Exact solutions for universal holonomic quantum gates

We show how one can implement any local quantum gate on specific qubits in an array of qubits by carrying adiabatically a Hamiltonian around a closed loop. We find the exact form of the loop and the Hamiltonian for implementing general one and two qubits gates. Our method is analytical and is not based on numerical search in the space of all loops.Comment: 10 pages, no figures, Accepted for Publication in Phys. Rev.

Parameterization adaption for 3D shape optimization in aerodynamics

When solving a PDE problem numerically, a certain mesh-refinement process is always implicit, and very classically, mesh adaptivity is a very effective means to accelerate grid convergence. Similarly, when optimizing a shape by means of an explicit geometrical representation, it is natural to seek for an analogous concept of parameterization adaptivity. We propose here an adaptive parameterization for three-dimensional optimum design in aerodynamics by using the so-called "Free-Form Deformation" approach based on 3D tensorial B\'ezier parameterization. The proposed procedure leads to efficient numerical simulations with highly reduced computational costs

On the structure of sequentially Cohen--Macaulay bigraded modules

Let $K$ be a field and $S=K[x_1,\ldots,x_m, y_1,\ldots,y_n]$ be the standard bigraded polynomial ring over $K$. In this paper, we explicitly describe the structure of finitely generated bigraded "sequentially Cohen--Macaulay" $S$-modules with respect to $Q=(y_1,\ldots,y_n)$. Next, we give a characterization of sequentially Cohen--Macaulay modules with respect to $Q$ in terms of local cohomology modules. Cohen--Macaulay modules that are sequentially Cohen--Macaulay with respect to $Q$ are considered

Fixed parity of the exchange rate and economic performance in the CFA zone : a comparative study

The authors compare economic performance in the CFA (franc) zone with the economic performance in similar countries outside the CFA zone in recent years. The results of their model estimates indicate that the competitive position for CFA members was weaker in the second half of the 1980s than in the first half and weaker than in non-CFA countries in terms of output growth as well as the performance of exports, investment, and savings. The exception was domestic inflation: the CFA fared better on that front. Results for a longer-term comparison are somewhat mixed. The CFA zone performed better than the others in exports, domestic savings and investment, and inflation, but failed in the long run to distinguish itself in terms of economic growth. The authors use a modified control group approach to compare changes in macroeconomic indicators in the CFA countries with those in countries elsewhere in sub-Saharan Africa and similar low-income developing countries. They control for initial conditions, changing exogenous internal and world environment, and policy stance. Their approach allows for a formal testing of whether zone membership is a random choice. The implication of randomness is that the CFA zone economies would have performed the same as the rest of sub-Saharan Africa, for example, if there had been no zone. Their results show the assumption of randomness to be valid only for GDP growth and inflation.Economic Theory&Research,Environmental Economics&Policies,Economic Stabilization,Achieving Shared Growth,Financial Intermediation

StreamLearner: Distributed Incremental Machine Learning on Event Streams: Grand Challenge

Today, massive amounts of streaming data from smart devices need to be analyzed automatically to realize the Internet of Things. The Complex Event Processing (CEP) paradigm promises low-latency pattern detection on event streams. However, CEP systems need to be extended with Machine Learning (ML) capabilities such as online training and inference in order to be able to detect fuzzy patterns (e.g., outliers) and to improve pattern recognition accuracy during runtime using incremental model training. In this paper, we propose a distributed CEP system denoted as StreamLearner for ML-enabled complex event detection. The proposed programming model and data-parallel system architecture enable a wide range of real-world applications and allow for dynamically scaling up and out system resources for low-latency, high-throughput event processing. We show that the DEBS Grand Challenge 2017 case study (i.e., anomaly detection in smart factories) integrates seamlessly into the StreamLearner API. Our experiments verify scalability and high event throughput of StreamLearner.Comment: Christian Mayer, Ruben Mayer, and Majd Abdo. 2017. StreamLearner: Distributed Incremental Machine Learning on Event Streams: Grand Challenge. In Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems (DEBS '17), 298-30