scholarly journals Contextual information and perceptual-cognitive expertise in a dynamic, temporally-constrained task.

2016 ◽  
Vol 22 (4) ◽  
pp. 455-470 ◽  
Author(s):  
Colm P. Murphy ◽  
Robin C. Jackson ◽  
Karl Cooke ◽  
André Roca ◽  
Nicolas Benguigui ◽  
...  
Keyword(s):  
Contextual Information ◽  
Cognitive Expertise

A Medieval Burial from Graham Street, Leith, Edinburgh

2003 ◽  
Vol 25 (2) ◽  
pp. 165-169
Author(s):  
Paul R. J. Duffy ◽  
Olivia Lelong
Keyword(s):  
Contextual Information ◽  
Human Remains ◽  
Housing Development ◽  
Archaeological Research ◽  
Archaeological Excavation ◽  
Radiocarbon Dates ◽  
Physical Evidence ◽  
Research Division ◽  
The North ◽  
The Individual

Summary An archaeological excavation was carried out at Graham Street, Leith, Edinburgh by Glasgow University Archaeological Research Division (GUARD) as part of the Historic Scotland Human Remains Call-off Contract following the discovery of human remains during machine excavation of a foundation trench for a new housing development. Excavation demonstrated that the burial was that of a young adult male who had been interred in a supine position with his head orientated towards the north. Radiocarbon dates obtained from a right tibia suggest the individual died between the 15th and 17th centuries AD. Little contextual information exists in documentary or cartographic sources to supplement this scant physical evidence. Accordingly, it is difficult to further refine the context of burial, although a possible link with a historically attested siege or a plague cannot be discounted.

"He seems odd": The effects of risk-irrelevant information and actuarial risk estimates on mock jurors' perceptions of sexual recidivism risk

2020 ◽  
Author(s):  
Jennifer Kamorowski ◽  
Karl Ask ◽  
Maartje Schreuder ◽  
Marko Jelicic ◽  
Corine de Ruiter
Keyword(s):  
Contextual Information ◽  
Individual Characteristics ◽  
Risk Estimate ◽  
Case Vignette ◽  
Future Research ◽  
Sexual Recidivism ◽  
Mock Jurors ◽  
Related Factors ◽  
Risk Estimates ◽  
Actuarial Risk

Previous research has shown that mock and actual jurors give little weight to actuarial sexual offending recidivism risk estimates when making decisions regarding civil commitment for so-called sexually violent predators (SVPs). We hypothesized that non-risk related factors, such as irrelevant contextual information and jurors’ information-processing style, would influence mock jurors’ perceptions of sexual recidivism risk. This preregistered experimental study examined the effects of mock jurors’ (N = 427) need for cognition (NFC), irrelevant contextual information in the form of the offender’s social attractiveness, and an actuarial risk estimate on mock jurors’ estimates of sexual recidivism risk related to a simulated SVP case vignette. Mock jurors exposed to negative risk-irrelevant characteristics of the offender estimated sexual recidivism risk as higher than mock jurors exposed to positive information about the offender. However, this effect was no longer significant after mock jurors had reviewed Static-99R actuarial risk estimate information. We found no support for the hypothesis that the level of NFC moderates the relationship between risk-irrelevant contextual information and risk estimates. Future research could explore additional individual characteristics or attitudes among mock jurors that may influence perceptions of sexual recidivism risk and insensitivity to actuarial risk estimates.

Minimizing Bias in Forensic Handwriting Examinations

2015 ◽  
Vol 25 ◽  
pp. 17-26 ◽  
Author(s):  
L. C. Alewijnse ◽  
E.J.A.T. Mattijssen ◽  
R.D. Stoel
Keyword(s):  
Decision Making ◽  
Information Management ◽  
Contextual Information ◽  
Context Information ◽  
Decision Making Process ◽  
Potential Bias ◽  
Potential Influence ◽  
Management Procedures

The purpose of this paper is to contribute to the increasing awareness about the potential bias on the interpretation and conclusions of forensic handwriting examiners (FHEs) by contextual information. We briefly provide the reader with an overview of relevant types of bias, the difficulties associated with studying bias, the sources of bias and their potential influence on the decision making process in casework, and solutions to minimize bias in casework. We propose that the limitations of published studies on bias need to be recognized and that their conclusions must be interpreted with care. Instead of discussing whether bias is an issue in casework, the forensic handwriting community should actually focus on how bias can be minimized in practice. As some authors have already shown (e.g., Found & Ganas, 2014), it is relatively easy to implement context information management procedures in practice. By introducing appropriate procedures to minimize bias, not only forensic handwriting examination will be improved, it will also increase the acceptability of the provided evidence during court hearings. Purchase Article - $10

Multi-level 3D Densenets for False-positive Reduction in Lung Nodule Detection Based on Chest Computed Tomography

2020 ◽  
Vol 16 (8) ◽  
pp. 1004-1021
Author(s):  
Xiaoqi Lu ◽  
Yu Gu ◽  
Lidong Yang ◽  
Baohua Zhang ◽  
Ying Zhao ◽  
...  
Keyword(s):  
False Positive ◽  
Contextual Information ◽  
Lung Nodule ◽  
Positive Sample ◽  
Lung Nodules ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Level Information ◽  
Multi Level ◽  
Cade System

Objective: False-positive nodule reduction is a crucial part of a computer-aided detection (CADe) system, which assists radiologists in accurate lung nodule detection. In this research, a novel scheme using multi-level 3D DenseNet framework is proposed to implement false-positive nodule reduction task. Methods: Multi-level 3D DenseNet models were extended to differentiate lung nodules from falsepositive nodules. First, different models were fed with 3D cubes with different sizes for encoding multi-level contextual information to meet the challenges of the large variations of lung nodules. In addition, image rotation and flipping were utilized to upsample positive samples which consisted of a positive sample set. Furthermore, the 3D DenseNets were designed to keep low-level information of nodules, as densely connected structures in DenseNet can reuse features of lung nodules and then boost feature propagation. Finally, the optimal weighted linear combination of all model scores obtained the best classification result in this research. Results: The proposed method was evaluated with LUNA16 dataset which contained 888 thin-slice CT scans. The performance was validated via 10-fold cross-validation. Both the Free-response Receiver Operating Characteristic (FROC) curve and the Competition Performance Metric (CPM) score show that the proposed scheme can achieve a satisfactory detection performance in the falsepositive reduction track of the LUNA16 challenge. Conclusion: The result shows that the proposed scheme can be significant for false-positive nodule reduction task.

Fortunae in Italy

2018 ◽  
Author(s):  
Daniele Miano
Keyword(s):  
Contextual Information ◽  
First Century ◽  
Latin Language ◽  
City Walls

This chapter analyses the cults of Fortuna through Italy up to the first century BC. Although the evidence for the cults is mostly fragmentary, contextual information shows that diverse meanings were attached to Fortuna by a variety of agents. Latium and Campania are the regions where most of the cults are attested, and the diffusion of the deity seems to have followed that of the Latin language. There are certain recurring features common to many local cults and sanctuaries, e.g. a tendency to worship Fortuna near liminal places, with sanctuaries attested at the border of different territories and near city walls.

scholarly journals Knowledge and Geo-Object Based Graph Convolutional Network for Remote Sensing Semantic Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3848
Author(s):  
Wei Cui ◽  
Meng Yao ◽  
Yuanjie Hao ◽  
Ziwei Wang ◽  
Xin He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Prior Knowledge ◽  
Contextual Information ◽  
Information Aggregation ◽  
Semantic Segmentation ◽  
Spatial Correlations ◽  
Convolutional Network ◽  
Object Based ◽  
Graph Neural Networks ◽  
Salt And Pepper

Pixel-based semantic segmentation models fail to effectively express geographic objects and their topological relationships. Therefore, in semantic segmentation of remote sensing images, these models fail to avoid salt-and-pepper effects and cannot achieve high accuracy either. To solve these problems, object-based models such as graph neural networks (GNNs) are considered. However, traditional GNNs directly use similarity or spatial correlations between nodes to aggregate nodes’ information, which rely too much on the contextual information of the sample. The contextual information of the sample is often distorted, which results in a reduction in the node classification accuracy. To solve this problem, a knowledge and geo-object-based graph convolutional network (KGGCN) is proposed. The KGGCN uses superpixel blocks as nodes of the graph network and combines prior knowledge with spatial correlations during information aggregation. By incorporating the prior knowledge obtained from all samples of the study area, the receptive field of the node is extended from its sample context to the study area. Thus, the distortion of the sample context is overcome effectively. Experiments demonstrate that our model is improved by 3.7% compared with the baseline model named Cluster GCN and 4.1% compared with U-Net.

scholarly journals Semi-automatic liver segmentation based on probabilistic models and anatomical constraints

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Doan Cong Le ◽  
Krisana Chinnasarn ◽  
Jirapa Chansangrat ◽  
Nattawut Keeratibharat ◽  
Paramate Horkaew
Keyword(s):  
Probabilistic Models ◽  
Contextual Information ◽  
Automatic Segmentation ◽  
Anatomical Variations ◽  
Graph Cut ◽  
Computing Methods ◽  
Liver Segmentation ◽  
Seed Point ◽  
Tissue Separation ◽  
Human Interactions

AbstractSegmenting a liver and its peripherals from abdominal computed tomography is a crucial step toward computer aided diagnosis and therapeutic intervention. Despite the recent advances in computing methods, faithfully segmenting the liver has remained a challenging task, due to indefinite boundary, intensity inhomogeneity, and anatomical variations across subjects. In this paper, a semi-automatic segmentation method based on multivariable normal distribution of liver tissues and graph-cut sub-division is presented. Although it is not fully automated, the method minimally involves human interactions. Specifically, it consists of three main stages. Firstly, a subject specific probabilistic model was built from an interior patch, surrounding a seed point specified by the user. Secondly, an iterative assignment of pixel labels was applied to gradually update the probabilistic map of the tissues based on spatio-contextual information. Finally, the graph-cut model was optimized to extract the 3D liver from the image. During post-processing, overly segmented nodal regions due to fuzzy tissue separation were removed, maintaining its correct anatomy by using robust bottleneck detection with adjacent contour constraint. The proposed system was implemented and validated on the MICCAI SLIVER07 dataset. The experimental results were benchmarked against the state-of-the-art methods, based on major clinically relevant metrics. Both visual and numerical assessments reported herein indicated that the proposed system could improve the accuracy and reliability of asymptomatic liver segmentation.

scholarly journals Individual differences in experienced and observational decision-making illuminate interactions between reinforcement learning and declarative memory

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Batel Yifrah ◽  
Ayelet Ramaty ◽  
Genela Morris ◽  
Avi Mendelsohn
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Declarative Memory ◽  
Contextual Information ◽  
Memory Performance ◽  
Relevant Information ◽  
Subjective Memory ◽  
Types Of Information ◽  
Reinforcement Learning Models ◽  
Implicit And Explicit

AbstractDecision making can be shaped both by trial-and-error experiences and by memory of unique contextual information. Moreover, these types of information can be acquired either by means of active experience or by observing others behave in similar situations. The interactions between reinforcement learning parameters that inform decision updating and memory formation of declarative information in experienced and observational learning settings are, however, unknown. In the current study, participants took part in a probabilistic decision-making task involving situations that either yielded similar outcomes to those of an observed player or opposed them. By fitting alternative reinforcement learning models to each subject, we discerned participants who learned similarly from experience and observation from those who assigned different weights to learning signals from these two sources. Participants who assigned different weights to their own experience versus those of others displayed enhanced memory performance as well as subjective memory strength for episodes involving significant reward prospects. Conversely, memory performance of participants who did not prioritize their own experience over others did not seem to be influenced by reinforcement learning parameters. These findings demonstrate that interactions between implicit and explicit learning systems depend on the means by which individuals weigh relevant information conveyed via experience and observation.

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