General Mass Scheme for Jet Production in DIS

We propose a method for calculating DIS jet production cross sections in QCD at NLO accuracy with consistent treatment of heavy quarks. The scheme relies on the dipole subtraction method for jets, which we extend to all possible initial state splittings with heavy partons, so that the Aivazis-Collins-Olness-Tung massive collinear factorization scheme (ACOT) can be applied. As a first check of the formalism we recover the ACOT result for the heavy quark structure function using a dedicated Monte Carlo program.Comment: 6 pages, 2 figure

Design of prototype charged particle fog dispersal unit

The unit was designed to be easily modified so that certain features that influence the output current and particle size distribution could be examined. An experimental program was designed to measure the performance of the unit. The program described includes measurements in a fog chamber and in the field. Features of the nozzle and estimated nozzle characteristics are presented

Next-to-leading order QCD corrections to single-inclusive hadron production in transversely polarized p-p and pbar-p collisions

We present a calculation of the next-to-leading order QCD corrections to the partonic cross sections contributing to single-inclusive high-p_T hadron production in collisions of transversely polarized hadrons. We use a recently developed projection technique for treating the phase space integrals in the presence of the cos(2Phi) azimuthal-angular dependence associated with transverse polarization. Our phenomenological results show that the double-spin asymmetry A_TT^pi for neutral-pion production is expected to be very small for polarized pp scattering at RHIC and could be much larger for the proposed experiments with an asymmetric pbar-p collider at the GSIComment: 7 pages, 5 figure

Programmable networks for quantum algorithms

The implementation of a quantum computer requires the realization of a large number of N-qubit unitary operations which represent the possible oracles or which are part of the quantum algorithm. Until now there are no standard ways to uniformly generate whole classes of N-qubit gates. We have developed a method to generate arbitrary controlled phase shift operations with a single network of one-qubit and two-qubit operations. This kind of network can be adapted to various physical implementations of quantum computing and is suitable to realize the Deutsch-Jozsa algorithm as well as Grover's search algorithm.Comment: 4 pages. Accepted version; Journal-ref. adde

Seed Yield Prediction Models of Four Common Moist-Soil Plant Species in Texas

Seed production by moist-soil plant species often varies within and among managed wetlands and on larger landscapes. Quantifying seed production of moist-soil plants can be used to evaluate wetland management strategies and estimate wetland energetic carrying capacity, specifically for waterfowl. In the past, direct estimation techniques were used, but due to excessive personnel and time costs, other indirect methods have been developed. Because indirect seed yield models do not exist for moist-soil plant species in east-central or coastal Texas, we developed direct and indirect methods to model seed production on regional managed wetlands. In September 2004 and 2005, we collected Echinochloa crusgalli (barnyard grass), E. walterii (wild millet), E. colona (jungle rice), and Oryza sativa (cultivated rice) for phytomorphological measurements and seed yield modeling. Initial simple linear and point of origin regression analyses demonstrate strong relationships (P \u3c 0.001) among phytomorphological and dot grid methods in predicting seed production for all four species. These models should help regional wetland managers evaluate moist-soil management success and create models for seed production for other moist-soil plants in this region

Real-time Tactical and Strategic Sales Management for Intelligent Agents Guided By Economic Regimes

Many enterprises that participate in dynamic markets need to make product pricing and inventory resource utilization decisions in real-time. We describe a family of statistical models that address these needs by combining characterization of the economic environment with the ability to predict future economic conditions to make tactical (short-term) decisions, such as product pricing, and strategic (long-term) decisions, such as level of finished goods inventories. Our models characterize economic conditions, called economic regimes, in the form of recurrent statistical patterns that have clear qualitative interpretations. We show how these models can be used to predict prices, price trends, and the probability of receiving a customer order at a given price. These “regime†models are developed using statistical analysis of historical data, and are used in real-time to characterize observed market conditions and predict the evolution of market conditions over multiple time scales. We evaluate our models using a testbed derived from the Trading Agent Competition for Supply Chain Management (TAC SCM), a supply chain environment characterized by competitive procurement and sales markets, and dynamic pricing. We show how regime models can be used to inform both short-term pricing decisions and longterm resource allocation decisions. Results show that our method outperforms more traditional shortand long-term predictive modeling approaches.dynamic pricing;trading agent competition;agent-mediated electronic commerce;dynamic markets;economic regimes;enabling technologies;price forecasting;supply-chain

Detecting and Forecasting Economic Regimes in Multi-Agent Automated Exchanges

We show how an autonomous agent can use observable market conditions to characterize the microeconomic situation of the market and predict future market trends. The agent can use this information to make both tactical decisions, such as pricing, and strategic decisions, such as product mix and production planning. We develop methods to learn dominant market conditions, such as over-supply or scarcity, from historical data using Gaussian mixture models to construct price density functions. We discuss how this model can be combined with real-time observable information to identify the current dominant market condition and to forecast market changes over a planning horizon. We forecast market changes via both a Markov correction-prediction process and an exponential smoother. Empirical analysis shows that the exponential smoother yields more accurate predictions for the current and the next day (supporting tactical decisions), while the Markov correction-prediction process is better for longer term predictions (supporting strategic decisions). Our approach offers more flexibility than traditional regression based approaches, since it does not assume a fixed functional relationship between dependent and independent variables. We validate our methods by presenting experimental results in a case study, the Trading Agent Competition for Supply Chain Management.dynamic pricing;machine learning;market forecasting;Trading agents

Speeding up Cylindrical Algebraic Decomposition by Gr\"obner Bases

Gr\"obner Bases and Cylindrical Algebraic Decomposition are generally thought of as two, rather different, methods of looking at systems of equations and, in the case of Cylindrical Algebraic Decomposition, inequalities. However, even for a mixed system of equalities and inequalities, it is possible to apply Gr\"obner bases to the (conjoined) equalities before invoking CAD. We see that this is, quite often but not always, a beneficial preconditioning of the CAD problem. It is also possible to precondition the (conjoined) inequalities with respect to the equalities, and this can also be useful in many cases.Comment: To appear in Proc. CICM 2012, LNCS 736