Tree-code simulations of proton acceleration from laser-irradiated wire targets

Recent experiments using Terawatt lasers to accelerate protons deposited on thin wire targets are modeled with a new type of gridless plasma simulation code. In contrast to conventional mesh-based methods, this technique offers a unique capability in emulating the complex geometry and open-ended boundary conditions characteristic of contemporary experimental conditions. Comparisons of ion acceleration are made between the tree code and standard particle-in-cell simulations for a typical collisionless

Speaker Recognition In Noisy Conditions With Limited Training Data

In this paper we present a novel method for performing speaker recognition with very limited training data and in the presence of background noise. Similarity-based speaker recognition is considered so that speaker models can be created with limited training speech data. The proposed similarity is a form of cosine similarity used as a distance measure between speech feature vectors. Each speech frame is modelled using subband features, and into this framework, multicondition training and optimal feature selection are introduced, making the system capable of performing speaker recognition in the presence of realistic, time-varying noise, which is unknown during training. Speaker identi?cation experiments were carried out using the SPIDRE database. The performance of the proposed new system for noise compensation is compared to that of an oracle model; the speaker identi?cation accuracy for clean speech by the new system trained with limited training data is compared to that of a GMM trained with several minutes of speech. Both comparisons have demonstrated the effectiveness of the new model. Finally, experiments were carried out to test the new model for speaker identi?cation given limited training data and with differing levels and types of realistic background noise. The results have demonstrated the robustness of the new system.

Why and how FDI stocks are a biased measure of MNE affiliate activity

Many international business (IB) studies have used foreign direct investment (FDI) stocks to measure the aggregate value-adding activity of multinational enterprises (MNE) affiliates in host countries. We argue that FDI stocks are a biased measure of that activity, because the degree to which they overestimate or underestimate affiliate activity varies systematically with host-country characteristics. First, most FDI into countries that serve as tax havens generate no actual productive activity; thus FDI stocks in such countries overestimate affiliate activity. Second, FDI stocks do not include locally raised external funds, funds widely used in countries with well-developed financial markets or volatile exchange rates, resulting in an underestimation of affiliate activity in such countries. Finally, the extent to which FDI translates into affiliate activity increases with affiliate labor productivity, so in countries where labor is more productive, FDI stocks also result in an underestimation of affiliate activity. We test these hypotheses by first regressing affiliate value-added and affiliate sales on FDI stocks to calculate a country-specific mismatch, and then by regressing this mismatch on a host country's tax haven status, level of financial market development, exchange rate volatility, and affiliate labor productivity. All hypotheses are supported, implying that FDI stocks are a biased measure of MNE affiliate activity, and hence that the results of FDI-data-based studies of such activity need to be reconsidered. [ABSTRACT FROM AUTHOR]

Tree-Based Adaptive Spatial Detection for Adaptive Modulated MIMO Systems

Adaptive Multiple-Input Multiple-Output (MIMO) systems achieve a much higher information rate than conventional fixed schemes due to their ability to adapt their configurations according to the wireless communications environment. However, current adaptive MIMO detection schemes exhibit either low performance (and hence low spectral efficiency) or huge computational
complexity. In particular, whilst deterministic Sphere Decoder (SD) detection schemes are well established for static MIMO systems, exhibiting deterministic parallel structure, low computational complexity and quasi-ML detection performance, there are no corresponding adaptive schemes. This paper solves
this problem, describing a hybrid tree based adaptive modulation detection scheme. Fixed Complexity Sphere Decoding (FSD) and Real-Values FSD (RFSD) are modified and combined into a hybrid scheme exploited at low and medium SNR to provide the highest possible information rate with quasi-ML Bit Error
Rate (BER) performance, while Reduced Complexity RFSD, BChase and Decision Feedback (DFE) schemes are exploited in the high SNR regions. This algorithm provides the facility to balance the detection complexity with BER performance with compatible information rate in dynamic, adaptive MIMO communications
environments.