On the adequacy of current empirical evaluations of formal models of categorization

Categorization is one of the fundamental building blocks of cognition, and the study of categorization is notable for the extent to which formal modeling has been a central and influential component of research. However, the field has seen a proliferation of noncomplementary models with little consensus on the relative adequacy of these accounts. Progress in assessing the relative adequacy of formal categorization models has, to date, been limited because (a) formal model comparisons are narrow in the number of models and phenomena considered and (b) models do not often clearly define their explanatory scope. Progress is further hampered by the practice of fitting models with arbitrarily variable parameters to each data set independently. Reviewing examples of good practice in the literature, we conclude that model comparisons are most fruitful when relative adequacy is assessed by comparing well-defined models on the basis of the number and proportion of irreversible, ordinal, penetrable successes (principles of minimal flexibility, breadth, good-enough precision, maximal simplicity, and psychological focus)

Virtual reality simulation for the optimization of endovascular procedures : current perspectives

Endovascular technologies are rapidly evolving, often - requiring coordination and cooperation between clinicians and technicians from diverse specialties. These multidisciplinary interactions lead to challenges that are reflected in the high rate of errors occurring during endovascular procedures. Endovascular virtual reality (VR) simulation has evolved from simple benchtop devices to full physic simulators with advanced haptics and dynamic imaging and physiological controls. The latest developments in this field include the use of fully immersive simulated hybrid angiosuites to train whole endovascular teams in crisis resource management and novel technologies that enable practitioners to build VR simulations based on patient-specific anatomy. As our understanding of the skills, both technical and nontechnical, required for optimal endovascular performance improves, the requisite tools for objective assessment of these skills are being developed and will further enable the use of VR simulation in the training and assessment of endovascular interventionalists and their entire teams. Simulation training that allows deliberate practice without danger to patients may be key to bridging the gap between new endovascular technology and improved patient outcomes

Civil liability of credit rating agencies - regulatory all-or-nothing approaches between immunity and over-deterrence

The European Commission recently put forward a proposal for a regulation to amend and strengthen the 2009 version of the EU's rules on the regulation of credit rating agencies ("CRA3"). Among other things, Art. 35a of the draft proposal introduces strict liability for rating agencies. This liability proposal is at odds with the aim to strengthen competition in the rating sector and could have a chilling effect on capital markets. The paper analyses existing rules on civil liability of rating agencies under different legal systems. Subsequently, the provision under Art. 35a of the Draft Proposal is examinded more closely. Suggestions on possible improvemts of the proposal are made

The role of cognitive abilities in laparoscopic simulator training

Learning minimally invasive surgery (MIS) differs substantially from learning open surgery and trainees differ in their ability to learn MIS. Previous studies mainly focused on the role of visuo-spatial ability (VSA) on the learning curve for MIS. In the current study, the relationship between spatial memory, perceptual speed, and general reasoning ability, in addition to VSA, and performance on a MIS simulator is examined. Fifty-three laparoscopic novices were tested for cognitive aptitude. Laparoscopic performance was assessed with the LapSim simulator (Surgical Science Ltd., Gothenburg, Sweden). Participants trained multiple sessions on the simulator until proficiency was reached. Participants showed significant improvement on the time to complete the task and efficiency of movement. Performance was related to different cognitive abilities, depending on the performance measure and type of cognitive ability. No relationship between cognitive aptitude and duration of training or steepness of the learning curve was found. Cognitive aptitude mediates certain aspects of performance during training on a laparoscopic simulator. Based on the current study, we conclude that cognitive aptitude tests cannot be used for resident selection but are potentially useful for developing individualized training programs. More research will be performed to examine how cognitive aptitude testing can be used to design training programs

Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications

We present an overview and evaluation of a new, systematic approach for generation of highly realistic, annotated synthetic data for training of deep neural networks in computer vision tasks. The main contribution is a procedural world modeling approach enabling high variability coupled with physically accurate image synthesis, and is a departure from the hand-modeled virtual worlds and approximate image synthesis methods used in real-time applications. The benefits of our approach include flexible, physically accurate and scalable image synthesis, implicit wide coverage of classes and features, and complete data introspection for annotations, which all contribute to quality and cost efficiency. To evaluate our approach and the efficacy of the resulting data, we use semantic segmentation for autonomous vehicles and robotic navigation as the main application, and we train multiple deep learning architectures using synthetic data with and without fine tuning on organic (i.e. real-world) data. The evaluation shows that our approach improves the neural network's performance and that even modest implementation efforts produce state-of-the-art results.Comment: The project web page at http://vcl.itn.liu.se/publications/2017/TKWU17/ contains a version of the paper with high-resolution images as well as additional materia

Sensor-Based Safety Performance Assessment of Individual Construction Workers

Over the last decade, researchers have explored various technologies and methodologies to enhance worker safety at construction sites. The use of advanced sensing technologies mainly has focused on detecting and warning about safety issues by directly relying on the detection capabilities of these technologies. Until now, very little research has explored methods to quantitatively assess individual workers’ safety performance. For this, this study uses a tracking system to collect and use individuals’ location data in the proposed safety framework. A computational and analytical procedure/model was developed to quantify the safety performance of individual workers beyond detection and warning. The framework defines parameters for zone-based safety risks and establishes a zone-based safety risk model to quantify potential risks to workers. To demonstrate the model of safety analysis, the study conducted field tests at different construction sites, using various interaction scenarios. Probabilistic evaluation showed a slight underestimation and overestimation in certain cases; however, the model represented the overall safety performance of a subject quite well. Test results showed clear evidence of the model’s ability to capture safety conditions of workers in pre-identified hazard zones. The developed approach presents a way to provide visualized and quantified information as a form of safety index, which has not been available in the industry. In addition, such an automated method may present a suitable safety monitoring method that can eliminate human deployment that is expensive, error-prone, and time-consuming

Adaptive multiagent system for seismic emergency management

Presently, most multiagent frameworks are typically programmed in Java. Since the JADE platform has been recently ported to .NET, we used it to create an adaptive multiagent system where the knowledge base of the agents is managed using the CLIPS language, also called from .NET. The multiagent system is applied to create seismic risk scenarios, simulations of emergency situations, in which different parties, modeled as adaptive agents, interact and cooperate.adaptive systems, risk management, seisms.