pwc-archive/papers-with-abstracts huggingface dataset:
GCI detection from raw speech using a fully-convolutional network
Authors: Luc Ardaillon, Axel Roebel
Date: 2019-10-22 00:00:00

Glottal Closure Instants (GCI) detection consists in automatically detecting the temporal locations of most significant excitation to the vocal tract from the speech signal. It is used in many speech analysis and processing applications, and various algorithms have been proposed for this purpose. Recently, new approaches using convolutional neural networks have emerged , with encouraging results. Following this trend, we propose here a simple approach that performs a regression from the speech waveform to a target signal from which the GCI are easily obtained by peak-picking. However, the ground truth GCI used for training and evaluation are usually extracted from EGG signals, which are not reliable and often not available. To overcome this problem, we propose to train our network on high-quality synthetic speech with perfect ground truth. The performances of the proposed algorithm are compared with three other state-of-the-art approaches using publicly available datasets, and the impact of using controlled synthetic or real speech signals in the training stage is investigated. The experimental results demonstrate that the proposed method obtains similar or better results than other state-of-the-art algorithms and that using large synthetic datasets with many speaker offers better generalization ability than using a smaller database of real speech and EGG signals
A Study on Estimating the Next Failure Time of Compressor Equipment in an Offshore Plant
Authors: SangJe Cho, Jong-Ho Shin, Hong-Bae Jun, Ho-Jin Hwang, Chunghun Ha, Jinsang Hwang
Date: 2016-10-03 00:00:00
Proceeding: Mathematical Problems in Engineering, Volume 2016 2016 10
Tasks: regression

The offshore plant equipment usually has a long life cycle. During its O&M (Operation and Maintenance) phase, since the
accidental occurrence of offshore plant equipment causes catastrophic damage, it is necessary to make more efforts for managing
critical offshore equipment. Nowadays, due to the emerging ICTs (Information Communication Technologies), it is possible to
send health monitoring information to administrator of an offshore plant, which leads to much concern on CBM (ConditionBased Maintenance). This study introduces three approaches for predicting the next failure time of offshore plant equipment (gas
compressor) with case studies, which are based on finite state continuous time Markov model, linear regression method, and their
hybrid model
Pulmonary vessel tree matching for quantifying changes in vascular morphology
Authors: Zhiwei Zhai, Marius Staring, Hideki Ota, Berend C. Stoel
Date: 2018-09-26 00:00:00
Proceeding: MICCAI2018 2018 9

Invasive right-sided heart catheterization (RHC) is currently the gold standard for assessing treatment effects in pulmonary vascular diseases, such as chronic thromboembolic pulmonary hypertension (CTEPH). Quantifying morphological changes by matching vascular trees (pre- and post-treatment) may provide a non-invasive alternative for assessing hemodynamic changes. In this work, we propose a method for quantifying morphological changes, consisting of three steps: constructing vascular trees from the detected pulmonary vessels, matching vascular trees with preserving local tree topology, and quantifying local morphological changes based on Poiseuille’s law (changes in   radius−4 ,   △r−4 ). Subsequently, median and interquartile range (IQR) of all local   △r−4  were calculated as global measurements for assessing morphological changes. The vascular tree matching method was validated with 10 synthetic trees and the relation between clinical RHC parameters and quantifications of morphological changes was investigated in 14 CTEPH patients, pre- and post-treatment. In the evaluation with synthetic trees, the proposed method achieved an average residual distance of   3.09±1.28  mm, which is a substantial improvement over the coherent point drift method (  4.32±1.89  mm) and a method with global-local topology preservation (  3.92±1.59  mm). In the clinical evaluation, the morphological changes (IQR of   △r−4 ) was significantly correlated with the changes in RHC examinations,   △sPAP  (  R=−0.62 , p-value = 0.019) and   △mPAP  (  R=−0.56 , p-value = 0.038). Quantifying morphological changes may provide a non-invasive assessment of treatment effects in CTEPH patients, consistent with hemodynamic changes from invasive RHC.
Robust Nucleus Detection from Microscopical Image with Partially Labeled Exemplars
Authors: Linqing Feng, Jun Ho Song, Jiwon Kim, Soomin Jeong, Jin Sung Park, Jinhyun Kim
Date: 2019-07-23 00:00:00
Tasks: Region Proposal

Quantitative analysis of cell nuclei in microscopic images is an essential yet still challenging source of biological and pathological information. The major challenge is accurate detection and segmentation of densely packed nuclei in images acquired under a variety of conditions. With sufficient training examples, Mask R-CNN-based methods have achieved state-of-the-art nucleus segmentation. However, the current pipeline requires fully annotated training images, which are time consuming to create and sometimes infeasible because of the noisy nature of microscopic images. Importantly, nuclei often have similar appearances within the same image; this similarity could be utilized to segment nuclei with only partially labeled training examples. We propose a simple yet effective region proposal module for the current Mask R-CNN pipeline to perform few-exemplar learning. To capture the similarities between the unlabeled regions and labeled nuclei, we apply decomposed self-attention to the learned features. On the self-attention map, we observe strong activation at the centers and edges of all nuclei, including the unlabeled ones. On this basis, our region proposal module propagates the partial annotations to the whole image and then proposes effective bounding boxes for the bounding box regression and binary mask generation modules. When trained with only 1/4 of the nuclei annotated, the baseline pipeline gives frequent false negatives, while our approach retains detection accuracy comparable to that of training with the fully annotated data. Moreover, our method can serve as a bootstrapping step to create a full annotation of a dataset, where annotations are iteratively generated and corrected until the predetermined coverage and accuracy are reached. The source code is available at https://github.com/feng-lab/nuclei.
Language coverage and generalization in RNN-based continuous sentence embeddings for interacting agents
Authors: Luca Celotti, Simon Brodeur, Jean Rouat
Date: 2019-11-05 00:00:00
Tasks: Dialogue Generation, Sentence, Sentence Embeddings

Continuous sentence embeddings using recurrent neural networks (RNNs), where variable-length sentences are encoded into fixed-dimensional vectors, are often the main building blocks of architectures applied to language tasks such as dialogue generation. While it is known that those embeddings are able to learn some structures of language (e.g. grammar) in a purely data-driven manner, there is very little work on the objective evaluation of their ability to cover the whole language space and to generalize to sentences outside the language bias of the training data. Using a manually designed context-free grammar (CFG) to generate a large-scale dataset of sentences related to the content of realistic 3D indoor scenes, we evaluate the language coverage and generalization abilities of the most common continuous sentence embeddings based on RNNs. We also propose a new embedding method based on arithmetic coding, AriEL, that is not data-driven and that efficiently encodes in continuous space any sentence from the CFG. We find that RNN-based embeddings underfit the training data and cover only a small subset of the language defined by the CFG. They also fail to learn the underlying CFG and generalize to unbiased sentences from that same CFG. We found that AriEL provides an insightful baseline.
Dancing to Music
Authors: Hsin-Ying Lee, Xiaodong Yang, Ming-Yu Liu, Ting-Chun Wang, Yu-Ding Lu, Ming-Hsuan Yang, Jan Kautz
Date: 2019-11-05 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Motion Synthesis, Pose Estimation

Dancing to music is an instinctive move by humans. Learning to model the music-to-dance generation process is, however, a challenging problem. It requires significant efforts to measure the correlation between music and dance as one needs to simultaneously consider multiple aspects, such as style and beat of both music and dance. Additionally, dance is inherently multimodal and various following movements of a pose at any moment are equally likely. In this paper, we propose a synthesis-by-analysis learning framework to generate dance from music. In the analysis phase, we decompose a dance into a series of basic dance units, through which the model learns how to move. In the synthesis phase, the model learns how to compose a dance by organizing multiple basic dancing movements seamlessly according to the input music. Experimental qualitative and quantitative results demonstrate that the proposed method can synthesize realistic, diverse,style-consistent, and beat-matching dances from music.
Data Diversification: A Simple Strategy For Neural Machine Translation
Authors: Xuan-Phi Nguyen, Shafiq Joty, Wu Kui, Ai Ti Aw
Date: 2019-11-05 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Knowledge Distillation, Machine Translation, NMT, Translation

We introduce Data Diversification: a simple but effective strategy to boost neural machine translation (NMT) performance. It diversifies the training data by using the predictions of multiple forward and backward models and then merging them with the original dataset on which the final NMT model is trained. Our method is applicable to all NMT models. It does not require extra monolingual data like back-translation, nor does it add more computations and parameters like ensembles of models. Our method achieves state-of-the-art BLEU scores of 30.7 and 43.7 in the WMT'14 English-German and English-French translation tasks, respectively. It also substantially improves on 8 other translation tasks: 4 IWSLT tasks (English-German and English-French) and 4 low-resource translation tasks (English-Nepali and English-Sinhala). We demonstrate that our method is more effective than knowledge distillation and dual learning, it exhibits strong correlation with ensembles of models, and it trades perplexity off for better BLEU score. We have released our source code at https://github.com/nxphi47/data_diversification
Band Selection from Hyperspectral Images Using Attention-based Convolutional Neural Networks
Authors: Pablo Ribalta Lorenzo, Lukasz Tulczyjew, Michal Marcinkiewicz, Jakub Nalepa
Date: 2018-10-24 00:00:00

This paper introduces new attention-based convolutional neural networks for selecting bands from hyperspectral images. The proposed approach re-uses convolutional activations at different depths, identifying the most informative regions of the spectrum with the help of gating mechanisms. Our attention techniques are modular and easy to implement, and they can be seamlessly trained end-to-end using gradient descent. Our rigorous experiments showed that deep models equipped with the attention mechanism deliver high-quality classification, and repeatedly identify significant bands in the training data, permitting the creation of refined and extremely compact sets that retain the most meaningful features.
Bipolar Morphological Neural Networks: Convolution Without Multiplication
Authors: Elena Limonova, Daniil Matveev, Dmitry Nikolaev, Vladimir V. Arlazarov
Date: 2019-11-05 00:00:00

In the paper we introduce a novel bipolar morphological neuron and bipolar morphological layer models. The models use only such operations as addition, subtraction and maximum inside the neuron and exponent and logarithm as activation functions for the layer. The proposed models unlike previously introduced morphological neural networks approximate the classical computations and show better recognition results. We also propose layer-by-layer approach to train the bipolar morphological networks, which can be further developed to an incremental approach for separate neurons to get higher accuracy. Both these approaches do not require special training algorithms and can use a variety of gradient descent methods. To demonstrate efficiency of the proposed model we consider classical convolutional neural networks and convert the pre-trained convolutional layers to the bipolar morphological layers. Seeing that the experiments on recognition of MNIST and MRZ symbols show only moderate decrease of accuracy after conversion and training, bipolar neuron model can provide faster inference and be very useful in mobile and embedded systems.
A Note on Quantum Markov Models
Authors: Christino Tamon, Weichen Xie
Date: 2019-11-05 00:00:00

The study of Markov models is central to control theory and machine learning. A quantum analogue of partially observable Markov decision process was studied in (Barry, Barry, and Aaronson, Phys. Rev. A, 90, 2014). It was proved that goal-state reachability is undecidable in the quantum setting, whereas it is decidable classically. In contrast to this classical-to-quantum transition from decidable to undecidable, we observe that the problem of approximating the optimal policy which maximizes the average discounted reward over an infinite horizon remains decidable in the quantum setting. Given that most relevant problems related to Markov decision process are undecidable classically (which immediately implies undecidability in the quantum case), this provides one of the few examples where the quantum problem is tractable.
Coverage Guided Testing for Recurrent Neural Networks
Authors: Wei Huang, Youcheng Sun, Xingyu Zhao, James Sharp, Wenjie Ruan, Jie Meng, Xiaowei Huang
Date: 2019-11-05 00:00:00
Tasks: Defect Detection, Drug Discovery, Image Classification, Sentiment Analysis, Video Recognition

Recurrent neural networks (RNNs) have been applied to a broad range of applications, including natural language processing, drug discovery, and video recognition. Their vulnerability to input perturbation is also known. Aligning with a view from software defect detection, this paper aims to develop a coverage guided testing approach to systematically exploit the internal behaviour of RNNs, with the expectation that such testing can detect defects with high possibility. Technically, the long short term memory network (LSTM), a major class of RNNs, is thoroughly studied. A family of three test metrics are designed to quantify not only the values but also the temporal relations (including both step-wise and bounded-length) exhibited when LSTM processing inputs. A genetic algorithm is applied to efficiently generate test cases. The test metrics and test case generation algorithm are implemented into a tool TestRNN, which is then evaluated on a set of LSTM benchmarks. Experiments confirm that TestRNN has advantages over the state-of-art tool DeepStellar and attack-based defect detection methods, owing to its working with finer temporal semantics and the consideration of the naturalness of input perturbation. Furthermore, TestRNN enables meaningful information to be collected and exhibited for users to understand the testing results, which is an important step towards interpretable neural network testing.
Dynamic Time Warp Convolutional Networks
Authors: Yaniv Shulman
Date: 2019-11-05 00:00:00
Tasks: Temporal Sequences, Time Series, Time Series Analysis, Time Series Classification

Where dealing with temporal sequences it is fair to assume that the same kind of deformations that motivated the development of the Dynamic Time Warp algorithm could be relevant also in the calculation of the dot product ("convolution") in a 1-D convolution layer. In this work a method is proposed for aligning the convolution filter and the input where they are locally out of phase utilising an algorithm similar to the Dynamic Time Warp. The proposed method enables embedding a non-parametric warping of temporal sequences for increasing similarity directly in deep networks and can expand on the generalisation capabilities and the capacity of standard 1-D convolution layer where local sequential deformations are present in the input. Experimental results demonstrate the proposed method exceeds or matches the standard 1-D convolution layer in terms of the maximum accuracy achieved on a number of time series classification tasks. In addition the impact of different hyperparameters settings is investigated given different datasets and the results support the conclusions of previous work done in relation to the choice of DTW parameter values. The proposed layer can be freely integrated with other typical layers to compose deep artificial neural networks of an arbitrary architecture that are trained using standard stochastic gradient descent.
Deepening Hidden Representations from Pre-trained Language Models
Authors: Junjie Yang, Hai Zhao
Date: 2019-11-05 00:00:00
Tasks: Natural Language Understanding

Transformer-based pre-trained language models have proven to be effective for learning contextualized language representation. However, current approaches only take advantage of the output of the encoder's final layer when fine-tuning the downstream tasks. We argue that only taking single layer's output restricts the power of pre-trained representation. Thus we deepen the representation learned by the model by fusing the hidden representation in terms of an explicit HIdden Representation Extractor (HIRE), which automatically absorbs the complementary representation with respect to the output from the final layer. Utilizing RoBERTa as the backbone encoder, our proposed improvement over the pre-trained models is shown effective on multiple natural language understanding tasks and help our model rival with the state-of-the-art models on the GLUE benchmark.
Training Neural Machine Translation (NMT) Models using Tensor Train Decomposition on TensorFlow (T3F)
Authors: Amelia Drew, Alexander Heinecke
Date: 2019-11-05 00:00:00
Tasks: Machine Translation, NMT, Translation

We implement a Tensor Train layer in the TensorFlow Neural Machine Translation (NMT) model using the t3f library. We perform training runs on the IWSLT English-Vietnamese '15 and WMT German-English '16 datasets with learning rates $\in \{0.0004,0.0008,0.0012\}$, maximum ranks $\in \{2,4,8,16\}$ and a range of core dimensions. We compare against a target BLEU test score of 24.0, obtained by our benchmark run. For the IWSLT English-Vietnamese training, we obtain BLEU test/dev scores of 24.0/21.9 and 24.2/21.9 using core dimensions $(2, 2, 256) \times (2, 2, 512)$ with learning rate 0.0012 and rank distributions $(1,4,4,1)$ and $(1,4,16,1)$ respectively. These runs use 113\% and 397\% of the flops of the benchmark run respectively. We find that, of the parameters surveyed, a higher learning rate and more `rectangular' core dimensions generally produce higher BLEU scores. For the WMT German-English dataset, we obtain BLEU scores of 24.0/23.8 using core dimensions $(4, 4, 128) \times (4, 4, 256)$ with learning rate 0.0012 and rank distribution $(1,2,2,1)$. We discuss the potential for future optimization and application of Tensor Train decomposition to other NMT models.
Online matrix factorization for Markovian data and applications to Network Dictionary Learning
Authors: Hanbaek Lyu, Deanna Needell, Laura Balzano
Date: 2019-11-05 00:00:00
Tasks: Denoising, Dictionary Learning

Online Matrix Factorization (OMF) is a fundamental tool for dictionary learning problems, giving an approximate representation of complex data sets in terms of a reduced number of extracted features. Convergence guarantees for most of the OMF algorithms in the literature assume independence between data matrices, and the case of dependent data streams remains largely unexplored. In this paper, we show that a non-convex generalization of the well-known OMF algorithm for i.i.d. stream of data in \citep{mairal2010online} converges almost surely to the set of critical points of the expected loss function, even when the data matrices are functions of some underlying Markov chain satisfying a mild mixing condition. This allows one to extract features more efficiently from dependent data streams, as there is no need to subsample the data sequence to approximately satisfy the independence assumption. As the main application, by combining online non-negative matrix factorization and a recent MCMC algorithm for sampling motifs from networks, we propose a novel framework of Network Dictionary Learning, which extracts ``network dictionary patches' from a given network in an online manner that encodes main features of the network. We demonstrate this technique and its application to network denoising problems on real-world network data.
GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models
Authors: Pavel Berkovich, Eric Perim, Wessel Bruinsma
Date: 2019-11-05 00:00:00
Proceeding: pproximateinference AABI Symposium 2019 12
Tasks: Gaussian Processes, Variational Inference

A simple and widely adopted approach to extend Gaussian processes (GPs) to multiple outputs is to model each output as a linear combination of a collection of shared, unobserved latent GPs. An issue with this approach is choosing the number of latent processes and their kernels. These choices are typically done manually, which can be time consuming and prone to human biases. We propose Gaussian Process Automatic Latent Process Selection (GP-ALPS), which automatically chooses the latent processes by turning off those that do not meaningfully contribute to explaining the data. We develop a variational inference scheme, assess the quality of the variational posterior by comparing it against the gold standard MCMC, and demonstrate the suitability of GP-ALPS in a set of preliminary experiments.
DLA: Dense-Layer-Analysis for Adversarial Example Detection
Authors: Philip Sperl, Ching-Yu Kao, Peng Chen, Konstantin Böttinger
Date: 2019-11-05 00:00:00
Tasks: Autonomous Driving, General Classification

In recent years Deep Neural Networks (DNNs) have achieved remarkable results and even showed super-human capabilities in a broad range of domains. This led people to trust in DNNs' classifications and resulting actions even in security-sensitive environments like autonomous driving. Despite their impressive achievements, DNNs are known to be vulnerable to adversarial examples. Such inputs contain small perturbations to intentionally fool the attacked model. In this paper, we present a novel end-to-end framework to detect such attacks during classification without influencing the target model's performance. Inspired by recent research in neuron-coverage guided testing we show that dense layers of DNNs carry security-sensitive information. With a secondary DNN we analyze the activation patterns of the dense layers during classification runtime, which enables effective and real-time detection of adversarial examples. Our prototype implementation successfully detects adversarial examples in image, natural language, and audio processing. Thereby, we cover a variety of target DNNs, including Long Short Term Memory (LSTM) architectures. In addition, to effectively defend against state-of-the-art attacks, our approach generalizes between different sets of adversarial examples. Thus, our method most likely enables us to detect even future, yet unknown attacks. Finally, during white-box adaptive attacks, we show our method cannot be easily bypassed.