Implicit social cognition: Attitudes, self-esteem, and stereotypes.

Social behavior is ordinarily treated as being under conscious (if not always thoughtful) control. However, considerable evidence now supports the view that social behavior often operates in an implicit or unconscious fashion. The identifying feature of implicit cognition is that past experience influences judgment in a fashion not introspectively known by the actor. The present conclusion--that attitudes, self-esteem, and stereotypes have important implicit modes of operation--extends both the construct validity and predictive usefulness of these major theoretical constructs of social psychology. Methodologically, this review calls for increased use of indirect measures--which are imperative in studies of implicit cognition. The theorized ordinariness of implicit stereotyping is consistent with recent findings of discrimination by people who explicitly disavow prejudice. The finding that implicit cognitive effects are often reduced by focusing judges' attention on their judgment task provides a basis for evaluating applications (such as affirmative action) aimed at reducing such unintended discrimination. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Incorporating statistical information into expert classification systems to reduce classification costs

Interactive expert systems seek relevant information from a user in order to answer a query or to solve a problem that the user has posed. A fundamental design issue for such a system is therefore itsinformation-seeking strategy, which determines the order in which it asks questions or performs experiments to gain the information that it needs to respond to the user. This paper examines the problem of optimal knowledge acquisition through questioning in contexts where it is expensive or time-consuming to obtain the answers to questions. An abstract model of an expert classification system — considered as a set of logical classification rules supplemented by some statistical knowledge about attribute frequencies — is developed and applied to analyze the complexity and to present constructive algorithms for doing probabilistic question-based classification. New heuristics are presented that generalize previous results for optimal identification keys and questionnaires. For an important class of discrete discriminant analysis problems, these heuristics find optimal or near-optimal questioning strategies in a small fraction of the time required by an exact solution algorithm.     

An Operator-integration-factor splitting method for time-dependent problems: Application to incompressible fluid flow

In this paper we present a simple, general methodology for the generation of high-order operator decomposition (splitting) techniques for the solution of time-dependent problems arising in ordinary and partial differential equations. The new approach exploits operator integration factors to reduce multiple-operator equations to an associated series of single-operator initial-value subproblems. Two illustrations of the procedure are presented: the first, a second-order method in time applied to velocity-pressure decoupling in the incompressible Stokes problem; the second, a third-order method in time applied to convection-Stokes decoupling in the incompressible Navier-Stokes equations. Critical open questions are briefly described.     

Specification and initialization of a logic computer system

A logic computer system consists of an inference machine and a compatible logic operating system. This paper describes prospective models for a logic computer system, and its hardware and software components. The language Concurrent Prolog serves as the single implementation, specification, and machine language. The computer system is represented as a logic programming goallogic_computer_system. Specification of the system corresponds to resolution of this goal. Clauses used to solve the goal — and ensuing subgoals — progressively refine the machine, operating system, and computer system designs. In addition, the accumulation of all clauses describing the logic operating system constitute its implementation. Logic computer systems with vastly different fundamental characteristics can be concisely specified in this manner. Two contrasting examples are given and discussed. An important characteristic of both peripheral devices and the overall computer system, whether they are restartable or perpetual, is examined. As well, a method for operational initialization of the logic computer system is presented. The same clauses which incrementally specify characteristics of the computer system also describe the manner in which this initialization takes place.     

Correction to: An Updated Index Including Toughness for Hot-Cracking Susceptibility

Metallurgical and Materials Transactions A -     

Collaborative test of determination of iodine value in fish oils. 1. Reaction time and carbon tetrachloride or cyclohexane as solvents

Twenty-two laboratories participated in a collaborative test to determine the iodine value (IV) of eight samples of fish oil (four with IV<150, four with IV>150) with either carbon tetrachloride (AOCS Official Method Cd 1–25) or cyclohexane (AOCS Recommended Practice Cd 1b-87) as solvent and either 1 or 2 h of reaction time. Laboratories received coded duplicate samples (hidden duplicates) and carried out duplicate determinations on each oil by each solvent-time combination (open duplicates). Replacing carbon tetrachloride with cyclohexane resulted in a lower IV (P<0.001). The decrease averaged 1.6 IV units for low-IV oils and 3.8 IV units for high-IV oils; this difference in response of 2.2 IV units between low- and high-IV oils was significant (P<0.001). Increasing the reaction time had a relatively small effect (0.34±0.18). There was no interaction of reaction time with solvent or oil type. Cyclohexane caused emulsions, which made it difficult to titrate residual iodine and thus increased the variability of the determination. The repeatability standard deviations (s _r ), based on hidden duplicates, for 1-h reaction time with carbon tetrachloride and cyclohexane were 2.17 and 3.35, respectively. The corresponding reproducibility standard deviations were 2.73 and 4.53.     

High Throughput Screening of Low Temperature SCR and SCD De-NOx Catalysts in Scanning Mass Spectrometer

High-throughput synthesis and screening methods have been developed for the discovery of highly active lead compounds for the selective catalytic reduction as well as direct decomposition of NO in the temperature range 200–300 °C. The discovery libraries for primary screening consisted of 16 × 16 catalyst arrays on 4in. square quartz wafers. Catalysts were prepared by robotic liquid dispensing techniques and screened for catalytic activity in Symyx' scanning mass spectrometer. The scanning mass spectrometer is a fast serial screening tool that uses flat wafer catalyst surfaces, local laser heating, a scanning/sniffing nozzle and a quadrupolar mass spectrometer to compare relative catalytic activities. The feed consisted of NO/NH₃ mixtures with optional O₂ cofeed and Kr as the internal standard in Ar carrier gas. QMS detection allowed for tracking of H₂O, N₂, NO, O₂, N₂O and Kr. Screening protocols for catalytic materials encompassed metal precursors and carriers for supported vanadia systems, extensive doping of V₂O₅/TiO₂, and broad screening of mixed redox metal oxides and supported base and noble metal systems. More than 500 samples could be screened in a single day. Active hits (high NO consumption accompanied by corresponding N₂ production) identified in discovery libraries were re-synthesized as focus libraries for lead confirmation and further optimization. These libraries used shallower compositional gradients, for example 56 points (compositions) per ternary, with four 56-point ternaries per 4in. wafer. Broad screening ternaries were generally 8 or 15 points. The focus libraries more clearly reveal the trends and provide guidelines for secondary screening and scale-up. High conversions were achieved in scanning mass spectrometer so the scalability risk is small for the short contact time reactions.     

Data-based adaptive online prediction model for plant-wide production indices

A data-based adaptive online prediction model is proposed for plant-wide production indices based on support vector regression, a general method which we customized specifically to model very large data sets that are generated dynamically and periodically. The proposed model can update its parameters online according to the statistical properties of the training samples. Further, in order to improve the prediction precision, each sample is weighted with a dynamic penalty factor that considers the effect of each sample on the prediction model accuracy. Moreover, a customized procedure is introduced to handle large training sets. After having been convincingly evaluated on benchmark data, effectiveness and performance of our approach for plant-wide production indices is demonstrated using industrial data from an operating ore dressing plant over a range of scale in training data set size. The higher accuracy and shorter computation times than existing methods suggest that it may prove advantageous in actual application to dynamic production processes.     

Perceiving,learning, and exploiting object affordances for autonomous pile manipulation

Autonomous manipulation in unstructured environments will enable a large variety of exciting and important applications. Despite its promise, autonomous manipulation remains largely unsolved. Even the most rudimentary manipulation task—such as removing objects from a pile—remains challenging for robots. We identify three major challenges that must be addressed to enable autonomous manipulation: object segmentation, action selection, and motion generation. These challenges become more pronounced when unknown man-made or natural objects are cluttered together in a pile. We present a system capable of manipulating unknown objects in such an environment. Our robot is tasked with clearing a table by removing objects from a pile and placing them into a bin. To that end, we address the three aforementioned challenges. Our robot perceives the environment with an RGB-D sensor, segmenting the pile into object hypotheses using non-parametric surface models. Our system then computes the affordances of each object, and selects the best affordance and its associated action to execute. Finally, our robot instantiates the proper compliant motion primitive to safely execute the desired action. For efficient and reliable action selection, we developed a framework for supervised learning of manipulation expertise. To verify the performance of our system, we conducted dozens of trials and report on several hours of experiments involving more than 1,500 interactions. The results show that our learning-based approach for pile manipulation outperforms a common sense heuristic as well as a random strategy, and is on par with human action selection.     

离子取代凝胶——ACA离子取代动力学模型

用动边界模型描述了海藻酸-壳聚糖-海藻酸(ACA)离子取代凝胶对2价离子的取代动力学过程.模型具有较好的可靠性，ACA离子取代凝胶对2价离子的取代属于颗粒扩散控制.ACA离子取代凝胶对Pb²⁺的反应级数是0.76.