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关键词： Autonomous Driving   Computer Science   Computer Vision   High Definition Map   Mapping   Transportation  

关键词： Embedded programming   High school computer science   Robotics   Secondary computer science   Teaching computer science  

摘要： This study sought to understand the influence of robotics on self-efficacy and interest in computer science. It provides the background that has led to the current research need. The study conducted was a double pre-test, repeated measures post-test quasi-experimental study, performed by the researcher to determine the effects of a robotics treatment on the interest and self-efficacy of introductory computer science students in secondary education in the United States. The Social Cognitive Career Theory of Lent et al. (2020) was used as the theoretical model for this study.

关键词： Computer science   Computer engineering   Transportation  

关键词： antioxidant activity   Ballatinao black rice   fermentation   rice wine  

摘要： This study described and evaluated the acidity, alcohol content, total phenolic content, antioxidant activity, and sensory attributes of rice wine (Tapuy) made from black, red, brown and white rice during a 7-day fermentation. Acidity of Tapuy made from the different pigmented rice had similar pH profiles characterized by a steep decrease at day 1 from 6.38 +/- 0.4 to 3.38 +/- 0.2, and remained relatively unchanged until the end of fermentation. Tapuy made from black rice (Ballatinao) contained 16.07% alcohol, second to white rice (Bongkitan) that contained 18.58% alcohol, although they were not significantly different from each other (p 0.05). At the end of fermentation, the total phenolic content and antioxidant activity of Tapuy made from black rice was significantly higher (p<0.05 and p<0.01, respectively) than Tapuy made from the other pigmented rice. Total phenolic content of Tapuy made from black rice was at 9.73 mg GAE/mL and antioxidant activity leveled-off at 70.63% DPPH radical scavenging activity;whereas Tapuy made from the other pigmented rice were declining. Thus, Ballatinao black rice is a suitable rice variety for making Tapuy and has the potential to be a health-promoting functional food.

关键词： Deep learning   Remote sensing   Inverse problems   Neural networks   Signal processing   Sensors   Algorithms   Satellites   Rain   Aerospace engineering   Computer science   Electrical engineering   Engineering   Mathematics   Meteorology  

摘要： Hyperspectral image unmixing (HU) is a remote sensing technique that has numerous applications in ecology, earth observation, and agronomy. Recently the research surrounding hyperspectral unmixing are using deep convolutional neural networks (CNNs). This helps to extract more detailed features of the image scene through multi-layer non-linear transformation and it has better performance than previous unmixing approaches. However, there are challenges that affect the performance of CNNs in the unmixing process. First, training the CNN with pixels which have different non-linearity mixing functions lead to miss-estimation of network parameters. Second, hundreds of spectral bands in the hyperspectral image (HSI) may cause the curse of dimensionality in data processing by the CNNs. Third, the sparsity of annotations is another concern in CNNs due to loss of detailed information. Fourth, over-fitting is another problem in Deep CNNs (DCNN) when the training data has a small number of endmembers. In this thesis, we will address some of the challenges of deep CNN applied in the unmixing process by proposing novel pixel-wise spectral unmixing methods. First, we use a graph model for the HSI to classify the pixels into homogeneous and heterogeneous classes based on minimizing the Structural Intervention Distance. We then train a dedicated DCNN for each class of pixels. In the next step, we propose another HU method called HU-DCNN-MF. Taking interference, distortion and noise into account, we design a bank of Matched Filters (MF) in the HU-DCNN-MF approach as a linear preprocessing stage before passing the data pixels through deep CNNs. The bank of matched filters improve the signal to interference, distortion and noise ratio (SIDNR) of the input data for the DCNNs. To consider the spatial correlations in HU, we develop a bank of Optimal Filters (OF) by using a graph model for the HSI. In this method called HU-DCNN-OF, we employ a bank of optimal filters cascaded with the DCNN

关键词： Deep learning   Machine translation   Back propagation   Neural networks   Semantics   Natural language   Computer science   Artificial intelligence  

摘要： We develop a novel unified representation for the argumentation mining task facilitating the extracting from text and the labelling of the non-argumentative units and argumentation components—premises, claims, and major claims—and the argumentative relations—premise to claim or premise in a support or attack relation, and claim to major claim in a for or against relation—in an end-to-end machine learning pipeline. This tightly integrated representation combines the component and relation identification sub-problems and enables a unitary solution for detecting argumentation structures. This new representation together with a new deep learning architecture composed of a mixed embedding method, a multi-head attention layer, two biLSTM layers, and a final linear layer obtains state-of-the-art accuracy and F1 scores on the Persuasive Essays (PE) dataset. Furthermore, the augmentation of the PE corpus by including copies of major claims substituting the n-gram tokens that occur right before the major claim tokens with other major claim-introducing n-grams has aided in this increased performance on the PE dataset.

关键词： Prostate   Mass spectrometry   Deep learning   Histology   Ions   Neural networks   Maps   Chemistry   Biomarkers   Scientific imaging   Algorithms   Clustering   Morphology   Analytical chemistry   Artificial intelligence   Biology   Computer science   Medicine  

摘要： Mass spectrometry is an analysis technique used to investigate the molecular profile of both biological and non-biological samples. Mass spectrometry imaging (MSI) allows for the spatial mapping of these molecules directly from a physical sample. Clinical applications of MSI are limited, in part because of the difficulty of analyzing the high-dimensional data that is produced. MSI images are commonly analyzed by reducing the dimensionality of the data and performing unsupervised analysis. In this work, we analyzed MSI data by using a deep neural network composed of a variational autoencoder and a Gaussian mixture model. The model was trained to jointly optimize the latent space representation of the MSI data and the cluster assignments within the latent space. We also used a path-based gradient-analysis method to determine the contribution of each analyzed molecule to its corresponding cluster. Our study demonstrated the robustness of the proposed method when compared to conventional MSI data-analysis methods. Our gradient-analysis method was able to discover patterns of molecules that had distribution throughout the samples which matched their corresponding cluster assignments.

关键词： Computer & video games   Algorithms   Graphs   Neighborhoods   Roads & highways   Benchmarks   Computer science   Transportation  

摘要： In the path planning problem on grid graphs, one of the main state-of-art preprocessing techniques are subgoal graphs. These graphs consist of a subset of important nodes called subgoals that are connected by a reachability relation. When solving a path planning problem, one must connect the origin node and the destination node to the subgoal graph, search for a path in the subgoal graph, and refine the path into a path in the original graph. In this thesis, we present Directed Subgoal Graphs (DSG), a new subgoal graph which is built on a grid graph augmented with the incoming direction in order to prune nonshortest paths. The reachability relation ensures that all shortest path and the beginning of any diagonal-first path between two subgoals are unblocked. In DSG the connection between subgoals is performed in a novel cardinal-first order. The connection process shows to be fastest among these in all subgoal graphs while using the minimum amount of memory for this stage. When DSG is enhanced with Contraction Hierarchies (CH), it improves the state-ofart performance in several instances of the MovingAI benchmark set. In game maps it provides a speedup of up to 3.0% over the state-of-art subgoal graph while using 47% less memory. We observed most of the benefits are obtained in mainly large maps in which the traversable space allows long diagonal movements and there is a large number of isles of obstacles. When this happens, the speedups can reach up to 27%. Additionally, we provide several improvements to the subgoal graph framework, which includes the extension of the avoidance of unimportant subgoals and a new technique to reduce redundant shortcuts generated by CH.

关键词： Robotics   Computer science   Mechanical engineering   Artificial intelligence  

关键词： Artificial intelligence   Computer science   Hydraulic engineering   Optics   Petroleum engineering  

摘要： Production logs have proved invaluable for identifying production and completion anomalies and problems in oil and gas wells. Conventional production logging especially in horizontal wells come with challenges such as the risks associated with lowering the logging tools into the wellbore, harsh downhole conditions, and the cost of well intervention. More importantly, measurements acquired from conventional production logging is only a snapshot of the downhole condition for that moment in time. Over the last two decades, there has been advances in downhole monitoring in oil and gas wells with the use of Fiber-Optic sensing technology such as the Distributed Temperature Sensing (DTS). Unlike a conventional production log that provides only snapshots of the well performance, DTS provides continuous temperature measurements along the entire wellbore. Unfortunately, current DTS interpretation methods are based on visualization of the temperature change in the DTS measurements, and are qualitative in nature, at best. Whether by fluid extraction or injection, oil and gas production changes reservoir conditions, and continuous monitoring of downhole conditions is highly desirable. This research study presents a tool for real-time quantification of production from individual perforation clusters in a multi-stage shale well using Artificial Intelligence and Machine Learning. The technique presented provides continuous production log on demand thereby providing opportunities for the optimization of completions design and hydraulic fracture treatments of future planned wells. A Fiber-Optic sensing enabled horizontal well MIP-3H in the Marcellus Shale has been selected for this work. MIP-3H is a 28-stage horizontal well drilled in July 2015, as part of a Department of Energy (DOE)-sponsored project - Marcellus Shale Energy & Environment Laboratory (MSEEL). A oneday conventional production logging operation has been performed on MIP-3H using a flow scanner while the installed Fib