AstroGrid-D: Enhancing Astronomic Science with Grid Technology

We present AstroGrid-D, a project bringing together astronomers and experts in Grid technology to enhance astronomic science in many aspects. First, by sharing currently dispersed resources, scientists can calculate their models in more detail. Second, by developing new mechanisms to efficiently access and process existing datasets, scientific problems can be investigated that were until now impossible to solve. Third, by adopting Grid technology large instruments such as robotic telescopes and complex scientific workflows from data aquisition to analysis can be managed in an integrated manner. In this paper, we present prominent astronomic use cases, discuss requirements on a Grid middleware and present our approach to extend/augment existing middleware to facilitate the improvements mentioned above

Punch-through jets in A+AA+A collisions at RHIC/LHC

High $p_T$ single and dihadron production is studied within a NLO pQCD parton model with jet quenching in high energy $A+A$ collisions at the RHIC/LHC energy. A simultaneous $\chi^2$-fit to both single and dihadron spectra can be achieved within a narrow range of energy loss parameter. Punch-through jets are found to result in the dihadron suppression factor slightly more sensitive to medium than the single hadron suppression factor at RHIC. Such jets at LHC are found to dominate high $p_T$ dihadron production and the resulting dihadron spectra are more sensitive to the initial parton distribution functions than the single hadron spectra.Comment: 4 pages, 4 figures, proceedings for the 20th international conference on ultra-relativistic nucleus-nucleus collisions (QM2008), Jaipur, India, February 4-10, 200

Forecasting Series-Based Stock Price Data using Direct Reinforcement Learning

A significant amount of work has been done in the area of price series forecasting using soft computing techniques, most of which are based upon supervised learning. Unfortunately, there has been evidence that such models suffer from fundamental drawbacks. Given that the short-term performance of the financial forecasting architecture can be immediately measured, it is possible to integrate reinforcement learning into such applications. In this paper, we present the novel hybrid view for a financial series and critic adaptation stock price forecasting architecture using direct reinforcement. A new utility function called policies-matching ratio is also proposed. The need for the common tweaking work of supervised learning is reduced and the empirical results using real financial data illustrate the effectiveness of such a learning framework

Cost Allocation for Transmission Investment using Agent-Based Game Theory

Due to electrical power restructuring, a dramatic change has been made to the generation and transmission sectors of the power industry. Rules and legislation are continuously changing. To promote more competition, transmission has to be expanded or upgraded to remove congestion and market power. The cost allocation of new investment in transmission has to be recalculated. The socialization methods of the past have been shown to be unfair to some market and network participants. The decentralization of cost allocation must be considered. The proposed paper provides a comparison between traditional cost allocation methods and a new cost allocation method based on agent-based game theory. A multigenerator/bus system will be used to compare the cost allocation methods

Short-Term Stock Market Timing Prediction under Reinforcement Learning Schemes

There are fundamental difficulties when only using a supervised learning philosophy to predict financial stock short-term movements. We present a reinforcement-oriented forecasting framework in which the solution is converted from a typical error-based learning approach to a goal-directed match-based learning method. The real market timing ability in forecasting is addressed as well as traditional goodness-of-fit-based criteria. We develop two applicable hybrid prediction systems by adopting actor-only and actor-critic reinforcement learning, respectively, and compare them to both a supervised-only model and a classical random walk benchmark in forecasting three daily-based stock indices series within a 21-year learning and testing period. The performance of actor-critic-based systems was demonstrated to be superior to that of other alternatives, while the proposed actor-only systems also showed efficac

Using a Neuro-Fuzzy-Genetic Data Mining Architecture to Determine a Marketing Strategy in a Charitable Organization\u27s Donor Database

This paper describes the use of a neuro-fuzzy-genetic data mining architecture for finding hidden knowledge and modeling the data of the 1997 donation campaign of an American charitable organization. This data was used during the 1998 KDD Cup competition. In the architecture, all input variables are first preprocessed and all continuous variables are fuzzified. Principal component analysis (PCA) is then applied to reduce the dimensions of the input variables in finding combinations of variables, or factors, that describe major trends in the data. The reduced dimensions of the input variables are then used to train probabilistic neural networks (PNN) to classify the dataset according to the groups considered. A rule extraction technique is then applied in order to extract hidden knowledge from the trained neural networks and represent the knowledge in the form of crisp and fuzzy if-then-rules. In the final stage a genetic algorithm is used as a rule-pruning module to eliminate weak rules that are still in the rule base while insuring that the classification accuracy of the rule base is improved or not changed. The pruned rule base helps the charitable organization to maximize the donation and to understand the characteristics of the respondents of the direct mail fund raising campaig

Measurement Error in Subjective Expectations and the Empirical Content of Economic Models

While stock market expectations are among the most important primitives of portfolio choice models, their measurement has proved challenging for some respondents. We ar-gue that the magnitude of measurement error in subjective expectations can be used as an indicator of the degree to which economic models of portfolio choice provide an ade-quate representation of individual decision processes. In order to explore this conjecture empirically, we estimate a semiparametric double index model on a dataset specifically collected for this purpose. Stock market participation reacts strongly to changes in model parameters for respondents at the lower end of the measurement error distribution; these effects are much less pronounced for individuals at the upper end. Our findings indicate that measurement error in subjective expectations provides useful information to uncover heterogeneity in choice behavior

Occult Breast Cancer Presenting as Metastatic Adenocarcinoma of Unknown Primary: Clinical Presentation, Immunohistochemistry, and Molecular Analysis

We report a rare presentation of a 66-year-old female with diffuse metastatic adenocarcinoma of unknown primary involving liver, lymphatic system and bone metastases. The neoplastic cells were positive for CK7 and OC125, while negative for CK20, thyroid transcription factor 1, CDX2, BRST-2, chromogranin, synaptophysin, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2/neu). Fluorescence in situ hybridization showed no amplification of the HER2/neu gene. Molecular profiling reported a breast cancer origin with a very high confidence score of 98%. The absence of immunohistochemistry staining for ER, PR, and HER2/neu further classified her cancer as triple-negative breast cancer. Additional studies revealed high expression levels of topoisomerase (Topo) I, androgen receptor, and ribonucleoside-diphosphate reductase large subunit; the results were negative for thymidylate synthase, Topo II-a and O6-methylguanine-DNA methyltransferase. The patient was initially treated with a combination regimen of cisplatin and etoposide, and she experienced a rapid resolution of cancer-related symptoms. Unfortunately, her therapy was complicated by a cerebrovascular accident (CVA), which was thought to be related to cisplatin and high serum mucin. After recovery from the CVA, the patient was successfully treated with second-line chemotherapy based on her tumor expression profile. We highlight the role of molecular profiling in the diagnosis and management of this patient and the implication of personalized chemotherapy in this challenging disease