Remembering the Good, Forgetting the Bad: Intentional Forgetting of Emotional Material in Depression.

The authors examined intentional forgetting of negative material in depression. Participants were instructed to not think about emotional nouns that they had learned to associate with a neutral cue word. The authors provided participants with multiple occasions to suppress the unwanted words. Overall, depressed participants successfully forgot negative words. Moreover, the authors obtained a clear practice effect. However, forgetting came at a cost: Compared with the nondepressed participants and with the depressed participants who were instructed to forget positive words, depressed participants who were instructed to forget negative words showed significantly worse recall of the baseline words. These results indicate that training depressed individuals in intentional forgetting could prove to be an effective strategy to counteract automatic ruminative tendencies and mood-congruent biases. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The Psychological Price of Media Bias.

Media bias was investigated through the effects of a TV interviewer's preferential behavior on the image of the interviewee in the eyes of the viewers. Judges viewed a political interview with either a friendly or a hostile interviewer then rated their impressions of the interviewed politician, whose behavior was identical in all conditions. The preferential nonverbal behavior of the interviewer (controlling for recognition and comprehension of verbal content) systematically influenced viewers' ratings of the politician. The effect consisted mainly of damage to the politician in the hostile interviewer condition. Describing the interviewee as a professor yielded a similar preferential behavior effect. A strong halo effect was identified, but it was ruled out as the mechanism accounting for the interviewer effect. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Influence diagnostics in regression with complex designs through conditional bias

One of the areas of Statistics in which the influence analysis has been widely studied is the multiple linear regression model. Nevertheless, the influence diagnostics proposed in this context cannot be applied to regression in complex survey, under randomized inference, since the i.i.d. case does not incorporate any probability weighting or population structure, such as clustering, stratification or measures of size into the analysis. In this paper we introduce some influence diagnostics in regression in complex survey. They are built on the conditional bias concept (Moreno-Rebollo et al., 1999). We emphasize the similarities and differences of the proposed measures with respect to the existing ones for the i.i.d. case. Research partly supported by the Spanish MCYT grant BFM2001-3844 and Spanish MEC grant MTM2004-0111433.     

一种基于启发式搜索的视差匹配的快速算法

提出了一种基于启发式搜索的视差匹配的快速算法，根据立体摄像系统的特性和图像本身的性质，迅速准确地进行视差估计。与完全搜索算法相比，在基本保持重建图像质量的前提下，能降低计算复杂度。     

消除卷积数值计算中的截尾误差的方法

有始无终信号的卷积在用数值计算方法来计算时会带来误差 ,包括截尾误差、近似误差和字长舍入误差 ,其中截尾误差往往较大。适当选取信号的长度 ,可以在相应时间段内消除截尾误差     

Algebraic Riccati equation and J-spectral factorization for estimation

In this paper we investigate on the existence of the stabilizing solution of the algebraic Riccati equation (ARE) related to the filtering problem with a prescribed attenuation level γ. It is well known that such a solution exists and is positive definite for γ larger than a certain γ_F and it does not exist for γ smaller than a certain γ₀. We consider the intermediate case γ(γ₀,γ_F] and show that in this interval the stabilizing solution does exist, except for a finite number of values of γ. We show how the solution of the ARE may be employed to obtain a minimum-phase J-spectral factor of the J-spectrum associated with the filtering problem.     

噪声对贝叶斯频谱估计精度的影响

研究了贝叶斯频谱估计的基本理论和基本方法 ,分别对无噪声和有噪声的简谐正弦信号进行了频谱估计。计算结果显示 :无噪声信号频率估计偏倚为零 ;当信号噪声系数S/N =1时 ,贝叶斯频率估计的偏倚与Jaynes的理论值一致     

Objectivity in the Eye of the Beholder: Divergent Perceptions of Bias in Self Versus Others.

Important asymmetries between self-perception and social perception arise from the simple fact that other people's actions, judgments, and priorities sometimes differ from one's own. This leads people not only to make more dispositional inferences about others than about themselves (E. E. Jones & R. E. Nisbett, 1972) but also to see others as more susceptible to a host of cognitive and motivational biases. Although this blind spot regarding one's own biases may serve familiar self-enhancement motives, it is also a product of the phenomenological stance of naive realism. It is exacerbated, furthermore, by people's tendency to attach greater credence to their own introspections about potential influences on judgment and behavior than they attach to similar introspections by others. The authors review evidence, new and old, of this asymmetry and its underlying causes and discuss its relation to other psychological phenomena and to interpersonal and intergroup conflict. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Blink and Shrink: The Effect of the Attentional Blink on Spatial Processing.

The detection or discrimination of the second of 2 targets in a rapid serial visual presentation (RSVP) task is often temporarily impaired-a phenomenon termed the attentional blink. This study demonstrated that the attentional blink also affects localization performance. Spatial cues pointed out the possible target positions in a subsequent visual search display. When cues were presented inside an attentional blink (as induced by an RSVP task), the observers' capacity to use them was reduced. This effect was not due to attention being highly focused, to general task switching costs, or to complete unawareness of the cues. Instead, the blink induced a systematic localization bias toward the fovea, reflecting what appears to be spatial compression. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Qualitative estimation of camera motion parameters from the linear composition of optical flow

In this paper, we propose a new method for estimating camera motion parameters based on optical flow models. Camera motion parameters are generated using linear combinations of optical flow models. The proposed method first creates these optical flow models, and then linear decompositions are performed on the input optical flows calculated from adjacent images in the video sequence, which are used to estimate the coefficients of each optical flow model. These coefficients are then applied to the parameters used to create each optical flow model, and the camera motion parameters implied in the adjacent images can be estimated through a linear composition of the weighted parameters.We demonstrated that the proposed method estimates the camera motion parameters accurately and at a low computational cost as well as robust to noise residing in the video sequence being analyzed.