The application of kernel-based Machine Learning (ML) techniques to discrete choice modelling using large datasets often faces challenges due to memory requirements and the considerable number of parameters involved in these models. This complexity hampers the efficient training of large-scale models. This paper addresses these problems of scalability by introducing the Nyström approximation for Kernel Logistic Regression (KLR) on large datasets. The study begins by presenting a theoretical analysis in which: (i) the set of KLR solutions is characterised, (ii) an upper bound to the solution of KLR with Nyström approximation is provided, and finally (iii) a specialisation of the optimisation algorithms to Nyström KLR is described. After this, the Nyström KLR is computationally validated. Four landmark selection methods are tested, including basic uniform sampling, a k -means sampling strategy, and two non-uniform methods grounded in leverage scores. The performance of these strategies is evaluated using large-scale transport mode choice datasets and is compared with traditional methods such as Multinomial Logit (MNL) and contemporary ML techniques. The study also assesses the efficiency of various optimisation techniques for the proposed Nyström KLR model. The performance of gradient descent, Momentum, Adam, and L-BFGS-B optimisation methods is examined on these datasets. Among these strategies, the k -means Nyström KLR approach emerges as a successful solution for applying KLR to large datasets, particularly when combined with the L-BFGS-B and Adam optimisation methods. The results highlight the ability of this strategy to handle datasets exceeding 200,000 observations while maintaining robust performance.

Electric vertical take-off and landing aircraft-based urban air mobility (UAM) service in conjunction with personal flying cars are anticipated to offer mobility benefits in terms of reduced travel time, alleviate demand from overburdened ground transportation systems; and bring forth a paradigm shift in travel patterns. Furthermore, uncrewed aerial vehicles or drones have significant potential in package and food delivery and in various disaster responses. In this context, this paper aims to provide a systematic review of current research and studies covering crucial aspects of urban air mobility and flying car ecosystems including public perception, potential market demand, infrastructure requirements, operations and traffic management processes, and policy formulation. Insights offered by the current studies encompassing these areas are summarized and discussed in this paper.

Social Engagement is a novel business model transforming final users of a service from passive into active components.In this framework, people are contacted by a company and they are asked to perform tasks in exchange for a reward.This arises the complicated optimization problem of allocating the different types of workforce so as to minimize costs.We address this problem by explicitly modeling the behavior of contacted candidates through consolidated concepts from utility theory and proposing a chance-constrained optimization model aiming at optimally deciding which user to contact, the amount of the reward proposed, and how many employees to use in order to minimize the total expected costs of the operations.A solution approach is proposed and its computational efficiency is investigated through experiments.

Specifying utility functions is a key step towards applying the discrete choice framework for understanding the behaviour processes that govern user choices. However, identifying the utility function specifications that best model and explain the observed choices can be a very challenging and time-consuming task. This paper seeks to help modellers by leveraging the Bayesian framework and the concept of automatic relevance determination (ARD), in order to automatically determine an optimal utility function specification from an exponentially large set of possible specifications in a purely data-driven manner. Based on recent advances in approximate Bayesian inference, a doubly stochastic variational inference is developed, which allows the proposed MNL-ARD model to scale to very large and high-dimensional datasets. Using semi-artificial choice data, the proposed approach is shown to be able to accurately recover the true utility function specifications that govern the observed choices. Moreover, when applied to real choice data, MNL-ARD is able discover high quality specifications that can outperform previous ones from the literature according to multiple criteria, thereby demonstrating its practical applicability.

In this paper, we introduce the incremental temporally weighted principal component analysis (ITWPCA) algorithm, based on singular value decomposition update, and the incremental temporally weighted visual tracking with spatial penalty (ITWVTSP) algorithm for robust visual tracking. ITWVTSP uses ITWPCA for computing incrementally a robust low dimensional subspace representation (model) of the tracked object. The robustness is based on the capacity of weighting the contribution of each single sample to the subspace generation to reduce the impact of bad quality samples, reducing the risk of model drift. Furthermore, ITWVTSP can exploit the a priori knowledge about important regions of a tracked object. This is done by penalizing the tracking error on some predefined regions of the tracked object, which increases the accuracy of tracking. Several tests are performed on several challenging video sequences, showing the robustness and accuracy of the proposed algorithm, as well as its superiority with respect to state-of-the-art techniques.

Abstract Optimization problems with noisy data solved using stochastic programming or robust optimization approaches require the explicit characterization of an uncertainty set U that models the nature of the noise. Such approaches depend on the modeling of the uncertainty set and suffer from an erroneous estimation of the noise. In this article, we introduce a framework that considers the uncertain data implicitly. We define the concept of Uncertainty Features (UF), which are problem‐specific structural properties of a solution. We show how to formulate an uncertain problem using the Uncertainty Feature Optimization (UFO) framework as a multi‐objective problem. We show that stochastic programming and robust optimization are particular cases of the UFO framework. We present computational results for the Multi‐Dimensional Knapsack Problem (MDKP) and discuss the application of the framework to the airline scheduling problem. © 2011 Wiley Periodicals, Inc. NETWORKS, 2011

We propose a new heuristic for nonlinear global optimization combining a variable neighborhood search framework with a modified trust-region algorithm as local search. The proposed method presents the capability to prematurely interrupt the local search if the iterates are converging to a local minimum that has already been visited or if they are reaching an area where no significant improvement can be expected. The neighborhoods, as well as the neighbors selection procedure, are exploiting the curvature of the objective function. Numerical tests are performed on a set of unconstrained nonlinear problems from the literature. Results illustrate that the new method significantly outperforms existing heuristics from the literature in terms of success rate, CPU time, and number of function evaluations.

In recent years, works on geometric multidimensional signal representations have established a close relation with signal expansions on redundant dictionaries. For this purpose, matching pursuits (MP) have shown to be an interesting tool. Recently, most important limitations of MP have been underlined, and alternative algorithms like weighted-MP have been proposed. This work explores the use of weighted-MP as a new framework for motion-adaptive geometric video approximations. We study a novel algorithm to decompose video sequences in terms of few, salient video components that jointly represent the geometric and motion content of a scene. Experimental coding results on highly geometric content reflect how the proposed paradigm exploits spatio-temporal video geometry. Two-dimensional weighted-MP improves the representation compared to those based on 2-D MP. Furthermore, the extracted video components represent relevant visual structures with high saliency. In an example application, such components are effectively used as video descriptors for the joint audio-video analysis of multimedia sequences.

Data collected through a recent web-based survey show that the perception (i.e. labeling) of a human facial expression by a human observer is a subjective process, which results in a lack of a unique ground-truth, as intended in the standard classification framework. In this paper we propose the use of discrete choice models (DCM) for human perception of static facial expressions. Random utility functions are defined in order to capture the attractiveness, perceived by the human observer for an expression class, when asked to assign a label to an actual expression image. The utilities represent a natural way for the modeler to formalize her prior knowledge on the process. Starting with a model based on facial action coding systems (FACS), we subsequently defines two other models by adding two new sets of explanatory variables. The model parameters are learned through maximum likelihood estimation and a cross-validation procedure is used for validation purposes.

Typical object detection algorithms on mobile cameras suffer from the lack of a-priori knowledge on the object to be detected. The variability in the shape, pose, color distribution, and behavior affect the robustness of the detection process. In general, such variability is addressed by using a large training data. However, only objects present in the training data can be detected. This paper introduces a vision-based system to address such problem. A master-slave approach is presented where a mobile camera (the slave) can match any object detected by a fixed camera (the master). Features extracted by the master camera are used to detect the object of interest in the slave camera without the use of any training data. A single observation is enough regardless of the changes in illumination, viewpoint, color distribution and image quality. A coarse to fine description of the object is presented built upon image statistics robust to partial occlusions. Qualitative and quantitative results are presented in an indoor and an outdoor urban scene.

In this paper we deal with the multiobject tracking problem in the particular case of pedestrians, assuming the detection step already done. We use a Bayesian framework to combine the likelihood term provided by an image correlation algorithm with a prior distribution given by a discrete choice model for pedestrian behavior, calibrated on real data. We aim to show how the combination of the image information with a model of pedestrian behavior can provides appreciable results in real and complex scenarios.