RSVP任务下双刺激重复的优势与劣势

本研究探讨重复知盲发生在知觉阶段还是在记忆阶段。采用快速视觉系列呈现(rapid serial visual representation, 简称RSVP)任务, 让被试即时回忆80个词表中呈现的词。操作了词表中目标词的重复数, 分为无重复、单刺激重复(重复一对)和双刺激重复(重复两对)三种, 和目标词的情绪效价, 分为中性词和情绪词两种。结果发现, 对目标词回忆的正确率是：当目标词和非目标词都是中性词汇, 即二者的情绪效价强度相当时(实验1), 在无重复条件与双刺激重复条件下无差异, 二者均大于单刺激重复条件; 当目标词是消极词汇, 非目标词是中性词汇, 即目标刺激的情绪效价强度大于非目标刺激时(实验2), 无重复条件与单刺激重复条件无差异, 二者均大于双刺激重复条件。结果表明：(1)RSVP 任务下词表中有双刺激重复时, 如果刺激的效价强度相当, 出现重复优势; 如果目标刺激的效价强度高于非目标刺激, 出现重复劣势; (2)人们会主动分配更多的注意资源给效价高的刺激, 重复知盲产生在记忆阶段, 支持注意资源分配最优化假设。     

Extending Energy Optimization in Goal-Directed Aiming from Movement Kinematics to Joint Angles

Energy optimization in goal-directed aiming has been demonstrated as an undershoot bias in primary movement endpoint locations, especially in conditions where corrections to target overshoots must be made against gravity. Two-component models of upper limb movement have not yet considered how joint angles are organized to deal with the energy constraints associated with moving the upper limb in goal-directed aiming tasks. To address this limitation, participants performed aiming movements to targets in the up and down directions with the index finger and two types of rod extensions attached to the index finger. The rod extensions were expected to invoke different energy optimizing strategies in the up and down directions by allowing the distal joints the opportunity to contribute to end effector displacement. Primary movements undershot the farthest target to a greater extent in the downward direction compared to the upward direction, showing that movement kinematics optimize energy expenditure in consideration of the effects of gravity. As rod length increased, shoulder elevation was optimized in movements to the far-up target and elbow flexion was optimally minimized in movements to the far-down target. The results suggest energy optimization in the control of joint angles independent of the force of gravity.     

Reducing Disparities in Cancer Screening and Prevention through Community‐Based Participatory Research Partnerships with Local Libraries: A Comprehensive Dynamic Trial

Reduction of cancer‐related disparities requires strategies that link medically underserved communities to preventive care. In this community‐based participatory research project, a public library system brought together stakeholders to plan and undertake programs to address cancer screening and risk behavior. This study was implemented over 48 months in 20 large urban neighborhoods, selected to reach diverse communities disconnected from care. In each neighborhood, Cancer Action Councils were organized to conduct a comprehensive dynamic trial, an iterative process of program planning, implementation and evaluation. This process was phased into neighborhoods in random, stepped‐wedge sequence. Population‐level outcomes included self‐reported screening adherence and smoking cessation, based on street intercept interviews. Event‐history regressions (n = 9374) demonstrated that adherence outcomes were associated with program implementation, as were mediators such as awareness of screening programs and cancer information seeking. Findings varied by ethnicity, and were strongest among respondents born outside the U.S. or least engaged in care. This intervention impacted health behavior in diverse, underserved and vulnerable neighborhoods. It has been sustained as a routine library system program for several years after conclusion of grant support. In sum, participatory research with the public library system offers a flexible, scalable approach to reduce cancer health disparities.     

Output‐sensitive complexity of multiobjective combinatorial optimization

We study output‐sensitive algorithms and complexity for multiobjective combinatorial optimization problems. In this computational complexity framework, an algorithm for a general enumeration problem is regarded efficient if it is output‐sensitive, that is, its running time is bounded by a polynomial in the input and the output size. We provide both practical examples of multiobjective combinatorial optimization problems for which such an efficient algorithm exists as well as problems for which no efficient algorithm exists under mild complexity theoretic assumptions.     

Nice Guys Finish Fast and Bad Guys Finish Last: Facilitatory vs. Inhibitory Interaction in Parallel Systems

Systems Factorial Technology is a powerful framework for investigating the fundamental properties of human information processing such as architecture (i.e., serial or parallel processing) and capacity (how processing efficiency is affected by increased workload). The Survivor Interaction Contrast (SIC) and the Capacity Coefficient are effective measures in determining these underlying properties, based on response-time data. Each of the different architectures, under the assumption of independent processing, predicts a specific form of the SIC along with some range of capacity. In this study, we explored SIC predictions of discrete-state (Markov process) and continuous-state (Linear Dynamic) models that allow for certain types of cross-channel interaction. The interaction can be facilitatory or inhibitory: one channel can either facilitate, or slow down processing in its counterpart. Despite the relative generality of these models, the combination of the architecture oriented plus the capacity oriented analyses provide for precise identification of the underlying system.     

耐多药肺结核的最优化治疗模式的几点思考

耐多药结核病是结核病治疗中的一个难点,由于耐药成因和耐药种类的多样性决定了治疗应该个体化,对耐多药肺结核的最优化治疗模式的应用和价值进行了思考和总结,这对结核病的治疗有极为重要的意义。     

A branch-and-bound algorithm for fitting anti-robinson structures to symmetric dissimilarity matrices

The seriation of proximity matrices is an important problem in combinatorial data analysis and can be conducted using a variety of objective criteria. Some of the most popular criteria for evaluating an ordering of objects are based on (anti-) Robinson forms, which reflect the pattern of elements within each row and/or column of the reordered matrix when moving away from the main diagonal. This paper presents a branch-and-bound algorithm that can be used to seriate a symmetric dissimilarity matrix by identifying a reordering of rows and columns of the matrix optimizing an anti-Robinson criterion. Computational results are provided for several proximity matrices from the literature using four different anti-Robinson criteria. The results suggest that with respect to computational efficiency, the branch-and-bound algorithm is generally competitive with dynamic programming. Further, because it requires much less storage than dynamic programming, the branch-and-bound algorithm can provide guaranteed optimal solutions for matrices that are too large for dynamic programming implementations.     

Riemannian Newton and trust-region algorithms for analytic rotation in exploratory factor analysis

In exploratory factor analysis, latent factors and factor loadings are seldom interpretable until analytic rotation is performed. Typically, the rotation problem is solved by numerically searching for an element in the manifold of orthogonal or oblique rotation matrices such that the rotated factor loadings minimize a pre-specified complexity function. The widely used gradient projection (GP) algorithm, although simple to program and able to deal with both orthogonal and oblique rotation, is found to suffer from slow convergence when the number of manifest variables and/or the number of latent factors is large. The present work examines the effectiveness of two Riemannian second-order algorithms, which respectively generalize the well-established truncated Newton and trust-region strategies for unconstrained optimization in Euclidean spaces, in solving the rotation problem. When approaching a local minimum, the second-order algorithms usually converge superlinearly or even quadratically, better than first-order algorithms that only converge linearly. It is further observed in Monte Carlo studies that, compared to the GP algorithm, the Riemannian truncated Newton and trust-region algorithms require not only much fewer iterations but also much less processing time to meet the same convergence criterion, especially in the case of oblique rotation.     

On the advancement and software support of decision‐making in focused ultrasound therapy

Focused ultrasound therapy (FUS) is a modern and promising way for minimally invasive cancer treatment. Recent advances in treatment technology, bio‐physical models, and numerical simulation methods have given rise to a significant curative potential. However, clinical routine of FUS still features classical planning approaches, which widely fail in exploiting this potential. The structure of FUS planning problems strongly suggests interactive multi‐criteria decision‐making concepts in order to improve treatment quality. This research work introduces an multi‐criteria decision‐making approach to FUS planning and explains how to bridge the conceptual gap between the clinical state of the art and this new planning paradigm.     

A bare bones bacterial foraging optimization algorithm

Bacterial foraging optimization (BFO), based on the social foraging behaviors of bacteria, is a new intelligent optimizer. It has been widely accepted as an optimization algorithm of current interest for a variety of fields. However, compared with other optimizers, the BFO possesses a poor convergence performance over complex optimization problems. To improve the optimization capability of the BFO, in this paper a bare bones bacterial foraging optimization (BBBFO) algorithm is developed. First, a chemotactic strategy based on Gaussian distribution is incorporated into this method through making use of both the historical information of individual and the share information of group. Then the swarm diversity is introduced in the reproduction strategy to promote the exploration ability of the algorithm. The performance of BBBFO is verified on various benchmark functions, the comparative results reveal that the proposed approach is more superior to its counterparts.