October 19, 2019

2915 words 14 mins read

Paper Group ANR 244

Counting Complexity for Reasoning in Abstract Argumentation. Non-invasive measuring method of skin temperature based on skin sensitivity index and deep learning. Nonlinear integro-differential operator regression with neural networks. Automatic Diagnosis of Short-Duration 12-Lead ECG using a Deep Convolutional Network. A Multi-channel Network with …

Counting Complexity for Reasoning in Abstract Argumentation


Title	Counting Complexity for Reasoning in Abstract Argumentation
Authors	Johannes K. Fichte, Markus Hecher, Arne Meier
Abstract	In this paper, we consider counting and projected model counting of extensions in abstract argumentation for various semantics. When asking for projected counts we are interested in counting the number of extensions of a given argumentation framework while multiple extensions that are identical when restricted to the projected arguments count as only one projected extension. We establish classical complexity results and parameterized complexity results when the problems are parameterized by treewidth of the undirected argumentation graph. To obtain upper bounds for counting projected extensions, we introduce novel algorithms that exploit small treewidth of the undirected argumentation graph of the input instance by dynamic programming (DP). Our algorithms run in time double or triple exponential in the treewidth depending on the considered semantics. Finally, we take the exponential time hypothesis (ETH) into account and establish lower bounds of bounded treewidth algorithms for counting extensions and projected extension.
Tasks	Abstract Argumentation
Published	2018-11-28
URL	http://arxiv.org/abs/1811.11501v1
PDF	http://arxiv.org/pdf/1811.11501v1.pdf
PWC	https://paperswithcode.com/paper/counting-complexity-for-reasoning-in-abstract
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Non-invasive measuring method of skin temperature based on skin sensitivity index and deep learning


Title	Non-invasive measuring method of skin temperature based on skin sensitivity index and deep learning
Authors	Xiaogang Cheng, Bin Yang, Kaige Tan, Erik Isaksson, Liren Li, Anders Hedman, Thomas Olofsson, Haibo Li
Abstract	In human-centered intelligent building, real-time measurements of human thermal comfort play critical roles and supply feedback control signals for building heating, ventilation, and air conditioning (HVAC) systems. Due to the challenges of intra- and inter-individual differences and skin subtleness variations, there is no satisfactory solution for thermal comfort measurements until now. In this paper, a non-invasive measuring method based on skin sensitivity index and deep learning (NISDL) was proposed to measure real-time skin temperature. A new evaluating index, named skin sensitivity index (SSI), was defined to overcome individual differences and skin subtleness variations. To illustrate the effectiveness of SSI proposed, two multi-layers deep learning framework (NISDL method I and II) was designed and the DenseNet201 was used for extracting features from skin images. The partly personal saturation temperature (NIPST) algorithm was use for algorithm comparisons. Another deep learning algorithm without SSI (DL) was also generated for algorithm comparisons. Finally, a total of 1.44 million image data was used for algorithm validation. The results show that 55.6180% and 52.2472% error values (NISDL method I, II) are scattered at [0, 0.25), and the same error intervals distribution of NIPST is 35.3933%.
Tasks
Published	2018-12-16
URL	http://arxiv.org/abs/1812.06509v1
PDF	http://arxiv.org/pdf/1812.06509v1.pdf
PWC	https://paperswithcode.com/paper/non-invasive-measuring-method-of-skin
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Nonlinear integro-differential operator regression with neural networks


Title	Nonlinear integro-differential operator regression with neural networks
Authors	Ravi G. Patel, Olivier Desjardins
Abstract	This note introduces a regression technique for finding a class of nonlinear integro-differential operators from data. The method parametrizes the spatial operator with neural networks and Fourier transforms such that it can fit a class of nonlinear operators without needing a library of a priori selected operators. We verify that this method can recover the spatial operators in the fractional heat equation and the Kuramoto-Sivashinsky equation from numerical solutions of the equations.
Tasks
Published	2018-10-19
URL	http://arxiv.org/abs/1810.08552v1
PDF	http://arxiv.org/pdf/1810.08552v1.pdf
PWC	https://paperswithcode.com/paper/nonlinear-integro-differential-operator
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Automatic Diagnosis of Short-Duration 12-Lead ECG using a Deep Convolutional Network


Title	Automatic Diagnosis of Short-Duration 12-Lead ECG using a Deep Convolutional Network
Authors	Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela Paixão, Derick Oliveira, Paulo R. Gomes, Jéssica A. Canazart, Milton Pifano, Wagner Meira Jr., Thomas B. Schön, Antonio Luiz Ribeiro
Abstract	We present a model for predicting electrocardiogram (ECG) abnormalities in short-duration 12-lead ECG signals which outperformed medical doctors on the 4th year of their cardiology residency. Such exams can provide a full evaluation of heart activity and have not been studied in previous end-to-end machine learning papers. Using the database of a large telehealth network, we built a novel dataset with more than 2 million ECG tracings, orders of magnitude larger than those used in previous studies. Moreover, our dataset is more realistic, as it consist of 12-lead ECGs recorded during standard in-clinics exams. Using this data, we trained a residual neural network with 9 convolutional layers to map 7 to 10 second ECG signals to 6 classes of ECG abnormalities. Future work should extend these results to cover a large range of ECG abnormalities, which could improve the accessibility of this diagnostic tool and avoid wrong diagnosis from medical doctors.
Tasks	Electrocardiography (ECG)
Published	2018-11-28
URL	http://arxiv.org/abs/1811.12194v2
PDF	http://arxiv.org/pdf/1811.12194v2.pdf
PWC	https://paperswithcode.com/paper/automatic-diagnosis-of-short-duration-12-lead
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A Multi-channel Network with Image Retrieval for Accurate Brain Tissue Segmentation


Title	A Multi-channel Network with Image Retrieval for Accurate Brain Tissue Segmentation
Authors	Yao Sun, Yang Deng, Yue Xu, Shuo Zhang, Mingwang Zhu, Kehong Yuan
Abstract	Magnetic Resonance Imaging (MRI) is widely used in the pathological and functional studies of the brain, such as epilepsy, tumor diagnosis, etc. Automated accurate brain tissue segmentation like cerebro-spinal fluid (CSF), gray matter (GM), white matter (WM) is the basis of these studies and many researchers are seeking it to the best. Based on the truth that multi-channel segmentation network with its own ground truth achieves up to average dice ratio 0.98, we propose a novel method that we add a fourth channel with the ground truth of the most similar image’s obtained by CBIR from the database. The results show that the method improves the segmentation performance, as measured by average dice ratio, by approximately 0.01 in the MRBrainS18 database. In addition, our method is concise and robust, which can be used to any network architecture that needs not be modified a lot.
Tasks	Image Retrieval
Published	2018-08-01
URL	http://arxiv.org/abs/1808.00457v2
PDF	http://arxiv.org/pdf/1808.00457v2.pdf
PWC	https://paperswithcode.com/paper/a-multi-channel-network-with-image-retrieval
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Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression


Title	Affect Estimation in 3D Space Using Multi-Task Active Learning for Regression
Authors	Dongrui Wu, Jian Huang
Abstract	Acquisition of labeled training samples for affective computing is usually costly and time-consuming, as affects are intrinsically subjective, subtle and uncertain, and hence multiple human assessors are needed to evaluate each affective sample. Particularly, for affect estimation in the 3D space of valence, arousal and dominance, each assessor has to perform the evaluations in three dimensions, which makes the labeling problem even more challenging. Many sophisticated machine learning approaches have been proposed to reduce the data labeling requirement in various other domains, but so far few have considered affective computing. This paper proposes two multi-task active learning for regression approaches, which select the most beneficial samples to label, by considering the three affect primitives simultaneously. Experimental results on the VAM corpus demonstrated that our optimal sample selection approaches can result in better estimation performance than random selection and several traditional single-task active learning approaches. Thus, they can help alleviate the data labeling problem in affective computing, i.e., better estimation performance can be obtained from fewer labeling queries.
Tasks	Active Learning
Published	2018-08-08
URL	http://arxiv.org/abs/1808.04244v2
PDF	http://arxiv.org/pdf/1808.04244v2.pdf
PWC	https://paperswithcode.com/paper/affect-estimation-in-3d-space-using-multi
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A New Registration Approach for Dynamic Analysis of Calcium Signals in Organs


Title	A New Registration Approach for Dynamic Analysis of Calcium Signals in Organs
Authors	Peixian Liang, Jianxu Chen, Pavel A. Brodskiy, Qinfeng Wu, Yejia Zhang, Yizhe Zhang, Lin Yang, Jeremiah J. Zartman, Danny Z. Chen
Abstract	Wing disc pouches of fruit flies are a powerful genetic model for studying physiological intercellular calcium ($Ca^{2+}$) signals for dynamic analysis of cell signaling in organ development and disease studies. A key to analyzing spatial-temporal patterns of $Ca^{2+}$ signal waves is to accurately align the pouches across image sequences. However, pouches in different image frames may exhibit extensive intensity oscillations due to $Ca^{2+}$ signaling dynamics, and commonly used multimodal non-rigid registration methods may fail to achieve satisfactory results. In this paper, we develop a new two-phase non-rigid registration approach to register pouches in image sequences. First, we conduct segmentation of the region of interest. (i.e., pouches) using a deep neural network model. Second, we obtain an optimal transformation and align pouches across the image sequences. Evaluated using both synthetic data and real pouch data, our method considerably outperforms the state-of-the-art non-rigid registration methods.
Tasks
Published	2018-02-01
URL	http://arxiv.org/abs/1802.00491v1
PDF	http://arxiv.org/pdf/1802.00491v1.pdf
PWC	https://paperswithcode.com/paper/a-new-registration-approach-for-dynamic
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A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification


Title	A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification
Authors	Yunan Wu, Feng Yang, Ying Liu, Xuefan Zha, Shaofeng Yuan
Abstract	Effective detection of arrhythmia is an important task in the remote monitoring of electrocardiogram (ECG). The traditional ECG recognition depends on the judgment of the clinicians’ experience, but the results suffer from the probability of human error due to the fatigue. To solve this problem, an ECG signal classification method based on the images is presented to classify ECG signals into normal and abnormal beats by using two-dimensional convolutional neural networks (2D-CNNs). First, we compare the accuracy and robustness between one-dimensional ECG signal input method and two-dimensional image input method in AlexNet network. Then, in order to alleviate the overfitting problem in two-dimensional network, we initialize AlexNet-like network with weights trained on ImageNet, to fit the training ECG images and fine-tune the model, and to further improve the accuracy and robustness of ECG classification. The performance evaluated on the MIT-BIH arrhythmia database demonstrates that the proposed method can achieve the accuracy of 98% and maintain high accuracy within SNR range from 20 dB to 35 dB. The experiment shows that the 2D-CNNs initialized with AlexNet weights performs better than one-dimensional signal method without a large-scale dataset.
Tasks	ECG Classification, Electrocardiography (ECG)
Published	2018-10-16
URL	http://arxiv.org/abs/1810.07088v1
PDF	http://arxiv.org/pdf/1810.07088v1.pdf
PWC	https://paperswithcode.com/paper/a-comparison-of-1-d-and-2-d-deep
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Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution


Title	Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution
Authors	Ying Qu, Hairong Qi, Chiman Kwan
Abstract	In many computer vision applications, obtaining images of high resolution in both the spatial and spectral domains are equally important. However, due to hardware limitations, one can only expect to acquire images of high resolution in either the spatial or spectral domains. This paper focuses on hyperspectral image super-resolution (HSI-SR), where a hyperspectral image (HSI) with low spatial resolution (LR) but high spectral resolution is fused with a multispectral image (MSI) with high spatial resolution (HR) but low spectral resolution to obtain HR HSI. Existing deep learning-based solutions are all supervised that would need a large training set and the availability of HR HSI, which is unrealistic. Here, we make the first attempt to solving the HSI-SR problem using an unsupervised encoder-decoder architecture that carries the following uniquenesses. First, it is composed of two encoder-decoder networks, coupled through a shared decoder, in order to preserve the rich spectral information from the HSI network. Second, the network encourages the representations from both modalities to follow a sparse Dirichlet distribution which naturally incorporates the two physical constraints of HSI and MSI. Third, the angular difference between representations are minimized in order to reduce the spectral distortion. We refer to the proposed architecture as unsupervised Sparse Dirichlet-Net, or uSDN. Extensive experimental results demonstrate the superior performance of uSDN as compared to the state-of-the-art.
Tasks	Image Super-Resolution, Super-Resolution
Published	2018-04-13
URL	http://arxiv.org/abs/1804.05042v3
PDF	http://arxiv.org/pdf/1804.05042v3.pdf
PWC	https://paperswithcode.com/paper/unsupervised-sparse-dirichlet-net-for
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Implementation of Neural Network and feature extraction to classify ECG signals


Title	Implementation of Neural Network and feature extraction to classify ECG signals
Authors	R Karthik, Dhruv Tyagi, Amogh Raut, Soumya Saxena, Rajesh Kumar M
Abstract	This paper presents a suitable and efficient implementation of a feature extraction algorithm (Pan Tompkins algorithm) on electrocardiography (ECG) signals, for detection and classification of four cardiac diseases: Sleep Apnea, Arrhythmia, Supraventricular Arrhythmia and Long Term Atrial Fibrillation (AF) and differentiating them from the normal heart beat by using pan Tompkins RR detection followed by feature extraction for classification purpose .The paper also presents a new approach towards signal classification using the existing neural networks classifiers.
Tasks	Electrocardiography (ECG)
Published	2018-02-17
URL	http://arxiv.org/abs/1802.06288v1
PDF	http://arxiv.org/pdf/1802.06288v1.pdf
PWC	https://paperswithcode.com/paper/implementation-of-neural-network-and-feature
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Batch Normalization in the final layer of generative networks


Title	Batch Normalization in the final layer of generative networks
Authors	Sean Mullery, Paul F. Whelan
Abstract	Generative Networks have shown great promise in generating photo-realistic images. Despite this, the theory surrounding them is still an active research area. Much of the useful work with Generative networks rely on heuristics that tend to produce good results. One of these heuristics is the advice not to use Batch Normalization in the final layer of the generator network. Many of the state-of-the-art generative network architectures use this heuristic, but the reasons for doing so are inconsistent. This paper will show that this is not necessarily a good heuristic and that Batch Normalization can be beneficial in the final layer of the generator network either by placing it before the final non-linear activation, usually a $tanh$ or replacing the final $tanh$ activation altogether with Batch Normalization and clipping. We show that this can lead to the faster training of Generator networks by matching the generator to the mean and standard deviation of the target distribution’s image colour values.
Tasks
Published	2018-05-18
URL	http://arxiv.org/abs/1805.07389v1
PDF	http://arxiv.org/pdf/1805.07389v1.pdf
PWC	https://paperswithcode.com/paper/batch-normalization-in-the-final-layer-of
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A Model of Free Will for Artificial Entities


Title	A Model of Free Will for Artificial Entities
Authors	Eric Sanchis
Abstract	The impression of free will is the feeling according to which our choices are neither imposed from our inside nor from outside. It is the sense we are the ultimate cause of our acts. In direct opposition with the universal determinism, the existence of free will continues to be discussed. In this paper, free will is linked to a decisional mechanism: an agent is provided with free will if having performed a predictable choice Cp, it can immediately perform another choice Cr in a random way. The intangible feeling of free will is replaced by a decision-making process including a predictable decision-making process immediately followed by an unpredictable decisional one.
Tasks	Decision Making
Published	2018-02-26
URL	http://arxiv.org/abs/1802.09317v1
PDF	http://arxiv.org/pdf/1802.09317v1.pdf
PWC	https://paperswithcode.com/paper/a-model-of-free-will-for-artificial-entities
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Evaluation of Interactive Machine Learning Systems


Title	Evaluation of Interactive Machine Learning Systems
Authors	Nadia Boukhelifa, Anastasia Bezerianos, Evelyne Lutton
Abstract	The evaluation of interactive machine learning systems remains a difficult task. These systems learn from and adapt to the human, but at the same time, the human receives feedback and adapts to the system. Getting a clear understanding of these subtle mechanisms of co-operation and co-adaptation is challenging. In this chapter, we report on our experience in designing and evaluating various interactive machine learning applications from different domains. We argue for coupling two types of validation: algorithm-centered analysis, to study the computational behaviour of the system; and human-centered evaluation, to observe the utility and effectiveness of the application for end-users. We use a visual analytics application for guided search, built using an interactive evolutionary approach, as an exemplar of our work. Our observation is that human-centered design and evaluation complement algorithmic analysis, and can play an important role in addressing the “black-box” effect of machine learning. Finally, we discuss research opportunities that require human-computer interaction methodologies, in order to support both the visible and hidden roles that humans play in interactive machine learning.
Tasks
Published	2018-01-24
URL	http://arxiv.org/abs/1801.07964v1
PDF	http://arxiv.org/pdf/1801.07964v1.pdf
PWC	https://paperswithcode.com/paper/evaluation-of-interactive-machine-learning
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Stochastic EM for Shuffled Linear Regression


Title	Stochastic EM for Shuffled Linear Regression
Authors	Abubakar Abid, James Zou
Abstract	We consider the problem of inference in a linear regression model in which the relative ordering of the input features and output labels is not known. Such datasets naturally arise from experiments in which the samples are shuffled or permuted during the protocol. In this work, we propose a framework that treats the unknown permutation as a latent variable. We maximize the likelihood of observations using a stochastic expectation-maximization (EM) approach. We compare this to the dominant approach in the literature, which corresponds to hard EM in our framework. We show on synthetic data that the stochastic EM algorithm we develop has several advantages, including lower parameter error, less sensitivity to the choice of initialization, and significantly better performance on datasets that are only partially shuffled. We conclude by performing two experiments on real datasets that have been partially shuffled, in which we show that the stochastic EM algorithm can recover the weights with modest error.
Tasks
Published	2018-04-02
URL	http://arxiv.org/abs/1804.00681v1
PDF	http://arxiv.org/pdf/1804.00681v1.pdf
PWC	https://paperswithcode.com/paper/stochastic-em-for-shuffled-linear-regression
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Tight Lower Bounds for Locally Differentially Private Selection


Title	Tight Lower Bounds for Locally Differentially Private Selection
Authors	Jonathan Ullman
Abstract	We prove a tight lower bound (up to constant factors) on the sample complexity of any non-interactive local differentially private protocol for optimizing a linear function over the simplex. This lower bound also implies a tight lower bound (again, up to constant factors) on the sample complexity of any non-interactive local differentially private protocol implementing the exponential mechanism. These results reveal that any local protocol for these problems has exponentially worse dependence on the dimension than corresponding algorithms in the central model. Previously, Kasiviswanathan et al. (FOCS 2008) proved an exponential separation between local and central model algorithms for PAC learning the class of parity functions. In contrast, our lower bound are quantitatively tight, apply to a simple and natural class of linear optimization problems, and our techniques are arguably simpler.
Tasks
Published	2018-02-07
URL	http://arxiv.org/abs/1802.02638v1
PDF	http://arxiv.org/pdf/1802.02638v1.pdf
PWC	https://paperswithcode.com/paper/tight-lower-bounds-for-locally-differentially
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