NeuroMask: Explaining Predictions of Deep Neural Networks through Mask Learning

Deep Neural Networks (DNNs) deliver state-of-the-art performance in many image recognition and understanding applications. However, despite their outstanding performance, these models are black-boxes and it is hard to understand how they make their decisions. Over the past few years, researchers have studied the problem of providing explanations of why DNNs predicted their results. However, existing techniques are either obtrusive, requiring changes in model training, or suffer from low output quality. In this paper, we present a novel method, NeuroMask, for generating an interpretable explanation of classification model results. When applied to image classification models, NeuroMask identifies the image parts that are most important to classifier results by applying a mask that hides/reveals different parts of the image, before feeding it back into the model. The mask values are tuned by minimizing a properly designed cost function that preserves the classification result and encourages producing an interpretable mask. Experiments using state-of-art Convolutional Neural Networks for image recognition on different datasets (CIFAR-10 and ImageNet) show that NeuroMask successfully localizes the parts of the input image which are most relevant to the DNN decision. By showing a visual quality comparison between NeuroMask explanations and those of other methods, we find NeuroMask to be both accurate and interpretable

Structured prediction of unobserved voxels from a single depth image

Building a complete 3D model of a scene, given only a single depth image, is underconstrained. To gain a full volumetric model, one needs either multiple views, or a single view together with a library of unambiguous 3D models that will fit the shape of each individual object in the scene. We hypothesize that objects of dissimilar semantic classes often share similar 3D shape components, enabling a limited dataset to model the shape of a wide range of objects, and hence estimate their hidden geometry. Exploring this hypothesis, we propose an algorithm that can complete the unobserved geometry of tabletop-sized objects, based on a supervised model trained on already available volumetric elements. Our model maps from a local observation in a single depth image to an estimate of the surface shape in the surrounding neighborhood. We validate our approach both qualitatively and quantitatively on a range of indoor object collections and challenging real scenes

Misclassification Risk and Uncertainty Quantification in Deep Classifiers

In this paper, we propose risk-calibrated evidential deep classifiers to reduce the costs associated with classification errors. We use two main approaches. The first is to develop methods to quantify the uncertainty of a classifier’s predictions and reduce the likelihood of acting on erroneous predictions. The second is a novel way to train the classifier such that erroneous classifications are biased towards less risky categories. We combine these two approaches in a principled way. While doing this, we extend evidential deep learning with pignistic probabilities, which are used to quantify uncertainty of classification predictions and model rational decision making under uncertainty.We evaluate the performance of our approach on several image classification tasks. We demonstrate that our approach allows to (i) incorporate misclassification cost while training deep classifiers, (ii) accurately quantify the uncertainty of classification predictions, and (iii) simultaneously learn how to make classification decisions to minimize expected cost of classification errors

Exploiting semantic and public prior information in MonoSLAM

In this paper, we propose a method to use semantic information to improve the use of map priors in a sparse, feature-based MonoSLAM system. To incorporate the priors, the features in the prior and SLAM maps must be associated with one another. Most existing systems build a map using SLAM and then align it with the prior map. However, this approach assumes that the local map is accurate, and the majority of the features within it can be constrained by the prior. We use the intuition that many prior maps are created to provide semantic information. Therefore, valid associations only exist if the features in the SLAM map arise from the same kind of semantic object as the prior map. Using this intuition, we extend ORB-SLAM2 using an open source pre-trained semantic segmentation network (DeepLabV3+) to incorporate prior information from Open Street Map building footprint data. We show that the amount of drift, before loop closing, is significantly smaller than that for original ORB-SLAM2. Furthermore, we show that when ORB-SLAM2 is used as a prior-aided visual odometry system, the tracking accuracy is equal to or better than the full ORB-SLAM2 system without the need for global mapping or loop closure

QTLs for Morphogenetic Traits in Medicago Truncatula

Plant morphogenesis that includes growth, development and flowering date, drives a large number of agronomical important traits in both grain and forage crops. Quantitative trait locus (QTL) mapping is a way to locate zones of the genome that are involved in the variations observed in a segregating population. Co-location of QTLs and candidate genes is an indication of the involvement of the genes in the variation. The objective of this study was to analyse segregation of aerial morphogenetic traits in a mapping population of recombinant inbred lines of the model legume species M. truncatula , to locate QTLs and candidate genes

Rofecoxib and cardiovascular adverse events in adjuvant treatment of colorectal cancer

Background Selective cyclooxygenase inhibitors may retard the progression of cancer, but they have enhanced thrombotic potential. We report on cardiovascular adverse events in patients receiving rofecoxib to reduce rates of recurrence of colorectal cancer. Methods All serious adverse events that were cardiovascular thrombotic events were reviewed in 2434 patients with stage II or III colorectal cancer participating in a randomized, placebo-controlled trial of rofecoxib, 25 mg daily, started after potentially curative tumor resection and chemotherapy or radiotherapy as indicated. The trial was terminated prematurely owing to worldwide withdrawal of rofecoxib. To examine possible persistent risks, we examined cardiovascular thrombotic events reported up to 24 months after the trial was closed. Results The median duration of active treatment was 7.4 months. The 1167 patients receiving rofecoxib and the 1160 patients receiving placebo were well matched, with a median follow-up period of 33.0 months (interquartile range, 27.6 to 40.1) and 33.4 months (27.7 to 40.4), respectively. Of the 23 confirmed cardiovascular thrombotic events, 16 occurred in the rofecoxib group during or within 14 days after the treatment period, with an estimated relative risk of 2.66 (from the Cox proportional-hazards model; 95% confidence interval [CI], 1.03 to 6.86; P = 0.04). Analysis of the Antiplatelet Trialists’ Collaboration end point (the combined incidence of death from cardiovascular, hemorrhagic, and unknown causes; of nonfatal myocardial infarction; and of nonfatal ischemic and hemorrhagic stroke) gave an unadjusted relative risk of 1.60 (95% CI, 0.57 to 4.51; P = 0.37). Fourteen more cardiovascular thrombotic events, six in the rofecoxib group, were reported within the 2 years after trial closure, with an overall unadjusted relative risk of 1.50 (95% CI, 0.76 to 2.94; P = 0.24). Four patients in the rofecoxib group and two in the placebo group died from thrombotic causes during or within 14 days after the treatment period, and during the follow-up period, one patient in the rofecoxib group and five patients in the placebo group died from cardiovascular causes. Conclusions Rofecoxib therapy was associated with an increased frequency of adverse cardiovascular events among patients with a median study treatment of 7.4 months’ duration. (Current Controlled Trials number, ISRCTN98278138.

Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of Bayesian filters

Modern service robots will soon become an essential part of modern society. As they have to move and act in human environments, it is essential for them to be provided with a fast and reliable tracking system that localizes people in the neighbourhood. It is therefore important to select the most appropriate filter to estimate the position of these persons. This paper presents three efficient implementations of multisensor-human tracking based on different Bayesian estimators: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Sampling Importance Resampling (SIR) particle filter. The system implemented on a mobile robot is explained, introducing the methods used to detect and estimate the position of multiple people. Then, the solutions based on the three filters are discussed in detail. Several real experiments are conducted to evaluate their performance, which is compared in terms of accuracy, robustness and execution time of the estimation. The results show that a solution based on the UKF can perform as good as particle filters and can be often a better choice when computational efficiency is a key issue

The modern pollen-vegetation relationship of a tropical forest-savannah mosaic landscape, Ghana, West Africa

Transitions between forest and savannah vegetation types in fossil pollen records are often poorly understood due to over-production by taxa such as Poaceae and a lack of modern pollen-vegetation studies. Here, modern pollen assemblages from within a forest-savannah transition in West Africa are presented and compared, their characteristic taxa discussed, and implications for the fossil record considered. Fifteen artificial pollen traps were deployed for 1 year, to collect pollen rain from three vegetation plots within the forest-savannah transition in Ghana. High percentages of Poaceae and Melastomataceae/Combretaceae were recorded in all three plots. Erythrophleum suaveolens characterised the forest plot, Manilkara obovata the transition plot and Terminalia the savannah plot. The results indicate that Poaceae pollen influx rates provide the best representation of the forest-savannah gradient, and that a Poaceae abundance of >40% should be considered as indicative of savannah-type vegetation in the fossil record

UAV-based SLAM and 3D reconstruction system

3D reconstructing a landscape is a prevalent problem that attracts a lot of interest in recent years. This project intended to verify whether the hypothesis of a UAV-based SLAM and 3D reconstruction system is practical. A GPS-Fused SLAM system is built based on ORB-SLAM. Inverse depth is also implemented to make the system suitable for a UAV-based platform. Meanwhile, REMODE is a depth filter and is tested as not being well enough as a dense mapping module. In the end, PMVS is implemented to build a dense map of the environment which produces a reasonable result. The small-scale-scene experiments produce the total error ratio of 5.60% in the x-y plane and 6.59% in the z axis

A bank of unscented Kalman filters for multimodal human perception with mobile service robots

A new generation of mobile service robots could be ready soon to operate in human environments if they can robustly estimate position and identity of surrounding people. Researchers in this field face a number of challenging problems, among which sensor uncertainties and real-time constraints. In this paper, we propose a novel and efficient solution for simultaneous tracking and recognition of people within the observation range of a mobile robot. Multisensor techniques for legs and face detection are fused in a robust probabilistic framework to height, clothes and face recognition algorithms. The system is based on an efficient bank of Unscented Kalman Filters that keeps a multi-hypothesis estimate of the person being tracked, including the case where the latter is unknown to the robot. Several experiments with real mobile robots are presented to validate the proposed approach. They show that our solutions can improve the robot's perception and recognition of humans, providing a useful contribution for the future application of service robotics