In this paper an imaging system for road traffic conflict analysis is proposed. The system exploits geo-referenced stereo sequences and tracking procedure to compute traffic conflict measures which can be analysed by experts. Using the potentiality of the traffic conflict technique as a surrogate safety measure could constitute an effective tool in understanding how the driver interacts and ada...

We propose a Markov chain method to efficiently generate surrogate networks that are random under the constraint of given vertex strengths. With these strength-preserving surrogates and with edge-weight-preserving surrogates we investigate the clustering coefficient and the average shortest path length of functional networks of the human brain as well as of the International Trade Networks. We ...

Currently surrogate data analysis can be used to determine if data is consistent with various linear systems, or something else (a nonlinear system). In this paper we propose an extension of these methods in an attempt to make more specific classifications within the class of nonlinear systems. In the method of surrogate data one estimates the probability distribution of values of a test statis...

A learning-based exploration approach is proposed to escape from the basins of attraction of converged-to optima, by selecting on what is termed the interestingness of a solution. This interestingness is based on the modeling error made by a surrogate model that is trained on all solutions encountered earlier during the search. Compared to multiple standard optimization runs, a learning-guided ...

SUMMARY Frangakis and Rubin (2002, Biometrics 58, 21-29) proposed a new definition of a surrogate endpoint (a "principal" surrogate) based on causal effects. We introduce an estimand for evaluating a principal surrogate, the causal effect predictiveness (CEP) surface, which quantifies how well causal treatment effects on the biomarker predict causal treatment effects on the clinical endpoint. A...

Quantitative analysis of MR images is becoming increasingly important as a surrogate marker in clinical trials in multiple sclerosis (MS). This paper describes a fully automated model-based method for segmentation of MS lesions from multi-channel MR images. The method simultaneously corrects for MR eld inhomogeneities, estimates tissue class distribution parameters and classiies the image voxel...

Consider a set of 6 points in R × {0, 1}: {(−1, 1), (−1, 1), (−2, 1), (−3, 0), (−3, 0), (−3, 0)}, and suppose we are interested in Prec@1. Note that the optimum model that maximizes prec@1 on these points has a positive sign. We will now show that the model w∗ ∈ R that maximizes the above structural SVM surrogate on these points has a negative sign. On the contrary, let us assume that w∗ has a ...

In this paper, we first develop a new feature from the last pooling layer (i.e. pool5) of VGG, called Bag of Surrogate Parts (BoSP), and its spatial variant, Spatial BoSP (S-BoSP). Next, we propose a scale pooling scheme for better handling the objects that may appear in different shape, positions and scales. Finally, aiming that the traditional data augmentation focuses more on part the origin...

Often, the performance on a supervised machine learning task is evaluated with a task loss function that cannot be optimized directly. Examples of such loss functions include the classification error, the edit distance and the BLEU score. A common workaround for this problem is to instead optimize a surrogate loss function, such as for instance cross-entropy or hinge loss. In order for this rem...