Human pavlovian-instrumental transfer.

The vigor with which a participant performs actions that produce valuable outcomes is subject to a complex set of motivational influences. Many of these are believed to involve the amygdala and the nucleus accumbens, which act as an interface between limbic and motor systems. One prominent class of influences is called pavlovian-instrumental transfer (PIT), in which the motivational characteristics of a predictor influence the vigor of an action with respect to which it is formally completely independent. We provide a demonstration of behavioral PIT in humans, with an audiovisual predictor of the noncontingent delivery of money inducing participants to perform more avidly an action involving squeezing a handgrip to earn money. Furthermore, using functional magnetic resonance imaging, we show that this enhanced motivation was associated with a trial-by-trial correlation with the blood oxygenation level-dependent (BOLD) signal in the nucleus accumbens and a subject-by-subject correlation with the BOLD signal in the amygdala. Our data dovetails well with the animal literature and sheds light on the neural control of vigor.

N-gram posterior probability confidence measures for statistical machine translation: An empirical study

We report an empirical study of n-gram posterior probability confidence measures for statistical machine translation (SMT). We first describe an efficient and practical algorithm for rapidly computing n-gram posterior probabilities from large translation word lattices. These probabilities are shown to be a good predictor of whether or not the n-gram is found in human reference translations, motivating their use as a confidence measure for SMT. Comprehensive n-gram precision and word coverage measurements are presented for a variety of different language pairs, domains and conditions. We analyze the effect on reference precision of using single or multiple references, and compare the precision of posteriors computed from k-best lists to those computed over the full evidence space of the lattice. We also demonstrate improved confidence by combining multiple lattices in a multi-source translation framework. © 2012 The Author(s).

Statistics anxiety, trait anxiety, learning behavior, and academic performance

The present study investigated the relationship between statistics anxiety, individual characteristics (e.g., trait anxiety and learning strategies), and academic performance. Students enrolled in a statistics course in psychology (N=147) filled in a questionnaire on statistics anxiety, trait anxiety, interest in statistics, mathematical selfconcept, learning strategies, and procrastination. Additionally, their performance in the examination was recorded. The structural equation model showed that statistics anxiety held a crucial role as the strongest direct predictor of performance. Students with higher statistics anxiety achieved less in the examination and showed higher procrastination scores. Statistics anxiety was related indirectly to spending less effort and time on learning. Trait anxiety was related positively to statistics anxiety and, counterintuitively, to academic performance. This result can be explained by the heterogeneity of the measure of trait anxiety. The part of trait anxiety that is unrelated to the specific part of statistics anxiety correlated positively with performance.

Probability-Matching Predictors for Extreme Extremes

A location- and scale-invariant predictor is constructed which exhibits good probability matching for extreme predictions outside the span of data drawn from a variety of (stationary) general distributions. It is constructed via the three-parameter {\mu, \sigma, \xi} Generalized Pareto Distribution (GPD). The predictor is designed to provide matching probability exactly for the GPD in both the extreme heavy-tailed limit and the extreme bounded-tail limit, whilst giving a good approximation to probability matching at all intermediate values of the tail parameter \xi. The predictor is valid even for small sample sizes N, even as small as N = 3. The main purpose of this paper is to present the somewhat lengthy derivations which draw heavily on the theory of hypergeometric functions, particularly the Lauricella functions. Whilst the construction is inspired by the Bayesian approach to the prediction problem, it considers the case of vague prior information about both parameters and model, and all derivations are undertaken using sampling theory.

Temporal Coherence of Chlorophyll a during a Spring Phytoplankton Bloom in Xiangxi Bay of Three-Gorges Reservoir, China

Algal bloom phenomenon was defined as "the rapid growth of one or more phytoplankton species which leads to a rapid increase in the biomass of phytoplankton", yet most estimates of temporal coherence are based on yearly or monthly sampling frequencies and little is known of how synchrony varies among phytoplankton or of the causes of temporal coherence during spring algal bloom. In this study, data of chlorophyll a and related environmental parameters were weekly gathered at 15 sampling sites in Xiangxi Bay of Three-Gorges Reservoir (TGR, China) to evaluate patterns of temporal coherence for phytoplankton during spring bloom and test if spatial heterogeneity of nutrient and inorganic suspended particles within a single ecosystem influences synchrony of spring phytoplankton dynamics. There is a clear spatial and temporal variation in chlorophyll a across Xiangxi Bay. The degree of temporal coherence for chlorophyll a between pairs of sites located in Xiangxi Bay ranged from -0.367 to 0.952 with mean and median values of 0.349 and 0.321, respectively. Low levels of temporal coherence were often detected among the three stretches of the bay (Down reach, middle reach and upper reach), while high levels of temporal coherence were often found within the same reach of the bay. The relative difference of DIN between pair sites was the strong predictor of temporal coherence for chlorophyll a in down and middle reach of the bay, while the relative difference in Anorganic Suspended Solids was the important factor regulating temporal coherence in middle and upper reach. Contrary to many studies, these results illustrate that, in a small geographic area (a single reservoir bay of approximately 25 km), spatial heterogeneity influence synchrony of phytoplankton dynamics during spring bloom and local processes may override the effects of regional processes or dispersal.

Low-rank optimization with trace norm penalty

The paper addresses the problem of low-rank trace norm minimization. We propose an algorithm that alternates between fixed-rank optimization and rank-one updates. The fixed-rank optimization is characterized by an efficient factorization that makes the trace norm differentiable in the search space and the computation of duality gap numerically tractable. The search space is nonlinear but is equipped with a Riemannian structure that leads to efficient computations. We present a second-order trust-region algorithm with a guaranteed quadratic rate of convergence. Overall, the proposed optimization scheme converges superlinearly to the global solution while maintaining complexity that is linear in the number of rows and columns of the matrix. To compute a set of solutions efficiently for a grid of regularization parameters we propose a predictor-corrector approach that outperforms the naive warm-restart approach on the fixed-rank quotient manifold. The performance of the proposed algorithm is illustrated on problems of low-rank matrix completion and multivariate linear regression. © 2013 Society for Industrial and Applied Mathematics.

Plankton Community Succession in Artificial Systems Subjected to Cyanobacterial Blooms Removal using Chitosan-Modified Soils

Using artificial systems to simulate natural lake environments with cyanobacterial blooms, we investigated plankton community succession by polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) fingerprinting and morphological method. With this approach, we explored potential ecological effects of a newly developed cyanobacterial blooms removal method using chitosan-modified soils. Results of PCR-DGGE and morphological identification showed that plankton communities in the four test systems were nearly identical at the beginning of the experiment. After applying the newly developed and standard removal methods, there was a shift in community composition, but neither chemical conditions nor plankton succession were significantly affected by the cyanobacteria removal process. The planted Vallisneria natans successfully recovered after cyanobacteria removal, whereas that in the box without removal process did not. Additionally, canonical correspondence analysis indicated that other than for zooplankton abundance, total phosphorus was the most important environmental predictor of planktonic composition. The present study and others suggest that dealing with cyanobacteria removal using chitosan-modified soils can play an important role in controlling cyanobacterial blooms in eutrophicated freshwater systems.

Prenatal exposures to persistent organic pollutants as measured in cord blood and meconium from three localities of Zhejiang, China

Prenatal exposures to persistent organic pollutants were assessed using the levels of PCBs and organochlorine pesticides (OCPs) measured in cord blood and meconium samples from Luqiao and two other localities of the Zhejiang province in China. Luqiao is a town with the largest site for disassembly of PCB-containing obsolete transformers and electrical waste in China. The other two localities Pingqiao (100 km NW of Luqiao) and Lin'an (500 km NW of Luqiao) are towns without known electronic or electrical waste sites. A total of 23 PCB congeners (including 12 dioxin-like) and 6 OCPs were measured using the traditional GC-mu ECD technique. Micro-EROD bioassay was additionally used to measure TCDD-based TEQ levels of the 12 dioxin-like PCBs. Significant correlations were found between the TEQs measured by the two methods, supporting the application of micro-EROD as a practical toot for complementing the chemical analysis. The data showed that beta-HCH, p,p'-DDE, and 6 PCB congeners (101, 138 153, 180, 183, and 187) were the predominant pollutants, with PCB 138 being the best indicator (predictor) for total PCB levels. Cord blood and meconium from Luqiao have higher levels of PCBs than those from the other two localities, suggesting that a disassembly site for electronic and electric waste would provide an environment for greater exposure to these chemicals. The cord blood or meconium levels of beta-HCH, though likewise considerably high, were comparable in the three localities. Similar findings were observed for p,p'-DDE. Pollution by these OCPs might have come from past use of agricultural pesticides in the three localities. (c) 2007 Published by Elsevier B.V.

Hybrid numerical methods for exponential models of growth

A series of novel numerical methods for the exponential models of growth are proposed. Based on these methods, hybrid predictor-corrector methods are constructed. The hybrid numerical methods can increase the accuracy and the computing speed obviously, as well as enlarge the stability domain greatly. (c) 2005 Published by Elsevier Inc.

Partially Supervised Neighbor Embedding for Example-Based Image Super-Resolution

Neighbor embedding algorithm has been widely used in example-based super-resolution reconstruction from a single frame, which makes the assumption that neighbor patches embedded are contained in a single manifold. However, it is not always true for complicated texture structure. In this paper, we believe that textures may be contained in multiple manifolds, corresponding to classes. Under this assumption, we present a novel example-based image super-resolution reconstruction algorithm with clustering and supervised neighbor embedding (CSNE). First, a class predictor for low-resolution (LR) patches is learnt by an unsupervised Gaussian mixture model. Then by utilizing class label information of each patch, a supervised neighbor embedding is used to estimate high-resolution (HR) patches corresponding to LR patches. The experimental results show that the proposed method can achieve a better recovery of LR comparing with other simple schemes using neighbor embedding.

一种新的角加速度估计方法及其应用

首先提出了一种新的基于卡尔曼滤波及牛顿预测的角加速度估计方法,在已知电机驱动系统位置信息的情况下,利用卡尔曼滤波实时估计系统的角加速度;同时采用牛顿预测方法解决估计算法的滞后问题,进一步提高了估计加速度的响应频带．以此为基础,本文进一步分析了利用估计加速度进行反馈控制以增强系统对外扰动的鲁棒性问题,提出了加速度反馈控制策略的设计准则并分析了稳定性．在一个直接驱动机器人关节上针对上述加速度估计及控制方法进行了实验研究:将估计加速度的实验结果与实测加速度(利用加速度计)的实验结果进行了比较分析,从而定量地揭示出估计加速度及其反馈控制在实际系统中的可行性及有效性．

基于web的遥操作系统时延分析

采用预测控制算法给出了一种带有时延补偿器的新的控制结构,分别在前向通道和反馈通道设计补偿器对网络时延进行补偿.实验结果表明:带有预测器及补偿器的新的控制结构可以改善系统的动态性能,并且能够保证系统在具有时延和数据丢失的环境下的稳定性.

基于卡尔曼滤波及牛顿预测的角加速度估计方法试验研究

基于角位置测量的角加速度实时估计问题是机电系统控制中一个非常重要的问题,在分析现有的线性回归平滑牛顿法和卡尔曼滤波法的基础上,提出了一种新的基于卡尔曼滤波和牛顿预测相结合的角加速度估计方法。该方法旨在利用牛顿预测进一步增强卡尔曼滤波的预测能力,减小由于滤波造成的相位滞后,提高估计加速度与实测加速度的一致性。为了验证新方法的有效性,以直接驱动机器人作为试验对象,采用将估计加速度的频率特性与实测加速度相比较的方法,分别对上述三种估计算法进行了试验比较研究,从而为利用估计加速度(取代测量加速度)实现加速度反馈控制提供了试验依据。

Smith预估器在纳米操作系统中的应用

针对纳米操作系统中存在的滞后性质,提出了带Smith预估器的PID控制方法。从理论上解决了纳米操作系统中由于纯滞后性质而引起的系统超调或振荡。从而保证了纳米操作中观测的精度和准确性。

水下机器人神经网络自适应逆控制

水下环境的复杂性以及自身模型的不确定性,给水下机器人的控制带来很大困难。针对水下机器人的特点和控制方面所存在的问题,提出了基于预测 校正控制策略的水下机器人神经网络自适应逆控制结构及训练算法。通过在线辨识系统的前向模型,估计出系统的Jacobian矩阵,然后采用预报误差法实现控制器的自适应。同时,为了提高系统对于外扰的鲁棒性,在伪线性回归算法的基础上,在评价函数中引入微分项。理论分析和仿真结果表明,与原来的算法相比,微分项的引入改善了系统对于外扰的鲁棒性和动态性能。