pwc-archive/papers-with-abstracts huggingface dataset:
Integrated Construction of Multimodal Atlases with Structural Connectomes in the Space of Riemannian Metrics
Authors: Kristen M. Campbell, Haocheng Dai, Zhe Su, Martin Bauer, P. Thomas Fletcher, Sarang C. Joshi
Date: 2021-09-20 00:00:00

The structural network of the brain, or structural connectome, can be represented by fiber bundles generated by a variety of tractography methods. While such methods give qualitative insights into brain structure, there is controversy over whether they can provide quantitative information, especially at the population level. In order to enable population-level statistical analysis of the structural connectome, we propose representing a connectome as a Riemannian metric, which is a point on an infinite-dimensional manifold. We equip this manifold with the Ebin metric, a natural metric structure for this space, to get a Riemannian manifold along with its associated geometric properties. We then use this Riemannian framework to apply object-oriented statistical analysis to define an atlas as the Fr\'echet mean of a population of Riemannian metrics. This formulation ties into the existing framework for diffeomorphic construction of image atlases, allowing us to construct a multimodal atlas by simultaneously integrating complementary white matter structure details from DWMRI and cortical details from T1-weighted MRI. We illustrate our framework with 2D data examples of connectome registration and atlas formation. Finally, we build an example 3D multimodal atlas using T1 images and connectomes derived from diffusion tensors estimated from a subset of subjects from the Human Connectome Project.
An Optimal Control Framework for Joint-channel Parallel MRI Reconstruction without Coil Sensitivities
Authors: Wanyu Bian, YunMei Chen, Xiaojing Ye
Date: 2021-09-20 00:00:00
Tasks: Image Reconstruction, MRI Reconstruction

Goal: This work aims at developing a novel calibration-free fast parallel MRI (pMRI) reconstruction method incorporate with discrete-time optimal control framework. The reconstruction model is designed to learn a regularization that combines channels and extracts features by leveraging the information sharing among channels of multi-coil images. We propose to recover both magnitude and phase information by taking advantage of structured convolutional networks in image and Fourier spaces. Methods: We develop a novel variational model with a learnable objective function that integrates an adaptive multi-coil image combination operator and effective image regularization in the image and Fourier spaces. We cast the reconstruction network as a structured discrete-time optimal control system, resulting in an optimal control formulation of parameter training where the parameters of the objective function play the role of control variables. We demonstrate that the Lagrangian method for solving the control problem is equivalent to back-propagation, ensuring the local convergence of the training algorithm. Results: We conduct a large number of numerical experiments of the proposed method with comparisons to several state-of-the-art pMRI reconstruction networks on real pMRI datasets. The numerical results demonstrate the promising performance of the proposed method evidently. Conclusion: We conduct a large number of numerical experiments of the proposed method with comparisons to several state-of-the-art pMRI reconstruction networks on real pMRI datasets. The numerical results demonstrate the promising performance of the proposed method evidently. Significance: By learning multi-coil image combination operator and performing regularizations in both image domain and k-space domain, the proposed method achieves a highly efficient image reconstruction network for pMRI.
Unsupervised Domain Adaptation with Semantic Consistency across Heterogeneous Modalities for MRI Prostate Lesion Segmentation
Authors: Eleni Chiou, Francesco Giganti, Shonit Punwani, Iasonas Kokkinos, Eleftheria Panagiotaki
Date: 2021-09-19 00:00:00
Tasks: Domain Adaptation, Lesion Segmentation, Translation, Unsupervised Domain Adaptation

Any novel medical imaging modality that differs from previous protocols e.g. in the number of imaging channels, introduces a new domain that is heterogeneous from previous ones. This common medical imaging scenario is rarely considered in the domain adaptation literature, which handles shifts across domains of the same dimensionality. In our work we rely on stochastic generative modeling to translate across two heterogeneous domains at pixel space and introduce two new loss functions that promote semantic consistency. Firstly, we introduce a semantic cycle-consistency loss in the source domain to ensure that the translation preserves the semantics. Secondly, we introduce a pseudo-labelling loss, where we translate target data to source, label them by a source-domain network, and use the generated pseudo-labels to supervise the target-domain network. Our results show that this allows us to extract systematically better representations for the target domain. In particular, we address the challenge of enhancing performance on VERDICT-MRI, an advanced diffusion-weighted imaging technique, by exploiting labeled mp-MRI data. When compared to several unsupervised domain adaptation approaches, our approach yields substantial improvements, that consistently carry over to the semi-supervised and supervised learning settings.
Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling
Authors: Cheng Chen, Quande Liu, Yueming Jin, Qi Dou, Pheng-Ann Heng
Date: 2021-09-19 00:00:00
Tasks: Denoising, Domain Adaptation, Image Segmentation, Semantic Segmentation, Unsupervised Domain Adaptation

Domain adaptation typically requires to access source domain data to utilize their distribution information for domain alignment with the target data. However, in many real-world scenarios, the source data may not be accessible during the model adaptation in the target domain due to privacy issue. This paper studies the practical yet challenging source-free unsupervised domain adaptation problem, in which only an existing source model and the unlabeled target data are available for model adaptation. We present a novel denoised pseudo-labeling method for this problem, which effectively makes use of the source model and unlabeled target data to promote model self-adaptation from pseudo labels. Importantly, considering that the pseudo labels generated from source model are inevitably noisy due to domain shift, we further introduce two complementary pixel-level and class-level denoising schemes with uncertainty estimation and prototype estimation to reduce noisy pseudo labels and select reliable ones to enhance the pseudo-labeling efficacy. Experimental results on cross-domain fundus image segmentation show that without using any source images or altering source training, our approach achieves comparable or even higher performance than state-of-the-art source-dependent unsupervised domain adaptation methods.
MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentation
Authors: Anastasia Makarevich, Azade Farshad, Vasileios Belagiannis, Nassir Navab
Date: 2021-09-18 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Meta-Learning, Organ Segmentation, Semantic Segmentation

The lack of sufficient annotated image data is a common issue in medical image segmentation. For some organs and densities, the annotation may be scarce, leading to poor model training convergence, while other organs have plenty of annotated data. In this work, we present MetaMedSeg, a gradient-based meta-learning algorithm that redefines the meta-learning task for the volumetric medical data with the goal to capture the variety between the slices. We also explore different weighting schemes for gradients aggregation, arguing that different tasks might have different complexity, and hence, contribute differently to the initialization. We propose an importance-aware weighting scheme to train our model. In the experiments, we present an evaluation of the medical decathlon dataset by extracting 2D slices from CT and MRI volumes of different organs and performing semantic segmentation. The results show that our proposed volumetric task definition leads to up to 30% improvement in terms of IoU compared to related baselines. The proposed update rule is also shown to improve the performance for complex scenarios where the data distribution of the target organ is very different from the source organs.
Relation-Guided Pre-Training for Open-Domain Question Answering
Authors: Ziniu Hu, Yizhou Sun, Kai-Wei Chang
Date: 2021-09-21 00:00:00
Proceeding: Findings (EMNLP) 2021 11
Tasks: Natural Questions, Open-Domain Question Answering, Question Answering, Relation, TriviaQA

Answering complex open-domain questions requires understanding the latent relations between involving entities. However, we found that the existing QA datasets are extremely imbalanced in some types of relations, which hurts the generalization performance over questions with long-tail relations. To remedy this problem, in this paper, we propose a Relation-Guided Pre-Training (RGPT-QA) framework. We first generate a relational QA dataset covering a wide range of relations from both the Wikidata triplets and Wikipedia hyperlinks. We then pre-train a QA model to infer the latent relations from the question, and then conduct extractive QA to get the target answer entity. We demonstrate that by pretraining with propoed RGPT-QA techique, the popular open-domain QA model, Dense Passage Retriever (DPR), achieves 2.2%, 2.4%, and 6.3% absolute improvement in Exact Match accuracy on Natural Questions, TriviaQA, and WebQuestions. Particularly, we show that RGPT-QA improves significantly on questions with long-tail relations
Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documents
Authors: Biao Zhang, Ankur Bapna, Melvin Johnson, Ali Dabirmoghaddam, Naveen Arivazhagan, Orhan Firat
Date: 2021-09-21 00:00:00
Proceeding: ACL 2022 5
Tasks: Machine Translation, Sentence, Transfer Learning, Translation

Document-level neural machine translation (DocNMT) achieves coherent translations by incorporating cross-sentence context. However, for most language pairs there's a shortage of parallel documents, although parallel sentences are readily available. In this paper, we study whether and how contextual modeling in DocNMT is transferable via multilingual modeling. We focus on the scenario of zero-shot transfer from teacher languages with document level data to student languages with no documents but sentence level data, and for the first time treat document-level translation as a transfer learning problem. Using simple concatenation-based DocNMT, we explore the effect of 3 factors on the transfer: the number of teacher languages with document level data, the balance between document and sentence level data at training, and the data condition of parallel documents (genuine vs. backtranslated). Our experiments on Europarl-7 and IWSLT-10 show the feasibility of multilingual transfer for DocNMT, particularly on document-specific metrics. We observe that more teacher languages and adequate data balance both contribute to better transfer quality. Surprisingly, the transfer is less sensitive to the data condition, where multilingual DocNMT delivers decent performance with either backtranslated or genuine document pairs.
Community detection for weighted bipartite networks
Authors: Huan Qing, Jingli Wang
Date: 2021-09-21 00:00:00
Tasks: Community Detection, Sociology, Stochastic Block Model

The bipartite network appears in various areas, such as biology, sociology, physiology, and computer science. \cite{rohe2016co} proposed Stochastic co-Blockmodel (ScBM) as a tool for detecting community structure of binary bipartite graph data in network studies. However, ScBM completely ignores edge weight and is unable to explain the block structure of a weighted bipartite network. Here, to model a weighted bipartite network, we introduce a Bipartite Distribution-Free model by releasing ScBM's distribution restriction. We also build an extension of the proposed model by considering the variation of node degree. Our models do not require a specific distribution on generating elements of the adjacency matrix but only a block structure on the expected adjacency matrix. Spectral algorithms with theoretical guarantees on the consistent estimation of node labels are presented to identify communities. Our proposed methods are illustrated by simulated and empirical examples.
Assured Neural Network Architectures for Control and Identification of Nonlinear Systems
Authors: James Ferlez, Yasser Shoukry
Date: 2021-09-21 00:00:00

In this paper, we consider the problem of automatically designing a Rectified Linear Unit (ReLU) Neural Network (NN) architecture (number of layers and number of neurons per layer) with the assurance that it is sufficiently parametrized to control a nonlinear system; i.e. control the system to satisfy a given formal specification. This is unlike current techniques, which provide no assurances on the resultant architecture. Moreover, our approach requires only limited knowledge of the underlying nonlinear system and specification. We assume only that the specification can be satisfied by a Lipschitz-continuous controller with a known bound on its Lipschitz constant; the specific controller need not be known. From this assumption, we bound the number of affine functions needed to construct a Continuous Piecewise Affine (CPWA) function that can approximate any Lipschitz-continuous controller that satisfies the specification. Then we connect this CPWA to a NN architecture using the authors' recent results on the Two-Level Lattice (TLL) NN architecture; the TLL architecture was shown to be parameterized by the number of affine functions present in the CPWA function it realizes.
Ranking Feature-Block Importance in Artificial Multiblock Neural Networks
Authors: Anna Jenul, Stefan Schrunner, Bao Ngoc Huynh, Runar Helin, Cecilia Marie Futsæther, Kristian Hovde Liland, Oliver Tomic
Date: 2021-09-21 00:00:00
Tasks: Feature Importance

In artificial neural networks, understanding the contributions of input features on the prediction fosters model explainability and delivers relevant information about the dataset. While typical setups for feature importance ranking assess input features individually, in this study, we go one step further and rank the importance of groups of features, denoted as feature-blocks. A feature-block can contain features of a specific type or features derived from a particular source, which are presented to the neural network in separate input branches (multiblock ANNs). This work presents three methods pursuing distinct strategies to rank features in multiblock ANNs by their importance: (1) a composite strategy building on individual feature importance rankings, (2) a knock-in, and (3) a knock-out strategy. While the composite strategy builds on state-of-the-art feature importance rankings, knock-in and knock-out strategies evaluate the block as a whole via a mutual information criterion. Our experiments consist of a simulation study validating all three approaches, followed by a case study on two distinct real-world datasets to compare the strategies. We conclude that each strategy has its merits for specific application scenarios.
AutoGCL: Automated Graph Contrastive Learning via Learnable View Generators
Authors: Yihang Yin, Qingzhong Wang, Siyu Huang, Haoyi Xiong, Xiang Zhang
Date: 2021-09-21 00:00:00
Tasks: Contrastive Learning, Graph Representation Learning, Representation Learning, Semantic Similarity, Semantic Textual Similarity, Transfer Learning

Contrastive learning has been widely applied to graph representation learning, where the view generators play a vital role in generating effective contrastive samples. Most of the existing contrastive learning methods employ pre-defined view generation methods, e.g., node drop or edge perturbation, which usually cannot adapt to input data or preserve the original semantic structures well. To address this issue, we propose a novel framework named Automated Graph Contrastive Learning (AutoGCL) in this paper. Specifically, AutoGCL employs a set of learnable graph view generators orchestrated by an auto augmentation strategy, where every graph view generator learns a probability distribution of graphs conditioned by the input. While the graph view generators in AutoGCL preserve the most representative structures of the original graph in generation of every contrastive sample, the auto augmentation learns policies to introduce adequate augmentation variances in the whole contrastive learning procedure. Furthermore, AutoGCL adopts a joint training strategy to train the learnable view generators, the graph encoder, and the classifier in an end-to-end manner, resulting in topological heterogeneity yet semantic similarity in the generation of contrastive samples. Extensive experiments on semi-supervised learning, unsupervised learning, and transfer learning demonstrate the superiority of our AutoGCL framework over the state-of-the-arts in graph contrastive learning. In addition, the visualization results further confirm that the learnable view generators can deliver more compact and semantically meaningful contrastive samples compared against the existing view generation methods.
Arterial blood pressure waveform in liver transplant surgery possesses variability of morphology reflecting recipients' acuity and predicting short term outcomes
Authors: Shen-Chih Wang, Chien-Kun Ting, Cheng-Yen Chen, Chin-Su Liu, Niang-Cheng Lin, Che-Chuan Loon, Hau-Tieng Wu, Yu-Ting Lin
Date: 2021-09-21 00:00:00

Background: We investigated clinical information underneath the beat-to-beat fluctuation of the arterial blood pressure (ABP) waveform morphology. We proposed the Dynamical Diffusion Map algorithm (DDMap) to quantify the variability of morphology. The underlying physiology could be the compensatory mechanisms involving complex interactions between various physiological mechanisms to regulate the cardiovascular system. As a liver transplant surgery contains distinct periods, we investigated its clinical behavior in different surgical steps. Methods: Our study used DDmap algorithm, based on unsupervised manifold learning, to obtain a quantitative index for the beat-to-beat variability of morphology. We examined the correlation between the variability of ABP morphology and disease acuity as indicated by Model for End-Stage Liver Disease (MELD) scores, the postoperative laboratory data, and 4 early allograft failure (EAF) scores. Results: Among the 85 enrolled patients, the variability of morphology obtained during the presurgical phase was best correlated with MELD-Na scores. The neohepatic phase variability of morphology was associated with EAF scores as well as postoperative bilirubin levels, international normalized ratio, aspartate aminotransferase levels, and platelet count. Furthermore, variability of morphology presents more associations with the above clinical conditions than the common BP measures and their BP variability indices. Conclusions: The variability of morphology obtained during the presurgical phase is indicative of patient acuity, whereas those during the neohepatic phase are indicative of short-term surgical outcomes.
Short-term traffic prediction using physics-aware neural networks
Authors: Mike Pereira, Annika Lang, Balázs Kulcsár
Date: 2021-09-21 00:00:00
Tasks: Traffic Prediction

In this work, we propose an algorithm performing short-term predictions of the flux of vehicles on a stretch of road, using past measurements of the flux. This algorithm is based on a physics-aware recurrent neural network. A discretization of a macroscopic traffic flow model (using the so-called Traffic Reaction Model) is embedded in the architecture of the network and yields flux predictions based on estimated and predicted space-time dependent traffic parameters. These parameters are themselves obtained using a succession of LSTM ans simple recurrent neural networks. Besides, on top of the predictions, the algorithm yields a smoothing of its inputs which is also physically-constrained by the macroscopic traffic flow model. The algorithm is tested on raw flux measurements obtained from loop detectors.
Audiomer: A Convolutional Transformer For Keyword Spotting
Authors: Surya Kant Sahu, Sai Mitheran, Juhi Kamdar, Meet Gandhi
Date: 2021-09-21 00:00:00
Proceeding: Submitted to NeurIPS 2021 ENLSP Workshop 2021 9
Tasks: Keyword Spotting

Transformers have seen an unprecedented rise in Natural Language Processing and Computer Vision tasks. However, in audio tasks, they are either infeasible to train due to extremely large sequence length of audio waveforms or incur a performance penalty when trained on Fourier-based features. In this work, we introduce an architecture, Audiomer, where we combine 1D Residual Networks with Performer Attention to achieve state-of-the-art performance in keyword spotting with raw audio waveforms, outperforming all previous methods while being computationally cheaper and parameter-efficient. Additionally, our model has practical advantages for speech processing, such as inference on arbitrarily long audio clips owing to the absence of positional encoding. The code is available at https://github.com/The-Learning-Machines/Audiomer-PyTorch.
Adaptive Reliability Analysis for Multi-fidelity Models using a Collective Learning Strategy
Authors: Chi Zhang, Chaolin Song, Abdollah Shafieezadeh
Date: 2021-09-21 00:00:00

In many fields of science and engineering, models with different fidelities are available. Physical experiments or detailed simulations that accurately capture the behavior of the system are regarded as high-fidelity models with low model uncertainty, however, they are expensive to run. On the other hand, simplified physical experiments or numerical models are seen as low-fidelity models that are cheaper to evaluate. Although low-fidelity models are often not suitable for direct use in reliability analysis due to their low accuracy, they can offer information about the trend of the high-fidelity model thus providing the opportunity to explore the design space at a low cost. This study presents a new approach called adaptive multi-fidelity Gaussian process for reliability analysis (AMGPRA). Contrary to selecting training points and information sources in two separate stages as done in state-of-the-art mfEGRA method, the proposed approach finds the optimal training point and information source simultaneously using the novel collective learning function (CLF). CLF is able to assess the global impact of a candidate training point from an information source and it accommodates any learning function that satisfies a certain profile. In this context, CLF provides a new direction for quantifying the impact of new training points and can be easily extended with new learning functions to adapt to different reliability problems. The performance of the proposed method is demonstrated by three mathematical examples and one engineering problem concerning the wind reliability of transmission towers. It is shown that the proposed method achieves similar or higher accuracy with reduced computational costs compared to state-of-the-art single and multi-fidelity methods. A key application of AMGPRA is high-fidelity fragility modeling using complex and costly physics-based computational models.
Learning low-degree functions from a logarithmic number of random queries
Authors: Alexandros Eskenazis, Paata Ivanisvili
Date: 2021-09-21 00:00:00

We prove that every bounded function $f:\{-1,1\}^n\to[-1,1]$ of degree at most $d$ can be learned with $L_2$-accuracy $\varepsilon$ and confidence $1-\delta$ from $\log(\tfrac{n}{\delta})\,\varepsilon^{-d-1} C^{d^{3/2}\sqrt{\log d}}$ random queries, where $C>1$ is a universal finite constant.