Freeway travel time estimation and prediction using dynamic neural networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. ^ This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.^

Practical dynamic thermal management on Intel desktop computer

Fueled by increasing human appetite for high computing performance, semiconductor technology has now marched into the deep sub-micron era. As transistor size keeps shrinking, more and more transistors are integrated into a single chip. This has increased tremendously the power consumption and heat generation of IC chips. The rapidly growing heat dissipation greatly increases the packaging/cooling costs, and adversely affects the performance and reliability of a computing system. In addition, it also reduces the processor's life span and may even crash the entire computing system. Therefore, dynamic thermal management (DTM) is becoming a critical problem in modern computer system design. Extensive theoretical research has been conducted to study the DTM problem. However, most of them are based on theoretically idealized assumptions or simplified models. While these models and assumptions help to greatly simplify a complex problem and make it theoretically manageable, practical computer systems and applications must deal with many practical factors and details beyond these models or assumptions. The goal of our research was to develop a test platform that can be used to validate theoretical results on DTM under well-controlled conditions, to identify the limitations of existing theoretical results, and also to develop new and practical DTM techniques. This dissertation details the background and our research efforts in this endeavor. Specifically, in our research, we first developed a customized test platform based on an Intel desktop. We then tested a number of related theoretical works and examined their limitations under the practical hardware environment. With these limitations in mind, we developed a new reactive thermal management algorithm for single-core computing systems to optimize the throughput under a peak temperature constraint. We further extended our research to a multicore platform and developed an effective proactive DTM technique for throughput maximization on multicore processor based on task migration and dynamic voltage frequency scaling technique. The significance of our research lies in the fact that our research complements the current extensive theoretical research in dealing with increasingly critical thermal problems and enabling the continuous evolution of high performance computing systems.

The effect of active student responding in computer -assisted instruction on social -studies learning by students with learning disabilities

The purpose of this study was to compare the effects of three student response conditions during computer-assisted instruction on the acquisition and maintenance of social-studies facts. Two of the conditions required active student responding (ASR), whereas the other required an on-task (OT) response. Participants were five fifth-grade students, with learning disabilities enrolled in a private school. An alternating treatments design with a best treatments phase was used to compare the effects of the response procedures on three major dependent measures: same-day tests, next-day tests, and maintenance tests. ^ Each week for six weeks, participants were provided daily one-to-one instruction on sets of 21 unknown social-studies facts using a hypermedia computer program, with a new set of facts being practiced each week. Each set of 21 facts was divided randomly into three conditions: Clicking-ASR, Repeating-ASR, and Listening-OT. Hypermedia lesson began weekly with the concept introduction lesson, followed by practice and testing. Practice and testing occurred four days per week, per set. During Clicking-ASR, student practice involved the selection of a social-studies response by clicking on an item with the mouse on the hypermedia card. Repeating-ASR instruction required students to orally repeat the social-studies facts when prompted by the computer. During Listening-OT, students listened to the social-studies facts being read by the computer. During weeks seven and eight, instruction occurred with seven unknown facts using only the best treatment. ^ Test results show that all for all 5 students, the Repeating-ASR practice procedure resulted in more social-studies facts stated correctly on same-day tests, next-day tests, and one-and two-week maintenance tests. Clicking-ASR was the next most effective procedure. During the seventh and eighth week of instruction when only the best practice condition was implemented, Repeating-ASR produced higher scores than all conditions (including Repeating-ASR) during the first six weeks of the study. ^ The results lend further support to the growing body of literature that demonstrates the positive relation between ASR and student achievement. Much of the ASR literature has focused on the effects of increased ASR during teacher-led or peer-mediated instruction. This study adds a dimension to that research in that it demonstrated the importance of ASR during computer-assisted instruction and further suggests that the type of ASR used during computer-assisted instruction may influence learning. Future research is needed to investigate the effectiveness of other types of ASR during computer-assisted instruction and to identify other fundamental characteristics of an effective computer-assisted instruction. ^

Factors affecting the alignment of *grades and reading scores for third-grade students on the Florida Comprehensive Assessment Test

The purpose of this study was to aid in understanding the relationship between current Reading report card grading practices and standards-based state standardized testing results in Reading and factors associated with the alignment of this relationship. Report card and Florida Comprehensive Assessment Test (FLAT) data for 2004 were collected for 1064 third grade students in nine schools of one feeder pattern in Florida's Miami-Dade County Public Schools. A Third Grade Teacher Questionnaire was administered to 48 Reading teachers. The questionnaire contained items relating to teachers' education, teaching experience, grading practices, and beliefs about the FCAT, instructional Reading activities, methods, and materials. ^ Findings of this study support a strong relationship between report card grades and FCAT Reading achievement levels. However, individual school correlational analysis showed significant differences among schools' alignment measures. Higher teacher alignment between grades and FCAT levels was associated with teachers spending more time on individualized methods of Reading instruction and to teachers feeling there was not enough time to teach and help individual students. Lower teacher alignment of grades and achievement levels was associated with teachers taking homework into account in the final Reading grade. Teacher alignment of grades and achievement levels was not associated with teacher beliefs concerning the FCAT, instructional activities in Reading and Language Arts, the Reading program used, the model of delivery of the Reading program, instruction or type of instructional planning done by the teachers. ^ This study highlights the need for further investigations related to determining additional teacher factors that may affect the alignment relationship between report card grades and standards-based state standardized testing results. ^

Using sonic enhancement to augment non-visual tabular navigation

More information is now readily available to computer users than at any time in human history; however, much of this information is often inaccessible to people with blindness or low-vision, for whom information must be presented non-visually. Currently, screen readers are able to verbalize on-screen text using text-to-speech (TTS) synthesis; however, much of this vocalization is inadequate for browsing the Internet. An auditory interface that incorporates auditory-spatial orientation was created and tested. For information that can be structured as a two-dimensional table, links can be semantically grouped as cells in a row within an auditory table, which provides a consistent structure for auditory navigation. An auditory display prototype was tested.^ Sixteen legally blind subjects participated in this research study. Results demonstrated that stereo panning was an effective technique for audio-spatially orienting non-visual navigation in a five-row, six-column HTML table as compared to a centered, stationary synthesized voice. These results were based on measuring the time- to-target (TTT), or the amount of time elapsed from the first prompting to the selection of each tabular link. Preliminary analysis of the TTT values recorded during the experiment showed that the populations did not conform to the ANOVA requirements of normality and equality of variances. Therefore, the data were transformed using the natural logarithm. The repeated-measures two-factor ANOVA results show that the logarithmically-transformed TTTs were significantly affected by the tonal variation method, F(1,15) = 6.194, p= 0.025. Similarly, the results show that the logarithmically transformed TTTs were marginally affected by the stereo spatialization method, F(1,15) = 4.240, p=0.057. The results show that the logarithmically transformed TTTs were not significantly affected by the interaction of both methods, F(1,15) = 1.381, p=0.258. These results suggest that some confusion may be caused in the subject when employing both of these methods simultaneously. The significant effect of tonal variation indicates that the effect is actually increasing the average TTT. In other words, the presence of preceding tones increases task completion time on average. The marginally-significant effect of stereo spatialization decreases the average log(TTT) from 2.405 to 2.264.^

Freeway Travel Time Estimation and Prediction Using Dynamic Neural Networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.

The relationship between the teacher's experience, the teacher's college major, and the teacher's level of education in predicting classroom attitudes in high school science students

A review of the literature reveals few research has attempted to demonstrate if a relationship exists between the type of teacher training a science teacher has received and the perceived attitudes of his/her students. Considering that a great deal of time and energy has been devoted by university colleges, school districts, and educators towards refining the teacher education process, it would be more efficient for all parties involved, if research were available that could discern if certain pathways in achieving that education, would promote the tendency towards certain teacher behaviors occurring in the classroom, while other pathways would lead towards different behaviors. Some of the teacher preparation factors examined in this study include the college major chosen by the science teacher, the highest degree earned, the number of years of teaching experience, the type of science course taught, and the grade level taught by the teacher. This study examined how the various factors mentioned, could influence the behaviors which are characteristic of the teacher, and how these behaviors could be reflective in the classroom environment experienced by the students. The instrument used in the study was the Classroom Environment Scale (CES), Real Form. The measured classroom environment was broken down into three separate dimensions, with three components within each dimension in the CES. Multiple Regression statistical analyses examined how components of the teachers' education influenced the perceived dimensions of the classroom environment from the students. The study occurred in Miami-Dade County Florida, with a predominantly urban high school student population. There were 40 secondary science teachers involved, each with an average of 30 students. The total number of students sampled in the study was 1200. The teachers who participated in the study taught the entire range of secondary science courses offered at this large school district. All teachers were selected by the researcher so that a balance would occur in the sample between teachers who were education major versus science major. Additionally, the researcher selected teachers so that a balance occurred in regards to the different levels of college degrees earned among those involved in the study. Several research questions sought to determine if there was significant difference between the type of the educational background obtained by secondary science teachers and the students' perception of the classroom environment. Other research questions sought to determine if there were significant differences in the students' perceptions of the classroom environment for secondary science teachers who taught biological content, or non-biological content sciences. An additional research question sought to evaluate if the grade level taught would affect the students' perception of the classroom environment. Analysis of the multiple regression were run for each of four scores from the CES, Real Form. For score 1, involvement of students, the results showed that teachers with the highest number of years of experience, with masters or masters plus degrees, who were education majors, and who taught twelfth grade students, had greater amounts of students being attentive and interested in class activities, participating in discussions, and doing additional work on their own, as compared with teachers who had lower experience, a bachelors degree, were science majors, and who taught a grade lower than twelfth. For score 2, task orientation, which emphasized completing the required activities and staying on-task, the results showed that teachers with the highest and intermediate experience, a science major, and with the highest college degree, showed higher scores as compared with the teachers indicating lower experiences, education major and a bachelors degree. For Score 3, competition, which indicated how difficult it was to achieve high grades in the class, the results showed that teachers who taught non-biology content subjects had the greatest effect on the regression. Teachers with a masters degree, low levels of experience, and who taught twelfth grade students were also factored into the regression equation. For Score 4, innovation, which indicated the extent in which the teachers used new and innovative techniques to encourage diverse and creative thinking included teachers with an education major as the first entry into the regression equation. Teachers with the least experience (0 to 3 years), and teachers who taught twelfth and eleventh grade students were also included into the regression equation.

Data compilation on the biological response to ocean acidification: environmental and experimental context of data sets and related literature

The exponential growth of studies on the biological response to ocean acidification over the last few decades has generated a large amount of data. To facilitate data comparison, a data compilation hosted at the data publisher PANGAEA was initiated in 2008 and is updated on a regular basis (doi:10.1594/PANGAEA.149999). By January 2015, a total of 581 data sets (over 4 000 000 data points) from 539 papers had been archived. Here we present the developments of this data compilation five years since its first description by Nisumaa et al. (2010). Most of study sites from which data archived are still in the Northern Hemisphere and the number of archived data from studies from the Southern Hemisphere and polar oceans are still relatively low. Data from 60 studies that investigated the response of a mix of organisms or natural communities were all added after 2010, indicating a welcomed shift from the study of individual organisms to communities and ecosystems. The initial imbalance of considerably more data archived on calcification and primary production than on other processes has improved. There is also a clear tendency towards more data archived from multifactorial studies after 2010. For easier and more effective access to ocean acidification data, the ocean acidification community is strongly encouraged to contribute to the data archiving effort, and help develop standard vocabularies describing the variables and define best practices for archiving ocean acidification data.

Differential activation in temporal, cingulate and prefrontal cortices during response inhibition in bipolar disorder

Background: Increased impulsivity and aberrant response inhibition have been observed in bipolar disorder (BD). This study examined the functional abnormalities and underlying neural processes during response inhibition in BD, and its relationship to impulsivity. Methods: We assessed impulsivity using the Barratt Impulsiveness Scale (BIS) and, using functional magnetic resonance imaging (fMRI), measured neural activity in response to an Affective Go-NoGo Task, consisting of emotional facial stimuli (fear, happy, anger faces) and non-emotional control stimuli (neutral female and male faces) in euthymic BD (n=23) and healthy individuals (HI; n=25). Results: BD patients were significantly more impulsive, yet did not differ from HI on accuracy or reaction time on the emotional go/no-go task. Comparing neural patterns of activation when processing emotional Go versus emotional NoGo trials yielded increased activation in BD within temporal and cingulate cortices and within prefrontal-cortical regions in HI. Furthermore, higher BIS scores for BD were associated with slower reaction times, and indicative of compensatory cognitive strategies to counter increased impulsivity. Conclusions: These findings illustrate cognition-emotion interference in BD and the observed differences in neural activation indicate potentially altered emotion modulation. Increased activation in brain regions previously shown in emotion regulation and response inhibition tasks could represent a disease-specific marker for BD

Computational models for improved diagnosis and prognosis of stroke using robot-based biomarkers

Stroke is a leading cause of death and permanent disability worldwide, affecting millions of individuals. Traditional clinical scores for assessment of stroke-related impairments are inherently subjective and limited by inter-rater and intra-rater reliability, as well as floor and ceiling effects. In contrast, robotic technologies provide objective, highly repeatable tools for quantification of neurological impairments following stroke. KINARM is an exoskeleton robotic device that provides objective, reliable tools for assessment of sensorimotor, proprioceptive and cognitive brain function by means of a battery of behavioral tasks. As such, KINARM is particularly useful for assessment of neurological impairments following stroke. This thesis introduces a computational framework for assessment of neurological impairments using the data provided by KINARM. This is done by achieving two main objectives. First, to investigate how robotic measurements can be used to estimate current and future abilities to perform daily activities for subjects with stroke. We are able to predict clinical scores related to activities of daily living at present and future time points using a set of robotic biomarkers. The findings of this analysis provide a proof of principle that robotic evaluation can be an effective tool for clinical decision support and target-based rehabilitation therapy. The second main objective of this thesis is to address the emerging problem of long assessment time, which can potentially lead to fatigue when assessing subjects with stroke. To address this issue, we examine two time reduction strategies. The first strategy focuses on task selection, whereby KINARM tasks are arranged in a hierarchical structure so that an earlier task in the assessment procedure can be used to decide whether or not subsequent tasks should be performed. The second strategy focuses on time reduction on the longest two individual KINARM tasks. Both reduction strategies are shown to provide significant time savings, ranging from 30% to 90% using task selection and 50% using individual task reductions, thereby establishing a framework for reduction of assessment time on a broader set of KINARM tasks. All in all, findings of this thesis establish an improved platform for diagnosis and prognosis of stroke using robot-based biomarkers.

Vibrational Feedback Training System for Use with SEMG Controlled Powered Prostheses

Loss of limb results in loss of function and a partial loss of freedom. A powered prosthetic device can partially assist an individual with everyday tasks and therefore return some level of independence. Powered upper limb prostheses are often controlled by the user generating surface electromyographic (SEMG) signals. The goal of this thesis is to develop a virtual environment in which a user can control a virtual hand to safely grasp representations of everyday objects using EMG signals from his/her forearm muscles, and experience visual and vibrotactile feedback relevant to the grasping force in the process. This can then be used to train potential wearers of real EMG controlled prostheses, with or without vibrotactile feedback. To test this system an experiment was designed and executed involving ten subjects, twelve objects, and three feedback conditions. The tested feedback conditions were visual, vibrotactile, and both visual and vibrotactile. In each experimental exercise the subject attempted to grasp a virtual object on the screen using the virtual hand controlled by EMG electrodes placed on his/her forearm. Two metrics were used: score, and time to task completion, where score measured grasp dexterity. It was hypothesized that with the introduction of vibrotactile feedback, dexterity, and therefore score, would improve and time to task completion would decrease. Results showed that time to task completion increased, and score did not improve with vibrotactile feedback. Details on the developed system, the experiment, and the results are presented in this thesis.

Combining Taxonomies using Word2vec

Taxonomies have gained a broad usage in a variety of fields due to their extensibility, as well as their use for classification and knowledge organization. Of particular interest is the digital document management domain in which their hierarchical structure can be effectively employed in order to organize documents into content-specific categories. Common or standard taxonomies (e.g., the ACM Computing Classification System) contain concepts that are too general for conceptualizing specific knowledge domains. In this paper we introduce a novel automated approach that combines sub-trees from general taxonomies with specialized seed taxonomies by using specific Natural Language Processing techniques. We provide an extensible and generalizable model for combining taxonomies in the practical context of two very large European research projects. Because the manual combination of taxonomies by domain experts is a highly time consuming task, our model measures the semantic relatedness between concept labels in CBOW or skip-gram Word2vec vector spaces. A preliminary quantitative evaluation of the resulting taxonomies is performed after applying a greedy algorithm with incremental thresholds used for matching and combining topic labels.

Commentary on Baller v. Watson (1737)

This case marks the first occasion, following the passage of the Statute of Anne 1710 (uk_1710), on which a living author sought to prevent the infringement of his own copyright before the courts, as well as the first time on which a ‘perpetual' injunction was granted to prevent the further unauthorised reproduction of the work.
The commentary describes the circumstances which led Gay to publish the work himself, by subscription, as well as the success he enjoyed (albeit posthumously) in preventing unauthorised versions of the work from being published. That a ‘perpetual' injunction was granted at the conclusion of the litigation was subsequently interpreted, by advocates of common law copyright, to suggest that, regardless of the Statute of Anne, the Lord Chancellor considered copyright to be a perpetual right.

A bimetallic catalyst on a dual component support for low temperature total methane oxidation

Palladium, platinum bimetallic catalysts supported on η-Al₂O₃, ZSM-5(23) and ZSM-5(80), with and without the addition of TiO₂, were prepared and used for low temperature total methane oxidation (TMO). The catalysts were tested under reaction temperatures of 200-500 °C with a GHSV of 100,000 mL g^-1 h^-1. It was found that all four components, palladium, platinum, an acidic support and oxygen carrier were needed to achieve a highly active and stable catalyst. The optimum support being 17.5% TiO₂ on ZSM-5(80) where the T_10% was observed at only 200 °C. On addition of platinum, longer time on stream experiments showed no decrease in the catalyst activity over 50 h at 250 °C.

Ecological and evolutionary effects of dispersal on freshwater zooplankton

A recent focus on contemporary evolution and the connections between communities has sought to more closely integrate the fields of ecology and evolutionary biology. Studies of coevolutionary dynamics, life history evolution, and rapid local adaptation demonstrate that ecological circumstances can dictate evolutionary trajectories. Thus, variation in species identity, trait distributions, and genetic composition may be maintained among ecologically divergent habitats. New theories and hypotheses (e.g., metacommunity theory and the Monopolization hypothesis) have been developed to understand better the processes occurring in spatially structured environments and how the movement of individuals among habitats contributes to ecology and evolution at broader scales. As few empirical studies of these theories exist, this work seeks to further test these concepts. Spatial and temporal dispersal are the mechanisms that connect habitats to one another. Both processes allow organisms to leave conditions that are suboptimal or unfavorable, and enable colonization and invasion, species range expansion, and gene flow among populations. Freshwater zooplankton are aquatic crustaceans that typically develop resting stages as part of their life cycle. Their dormant propagules allow organisms to disperse both temporally and among habitats. Additionally, because a number of species are cyclically parthenogenetic, they make excellent model organisms for studying evolutionary questions in a controlled environment. Here, I use freshwater zooplankton communities as model systems to explore the mechanisms and consequences of dispersal and to test these nascent theories on the influence of spatial structure in natural systems. In Chapter one, I use field experiments and mathematical models to determine the range of adult zooplankton dispersal over land and what vectors are moving zooplankton. Chapter two focuses on prolonged dormancy of one aquatic zooplankter, Daphnia pulex. Using statistical models with field and mesocosm experiments, I show that variation in Daphnia dormant egg hatching is substantial among populations in nature, and some of that variation can be attributed to genetic differences among the populations. Chapters three and four explore the consequences of dispersal at multiple levels of biological organization. Chapter three seeks to understand the population level consequences of dispersal over evolutionary time on current patterns of population genetic differentiation. Nearby populations of D. pulex often exhibit high population genetic differentiation characteristic of very low dispersal. I explore two alternative hypotheses that seek to explain this pattern. Finally, chapter four is a case study of how dispersal has influenced patterns of variation at the community, trait and genetic levels of biodiversity in a lake metacommunity.