Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural and social dimensions of an international joint venture

Purpose - The purpose of this paper is to advance knowledge in the area of international joint ventures, more specifically in China. Design/methodology/approach - The analysis considers events prior to 26 April 2006, when the joint venture was announced. Organizations involved are considered as units of analysis for the current study. Using Yin's methodology for exploratory theory development, this study builds on existing theories through a case study that explores the execution of international joint ventures. This study analyzes holistic data in relation to Hypor Canada, Sichuan South Hope Company Ltd, and Shangdong Liu He Group Ltd. Findings - The findings suggest that strategic intents and resources need to be aligned between parties involved, and that foreign-based North American firms should cultivate both the structural and social dimensions of a relationship with a Chinese-based company. Research limitations/implications - The sample was limited to one case in swine genetics. The findings of this research may only be fully applicable for explaining joint venture operations in this particular area. Practical implications - The Hypor and New Hope agreement shows that a project needs to address both structural and social dimensions at once. Managers and marketers need to be aware of this and to consider both dimensions when assessing situations that may lead to a new joint venture. Originality/value - For various reasons, many joint ventures between North American and Chinese companies fail. On 26 April 2006, however, Hypor Canada, a leader in swine genetics headquartered in Regina, Saskatchewan, signed what would prove to be a successful joint venture with China-based New Hope (Sichuan South Hope Company Ltd). The agreements with Sichuan South Hope Company Ltd (New Hope) and Shandong Liu He Group Company Ltd (Liu He) were at the production and distribution of breeding pigs in China.
A preliminary study on a recommender system for the job recommendation challenge

In this paper we present our method used in the RecSys '16 Challenge. In particular, we propose a general collaborative filtering framework where many predictors can be cast. The framework is able to incorporate information about the content but in a collaborative fashion. Using this framework we instantiate a set of different predictors that consider different aspects of the dataset provided for the challenge. In order to merge all these aspects together, we also provide a method able to linearly combine the predictors. This method learns the weights of the predictors by solving a quadratic optimization problem. In the experimental section we show the performance using different predictors combinations. Results highlight the fact that the combination always outperforms the single predictor.
First Tier Health Care at Stanford

This study employs a survey to elicit students’ willingness to pay for health care services at the on-campus student clinic. While analyzing the utility of cost sharing in this context, this study is as much a focus on the survey methods involved in accurately measuring willingness to pay. The study finds that both the possibility of anchoring and acquiescence bias must be seriously addressed in contingent valuation for health care services. The study also finds that charging a co-pay should be strongly considered by the Stanford Administration. *I would like to thank Jay Bhattacharya for his invaluable advice, constant encouragement, and steadfast support and patience all throughout the long process.
Exploration of Feature Selection and Advanced Classification Models for High-Stakes Deception Detection

Recent research has demonstrated the effectiveness of automated text-based deception detection. In this study, using a variety of data sets and common classification techniques, this has been shown to be an accurate technique. Previous results have shown the need to reduce the number of inputs to these models in order to prevent overfitting. While previous results have been promising, there is a need to improve accuracy and reduce the number of false positives. Using 5 classification models and 3 variable sets, we have achieved accuracy level of 76% in this study.
Fast defect detection algorithm on the variety surface with random forest using GPUs

This paper proposes a defect detection method that can find the surface irregularity of the variety surface. The human like, the Variance of Variance (VOV) method can calculate the characteristic pattern of surface with mathematization. Also defect with unusual pattern appear using the VOV method. The object surface usually has smooth or textured plane, including defects with intensive irregularity. Conventional variance detection algorithms are not adequate for detecting various defect types of multiple scales and intensity variations. The purpose of this paper is to propose a method that can find defections of different scales in a single framework. This method can detect defects of different sizes by changing the window's size, which is combined with GPU computing for real time processing of inspection. The local VOV value does not affect other calculation result that can good for parallel processing. GPU has the high performance with parallel processing and it solved the typical processing speed problem of VOV method. For robust detection, we combined result of single VOV detection using machine learning process. From the results of the experiments that used in real images, it is verified that the method can be applied to detect the defects of images for irregular and textured intensity under uneven illumination.
Mapping of Vegetation Using Multi-Temporal Downscaled Satellite Images of a Reclaimed Area in Saemangeum, Republic of Korea

The aim of this study is to adapt and evaluate the effectiveness of a multi-temporal downscaled images technique for classifying the typical vegetation types of a reclaimed area. The areas reclaimed from estuarine tidal flats show high spatial heterogeneity in soil salinity conditions. There are three typical vegetation types for which the distribution is restricted by the soil conditions. A halophyte-dominated vegetation is located in a high saline area, grass vegetation is found in a mid- or low saline area, and reed/small-reed vegetation is situated in a low saline area. Multi-temporal satellite images were used to classify the vegetation types. Landsat images were downscaled to take into account spatial heterogeneity using cokriging. A random forest classifier was used for the classification, with downscaled Landsat and RapidEye images. Classification with RapidEye images alone demonstrated a lower level of accuracy than when combined with multi-temporal downscaled images. The results demonstrate the usefulness of a downscaling technique for mapping. This approach can provide a framework which is able to maintain low costs whilst producing richer images for the monitoring of a large and heterogeneous ecosystem.
Algorithms for user detection and authentication based on face analysis

Nowadays, there are many methods for identifying people, but most common and probably the oldest one is identification based on face recognition. Even though for people this method is natural, for computers it is a very challenging and complex problem. This paper describes one the known approaches for face recognition, which uses principal component analysis to compare and recognize faces. Obviously, a before face is recognized, it has to be detected. For process of detection, method based on Haar features is used.
How does Future Income Affect Current Consumption

This paper tests a straightforward implication of the basic Life Cycle model of consumption: that current consumption depends on expected lifetime income. The paper projects future income for a panel of households and finds that consumption is closely related to projected current income, but unrelated to predictable changes in income. However, future income uncertainty has an important effect: consumers facing greater income uncertainty consume less. The results are consistent with "buffer-stock" models of consumption like those of Deaton [1991] or Carroll [1992a, 1992b], where precautionary motives greatly reduce the willingness of prudent consumers to consume out of uncertain future income.
Changing Patterns in the Clinical Pathological Features of Hodgkin Lymphoma: A Report from Debrecen, Hungary

Introduction. Hodgkin lymphoma shows a well-known geographic pattern, but temporal changes have been found recently as well. Patients and Methods. 439 Hodgkin lymphoma patients' clinicopathological and treatment data were processed in calendar periods of approximately ten years. The patients were treated at our department from 1980 until the end of 2008. Results. The first period (1980–89) contained 177 patients, the second (1990–99) 147, and the third (2000–08) 115 Hodgkin lymphoma patients. The mean age of the patients was 40.1, 35.9, and 36.8 years in order. The male/female ratio: 1.42, 1.45, 1.05 in order. Contrary-wise a unimodal age group pattern could have been seen with an incidence peak between 30 and 39 in the past decades. The incidence of classical mixed cellularity histological subtype is decreasing (43.7%, 58.23%, 42.6%, P = 0.0098 (it is only significant in the second period)); classical nodular sclerosis shows an increasing tendency (25%, 27.32%, 34.78%, P = 0.1734). The first incidence peak is predominantly created by classical nodular sclerosis, meanwhile the second peak by classical mixed cellularity. The number of early-stage patients (59.12%) is beyond the advanced stage (40%) in the last decade. Meanwhile, the number of second-stage patients was increasing (25.8%, 26.35%, 49.56% P < 0.0001) and of patients in third stage was decreasing (53.4 %, 50.67%, 20% P < 0.0001). The 5- and 10-year overall survival data were progressing: 59.7 %, 77.4 %, and 90.5 % and 44.1 %, 70.6 % and 90.5 % (expected survival) in the last decade. Conclusions. Changes can be explained by the altered nature of Hodgkin lymphoma, the changes in socioeconomic status and the development of diagnostic and therapy methods.
Technology Use and Worker Outcomes: Direct Evidence from Linked Employee-Employer Data

We investigate the impact of technology adoption on workers’ wages and mobility in U.S. manufacturing plants by constructing and exploiting a unique Linked Employee-Employer data set containing longitudinal worker and plant information. We first examine the effect of technology use on wage determination, and find that technology adoption does not have a significant effect on high-skill workers, but negatively affects the earnings of low-skill workers after controlling for worker-plant fixed effects. This result seems to support the skill-biased technological change hypothesis. We next explore the impact of technology use on worker mobility, and find that mobility rates are higher in high-technology plants, and that high-skill workers are more mobile than their low and medium-skill counterparts. However, our technology-skill interaction term indicates that as the number of adopted technologies increases, the probability of exit of skilled workers decreases while that of unskilled workers increases.
The relevance of using 3D cell cultures, in addition to 2D monolayer cultures, when evaluating breast cancer drug sensitivity and resistance

Solid tumours naturally grow in 3D wherein the spatial arrangement of cells affects how they interact with each other. This suggests that 3D cell culture may mimic the natural in vivo setting better than traditional monolayer (2D) cell culture, where cells are grown attached to plastic. Here, using HER2-positive breast cancer cell lines as models (BT474, HCC1954, EFM192A), the effects of culturing cells in 3D using the poly-HEMA method compared to 2D cultures were assessed in terms of cellular viability, response/resistance to anti-cancer drugs, protein expression and enzyme activity. Scanning electron microscopy showed the morphology of cells in 3D to be substantially different to those cultured in 2D. Cell viability in 3D cells was substantially lower than that of cells in 2D cultures, while 3D cultures were more resistant to the effects of HER-targeted (neratinib) and classical chemotherapy (docetaxel) drugs. Expression of proteins involved in cell survival, transporters associated with drug resistance and drug targets were increased in 3D cultures. Finally, activity of drug metabolising enzyme CYP3A4 was substantially increased in 3D compared to 2D cultures. Together this data indicates that the biological information represented by 3D and 2D cell cultures is substantially different i.e. 3D cell cultures demonstrate higher innate resistance to anti-cancer drugs compared to 2D cultures, which may be facilitated by the altered receptor proteins, drug transporters and metabolising enzyme activity. This highlights the importance of considering 3D in addition to 2D culture methods in pre-clinical studies of both newer targeted and more traditional anti-cancer drugs.
A Fast Algorithm for Mining Multilevel Association Rule Based on Boolean Matrix

In this paper an algorithm is proposed for mining multilevel association rules. A Boolean Matrix based approach has been employed to discover frequent itemsets, the item forming a rule come from different levels. It adopts Boolean relational calculus to discover maximum frequent itemsets at lower level. When using this algorithm first time, it scans the database once and will generate the association rules. Apriori property is used in prune the item sets. It is not necessary to scan the database again; it uses Boolean logical operation to generate the multilevel association rules and also use top-down progressive deepening method.
Using line-integral convolution to visualize dense vector fields

s high-performance computers containing large amounts of memory and disk space become more accessible, demands for more effective visualization methods that can analyze large-scale numerical data also continue to grow. Among many problems that are faced by visualization researchers , how to effectively visualize numerical vector field data is one of the most challenging research subjects. In 1993, Cabral and Leedom 1 proposed an innovative approach called line-integral convolution (LIC), which can produce realistic visualizations of flow direction everywhere in a vector-field. This technique has drawn a lot of attention from researchers as well as practical users in the past several years. Generally, people are impressed by the results of using LIC to visualize vector-field data. In this article, I briefly review this new visu-alization technique, along with some recent extensions, in the hope that you will find it useful for your own applications. Visualizing dense vector-field data is a difficult problem. This is because the graphical icons that can naturally represent a vector tend to use up too much of the spatial resolution of the display. Suppose we were to draw a line segment representing a vector's direction and make the length of the segment proportional to the vector magnitude. Unlike displaying a scalar quantity, in which we can just paint a color to a single pixel to illustrate its value, drawing a line segment inevitably requires more screen space. As a result, the display window quickly gets cluttered as the size of vector data increases. Another way to visualize the vector field is to specify seed locations and then compute streamlines, curves that are tangent everywhere to the flow direction in the field. This is one of the most popular visualization techniques in computational fluid dynamics. The disadvantage of using streamlines, however, is that in order not to miss any subtle features in the data, you have to know where to place those seed points. For large-scale simulations with complex geometric models, knowing for sure where the interesting features are is nontrivial. You probably will need to place a lot of streamlines, which again clutter the display, or you can probe around by trial-and-error , but you never know if you have missed anything. To solve the problems, visualization researchers have been working on " global techniques , " trying to image the flow direction everywhere in the field into a single picture. Such techniques attempt to represent …
Kalman Filtering with Intermittent Observations: Critical Value for Second Order System

Abstract Sinopoli et al. (2004) analyze the problem of optimal estimation for linear Gaussian systems where packets containing observations are dropped according to an i.i. d. Bernoulli process, modeling a memoryless erasure channel. In this case the authors show that the Kalman Filter is still the optimal estimator, although boundedness of the error depends directly upon the channel arrival probability, p. In particular they also prove the existence of a critical value, p c , for such probability, below which the Kalman filter will diverge. While it has been shown that the critical value for diagonalizable systems with eigenvalues of different magnitude coincides with the lower bound determined by Mo and Sinopoli (2008), the problem is still open in the case where some eigenvalues have equal magnitude. This paper provides a complete characterization of the critical arrival probability for diagonalizable second order systems with eigenvalues of equal magnitude. In general the critical value for these systems is higher than the lower bound, unless the transmission from the sensor includes both the current and the previous measurement. In this case it is possible to construct a filter that whose critical value achieves the lower bound. Although clearly restrictive, the analysis of second order systems presented herein can be used to bound the critical value of higher dimensional systems of this kind.
What ocular and systemic variables affect choroidal circulation in healthy eyes

Abstract The aim of the study was to investigate the relationship between choroidal blood flow and systemic and ocular variables in patients with healthy eyes. In this prospective cross-sectional study, we examined 241 eyes of 241 healthy Japanese subjects (92 males and 149 females; mean age, 37.8 ± 17.0 years). The mean blur rate, a measure of the relative blood flow of the choroid, was determined using laser speckle flowgraphy. The total cross-sectional choroidal, luminal, and stromal areas of the choroid were determined by the binarization method. We investigated the correlation between choroidal MBR and systemic and ocular variables. Choroidal mean blur rate correlated with age (r = −0.385, P < 0.001) and choroidal thickness (r = 0.264, P < 0.001). The choroidal area correlated with choroidal mean blur rate (r = 0.374, P < 0.001), age (r = −0.184, P = 0.004), axial length (r = −0.251, P < 0.001), and choroidal thickness (r = 0.468, P < 0.001). The luminal area correlated with choroidal mean blur rate (r = 0.403, P < 0.001), age (r = −0.244, P < 0.001), axial length (r = −0.218, P = 0.001), and choroidal thickness (r = 0.435, P < 0.001). On multiple stepwise regression analyses, age (&bgr; = −0.321, P < 0.001) and luminal area (&bgr; = 0.320, P < 0.001), heart rate (&bgr; = 0.136, P = 0.018), and mean ocular perfusion pressure (&bgr; = 0.126, P = 0.045) were independent factors indicating the choroidal mean blur rate. Furthermore, axial length (&bgr; = −0.352, P < 0.001), choroidal mean blur rate (&bgr; = 0.273, P < 0.001), age (&bgr; = −0.247, P < 0.001), gender (&bgr; = −0.226, P < 0.001), and mean ocular perfusion pressure (&bgr; = 0.193, P = 0.002) were independent factors indicating the luminal area. The choroidal blood flow positively correlated with the luminal area and negatively correlating with age. In addition, the luminal area was negative correlated with age. It is suggested that aging causes a reduction in choroidal blood flow and luminal area, and as a result of aging effect, decreased choroidal blood flow would correlate with decreased luminal area.
Process Variability in Micro-Embossing

A promising technique for the large-scale manufacture of micro-fluidic devices and photonic devices is hot embossing of polymers such as PMMA. Micro-embossing is a deformation process where the workpiece material is heated to permit easier material flow and then forced over a planar pat-terned tool. While there has been considerable, attention paid to process feasibility very little effort has been put into production issues such as process capability and eventual process control. In this paper, we present initial studies aimed at identifying the origins and magnitude of variability for embossing features at the micron scale in PMMA. Test parts with features ranging from 3.5-630 µm wide and 0.9 µm deep were formed. Measurements at this scale proved very difficult, and only atomic force micros-copy was able to provide resolution sufficient to identify process variations. It was found that standard deviations of widths at the 3-4 µm scale were on the order of 0.5 µm leading to a coefficient of variation as high as 13%. Clearly, the transition from test to manufacturing for this process will require understanding the causes of this variation and devising control methods to minimize its magnitude over all types of parts.
Maximum likelihood and discriminative training of direct translation models

We consider translating natural language sentences into a formal language using direct translation models built automatically from training data. Direct translation models have three components: an arbitrary prior conditional probability distribution, features that capture correlations between automatically determined key phrases or sets of words in both languages, and weights associated with these features. The features and the weights are selected using a training corpus of matched pairs of source and target language sentences to maximize the entropy or a new discrimination measure of the resulting conditional probability model. We report results in the air travel information system domain and compare the two methods of training.
Exploiting model graph analysis for simplified modeling and improved diagnostics

In systems modeling, the dynamic behavior is approximated by considering certain properties to be constant in space in each physical component of the system. Such models are called lumped element models and are composed of connected components. Each component is described by ordinary differential equations and algebraic equations. The theory of linear graphs or model graphs can be helpful for analysis of such models. This paper presents a unified methodology for describing and analyzing network topology, using both linear graphs and the underlying algebraic equations, i.e. to take advantage of linear graph methods to enhance symbolic manipulation of model equations in order to handle underdetermined sets of potential variables and overdetermined sets of flow variables. The methodology is extended to handle overdetermined potential variables which arise, for example, when dealing with 3D rotations in multi body systems. It also allows handling of planar loops without any special attention by the user. This methodology for treating lumped models is in particular applicable to Modelica tools and would in certain cases allow simulation of ungrounded circuits and otherwise enable better diagnostics, for example when forgetting to ground an electrical circuit. It will also simplify the Multibody library of Modelica Standard Library.