Finding New Rules for Incomplete Theories: Induction with Explicit Biases in Varying Contexts

Many AI problem solvers possess explicitly encoded knowledge - a domain theory ““ that they use to solve problems. If these problem solvers are to be autonomous, they must be able to detect and to fill gaps in their own knowledge. The field of machine learning addresses this issue. Recently two disparate machine learning approaches have emerged as predominant in the field: explanation-based learning (EBL) and similarity-based learning (SBL), EBL and SBL have been applied to problems in a variety of domains. Both methods have clear problems, however, EBL assumes that a system is given an explicit theory of the domain that is complete, correct, and tractable. These assumptions are clearly unrealistic for most complex, real-world problems. SBL suffers because of its lack of an explicit theory of the domain. The simplicity of the method requires that human intervention playa large role in tailoring input examples and the features describing them in such a way as to allow a system to choose an appropriate set of features to define a concept. Biasing a system in this way may result in its being unable to discover all concepts in even a Single domain. Less tailoring of the examples leaves a system open to the possibility of not converging on the best definition for a concept, or any at all, due to the computational complexity. The research described in this proposal addresses a number of the problems found in explanation-based and similarity-based learning. The major focus of the research is the elimination of the assumption that the domain theory of an EBL system is complete. In particular, it considers the problem of working with an incomplete theory by suggesting a method by which gaps in an EBL system's knowledge can be detected and filled. We suggest that when EBL cannot derive a complete explanation, the partial explanation focus a context in which learning takes place. Information extracted from partial explanations, as well as from complete explanations, can be exploited by SBL to do better induction of the missing domain knowledge. The extracted information constitutes an explicit bias for similarity-based learning. A second problem to be addressed is that of making the biases of SBL explicit. Finally, all testing of the claims made in this proposal is to be done in the Gemini learning system. The development of the system addresses the goal of constructing an integrated learning architecture utilizing both EBL and SBL

Data Analytic Approach to Support the Activation of Special Signal Timing Plans in Response to Congestion

Improving arterial network performance has become a major challenge that is significantly influenced by signal timing control. In recent years, transportation agencies have begun focusing on Active Arterial Management Program (AAM) strategies to manage the performance of arterial streets under the flagship of Transportation Systems Management & Operations (TSM&O) initiatives. The activation of special traffic signal plans during non-recurrent events is an essential component of AAM and can provide significant benefits in managing congestion. Events such as surges in demands or lane blockages can create queue spillbacks, even during off-peak periods resulting in delays and spillbacks to upstream intersections. To address this issue, some transportation agencies have started implementing processes to change the signal timing in real time based on traffic signal engineer/expert observations of incident and traffic conditions at the intersections upstream and downstream of congested locations. This dissertation develops methods to automate and enhance such decisions made at traffic management centers. First, a method is developed to learn from experts’ decisions by utilizing a combination of Recursive Partitioning and Regression Decision Tree (RPART) and Fuzzy Rule-Based System (FRBS) to deal with the vagueness and uncertainty of human decisions. This study demonstrates the effectiveness of this method in selecting plans to reduce congestion during non-recurrent events. However, the method can only recommend the changes in green time to the movement affected by the incident and does not give an optimized solution that considers all movements. Thus, there was a need to extend the method to decide how the reduction of green times should be distributed to other movements at the intersection. Considering the above, this dissertation further develops a method to derive optimized signal timing plans during non-recurrent congestion that considers the operations of the critical direction impacted by the incident, the overall corridor, as well as the critical intersection movement performance. The prerequisite of optimizing the signal plans is the accurate measurements of traffic flow conditions and turning movement counts. It is also important to calibrate any utilized simulation and optimization models to replicate the field traffic states according to field traffic conditions and local driver behaviors. This study evaluates the identified special signal-timing plan based on both the optimization and the DT and FRBS approaches. Although the DT and FRBS model outputs are able to reduce the existing queue and improve all other performance measures, the evaluation results show that the special signal timing plan obtained from the optimization method produced better performance compared to the DT and FRBS approaches for all of the evaluated non-recurrent conditions. However, there are opportunities to combine both approaches for the best selection of signal plans

Enhanced Methods for Utilization of Data to Support Multi-Scenario Analysis and Multi-Resolution Modeling

The success of analysis and simulation in transportation systems depends on the availability, quality, reliability, and consistency of real-world data and the methods for utilizing the data. Additional data and data requirements are needed to support advanced analysis and simulation strategies such as multi-resolution modeling (MRM) and multi-scenario analysis. This study has developed, demonstrated, and assessed a systematic approach for the use of data to support MRM and multi-scenario analysis. First, the study developed and examined approaches for selecting one or more representative days for the analysis, considering the variability in travel conditions throughout the year based on cluster analysis. Second, this study developed and analyzed methods for using crowdsourced data vii to estimate origin-destination demands and link-level volumes for use as part of an MRM with consideration of the modeling scenario(s). The assessment of the methods to select the representative day(s) utilizes statistical measures, in addition to measures and visualization techniques that are specific to traffic operations. The results of the assessment indicate that the utilization of the K-means clustering algorithm with four clusters and spatio-temporal segregation of the variables demonstrated superior performance over other tested approaches, such as the use of the Gaussian Mixture clustering algorithm and the use of different segregation levels. The study assessed methods for the use of third-party crowdsourced data from StreetLight (SL) as part of the Origin-Destination Matrix Estimation (ODME), which identifies the method resulting in the closest origin-destination demands to the original seed matrices and real-world link counts. The results of the study indicate that Method 3(b) produced the best performance, which utilized combined data from demand forecasting models, crowdsourced data, and traffic counts. Additionally, this study examined regression models between crowdsourced data and count station data developed for link-level estimation of the volumes. This study also examined the accuracy and transferability of the link-level estimation of the volumes to determine if the crowdsourced data combined with available volume data at several locations can be used to predict missing or unavailable volumes in different locations on different days and times within the network. Regression models produced low errors than the default SL estimates when hourly or daily traffic volumes were taken into account. For similar traffic conditions, the models predicted directional traffic volume close to the real-world value

Design of nanomanufacturing systems

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 121-124).Over 100 years of manufacturing knowledge and experience are available to a design engineer when considering the integration of a machine tool enabling macro-scale processes (milling, turning, welding, water-jet cutting) into a production or manufacturing line, and this thesis seeks to provide a design engineer with the insight so that the same can be done for a nano-scale process such as Dip Pen Nanolithography and Nanoimprint Lithography. Accordingly this work presents methods for designing nanomanufacturing systems, including the development of new technology to fulfill the unique performance requirements of nanomanufacturing processes. First, an introduction to nanomanufacturing and the differences between macro-scale and nano-scale manufacturing will be presented. Second, a "metric mapping" method will be illustrated which can be used to identify areas of nano-manufacturing where the need for the development of new technology is critical. Thirdly, this new method is capable of helping a design engineer synthesize technology for nano-manufacturing, as will be shown through a case-study in which a modular, precision belt-drive machine which is capable of enabling high-throughput nanomanufacturing was designed and built. This machine for highrate nanomanufacturing not only exceeds the performance requirements for a process (Dip Pen Nanolithography, or DPN) that has been called "not suitable for high-rate nanomanufacturing", but also is capable of implementing DPN at a rate almost 200 times that of previous machines.by Alexander H. Slocum, Jr.S.M

Developing Emergency Preparedness Plans For Orlando International Airport (MCO) Using Microscopic Simulator WATSim

Emergency preparedness typically involves the preparation of detailed plans that can be implemented in response to a variety of possible emergencies or disruptions to the transportation system. One shortcoming of past response plans was that they were based on only rudimentary traffic analysis or in many cases none at all. With the advances in traffic simulation during the last decade, it is now possible to model many traffic problems, such as emergency management, signal control and testing of Intelligent Transportation System technologies. These problems are difficult to solve using the traditional tools, which are based on analytical methods. Therefore, emergency preparedness planning can greatly benefit from the use of micro-simulation models to evaluate the impacts of natural and man-made incidents and assess the effectiveness of various responses. This simulation based study assessed hypothetical emergency preparedness plans and what geometric and/or operational improvements need to be done in response to emergency incidents. A detailed framework outlining the model building, calibration and validation of the model using microscopic traffic simulation model WATSim (academic version) is provided. The Roadway network data consists of geometric layout of the network, number of lanes, intersection description which include the turning bays, signal timings, phasing sequence, turning movement information etc. The network in and around the OIA region is coded into WATSim with 3 main signalized intersections, 180 nodes and 235 links. The travel demand data includes the vehicle counts in each link of the network and was modeled as percentage turning count movements. After the OIA network was coded into WATSim, the road network was calibrated and validated for the peak hour mostly obtained from ADT with 8% K factor by comparing the simulated and actual link counts at 15 different key locations in the network and visual verification done. Ranges of scenarios were tested that includes security checkpoint, route diversion incase of incident in or near the airport and increasing demand on the network. Travel time, maximum queue length and delay were used as measures of effectiveness and the results tabulated. This research demonstrates the potential benefits of using microscopic simulation models when developing emergency preparedness strategies. In all 4 main Events were modeled and analyzed. In Event 1, occurrence of 15 minutes traffic incident on a section of South Access road was simulated and its impact on the network operations was studied. The averaged travel time under the incident duration to Side A was more than doubled (29 minutes, more than a 100% increase) compared to the base case and similarly that of Side B two and a half times more (23 minutes, also more than a 100% increase). The overall network performance in terms of delay was found to be 231.09 sec/veh. and baseline 198.9 sec/veh. In Event 2, two cases with and without traffic diversions were assumed and evaluated under 15 minutes traffic incident modeled at the same link and spot as in Event 1. It was assumed that information about the traffic incident was disseminated upstream of the incident 2 minutes after the incident had occurred. This scenario study demonstrated that on the average, 17% (4 minutes) to 41% (12 minutes) per vehicle of travel time savings are achieved when real-time traffic information was provided to 26% percent of the drivers diverted. The overall network performance in delay for this event was also found to improve significantly (166.92 sec/veh). These findings led to the conclusion that investment in ITS technologies that support dissemination of traffic information (such as Changeable Message Signs, Highway Advisory Radio, etc) would provide a great advantage in traffic management under emergency situations and road diversion strategies. Event 3 simulated a Security Check point. It was observed that on the average, travel times to Sides A and B was 3 and 5 minutes more respectively compared to its baseline. Averaged queue length of 650 feet and 890 feet worst case was observed. Event 4 determined when and where the network breaks down when loaded. Among 10 sets of demand created, the network appeared to be breaking down at 30% increase based on the network-wide delay and at 15% based on Level of Service (LOS). The 90% increase appeared to have the most effect on the network with a total network-wide delay close to 620 seconds per vehicle which is 3 and a half times compared to the baseline. Conclusions and future scope were provided to ensure continued safe and efficient traffic operations inside and outside the Orlando International Airport region and to support efficient and informed decision making in the face of emergency situations

Synthetic lethality driven by N-Myristoyltransferase inhibition in MYC deregulated cancers.

N-myristoylation is the irreversible attachment of myristate (a C14 fatty acid) to the N-terminal glycine of a given substrate. The enzyme responsible for this reaction is N-myristoyl transferase (NMT), a protein shown to be essential for many organisms, ranging from eukaryotes, plants, fungi, to infectious parasites. Initially, the NMTs of the fungi and infectious parasites sparked the interest of researchers in drug discovery to target these pathogens. However, the question remained whether one could target NMT in cancer. While early studies suggested potential upregulation of NMT1 in some early stage cancers, it remained unclear which cancer types to target and for which mechanistical reason. In this study, data from pharmacogenomics screens across hundreds of cancer cell lines, treated with three different NMT inhibitors, were analysed. Haematological malignancies were amongst the most responsive cell lines; however, also cancers originating from other tissues were sensitive, indicating a more complicated picture. Detailed phenotypical and omics-based analysis of the effects of NMT inhibition in an example cancer cell line from the haematological malignancies, and an unbiased bioinformatics approach across the pharmacogenomics data hinted at the same protooncogene: MYC. Two different isogenic system with inducible MYC confirmed that MYC deregulated cells are highly dependent on myristoylation. This newly uncovered synthetic lethality has potentially wide implications as MYC, a key transcription factor, is commonly deregulated in cancer and involved in most of the hallmarks of cancer. Targeting MYC or its downstream program attracted wide attention of the field; however, to date no drug has been approved to specifically target either. Novel approaches to target MYC, in the context of cancer, are urgently needed, and this study identified a potential new one.Open Acces