April 2, 2020

3126 words 15 mins read

Paper Group ANR 298

Delineating Bone Surfaces in B-Mode Images Constrained by Physics of Ultrasound Propagation. A Structured Approach to Trustworthy Autonomous/Cognitive Systems. Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Image. The Human Visual System and Adversarial AI. Imputing Missing Boarding Stations With Machine Learning Meth …

Delineating Bone Surfaces in B-Mode Images Constrained by Physics of Ultrasound Propagation


Title	Delineating Bone Surfaces in B-Mode Images Constrained by Physics of Ultrasound Propagation
Authors	Firat Ozdemir, Christine Tanner, Orcun Goksel
Abstract	Bone surface delineation in ultrasound is of interest due to its potential in diagnosis, surgical planning, and post-operative follow-up in orthopedics, as well as the potential of using bones as anatomical landmarks in surgical navigation. We herein propose a method to encode the physics of ultrasound propagation into a factor graph formulation for the purpose of bone surface delineation. In this graph structure, unary node potentials encode the local likelihood for being a soft tissue or acoustic-shadow (behind bone surface) region, both learned through image descriptors. Pair-wise edge potentials encode ultrasound propagation constraints of bone surfaces given their large acoustic-impedance difference. We evaluate the proposed method in comparison with four earlier approaches, on in-vivo ultrasound images collected from dorsal and volar views of the forearm. The proposed method achieves an average root-mean-square error and symmetric Hausdorff distance of 0.28mm and 1.78mm, respectively. It detects 99.9% of the annotated bone surfaces with a mean scanline error (distance to annotations) of 0.39mm.
Tasks
Published	2020-01-07
URL	https://arxiv.org/abs/2001.02001v1
PDF	https://arxiv.org/pdf/2001.02001v1.pdf
PWC	https://paperswithcode.com/paper/delineating-bone-surfaces-in-b-mode-images
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Framework

A Structured Approach to Trustworthy Autonomous/Cognitive Systems


Title	A Structured Approach to Trustworthy Autonomous/Cognitive Systems
Authors	Henrik J. Putzer, Ernest Wozniak
Abstract	Autonomous systems with cognitive features are on their way into the market. Within complex environments, they promise to implement complex and goal oriented behavior even in a safety related context. This behavior is based on a certain level of situational awareness (perception) and advanced de-cision making (deliberation). These systems in many cases are driven by artificial intelligence (e.g. neural networks). The problem with such complex systems and with using AI technology is that there is no generally accepted approach to ensure trustworthiness. This paper presents a framework to exactly fill this gap. It proposes a reference lifecycle as a structured approach that is based on current safety standards and enhanced to meet the requirements of autonomous/cog-nitive systems and trustworthiness.
Tasks
Published	2020-02-19
URL	https://arxiv.org/abs/2002.08210v1
PDF	https://arxiv.org/pdf/2002.08210v1.pdf
PWC	https://paperswithcode.com/paper/a-structured-approach-to-trustworthy
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Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Image


Title	Robust Method for Semantic Segmentation of Whole-Slide Blood Cell Microscopic Image
Authors	Muhammad Shahzad, Arif Iqbal Umar, Muazzam A. Khan, Syed Hamad Shirazi, Zakir Khan, Waqas Yousaf
Abstract	Previous works on segmentation of SEM (scanning electron microscope) blood cell image ignore the semantic segmentation approach of whole-slide blood cell segmentation. In the proposed work, we address the problem of whole-slide blood cell segmentation using the semantic segmentation approach. We design a novel convolutional encoder-decoder framework along with VGG-16 as the pixel-level feature extraction model. -e proposed framework comprises 3 main steps: First, all the original images along with manually generated ground truth masks of each blood cell type are passed through the preprocessing stage. In the preprocessing stage, pixel-level labeling, RGB to grayscale conversion of masked image and pixel fusing, and unity mask generation are performed. After that, VGG16 is loaded into the system, which acts as a pretrained pixel-level feature extraction model. In the third step, the training process is initiated on the proposed model. We have evaluated our network performance on three evaluation metrics. We obtained outstanding results with respect to classwise, as well as global and mean accuracies. Our system achieved classwise accuracies of 97.45%, 93.34%, and 85.11% for RBCs, WBCs, and platelets, respectively, while global and mean accuracies remain 97.18% and 91.96%, respectively.
Tasks	Cell Segmentation, Semantic Segmentation
Published	2020-01-28
URL	https://arxiv.org/abs/2001.10188v1
PDF	https://arxiv.org/pdf/2001.10188v1.pdf
PWC	https://paperswithcode.com/paper/robust-method-for-semantic-segmentation-of
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The Human Visual System and Adversarial AI


Title	The Human Visual System and Adversarial AI
Authors	Yaoshiang Ho, Samuel Wookey
Abstract	This paper applies theories about the Human Visual System to make Adversarial AI more effective. To date, Adversarial AI has modeled perceptual distances between clean and adversarial examples of images using Lp norms. These norms have the benefit of simple mathematical description and reasonable effectiveness in approximating perceptual distance. However, in prior decades, other areas of image processing have moved beyond simpler models like Mean Squared Error (MSE) towards more complex models that better approximate the Human Visual System (HVS). We demonstrate a proof of concept of incorporating HVS models into Adversarial AI.
Tasks
Published	2020-01-05
URL	https://arxiv.org/abs/2001.01172v2
PDF	https://arxiv.org/pdf/2001.01172v2.pdf
PWC	https://paperswithcode.com/paper/the-human-visual-system-and-adversarial-ai
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Framework

Imputing Missing Boarding Stations With Machine Learning Methods


Title	Imputing Missing Boarding Stations With Machine Learning Methods
Authors	Nadav Shalit, Michael Fire, Eran Ben Elia
Abstract	With the increase in population densities and environmental awareness, public transport has become an important aspect of urban life. Consequently, large quantities of transportation data are generated, and mining data from smart card use has become a standardized method to understand the travel habits of passengers. Public transport datasets, however, often may lack data integrity; boarding stop information may be missing due to either imperfect acquirement processes or inadequate reporting. As a result, large quantities of observations and even complete sections of cities might be absent from the smart card database. We have developed a machine (supervised) learning method to impute missing boarding stops based on ordinal classification. In addition, we present a new metric, Pareto Accuracy, to evaluate algorithms where classes have an ordinal nature. Results are based on a case study in the Israeli city of Beer Sheva for one month of data. We show that our proposed method significantly notably outperforms current imputation methods and can improve the accuracy and usefulness of large-scale transportation data.
Tasks	Imputation
Published	2020-03-10
URL	https://arxiv.org/abs/2003.05285v1
PDF	https://arxiv.org/pdf/2003.05285v1.pdf
PWC	https://paperswithcode.com/paper/imputing-missing-boarding-stations-with
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Novel Meta-Heuristic Model for Discrimination between Iron Deficiency Anemia and B-Thalassemia with CBC Indices Based on Dynamic Harmony Search


Title	Novel Meta-Heuristic Model for Discrimination between Iron Deficiency Anemia and B-Thalassemia with CBC Indices Based on Dynamic Harmony Search
Authors	Sultan Noman Qasem, Amir Mosavi
Abstract	In recent decades, attention has been directed at anemia classification for various medical purposes, such as thalassemia screening and predicting iron deficiency anemia (IDA). In this study, a new method has been successfully tested for discrimination between IDA and \b{eta}-thalassemia trait (\b{eta}-TT). The method is based on a Dynamic Harmony Search (DHS). Complete blood count (CBC), a fast and inexpensive laboratory test, is used as the input of the system. Other models, such as a genetic programming method called structured representation on genetic algorithm in non-linear function fitting (STROGANOFF), an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), a support vector machine (SVM), k-nearest neighbor (KNN), and certain traditional methods, are compared with the proposed method.
Tasks
Published	2020-03-03
URL	https://arxiv.org/abs/2004.00480v1
PDF	https://arxiv.org/pdf/2004.00480v1.pdf
PWC	https://paperswithcode.com/paper/novel-meta-heuristic-model-for-discrimination
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Framework

Distributed Learning in Ad-Hoc Networks: A Multi-player Multi-armed Bandit Framework


Title	Distributed Learning in Ad-Hoc Networks: A Multi-player Multi-armed Bandit Framework
Authors	Sumit J. Darak, Manjesh K. Hanawal
Abstract	Next-generation networks are expected to be ultra-dense with a very high peak rate but relatively lower expected traffic per user. For such scenario, existing central controller based resource allocation may incur substantial signaling (control communications) leading to a negative effect on the quality of service (e.g. drop calls), energy and spectrum efficiency. To overcome this problem, cognitive ad-hoc networks (CAHN) that share spectrum with other networks are being envisioned. They allow some users to identify and communicate in `free slots’ thereby reducing signaling load and allowing the higher number of users per base stations (dense networks). Such networks open up many interesting challenges such as resource identification, coordination, dynamic and context-aware adaptation for which Machine Learning and Artificial Intelligence framework offers novel solutions. In this paper, we discuss state-of-the-art multi-armed multi-player bandit based distributed learning algorithms that allow users to adapt to the environment and coordinate with other players/users. We also discuss various open research problems for feasible realization of CAHN and interesting applications in other domains such as energy harvesting, Internet of Things, and Smart grids. \|
Tasks
Published	2020-03-06
URL	https://arxiv.org/abs/2004.00367v1
PDF	https://arxiv.org/pdf/2004.00367v1.pdf
PWC	https://paperswithcode.com/paper/distributed-learning-in-ad-hoc-networks-a
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Neuro-symbolic Architectures for Context Understanding


Title	Neuro-symbolic Architectures for Context Understanding
Authors	Alessandro Oltramari, Jonathan Francis, Cory Henson, Kaixin Ma, Ruwan Wickramarachchi
Abstract	Computational context understanding refers to an agent’s ability to fuse disparate sources of information for decision-making and is, therefore, generally regarded as a prerequisite for sophisticated machine reasoning capabilities, such as in artificial intelligence (AI). Data-driven and knowledge-driven methods are two classical techniques in the pursuit of such machine sense-making capability. However, while data-driven methods seek to model the statistical regularities of events by making observations in the real-world, they remain difficult to interpret and they lack mechanisms for naturally incorporating external knowledge. Conversely, knowledge-driven methods, combine structured knowledge bases, perform symbolic reasoning based on axiomatic principles, and are more interpretable in their inferential processing; however, they often lack the ability to estimate the statistical salience of an inference. To combat these issues, we propose the use of hybrid AI methodology as a general framework for combining the strengths of both approaches. Specifically, we inherit the concept of neuro-symbolism as a way of using knowledge-bases to guide the learning progress of deep neural networks. We further ground our discussion in two applications of neuro-symbolism and, in both cases, show that our systems maintain interpretability while achieving comparable performance, relative to the state-of-the-art.
Tasks	Decision Making
Published	2020-03-09
URL	https://arxiv.org/abs/2003.04707v1
PDF	https://arxiv.org/pdf/2003.04707v1.pdf
PWC	https://paperswithcode.com/paper/neuro-symbolic-architectures-for-context
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Framework

Adaptive Experimental Design for Efficient Treatment Effect Estimation: Randomized Allocation via Contextual Bandit Algorithm


Title	Adaptive Experimental Design for Efficient Treatment Effect Estimation: Randomized Allocation via Contextual Bandit Algorithm
Authors	Masahiro Kato, Takuya Ishihara, Junya Honda, Yusuke Narita
Abstract	Many scientific experiments have an interest in the estimation of the average treatment effect (ATE), which is defined as the difference between the expected outcomes of two or more treatments. In this paper, we consider a situation called adaptive experimental design where research subjects sequentially visit a researcher, and the researcher assigns a treatment. For estimating the ATE efficiently, we consider changing the probability of assigning a treatment at a period by using past information obtained until the period. However, in this approach, it is difficult to apply the standard statistical method to construct an estimator because the observations are not independent and identically distributed. In this paper, to construct an efficient estimator, we overcome this conventional problem by using an algorithm of the multi-armed bandit problem and the theory of martingale. In the proposed method, we use the probability of assigning a treatment that minimizes the asymptotic variance of an estimator of the ATE. We also elucidate the theoretical properties of an estimator obtained from the proposed algorithm for both infinite and finite samples. Finally, we experimentally show that the proposed algorithm outperforms the standard RCT in some cases.
Tasks
Published	2020-02-13
URL	https://arxiv.org/abs/2002.05308v1
PDF	https://arxiv.org/pdf/2002.05308v1.pdf
PWC	https://paperswithcode.com/paper/adaptive-experimental-design-for-efficient
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Framework

NN-PARS: A Parallelized Neural Network Based Circuit Simulation Framework


Title	NN-PARS: A Parallelized Neural Network Based Circuit Simulation Framework
Authors	Mohammad Saeed Abrishami, Hao Ge, Justin F. Calderon, Massoud Pedram, Shahin Nazarian
Abstract	The shrinking of transistor geometries as well as the increasing complexity of integrated circuits, significantly aggravate nonlinear design behavior. This demands accurate and fast circuit simulation to meet the design quality and time-to-market constraints. The existing circuit simulators which utilize lookup tables and/or closed-form expressions are either slow or inaccurate in analyzing the nonlinear behavior of designs with billions of transistors. To address these shortcomings, we present NN-PARS, a neural network (NN) based and parallelized circuit simulation framework with optimized event-driven scheduling of simulation tasks to maximize concurrency, according to the underlying GPU parallel processing capabilities. NN-PARS replaces the required memory queries in traditional techniques with parallelized NN-based computation tasks. Experimental results show that compared to a state-of-the-art current-based simulation method, NN-PARS reduces the simulation time by over two orders of magnitude in large circuits. NN-PARS also provides high accuracy levels in signal waveform calculations, with less than $2%$ error compared to HSPICE.
Tasks
Published	2020-02-13
URL	https://arxiv.org/abs/2002.05292v1
PDF	https://arxiv.org/pdf/2002.05292v1.pdf
PWC	https://paperswithcode.com/paper/nn-pars-a-parallelized-neural-network-based
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Framework

Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction


Title	Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction
Authors	Eunji Jun, Ahmad Wisnu Mulyadi, Jaehun Choi, Heung-Il Suk
Abstract	Electronic health records (EHR) are characterized as non-stationary, heterogeneous, noisy, and sparse data; therefore, it is challenging to learn the regularities or patterns inherent within them. In particular, sparseness caused mostly by many missing values has attracted the attention of researchers, who have attempted to find a better use of all available samples for determining the solution of a primary target task through the defining a secondary imputation problem. Methodologically, existing methods, either deterministic or stochastic, have applied different assumptions to impute missing values. However, once the missing values are imputed, most existing methods do not consider the fidelity or confidence of the imputed values in the modeling of downstream tasks. Undoubtedly, an erroneous or improper imputation of missing variables can cause difficulties in modeling as well as a degraded performance. In this study, we present a novel variational recurrent network that (i) estimates the distribution of missing variables allowing to represent uncertainty in the imputed values, (ii) updates hidden states by explicitly applying fidelity based on a variance of the imputed values during a recurrence (i.e., uncertainty propagation over time), and (iii) predicts the possibility of in-hospital mortality. It is noteworthy that our model can conduct these procedures in a single stream and learn all network parameters jointly in an end-to-end manner. We validated the effectiveness of our method using the public datasets of MIMIC-III and PhysioNet challenge 2012 by comparing with and outperforming other state-of-the-art methods for mortality prediction considered in our experiments. In addition, we identified the behavior of the model that well represented the uncertainties for the imputed estimates, which indicated a high correlation between the calculated MAE and the uncertainty.
Tasks	Imputation, Mortality Prediction
Published	2020-03-02
URL	https://arxiv.org/abs/2003.00655v1
PDF	https://arxiv.org/pdf/2003.00655v1.pdf
PWC	https://paperswithcode.com/paper/uncertainty-gated-stochastic-sequential-model
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Framework

Multiscale Non-stationary Stochastic Bandits


Title	Multiscale Non-stationary Stochastic Bandits
Authors	Qin Ding, Cho-Jui Hsieh, James Sharpnack
Abstract	Classic contextual bandit algorithms for linear models, such as LinUCB, assume that the reward distribution for an arm is modeled by a stationary linear regression. When the linear regression model is non-stationary over time, the regret of LinUCB can scale linearly with time. In this paper, we propose a novel multiscale changepoint detection method for the non-stationary linear bandit problems, called Multiscale-LinUCB, which actively adapts to the changing environment. We also provide theoretical analysis of regret bound for Multiscale-LinUCB algorithm. Experimental results show that our proposed Multiscale-LinUCB algorithm outperforms other state-of-the-art algorithms in non-stationary contextual environments.
Tasks
Published	2020-02-13
URL	https://arxiv.org/abs/2002.05289v1
PDF	https://arxiv.org/pdf/2002.05289v1.pdf
PWC	https://paperswithcode.com/paper/multiscale-non-stationary-stochastic-bandits
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Framework

Uncertainty-Aware Variational-Recurrent Imputation Network for Clinical Time Series


Title	Uncertainty-Aware Variational-Recurrent Imputation Network for Clinical Time Series
Authors	Ahmad Wisnu Mulyadi, Eunji Jun, Heung-Il Suk
Abstract	Electronic health records (EHR) consist of longitudinal clinical observations portrayed with sparsity, irregularity, and high-dimensionality, which become major obstacles in drawing reliable downstream clinical outcomes. Although there exist great numbers of imputation methods to tackle these issues, most of them ignore correlated features, temporal dynamics and entirely set aside the uncertainty. Since the missing value estimates involve the risk of being inaccurate, it is appropriate for the method to handle the less certain information differently than the reliable data. In that regard, we can use the uncertainties in estimating the missing values as the fidelity score to be further utilized to alleviate the risk of biased missing value estimates. In this work, we propose a novel variational-recurrent imputation network, which unifies an imputation and a prediction network by taking into account the correlated features, temporal dynamics, as well as the uncertainty. Specifically, we leverage the deep generative model in the imputation, which is based on the distribution among variables, and a recurrent imputation network to exploit the temporal relations, in conjunction with utilization of the uncertainty. We validated the effectiveness of our proposed model on two publicly available real-world EHR datasets: PhysioNet Challenge 2012 and MIMIC-III, and compared the results with other competing state-of-the-art methods in the literature.
Tasks	Imputation, Time Series
Published	2020-03-02
URL	https://arxiv.org/abs/2003.00662v1
PDF	https://arxiv.org/pdf/2003.00662v1.pdf
PWC	https://paperswithcode.com/paper/uncertainty-aware-variational-recurrent-1
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Framework

Unsupervised text line segmentation


Title	Unsupervised text line segmentation
Authors	Berat Kurar Barakat, Ahmad Droby, Rym Alasam, Boraq Madi, Irina Rabaev, Raed Shammes, Jihad El-Sana
Abstract	We present an unsupervised text line segmentation method that is inspired by the relative variance between text lines and spaces among text lines. Handwritten text line segmentation is important for the efficiency of further processing. A common method is to train a deep learning network for embedding the document image into an image of blob lines that are tracing the text lines. Previous methods learned such embedding in a supervised manner, requiring the annotation of many document images. This paper presents an unsupervised embedding of document image patches without a need for annotations. The main idea is that the number of foreground pixels over the text lines is relatively different from the number of foreground pixels over the spaces among text lines. Generating similar and different pairs relying on this principle definitely leads to outliers. However, as the results show, the outliers do not harm the convergence and the network learns to discriminate the text lines from the spaces between text lines. We experimented with a challenging Arabic handwritten text line segmentation dataset, VML-AHTE, and achieved a superior performance even over the supervised methods.
Tasks
Published	2020-03-19
URL	https://arxiv.org/abs/2003.08632v1
PDF	https://arxiv.org/pdf/2003.08632v1.pdf
PWC	https://paperswithcode.com/paper/unsupervised-text-line-segmentation
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Framework

MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models


Title	MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models
Authors	Imke Mayer, Julie Josse, Félix Raimundo, Jean-Philippe Vert
Abstract	Inferring causal effects of a treatment, intervention or policy from observational data is central to many applications. However, state-of-the-art methods for causal inference seldom consider the possibility that covariates have missing values, which is ubiquitous in many real-world analyses. Missing data greatly complicate causal inference procedures as they require an adapted unconfoundedness hypothesis which can be difficult to justify in practice. We circumvent this issue by considering latent confounders whose distribution is learned through variational autoencoders adapted to missing values. They can be used either as a pre-processing step prior to causal inference but we also suggest to embed them in a multiple imputation strategy to take into account the variability due to missing values. Numerical experiments demonstrate the effectiveness of the proposed methodology especially for non-linear models compared to competitors.
Tasks	Causal Inference, Imputation, Latent Variable Models
Published	2020-02-25
URL	https://arxiv.org/abs/2002.10837v1
PDF	https://arxiv.org/pdf/2002.10837v1.pdf
PWC	https://paperswithcode.com/paper/missdeepcausal-causal-inference-from-1
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Framework