Query: Moral development, executive functioning, peak experiences and brain patterns in professional and amateur classical musicians: Interpreted in light of a Unified Theory of Performance

Relevant passages:
Brain structures differ between musicians and non-musician <=SEP=> From an early age, musicians learn complex motor and auditory skills (e.g., the translation of visually perceived musical symbols into motor commands with simultaneous auditory monitoring of output), which they practice extensively from childhood throughout their entire careers. Using a voxel-by-voxel morphometric technique, we found gray matter volume differences in motor, auditory, and visual-spatial brain regions when comparing professional musicians (keyboard players) with a matched group of amateur musicians and non-musicians. Although some of these multiregional differences could be attributable to innate predisposition, we believe they may represent structural adaptations in response to long-term skill acquisition and the repetitive rehearsal of those skills. This hypothesis is supported by the strong association we found between structural differences, musician status, and practice intensity, as well as the wealth of supporting animal data showing structural changes in response to long-term motor training. However, only future experiments can determine the relative contribution of predisposition and practice.

Updating P300: An integrative theory of P3a and P3b <=SEP=> The empirical and theoretical development of the P300 event-related brain potential (ERP) is reviewed by considering factors that contribute to its amplitude, latency, and general characteristics. The neuropsychological origins of the P3a and P3b subcomponents are detailed, and how target/standard discrimination difficulty modulates scalp topography is discussed. The neural loci of P3a and P3b generation are outlined, and a cognitive model is proffered: P3a originates from stimulus-driven frontal attention mechanisms during task processing, whereas P3b originates from temporal-parietal activity associated with attention and appears related to subsequent memory processing. Neurotransmitter actions associating P3a to frontal/dopaminergic and P3b to parietal/norepinephrine pathways are highlighted. Neuroinhibition is suggested as an overarching theoretical mechanism for P300, which is elicited when stimulus detection engages memory operations.

Increased auditory cortical representation in musicians <=SEP=> Acoustic stimuli are processed throughout the auditory projection pathway, including the neocortex, by neurons that are aggregated into ‘tonotopic’ maps according to their specific frequency tunings. Research on animals has shown that tonotopic representations are not statically fixed in the adult organism but can reorganize after damage to the cochlea or after training the intact subject to discriminate between auditory stimuli. Here we used functional magnetic source imaging (single dipole model) to measure cortical representations in highly skilled musicians. Dipole moments for piano tones, but not for pure tones of similar fundamental frequency (matched in loudness), were found to be enlarged by about 25% in musicians compared with control subjects who had never played an instrument. Enlargement was correlated with the age at which musicians began to practise and did not differ between musicians with absolute or relative pitch. These results, when interpreted with evidence for modified somatosensory representations of the fingering digits in skilled violinists, suggest that use-dependent functional reorganization extends across the sensory cortices to reflect the pattern of sensory input processed by the subject during development of musical skill.

Navigation-related structural change in the hippocampi of taxi drivers. <=SEP=> Structural MRIs of the brains of humans with extensive navigation experience, licensed London taxi drivers, were analyzed and compared with those of control subjects who did not drive taxis. The posterior hippocampi of taxi drivers were significantly larger relative to those of control subjects. A more anterior hippocampal region was larger in control subjects than in taxi drivers. Hippocampal volume correlated with the amount of time spent as a taxi driver (positively in the posterior and negatively in the anterior hippocampus). These data are in accordance with the idea that the posterior hippocampus stores a spatial representation of the environment and can expand regionally to accommodate elaboration of this representation in people with a high dependence on navigational skills. It seems that there is a capacity for local plastic change in the structure of the healthy adult human brain in response to environmental demands.

Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 1 . Background <=SEP=> Battery management systems (BMS) in hybrid-electric-vehicle (HEV) battery packs must estimate values descriptive of the pack’s present operating condition. These include: battery state of charge, power fade, capacity fade, and instantaneous available power. The estimation mechanism must adapt to changing cell characteristics as cells age and therefore provide accurate estimates over the lifetime of the pack. In a series of three papers, we propose a method, based on extended Kalman filtering (EKF), that is able to accomplish these goals on a lithium-ion polymer battery pack. We expect that it will also work well on other battery chemistries. These papers cover the required mathematical background, cell modeling and system identification requirements, and the final solution, together with results. This first paper investigates the estimation requirements for HEV BMS in some detail, in parallel to the requirements for other battery-powered applications. The comparison leads us to understand that the HEV environment is very challenging on batteries and the BMS, and that precise estimation of some parameters will improve performance and robustness, and will ultimately lengthen the useful lifetime of the pack. This conclusion motivates the use of more complex algorithms than might be used in other applications. Our premise is that EKF then becomes a very attractive approach. This paper introduces the basic method, gives some intuitive feel to the necessary computational steps, and concludes by presenting an illustrative example as to the type of results that may be obtained using EKF. © 2004 Elsevier B.V. All rights reserved.

Price-Based Global Market Segmentation for Services <=SEP=> In business-to-business marketing, managers are often tasked with developing effective global pricing strategies for customers characterized by different cultures and different utilities for product attributes. The challenges of formulating international pricing schedules are especially evident in global markets for service offerings, where intensive customer contact, extensive customization requirements, and reliance on extrinsic cues for service quality make pricing particularly problematic. The purpose of this article is to develop and test a model of the antecedents of business customers’ price elasticities of demand for services in an international setting. The article begins with a synthesis of the services, pricing, and global marketing literature streams and then identifies factors that account for differences in business customers’ price elasticities for service offerings across customers in Asia Pacific, Europe, and North America. The findings indicate that price elasticities depend on service quality, service type, and level of service support and that horizontal segments do exist, which provides support for pricing strategies transcending national borders. The article concludes with a discussion of the managerial implications of these results for effective segmentation of global markets for services.

Adversarial Patch <=SEP=> We present a method to create universal, robust, targeted adversarial image patches in the real world. The patches are universal because they can be used to attack any scene, robust because they work under a wide variety of transformations, and targeted because they can cause a classifier to output any target class. These adversarial patches can be printed, added to any scene, photographed, and presented to image classifiers; even when the patches are small, they cause the classifiers to ignore the other items in the scene and report a chosen target class.

Efficacy and safety of Meriva®, a curcumin-phosphatidylcholine complex, during extended administration in osteoarthritis patients. <=SEP=> In a previous three-month study of Meriva, a proprietary curcumin-phosphatidylcholine phytosome complex, decreased joint pain and improvement in joint function were observed in 50 osteoarthritis (OA) patients. Since OA is a chronic condition requiring prolonged treatment, the long-term efficacy and safety of Meriva were investigated in a longer (eight months) study involving 100 OA patients. The clinical end points (Western Ontario and McMaster Universities [WOMAC] score, Karnofsky Performance Scale Index, and treadmill walking performance) were complemented by the evaluation of a series of inflammatory markers (interleukin [IL]-1beta, IL-6, soluble CD40 ligand [sCD40L], soluble vascular cell adhesion molecule (sVCAM)-1, and erythrocyte sedimentation rate [ESR]). This represents the most ambitious attempt, to date, to evaluate the clinical efficacy and safety of curcumin as an anti-inflammatory agent. Significant improvements of both the clinical and biochemical end points were observed for Meriva compared to the control group. This, coupled with an excellent tolerability, suggests that Meriva is worth considering for the long-term complementary management of osteoarthritis.

An Ontology based Dialog Interface to Database <=SEP=> In this paper, we extend the state-of-the-art NLIDB system and present a dialog interface to relational databases. Dialog interface enables users to automatically exploit the semantic context of the conversation while asking natural language queries over RDBMS, thereby making it simpler to express complex questions in a natural, piece-wise manner. We propose novel ontology-driven techniques for addressing each of the dialog-specific challenges such as co-reference resolution, ellipsis resolution, and query disambiguation, and use them in determining the overall intent of the user query. We demonstrate the applicability and usefulness of dialog interface over two different domains viz. finance and healthcare.

Consumers ’ Trust in Electronic Commerce Transactions : The Role of Perceived Privacy and Perceived Security 1 Introduction <=SEP=> Acknowledgement I am greatly indebted to Omar El Sawy and Ann Majchrzak for their guidance and suggestion, and I would like to thank Ricky Lim and Raymond for their help with the data analysis. Abstract Consumers' trust in their online transactions is vital for the sustained progress and development of electronic commerce. Our paper proposes that in addition to known factors of trust such a vendor's reputation, consumers' perception of privacy and security influence their trust in online transactions. Our research shows that consumers exhibit variability in their perceptions of privacy, security and trust between online and offline transactions even if it is conducted with the same store. We build upon this finding to develop and validate measures of consumers' perceived privacy and perceived security of their online transactions which are then theorized to influence their trust in EC transactions. We propose that the perceptions of privacy and security are factors that affect the consumers' trust in the institutional governance mechanisms underlying the Internet. We conduct two distinct empirical studies and through successive refinement and analysis using the Partial Least Squares technique, we test our hypothesized relationships while verifying the excellent measurement properties associated with our instrument. Our study finds that the consumers' perceived privacy and perceived security are indeed distinct constructs but the effect of perceived privacy on trust in EC transactions is strongly mediated by perceived security. A major implication of our findings is that while the much studied determinants of trust such as reputation of the transacting firm should not be neglected, vendors should also engage in efforts to positively influence consumer perceptions of privacy and security. We discuss the significance of this observation in the context of increasing importance in acquiring customer information for personalization and other online strategies.

Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models <=SEP=> Inspired by recent advances in multimodal learning and machine translation, we introduce an encoder-decoder pipeline that learns (a): a multimodal joint embedding space with images and text and (b): a novel language model for decoding distributed representations from our space. Our pipeline effectively unifies joint image-text embedding models with multimodal neural language models. We introduce the structure-content neural language model that disentangles the structure of a sentence to its content, conditioned on representations produced by the encoder. The encoder allows one to rank images and sentences while the decoder can generate novel descriptions from scratch. Using LSTM to encode sentences, we match the state-of-the-art performance on Flickr8K and Flickr30K without using object detections. We also set new best results when using the 19-layer Oxford convolutional network. Furthermore we show that with linear encoders, the learned embedding space captures multimodal regularities in terms of vector space arithmetic e.g. *image of a blue car* "blue" + "red" is near images of red cars. Sample captions generated for 800 images are made available for comparison.

DC Motor Drive with P, PI, and Particle Swarm Optimization Speed Controllers <=SEP=> This paper implements a Particle Swarm Optimization (PSO) speed controller for controlling the speed of DC motor. Also traditional Proportional (P), Proportional-Integral (PI) controller have been developed and simulated using MATLAB/SIMULINK. The simulation results indicate that the PI controller has big overshoot, sensitivity to controller gains. The optimization with particle swarm verified the reduction of oscillations as well as improve the steady state error, rise time and overshot in speed response. The simulation results confirmed the system is less sensitive to gain.

Modeling crowdsourcing systems: design and analysis of incentive mechanism and rating system <=SEP=> Over the past few years, we have seen an increasing popularity of crowdsourcing services [5]. Many companies are now providing such services, e.g., Amazon Mechanical Turk [1], Google Helpouts [3], and Yahoo! Answers [8], etc. Briefly speaking, crowdsourcing is an online, distributed problem solving paradigm and business production platform. It uses the power of today’s Internet to solicit the collective intelligence of large number of users. Relying on the wisdom of the crowd to solve posted tasks (or problems), crowdsourcing has become a promising paradigm to obtain “solutions” which can have higher quality or lower costs than the conventional method of solving problems via specialized employees or contractors in a company. Typically, a crowdsourcing system operates with three basic components: Users, tasks and rewards. Users are classified into requesters and workers. A user can be a requester or a worker, and in some cases, a user can be a requester/worker at the same time. Requesters outsource tasks to workers and associate each task with certain rewards, which will be granted to the workers who solve the task. Workers, on the other hand, solve the assigned tasks and reply to requesters with solutions, and then take the reward, which can be in form of money [1], entertainment [7] or altruism [8], etc. To have a successful crowdsourcing website, it is pertinent to attract high volume of participation of users (requesters and workers), and at the same time, solutions by workers have to be of high quality. In this paper we design a rating system and a mechanism to encourage users to participate, and incentivize workers to high quality solutions. First, we develop a game-theoretic model to characterize workers’ strategic behavior. We then design a class effective incentive mechanisms which consist of a task bundling scheme and a rating system, and pay workers according to solution ratings from requesters. We develop a model to characterize the design space of a class of commonly users rating systems– threshold based rating system. We quantify the impact of such rating systems, and the bundling scheme on reducing requesters’ reward payment in guaranteeing high quality solutions. We find out that a simplest rating system, e.g., two rating points, is an effective system in which requesters only need to provide binary feedbacks to indicate whether they are satisfied or not with a solution.

An analysis of player affect transitions in survival horror games <=SEP=> The trend of multimodal interaction in interactive gaming has grown significantly as demonstrated for example by the wide acceptance of the Wii Remote and the Kinect as tools not just for commercial games but for game research as well. Furthermore, using the player’s affective state as an additional input for game manipulation has opened the realm of affective gaming. In this paper, we analyzed the affective states of players prior to and after witnessing a scary event in a survival horror game. Player affect data were collected through our own affect annotation tool that allows the player to report his affect labels while watching his recorded gameplay and facial expressions. The affect data were then used for training prediction models with the player’s brainwave and heart rate signals, as well as keyboard–mouse activities collected during gameplay. Our results show that (i) players are likely to get more fearful of a scary event when they are in the suspense state and that (ii) heart rate is a good candidate for detecting player affect. Using our results, game designers can maximize the fear level of the player by slowly building tension until the suspense state and showing a scary event after that. We believe that this approach can be applied to the analyses of different sets of emotions in other games as well.

Asymptotic Convergence in Online Learning with Unbounded Delays <=SEP=> We study the problem of predicting the results of computations that are too expensive to run, via the observation of the results of smaller computations. We model this as an online learning problem with delayed feedback, where the length of the delay is unbounded, which we study mainly in a stochastic setting. We show that in this setting, consistency is not possible in general, and that optimal forecasters might not have average regret going to zero. However, it is still possible to give algorithms that converge asymptotically to Bayesoptimal predictions, by evaluating forecasters on specific sparse independent subsequences of their predictions. We give an algorithm that does this, which converges asymptotically on good behavior, and give very weak bounds on how long it takes to converge. We then relate our results back to the problem of predicting large computations in a deterministic setting.

Degradation of methyl orange using short-wavelength UV irradiation with oxygen microbubbles. <=SEP=> A novel wastewater treatment technique using 8 W low-pressure mercury lamps in the presence of uniform-sized microbubbles (diameter = 5.79 microm) was investigated for the decomposition of methyl orange as a model compound in aqueous solution. Photodegradation experiments were conducted with a BLB black light blue lamp (365 nm), a UV-C germicidal lamp (254 nm) and an ozone lamp (185 nm+254 nm) both with and without oxygen microbubbles. The results show that the oxygen microbubbles accelerated the decolorization rate of methyl orange under 185+254 nm irradiation. In contrast, the microbubbles under 365 and 254 nm irradiation were unaffected on the decolorization of methyl orange. It was found that the pseudo-zero order decolorization reaction constant in microbubble system is 2.1 times higher than that in conventional large bubble system. Total organic carbon (TOC) reduction rate of methyl orange was greatly enhanced by oxygen microbubble under 185+254 nm irradiation, however, TOC reduction rate by nitrogen microbubble was much slower than that with 185+254 nm irradiation only. Possible reaction mechanisms for the decolorization and mineralization of methyl orange both with oxygen and nitrogen mirobubbles were proposed in this study.

Cost Models for Future Software Life Cycle Processes: COCOMO 2.0 <=SEP=> Current software cost estimation models, such as the 1981 Constructive Cost Model (COCOMO) for software cost estimation and its 1987 Ada COCOMO update, have been experiencing increasing difficulties in estimating the costs of software developed to new life cycle processes and capabilities. These include non-sequential and rapid-development process models; reuse-driven approaches involving commercial off the shelf (COTS) packages, reengineering, applications composition, and applications generation capabilities; object-oriented approaches supported by distributed middleware; and software process maturity initiatives. This paper summarizes research in deriving a baseline COCOMO 2.0 model tailored to these new forms of software development, including rationales for the model decisions. The major new modeling capabilities of COCOMO 2.0 are a tailorable family of software sizing models, involving Object Points, Function Points, and Source Lines of Code; nonlinear models for software reuse and reengineering; an exponent-driver approach for modeling relative software diseconomies of scale; and several additions, deletions, and updates to previous COCOMO effort-multiplier cost drivers. This model is serving as a framework for an extensive current data collection and analysis effort to further refine and calibrate the model’s estimation capabilities.

Multimodal Classification of Remote Sensing Images: A Review and Future Directions <=SEP=> Earth observation through remote sensing images allows the accurate characterization and identification of materials on the surface from space and airborne platforms. Multiple and heterogeneous image sources can be available for the same geographical region: multispectral, hyperspectral, radar, multitemporal, and multiangular images can today be acquired over a given scene. These sources can be combined/fused to improve classification of the materials on the surface. Even if this type of systems is generally accurate, the field is about to face new challenges: the upcoming constellations of satellite sensors will acquire large amounts of images of different spatial, spectral, angular, and temporal resolutions. In this scenario, multimodal image fusion stands out as the appropriate framework to address these problems. In this paper, we provide a taxonomical view of the field and review the current methodologies for multimodal classification of remote sensing images. We also highlight the most recent advances, which exploit synergies with machine learning and signal processing: sparse methods, kernel-based fusion, Markov modeling, and manifold alignment. Then, we illustrate the different approaches in seven challenging remote sensing applications: 1) multiresolution fusion for multispectral image classification; 2) image downscaling as a form of multitemporal image fusion and multidimensional interpolation among sensors of different spatial, spectral, and temporal resolutions; 3) multiangular image classification; 4) multisensor image fusion exploiting physically-based feature extractions; 5) multitemporal image classification of land covers in incomplete, inconsistent, and vague image sources; 6) spatiospectral multisensor fusion of optical and radar images for change detection; and 7) cross-sensor adaptation of classifiers. The adoption of these techniques in operational settings will help to monitor our planet from space in the very near future.

OCCUPATIONAL THERAPY FOR THE POLY- TRAUMA CASUALTY WITH LIMB LOSS <=SEP=> 

Histogram-based image hashing scheme robust against geometric deformations <=SEP=> In this paper, we propose a robust image hash algorithm by using the invariance of the image histogram shape to geometric deformations. Robustness and uniqueness of the proposed hash function are investigated in detail by representing the histogram shape as the relative relations in the number of pixels among groups of two different bins. It is found from extensive testing that the histogram-based hash function has a satisfactory performance to various geometric deformations, and is also robust to most common signal processing operations thanks to the use of Gaussian kernel low-pass filter in the preprocessing phase.

Blob Enhancement and Visualization for Improved Intracranial Aneurysm Detection <=SEP=> Several researches have established that the sensitivity of visual assessment of smaller intracranial aneurysms is not satisfactory. Computer-aided diagnosis based on volume rendering of the response of blob enhancement filters may shorten visual inspection and increase detection sensitivity by directing a diagnostician to suspicious locations in cerebral vasculature. We proposed a novel blob enhancement filter based on a modified volume ratio of Hessian eigenvalues that has a more uniform response inside the blob-like structures compared to state-of-the-art filters. Because the response of proposed filter is independent of the size and intensity of structures, it is especially sensitive for detecting small blob-like structures such as aneuryms. We proposed a novel volume rendering method, which is sensitive to signal energy along the viewing ray and which visually enhances the visualization of true positives and suppresses usually sharp false positive responses. The proposed and state-of-the-art methods were quantitatively evaluated on a synthetic dataset and 42 clinical datasets of patients with aneurysms. Because of the capability to accurately enhance the aneurysm's boundary and due to a low number of visualized false positive responses, the combined use of the proposed filter and visualization method ensures a reliable detection of (small) intracranial aneurysms.

Spatio-Temporal Recurrent Convolutional Networks for Citywide Short-term Crowd Flows Prediction <=SEP=> With the rapid development of urban traffic, forecasting the flows of crowd plays an increasingly important role in traffic management and public safety. However, it is very challenging as it is affected by many complex factors, including spatio-temporal dependencies of regions and other external factors such as weather and holiday. In this paper, we proposed a deep-learning-based approach, named STRCNs, to forecast both inflow and outflow of crowds in every region of a city. STRCNs combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network structures to capture spatio-temporal dependencies, simultaneously. More particularly, our model can be decomposed into four components: Closeness captures the changes of instantaneous flows; Daily influence detects the changes of daily influence flows regularly; Weekly influence reacts weekly patterns of influence flows and External influence gets the influence of external factors. For the first three properties (Closeness, Daily influence and Weekly influence), we give a branch of recurrent convolutional network units to learn both spatial and temporal dependencies in crowd flows. External factors are fed into a two-layers fully connected neural network. STRCNs assigns different weights to different branches, and then merges the outputs of the four parts together. Experimental results on two data sets (MobileBJ and TaxiBJ) demonstrate that STRCNs outperforms classical time series and other deep-learning-based prediction methods.

Diagnosing Error in Temporal Action Detectors <=SEP=> Despite the recent progress in video understanding and the continuous rate of improvement in temporal action localization throughout the years, it is still unclear how far (or close?) we are to solving the problem. To this end, we introduce a new diagnostic tool to analyze the performance of temporal action detectors in videos and compare different methods beyond a single scalar metric. We exemplify the use of our tool by analyzing the performance of the top rewarded entries in the latest ActivityNet action localization challenge. Our analysis shows that the most impactful areas to work on are: strategies to better handle temporal context around the instances, improving the robustness w.r.t. the instance absolute and relative size, and strategies to reduce the localization errors. Moreover, our experimental analysis finds the lack of agreement among annotator is not a major roadblock to attain progress in the field. Our diagnostic tool is publicly available to keep fueling the minds of other researchers with additional insights about their algorithms.

Cloud computing paradigms for pleasingly parallel biomedical applications <=SEP=> Cloud computing offers exciting new approaches for scientific computing that leverages the hardware and software investments on large scale data centers by major commercial players. Loosely coupled problems are very important in many scientific fields and are on the rise with the ongoing move towards data intensive computing. There exist several approaches to leverage clouds & cloud oriented data processing frameworks to perform pleasingly parallel computations. In this paper we present two pleasingly parallel biomedical applications, 1) assembly of genome fragments 2) dimension reduction in the analysis of chemical structures, implemented utilizing cloud infrastructure service based utility computing models of Amazon AWS and Microsoft Windows Azure as well as utilizing MapReduce based data processing frameworks, Apache Hadoop and Microsoft DryadLINQ. We review and compare each of the frameworks and perform a comparative study among them based on performance, efficiency, cost and the usability. Cloud service based utility computing model and the managed parallelism (MapReduce) exhibited comparable performance and efficiencies for the applications we considered. We analyze the variations in cost between the different platform choices (eg: EC2 instance types), highlighting the need to select the appropriate platform based on the nature of the computation.

The psychometric properties and utility of the Short Sadistic Impulse Scale (SSIS). <=SEP=> Sadistic personality disorder (SPD) has been underresearched and often misunderstood in forensic settings. Furthermore, personality disorders in general are the subject of much controversy in terms of their classification (i.e., whether they should be categorical or dimensional). The Sadistic Attitudes and Behaviors Scale (SABS; Davies & Hand, 2003; O'Meara, Davies, & Barnes-Holmes, 2004) is a recently developed scale for measuring sadistic inclinations. Derived from this is the Short Sadistic Impulse Scale (SSIS), which has proved to be a strong unidimensional measure of sadistic inclination. Through cumulative scaling, it was investigated whether the SSIS could measure sadism on a continuum of interest, thus providing a dimensional view of the construct. Further, the SSIS was administered along with a number of other measures related to sadism in order to assess the validity of the scale. Results showed that the SSIS has strong construct and discriminant validity and may be useful as a screening measure for sadistic impulse.

Cyber military strategy for cyberspace superiority in cyber warfare <=SEP=> In this paper, we proposed robust and operational cyber military strategy for cyberspace superiority in cyber warfare. We considered cyber forces manpower, cyber intelligence capability, and an organization of cyber forces for improving cyber military strategy. In cyber forces power field, we should cultivated personals that can perform network computer operations and hold cyber security technology such as cyber intelligence collection, cyber-attack, cyber defence, and cyber forensics. Cyber intelligence capability includes cyber surveillance/reconnaissance, cyber order of battle, pre-CTO, and cyber damage assessment. An organization of cyber forces has to change tree structure to network structure and has to organize task or functional centric organizations. Our proposed cyber military strategy provides prior decision of action to operate desired effects in cyberspace.

The emulation theory of representation: motor control, imagery, and perception. <=SEP=> The emulation theory of representation is developed and explored as a framework that can revealingly synthesize a wide variety of representational functions of the brain. The framework is based on constructs from control theory (forward models) and signal processing (Kalman filters). The idea is that in addition to simply engaging with the body and environment, the brain constructs neural circuits that act as models of the body and environment. During overt sensorimotor engagement, these models are driven by efference copies in parallel with the body and environment, in order to provide expectations of the sensory feedback, and to enhance and process sensory information. These models can also be run off-line in order to produce imagery, estimate outcomes of different actions, and evaluate and develop motor plans. The framework is initially developed within the context of motor control, where it has been shown that inner models running in parallel with the body can reduce the effects of feedback delay problems. The same mechanisms can account for motor imagery as the off-line driving of the emulator via efference copies. The framework is extended to account for visual imagery as the off-line driving of an emulator of the motor-visual loop. I also show how such systems can provide for amodal spatial imagery. Perception, including visual perception, results from such models being used to form expectations of, and to interpret, sensory input. I close by briefly outlining other cognitive functions that might also be synthesized within this framework, including reasoning, theory of mind phenomena, and language.

DC Bus Voltage Control for a Distributed Power System <=SEP=> This paper addresses voltage control of distributed dc power systems. DC power systems have been discussed as a result of the introduction of renewable, small-scale power generation units. Also, telecommunication power systems featuring UPS properties might benefit from a broader introduction of dc power systems. Droop control is utilized to distribute the load between the source converters. In order to make the loading of the source converters equal, in per unit, the voltage control algorithm for each converter has to be designed to act similar. The dc side capacitor of each converter, needed for filtering, is also determined as a consequence. The root locus is investigated for varying dc bus impedance. It is found that the risk of entering converter over-modulation is a stronger limitation than stability, at least for reasonable dc bus cable parameters. The stationary and dynamic properties during load variations are also investigated.

Diagnostic Accuracy of Clinical Tests for Morton's Neuroma Compared With Ultrasonography. <=SEP=> The aim of the present study was to assess the diagnostic accuracy of 7 clinical tests for Morton's neuroma (MN) compared with ultrasonography (US). Forty patients (54 feet) were diagnosed with MN using predetermined clinical criteria. These patients were subsequently referred for US, which was performed by a single, experienced musculoskeletal radiologist. The clinical test results were compared against the US findings. MN was confirmed on US at the site of clinical diagnosis in 53 feet (98%). The operational characteristics of the clinical tests performed were as follows: thumb index finger squeeze (96% sensitivity, 96% accuracy), Mulder's click (61% sensitivity, 62% accuracy), foot squeeze (41% sensitivity, 41% accuracy), plantar percussion (37% sensitivity, 36% accuracy), dorsal percussion (33% sensitivity, 26% accuracy), and light touch and pin prick (26% sensitivity, 25% accuracy). No correlation was found between the size of MN on US and the positive clinical tests, except for Mulder's click. The size of MN was significantly larger in patients with a positive Mulder's click (10.9 versus 8.5 mm, p = .016). The clinical assessment was comparable to US in diagnosing MN. The thumb index finger squeeze test was the most sensitive screening test for the clinical diagnosis of MN.