A Novel Apex-Time Network for Cross-Dataset Micro-Expression Recognition

The automatic recognition of micro-expression has been boosted ever since the successful introduction of deep learning approaches. As researchers working on such topics are moving to learn from the nature of micro-expression, the practice of using deep learning techniques has evolved from processing the entire video clip of micro-expression to the recognition on apex frame. Using the apex frame is able to get rid of redundant video frames, but the relevant temporal evidence of micro-expression would be thereby left out. This paper proposes a novel Apex-Time Network (ATNet) to recognize micro-expression based on spatial information from the apex frame as well as on temporal information from the respective-adjacent frames. Through extensive experiments on three benchmarks, we demonstrate the improvement achieved by learning such temporal information. Specially, the model with such temporal information is more robust in cross-dataset validations.Comment: 6 pages, 3 figures, 3 tables, code available, accepted in ACII 201

An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service

In this paper, we present machine learning approaches for characterizing and forecasting the short-term demand for on-demand ride-hailing services. We propose the spatio-temporal estimation of the demand that is a function of variable effects related to traffic, pricing and weather conditions. With respect to the methodology, a single decision tree, bootstrap-aggregated (bagged) decision trees, random forest, boosted decision trees, and artificial neural network for regression have been adapted and systematically compared using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and slope. To better assess the quality of the models, they have been tested on a real case study using the data of DiDi Chuxing, the main on-demand ride hailing service provider in China. In the current study, 199,584 time-slots describing the spatio-temporal ride-hailing demand has been extracted with an aggregated-time interval of 10 mins. All the methods are trained and validated on the basis of two independent samples from this dataset. The results revealed that boosted decision trees provide the best prediction accuracy (RMSE=16.41), while avoiding the risk of over-fitting, followed by artificial neural network (20.09), random forest (23.50), bagged decision trees (24.29) and single decision tree (33.55).Comment: Currently under review for journal publicatio

Beyond Physical Connections: Tree Models in Human Pose Estimation

Simple tree models for articulated objects prevails in the last decade. However, it is also believed that these simple tree models are not capable of capturing large variations in many scenarios, such as human pose estimation. This paper attempts to address three questions: 1) are simple tree models sufficient? more specifically, 2) how to use tree models effectively in human pose estimation? and 3) how shall we use combined parts together with single parts efficiently? Assuming we have a set of single parts and combined parts, and the goal is to estimate a joint distribution of their locations. We surprisingly find that no latent variables are introduced in the Leeds Sport Dataset (LSP) during learning latent trees for deformable model, which aims at approximating the joint distributions of body part locations using minimal tree structure. This suggests one can straightforwardly use a mixed representation of single and combined parts to approximate their joint distribution in a simple tree model. As such, one only needs to build Visual Categories of the combined parts, and then perform inference on the learned latent tree. Our method outperformed the state of the art on the LSP, both in the scenarios when the training images are from the same dataset and from the PARSE dataset. Experiments on animal images from the VOC challenge further support our findings.Comment: CVPR 201

Large Scale Question Paraphrase Retrieval with Smoothed Deep Metric Learning

The goal of a Question Paraphrase Retrieval (QPR) system is to retrieve equivalent questions that result in the same answer as the original question. Such a system can be used to understand and answer rare and noisy reformulations of common questions by mapping them to a set of canonical forms. This has large-scale applications for community Question Answering (cQA) and open-domain spoken language question answering systems. In this paper we describe a new QPR system implemented as a Neural Information Retrieval (NIR) system consisting of a neural network sentence encoder and an approximate k-Nearest Neighbour index for efficient vector retrieval. We also describe our mechanism to generate an annotated dataset for question paraphrase retrieval experiments automatically from question-answer logs via distant supervision. We show that the standard loss function in NIR, triplet loss, does not perform well with noisy labels. We propose smoothed deep metric loss (SDML) and with our experiments on two QPR datasets we show that it significantly outperforms triplet loss in the noisy label setting

Building a Nomogram for Survey-Weighted Cox Models Using R

Nomograms have become a very useful tool among clinicians as they provide individualized predictions based on the characteristics of the patient. For complex design survey data with survival outcome, Binder (1992) proposed methods for fitting survey-weighted Cox models, but to the best of our knowledge there is no available software to build a nomogram based on such models. This paper introduces R software to accomplish this goal and illustrates its use on a gastric cancer dataset. Validation and calibration routines are also included

Cancer recording and mortality in the General Practice Research Database and linked cancer registries.

PURPOSE: Large electronic datasets are increasingly being used to evaluate healthcare delivery. The aim of this study was to compare information held by cancer registries with that of the General Practice Research Database (GPRD). METHODS: A convenience sample of 101 020 patients aged 40+ years drawn from GPRD formed the primary data source. This cohort was derived from a larger sample originally established for a cohort study of diabetes. GPRD records were linked with those from cancer registries in the National Cancer Data Repository (NCDR). Concordance between the two datasets was then evaluated. For cases recorded only on one dataset, validation was sought from other datasets (Hospital Episode Statistics and death registration) and by detailed analysis of a subset of GPRD records. RESULTS: A total of 5797 cancers (excluding non-melanomatous skin cancer) were recorded on GPRD. Of these cases, 4830 were also recorded on NCDR (concordance rate of 83.3%). Of the 976 cases recorded on GPRD but not on NCDR, 528 were present also in the hospital records or death certificates. Of the 341 cases recorded on NCDR but not on GPRD, 307 were recorded in these other two datasets. Rates of concordance varied by cancer type. Cancer registries recorded larger numbers of patients with lung, colorectal, and pancreatic cancers, whereas GPRD recorded more haematological cancers and melanomas. As expected, GPRD recorded significantly more non-melanomatous skin cancer. Concordance decreased with increasing age. CONCLUSION: Although concordance levels were reasonably high, the findings from this study can be used to direct efforts for better recording in both datasets