Neural networks for contract bridge bidding

The objective of this study is to explore the possibility of capturing the reasoning process used in bidding a hand in a bridge game by an artificial neural network. We show that a multilayer feedforward neural network can be trained to learn to make an opening bid with a new hand. The game of bridge, like many other games used in artificial intelligence, can easily be represented in a machine. But, unlike most games used in artificial intelligence, bridge uses subtle reasoning over and above the agreed conventional system, to make a bid from the pattern of a given hand. Although it is difficult for a player to spell out the precise reasoning process he uses, we find that a neural network can indeed capture it. We demonstrate the results for the case of one-level opening bids, and discuss the need for a hierarchical architecture to deal with bids at all levels.     

用广播与选择型OADM实现低成本透明光传输

互联网业务和数据业务的不断增长，使长途骨干网中的业务传送模式发生了显著变化。目前，互联网业务流比传统的话音业务流的传送距离远得多。研究表明，从2000年到2005年，北美地区的互联网业务流的平均传输距离将从目前的740公里增长到2700公里。业务传送模式的根本性变化，要求网络设计与运营模式也作相应的更新。今后，网络节点必须能提供更多的带宽和更长的电路连接。如果采用不透明的光网络，所有的信号都必须在每个中间节点进行再生，对于60%的传输距离不超过600公里的话音业务而言是不成问题的；但对于60%的传输距离超过3000公里的互…     

Optimal size,hydraulic retention time and volatile solids loading rate of biogas unit using water hyacinth

With a view to evaluate the effects of hydraulic retention time and volatile solids loading rate on methane production using water hyacinth, experiments were conducted in a 160 dm³ capacity, well-mixed continuous digestion unit at ambient temperature. The optimal design of biogas plant using water hyacinth was elucidated, to provide a unit giving a maximum methane yield of 0.16 nm³ kg^?1 volatile solids added, which was 60% greater than the conventional plant using cowdung. The calculations of optimal design factors were based on reactor size, hydraulic retention time and volatile solids loading rate; considerations were made of capital cost, running costs and operational revenue.     

A 4.5-GHz 130-nm 32-KB L0 cache with a leakage-tolerant self reverse-bias bitline scheme

This paper describes a 32-KB two-read, one-write ported L0 cache for 4.5-GHz operation in 1.2-V 130-nm dual-V/sub TH/ CMOS technology. The local bitline uses a leakage-tolerant self reverse-bias (SRB) scheme with nMOS source-follower pullup access transistors, while preserving robust full-swing operation. Gate-source underdrive of -220 mV on the bitline read-select transistors is established without external bias voltages or gate-oxide overstress. Device-level measurements in the 130-nm technology show 72/spl times/ bitline active leakage reduction, enabling low-V/sub TH/ usage, 40% bitline keeper downsizing, and 16 bitcells/bitline. 11% faster read delay and 2/spl times/ higher dc noise robustness are achieved compared with high-performance dual-V/sub TH/ bitline scheme. Sustained performance and robustness benefits of the SRB technique against conventional dynamic bitline with scaling to 100- and 70-nm technology is also presented.     

A finite range failure model

In the present article the characterstics of a finite range failure time distribution, that includes the rectangular distribution as a particular case, are studied. When shape parameter surpass unity then the distribution is IFR otherwise it always remains IFRA. A graphic shape of the distribution is also formed for certain admissible values of the constants.     

Thermal stability in bulk cryomilled ultrafine-grained 5083 Al alloy

Thermal stability in bulk ultrafine-grained (UFG) 5083 Al that was processed by gas atomization followed by cryomilling, consolidation, and extrusion, and that exhibited an average grain size of 305 nm, was investigated in the temperature range of 473 to 673 K (0.55 to 0.79 T _m , where T _m is the melting temperature of the material) for different annealing times. Appreciable grain growth was observed at temperatures > 573 K, whereas there was limited grain growth at temperatures < 573 K even after long annealing times. The values of the grain growth exponent, n, deduced from the grain growth data were higher than the value of 2 predicted from elementary grain growth theories. The discrepancy was attributed to the operation of strong pinning forces on boundaries during the annealing treatment. An examination of the microstructure of the alloy suggests that the origin of the pinning forces is most likely related to the presence of dispersion particles, which are mostly introduced during cryomilling. Two-grain growth regimes were identified: the low-temperature region (<573 K) and the high-temperature region (>573 K). For temperatures lower than 573 K, the activation energy of 25 ± 5 kJ/mol was determined. It is suggested that this low activation energy represents the energy for the reordering of grain boundaries in the UFG material. For temperatures higher than 573 K, an activation energy of 124 ± 5 kJ/mol was measured. This value of activation energy, 124 ± 5 kJ/mol, lies between that for grain boundary diffusion and lattice diffusion in analogous aluminum polycrystalline systems. The results show that the strength and ductility of bulk UFG 5083 Al, as obtained from tensile tests, correlate well with substructural changes introduced in the alloy by the annealing treatment.     

An actor-critic algorithm for constrained Markov decision processes

An actor-critic type reinforcement learning algorithm is proposed and analyzed for constrained controlled Markov decision processes. The analysis uses multiscale stochastic approximation theory and the envelope theorem' of mathematical economics.     

Energy-Efficient Thermal-Aware Autonomic Management of Virtualized HPC Cloud Infrastructure

Virtualized datacenters and clouds are being increasingly considered for traditional High-Performance Computing (HPC) workloads that have typically targeted Grids and conventional HPC platforms. However, maximizing energy efficiency and utilization of datacenter resources, and minimizing undesired thermal behavior while ensuring application performance and other Quality of Service (QoS) guarantees for HPC applications requires careful consideration of important and extremely challenging tradeoffs. Virtual Machine (VM) migration is one of the most common techniques used to alleviate thermal anomalies (i.e., hotspots) in cloud datacenter servers as it reduces load and, hence, the server utilization. In this article, the benefits of using other techniques such as voltage scaling and pinning (traditionally used for reducing energy consumption) for thermal management over VM migrations are studied in detail. As no single technique is the most efficient to meet temperature/performance optimization goals in all situations, an autonomic approach that performs energy-efficient thermal management while ensuring the QoS delivered to the users is proposed. To address the problem of VM allocation that arises during VM migrations, an innovative application-centric energy-aware strategy for Virtual Machine (VM) allocation is proposed. The proposed strategy ensures high resource utilization and energy efficiency through VM consolidation while satisfying application QoS by exploiting knowledge obtained through application profiling along multiple dimensions (CPU, memory, and network bandwidth utilization). To support our arguments, we present the results obtained from an experimental evaluation on real hardware using HPC workloads under different scenarios.     

A stochastic Kaczmarz algorithm for network tomography

We develop a stochastic approximation version of the classical Kaczmarz algorithm that is incremental in nature and takes as input noisy real time data. Our analysis shows that with probability one it mimics the behavior of the original scheme: starting from the same initial point, our algorithm and the corresponding deterministic Kaczmarz algorithm converge to precisely the same point. The motivation for this work comes from network tomography where network parameters are to be estimated based upon end-to-end measurements. Numerical examples via Matlab based simulations demonstrate the efficacy of the algorithm.