pwc-archive/papers-with-abstracts huggingface dataset:
A CNN Accelerator on FPGA Using Depthwise Separable Convolution
Authors: Lin Bai, Yiming Zhao, Xinming Huang
Date: 2018-09-03 00:00:00
Tasks: CPU, GPU

Convolutional neural networks (CNNs) have been widely deployed in the fields of computer vision and pattern recognition because of their high accuracy. However, large convolution operations are computing-intensive that often requires a powerful computing platform such as Graphics Processing Unit (GPU). This makes it difficult to apply CNNs to portable devices. The state-of-the-art CNNs, such as MobileNetV2 and Xception, adopt depthwise separable convolution to replace the standard convolution for embedded platforms. That significantly reduces operations and parameters with only limited loss in accuracy. This highly structured model is very suitable for Field-Programmable Gate Array (FPGA) implementation. In this paper, a scalable high performance depthwise separable convolution optimized CNN accelerator is proposed. The accelerator can be fit into an FPGA of different sizes, provided the balancing between hardware resources and processing speed. As an example, MobileNetV2 is implemented on Arria 10 SoC FPGA, and the results show this accelerator can classify each picture from ImageNet in 3.75ms, which is about 266.6 frames per second. This achieves 20x speedup if compared to CPU.
E2GAN: End-to-End Generative Adversarial Network or Multivariate Time Series Imputation
Authors: Yonghong Luo, Ying Zhang, Xiangrui Cai, Xiaojie Yuan
Date: 2019-08-10 00:00:00
Proceeding: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19) 2019 8
Tasks: Generative Adversarial Network, Imputation, Missing Values, Multivariate Time Series Imputation, Time Series, Time Series Analysis

The missing values, appear in most of multivariate time series, prevent advanced analysis of multivariate time series data. Existing imputation approaches try to deal with missing values by deletion, statistical imputation, machine learning based imputation and generative imputation. However, these methods are either incapable of dealing with temporal information or multi-stage. This paper proposes an end-to-end generative model E2GAN to impute missing values in multivariate time series. With the help of the discriminative loss and the squared error loss, E2GAN can imputethe incomplete time series by the nearest generated complete time series at one stage. Experiments on multiple real-world datasets show that our model outperforms the baselines on the imputation accuracy and achieves state-of-the-art classification/regression results on the downstream applications. Additionally, our method also gains better time efficiency than multi-stage method on the training of neural networks.
Multiple imputation using chained equations: issues and guidance for practice
Authors: Ian R. White, Patrick Royston, Angela M. Wood
Date: 2010-11-30 00:00:00
Proceeding: Statistics in medicine 30(4):377–399, 2011 2010 11
Tasks: Imputation, Multivariate Time Series Imputation

Multiple imputation by chained equations (MICE) is a flexible and practical approach to handling missing data. We describe the principles of the method and show how to impute categorical and quantitative variables, including skewed variables. We give guidance on how to specify the imputation model and how many imputations are needed. We describe the practical analysis of multiply imputed data, including model building and model checking. We stress the limitations of the method and discuss the possible pitfalls. We illustrate the ideas using a data set in mental health, giving Stata code fragments.
ST-MVL: Filling Missing Values in Geo-Sensory Time Series Data
Authors: Xiuwen Yi, Yu Zheng, Junbo Zhang, Tianrui Li
Date: 2016-07-09 00:00:00
Proceeding: IJCAI 2016 2016 7
Tasks: Collaborative Filtering, Imputation, Missing Values, Multivariate Time Series Imputation, MULTI-VIEW LEARNING, Time Series, Time Series Analysis

Many sensors have been deployed in the physical world, generating massive geo-tagged time series data. In reality, readings of sensors are usually lost at various unexpected moments because of sensor or communication errors. Those missing readings do not only affect real-time monitoring but also compromise the performance of further data analysis. In this paper, we propose a spatio-temporal multi-view-based learning (ST-MVL) method to collectively fill missing readings in a collection of geosensory time series data, considering 1) the temporal correlation between readings at different timestamps in the same series and 2) the spatial correlation between different time series. Our method combines empirical statistic models, consisting of Inverse Distance Weighting and Simple Exponential Smoothing, with data-driven algorithms, comprised of User-based and Item-based Collaborative Filtering. The former models handle general missing cases based on empirical assumptions derived from history data over a long period, standing for two global views from spatial and temporal perspectives respectively. The latter algorithms deal with special cases where empirical assumptions may not hold, based on recent contexts of data, denoting two local views from spatial and temporal perspectives respectively. The predictions of the four views are aggregated to a final value in a multi-view learning algorithm. We evaluate our method based on Beijing air quality and meteorological data, finding advantages to our model compared with ten baseline approaches.
imputeTS: Time Series Missing Value Imputation in R
Authors: Steffen Moritz, Thomas Bartz-Beielstein
Date: 2017-06-01 00:00:00
Proceeding: The R Journal 9(1) 2017 6
Tasks: Attribute, Imputation, Missing Values, Multivariate Time Series Imputation, Time Series, Time Series Analysis

The imputeTS package specializes on univariate time series imputation. It offers multiple state-of-the-art imputation algorithm implementations along with plotting functions for time series missing data statistics. While imputation in general is a well-known problem and widely covered by R packages, finding packages able to fill missing values in univariate time series is more complicated. The reason for this lies in the fact that most imputation algorithms rely on inter-attribute correlations, while univariate time series imputation instead needs to employ time dependencies. This paper provides an introduction to the imputeTS package and its provided algorithms and tools. Furthermore, it gives a short overview about univariate time series imputation in R.
Patient Subtyping via Time-Aware LSTM Networks
Authors: Inci M. Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil K. Jain, Jiayu Zhou
Date: 2017-08-13 00:00:00
Proceeding: KDD '17 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2017 8
Tasks: Multivariate Time Series Forecasting

In the study of various diseases, heterogeneity among patients usually leads to different progression patterns and may require different types of therapeutic intervention. Therefore, it is important to study patient subtyping, which is grouping of patients into disease characterizing subtypes. Subtyping from complex patient data is challenging because of the information heterogeneity and temporal dynamics. Long-Short Term Memory (LSTM) has been successfully used in many domains for processing sequential data, and recently applied for analyzing longitudinal patient records. The LSTM units are designed to handle data with constant elapsed times between consecutive elements of a sequence. Given that time lapse between successive elements in patient records can vary from days to months, the design of traditional LSTM may lead to suboptimal performance. In this paper, we propose a novel LSTM unit called Time-Aware LSTM (T-LSTM) to handle irregular time intervals in longitudinal patient records. We learn a subspace decomposition of the cell memory which enables time decay to discount the memory content according to the elapsed time. We propose a patient subtyping model that leverages the proposed T-LSTM in an auto-encoder to learn a powerful single representation for sequential records of patients, which are then used to cluster patients into clinical subtypes. Experiments on synthetic and real world datasets show that the proposed T-LSTM architecture captures the underlying structures in the sequences with time irregularities.
Nuclei Segmentation via a Deep Panoptic Model with Semantic Feature Fusion
Authors: Dongnan Liu, Donghao Zhang, Yang song, Chaoyi Zhang, Fan Zhang, Lauren O’Donnell, Weidong Cai
Date: 2019-08-10 00:00:00
Proceeding: International Joint Conference on Artificial Intelligence (IJCAI-19) 2019 8
Tasks: Instance Segmentation, Nuclear Segmentation, Panoptic Segmentation, Prognosis, Segmentation, Semantic Segmentation

Automated detection and segmentation of individual nuclei in histopathology images is important for cancer diagnosis and prognosis. Due to the high variability of nuclei appearances and numerous overlapping objects, this task still remains challenging. 
 eep learning based semantic and instance segmentation models have been proposed to address the challenges, but these methods tend to concentrate on either the global or local features and hence still suffer from information loss. In this work, we propose a panoptic segmentation model which incorporates an auxiliary semantic segmentation branch with the instance branch 
to integrate global and local features. Furthermore, we design a feature map fusion mechanism in the instance branch and a new mask generator to prevent information loss. Experimental results on three different histopathology datasets demonstrate that our method outperforms the state-of-the-art nuclei segmentation methods and popular semantic and instance segmentation models by a large margin.
Panoptic Segmentation with an End-to-End Cell R-CNN for Pathology Image Analysis
Authors: Donghao Zhang, Yang song, Dongnan Liu, Haozhe Jia, Si-Qi Liu, Yong Xia, Heng Huang, Weidong Cai
Date: 2018-09-28 00:00:00
Proceeding: MICCAI 2018 2018 9
Tasks: Cell Segmentation, Nuclear Segmentation, Panoptic Segmentation, Region Proposal, Segmentation

The morphological clues of various cancer cells are essential for pathologists to determine the stages of cancers. In order to obtain the quantitative morphological information, we present an end-to-end network for panoptic segmentation of pathology images. Recently, many methods have been proposed, focusing on the semantic-level or instance-level cell segmentation. Unlike existing cell segmentation methods, the proposed network unifies detecting, localizing objects and assigning pixel-level class information to regions with large overlaps such as the background. This unifier is obtained by optimizing the novel semantic loss, the bounding box loss of Region Proposal Network (RPN), the classifier loss of RPN, the background-foreground classifier loss of segmentation Head instead of class-specific loss, the bounding box loss of proposed cell object, and the mask loss of cell object. The results demonstrate that the proposed method not only outperforms state-of-the-art approaches to the 2017 MICCAI Digital Pathology Challenge dataset, but also proposes an effective and end-to-end solution for the panoptic segmentation challenge.
Dense Transformer Networks for Brain Electron Microscopy Image Segmentation
Authors: Jun Li, Yongjun Chen, Lei Cai, Ian Davidson, Shuiwang Ji
Date: 2019-08-10 00:00:00
Proceeding: Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19) 2019 8
Tasks: Decoder, Electron Microscopy Image Segmentation, Image Segmentation, Semantic Segmentation

The key idea of current deep learning methods for dense prediction is to apply a model on a regular patch centered on each pixel to make pixel-wise predictions. These methods are limited in the sense that the patches are determined by network architecture instead of learned from data. In this work, we propose the dense transformer networks, which can learn the shapes and sizes of patches from data. The dense transformer networks employ an encoder-decoder architecture, and a pair of dense transformer modules are inserted into each of the encoder and decoder paths. The novelty of this work is that we provide technical solutions for learning the shapes and sizes of patches from data and efficiently restoring the spatial correspondence required for dense prediction. The proposed dense transformer modules are differentiable, thus the entire network can be trained. We apply the proposed networks on biological image segmentation tasks and show superior performance is achieved in comparison to baseline methods.
Long-term 3D Localization and Pose from Semantic Labellings
Authors: Carl Toft1, Carl Olsson1;2, Fredrik Kahl1;2
Date: 2017-08-01 00:00:00
Proceeding: ICCV 2017 8
Tasks: Camera Pose Estimation, Pose Estimation

One of the major challenges in camera pose estimation
and 3D localization is identifying features that are approximately invariant across seasons and in different weather
and lighting conditions. In this paper, we present a method
for performing accurate and robust six degrees-of-freedom
camera pose estimation based only on the pixelwise semantic labelling of a single query image. Localization is performed using a sparse 3D model consisting of semantically
labelled points and curves, and an error function based on
how well these project onto corresponding curves in the
query image is developed. The method is evaluated on the
recently released Oxford Robotcar dataset, showing that by
minimizing this error function, the pose can be recovered
with decimeter accuracy in many cases.
Evaluating shallow and deep learning strategies for the 2018 n2c2 shared task on clinical text classification
Authors: Michel Oleynik, Amila Kugic, Zdenko Kasáč, Markus Kreuzthaler
Date: 2019-09-12 00:00:00
Proceeding: Journal of the American Medical Informatics Association 2019 9
Tasks: BIG-bench Machine Learning, Binary text classification, Deep Learning, General Classification, Negation, regression, text-classification, Text Classification, Word Embeddings

Objective: Automated clinical phenotyping is challenging because word-based features quickly turn it into a high-dimensional problem, in which the small, privacy-restricted, training datasets might lead to overfitting. Pretrained embeddings might solve this issue by reusing input representation schemes trained on a larger dataset. We sought to evaluate shallow and deep learning text classifiers and the impact of pretrained embeddings in a small clinical dataset.
Materials and Methods: We participated in the 2018 National NLP Clinical Challenges (n2c2) Shared Task on cohort selection and received an annotated dataset with medical narratives of 202 patients for multilabel binary text classification. We set our baseline to a majority classifier, to which we compared a rule-based classifier and orthogonal machine learning strategies: support vector machines, logistic regression, and long short-term memory neural networks. We evaluated logistic regression and long short-term memory using both self-trained and pretrained BioWordVec word embeddings as input representation schemes.
Results: Rule-based classifier showed the highest overall micro F1 score (0.9100), with which we finished first in the challenge. Shallow machine learning strategies showed lower overall micro F1 scores, but still higher than deep learning strategies and the baseline. We could not show a difference in classification efficiency between self-trained and pretrained embeddings.
Discussion: Clinical context, negation, and value-based criteria hindered shallow machine learning approaches, while deep learning strategies could not capture the term diversity due to the small training dataset.
Conclusion: Shallow methods for clinical phenotyping can still outperform deep learning methods in small imbalanced data, even when supported by pretrained embeddings.
UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images
Authors: Hidetoshi Urakubo, Torsten Bullmann, Yoshiyuki Kubota, Shigeyuki Oba, Shin Ishii
Date: 2019-04-12 00:00:00
Proceeding: bioRxiv Neuroscience 2019 4
Tasks: 3D Reconstruction, Electron Microscopy Image Segmentation, Image Segmentation, Segmentation, Semantic Segmentation

Recently, there has been a rapid expansion in the field of micro-connectomics, which targets the three-dimensional (3D) reconstruction of neuronal networks from a stack of two-dimensional (2D) electron microscopic (EM) images. The spatial scale of the 3D reconstruction grows rapidly owing to deep neural networks (DNNs) that enable automated image segmentation. Several research teams have developed their own software pipelines for DNN-based segmentation. However, the complexity of such pipelines makes their use difficult even for computer experts and impossible for non-experts. In this study, we developed a new software program, called UNI-EM, that enables 2D- and 3D-DNN-based segmentation for non-computer experts. UNI-EM is a software collection for DNN-based EM image segmentation, including ground truth generation, training, inference, postprocessing, proofreading, and visualization. UNI-EM comes with a set of 2D DNNs, i.e., U-Net, ResNet, HighwayNet, and DenseNet. We further wrapped flood-filling networks (FFNs) as a representative 3D DNN-based neuron segmentation algorithm. The 2D- and 3D-DNNs are known to show state-of-the-art level segmentation performance. We then provided two-example workflows: mitochondria segmentation using a 2D DNN as well as neuron segmentation using FFNs. Following these example workflows, users can benefit from DNN-based segmentation without any knowledge of Python programming or DNN frameworks.
High-Precision Automated Reconstruction of Neurons with Flood-filling Networks
Authors: Michał Januszewski, Jörgen Kornfeld, Peter H. Li, Art Pope, Tim Blakely, Larry Lindsey, Jeremy Maitin-Shepard, Mike Tyka, Winfried Denk, Viren Jain
Date: 2017-10-09 00:00:00
Proceeding: Nature Methods 2017 10
Tasks: Electron Microscopy Image Segmentation, Image Reconstruction, Vocal Bursts Intensity Prediction

Reconstruction of neural circuits from volume electron microscopy data requires the tracing of complete cells including all their neurites. Automated approaches have been developed to perform the tracing, but without costly human proofreading their error rates are too high to obtain reliable circuit diagrams. We present a method for automated segmentation that, like the majority of previous efforts, employs convolutional neural networks, but contains in addition a recurrent pathway that allows the iterative optimization and extension of the reconstructed shape of individual neural processes. We used this technique, which we call flood-filling networks, to trace neurons in a data set obtained by serial block-face electron microscopy from a male zebra finch brain. Our method achieved a mean error-free neurite path length of 1.1 mm, an order of magnitude better than previously published approaches applied to the same dataset. Only 4 mergers were observed in a neurite test set of 97 mm path length.
TeraVR empowers precise reconstruction of complete 3-D neuronal morphology in the whole brain
Authors: Yimin Wang, Qi Li, Li-Juan Liu, Zhi Zhou, Zongcai Ruan, Lingsheng Kong, Yaoyao Li, Yun Wang, Ning Zhong, Renjie Chai, Xiangfeng Luo, Yike Guo, Michael Hawrylycz, Qingming Luo, Zhongze Gu, Wei Xie, Hongkui Zeng, Hanchuan Peng
Date: 2019-08-02 00:00:00
Proceeding: Nature Communicationsvolume 10, Article number: 3474 (2019) 2019 8
Tasks: Electron Microscopy Image Segmentation, Image Reconstruction

Neuron morphology is recognized as a key determinant of cell type, yet the quantitative profiling of a mammalian neuron’s complete three-dimensional (3-D) morphology remains arduous when the neuron has complex arborization and long projection. Whole-brain reconstruction of neuron morphology is even more challenging as it involves processing tens of teravoxels of imaging data. Validating such reconstructions is extremely laborious. We develop TeraVR, an open-source virtual reality annotation system, to address these challenges. TeraVR integrates immersive and collaborative 3-D visualization, interaction, and hierarchical streaming of teravoxel-scale images. Using TeraVR, we have produced precise 3-D full morphology of long-projecting neurons in whole mouse brains and developed a collaborative workflow for highly accurate neuronal reconstruction.
Recognizing Object Affordances to Support Scene Reasoning for Manipulation Tasks
Authors: Fu-Jen Chu, Ruinian Xu, Chao Tang, Patricio A. Vela
Date: 2019-09-12 00:00:00
Tasks: Affordance Detection, Affordance Recognition, Benchmarking, Object, Region Proposal, Robot Manipulation

Affordance information about a scene provides important clues as to what actions may be executed in pursuit of meeting a specified goal state. Thus, integrating affordance-based reasoning into symbolic action plannning pipelines would enhance the flexibility of robot manipulation. Unfortunately, the top performing affordance recognition methods use object category priors to boost the accuracy of affordance detection and segmentation. Object priors limit generalization to unknown object categories. This paper describes an affordance recognition pipeline based on a category-agnostic region proposal network for proposing instance regions of an image across categories. To guide affordance learning in the absence of category priors, the training process includes the auxiliary task of explicitly inferencing existing affordances within a proposal. Secondly, a self-attention mechanism trained to interpret each proposal learns to capture rich contextual dependencies through the region. Visual benchmarking shows that the trained network, called AffContext, reduces the performance gap between object-agnostic and object-informed affordance recognition. AffContext is linked to the Planning Domain Definition Language (PDDL) with an augmented state keeper for action planning across temporally spaced goal-oriented tasks. Manipulation experiments show that AffContext can successfully parse scene content to seed a symbolic planner problem specification, whose execution completes the target task. Additionally, task-oriented grasping for cutting and pounding actions demonstrate the exploitation of multiple affordances for a given object to complete specified tasks.
Modeling and inference of spatio-temporal protein dynamics across brain networks
Authors: Sara Garbarino, Marco Lorenzi
Date: 2019-01-18 00:00:00
Tasks: Uncertainty Quantification, Variational Inference

Models of misfolded proteins (MP) aim at discovering the bio-mechanical propagation properties of neurological diseases (ND) by identifying plausible associated dynamical systems. Solving these systems along the full disease trajectory is usually challenging, due to the lack of a well defined time axis for the pathology. This issue is addressed by disease progression models (DPM) where long-term progression trajectories are estimated via time reparametrization of individual observations. However, due to their loose assumptions on the dynamics, DPM do not provide insights on the bio-mechanical properties of MP propagation. Here we propose a unified model of spatio-temporal protein dynamics based on the joint estimation of long-term MP dynamics and time reparameterization of individuals observations. The model is expressed within a Gaussian Process (GP) regression setting, where constraints on the MP dynamics are imposed through non--linear dynamical systems. We use stochastic variational inference on both GP and dynamical system parameters for scalable inference and uncertainty quantification of the trajectories. Experiments on simulated data show that our model accurately recovers prescribed rates along graph dynamics and precisely reconstructs the underlying progression. When applied to brain imaging data our model allows the bio-mechanical interpretation of amyloid deposition in Alzheimer's disease, leading to plausible simulations of MP propagation, and achieving accurate predictions of individual MP deposition in unseen data.