Active Set Support Vector Regression

We present ASVR, a new active set strategy to solve a straightforward reformulation of the standard support vector regression problem. This new algorithm is based on the successful ASVM algorithm for classification problems, and consists of solving a finite number of linear equations with a typically large dimensionality equal to the number of points to be approximated. However, by making use of the Sherman-Morrison-Woodbury formula, a much smaller matrix of the order of the original input space is inverted at each step. The algorithm requires no specialized quadratic or linear programming code, but merely a linear equation solver which is publicly available. ASVR is extremely fast, produces comparable generalization error to other popular algorithms, and is available on the web for download

Mechanisms, Models, and Therapeutic Vulnerabilities of CRTC1/MAML2-Positive Salivary Mucoepidermoid Carcinoma

Mucoepidermoid carcinoma (MEC) is a life-threatening salivary gland cancer for which treatment options and targeted therapies are lacking. This tumor type is frequently characterized by a recurrent t(11;19)(q21;p13) chromosomal translocation which generates the transcriptional coactivator fusion CRTC1/MAML2. While it is clear that CRTC1/MAML2 plays a key role in maintaining a transformed state in MEC, the mechanisms by which this fusion oncogene rewires gene expression programs that promote tumorigenesis remain poorly understood. Here, we show that CRTC1/MAML2 induces transcriptional activation of a non-canonical PGC-1α splice variant, PGC-1α4, which regulates PPARγ-dependent expression of the IGF-1 growth hormone. This dependence on autocrine regulation of IGF-1 transcription via PGC-1α4/PPARγ renders MEC cells susceptible to IGF-1R inhibition with small molecules and to PPARγ inhibition with inverse agonists. These results yield insights into the aberrant co-regulatory functions of CRTC1/MAML2 and identify specific vulnerabilities that can be exploited for precision therapy. The tumor “cell of origin” is a normal cell in which a cancer-causing mutation or genetic hit occurs. Identification and characterization of the cell of origin is an essential undertaking, as a better understanding of this cell type can aid in earlier diagnosis, hint at future tumor behavior, and can even inform treatment strategies. MEC tumors exhibit extreme cellular heterogeneity, and so it has been challenging to determine what cell type is responsible for tumorigenesis in this cancer. We therefore also sought to characterize the MEC cell of origin by generating several genetically engineered mouse models in which the CRTC1/MAML2 fusion oncogene is inducibly expressed in specific salivary gland cell populations. Our results demonstrate that CRTC1/MAML2-positive MEC originates from a salivary ductal precursor cell and that development of MEC tumors is aided by loss of Tp53 gene expression. While further studies are required to reduce off-target effects of oncogene induction within the cutaneous epithelia, the importance of determining the salivary MEC cell of origin cannot be understated.Doctor of Philosoph

Integrating Kinematic- and Vision-Based Information to Better Understand Driving Behaviour

This study explored the use of two types of advanced driver assistance systems (ADAS) as tools for observing driving behavior. The first was a kinematic-based ADAS that uses speed and acceleration data to detect driving events such as hard braking, speeding and sharp turning. The second was a visionbased ADAS that uses video data to provide lane departure warnings (LDW), headway warnings (HW) and forward collision warnings (FCW). Data was collected for more than 4,500 trips and 2,200 driving hours during a period of 70 days. The sample consisted of 10 drivers that used both types of ADAS simultaneously. The information collected also included more than 17,000 records of various types of driving events. First, the events rates were estimated by the Poisson and the Poisson-lognormal models. Then, Pearson correlation and factor analysis were implemented to study the relationships among the events and to evaluate whether different types of events converged to describe the same behaviors. Significant correlations were observed between the braking and turning kinematic-based events and the FCW vision-based event, which converged under the same factor. High rates of these events may indicate that the person is driving in an urban style. The LDW, HW and speeding events converged to the second factor, which is more relevant in inter-urban areas. These findings, although based on a small-scale study, point to a potential for the use of commercial ADAS for driving behavior analysis. The integration of kinematic-based and vision-based information can provide deeper understanding of the measured behavior

Associating Vehicles Automation With Drivers Functional State Assessment Systems: A Challenge for Road Safety in the Future

In the near future, vehicles will gradually gain more autonomous functionalities. Drivers’ activity will be less about driving than about monitoring intelligent systems to which driving action will be delegated. Road safety, therefore, remains dependent on the human factor and we should identify the limits beyond which driver’s functional state (DFS) may no longer be able to ensure safety. Depending on the level of automation, estimating the DFS may have different targets, e.g., assessing driver’s situation awareness in lower levels of automation and his ability to respond to emerging hazard or assessing driver’s ability to monitor the vehicle performing operational tasks in higher levels of automation. Unfitted DFS (e.g., drowsiness) may impact the driver ability respond to taking over abilities. This paper reviews the most appropriate psychophysiological indices in naturalistic driving while considering the DFS through exogenous sensors, providing the more efficient trade-off between reliability and intrusiveness. The DFS also originates from kinematic data of the vehicle, thus providing information that indirectly relates to drivers behavior. The whole data should be synchronously processed, providing a diagnosis on the DFS, and bringing it to the attention of the decision maker in real time. Next, making the information available can be permanent or intermittent (or even undelivered), and may also depend on the automation level. Such interface can include recommendations for decision support or simply give neutral instruction. Mapping of relevant psychophysiological and behavioral indicators for DFS will enable practitioners and researchers provide reliable estimates, fitted to the level of automation

Evaluating Changes in the Driving Behavior of Young Drivers a Few Years After Licensure Using In-Vehicle Data Recorders

This paper aims to evaluate how young drivers drive a few years after licensure. Driving behavior in the fourth year of driving is compared to that of the first year, based on data from In-Vehicle Data Recorders (IVDR). Young drivers\u27 cars were equipped with the same IVDR systems in both study periods. The comparison revealed that, in general, driving patterns did not change significantly. The difference in risky behaviour between weekdays and weekends was more prominent in the fourth year than in the first year. In addition, an interesting improvement occurred at the end of the fourth-year study period. The analysis results obtained should also be considered an example of the potential of what may be done with this kind of data

The Potential for IVDR Feedback and Parental Guidance to Improve Novice Young Drivers’ Behavior

Young male drivers are well known for their increased involvement in road crashes when moving to the independent driving phase. This study examines the potential of IVDR (In-Vehicle Data Recorder) systems, which provide feedback on driving performances, and parental monitoring to restrain young male drivers’ aggressive driving behavior. The IVDR system was installed in the family car of young drivers for a period of 12 months, starting in the accompanied driving phase and continuing to the first nine months of independent driving. The system documents events based on measurements of extreme G-forces in the vehicles. 242 families of young male drivers participated in the study. They were randomly allocated into 4 groups: (1) FFNG- Family Feedback No Guidance- all members of the family were exposed to feedback on their own driving behavior and that of the other family members; (2) FFPG- Family Feedback Parental Guidance - similar to the previous group with the addition of personal guidance given to parents on ways to enhance their involvement and monitoring of their sons’ driving; (3) IFNG- Individual Feedback No Guidance- each driver received feedback only on his own driving behavior; (4) CNTL- a control group that received no feedback or parental guidance. The collected data from the IVDR was analyzed and the results indicate substantial benefits to drivers in the FFPG group in which parents received personal guidance to enhance their parental involvement and feedback on their son’s driving behavior, compared to the CNTL group which did not receive any feedback