There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

OUTPU T 29 OUTPU T 30 OUTPU T 31 OUTPU T 32 OUTPU T 25 OUTPU T 26 OUTPU T 27 OUTPU T 28 OUTPU T 21 OUTPU T 22 OUTPU T 23 OUTPU T 24 OUTPU T 17 OUTPU T 18 OUTPU T 19 OUTPU T 20 OUTPU T 13 OUTPU T 14 OUTPU T 15 OUTPU T 16 OUTPU T 9 OUTPU T 10 OUTPU T 11 OUTPU T 12 OUTPU T 5 OUTPU T 6 OUTPU T 7 OUTPU T 8 OUTPU T 1 OUTPU T 2 OUTPU T 3 OUTPU T 4 29 30 31 32 25 26 27 28 21 22 23 24 17 18 19 20 13 14 15 16 9

A User’s Guide

Highway Capacity Manual改訂の動向--テイラ-教授の講演より

Quantitative Risk Assessment (QRA) models have already been developed and used to assess various risks of road tunnels, including the societal risk and expected number of fatalities (ENF). Fire in a road tunnel is considered as the most severe disaster in that road tunnel is an enclosed space. The risk assessment for fire in road tunnels involves a number of parameters. Some of these parameters possess uncertainties in reality and they cannot be formulated as a crisp number. In this paper, these uncertain parameters are assumed to be random variables with given probability distribution functions. A novel QRA model is thus proposed to address the uncertainties in risk assessment caused by fire in road tunnel. In addition, the societal risk and ENF with distinct characteristics could be used to facilitate tunnel managers to make decisions. A case study is illustrated and the new characteristics of the risk indices are also discussed.

Quantitative Risk Assessment Model for Fire in Road Tunnels with Parameter Uncertainty

Urban aerial mobility: Reshaping the future of urban transportation

Soft-thresholding has been widely used in neural networks. Its basic network structure is a two-layer convolution neural network with soft-thresholding. Due to the network's nature of nonlinearity and nonconvexity, the training process heavily depends on an appropriate initialization of network parameters, resulting in the difficulty of obtaining a globally optimal solution. To address this issue, a convex dual network is designed here. We theoretically analyze the network convexity and numerically confirm that the strong duality holds. This conclusion is further verified in the linear fitting and denoising experiments. This work provides a new way to convexify soft-thresholding neural networks.

Convex Dual Theory Analysis of Two-Layer Convolutional Neural Networks with Soft-Thresholding

Drivers are the center of road/air/sea transportation systems, and they can be either human beings or artificial beings. Inconsistency between human driver behavior and artificial driver behavior will lead to accidents and congestion in intelligent transportation systems (ITSs) <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> . To make future ITSs trustworthy for traffic safety and acceptable for travel efficiency, developing industrial ITS applications based on drivers’ reliable behavioral and cognitive intelligence is essential <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref> . However, there are many challenges to be addressed, including real-time behavior prediction, reliable decision making, safe interaction among human and artificial drivers, and so on. 

Driver Behavior in Intelligent Transportation Systems [Guest Editorial]

• A location-allocation problem under uncertainty is investigated. • A two-stage stochastic programming model is proposed. • The mode is solved by sample average approximation. Emergencies, especially those considered routine (i.e., occurring on a daily basis), pose great threats to health, life, and property. Immediate response and treatment can greatly mitigate these threats. This research is conducted to optimize the locations of ambulance stations, deployment of ambulances, and dispatch of vehicles under demand and traffic uncertainty, which are the main factors that influence emergency response time. The research problem is formulated as a dynamic scenario-based two-stage stochastic programming model, aiming to minimize the total cost while responding to as much demand as possible. The Sample Average Approximation is proposed to approximate the original problem using a limited number of scenarios, and a two-phase Benders Decomposition solution scheme is proposed to accelerate computation, especially when solving a large-sized problem. Numerical experiments using real-world emergency data are conducted to validate the performance of the solution method. The results demonstrate the effectiveness and efficiency of the proposed algorithm. We additionally conduct a sensitivity analysis to evaluate the influences of crucial parameters, including the response time standard, facility capacity, service capacity, and facility heterogeneity. The managerial insights derived from sensitivity analysis will provide valuable guidance for the design of an emergency response system in practice. 

/pdf/ems-location-allocation-problem-under-uncertainties-1524z3y2.pdf

Xiaobo Qu

Papers

Quantitative Risk Assessment Model for Fire in Road Tunnels with Parameter Uncertainty

Urban aerial mobility: Reshaping the future of urban transportation

Convex Dual Theory Analysis of Two-Layer Convolutional Neural Networks with Soft-Thresholding

Driver Behavior in Intelligent Transportation Systems [Guest Editorial]

EMS location-allocation problem under uncertainties