Neural Dynamics of Saccadic and Smooth Pursuit Eye Movement Coordination during Visual Tracking of Unpredictably Moving Targets

How does the brain use eye movements to track objects that move in unpredictable directions and speeds? Saccadic eye movements rapidly foveate peripheral visual or auditory targets and smooth pursuit eye movements keep the fovea pointed toward an attended moving target. Analyses of tracking data in monkeys and humans reveal systematic deviations from predictions of the simplest model of saccade-pursuit interactions, which would use no interactions other than common target selection and recruitment of shared motoneurons. Instead, saccadic and smooth pursuit movements cooperate to cancel errors of gaze position and velocity, and thus to maximize target visibility through time. How are these two systems coordinated to promote visual localization and identification of moving targets? How are saccades calibrated to correctly foveate a target despite its continued motion during the saccade? A neural model proposes answers to such questions. The modeled interactions encompass motion processing areas MT, MST, FPA, DLPN and NRTP; saccade planning and execution areas FEF and SC; the saccadic generator in the brain stem; and the cerebellum. Simulations illustrate the model’s ability to functionally explain and quantitatively simulate anatomical, neurophysiological and behavioral data about SAC-SPEM tracking.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Target Selection by Frontal Cortex During Coordinated Saccadic and Smooth Pursuit Eye Movement

Oculomotor tracking of moving objects is an important component of visually based cognition and planning. Such tracking is achieved by a combination of saccades and smooth pursuit eye movements. In particular, the saccadic and smooth pursuit systems interact to often choose the same target, and to maximize its visibility through time. How do multiple brain regions interact, including frontal cortical areas, to decide the choice of a target among several competing moving stimuli? How is target selection information that is created by a bias (e.g., electrical stimulation) transferred from one movement system to another? These saccade-pursuit interactions are clarified by a new computational neural model, which describes interactions among motion processing areas MT, MST, FPA, DLPN; saccade specification, selection, and planning areas LIP, FEF, SNr, SC; the saccadic generator in the brain stem; and the cerebellum. Model simulations explain a broad range of neuroanatomical and neurophysiological data. These results are in contrast with the simplest parallel model with no interactions between saccades and pursuit than common-target selection and recruitment of shared motoneurons. Actual tracking episodes in primates reveal multiple systematic deviations from predictions of the simplest parallel model, which are explained by the current model.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Neural Dynamics of Saccadic and Smooth Pursuit Eye Movement Coordination during Visual Tracking of Unpredictably Moving Targets

How does the brain use eye movements to track objects that move in unpredictable directions and speeds? Saccadic eye movements rapidly foveate peripheral visual or auditory targets and smooth pursuit eye movements keep the fovea pointed toward an attended moving target. Analyses of tracking data in monkeys and humans reveal systematic deviations from predictions of the simplest model of saccade-pursuit interactions, which would use no interactions other than common target selection and recruitment of shared motoneurons. Instead, saccadic and smooth pursuit movements cooperate to cancel errors of gaze position and velocity, and thus to maximize target visibility through time. How are these two systems coordinated to promote visual localization and identification of moving targets? How are saccades calibrated to correctly foveate a target despite its continued motion during the saccade? A neural model proposes answers to such questions. The modeled interactions encompass motion processing areas MT, MST, FPA, DLPN and NRTP; saccade planning and execution areas FEF and SC; the saccadic generator in the brain stem; and the cerebellum. Simulations illustrate the model’s ability to functionally explain and quantitatively simulate anatomical, neurophysiological and behavioral data about SAC-SPEM tracking.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

ARSTREAM: A Neural Network Model of Auditory Scene Analysis and Source Segregation

Multiple sound sources often contain harmonics that overlap and may be degraded by environmental noise. The auditory system is capable of teasing apart these sources into distinct mental objects, or streams. Such an "auditory scene analysis" enables the brain to solve the cocktail party problem. A neural network model of auditory scene analysis, called the AIRSTREAM model, is presented to propose how the brain accomplishes this feat. The model clarifies how the frequency components that correspond to a give acoustic source may be coherently grouped together into distinct streams based on pitch and spatial cues. The model also clarifies how multiple streams may be distinguishes and seperated by the brain. Streams are formed as spectral-pitch resonances that emerge through feedback interactions between frequency-specific spectral representaion of a sound source and its pitch. First, the model transforms a sound into a spatial pattern of frequency-specific activation across a spectral stream layer. The sound has multiple parallel representations at this layer. A sound's spectral representation activates a bottom-up filter that is sensitive to harmonics of the sound's pitch. The filter activates a pitch category which, in turn, activate a top-down expectation that allows one voice or instrument to be tracked through a noisy multiple source environment. Spectral components are suppressed if they do not match harmonics of the top-down expectation that is read-out by the selected pitch, thereby allowing another stream to capture these components, as in the "old-plus-new-heuristic" of Bregman. Multiple simultaneously occuring spectral-pitch resonances can hereby emerge. These resonance and matching mechanisms are specialized versions of Adaptive Resonance Theory, or ART, which clarifies how pitch representations can self-organize durin learning of harmonic bottom-up filters and top-down expectations. The model also clarifies how spatial location cues can help to disambiguate two sources with similar spectral cures. Data are simulated from psychophysical grouping experiments, such as how a tone sweeping upwards in frequency creates a bounce percept by grouping with a downward sweeping tone due to proximity in frequency, even if noise replaces the tones at their interection point. Illusory auditory percepts are also simulated, such as the auditory continuity illusion of a tone continuing through a noise burst even if the tone is not present during the noise, and the scale illusion of Deutsch whereby downward and upward scales presented alternately to the two ears are regrouped based on frequency proximity, leading to a bounce percept. Since related sorts of resonances have been used to quantitatively simulate psychophysical data about speech perception, the model strengthens the hypothesis the ART-like mechanisms are used at multiple levels of the auditory system. Proposals for developing the model to explain more complex streaming data are also provided.Air Force Office of Scientific Research (F49620-01-1-0397, F49620-92-J-0225); Office of Naval Research (N00014-01-1-0624); Advanced Research Projects Agency (N00014-92-J-4015); British Petroleum (89A-1204); National Science Foundation (IRI-90-00530); American Society of Engineering Educatio

A Neural Network Model of Auditory Scene Anaysis and Source Segregation

In environments with multiple sound sources, the auditory system is capable of teasing apart the impinging jumbled signal into different mental objects, or streams, as in its ability to solve the cocktail party problem. A neural network model of auditory scene analysis, called the ARTSTREAM model, is presented that groups different frequency components based on pitch and spatial location cues, and selectively allocates the components to different streams. The grouping is accomplished through a resonance that develops between a given object's pitch, its harmonic spectral components, and (to a lesser extent) its spatial location. Those spectral components that are not reinforced by being rnatched with the top-down prototype read-out by the selected object's pitch representation are suppressed, thereby allowing another stream to capture these components, as in the "old-plus-new heuristic" of Bregman. These resonance and matching mechanisms are specialized versions of Adaptive Resonance Theory, or ART, mechanisms. The model is used to simulate data from psychophysical grouping experiments, such as how a. tone sweeping upwards in frequency creates a bounce percept by grouping with a downward sweeping tone clue to proximity in frequency, even if noise replaces the tones at their intersection point. The model also simulates illusory auditory percepts such as the auditory continuity illusion of a tone continuing through a noise burst even if the tone is not present during the noise, and the scale illusion of Deutsch whereby downward and upward scales presented alternately to the two ears are regrouped based on frequency proximity, leading to a bounce percept. The stream resonances provide the coherence that allows one voice or instrument to be tracked through a multiple source environment.Air Force Office of Scientific Research (F49620-92-J-0225, F49620-92-J-0225); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-92-J-4015); British Petroleum (89A-1204

ARSTREAM: A Neural Network Model of Auditory Scene Analysis and Source Segregation

Multiple sound sources often contain harmonics that overlap and may be degraded by environmental noise. The auditory system is capable of teasing apart these sources into distinct mental objects, or streams. Such an "auditory scene analysis" enables the brain to solve the cocktail party problem. A neural network model of auditory scene analysis, called the AIRSTREAM model, is presented to propose how the brain accomplishes this feat. The model clarifies how the frequency components that correspond to a give acoustic source may be coherently grouped together into distinct streams based on pitch and spatial cues. The model also clarifies how multiple streams may be distinguishes and seperated by the brain. Streams are formed as spectral-pitch resonances that emerge through feedback interactions between frequency-specific spectral representaion of a sound source and its pitch. First, the model transforms a sound into a spatial pattern of frequency-specific activation across a spectral stream layer. The sound has multiple parallel representations at this layer. A sound's spectral representation activates a bottom-up filter that is sensitive to harmonics of the sound's pitch. The filter activates a pitch category which, in turn, activate a top-down expectation that allows one voice or instrument to be tracked through a noisy multiple source environment. Spectral components are suppressed if they do not match harmonics of the top-down expectation that is read-out by the selected pitch, thereby allowing another stream to capture these components, as in the "old-plus-new-heuristic" of Bregman. Multiple simultaneously occuring spectral-pitch resonances can hereby emerge. These resonance and matching mechanisms are specialized versions of Adaptive Resonance Theory, or ART, which clarifies how pitch representations can self-organize durin learning of harmonic bottom-up filters and top-down expectations. The model also clarifies how spatial location cues can help to disambiguate two sources with similar spectral cures. Data are simulated from psychophysical grouping experiments, such as how a tone sweeping upwards in frequency creates a bounce percept by grouping with a downward sweeping tone due to proximity in frequency, even if noise replaces the tones at their interection point. Illusory auditory percepts are also simulated, such as the auditory continuity illusion of a tone continuing through a noise burst even if the tone is not present during the noise, and the scale illusion of Deutsch whereby downward and upward scales presented alternately to the two ears are regrouped based on frequency proximity, leading to a bounce percept. Since related sorts of resonances have been used to quantitatively simulate psychophysical data about speech perception, the model strengthens the hypothesis the ART-like mechanisms are used at multiple levels of the auditory system. Proposals for developing the model to explain more complex streaming data are also provided.Air Force Office of Scientific Research (F49620-01-1-0397, F49620-92-J-0225); Office of Naval Research (N00014-01-1-0624); Advanced Research Projects Agency (N00014-92-J-4015); British Petroleum (89A-1204); National Science Foundation (IRI-90-00530); American Society of Engineering Educatio

Early detection of capping risk in pharmaceutical compacts

Capping is a common mechanical defect in tablet manufacturing, exhibited during or after the compression process. Predicting tablet capping in terms of process variables (e.g. compaction pressure and speed) and formulation properties is essential in pharmaceutical industry. In current work, a non-destructive contact ultrasonic approach for detecting capping risk in the pharmaceutical compacts prepared under various compression forces and speeds is presented. It is shown that the extracted mechanical properties can be used as early indicators for invisible capping (prior to visible damage). Based on the analysis of X-ray cross-section images and a large set of waveform data, it is demonstrated that the mechanical properties and acoustic wave propagation characteristics is significantly modulated by the tablet’s internal cracks and capping at higher compaction speeds and pressures. In addition, the experimentally extracted properties were correlated to the directly-measured porosity and tensile strength of compacts of Pearlitol®, Anhydrous Mannitol and LubriTose® Mannitol, produced at two compaction speeds and at three pressure levels. The effect compaction speed and pressure on the porosity and tensile strength of the resulting compacts is quantified, and related to the compact acoustic characteristics and mechanical properties. The detailed experimental approach and reported wave propagation data could find key applications in determining the bounds of manufacturing design spaces in the development phase, predicting capping during (continuous) tablet manufacturing, as well as online monitoring of tablet mechanical integrity and reducing batch-to-batch end-product quality variations

The Political Economy of Low-carbon Investments: Insights from the Wind and Solar Power Sectors in India

The primary motivation behind this research is the need to accelerate the supply of renewable energy because of the important role that it plays in mitigating climate change and in fostering sustainable development. Understanding past drivers for low-carbon investment can help us identify those for the future, and what could accelerate such investment. Investment in renewable energy can be modelled as a problem of technical asset allocation or optimisation at the firm or sectoral level, but is not entirely explained by this approach – the context in which actors are involved, their motivations and the wider systems in which they operate must also be taken into account. The interactions between actors may sometimes accelerate investment and sometimes prevent it; however, understanding the dynamics of these processes is crucial if we are to shape them. This study, which focuses on the wind and solar power sectors in India and China, aims to find and compare drivers for investment in renewable energy. Our point of entry for this piece of the study is that India is already seeing significant investment activity in renewable energy. During 2010/11, investment in renewables grew by 62 per cent to US$13bn (although it slowed drastically in 2011/12 to US$6.5bn). In 2010 the Indian government announced a National Solar Mission that aimed to add 20 gigawatts (GW) of solar power generation capacity by 2020; wind power capacity has grown steadily at a compound annual growth rate of 17.9 per cent since 2007 and now contributes more than 20GW, or just over 70 per cent, of total renewable energy capacity. Almost all of this is private investment. However, these levels will need to increase sharply in the coming years and decades if India is to reach China’s levels (who, in 2013, became the world leader with US$67bn invested in renewables) and make a greater contribution to the US$1tn needed.UK Department for International Developmen

Radio Properties of Low Redshift Broad Line Active Galactic Nuclei Including Extended Radio Sources

We present a study of the extended radio emission in a sample of 8434 low redshift (z < 0.35) broad line active galactic nuclei (AGN) from the Sloan Digital Sky Survey (SDSS). To calculate the jet and lobe contributions to the total radio luminosity, we have taken the 846 radio core sources detected in our previous study of this sample and performed a systematic search in the Faint Images of the Radio Sky at Twenty-centimeters (FIRST) database for extended radio emission that is likely associated with the optical counterparts. We found 51 out of 846 radio core sources have extended emission (> 4" from the optical AGN) that is positively associated with the AGN, and we have identified an additional 12 AGN with extended radio emission but no detectable radio core emission. Among these 63 AGN, we found 6 giant radio galaxies (GRGs), with projected emission exceeding 750 kpc in length, and several other AGN with unusual radio morphologies also seen in higher redshift surveys. The optical spectra of many of the extended sources are similar to that of typical broad line radio galaxy spectra, having broad H$\alpha$ emission lines with boxy profiles and large M_BH. With extended emission taken into account, we find strong evidence for a bimodal distribution in the radio-loudness parameter R, where the lower radio luminosity core-only sources appear as a population separate from the extended sources, with a dividing line at log(R) $\approx 1.75$. This dividing line ensures that these are indeed the most radio-loud AGN, which may have different or extreme physical conditions in their central engines when compared to the more numerous radio quiet AGN.Comment: 25 pages, 6 figures, accepted to A

An Exploratory Survey of Drivers’ Knowledge of Right of Way at Freeway On-ramp Merging Areas

Motor vehicle crashes are one of the leading causes of death in the United States. The most recent data available show that in 2006 there were almost 6 million police-reported motor vehicle crashes in the United States where a total of 42,642 people were killed and an additional 2.6 million were injured. The majority of these motor vehicle crashes occurred at intersections or within the intersection influence areas. Traffic conflicts occur at intersections due to traffic streams moving in different directions interfering with each other, and as a result they become areas with high potential for traffic crashes compared with non-intersection areas of the roadways. In order to reduce the potential conflict points, access to a freeway is only provided through gradeseparated intersection ramps (also known as interchanges). Freeways play a major role in providing mobility due to their high operational speeds and their being fully access controlled. Like other intersections, freeway-ramp areas have also been identified as locations highly prone to crashes as compared to other segments of freeways. A number of studies have been conducted to determine the causes and characteristics of crashes that occur at freeway ramps in order to devise and apply preventive countermeasures to reduce the occurrences of such crashes. Most of these studies have focused on developing and calibrating factors that contribute to traffic crash occurrences such as highway geometry, traffic volume, ramp location, and type of interchange. Other studies analyzed the type and patterns of crashes on urban freeways. For instance, a study by McCartt et al highlighted types and characteristics of ramp-related crashes, which showed that the type of crashes that occur at entrance ramps and exiting ramps are generally different. The most common type of crashes at exit ramps involve vehicles running-off the road while speeding. For the entrance ramps, sideswipe and cut-off crash types are the most frequent ones, with lack of yielding of right of way involving merging drivers from entrance ramps identified as a major cause. What is not clear, however, is whether at-fault merging drivers (from entrance ramps) know who had a right of way at the freeway merging area. In the present study, we assumed that most of these atfault drivers think that they have a right of way over drivers already on mainlines. To date, we have not found any study that has examined the factors that influence on-ramp merging drivers not yielding the right of way to freeway mainline traffic. In particular, the contribution of drivers’ knowledge of who has the right of way at the freeway-entrance ramp merge area has not been addressed. By determining what drivers know about right of way at the freeway merge area, including their driving actions, appropriate countermeasures such as education, engineering, and legislative actions can be implemented as future crash countermeasures. In addition, some states’ driver’s license testing handbooks inform new drivers to accelerate at on-ramps to attain the freeway mainline speed. This is also in accordance with the American Association of State Highway and Transportation Officials (AASHTO) guidelines whereby auxiliary (acceleration) lanes are provided in order to minimally affect the through traffic operations. Normally no yield sign is needed for ramps having standard-length acceleration lanes. The abovementioned reasons may also cause some on-ramp merging drivers to think that they share equally the right of way with the mainline traffic; this misconception may be one of the contributing causes of collisions at on-ramp merging areas. Furthermore, traffic safety studies acknowledge that certain demographic factors contribute to most of the motor vehicle crashes. For instance, gender and age differences in traffic crash involvement are well documented. The youngest and oldest drivers are more likely to be involved in motor vehicle crashes; similarly, younger males are more likely than younger females to be involved in motor vehicle crashes. On the other hand, females older than 50 years of age are more likely than the same age males to be involved in fatal crashes. Specifically, half of fatal crashes involving old drivers (80 years and older) tend to occur at intersections, and young drivers (16–25 years old) have a risk of being involved in traffic crashes to the order of 2.5 times higher than that of other drivers. Therefore, in the present study, we assumed that gender and age will be associated with drivers’ knowledge of freeway merging areas’ right of way. Particularly, the objective of this paper is twofold: to explore the knowledge of drivers concerning who has the right of way between the one on mainline lanes of a freeway and the one entering the freeway through the on-ramp junction lane and to explore the drivers’ actions when driving in the vicinity of freeway-entrance ramp merge areas, whether driving on the freeway mainline lanes or entering through the ramp junction lanes