Due to rapid urbanization, large cities in developing countries have problems with heavy traffic congestion. International aid is being provided to construct modern traffic signal infrastructure. But often such an infrastructure does not work well due to the high operating and maintenance costs and the limited knowledge of the local engineers. In this paper, we propose a frugal signal control framework that uses image analysis to estimate traffic flows. It requires only low-cost Web cameras to support a signal control strategy based on the current traffic volume. We can estimate the traffic volumes of the roads near the traffic signals from a few observed points and then adjust the signal control. Through numerical experiments, we confirmed that the proposed framework can reduce an average travel time 20.6% compared to a fixed-time signal control even though the Web cameras are located at 500 m away from intersections.
Background: Diabetic kidney diseases (DKD) including diabetic nephropathy is the most frequent cause of hemodialysis (HD), and more precise prediction model could be useful to early intervention of DKD. Methods: We constructed new prediction model for DKD by using artificial intelligence (AI) based on electronic medical records (EMRs). From EMRs of 64,059 diabetes patients who visited our hospital, we extracted a variety of features. This model uses the stage of nephropathy as labels, and predicts whether the stage 1 patients will move up their stage after 180 days. Results: AI constructed new prediction model by big data machine learning. First, AI extracted raw features in past 6 months at reference period, and selected 22 factors. Then, time series data analysis using convolutional autoencoder was conducted to find time series patterns relating to 6-month DKD aggravation. AI then constructed the prediction model with 17raw features as well as time series and text as secondary features using logistic regression. Finally, AI predicted DKD aggravation with 0.74 AUC score at maximum. Furthermore, DKD aggravation group had significantly higher incidence of HD than non-aggravation group in 10 years. Conclusion: The new prediction model by AI could detect progress of DKD, which could contribute to more effective and accurate intervention to reduce HD. Disclosure M. Makino: Research Support; Spouse/Partner; THE DAI-ICHI LIFE INSURANCE COMPANY, LIMITED. Self. M. Ono: None. T. Itoko: Employee; Self; IBM. T. Katsuki: Employee; Self; IBM. A. Koseki: Employee; Self; IBM. M. Kudo: Employee; Self; IBM. K. Haida: Employee; Self; Daiichilife Insurance Company. J. Kuroda: Employee; Self; The Dai-ichi Life Insurance Company, Limited. R. Yanagiya: None. A. Suzuki: Research Support; Self; THE DAI-ICHI LIFE INSURANCE COMPANY, LIMITED.. Speaker's Bureau; Self; Astellas Pharma Inc., Mitsubishi Tanabe Pharma Corporation. Research Support; Self; EA Pharma Co Ltd, Daiichi Sankyo Company, Limited, Chugai Pharmaceutical Co., Ltd., Kyowa Hakko Kirin Co., Ltd., MSD K.K., Novo Nordisk Inc., Ono Pharmaceutical Co., Ltd., Pfizer Inc., Taisho Pharmaceutical Co., Ltd., Takeda Pharmaceutical Company Limited.
This manuscript proposes a posterior mean (PM) super-resolution (SR) method with a compound Gaussian Markov random field (MRF) prior. SR is a technique to estimate a spatially high-resolution image from observed multiple low-resolution images. A compound Gaussian MRF model provides a preferable prior for natural images that preserves edges. PM is the optimal estimator for the objective function of peak signal-to-noise ratio (PSNR). This estimator is numerically determined by using variational Bayes (VB). We then solve the conjugate prior problem on VB and the exponential-order calculation cost problem of a compound Gaussian MRF prior with simple Taylor approximations. In experiments, the proposed method roughly overcomes existing methods.
The authors propose a new approach to intelligent transportation systems for developing countries. Their system consists of two major components: (1) Web-camera-based traffic monitoring and (2) network flow estimation. The traffic monitoring module features a new algorithm for computing the vehicle count and velocity from very low-resolution Web camera images, while the network flow estimation module features a traffic flow estimation algorithm at every single link, based on measurements at a limited number of links with the cameras. Using real Web cameras deployed in Nairobi, Kenya, they assessed the accuracy of the approach. To the best of the authors’ knowledge, this is the first practical framework for monitoring an entire city’s traffic without special expensive infrastructure and time-consuming data calibration.
We address a regression problem from weakly labeled data that are correctly labeled only above a regression line, i.e., upper one-side labeled data. The label values of the data are the results of sensing the magnitude of some phenomenon. In this case, the labels often contain missing or incomplete observations whose values are lower than those of correct observations and are also usually lower than the regression line. It follows that data labeled with lower values than the estimations of a regression function (lower-side data) are mixed with data that should originally be labeled above the regression line (upper-side data). When such missing label observations are observed in a non-negligible amount, we thus should assume our lower-side data to be unlabeled data that are a mix of original upper- and lower-side data. We formulate a regression problem from these upper-side labeled and lower-side unlabeled data. We then derive a learning algorithm in an unbiased and consistent manner to ordinary regression that is learned from data labeled correctly in both upper- and lower-side cases. Our key idea is that we can derive a gradient that requires only upper-side data and unlabeled data as the equivalent expression of that for ordinary regression. We additionally found that a specific class of losses enables us to learn unbiased solutions practically. In numerical experiments on synthetic and real-world datasets, we demonstrate the advantages of our algorithm.
Two-sample feature selection is the problem of finding features that describe a difference between two probability distributions, which is a ubiquitous problem in both scientific and engineering studies. However, existing methods have limited applicability because of their restrictive assumptions on data distributoins or computational difficulty. In this paper, we resolve these difficulties by formulating the problem as a sparsest $k$-subgraph problem. The proposed method is nonparametric and does not assume any specific parametric models on the data distributions. We show that the proposed method is computationally efficient and does not require any extra computation for model selection. Moreover, we prove that the proposed method provides a consistent estimator of features under mild conditions. Our experimental results show that the proposed method outperforms the current method with regard to both accuracy and computation time.
This paper addresses the learning task of estimating driver drowsiness from the signals of car acceleration sensors. Since even drivers themselves cannot perceive their own drowsiness in a timely manner unless they use burdensome invasive sensors, obtaining labeled training data for each timestamp is not a realistic goal. To deal with this difficulty, we formulate the task as a weakly supervised learning. We only need to add labels for each complete trip, not for every timestamp independently. By assuming that some aspects of driver drowsiness increase over time due to tiredness, we formulate an algorithm that can learn from such weakly labeled data. We derive a scalable stochastic optimization method as a way of implementing the algorithm. Numerical experiments on real driving datasets demonstrate the advantages of our algorithm against baseline methods.
This paper describes a technology for predicting the aggravation of diabetic nephropathy from electronic health record (EHR). For the prediction, we used features extracted from event sequence of lab tests in EHR with a stacked convolutional autoencoder which can extract both local and global temporal information. The extracted features can be interpreted as similarities to a small number of typical sequences of lab tests, that may help us to understand the disease courses and to provide detailed health guidance. In our experiments on real-world EHRs, we confirmed that our approach performed better than baseline methods and that the extracted features were promising for understanding the disease.
This paper addresses the problem of object counting, which is to estimate the number of objects of interest from an input observation. We formalize the problem as a posterior inference of the count by introducing a particular type of Gaussian mixture for the input observation, whose mixture indexes correspond to the count. Unlike existing approaches in image analysis, which typically perform explicit object detection using labeled training images, our approach does not need any labeled training data. Our idea is to use the stick-breaking process as a constraint to make it possible to interpret the mixture indexes as the count. We apply our method to the problem of counting vehicles in real-world web camera images and demonstrate that the accuracy and robustness of the proposed approach without any labeled training data are comparable to those of supervised alternatives.
