Trial history biases the spatial programming of antisaccades

The historical context in which saccades are made influences their latency and error rates, but less is known about how context influences their spatial parameters. We recently described a novel spatial bias for antisaccades, in which the endpoints of these responses deviate towards alternative goal locations used in the same experimental block, and showed that expectancy (prior probability) is at least partly responsible for this 'alternate-goal bias'. In this report we asked whether trial history also plays a role. Subjects performed antisaccades to a stimulus randomly located on the horizontal meridian, on a 40° angle downwards from the horizontal meridian, or on a 40° upward angle, with all three locations equally probable on any given trial. We found that the endpoints of antisaccades were significantly displaced towards the goal location of not only the immediately preceding trial (n - 1) but also the penultimate (n - 2) trial. Furthermore, this bias was mainly present for antisaccades with a short latency of <250 ms and was rapidly corrected by secondary saccades. We conclude that the location of recent antisaccades biases the spatial programming of upcoming antisaccades, that this historical effect persists over many seconds, and that it influences mainly rapidly generated eye movements. Because corrective saccades eliminate the historical bias, we suggest that the bias arises in processes generating the response vector, rather than processes generating the perceptual estimate of goal location.

Assessing the Relevance of Anomie Theory for Explaining Spatial Variation in Lethal Criminal Violence: An Aggregate-Level Analysis of Homicide within the United States

One of the most influential statements in the anomie theory tradition has been Merton’s argument that the volume of instrumental property crime should be higher where there is a greater imbalance between the degree of commitment to monetary success goals and the degree of commitment to legitimate means of pursing such goals. Contemporary anomie theories stimulated by Merton’s perspective, most notably Messner and Rosenfeld’s institutional anomie theory, have expanded the scope conditions by emphasizing lethal criminal violence as an outcome to which anomie theory is highly relevant, and virtually all contemporary empirical studies have focused on applying the perspective to explaining spatial variation in homicide rates. In the present paper, we argue that current explications of Merton’s theory and IAT have not adequately conveyed the relevance of the core features of the anomie perspective to lethal violence. We propose an expanded anomie model in which an unbalanced pecuniary value system – the core causal variable in Merton’s theory and IAT – translates into higher levels of homicide primarily in indirect ways by increasing levels of firearm prevalence, drug market activity, and property crime, and by enhancing the degree to which these factors stimulate lethal outcomes. Using aggregate-level data collected during the mid-to-late 1970s for a sample of relatively large social aggregates within the U.S., we find a significant effect on homicide rates of an interaction term reflecting high levels of commitment to monetary success goals and low levels of commitment to legitimate means. Virtually all of this effect is accounted for by higher levels of property crime and drug market activity that occur in areas with an unbalanced pecuniary value system. Our analysis also reveals that property crime is more apt to lead to homicide under conditions of high levels of structural disadvantage. These and other findings underscore the potential value of elaborating the anomie perspective to explicitly account for lethal violence.

IDRA (IDeal Resource Allocation): Computing ideal speedups in parallel logic programming

We present a technique to estimate accurate speedups for parallel logic programs with relative independence from characteristics of a given implementation or underlying parallel hardware. The proposed technique is based on gathering accurate data describing one execution at run-time, which is fed to a simulator. Alternative schedulings are then simulated and estimates computed for the corresponding speedups. A tool implementing the aforementioned techniques is presented, and its predictions are compared to the performance of real systems, showing good correlation.

Some techniques for automated, resource-aware distributed and mobile computing in a multi-paradigm programming system

Distributed parallel execution systems speed up applications by splitting tasks into processes whose execution is assigned to different receiving nodes in a high-bandwidth network. On the distributing side, a fundamental problem is grouping and scheduling such tasks such that each one involves sufñcient computational cost when compared to the task creation and communication costs and other such practical overheads. On the receiving side, an important issue is to have some assurance of the correctness and characteristics of the code received and also of the kind of load the particular task is going to pose, which can be specified by means of certificates. In this paper we present in a tutorial way a number of general solutions to these problems, and illustrate them through their implementation in the Ciao multi-paradigm language and program development environment. This system includes facilities for parallel and distributed execution, an assertion language for specifying complex programs properties (including safety and resource-related properties), and compile-time and run-time tools for performing automated parallelization and resource control, as well as certification of programs with resource consumption assurances and efñcient checking of such certificates.

Regional analysis of production adjustments in the major field crops : historical and prospective : an application of spatial linear programming /

Literature cited and selected references: p. 38-41.

Aggregate economic effects of alternative land retirement programs : a linear programming analysis /

Includes bibliographical references (p. 53-54).

Do predators aggregate in response to pest density in agroecosystems? Assessing within-field spatial patterns

1. The spatial heterogeneity of predator populations is an important component of ecological theories pertaining to predator-prey dynamics. Most studies within agricultural fields show spatial correlation (positive or negative) between mean predator numbers and prey abundance across a whole field over time but generally ignore the within-field spatial dimension. We used explicit spatial mapping to determine if generalist predators aggregated within a soybean field, the size of these aggregations and if predator aggregation was associated with pest aggregation, plant damage and predation rate. 2. The study was conducted at Gatton in the Lockyer Valley, 90 km west of Brisbane, Australia. Intensive sampling grids were used to investigate within-field spatial patterns. The first row of each grid was located in a lucerne field (10 m from interface) and the remaining rows were in an adjacent soybean field. At each point on the grid the abundance of foliage-dwelling and ground-dwelling pests and predators was measured, predation rates [using sentinel Helicoverpa armigera (Hubner) egg cards] and plant damage were estimated. Eight grids were sampled across two summer cropping seasons (2000/01, 2001/02). 3. Predators exhibited strong spatial patterning with regions of high and low abundance and activity within what are considered to be uniform soybean fields. Ground-dwelling and foliage-dwelling predators were often aggregated in patches approximately 40 m across. 4. Lycosidae (wolf spiders) displayed aggregation and were consistently more abundant within the lucerne, with a decreasing trap catch with distance from the lucrene/soybean interface. This trend was consistent between subsequent grids in a single field and between fields. 5. The large amount of spatial variability in within-field arthropod abundance (pests and predators) and activity (egg predation and plant damage) indicates that whole field averages were misleading. This result has serious implications for sampling of arthropod abundance and pest management decision-making based on scouting data. 6. There was a great deal of temporal change in the significant spatial patterns observed within a field at each sampling time point during a single season. Predator and pest aggregations observed in these fields were generally not stable for the entire season. 7. Predator aggregation did not correlate consistently with pest aggregation, plant damage or predation rate. Spatial patterns in predator abundance were not associated consistently with any single parameter measured. The most consistent positive association was between foliage-dwelling predators and pests (significant in four of seven grids). Inferring associations between predators and prey based on an intensive one-off sampling grid is difficult, due to the temporal variability in the abundance of each group. 8. Synthesis and applications. This study demonstrated that generalist predator populations are rarely distributed randomly and field edges and adjacent crops can have an influence on within-field predator abundance. This must be considered when estimating arthropod (pest and predator) abundance from a set of samples taken at random locations within a field.

Heuristic algorithm for computing reversal distance with multigene families via binary integer programming

Hannenhalli and Pevzner developed the first polynomial-time algorithm for the combinatorial problem of sorting of signed genomic data. Their algorithm solves the minimum number of reversals required for rearranging a genome to another when gene duplication is nonexisting. In this paper, we show how to extend the Hannenhalli-Pevzner approach to genomes with multigene families. We propose a new heuristic algorithm to compute the reversal distance between two genomes with multigene families via the concept of binary integer programming without removing gene duplicates. The experimental results on simulated and real biological data demonstrate that the proposed algorithm is able to find the reversal distance accurately. ©2005 IEEE

Efficiently computing weighted proximity relationships in spatial databases

Spatial data mining recently emerges from a number of real applications, such as real-estate marketing, urban planning, weather forecasting, medical image analysis, road traffic accident analysis, etc. It demands for efficient solutions for many new, expensive, and complicated problems. In this paper, we investigate the problem of evaluating the top k distinguished “features” for a “cluster” based on weighted proximity relationships between the cluster and features. We measure proximity in an average fashion to address possible nonuniform data distribution in a cluster. Combining a standard multi-step paradigm with new lower and upper proximity bounds, we presented an efficient algorithm to solve the problem. The algorithm is implemented in several different modes. Our experiment results not only give a comparison among them but also illustrate the efficiency of the algorithm.

An Integrated Programming Environment for Undergraduate Computing Courses

The Computing Division of the Business School at University College Worcester provides computing and information technology education to a range of undergraduate students. Topics include various approaches to programming, artificial intelligence, operating systems and digital technologies. Each of these has its own potentially conflicting requirements for a pedagogically sound programming environment. This paper describes an endeavor to develop a common programming paradigm across all topics. This involves the combined use of autonomous robots and Java simulations.

An Assessment of Pharmacological Properties of Schinus Essential Oils: A Soft Computing Approach

Plants of genus Schinus are native South America and introduced in Mediterranean countries, a long time ago. Some Schinus species have been used in folk medicine, and Essential Oils of Schinus spp. (EOs) have been reported as having antimicrobial, anti-tumoural and anti-inflammatory properties. Such assets are related with the EOs chemical composition that depends largely on the species, the geographic and climatic region, and on the part of the plants used. Considering the difficulty to infer the pharmacological properties of EOs of Schinus species without a hard experimental setting, this work will focus on the development of an Artificial Intelligence grounded Decision Support System to predict pharmacological properties of Schinus EOs. The computational framework was built on top of a Logic Programming Case Base approach to knowledge representation and reasoning, which caters to the handling of incomplete, unknown, or even self-contradictory information. New clustering methods centered on an analysis of attribute’s similarities were used to distinguish and aggregate historical data according to the context under which it was added to the Case Base, therefore enhancing the prediction process.

Parametric model for the analysis of concrete expansion due to alkali-aggregate reaction

The alkali-aggregate reaction (AAR) is a chemical reaction that provokes a heterogeneous expansion of concrete and reduces important properties such as Young's modulus, leading to a reduction in the structure's useful life. In this study, a parametric model is employed to determine the spatial distribution of the concrete expansion, combining normalized factors that influence the reaction through an AAR expansion law. Optimization techniques were employed to adjust the numerical results and observations in a real structure. A three-dimensional version of the model has been implemented in a finite element commercial package (ANSYS(C)) and verified in the analysis of an accelerated mortar test. Comparisons were made between two AAR mathematical descriptions for the mechanical phenomenon, using the same methodology, and an expansion curve obtained from experiment. Some parametric studies are also presented. The numerical results compared very well with the experimental data validating the proposed method.

Optimized harvest scheduling in eucalyptus plantations under operational, spatial and climatic constraints

The representation of sustainability concerns in industrial forests management plans, in relation to environmental, social and economic aspects, involve a great amount of details when analyzing and understanding the interaction among these aspects to reduce possible future impacts. At the tactical and operational planning levels, methods based on generic assumptions usually provide non-realistic solutions, impairing the decision making process. This study is aimed at improving current operational harvesting planning techniques, through the development of a mixed integer goal programming model. This allows the evaluation of different scenarios, subject to environmental and supply constraints, increase of operational capacity, and the spatial consequences of dispatching harvest crews to certain distances over the evaluation period. As a result, a set of performance indicators was selected to evaluate all optimal solutions provided to different possible scenarios and combinations of these scenarios, and to compare these outcomes with the real results observed by the mill in the study case area. Results showed that it is possible to elaborate a linear programming model that adequately represents harvesting limitations, production aspects and environmental and supply constraints. The comparison involving the evaluated scenarios and the real observed results showed the advantage of using more holistic approaches and that it is possible to improve the quality of the planning recommendations using linear programming techniques.

Rough Notes on Programming AVR Microcontrollers in C

These notes follow on from the material that you studied in CSSE1000 Introduction to Computer Systems. There you studied details of logic gates, binary numbers and instruction set architectures using the Atmel AVR microcontroller family as an example. In your present course (METR2800 Team Project I), you need to get on to designing and building an application which will include such a microcontroller. These notes focus on programming an AVR microcontroller in C and provide a number of example programs to illustrate the use of some of the AVR peripheral devices.

MapScript: A map algebra programming language incorporating neighborhood analysis

Map algebra is a data model and simple functional notation to study the distribution and patterns of spatial phenomena. It uses a uniform representation of space as discrete grids, which are organized into layers. This paper discusses extensions to map algebra to handle neighborhood operations with a new data type called a template. Templates provide general windowing operations on grids to enable spatial models for cellular automata, mathematical morphology, and local spatial statistics. A programming language for map algebra that incorporates templates and special processing constructs is described. The programming language is called MapScript. Example program scripts are presented to perform diverse and interesting neighborhood analysis for descriptive, model-based and processed-based analysis.