Investigating proximate mechanisms and ultimate functions of memory for emotional events

This thesis is an investigation of the proximate mechanisms and ultimate functions of memory for emotional events. The theoretical basis of this Thesis is that in order to reach a full understanding of a biological phenomenon, it is important that both proximate and ultimate (functional) explanations for that phenomenon are explored. Chapters 2 and 3 present an examination of the proximate mechanisms involved in memory consolidation of emotional events. In Chapter 2, three experiments are presented each testing the hypothesis that stress hormone activation immediately following viewing an emotional event enhances memory for that event. Each of the three experiments failed to find an enhancing effect of stress hormone activation on memory consolidation. Chapter 3 describes an investigation into whether the reduced feedback from the body to the brain, which occurs as a result of total spinal cord transection, diminishes the intensity of emotional experience and therefore impairs memory for emotional events. The results of this investigation revealed no differences between spinal cord transection patients and matched control participants in emotional expressivity, emotional awareness and in memory for emotional material. Chapters 4 and 5 explore how memory and emotion may interact differently for males and females and in manner that facilitates their survival and reproduction. Evolutionary theory argues that males should be more concerned than females about threats to their social status, whereas females should be more concerned about threats to their physical appearance and sexual reputation. Chapter 4 describes two experiments testing whether a) males have enhanced emotional arousal and memory for words implying they are of low social status; b) females have enhanced emotional arousal and memory for words implying they are physically unattractive and sexually untrustworthy. The results of these experiments showed that females had enhanced memory for words relating to physical appearance, and partial evidence that males have 2 enhanced memory for words relating to social status. Chapter 5 tests the evolutionary theory that males should be more emotionally aroused and thus have greater memory for cues relating to sexual infidelity (the thought of their partner having sex with another man), whereas females should be more emotionally aroused and have greater memory for cues to emotional infidelity (the thought of their partner forming a close emotional attachment with another woman). It also examines whether relationship status affects emotional arousal and memory for these cues. The results did not find any support for these hypothesised sex difference in memory. However, those ‘currently in a relationship’ did show enhanced emotional arousal to cues to sexual infidelity compared to those ‘currently not in a relationship’. Chapter 6 presents an investigation concerning the evolutionary hypothesis that individuals tend to have enhanced recognition memory for the faces of deceivers or ‘liars’. This chapter describes a study in which participants viewed a series of short video clips of individuals, half of whom were lying, half telling the truth. Participants’ memory for the individuals that appeared in the video clips was tested but there was no evidence of enhanced memory for the faces of ‘liars’. Chapter 7 provides a general discussion of the findings of this thesis. The failure to find an enhancing effect of post learning stress hormone activation on memory for emotional material, and the failure to find an impairment in memory for emotional material in people with total spinal cord transection contradict two established views on the proximate mechanisms involved in emotion, and emotions effect of the brain. How these findings relate to the established mainstream views on emotion and memory are discussed. The findings of studies concerning the functional interaction of memory and emotion presented in this thesis are also discussed in relation to previous research

Model-Guided Data-Driven Optimization and Control for Internal Combustion Engine Systems

The incorporation of electronic components into modern Internal Combustion, IC, engine systems have facilitated the reduction of fuel consumption and emission from IC engine operations. As more mechanical functions are being replaced by electric or electronic devices, the IC engine systems are becoming more complex in structure. Sophisticated control strategies are called in to help the engine systems meet the drivability demands and to comply with the emission regulations. Different model-based or data-driven algorithms have been applied to the optimization and control of IC engine systems. For the conventional model-based algorithms, the accuracy of the applied system models has a crucial impact on the quality of the feedback system performance. With computable analytic solutions and a good estimation of the real physical processes, the model-based control embedded systems are able to achieve good transient performances. However, the analytic solutions of some nonlinear models are difficult to obtain. Even if the solutions are available, because of the presence of unavoidable modeling uncertainties, the model-based controllers are designed conservatively

Memory and information processing in neuromorphic systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system

Semiparametric Regression During 2003–2007

Semiparametric regression is a fusion between parametric regression and nonparametric regression and the title of a book that we published on the topic in early 2003. We review developments in the field during the five year period since the book was written. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application

International conference "Information technologies in education in the 21st century": Conference proceedings.

Proceedings of a conference which concluded TEMPUS project JEP 25008_200

Teaching Students with a Mild Intellectual Disability how to Respond to Strangers using Computer-Based Video Instruction

Computer-based video instruction (CBVI) has been effective in teaching students with disabilities various health, community, and safety skills. Research suggests that CBVI is often used in conjunction with community-based instruction (CBI). Frequently, students with severe disabilities or students who are of high school age participate in CBI and/or CBVI and its accompanying research. This study investigated the effectiveness of CBVI to teach students with a mild ID, ages 11-13, appropriate responses to lures from strangers. A single-case, multiple probe across participants design was used to examine the impact of CBVI on one dependent variable, a correct two-step response (verbal and motor) to a lure from a stranger. The two-step response was adapted from the Akmanoglu & Tekin-Iftar (2011) study investigating responses to strangers. Findings from the study suggest CBVI had a positive impact on all participants. Implications for practice and for future research are provided

Feature-driven Volume Visualization of Medical Imaging Data

Direct volume rendering (DVR) is a volume visualization technique that has been proved to be a very powerful tool in many scientific visualization domains. Diagnostic medical imaging is one such domain in which DVR provides new capabilities for the analysis of complex cases and improves the efficiency of image interpretation workflows. However, the full potential of DVR in the medical domain has not yet been realized. A major obstacle for a better integration of DVR in the medical domain is the time-consuming process to optimize the rendering parameters that are needed to generate diagnostically relevant visualizations in which the important features that are hidden in image volumes are clearly displayed, such as shape and spatial localization of tumors, its relationship with adjacent structures, and temporal changes in the tumors. In current workflows, clinicians must manually specify the transfer function (TF), view-point (camera), clipping planes, and other visual parameters. Another obstacle for the adoption of DVR to the medical domain is the ever increasing volume of imaging data. The advancement of imaging acquisition techniques has led to a rapid expansion in the size of the data, in the forms of higher resolutions, temporal imaging acquisition to track treatment responses over time, and an increase in the number of imaging modalities that are used for a single procedure. The manual specification of the rendering parameters under these circumstances is very challenging. This thesis proposes a set of innovative methods that visualize important features in multi-dimensional and multi-modality medical images by automatically or semi-automatically optimizing the rendering parameters. Our methods enable visualizations necessary for the diagnostic procedure in which 2D slice of interest (SOI) can be augmented with 3D anatomical contextual information to provide accurate spatial localization of 2D features in the SOI; the rendering parameters are automatically computed to guarantee the visibility of 3D features; and changes in 3D features can be tracked in temporal data under the constraint of consistent contextual information. We also present a method for the efficient computation of visibility histograms (VHs) using adaptive binning, which allows our optimal DVR to be automated and visualized in real-time. We evaluated our methods by producing visualizations for a variety of clinically relevant scenarios and imaging data sets. We also examined the computational performance of our methods for these scenarios

Feature-driven Volume Visualization of Medical Imaging Data

Direct volume rendering (DVR) is a volume visualization technique that has been proved to be a very powerful tool in many scientific visualization domains. Diagnostic medical imaging is one such domain in which DVR provides new capabilities for the analysis of complex cases and improves the efficiency of image interpretation workflows. However, the full potential of DVR in the medical domain has not yet been realized. A major obstacle for a better integration of DVR in the medical domain is the time-consuming process to optimize the rendering parameters that are needed to generate diagnostically relevant visualizations in which the important features that are hidden in image volumes are clearly displayed, such as shape and spatial localization of tumors, its relationship with adjacent structures, and temporal changes in the tumors. In current workflows, clinicians must manually specify the transfer function (TF), view-point (camera), clipping planes, and other visual parameters. Another obstacle for the adoption of DVR to the medical domain is the ever increasing volume of imaging data. The advancement of imaging acquisition techniques has led to a rapid expansion in the size of the data, in the forms of higher resolutions, temporal imaging acquisition to track treatment responses over time, and an increase in the number of imaging modalities that are used for a single procedure. The manual specification of the rendering parameters under these circumstances is very challenging. This thesis proposes a set of innovative methods that visualize important features in multi-dimensional and multi-modality medical images by automatically or semi-automatically optimizing the rendering parameters. Our methods enable visualizations necessary for the diagnostic procedure in which 2D slice of interest (SOI) can be augmented with 3D anatomical contextual information to provide accurate spatial localization of 2D features in the SOI; the rendering parameters are automatically computed to guarantee the visibility of 3D features; and changes in 3D features can be tracked in temporal data under the constraint of consistent contextual information. We also present a method for the efficient computation of visibility histograms (VHs) using adaptive binning, which allows our optimal DVR to be automated and visualized in real-time. We evaluated our methods by producing visualizations for a variety of clinically relevant scenarios and imaging data sets. We also examined the computational performance of our methods for these scenarios

Active timing margin management to improve microprocessor power efficiency

Improving power/performance efficiency is critical for today’s micro- processors. From edge devices to datacenters, lower power or higher performance always produces better systems, measured by lower cost of ownership or longer battery time. This thesis studies improving microprocessor power/performance efficiency by optimizing the pipeline timing margin. In particular, this thesis focuses on improving the efficacy of Active Timing Margin, a young technology that dynamically adjusts the margin. Active timing margin trims down the pipeline timing margin with a control loop that adjusts voltage and frequency based on real-time chip environment monitoring. The key insight of this thesis is that in order to maximize active timing margin’s efficiency enhancement benefits, synergistic management from processor architecture design and system software scheduling are needed. To that end, this thesis covers the major consumers of pipeline timing margin, including temperature, voltage, and process variation. For temperature variation, the thesis proposes a table-lookup based active timing margin mechanism, and an associated temperature management scheme to minimize power consumption. For voltage variation, the thesis characterizes the limiting factors of adaptive clocking’s power saving and proposes application scheduling to maximize total system power reduction. For process variation, the thesis proposes core-level adaptive clocking reconfiguration to automatically expose inter-core variation and discusses workload scheduling and throttling management to control critical application performance. The author believes the optimization presented in this thesis can potentially benefit a variety of processor architectures as the conclusions are based on the solid measurement on state-of-the-art processors, and the research objective, active timing margin, already has wide applicability in the latest microprocessors by the time this thesis is written.Electrical and Computer Engineerin