Miracle-dz/UniFAR-FTUs huggingface dataset:
Real Options: Managerial Flexibility and Strategy in Resource Allocation

In the 1970s and the 1980s, developments in the valuation of capital-investment opportunities based on options pricing revolutionized capital budgeting. Managerial flexibility to adapt and revise future decisions in order to capitalize on favorable future opportunities or to limit losses has proven vital to long-term corporate success in an uncertain and changing marketplace.In this book Lenos Trigeorgis, who has helped shape the field of real options, brings together a wealth of previously scattered knowledge and research on the new flexibility in corporate resource allocation and in the evaluation of investment alternatives brought about by the shift from static cash-flow approaches to the more dynamic paradigm of real options -- an approach that incorporates decisions on whether to defer, expand, contract, abandon, switch use, or otherwise alter a capital investment.Comprehensive in scope, Real Options reviews current techniques of capital budgeting and details an approach (based on the pricing of options) that provides a means of quantifying the elusive elements of managerial flexibility in the face of unexpected changes in the market. Also discussed are the strategic value of new technology, project interdependence, and competitive interaction.The ability to value real options has so dramatically altered the way in which corporate resources are allocated that future textbooks on capital budgeting will bear little resemblance to those of even the recent past. Real Options is a pioneer in this area, coupling a coherent picture of how option theory is used with practical insights in into real-world applications.
Privately contributing to public goods over time: An experimental study

Similar to Levati and Neugebauer (2001), a clock is used by which participants can vary their individual contributions for voluntarily providing a public good. As time goes by, participants either in(de)crease their contribution gradually or keep it constant. Groups of two poorly and two richly endowed participants encounter repeatedly the weakest link-, the usual average contribution- and the best shot-technology of public good provision in a within subject-design. Some striking findings are that the weakest link-technology fares much better than the other two technologies in terms of welfare, and that the willingness to voluntarily contribute is greatly affected by the (increasing or decreasing) clock mechanism.
Factored translation model in English-to-Thai translation

The objective of this paper is to find an appropriate combination of English factors to support English-to-Thai translation task. There are four English factors those are surface, lemma, part-of-speech (POS) and combinatory categorical grammar (CCG). Various combinations of those factors were trained to build the most efficient model for translation. We test the accuracy using the BLEU score. The result shows that the factored model which consists of lemma and CCG produce the highest accuracy at 24.21% (1.05% superior than baseline). In future work, we will extend this work to Thai-to-English translation.
Vitamin D Receptor Poly(A) Microsatellite Polymorphism and 25-Hydroxyvitamin D Serum Levels: Association with Susceptibility to Breast Cancer

Purpose According to previous studies, vitamin D exhibits protective effects against breast cancer via the vitamin D receptor (VDR). There is growing evidence that breast cancer incidence is associated with various polymorphisms of the VDR gene. This study investigates the association of VDR poly(A) microsatellite variants with 25-hydroxyvitamin D (25(OH)D) serum levels and breast cancer risk. Methods Polymorphism analysis was performed on a total of 261 blood samples, which were collected from 134 women with breast cancer and 127 controls. Single strand conformation polymorphism was assessed by polymerase chain reaction in combination with sequencing to detect poly(A) lengths for each sample. The vitamin D levels of samples were determined by electrochemiluminescence. Results The poly(A) variant L allele frequency was significantly higher in cancer patients than in controls (odds ratio [OR], 1.73; 95% confidence interval [CI], 1.16-2.57; p=0.006). Thus, carriers of the L allele (LS and LL genotypes) have a higher risk for breast cancer (OR, 1.86; 95% CI, 1.13-3.05; p=0.013). A larger increase in the risk for breast cancer was found in individuals with the L carrier genotype and lowered 25(OH)D levels. Conclusion The results primarily suggest that VDR gene polymorphism in the poly(A) microsatellite is associated with 25(OH)D levels and that it can affect the breast cancer risk in the female population from northern Iran.
Streaming spatio-temporal video segmentation using Gaussian Mixture Model

Development of an automatic streaming video segmentation method is crucial for many video analysis applications. However, consistency of temporal segmentation and scalability for real-time applications are difficult to achieve. This work proposes a linear-time video segmentation method which is scalable and temporally consistent for streaming videos. A Gaussian Mixture Model (GMM) is used to segment each frame while a recursive filtering updates the parameters of the GMM. This hybrid methodology can uniquely propagate Gaussian clusters through each new frame, update the variance recursively, and create or remove clusters as necessary. In this way, the model automatically manipulates the number of clusters in run-time and adapts to any video sequence over streaming frames maintaining temporal coherence. The method needs a distance threshold value as the main parameter. The creation and removal of new clusters are governed by a cluster similarity criterion that can be based on user-defined distance measure. The experimental results are presented with two possible distance measures. The performance of the proposed method on several datasets is found to be comparable to state-of-the-art video segmentation algorithms.
A Model and Architecture for Conceptualized Data Annotations

In many collaborative research environments, in particular computational sciences, novel tools and techniques allow researchers to generate data from experiments and observations at a staggering rate. Researchers in these areas are now facing the strong need for querying, sharing and exchanging the data in a uniform and transparent fashion to further leverage their findings. However, due to the nature of the various types of heterogeneous data and lack of local and global database schema structures, standard data integration approaches fail or are not applicable. A viable solution to this problem is the extensive use of metadata. In this paper, we present the model and realization of a metadata management systems suitable for such research environments. The core component of the model are conceptualized data annotationsthat allow researchers to associate well-defined, agreed upon metadata, called concepts, with possibly remote Web accessible data sources. Annotations provide rich linkage structures between domain specific conceptual structures and fine-grained document components, including text and image data. We detail how annotations are managed and used in different data retrieval scenarios in the context of an application in the Neurosciences. We also present the realization of a general architecture for the proposed metadata information systems and outline some of the services embedded in this system.
Research on integration of ontology-based product knowledge: Framework, schema and algorithm

To deal with the problem that consistency in semantic representation of exchanged product knowledge in collaborative manufacturing, an ontology-based framework of knowledge integration and sharing was presented, and in the meantime an integration algorithm based on ontology mapping and merging was designed under this framework. First of all, a collaborative operation model for enterprise collaboration was discussed, and then an ontology-based framework of knowledge integration and sharing was established. Under the condition of analyzing the structure of product knowledge ontology, an ontology logic schema of knowledge concept was designed. Aiming at the ontology logic schema, an ontology integration algorithm of product knowledge was also improved. The algorithm is composed of ontology mapping and merging. Finally, the proposed ontology integration algorithm has been verified to be effective through the comparing experiment.
Robust Multi-Antenna Broadcasting with Imperfect Channel State Information

The problem of robust multi-antenna broadcasting is considered in the case of erroneous channel state information (CSI) at the transmitter. The criterion of minimum transmission power is used subject to the constraints guaranteeing that the signal-to-noise ratio (SNR) of each intended receiver satisfies a prescribed quality of service (QoS) requirement for the worst-case norm-bounded CSI mismatch. The resulting robust broadcasting problem is non-convex and, therefore, is difficult to solve. However, a suitable semi-definite relaxation (SDR) of this problem is proposed that enables to transform it to a convex form and to compute the optimal transmitter weight vector.
Two-component jet simulations - II. Combining analytical disk and stellar MHD outflow solutions

Context. Theoretical arguments along with observational data of YSO jets suggest the presence of two steady components: a disk wind type outflow needed to explain the observed high mass loss rates and a stellar wind type outflow probably accounting for the observed stellar spin down. Each component’s contribution depends on the intrinsic physical properties of the YSO-disk system and its evolutionary stage. Aims. The main goal of this paper is to understand some of the basic features of the evolution, interaction and co-existence of the two jet components over a parameter space and when time variability is enforced. Methods. Having studied separately the numerical evolution of each type of the complementary disk and stellar analytical wind solutions in Paper I of this series, we proceed here to mix together the two models inside the computational box. The evolution in time is performed with the PLUTO code, investigating the dynamics of the two-component jets, the modifications each solution undergoes and the potential steady state reached. Results. The co-evolution of the two components, indeed, results in final steady state configurations with the disk wind effectively collimating the inner stellar component. The final outcome stays close to the initial solutions, supporting the validity of the analytical studies. Moreover, a weak shock forms, disconnecting the launching region of both outflows with the propagation domain of the twocomponent jet. On the other hand, several cases are being investigated to identify the role of each two-component jet parameter. Time variability is not found to considerably affect the dynamics, thus making all the conclusions robust. However, the flow fluctuations generate shocks, whose large scale structures have a strong resemblance to observed YSO jet knots. Conclusions. Analytical disk and stellar solutions, even sub modified fast ones, provide a solid foundation to construct twocomponent jet models. Tuning their physical properties along with the two-component jet parameters allows a broad class of realistic scenarios to be addressed. The applied flow variability provides very promising perspectives for the comparison of the models with observations.
Towards in-situ sensor network assisted remote sensing of crop parameters: poster

Remote sensing data acquired from satellites are a vital information source for precision agriculture to assess current crop conditions. Field measurements of plant parameters, like the leaf area index (LAI), serve as a crucial basis to validate parameter maps derived from satellite images. Traditionally, in-situ LAI measurements are collected manually. Therefore, the assessment is cost-intensive and the temporal availability of measurements is limited. Measurements provided by small sensor devices organized in a wireless sensor network (WSN) are a low-cost alternative to manual field measurements. They allow a precise LAI determination with high temporal resolution at many different locations in a field or even an entire region. These information are highly demanded for the validation of spatial information on crop conditions derived from image data acquired by modern satellites like Sentinel-2.
A Robust and Accurate LED-BGK Solver on Unstructured Adaptive Meshes

Starting from the BGK model of the Boltzmann equation, we develop a robust and accurate finite volume gas kinetic scheme on unstructured triangular meshes. The proposed numerical approach is composed of two steps? an initial reconstruction step and a gas evolution step. In the initial reconstruction step, an unstructured version of the local extremum diminishing interpolation is applied to the conservative variables and to compute left and right states along a node edge. In the gas evolution step, the local integral solution of the BGK model is used to compute numerical fluxes at a cell interface. This approach provides an alternative to Riemann solvers and yields numerical schemes which possess many desirable properties that may not be found in Godunov-type schemes. A classich-refinement adaptive procedure is implemented to increase the spatial resolution of high-speed unsteady flow characteristics such as shock waves, contact discontinuities, or expansion waves with minimal computational costs and memory overheads. This procedure involves mesh enrichment/coarsening steps to either insert nodes on an edge center in high-gradient regions or delete nodes in over-resolved regions. Numerical results of several test cases for unsteady compressible inviscid flows are presented. In order to verify the accuracy and robustness of the current numerical approach, the computed results are compared with analytical solutions, experimental data, the results of structured mesh calculations, and the results obtained by widely used flux splitting methods.
Methylenetetrahydrofolate reductase gene polymorphisms in patients with nonalcoholic steatohepatitis (NASH)

Nonalcoholic fatty liver disease (NAFLD) is the most common cause of abnormal hepatic steatosis in the absence of a history of alcohol use. Nonalcoholic steatohepatitis (NASH) is the progressive form of NAFLD. Hyperhomocysteinemia causes steatosis, and the methylenetetrahydrofolate reductase (MTHFR) C677T and A1298C polymorphisms result in hyperhomocysteinemia. To examine whether the C677T and A1298C polymorphisms of the MTHFR gene were associated with NASH, we analysed the allele and genotype distribution of the MTHFR C677T and A1298C polymorphisms in 57 well‐diagnosed NASH patients, 324 healthy controls in a case‐control study of Turkish subjects of Caucasian origin. The diagnosis of the NASH patients was based on liver biopsy. The method used in the analysis of genotypes was PCR‐RFLP. The MTHFR A1298C polymorphism was significantly associated with NASH (χ2 = 8.439; p = 0.015) in the total NASH patients compared with healthy controls. The MTHFR 1298C allele (odds ratio (OR) = 2.480; 95%CI = 1.286–4.782; χ2 = 7.703; df = 1; p = 0.006) was significantly associated with NASH in the total NASH patients. The MTHFR C677C/A1298C compound genotype (OR = 2.218; 95%CI = 1.003–4.906; χ2 = 3.998; df = 1; p = 0.046) in men patients was also significantly associated with NASH. Likewise the MTHFR C1298C genotype was significantly associated with NASH in women patients with NASH (OR = 2.979; 95%CI = 1.027–8.641; χ2 = 4.343; df = 1; p = 0.037). In conclusion, the MTHFR 1298C allele in all NASH patients, C1298C genotype, C677C/C1298C compound genotype in women NASH patients and C677C/A1298C compound genotype in men NASH patients were genetic risk factors for NASH. Copyright © 2007 John Wiley & Sons, Ltd.
Accountability Effects on Auditors' Performance: Influence of Knowledge, Problem-Solving Ability, and Task Complexity

In this study, we examine three factors that moderate the relation between accountability to a superior and auditor performance: knowledge, problem-solving ability, and task complexity. Specifically, we predict that accountability works best when the requisite knowledge and abilities are matched with the characteristics of the task. In our study, auditors performed three tasks of varying complexity. In the low complexity task (listing compliance and substantive tests), accountability did not influence performance. As the task became more complex (listing financial statement errors associated with an internal control deviation), accountability improved performance only when knowledge was high. For the most complex task (listing causes associated with a change in ratios), accountability improved performance only when both knowledge and problem-solving ability were high. These results indicate that accountability works best in particular combinations of knowledge, problem-solving ability, and task complexity. Implications of the results and the directions for future research are discussed.
Effects of Walnuts on Endothelial Function in Overweight Adults with Visceral Obesity: A Randomized, Controlled, Crossover Trial

Objectives: Metabolic syndrome is a precursor of diabetes and cardiovascular disease (CVD). Walnut ingestion has been shown to reduce CVD risk indices in diabetes. This randomized controlled crossover trial was performed to investigate the effects of daily walnut consumption on endothelial function and other biomarkers of cardiac risk in a population of overweight individuals with visceral adiposity. Methods: Forty-six overweight adults (average age, 57.4 years; 28 women, 18 men) with elevated waist circumference and 1 or more additional signs of metabolic syndrome were randomly assigned to two 8-week sequences of walnut-enriched ad libitum diet and ad libitum diet without walnuts, which were separated by a 4-week washout period. The primary outcome measure was the change in flow-mediated vasodilation (FMD) of the brachial artery. Secondary measures included serum lipid panel, fasting glucose and insulin, Homeostasis Model Assessment–Insulin Resistance values, blood pressure, and anthropometric measures. Results: FMD improved significantly from baseline when subjects consumed a walnut-enriched diet as compared with the control diet (1.4% ± 2.4% versus 0.3% ± 1.5%; p = 0.019). Beneficial trends in systolic blood pressure reduction were seen, and maintenance of the baseline anthropometric values was also observed. Other measures were unaltered. Conclusion: Daily ingestion of 56 g of walnuts improves endothelial function in overweight adults with visceral adiposity. The addition of walnuts to the diet does not lead to weight gain. Further study of the potential role of walnut intake in diabetes and CVD prevention is warranted.
A generic nonlinear output error structure implemented on a PWL ASIC

In this paper, we present a Nonlinear Output Error (NOE) model structure implemented on an Application Specific Integrated Circuit (ASIC) chip dedicated to Piecewise Linear (PWL) calculation. Three examples are included to show the performance of the ASIC in quantization, truncation and fixed-point operation. Experimental results and simulation results are shown and compared.
Performance evaluation of routing strategies in MANETs

Ad hoc networks are gaining increasing popularity in recent years because of their ease of deployment. No wired base station or infrastructure is supported, and each host communicates one another via packet radios. In ad hoc networks, routing protocols are challenged with establishing and maintaining multihop routes in the face of mobility, bandwidth limitation and power constraints. In this paper, the distance vector based Wireless Routing Protocol (WRP) and Adaptive Distance Vector protocol (ADV), the link state based reactive protocol Fisheye State Routing (FSR), the on-demand routing protocol Dynamic Source Routing (DSR) and Associativity Based Routing protocol (ABR), and the location based reactive protocol Location-Aided Routing protocol (LAR) are simulated using OPNET Modeler® and compared under various network scenarios (e.g., different mobility patterns, mobility rates, traffic patterns, etc). Our study shows that overall, all protocols performed much better with the group mobility model than with the random waypoint model. WRP and FSR, especially, are the main beneficiaries of the group mobility model. Each protocol's performance degraded as mobility rates increased. On-demand protocols were highly effective and efficient in most of our scenarios. Extra delay in acquiring routes, though, makes them less attractive in delivering real-time traffic. LAR further improved an on-demand protocol by using location information, but produced more overhead to exchange location information.
Learning spectral embedding via iterative eigenvalue thresholding

Learning data representation is a fundamental problem in data mining and machine learning. Spectral embedding is one popular method for learning effective data representations. In this paper we propose a novel framework to learn enhanced spectral embedding, which not only considers the geometrical structure of the data space, but also takes advantage of the given pairwise constraints. The proposed formulation can be solved by an iterative eigenvalue thresholding (IET) algorithm. Specially, we convert the problem of learning spectral embedding with pairwise constraints into the one of completing an "ideal" kernel matrix. And we introduce the spectral embedding of graph Laplacian as the auxiliary information and cast it as a small-scale positive semidefinite (PSD) matrix optimization problem with nuclear norm regularization. Then, we develop an IET algorithm to solve it efficiently. Moreover, we also present an effective semi-supervised clustering (SSC) approach with learned spectral embedding (LSE). Finally, we validate the proposed IET algorithm and LSE approach by extensive experiments on real-world data sets.
Virtual reality training for the diagnosis of prostate cancer

Prostate cancer is the second leading cause of cancer death among men (25% of men with prostate cancer will die of the disease). The most common method of detecting this malignancy is digital rectal examination (DRE). Current DRE training requires medical students to examine a large number of patients before attaining adequate experience. We propose to solve this problem using a virtual reality digital rectal examination simulation. The prototype system consists of a PHANToM haptic interface which provides feedback to the trainee's index finger, a motion restricting board and an SGI workstation, which renders the patient's anatomy in the region of interest. Four types of prostate were modeled using OpenGL and GHOST haptic library-normal, enlarged with no tumor, incipient malignancy (single tumor), and advanced malignancy (tumor cluster). Results of initial human factors studies are encouraging, while pointing out the need for more realistic physical modeling.