when the data is not sparse in the multi-valued case (when the average number of equidistant nearest neighbors is well above a handful), unpacking can lead to improved generalization accuracy. 1. Multi-valued versus Binary Features and Classes When a natural language processing task is formulated as a classiication task to be learned by a machine learning system, it is common to formulate it as a mapping from a (typically xed-length) vector of multi-valued symbolic features to a single multi-valued symbolic class. Features and classes typically represent positions in language strings, in which language items occur. Mappings from features to classes represent either symbol conversion tasks (e.g. from letters to phonemes, from ambiguous syntactic word classes to disambiguated classes) or segmentations of the input string represented in the features (e.g. boundaries between morphemes within words; boundaries between syntactic phrases in sentences) (Daelemans, 1995). Supervised machine learning systems tend to have particular biases towards the representation of features and classes they can handle in principle. For example, when representing a language processing task such as grapheme-phoneme conversion in a multi-layer feed-forward network trained with the back-propagation learning rule, some recoding is necessary from the basic multi-valued features (letters) and classes (phonemes and stress markers) into real-valued or binary input and output unit activation patterns (Sejnowski & Rosenberg, 1987; Dietterich et al., 1995). Arguably the simplest coding of multi-valued features and classes is local or binary coding, where each individual value of each feature or class is represented by a binary value detector, that has value 1 when the original individual value is present, and 0 otherwise. This paper deals with using this unpacked binary coding with memory-based learning. Memory-based learning is founded on the hypothesis that performance …
SEGMENTATION OF LASER ALTIMETER DATA FOR BUILDING RECONSTRUCTION: DIFFERENT PROCEDURES AND COMPARISON

One important step in the reconstruction of buildings from laser altimeter data is the segmentation step. In this contribution four procedures known from literature are investigated and compared. One procedure, which was made for detecting straight lines, which produces a segmentation as an intermediate result, was used. Three other procedures were installed, which are based on region growing. For each of the procedures experiments with an extensive variation of the parameters were carried out. For the particular task of roof reconstruction and the speciality of height data an own procedure was developed. In this procedure the distance measure is not computed from derived properties but directly from the sets of points. Moreover it also uses the knowledge about the separation of background and foreground and it is regulated by only one parameter. In order to make the segmentation results comparable known comparison measures for single segments (the relative correspondence related to the model and the result) were taken. For the estimation of the whole segmentation several aspects of the evaluation of single segments are discussed and a complete evaluation function is developed. For a quantitative measurement some buildings were chosen as examples. 1 MOTIVATION
Conditional Cripto overexpression in satellite cells promotes myogenic commitment and enhances early regeneration

Skeletal muscle regeneration mainly depends on satellite cells, a population of resident muscle stem cells. Despite extensive studies, knowledge of the molecular mechanisms underlying the early events associated with satellite cell activation and myogenic commitment in muscle regeneration remains still incomplete. Cripto is a novel regulator of postnatal skeletal muscle regeneration and a promising target for future therapy. Indeed, Cripto is expressed both in myogenic and inflammatory cells in skeletal muscle after acute injury and it is required in the satellite cell compartment to achieve effective muscle regeneration. A critical requirement to further explore the in vivo cellular contribution of Cripto in regulating skeletal muscle regeneration is the possibility to overexpress Cripto in its endogenous configuration and in a cell and time-specific manner. Here we report the generation and the functional characterization of a novel mouse model for conditional expression of Cripto, i.e., the Tg:DsRedloxP/loxPCripto-eGFP mice. Moreover, by using a satellite cell specific Cre-driver line we investigated the biological effect of Cripto overexpression in vivo, and provided evidence that overexpression of Cripto in the adult satellite cell compartment promotes myogenic commitment and differentiation, and enhances early regeneration in a mouse model of acute injury.
Motion Planning of Multi-joint Underactuated Manipulator Based on Trigonometric Function Input

Based on the characteristics of nonholonomic constraints and the principle of nonlinear control, this paper proposes a structural scheme of multi-joint underactuated manipulator and the method of chained form transformation. It also presents the design of a multi-joint underactuated manipulator which can be transformed into a chained-form system and the analysis indicates its underactuated property is decided by its nonholonomic feature. Then a motion planning method based on trigonometric function input is proposed by using the trigonometric function input control method of the chained form system. Finally, the validity of the motion planning is demonstrated by the simulation analysis. This research idea and method can be applied effectively to developing new multi-joint manipulator and is indispensable in exploring the motion planning issue of multi-joint manipulator
Hedging of options in a general semimartingale model

In this thesis, we investigate the mathematical theory of option pricing and option hedging which is a generaUzation of the Black-Scholes approach. We consider a very general stochastic model where the price process X is a semimartingale. In contrast to the usual Uterature, we do not assume completeness, i.e., we work without the restriction that X generates the space of options in the sense of stochastic integration theory. Our aim is a sequential reduction of risk for both European and American options; in the latter case, this also involves a problem of optimal stopping. In discrete time, this leads to a sequential conditional regression problem which is expücitly solved by using backwards induction. In continuous time, we develop new results on the differentiation of semimartingales which allow us to derive a nonlinear stochastic optimaUty equation of a new kind. In the case of a European option, we solve this optimality equation by a suitable Cameron-MartinMaruyama-Girsanov transformation.
The Performance and Robustness of Simple Monetary Policy Rules in Models of the Canadian Economy

In this paper, we evaluate seven simple monetary policy rules in a wide range of models of the Canadian economy. Our results indicate that none of the seven simple policy rules we examined is robust to model uncertainty, in that no single rule performs well in all models. In fact, our results show that the performance of some of the simple rules, particularly rules with interest rate smoothing and rules with a high coefficient on the inflation gap, can substantially deviate from that of the optimal rule and can even be unstable in some models. Furthermore, we find that “open-economy” rules do not perform well in many models. We find that adding an exchange rate term to a simple policy rule often increases the value of the policy-maker's loss function. Although it is not robust, we find that a simple nominal Taylor-type rule that has a coefficient of 2 on the inflation gap and 0.5 on the output gap outperforms the other simple rules in a certain class of models. However, even in those models, the loss-function value of this simple rule can be substantially higher than that of the optimal or base-case rule.
A framework for describing correlated excitation of solar p‐modes

In a previous paper we suggested that, for a given p mode, the excitation function is the same as the component of the solar background noise that has an identical surface spherical harmonic projection (over the corresponding range of temporal frequency). An important consequence of this surmise is that the excitation of overtones of a given angular degree and azimuthal order will be correlated in time. In this note, we introduce the basic principles and a mathematical description of correlated mode excitation. We use simple, illustrative examples, involving two modes. Our treatment suggests that in the real observations, any signatures of the correlation would not appear as a correlation of the output amplitudes of overtones, but rather as subtle modifications to the power spectral density at frequencies between the central frequencies of the overtones. These modifications give a contribution to the observed peak asymmetries. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
66 RESEARCH PAPER Testing the relationship between product innovation and process innovation. A comparative analysis of tourism and manufacturing sectors

Innovation is a key issue for business competitiveness. The most popular kinds of innovation are product innovation and process innovation. The effect of both types of innovation within a company is seen in the specialist literature from two opposing views: the distinctive view, which considers that both kinds of innovation are independent, and the integrative view, which assumes the existence of complementarities between the two kinds of innovation. The aim of this paper is to explore which of the two approaches dominates the context of both the tourism sector and the Spanish manufacturing sector. With samples taken from the database of the Technological Innovation Panel (PITEC 2008), two binary logistic regressions were conducted in two stages and a correlation test was performed with their residuals. The results show that each of the two types of innovation is independent.
N ov 2 00 4 Quantum κ-Poincaré Algebra from de Sitter Space of Momenta

There is a growing number of physical models, like point part icle(s) in 2+1 gravity or Doubly Special Relativity, in which the space of m o enta is curved, de Sitter space. We show that for such models the algebra of spac e-time symmetries possesses a natural Hopf algebra structure. It turns out tha t this algebra is just the quantumκ-Poincaré algebra.
Agile Global Software Development Communication Challenges: a Systematic Review

Organizations have shown a significant interest in adopting human and communication-oriented agile practices for Global Software Development (GSD). Agile practices originated in the context of small and medium co-located project teams present a number of communication challenges when they are applied to the distributed GSD. There is a need to understand the underlying communication challenges of agile GSD environment. This paper adopts a Systematic Literature Review (SLR) approach and reports communication challenges in the agile GSD context. A customized literature search and selection criteria was first developed and then applied to initially identify a set of 449 papers. Finally, 22 of 449 papers, relevant to this research, were selected for this study. These final 22 papers were reviewed and 7 major categories of communication challenges were identified in the context of agile GSD. The review results of this paper are expected to help researchers and practitioners to understand communication challenges of agile GSD and develop tools, techniques and strategies to deal with these challenges. This paper is limited to the number of reviewed studies from selected databases.