ABSTRACT:
We describe a unified computational framework for learning causal dependencies between genotypes, biomarkers, and phenotypic outcomes from large-scale data. In contrast to previous studies, our framework allows for noisy measurements, hidden confounders, missing data, and pleiotropic effects of genotypes on outcomes. The method exploits the use of genotypes as “instrumental variables” to infer causal associations between phenotypic biomarkers and outcomes, without requiring the assumption that genotypic effects are mediated only through the observed biomarkers. The framework builds on sparse linear methods developed in statistics and machine learning and modified here for inferring structures of richer networks with latent variables. Where the biomarkers are gene transcripts, the method can be used for fine mapping of quantitative trait loci (QTLs) detected in genetic linkage studies. To demonstrate our method, we examined effects of gene transcript levels in the liver on plasma HDL cholesterol levels in a sample of 260 mice from a heterogeneous stock.

ABSTRACT:
Planning is a critical, early step on the path to successful program writing and a skill that is often lacking in novice programmers. As practitioners we are continually searching for or creating interventions to help our students, particularly those who struggle in the early stages of their computer science education. In this paper we report on our ongoing research of novice programming skills that utilizes the qualitative research method of grounded theory to develop theories and inform the construction of these interventions. We describe how grounded theory, a popular research method in the social sciences since the 1960’s, can lend formality and structure to the common practice of simply asking students what they did and why they did it. Further, we aim to inform the reader not only about our emerging theories on interventions for planning but also how they might collect and analyze their own data in this and other areas that trouble novice programmers. In this way those who lecture and design CS1 interventions can do so from a more informed perspective.

ABSTRACT:
Electrical resistance responses of multi-walled carbon nanotubes (MWCNT) reinforced polypropylene (PP) nanocomposites under mechanical tensile loading are studied in this paper. A standard tensile test was conducted while the electrical resistance was measured using 2-probe method. From our previous works on the CNT/PP nanocomposites, the percolation threshold of electrical conductivity is around 3.8 wt% of CNT. The influence of this percolation threshold on the electrical resistance upon mechanical loading was investigated. The results will be discussed and compared.

ABSTRACT:
In this study, moisture damage in plastomeric polymer modified asphalt binder is investigated using Atomic Force Microscopy (AFM) with chemically functionalized AFM tips. Four different percentages of plastomeric polymers and two antistripping agents such as Kling Beta and Lime are used to modify a base asphalt binder. Chemical functional groups such as -COOH, -CH3, -NH3, and –OH, that are commonly present in plastomeric polymer modified asphalt system, are used to functionalize the AFM tips. The force distance mode of AFM is used to measure the adhesion forces between a modified asphalt sample surface and the functionalized AFM tips. This enables the measurement of adhesion within an asphalt binder system. It is shown that the adhesion force values in dry sample changed substantially from that in wet conditioned samples. It is evident from this study that plastomeric modification does not help reduce moisture damage in asphalt. The percentage change in adhesion forces due to moisture is about 20 nN for the lime modified samples, and about 50 nN for the Kling Beta modified samples. This indicates that lime is more effective than Kling Beta for reducing moisture damage in plastomeric polymer modified asphalt.

ABSTRACT:
Information overload of the anesthesiologist through technological advances have threatened the safety of patients under anesthesia in the operating room (OR). Traditional monitoring and alarm systems provide independent, spatially distributed indices of patient physiological state. This creates the potential to distract caregivers from direct patient care tasks. To address this situation, a novel reactive agent decision support system with graphical human machine interface was developed. The system integrates the disparate data sources available in the operating room, passes the data though a decision matrix comprising a deterministic physiologic rule base established through medical research. Patient care is improved by effecting change to the care environment by displaying risk factors and alerts as an intuitive color coded animation. The system presents a unified, contextually appropriate snapshot of the patient state including current and potential risk factors, and alerts of critical patient events to the operating room team without requiring any user intervention. To validate the efficacy of the system, a retrospective analysis focusing on the hypotension rules were performed. Results show that even with vigilant and highly trained clinicians, deviations from ideal patient care exist and it is here that the proposed system may allow more standardized and improved patient care and potentially outcomes.

ABSTRACT:
The process of making complex and controversial decisions, that is, dealing with moral or ethical dilemmas, have intrigued people and inspired writers from time immemorial. Dilemmas give both color and depth to characters in good literary works. But beyond literary fiction, dilemmas occupy society in every day issues such as in introducing legislation or solving current political problems. One example of a current political dilemma is how to deal with Iran’s quest for nuclear weapons. If it were possible to assess and quantify each of the alternative solutions for a given problem, the process of decision making would be much easier. If a problem involves only two optional solutions, game theory techniques can be used. However, real life problems are usually multi-unit, multi-optional problems, as in Iran

ABSTRACT:
Lattice reduction algorithms have been used for crypt-analysis of many public key cryptosystems. Several lattice reduction algorithms have been proposed in the literature while the most popular among them is the BKZ algorithm. When BKZ fails to find a shortest vector, typically it returns a much longer vector than the shortest. We proposed the extended search space to find a shortest vector in such a case in our previous paper and confirmed the effectiveness of it experimentally. In this paper, we justify the effectiveness of the extended search space by additional analysis. For that, we analyzed coefficients of the shortest vector in a lattice based on some heuristic assumptions. Moreover, we examined the distribution of the coefficients that highly affect the inclusion probability in the extended search space. We showed that the inclusion probability can be estimated based on the distribution, and the estimated probability reflected the experimental results in our previous paper.

ABSTRACT:
Hand-held based optical imaging devices are currently developed as a noninvasive method for breast cancer diagnosis. However, the devices developed to date have not performed 3D tomography since they are unable to perform coregistration. In our Optical Imaging Laboratory we have developed a hand-held optical imager with automated coregistration facilities to enable 3D tomography of breast cancer. Herein, coregistered imaging and 3D tomography are demonstrated in vitro, and preliminary in vivo studies are performed to demonstrate 2D surface mapping and coregistered imaging in breast tissue of normal human subjects. The results demonstrate potential toward clinical translation of a portable and patient-comfortable method for breast cancer diagnosis.

ABSTRACT:
For controlling the prosthetic hand by only electroencephalogram (EEG), it has become the hot spot in robotics research to set up a direct communication and control channel between human brain and prosthetic hand. In this paper, the EEG signal is analyzed based on multi-complicated hand activities. And then, two methods of EEG pattern recognition are investigated, a neural prosthesis hand system driven by BCI is set up, which can complete four kinds of actions (arm’s free state, arm movement, hand crawl, hand open). Through several times of off-line and on-line experiments, the result shows that the neural prosthesis hand system driven by BCI is reasonable and feasible, the C-support vector classifiers-based method is better than BP neural network on the EEG pattern recognition for multi-complicated hand activities.

ABSTRACT:
The research is aimed at enabling special education pupils to use computers in everyday life, and improving spatial intelligence and mathematical achievements through computers. The method of training focuses on enabling pupils to create computer simulations, communicate by electronic mail while evaluating each other’s products and navigate Internet sites. The creation of such simulations is based on manipulations of the virtual environment similar to the real world as much as possible in order to utilize the unique characteristics of the computer such as spatial visualization. The researcher taught the teachers the basics of the use of computer and trained them how to use the method in their classroom. Then the teachers used the method with their special education pupils in accordance with their cognitive and motor abilities. The objects were taken from the pupils’ everyday environment. The teachers trained the pupils in pairs. Such procedures were held among different populations. The teachers improved their mastery of computers. In spite of their lack of experience before the experiment, they built high-level PowerPoint presentations and used them with their pupils in the classroom including even virtual simulations. They sent their products by Electronic mail (E-Mail) for the peer reviewing process and navigated relevant Internet sites. The teachers reported pupils’ high motivation and their success in the various virtual activities. As a result, the spatial intelligence and mathematical achievements of the pupils were improved. The teacher-pupil interaction and the social relationships between the pupils were also improved.

ABSTRACT:
Selection of relevant genes that will give higher accuracy for sample classication (for example, to distinguish cancerous from normal tissues) is a common task in most microarray data studies. An evolutionary method based on generalization error bound theory of support vector machine (SVM) can select a subset of potentially informative genes for SVM classifier very efficiently. The bound theories are developed for binary SVM, however multiclass SVMs do not have established bounds on the generalization error. Several multiclass SVMs have been proposed where multiclass SVMs are typically constructed by combining several binary SVMs. We evaluate an estimate of a generalization error bound for a multiclass SVM by combining the error bound of binary SVMs which are used to construct the multiclass SVM. In this paper our aims are to compare the performance of several multiclass SVMs in the SVM-based evolutionary method and then find the best multiclass SVM classifier in the SVM-based evolutionary method for multicategory cancer diagnosis using microarray gene expression data.

ABSTRACT:
How do individual characteristics, such as economic culture, influence the trading behaviors and the acceptance of any consensus reached through prediction market mechanisms? This research explores variations in the usage of prediction (or information) markets that are explained by some of the traders‟ cultural differences. Four forms of capitalism: state-guided, oligarchic, big-firm, and entrepreneurial, proposed by Baumol et al, are employed to capture aspects of traders‟ differences. To assess participants‟ economic culture along the spectrum of capitalist forms a survey instrument has been developed, validated, and tested. Moreover, several concepts related to trading participation, trading patterns, trader‟s overall performance and trader‟s acceptance of market outcomes are described and hypothesized against the economic culture forms. A series of research questions are proposed that explore how trader economic culture may affect prediction market use. The research landscape specified by Jones et al. is extended to recognize the potential differences between trader and market outcomes.

ABSTRACT:
Not only in Europe but also on other continents, it is felt that a reform of the doctoral phase in tertiary education is necessary. To make this reform a success, it is necessary to first define the skills and competences of a doctor in general and of a doctor of engineering in particular to dispose of measurable criteria for the outcomes of the reform. These criteria are intended to foster not only an academic career but also careers in industry and administration, i.e. to support the careers of future chief executives. It is also presented how these skills and competences are seen by industrial companies as well as by the young Doctors of Engineering. Finally, it is discussed how existing methods might be reformed to further improve the doctoral phase at and in cooperation with universities.

ABSTRACT:
In this paper we summarize the main idea and results of Yuen and Yang (2009, 2010a, 2010b) and provide some results on pricing of Parisian options under the Markov regime-switching model (MRSM). The MRSM allows the parameters of the market model depending on a Markovian process, and the model can reflect the information of the market environment which cannot be modeled solely by linear Gaussian process. However, when the parameters of the stock price model are not constant but governed by a Markovian process, the pricing of the options becomes complex. We present a fast and simple trinomial tree model to price options in MRSM. In recent years, the pricing of modern insurance products, such as Equity-Indexed annuity (EIA) and variable annuities (VAs), has become a popular topic. We show here that our trinomial tree model can been used to price EIA with strong path dependent exotic options in the regime switching model.

ABSTRACT:
In synchrotron diffraction experiments, it is typically assumed that the X-ray beam at the sample position is uniform, stable and has dimensions that are controlled by the focus and slits settings. As might be expected, this process is much more complex. We present here an investigation of the properties of a synchrotron X-ray beam at the sample position. The X-ray beam is visualized with a single crystal scintillator that converts X-ray photons into visible light photons, which can be imaged using Structure Biology Center (SBC) on-axis and off-axis microscope optics. The X-ray penetration is dependent on the composition of the scintillator (especially the effective Z), and X-ray energy. Several scintillators have been used to visualize X-ray beams. Here we compare CdWO4, PbWO4, Bi4Ge3O12, Y3Al5O12:Ce (YAG:Ce), and Gd2O2S:Tb (phosphor). We determined that scintillator crystals made of CdWO4 and similar high-Z materials are best suited for the energy range (7–20 keV) and are most suitable for beam visualization for macromolecular crystallography applications. These scintillators show excellent absorption, optical, and mechanical properties.