Paper Group ANR 145
Shared control schematic for brain controlled vehicle based on fuzzy logic. Learning Variable Impedance Control for Contact Sensitive Tasks. A Comparison of Policy Search in Joint Space and Cartesian Space for Refinement of Skills. Deep Learning for Low-Dose CT Denoising. CMR motion artifact correction using generative adversarial nets. Adaptive Ma …
Shared control schematic for brain controlled vehicle based on fuzzy logic
| Title | Shared control schematic for brain controlled vehicle based on fuzzy logic |
| Authors | Na Dong, Wen-qi Zhang, Zhong-ke Gao |
| Abstract | Brain controlled vehicle refers to the vehicle that obtains control commands by analyzing the driver’s EEG through Brain-Computer Interface (BCI). The research of brain controlled vehicles can not only promote the integration of brain machines, but also expand the range of activities and living ability of the disabled or some people with limited physical activity, so the research of brain controlled vehicles is of great significance and has broad application prospects. At present, BCI has some problems such as limited recognition accuracy, long recognition time and limited number of recognition commands in the process of analyzing EEG signals to obtain control commands. If only use the driver’s EEG signals to control the vehicle, the control performance is not ideal. Based on the concept of Shared control, this paper uses the fuzzy control (FC) to design an auxiliary controller to realize the cooperative control of automatic control and brain control. Designing a Shared controller which evaluates the current vehicle status and decides the switching mechanism between automatic control and brain control to improve the system control performance. Finally, based on the joint simulation platform of Carsim and MATLAB, with the simulated brain control signals, the designed experiment verifies that the control performance of the brain control vehicle can be improved by adding the auxiliary controller. |
| Tasks | EEG |
| Published | 2019-05-29 |
| URL | http://arxiv.org/abs/1905.13044v1 |
| http://arxiv.org/pdf/1905.13044v1.pdf | |
| PWC | https://paperswithcode.com/paper/shared-control-schematic-for-brain-controlled |
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Learning Variable Impedance Control for Contact Sensitive Tasks
| Title | Learning Variable Impedance Control for Contact Sensitive Tasks |
| Authors | Miroslav Bogdanovic, Majid Khadiv, Ludovic Righetti |
| Abstract | Reinforcement learning algorithms have shown great success in solving different problems ranging from playing video games to robotics. However, they struggle to solve delicate robotic problems, especially those involving contact interactions. Though in principle a policy outputting joint torques should be able to learn these tasks, in practice we see that they have difficulty to robustly solve the problem without any structure in the action space. In this paper, we investigate how the choice of action space can give robust performance in presence of contact uncertainties. We propose to learn a policy that outputs impedance and desired position in joint space as a function of system states without imposing any other structure to the problem. We compare the performance of this approach to torque and position control policies under different contact uncertainties. Extensive simulation results on two different systems, a hopper (floating-base) with intermittent contacts and a manipulator (fixed-base) wiping a table, show that our proposed approach outperforms policies outputting torque or position in terms of both learning rate and robustness to environment uncertainty. |
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| Published | 2019-07-17 |
| URL | https://arxiv.org/abs/1907.07500v1 |
| https://arxiv.org/pdf/1907.07500v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-variable-impedance-control-for |
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A Comparison of Policy Search in Joint Space and Cartesian Space for Refinement of Skills
| Title | A Comparison of Policy Search in Joint Space and Cartesian Space for Refinement of Skills |
| Authors | Alexander Fabisch |
| Abstract | Imitation learning is a way to teach robots skills that are demonstrated by humans. Transfering skills between these different kinematic structures seems to be straightforward in Cartesian space. Because of the correspondence problem, however, the result will most likely not be identical. This is why refinement is required, for example, by policy search. Policy search in Cartesian space is prone to reachability problems when using conventional inverse kinematic solvers. We propose a configurable approximate inverse kinematic solver and show that it can accelerate the refinement process considerably. We also compare empirically refinement in Cartesian space and refinement in joint space. |
| Tasks | Imitation Learning |
| Published | 2019-04-14 |
| URL | http://arxiv.org/abs/1904.06765v1 |
| http://arxiv.org/pdf/1904.06765v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-comparison-of-policy-search-in-joint-space |
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Deep Learning for Low-Dose CT Denoising
| Title | Deep Learning for Low-Dose CT Denoising |
| Authors | Maryam Gholizadeh-Ansari, Javad Alirezaie, Paul Babyn |
| Abstract | Low-dose CT denoising is a challenging task that has been studied by many researchers. Some studies have used deep neural networks to improve the quality of low-dose CT images and achieved fruitful results. In this paper, we propose a deep neural network that uses dilated convolutions with different dilation rates instead of standard convolution helping to capture more contextual information in fewer layers. Also, we have employed residual learning by creating shortcut connections to transmit image information from the early layers to later ones. To further improve the performance of the network, we have introduced a non-trainable edge detection layer that extracts edges in horizontal, vertical, and diagonal directions. Finally, we demonstrate that optimizing the network by a combination of mean-square error loss and perceptual loss preserves many structural details in the CT image. This objective function does not suffer from over smoothing and blurring effects caused by per-pixel loss and grid-like artifacts resulting from perceptual loss. The experiments show that each modification to the network improves the outcome while only minimally changing the complexity of the network. |
| Tasks | Denoising, Edge Detection |
| Published | 2019-02-25 |
| URL | http://arxiv.org/abs/1902.10127v1 |
| http://arxiv.org/pdf/1902.10127v1.pdf | |
| PWC | https://paperswithcode.com/paper/deep-learning-for-low-dose-ct-denoising |
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CMR motion artifact correction using generative adversarial nets
| Title | CMR motion artifact correction using generative adversarial nets |
| Authors | Yunxuan Zhang, Weiliang Zhang, Qinyan Zhang, Jijiang Yang, Xiuyu Chen, Shihua Zhao |
| Abstract | Cardiovascular Magnetic Resonance (CMR) plays an important role in the diagnoses and treatment of cardiovascular diseases while motion artifacts which are formed during the scanning process of CMR seriously affects doctors to find the exact focus. The current correction methods mainly focus on the K-space which is a grid of raw data obtained from the MR signal directly and then transfer to CMR image by inverse Fourier transform. They are neither effective nor efficient and can not be utilized in clinic. In this paper, we propose a novel approach for CMR motion artifact correction using deep learning. Specially, we use deep residual network (ResNet) as net framework and train our model in adversarial manner. Our approach is motivated by the connection between image motion blur and CMR motion artifact, so we can transfer methods from motion-deblur where deep learning has made great progress to CMR motion-correction successfully. To evaluate motion artifact correction methods, we propose a novel algorithm on how edge detection results are improved by deblurred algorithm. Boosted by deep learning and adversarial training algorithm, our model is trainable in an end-to-end manner, can be tested in real-time and achieves the state-of-art results for CMR correction. |
| Tasks | Edge Detection |
| Published | 2019-02-21 |
| URL | http://arxiv.org/abs/1902.11121v1 |
| http://arxiv.org/pdf/1902.11121v1.pdf | |
| PWC | https://paperswithcode.com/paper/cmr-motion-artifact-correction-using |
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Adaptive Margin Ranking Loss for Knowledge Graph Embeddings via a Correntropy Objective Function
| Title | Adaptive Margin Ranking Loss for Knowledge Graph Embeddings via a Correntropy Objective Function |
| Authors | Mojtaba Nayyeri, Xiaotian Zhou, Sahar Vahdati, Hamed Shariat Yazdi, Jens Lehmann |
| Abstract | Translation-based embedding models have gained significant attention in link prediction tasks for knowledge graphs. TransE is the primary model among translation-based embeddings and is well-known for its low complexity and high efficiency. Therefore, most of the earlier works have modified the score function of the TransE approach in order to improve the performance of link prediction tasks. Nevertheless, proven theoretically and experimentally, the performance of TransE strongly depends on the loss function. Margin Ranking Loss (MRL) has been one of the earlier loss functions which is widely used for training TransE. However, the scores of positive triples are not necessarily enforced to be sufficiently small to fulfill the translation from head to tail by using relation vector (original assumption of TransE). To tackle this problem, several loss functions have been proposed recently by adding upper bounds and lower bounds to the scores of positive and negative samples. Although highly effective, previously developed models suffer from an expansion in search space for a selection of the hyperparameters (in particular the upper and lower bounds of scores) on which the performance of the translation-based models is highly dependent. In this paper, we propose a new loss function dubbed Adaptive Margin Loss (AML) for training translation-based embedding models. The formulation of the proposed loss function enables an adaptive and automated adjustment of the margin during the learning process. Therefore, instead of obtaining two values (upper bound and lower bound), only the center of a margin needs to be determined. During learning, the margin is expanded automatically until it converges. In our experiments on a set of standard benchmark datasets including Freebase and WordNet, the effectiveness of AML is confirmed for training TransE on link prediction tasks. |
| Tasks | Knowledge Graph Embeddings, Knowledge Graphs, Link Prediction |
| Published | 2019-07-09 |
| URL | https://arxiv.org/abs/1907.05336v1 |
| https://arxiv.org/pdf/1907.05336v1.pdf | |
| PWC | https://paperswithcode.com/paper/adaptive-margin-ranking-loss-for-knowledge |
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Generalized Median Graph via Iterative Alternate Minimizations
| Title | Generalized Median Graph via Iterative Alternate Minimizations |
| Authors | Nicolas Boria, S’ebastien Bougleux, Benoit Gaüzère, Luc Brun |
| Abstract | Computing a graph prototype may constitute a core element for clustering or classification tasks. However, its computation is an NP-Hard problem, even for simple classes of graphs. In this paper, we propose an efficient approach based on block coordinate descent to compute a generalized median graph from a set of graphs. This approach relies on a clear definition of the optimization process and handles labeling on both edges and nodes. This iterative process optimizes the edit operations to perform on a graph alternatively on nodes and edges. Several experiments on different datasets show the efficiency of our approach. |
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| Published | 2019-06-26 |
| URL | https://arxiv.org/abs/1906.11009v1 |
| https://arxiv.org/pdf/1906.11009v1.pdf | |
| PWC | https://paperswithcode.com/paper/generalized-median-graph-via-iterative |
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Meta-QSM: An Image-Resolution-Arbitrary Network for QSM Reconstruction
| Title | Meta-QSM: An Image-Resolution-Arbitrary Network for QSM Reconstruction |
| Authors | Juan Liu, Kevin M. Koch |
| Abstract | Quantitative Susceptibility Mapping (QSM) can estimate the underlying tissue magnetic susceptibility and reveal pathology. Current deep-learning-based approaches to solve the QSM inverse problem are restricted on fixed image resolution. They trained a specific model for each image resolution which is inefficient in computing. In this work, we proposed a novel method called Meta-QSM to firstly solve QSM reconstruction of arbitrary image resolution with a single model. In Meta-QSM, weight prediction was used to predict the weights of kernels by taking the image resolution as input. The proposed method was evaluated on synthetic data and clinical data with comparison to existing QSM reconstruction methods. The experimental results showed the Meta-QSM can effectively reconstruct susceptibility maps with different image resolution using one neural network training. |
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| Published | 2019-08-01 |
| URL | https://arxiv.org/abs/1908.00206v1 |
| https://arxiv.org/pdf/1908.00206v1.pdf | |
| PWC | https://paperswithcode.com/paper/meta-qsm-an-image-resolution-arbitrary |
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Thickened 2D Networks for Efficient 3D Medical Image Segmentation
| Title | Thickened 2D Networks for Efficient 3D Medical Image Segmentation |
| Authors | Qihang Yu, Yingda Xia, Lingxi Xie, Elliot K. Fishman, Alan L. Yuille |
| Abstract | There has been a debate in 3D medical image segmentation on whether to use 2D or 3D networks, where both pipelines have advantages and disadvantages. 2D methods enjoy a low inference time and greater transfer-ability while 3D methods are superior in performance for hard targets requiring contextual information. This paper investigates efficient 3D segmentation from another perspective, which uses 2D networks to mimic 3D segmentation. To compensate the lack of contextual information in 2D manner, we propose to thicken the 2D network inputs by feeding multiple slices as multiple channels into 2D networks and thus 3D contextual information is incorporated. We also put forward to use early-stage multiplexing and slice sensitive attention to solve the confusion problem of information loss which occurs when 2D networks face thickened inputs. With this design, we achieve a higher performance while maintaining a lower inference latency on a few abdominal organs from CT scans, in particular when the organ has a peculiar 3D shape and thus strongly requires contextual information, demonstrating our method’s effectiveness and ability in capturing 3D information. We also point out that “thickened” 2D inputs pave a new method of 3D segmentation, and look forward to more efforts in this direction. Experiments on segmenting a few abdominal targets in particular blood vessels which require strong 3D contexts demonstrate the advantages of our approach. |
| Tasks | Medical Image Segmentation, Semantic Segmentation |
| Published | 2019-04-02 |
| URL | https://arxiv.org/abs/1904.01150v2 |
| https://arxiv.org/pdf/1904.01150v2.pdf | |
| PWC | https://paperswithcode.com/paper/thickened-2d-networks-for-3d-medical-image |
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Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization
| Title | Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization |
| Authors | Wei-Chiu Ma, Ignacio Tartavull, Ioan Andrei Bârsan, Shenlong Wang, Min Bai, Gellert Mattyus, Namdar Homayounfar, Shrinidhi Kowshika Lakshmikanth, Andrei Pokrovsky, Raquel Urtasun |
| Abstract | In this paper we propose a novel semantic localization algorithm that exploits multiple sensors and has precision on the order of a few centimeters. Our approach does not require detailed knowledge about the appearance of the world, and our maps require orders of magnitude less storage than maps utilized by traditional geometry- and LiDAR intensity-based localizers. This is important as self-driving cars need to operate in large environments. Towards this goal, we formulate the problem in a Bayesian filtering framework, and exploit lanes, traffic signs, as well as vehicle dynamics to localize robustly with respect to a sparse semantic map. We validate the effectiveness of our method on a new highway dataset consisting of 312km of roads. Our experiments show that the proposed approach is able to achieve 0.05m lateral accuracy and 1.12m longitudinal accuracy on average while taking up only 0.3% of the storage required by previous LiDAR intensity-based approaches. |
| Tasks | Self-Driving Cars |
| Published | 2019-08-08 |
| URL | https://arxiv.org/abs/1908.03274v1 |
| https://arxiv.org/pdf/1908.03274v1.pdf | |
| PWC | https://paperswithcode.com/paper/exploiting-sparse-semantic-hd-maps-for-self |
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Self-Supervised Flow Estimation using Geometric Regularization with Applications to Camera Image and Grid Map Sequences
| Title | Self-Supervised Flow Estimation using Geometric Regularization with Applications to Camera Image and Grid Map Sequences |
| Authors | Sascha Wirges, Johannes Gräter, Qiuhao Zhang, Christoph Stiller |
| Abstract | We present a self-supervised approach to estimate flow in camera image and top-view grid map sequences using fully convolutional neural networks in the domain of automated driving. We extend existing approaches for self-supervised optical flow estimation by adding a regularizer expressing motion consistency assuming a static environment. However, as this assumption is violated for other moving traffic participants we also estimate a mask to scale this regularization. Adding a regularization towards motion consistency improves convergence and flow estimation accuracy. Furthermore, we scale the errors due to spatial flow inconsistency by a mask that we derive from the motion mask. This improves accuracy in regions where the flow drastically changes due to a better separation between static and dynamic environment. We apply our approach to optical flow estimation from camera image sequences, validate on odometry estimation and suggest a method to iteratively increase optical flow estimation accuracy using the generated motion masks. Finally, we provide quantitative and qualitative results based on the KITTI odometry and tracking benchmark for scene flow estimation based on grid map sequences. We show that we can improve accuracy and convergence when applying motion and spatial consistency regularization. |
| Tasks | Optical Flow Estimation, Scene Flow Estimation |
| Published | 2019-04-17 |
| URL | http://arxiv.org/abs/1904.12599v1 |
| http://arxiv.org/pdf/1904.12599v1.pdf | |
| PWC | https://paperswithcode.com/paper/190412599 |
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Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory
| Title | Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory |
| Authors | Benjamin Plaster, Gautam Kumar |
| Abstract | Modeling brain dynamics to better understand and control complex behaviors underlying various cognitive brain functions are of interests to engineers, mathematicians, and physicists from the last several decades. With a motivation of developing computationally efficient models of brain dynamics to use in designing control-theoretic neurostimulation strategies, we have developed a novel data-driven approach in a long short-term memory (LSTM) neural network architecture to predict the temporal dynamics of complex systems over an extended long time-horizon in future. In contrast to recent LSTM-based dynamical modeling approaches that make use of multi-layer perceptrons or linear combination layers as output layers, our architecture uses a single fully connected output layer and reversed-order sequence-to-sequence mapping to improve short time-horizon prediction accuracy and to make multi-timestep predictions of dynamical behaviors. We demonstrate the efficacy of our approach in reconstructing the regular spiking to bursting dynamics exhibited by an experimentally-validated 9-dimensional Hodgkin-Huxley model of hippocampal CA1 pyramidal neurons. Through simulations, we show that our LSTM neural network can predict the multi-time scale temporal dynamics underlying various spiking patterns with reasonable accuracy. Moreover, our results show that the predictions improve with increasing predictive time-horizon in the multi-timestep deep LSTM neural network. |
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| Published | 2019-08-11 |
| URL | https://arxiv.org/abs/1908.07428v1 |
| https://arxiv.org/pdf/1908.07428v1.pdf | |
| PWC | https://paperswithcode.com/paper/data-driven-predictive-modeling-of-neuronal |
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Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images
| Title | Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images |
| Authors | Susanne Kimeswenger, Elisabeth Rumetshofer, Markus Hofmarcher, Philipp Tschandl, Harald Kittler, Sepp Hochreiter, Wolfram Hötzenecker, Günter Klambauer |
| Abstract | Diagnosing basal cell carcinomas (BCC), one of the most common cutaneous malignancies in humans, is a task regularly performed by pathologists and dermato-pathologists. Improving histological diagnosis by providing diagnosis suggestions, i.e. computer-assisted diagnoses is actively researched to improve safety, quality and efficiency. Increasingly, machine learning methods are applied due to their superior performance. However, typical images obtained by scanning histological sections often have a resolution that is prohibitive for processing with current state-of-the-art neural networks. Furthermore, the data pose a problem of weak labels, since only a tiny fraction of the image is indicative of the disease class, whereas a large fraction of the image is highly similar to the non-disease class. The aim of this study is to evaluate whether it is possible to detect basal cell carcinomas in histological sections using attention-based deep learning models and to overcome the ultra-high resolution and the weak labels of whole slide images. We demonstrate that attention-based models can indeed yield almost perfect classification performance with an AUC of 0.99. |
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| Published | 2019-11-14 |
| URL | https://arxiv.org/abs/1911.06616v3 |
| https://arxiv.org/pdf/1911.06616v3.pdf | |
| PWC | https://paperswithcode.com/paper/detecting-cutaneous-basal-cell-carcinomas-in |
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Multiple Policy Value Monte Carlo Tree Search
| Title | Multiple Policy Value Monte Carlo Tree Search |
| Authors | Li-Cheng Lan, Wei Li, Ting-Han Wei, I-Chen Wu |
| Abstract | Many of the strongest game playing programs use a combination of Monte Carlo tree search (MCTS) and deep neural networks (DNN), where the DNNs are used as policy or value evaluators. Given a limited budget, such as online playing or during the self-play phase of AlphaZero (AZ) training, a balance needs to be reached between accurate state estimation and more MCTS simulations, both of which are critical for a strong game playing agent. Typically, larger DNNs are better at generalization and accurate evaluation, while smaller DNNs are less costly, and therefore can lead to more MCTS simulations and bigger search trees with the same budget. This paper introduces a new method called the multiple policy value MCTS (MPV-MCTS), which combines multiple policy value neural networks (PV-NNs) of various sizes to retain advantages of each network, where two PV-NNs f_S and f_L are used in this paper. We show through experiments on the game NoGo that a combined f_S and f_L MPV-MCTS outperforms single PV-NN with policy value MCTS, called PV-MCTS. Additionally, MPV-MCTS also outperforms PV-MCTS for AZ training. |
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| Published | 2019-05-31 |
| URL | https://arxiv.org/abs/1905.13521v1 |
| https://arxiv.org/pdf/1905.13521v1.pdf | |
| PWC | https://paperswithcode.com/paper/multiple-policy-value-monte-carlo-tree-search |
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Adversarial attacks against Fact Extraction and VERification
| Title | Adversarial attacks against Fact Extraction and VERification |
| Authors | James Thorne, Andreas Vlachos |
| Abstract | This paper describes a baseline for the second iteration of the Fact Extraction and VERification shared task (FEVER2.0) which explores the resilience of systems through adversarial evaluation. We present a collection of simple adversarial attacks against systems that participated in the first FEVER shared task. FEVER modeled the assessment of truthfulness of written claims as a joint information retrieval and natural language inference task using evidence from Wikipedia. A large number of participants made use of deep neural networks in their submissions to the shared task. The extent as to whether such models understand language has been the subject of a number of recent investigations and discussion in literature. In this paper, we present a simple method of generating entailment-preserving and entailment-altering perturbations of instances by common patterns within the training data. We find that a number of systems are greatly affected with absolute losses in classification accuracy of up to $29%$ on the newly perturbed instances. Using these newly generated instances, we construct a sample submission for the FEVER2.0 shared task. Addressing these types of attacks will aid in building more robust fact-checking models, as well as suggest directions to expand the datasets. |
| Tasks | Information Retrieval, Natural Language Inference |
| Published | 2019-03-13 |
| URL | http://arxiv.org/abs/1903.05543v1 |
| http://arxiv.org/pdf/1903.05543v1.pdf | |
| PWC | https://paperswithcode.com/paper/adversarial-attacks-against-fact-extraction |
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