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Rationale, aims and objectives: Single‐group interrupted time series analysis (ITSA) is a popular evaluation methodology in which a single unit of observation is being studied, the outcome variable is serially ordered as a time series and the intervention is expected to ‘interrupt’ the level and/or trend of the time series, subsequent to its introduction. Given that the internal validity of the design rests on the premise that the interruption in the time series is associated with the introduction of the treatment, treatment effects may seem less plausible if a parallel trend already exists in the time series prior to the actual intervention. Thus, sensitivity analyses should focus on detecting structural breaks in the time series before the intervention. Method: In this paper, we introduce a machine‐learning algorithm called optimal discriminant analysis (ODA) as an approach to determine if structural breaks can be identified in years prior to the initiation of the intervention, using data from California's 1988 voter‐initiated Proposition 99 to reduce smoking rates. Results: The ODA analysis indicates that numerous structural breaks occurred prior to the actual initiation of Proposition 99 in 1989, including perfect structural breaks in 1983 and 1985, thereby casting doubt on the validity of treatment effects estimated for the actual intervention when using a single‐group ITSA design. Conclusions: Given the widespread use of ITSA for evaluating observational data and the increasing use of machine‐learning techniques in traditional research, we recommend that structural break sensitivity analysis is routinely incorporated in all research using the single‐group ITSA design. [ABSTRACT FROM AUTHOR]

The principles of irrigation and drainage in cracking soils differ markedly from non-cracking soils, and are not thoroughly understood. This paper presents a conceptual model to simulate water and salt flows in cracking soils of the Imperial Valley, CA, in the presence of ground water that contributes partially to ET demand of crops. A salt reactivity function is introduced in the model to account for mineral precipitation (salt deposition) and mineral dissolution (salt pick up). The conceptual water flow model assumes that surface irrigation water moves into the cracks, infiltrates horizontally to wet the soil profile and a fraction bypasses below the root zone into the shallow ground water and is retained for later crop extraction via upflow. Then, water drains vertically through the soil profile step by step, and root water extractions are calculated. When ET exceeds available water upflow of ground water is calculated. Provision for reclamation leaching before the next crop is also made. The associated conceptual salt transport model involves complete mixing of invading and resident soil water. Salt concentration from ET is subjected to a salt reactivity function to obtain salt deposition of calcite and gypsum to obtain salt concentration after precipitation. This reactivity function is also used in the inverse when two or more waters mix to transform salt after precipitation to salt concentration after ET. The flow of salts follows the water transport algorithum. The model has been applied to a point in the Imperial Valley and observed data from Bali et al. () was used for calibration. Simulated point data from four successive years of alfalfa, reclamation leaching, wheat and lettuce are evaluated in this paper. [ABSTRACT FROM AUTHOR]

This paper presents a behavior-based adaptive mission planner (AMP) to trace a chemical plume to its source and reliably declare the source location. The proposed AMP is implemented on a REMUS autonomous underwater vehicle (AUV) equipped with multiple types of sensors that measure chemical concentration, the flow velocity vector, and AUV position, depth, altitude, attitude, and speed. This paper describes the methods and results from experiments conducted in November 2002 on San Clemente Island, CA, using a plume of Rhodamine dye developed in a turbulent fluid flow (i.e., near-shore ocean conditions). These experiments demonstrated chemical plume tracing over 100 m and source declaration accuracy relative to the nominal source location on the order of tens of meters. The designed maneuvers are divided into four behavior types: finding a plume, tracing the plume, reacquiring the plume, and declaring the source location. The tracing and reacquiring behaviors are inspired by male moths flying upwind along a pheromone plume to locate a sexually receptive female. All behaviors are formulated by perception and action modules and translated into chemical plume-tracing algorithms suitable for implementation on a REMUS AUV. To coordinate the different behaviors, the subsumption architecture is adopted to define and arbitrate the behavior priorities. AUVs capable of such feats would have applicability in searching for environmentally interesting phenomena, unexploded ordnance, undersea wreckage, and sources of hazardous chemicals or pollutants. [ABSTRACT FROM AUTHOR]

This paper focuses on the benchmark control problems for seismically excited nonlinear buildings. The benchmark study focuses on three typical steel structures, 3-, 9-, and 20-story buildings designed for the SAC project for the Los Angeles region. This paper reports the application of the active control scheme on the 3- and 20-story benchmark buildings, where the control action is achieved by a fuzzy logic controller. The main advantage of the fuzzy controller is its inherent robustness and ability to handle any nonlinear behavior of the structure. The performance of the controller is validated through the computer simulation on MATLAB. The control scheme satisfies the control constraints and is presented according to the evaluation criteria provided with the benchmark problems for comparison with other schemes. Four different earthquakes (El Centro 1940, Hachinohe 1968, Northridge 1994, and Kobe 1995) will be used in the simulation. [ABSTRACT FROM AUTHOR]

Sandwave migration rate is of critical importance for the hydrodynamic research as well as the coastal engineering application. To acquire this information, the correlation matching algorithm is applied in this paper. By searching for similar seafloor areas between multi-temporal digital terrain models, the migration rate can be estimated. To account for the impact of the morphological distortion as well as outliers in the survey data, the correlation matching is adjusted based on the robust estimation theory. By executing the correlation matching based on the least median square criterion, a robust initial estimate is obtained. Afterwards, the potential outliers are detected based on the initial matching result, and the corresponding weights are adjusted to zero. Finally, correlation matching is executed with the updated weights. With the above procedure, the impact of outliers can be accounted for. The developed method is applied on the bathymetry time-series data collected in Monterey Canyon, California. Comparison shows that the presented algorithm significantly improves the credibility of the estimation. The generated migration vector field not only reveals the main tendency of the migration rate in the region, but also highlights the unique local migration patterns, which is helpful for understanding the mechanism of the sandwave migration. [ABSTRACT FROM AUTHOR]

Embedding of diagnostic and health care information requires secure encryption and watermarking. This research paper presents a comprehensive study for the behavior of some well established watermarking algorithms in frequency domain for the preservation of stroke-based diagnostic parameters. Two different sets of watermarking algorithms namely: two correlation-based (binary logo hiding) and two singular value decomposition (SVD)-based (gray logo hiding) watermarking algorithms are used for embedding ownership logo. The diagnostic parameters in atherosclerotic plaque ultrasound video are namely: (a) bulb identification and recognition which consists of identifying the bulb edge points in far and near carotid walls; (b) carotid bulb diameter; and (c) carotid lumen thickness all along the carotid artery. The tested data set consists of carotid atherosclerotic movies taken under IRB protocol from University of Indiana Hospital, USA-AtheroPoint™ (Roseville, CA, USA) joint pilot study. ROC (receiver operating characteristic) analysis was performed on the bulb detection process that showed an accuracy and sensitivity of 100 % each, respectively. The diagnostic preservation (DP) for SVD-based approach was above 99 % with PSNR (Peak signal-to-noise ratio) above 41, ensuring the retention of diagnostic parameter devalorization as an effect of watermarking. Thus, the fully automated proposed system proved to be an efficient method for watermarking the atherosclerotic ultrasound video for stroke application. [ABSTRACT FROM AUTHOR]

Cardiovascular Disease (CVD) is one of the most catastrophic and life threatening health issue nowadays. Early detection of CVD is an important solution to reduce its devastating effects on health. In this paper, an efficient CVD detection algorithm is identified. The algorithm uses patient demographic data as inputs, along with several ECG signal features extracted automatically through signal processing techniques. Cross-validation results show a 98.29 % accuracy for the decision tree classification algorithm. The algorithm has been integrated into a web based system that can be used at anytime by patients to check their heart health status. At one end of the system is the ECG sensor attached to the patient's body, while at the other end is the detection algorithm. Communication between the two ends is done through an Android application. [ABSTRACT FROM AUTHOR]

The Study to Explore Early Development (SEED) is a multi-site case-control study designed to explore the relationship between autism spectrum disorder (ASD) phenotypes and etiologies. The goals of this paper are to (1) describe the SEED algorithm that uses the Autism Diagnostic Interview-Revised (ADI-R) and Autism Diagnostic Observation Schedule (ADOS) to classify children with ASD, (2) examine psychometric properties of different ASD classification methods, including the SEED method that incorporates rules for resolving ADI-R and ADOS discordance, and (3) determine whether restricted interests and repetitive behaviors were noted for children who had instrument discordance resolved using ADI-R social and communication scores. Results support the utility of SEED criteria when well-defined groups of children are an important clinical or research outcome. [ABSTRACT FROM AUTHOR]

Abstract: Land use regression (LUR) models are widely employed in health studies to characterize chronic exposure to air pollution. The LUR is essentially an interpolation technique that employs the pollutant of interest as the dependent variable with proximate land use, traffic, and physical environmental variables used as independent predictors. Two major limitations with this method have not been addressed: (1) variable selection in the model building process, and (2) dealing with unbalanced repeated measures. In this paper, we address these issues with a modeling framework that implements the deletion/substitution/addition (DSA) machine learning algorithm that uses a generalized linear model to average over unbalanced temporal observations. Models were derived for fine particulate matter with aerodynamic diameter of 2.5 microns or less (PM2.5) and nitrogen dioxide (NO2) using monthly observations. We used 4119 observations at 108 sites and 15,301 observations at 138 sites for PM2.5 and NO2, respectively. We derived models with good predictive capacity (cross-validated-R 2 values were 0.65 and 0.71 for PM2.5 and NO2, respectively). By addressing these two shortcomings in current approaches to LUR modeling, we have developed a framework that minimizes arbitrary decisions during the model selection process. We have also demonstrated how to integrate temporally unbalanced data in a theoretically sound manner. These developments could have widespread applicability for future LUR modeling efforts. [Copyright &y& Elsevier]

Abstract: The Wildfire Automated Biomass Burning Algorithm (WF-ABBA) is a state-of-the-art algorithm for geostationary wildfire detection whose results have been increasingly used in a range of environmental applications. At present, the WF-ABBA validation activities and, in general, fire product validation methodologies are at a markedly less advanced stage than the algorithm itself. Particularly, little is known about detection timeliness, despite the value of such information for assessing the potential of geostationary observations to improve tactical decision making of first responders. This paper contributes to reducing this gap in two ways. Firstly, we describe a new methodology that is suitable for evaluating geostationary satellite wildfire detection in terms of incidents with regard to both timeliness and reliability. This methodology utilizes available official multi-agency wildfire reporting information and multitemporal Landsat imagery. Secondly, we apply the proposed validation method to temporally filtered GOES-West WF-ABBA (ver. 6.1) detections for the 2006 fire season over the State of California and present incident-wise and pixel-wise performance information. The results indicate highly reliable pixel-wise performance of WF-ABBA, with about 75% of fire pixels (or more) corresponding to actual recorded active wildfires. A substantial portion of wildfires were detected during their first hour of activity, and a few incidents—even before the initial reports from conventional sources. Although the WF-ABBA performs best at what it was designed for: consistently re-detecting (monitoring) active fires, we believe there is an additional potential for automated detection from current geostationary data to reduce wildfire ignition latencies in the Western U.S. Our results can serve as a guideline for algorithm developers and users of the WF-ABBA fire product. [Copyright &y& Elsevier]

Abstract: Estimating sediment volume carried by a river is an important issue in water resources engineering. This paper compares the accuracy of three different soft computing methods, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Gene Expression Programming (GEP), in estimating daily suspended sediment concentration on rivers by using hydro-meteorological data. The daily rainfall, streamflow and suspended sediment concentration data from Eel River near Dos Rios, at California, USA are used as a case study. The comparison results indicate that the GEP model performs better than the other models in daily suspended sediment concentration estimation for the particular data sets used in this study. Levenberg–Marquardt, conjugate gradient and gradient descent training algorithms were used for the ANN models. Out of three algorithms, the Conjugate gradient algorithm was found to be better than the others. [Copyright &y& Elsevier]

The quality of propensity scores is traditionally measured by assessing how well they make the distributions of covariates in the treatment and control groups match, which we refer to as 'good balance'. Good balance guarantees less biased estimates of the treatment effect. However, the cost of achieving good balance is that the variance of the estimates increases due to a reduction in effective sample size, either through the introduction of propensity score weights or dropping cases when propensity score matching. In this paper, we investigate whether it is best to optimize the balance or to settle for a less than optimal balance and use double robust estimation to adjust for remaining differences. We compare treatment effect estimates from regression, propensity score weighting, and double robust estimation with varying levels of effort expended to achieve balance using data from a study about the differences in outcomes by HIV status in heterosexually active homeless men residing in Los Angeles. Because of how costly data collection efforts are for this population, it is important to find an alternative estimation method that does not reduce effective sample size as much as methods that aggressively aim to optimize balance. Results from a simulation study suggest that there are instances in which we can obtain more precise treatment effect estimates without increasing bias too much by using a combination of regression and propensity score weights that achieve a less than optimal balance. There is a bias-variance tradeoff at work in propensity score estimation; every step toward better balance usually means an increase in variance and at some point a marginal decrease in bias may not be worth the associated increase in variance. [ABSTRACT FROM AUTHOR]

Path planning and trajectory design for autonomous underwater vehicles (AUVs) is of great importance to the oceanographic research community because automated data collection is becoming more prevalent. Intelligent planning is required to maneuver a vehicle to high-valued locations to perform data collection. In this paper, we present algorithms that determine paths for AUVs to track evolving features of interest in the ocean by considering the output of predictive ocean models. While traversing the computed path, the vehicle provides near-real-time, in situ measurements back to the model, with the intent to increase the skill of future predictions in the local region. The results presented here extend preliminary developments of the path planning portion of an end-to-end autonomous prediction and tasking system for aquatic, mobile sensor networks. This extension is the incorporation of multiple vehicles to track the centroid and the boundary of the extent of a feature of interest. Similar algorithms to those presented here are under development to consider additional locations for multiple types of features. The primary focus here is on algorithm development utilizing model predictions to assist in solving the motion planning problem of steering an AUV to high-valued locations, with respect to the data desired. We discuss the design technique to generate the paths, present simulation results and provide experimental data from field deployments for tracking dynamic features by use of an AUV in the Southern California coastal ocean. [ABSTRACT FROM AUTHOR]

Abstract: Object-based image analysis has proven its potentials for remote sensing applications, especially when using high-spatial resolution data. One of the first steps of object-based image analysis is to generate homogeneous regions from a pixel-based image, which is typically called the image segmentation process. This paper introduces a new automatic Region-based Image Segmentation Algorithm based on k-means clustering (RISA), specifically designed for remote sensing applications. The algorithm includes five steps: k-means clustering, segment initialization, seed generation, region growing, and region merging. RISA was evaluated using a case study focusing on land-cover classification for two sites: an agricultural area in the Republic of South Africa and a residential area in Fresno, CA. High spatial resolution SPOT 5 and QuickBird satellite imagery were used in the case study. RISA generated highly homogeneous regions based on visual inspection. The land-cover classification using the RISA-derived image segments resulted in higher accuracy than the classifications using the image segments derived from the Definiens software (eCognition) and original image pixels in combination with a minimum-distance classifier. Quantitative segmentation quality assessment using two object metrics showed RISA-derived segments successfully represented the reference objects. [Copyright &y& Elsevier]

This paper proposes a multiclassification algorithm using multilayer perceptron neural network models. It tries to boost two conflicting main objectives of multiclassifiers: a high correct classification rate level and a high classification rate for each class. This last objective is not usually optimized in classification, but is considered here given the need to obtain high precision in each class in real problems. To solve this machine learning problem, we use a Pareto-based multiobjective optimization methodology based on a memetic evolutionary algorithm. We consider a memetic Pareto evolutionary approach based on the NSGA2 evolutionary algorithm (MPENSGA2). Once the Pareto front is built, two strategies or automatic individual selection are used: the best model in accuracy and the best model in sensitivity (extremes in the Pareto front). These methodologies are applied to solve 17 classification benchmark problems obtained from the University of California at Irvine (UCI) repository and one complex real classification problem. The models obtained show high accuracy and a high classification rate for each class. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, I analyze immigration''s effect on the SAT-scores and college application patterns of high school students in California and Texas. The student-level dataset used is longitudinal in nature and is matched via a unique algorithm to the Census 2000 summary tabulation files to determine immigration at the local census-place level. The Census measure of immigration developed here is extremely specific and improves upon existing immigration measures. Using empirical strategies to account for issues of selection, this essay''s main finding is that the 1990s immigration did not harm, and possibly benefited the student outcomes of U.S. citizens. [Copyright &y& Elsevier]

The capacity to adaptively manage irrigation and associated contaminant transport is desirable from the perspectives of water conservation, groundwater quality protection, and other concerns. This paper introduces the application of a feedback-control strategy known as Receding Horizon Control (RHC) to the problem of irrigation management. The RHC method incorporates sensor measurements, predictive models, and optimization algorithms to maintain soil moisture at certain levels or prevent contaminant propagation beyond desirable thresholds. Theoretical test cases are first presented to examine the RHC scheme performance for the control of soil moisture and nitrate levels in a soil irrigation problem. Then, soil moisture control is successfully demonstrated for a center-pivot system in Palmdale, CA where reclaimed water is used for agricultural irrigation. Real-time soil moisture, temperature, and meteorological data were streamed wirelessly to a field computer to enable autonomous execution of the RHC algorithm. The RHC scheme is demonstrated to be a viable strategy for achieving water reuse and agricultural objectives while minimizing negative impacts on environmental quality. [Copyright &y& Elsevier]

This paper presents a new learning algorithm for multitask pattern recognition (MTPR) problems. We consider learning multiple multiclass classification tasks online where no information is ever provided about the task category of a training example. The algorithm thus needs an automated task recognition capability to properly learn the different classification tasks. The learning mode is "online" where training examples for different tasks are mixed in a random fashion and given sequentially one after another. We assume that the classification tasks are related to each other and that both the tasks and their training examples appear in random during "online training." Thus, the learning algorithm has to continually switch from learning one task to another whenever the training examples change to a different task. This also implies that the learning algorithm has to detect task changes automatically and utilize knowledge of previous tasks for learning new tasks fast. The performance of the algorithm is evaluated for ten MTPR problems using five University of California at Irvine (UCI) data sets. The experiments verify that the proposed algorithm can indeed acquire and accumulate task knowledge and that the transfer of knowledge from tasks already learned enhances the speed of knowledge acquisition on new tasks and the final classification accuracy. In addition, the task categorization accuracy is greatly improved for all MTPR problems by introducing the reorganization process even if the presentation order of class training examples is fairly biased. [ABSTRACT FROM AUTHOR]

Spectral similarity metrics have previously been used to select representative spectra from a class for use in spectral mixture modeling. Since the tasks of spectral selection for spectral mixture modeling and spectral selection for temporal compositing are similar, these metrics may have utility for temporal compositing. This paper explores the use of two spectral similarity metrics, endmember average root mean square error (EAR) and minimum average spectral angle (MASA), for constructing temporal composites. EAR and MASA compositing algorithms were compared against four previously used algorithms, including maximum NDVI, minimum view zenith angle, minimum blue, and median red. A total of 10 different algorithms were used to create 16-day composites of Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data over a 6-year period. Algorithm performance was assessed based on short-term temporal variability in spectral reflectance and in a selection of indices, both within a southwestern California study area and within five land-cover class subsets. EAR compositing produced the lowest variability for 4 out of 7 MODIS bands, as measured by the root mean square of time series residuals. MASA or EAR compositing produced the lowest root mean square residual values for all of the tested indices. To assess how compositing algorithms might affect remote sensing correlations with biophysical variables, correlations between indices calculated from different composites and live fuel moisture were compared. Correlations between indices and live fuel moisture were higher for shape-based composites compared with the standard composites. [Copyright &y& Elsevier]

The architectural landscape of high-performance computing stretches from superscalar uniprocessor to explicitly parallel systems to dedicated hardware implementations of algorithms. Single-purpose hardware can achieve the highest performance and uniprocessors can be the most programmable. Between these extremes, programmable and reconfigurable architectures provide a wide range of choice in flexibility, programmability, computational density, and performance. The UCSC Kestrel parallel processor strives to attain single-purpose performance while maintaining user programmability. Kestrel is a single-instruction stream, multiple- data stream (SIMD) parallel processor with a 512-element linear array of 8-bit processing elements. The system design focuses on efficient high-throughput DNA and protein sequence analysis, but its programmability enables high performance on computational chemistry, image processing, machine learning, and other applications. The Kestrel system has had unexpected longevity in its utility due to a careful design and analysis process. Experience with the system leads to the conclusion that programmable SIMD architectures can excel in both programmability and performance. This paper presents the architecture, implementation, applications, and observations of the Kestrel project at the University of California at Santa Cruz. [ABSTRACT FROM AUTHOR]

In this paper we present a parallel regret insertion heuristic to solve a dial-a-ride problem with time windows. A new route initialization procedure is implemented, that keeps into account both the spatial and the temporal aspects of the problem, and a regret insertion is then performed to serve the remaining requests. The considered operating scenario is representative of a large-scale dial-a-ride program in Los Angeles County. The proposed algorithm was tested on data sets of 500 and 1000 requests built from data of paratransit service in this area. The computational results show the effectiveness of this approach in terms of trading-off solution quality and computational times. The latter measure being especially important in large-scale systems where numerous daily requests need to be processed. [Copyright &y& Elsevier]

TransGuide is San Antonio’s multifunctional citywide center that manages traffic through surveillance, incident detection/management, and dissemination of traffic information. This paper reviews automatic incident detection technologies deployed in San Antonio freeways and managed by TransGuide. Traffic and incident data collected from the San Antonio network are used to compare the performance of inductive loop detectors (ILDs) and automatic vehicle identification (AVI) for automated incident detection. California No. 8 and the Texas algorithms were calibrated and tested using the ILD data collected for incident detection. The upper confidence limit algorithm and the Texas algorithm were calibrated and tested using the AVI data collected. When traffic and incident data from the San Antonio network are processed by the four different algorithms, the California No. 8 algorithms applied to ILD data performed best in terms of detection rate and false alarm rate. Automated incident detection (AID) is not currently worth implementing in the AVI system studied, but AID based on AVI data is generally feasible with denser tag penetration and sensor installation. [ABSTRACT FROM AUTHOR]

This paper presents the applications of a recently developed pattern classifier called support vector machine (SVM) in incident detection. Support vector machine is constructed from a unique learning algorithm that extracts training vectors that lie closest to the class boundary, and makes use of them to construct a decision boundary that optimally separates the different classes of data. Two SVMs, each with a different non-linear kernel function, were trained and tested with simulated incident data from an arterial network. Test results have shown that SVM offers a lower misclassification rate, higher correct detection rate, lower false alarm rate and slightly faster detection time than the multi-layer feed forward neural network (MLF) and probabilistic neural network models in arterial incident detection. Three different SVMs have also been developed and tested with real I-880 Freeway data in California. The freeway SVMs have exhibited incident detection performance as good as the MLF, one of the most promising incident detection model developed to date. [Copyright &y& Elsevier]