pwc-archive/papers-with-abstracts huggingface dataset:
Vision as an Interlingua: Learning Multilingual Semantic Embeddings of Untranscribed Speech
Authors: David Harwath, Galen Chuang, James Glass
Date: 2018-04-09 00:00:00
Tasks: Retrieval, speech-recognition, Speech Recognition

In this paper, we explore the learning of neural network embeddings for
natural images and speech waveforms describing the content of those images.
These embeddings are learned directly from the waveforms without the use of
linguistic transcriptions or conventional speech recognition technology. While
prior work has investigated this setting in the monolingual case using English
speech data, this work represents the first effort to apply these techniques to
languages beyond English. Using spoken captions collected in English and Hindi,
we show that the same model architecture can be successfully applied to both
languages. Further, we demonstrate that training a multilingual model
simultaneously on both languages offers improved performance over the
monolingual models. Finally, we show that these models are capable of
performing semantic cross-lingual speech-to-speech retrieval.
Approximate k-NN Graph Construction: a Generic Online Approach
Authors: Wan-Lei Zhao, Hui Wang, Chong-Wah Ngo
Date: 2018-04-09 00:00:00
Tasks: graph construction, Information Retrieval, Retrieval

Nearest neighbor search and k-nearest neighbor graph construction are two fundamental issues arise from many disciplines such as multimedia information retrieval, data-mining and machine learning. They become more and more imminent given the big data emerge in various fields in recent years. In this paper, a simple but effective solution both for approximate k-nearest neighbor search and approximate k-nearest neighbor graph construction is presented. These two issues are addressed jointly in our solution. On the one hand, the approximate k-nearest neighbor graph construction is treated as a search task. Each sample along with its k-nearest neighbors are joined into the k-nearest neighbor graph by performing the nearest neighbor search sequentially on the graph under construction. On the other hand, the built k-nearest neighbor graph is used to support k-nearest neighbor search. Since the graph is built online, the dynamic update on the graph, which is not possible from most of the existing solutions, is supported. This solution is feasible for various distance measures. Its effectiveness both as k-nearest neighbor construction and k-nearest neighbor search approaches is verified across different types of data in different scales, various dimensions and under different metrics.
Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation
Authors: Leon Bungert, David A. Coomes, Matthias J. Ehrhardt, Jennifer Rasch, Rafael Reisenhofer, Carola-Bibiane Schönlieb
Date: 2017-10-04 00:00:00
Tasks: Blind Super-Resolution, Super-Resolution

Hyperspectral imaging is a cutting-edge type of remote sensing used for
mapping vegetation properties, rock minerals and other materials. A major
drawback of hyperspectral imaging devices is their intrinsic low spatial
resolution. In this paper, we propose a method for increasing the spatial
resolution of a hyperspectral image by fusing it with an image of higher
spatial resolution that was obtained with a different imaging modality. This is
accomplished by solving a variational problem in which the regularization
functional is the directional total variation. To accommodate for possible
mis-registrations between the two images, we consider a non-convex blind
super-resolution problem where both a fused image and the corresponding
convolution kernel are estimated. Using this approach, our model can realign
the given images if needed. Our experimental results indicate that the
non-convexity is negligible in practice and that reliable solutions can be
computed using a variety of different optimization algorithms. Numerical
results on real remote sensing data from plant sciences and urban monitoring
show the potential of the proposed method and suggests that it is robust with
respect to the regularization parameters, mis-registration and the shape of the
kernel.
Learning at the Ends: From Hand to Tool Affordances in Humanoid Robots
Authors: Giovanni Saponaro, Pedro Vicente, Atabak Dehban, Lorenzo Jamone, Alexandre Bernardino, José Santos-Victor
Date: 2018-04-09 00:00:00
Tasks: Decision Making

One of the open challenges in designing robots that operate successfully in
the unpredictable human environment is how to make them able to predict what
actions they can perform on objects, and what their effects will be, i.e., the
ability to perceive object affordances. Since modeling all the possible world
interactions is unfeasible, learning from experience is required, posing the
challenge of collecting a large amount of experiences (i.e., training data).
Typically, a manipulative robot operates on external objects by using its own
hands (or similar end-effectors), but in some cases the use of tools may be
desirable, nevertheless, it is reasonable to assume that while a robot can
collect many sensorimotor experiences using its own hands, this cannot happen
for all possible human-made tools.
  Therefore, in this paper we investigate the developmental transition from
hand to tool affordances: what sensorimotor skills that a robot has acquired
with its bare hands can be employed for tool use? By employing a visual and
motor imagination mechanism to represent different hand postures compactly, we
propose a probabilistic model to learn hand affordances, and we show how this
model can generalize to estimate the affordances of previously unseen tools,
ultimately supporting planning, decision-making and tool selection tasks in
humanoid robots. We present experimental results with the iCub humanoid robot,
and we publicly release the collected sensorimotor data in the form of a hand
posture affordances dataset.
Knowledge Base Relation Detection via Multi-View Matching
Authors: Yang Yu, Kazi Saidul Hasan, Mo Yu, Wei zhang, Zhiguo Wang
Date: 2018-03-01 00:00:00
Tasks: Knowledge Base Question Answering, Question Answering, Relation

Relation detection is a core component for Knowledge Base Question Answering
(KBQA). In this paper, we propose a KB relation detection model via multi-view
matching which utilizes more useful information extracted from question and KB.
The matching inside each view is through multiple perspectives to compare two
input texts thoroughly. All these components are designed in an end-to-end
trainable neural network model. Experiments on SimpleQuestions and WebQSP yield
state-of-the-art results.
Multi-views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images
Authors: Gongning Luo, Suyu Dong, Kuanquan Wang, WangMeng Zuo, Shaodong Cao, Henggui Zhang
Date: 2018-04-09 00:00:00
Tasks: Prediction

Left ventricular (LV) volumes estimation is a critical procedure for cardiac
disease diagnosis. The objective of this paper is to address direct LV volumes
prediction task. Methods: In this paper, we propose a direct volumes prediction
method based on the end-to-end deep convolutional neural networks (CNN). We
study the end-to-end LV volumes prediction method in items of the data
preprocessing, networks structure, and multi-views fusion strategy. The main
contributions of this paper are the following aspects. First, we propose a new
data preprocessing method on cardiac magnetic resonance (CMR). Second, we
propose a new networks structure for end-to-end LV volumes estimation. Third,
we explore the representational capacity of different slices, and propose a
fusion strategy to improve the prediction accuracy. Results: The evaluation
results show that the proposed method outperforms other state-of-the-art LV
volumes estimation methods on the open accessible benchmark datasets. The
clinical indexes derived from the predicted volumes agree well with the ground
truth (EDV: R2=0.974, RMSE=9.6ml; ESV: R2=0.976, RMSE=7.1ml; EF: R2=0.828, RMSE
=4.71%). Conclusion: Experimental results prove that the proposed method may be
useful for LV volumes prediction task. Significance: The proposed method not
only has application potential for cardiac diseases screening for large-scale
CMR data, but also can be extended to other medical image research fields
Anomaly Detection for Industrial Big Data
Authors: Neil Caithness, David Wallom
Date: 2018-04-09 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis

As the Industrial Internet of Things (IIoT) grows, systems are increasingly
being monitored by arrays of sensors returning time-series data at
ever-increasing 'volume, velocity and variety' (i.e. Industrial Big Data). An
obvious use for these data is real-time systems condition monitoring and
prognostic time to failure analysis (remaining useful life, RUL). (e.g. See
white papers by Senseye.io, and output of the NASA Prognostics Center of
Excellence (PCoE).) However, as noted by Agrawal and Choudhary 'Our ability to
collect "big data" has greatly surpassed our capability to analyze it,
underscoring the emergence of the fourth paradigm of science, which is
data-driven discovery.' In order to fully utilize the potential of Industrial
Big Data we need data-driven techniques that operate at scales that process
models cannot. Here we present a prototype technique for data-driven anomaly
detection to operate at industrial scale. The method generalizes to application
with almost any multivariate dataset based on independent ordinations of
repeated (bootstrapped) partitions of the dataset and inspection of the joint
distribution of ordinal distances.
Convolutional Recurrent Neural Networks for Electrocardiogram Classification
Authors: Martin Zihlmann, Dmytro Perekrestenko, Michael Tschannen
Date: 2017-10-17 00:00:00
Tasks: Classification, Data Augmentation, General Classification

We propose two deep neural network architectures for classification of
arbitrary-length electrocardiogram (ECG) recordings and evaluate them on the
atrial fibrillation (AF) classification data set provided by the PhysioNet/CinC
Challenge 2017. The first architecture is a deep convolutional neural network
(CNN) with averaging-based feature aggregation across time. The second
architecture combines convolutional layers for feature extraction with
long-short term memory (LSTM) layers for temporal aggregation of features. As a
key ingredient of our training procedure we introduce a simple data
augmentation scheme for ECG data and demonstrate its effectiveness in the AF
classification task at hand. The second architecture was found to outperform
the first one, obtaining an $F_1$ score of $82.1$% on the hidden challenge
testing set.
AutoEncoder Inspired Unsupervised Feature Selection
Authors: Kai Han, Yunhe Wang, Chao Zhang, Chao Li, Chao Xu
Date: 2017-10-23 00:00:00
Tasks: BIG-bench Machine Learning, feature selection

High-dimensional data in many areas such as computer vision and machine
learning tasks brings in computational and analytical difficulty. Feature
selection which selects a subset from observed features is a widely used
approach for improving performance and effectiveness of machine learning models
with high-dimensional data. In this paper, we propose a novel AutoEncoder
Feature Selector (AEFS) for unsupervised feature selection which combines
autoencoder regression and group lasso tasks. Compared to traditional feature
selection methods, AEFS can select the most important features by excavating
both linear and nonlinear information among features, which is more flexible
than the conventional self-representation method for unsupervised feature
selection with only linear assumptions. Experimental results on benchmark
dataset show that the proposed method is superior to the state-of-the-art
method.
Face Sketch Synthesis Style Similarity:A New Structure Co-occurrence Texture Measure
Authors: Deng-Ping Fan, Shengchuan Zhang, Yu-Huan Wu, Ming-Ming Cheng, Bo Ren, Rongrong Ji, Paul L. Rosin
Date: 2018-04-09 00:00:00
Tasks: Face Sketch Synthesis

Existing face sketch synthesis (FSS) similarity measures are sensitive to
slight image degradation (e.g., noise, blur). However, human perception of the
similarity of two sketches will consider both structure and texture as
essential factors and is not sensitive to slight ("pixel-level") mismatches.
Consequently, the use of existing similarity measures can lead to better
algorithms receiving a lower score than worse algorithms. This unreliable
evaluation has significantly hindered the development of the FSS field. To
solve this problem, we propose a novel and robust style similarity measure
called Scoot-measure (Structure CO-Occurrence Texture Measure), which
simultaneously evaluates "block-level" spatial structure and co-occurrence
texture statistics. In addition, we further propose 4 new meta-measures and
create 2 new datasets to perform a comprehensive evaluation of several
widely-used FSS measures on two large databases. Experimental results
demonstrate that our measure not only provides a reliable evaluation but also
achieves significantly improved performance. Specifically, the study indicated
a higher degree (78.8%) of correlation between our measure and human judgment
than the best prior measure (58.6%). Our code will be made available.
Categorizing Variants of Goodhart's Law
Authors: David Manheim, Scott Garrabrant
Date: 2018-03-13 00:00:00

There are several distinct failure modes for overoptimization of systems on
the basis of metrics. This occurs when a metric which can be used to improve a
system is used to an extent that further optimization is ineffective or
harmful, and is sometimes termed Goodhart's Law. This class of failure is often
poorly understood, partly because terminology for discussing them is ambiguous,
and partly because discussion using this ambiguous terminology ignores
distinctions between different failure modes of this general type. This paper
expands on an earlier discussion by Garrabrant, which notes there are "(at
least) four different mechanisms" that relate to Goodhart's Law. This paper is
intended to explore these mechanisms further, and specify more clearly how they
occur. This discussion should be helpful in better understanding these types of
failures in economic regulation, in public policy, in machine learning, and in
Artificial Intelligence alignment. The importance of Goodhart effects depends
on the amount of power directed towards optimizing the proxy, and so the
increased optimization power offered by artificial intelligence makes it
especially critical for that field.
HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation
Authors: Jose Dolz, Karthik Gopinath, Jing Yuan, Herve Lombaert, Christian Desrosiers, Ismail Ben Ayed
Date: 2018-04-09 00:00:00
Tasks: Brain Segmentation, image-classification, Image Classification, Image Segmentation, Medical Image Segmentation, Multi-modal image segmentation, Representation Learning, Segmentation, Semantic Segmentation

Recently, dense connections have attracted substantial attention in computer
vision because they facilitate gradient flow and implicit deep supervision
during training. Particularly, DenseNet, which connects each layer to every
other layer in a feed-forward fashion, has shown impressive performances in
natural image classification tasks. We propose HyperDenseNet, a 3D fully
convolutional neural network that extends the definition of dense connectivity
to multi-modal segmentation problems. Each imaging modality has a path, and
dense connections occur not only between the pairs of layers within the same
path, but also between those across different paths. This contrasts with the
existing multi-modal CNN approaches, in which modeling several modalities
relies entirely on a single joint layer (or level of abstraction) for fusion,
typically either at the input or at the output of the network. Therefore, the
proposed network has total freedom to learn more complex combinations between
the modalities, within and in-between all the levels of abstraction, which
increases significantly the learning representation. We report extensive
evaluations over two different and highly competitive multi-modal brain tissue
segmentation challenges, iSEG 2017 and MRBrainS 2013, with the former focusing
on 6-month infant data and the latter on adult images. HyperDenseNet yielded
significant improvements over many state-of-the-art segmentation networks,
ranking at the top on both benchmarks. We further provide a comprehensive
experimental analysis of features re-use, which confirms the importance of
hyper-dense connections in multi-modal representation learning. Our code is
publicly available at https://www.github.com/josedolz/HyperDenseNet.
Generative Adversarial Networks for Extreme Learned Image Compression
Authors: Eirikur Agustsson, Michael Tschannen, Fabian Mentzer, Radu Timofte, Luc van Gool
Date: 2018-04-09 00:00:00
Proceeding: ICCV 2019 10
Tasks: Decoder, Image Compression

We present a learned image compression system based on GANs, operating at extremely low bitrates. Our proposed framework combines an encoder, decoder/generator and a multi-scale discriminator, which we train jointly for a generative learned compression objective. The model synthesizes details it cannot afford to store, obtaining visually pleasing results at bitrates where previous methods fail and show strong artifacts. Furthermore, if a semantic label map of the original image is available, our method can fully synthesize unimportant regions in the decoded image such as streets and trees from the label map, proportionally reducing the storage cost. A user study confirms that for low bitrates, our approach is preferred to state-of-the-art methods, even when they use more than double the bits.
A theory of consciousness: computation, algorithm, and neurobiological realization
Authors: J. H. van Hateren
Date: 2018-04-09 00:00:00

The most enigmatic aspect of consciousness is the fact that it is felt, as a subjective sensation. The theory proposed here aims to explain this particular aspect. The theory encompasses both the computation that is presumably involved and the way in which that computation may be realized in the brain's neurobiology. It is assumed that the brain makes an internal estimate of an individual's own evolutionary fitness, which can be shown to produce a special, distinct form of causation. Communicating components of the fitness estimate (either for external or internal use) requires inverting them. Such inversion can be performed by the thalamocortical feedback loop in the mammalian brain, if that loop is operating in a switched, dual-stage mode. A first (nonconscious) stage produces forward estimates, whereas the second (conscious) stage inverts those estimates. It is argued that inversion produces another special, distinct form of causation, which is spatially localized and is plausibly sensed as the feeling of consciousness.
Sample-Derived Disjunctive Rules for Secure Power System Operation
Authors: Jochen L. Cremer, Ioannis Konstantelos, Simon H. Tindemans, Goran Strbac
Date: 2018-04-09 00:00:00
Tasks: Decision Making

Machine learning techniques have been used in the past using Monte Carlo
samples to construct predictors of the dynamic stability of power systems. In
this paper we move beyond the task of prediction and propose a comprehensive
approach to use predictors, such as Decision Trees (DT), within a standard
optimization framework for pre- and post-fault control purposes. In particular,
we present a generalizable method for embedding rules derived from DTs in an
operation decision-making model. We begin by pointing out the specific
challenges entailed when moving from a prediction to a control framework. We
proceed with introducing the solution strategy based on generalized disjunctive
programming (GDP) as well as a two-step search method for identifying optimal
hyper-parameters for balancing cost and control accuracy. We showcase how the
proposed approach constructs security proxies that cover multiple contingencies
while facing high-dimensional uncertainty with respect to operating conditions
with the use of a case study on the IEEE 39-bus system. The method is shown to
achieve efficient system control at a marginal increase in system price
compared to an oracle model.
Crime prediction through urban metrics and statistical learning
Authors: Luiz G. A. Alves, Haroldo V. Ribeiro, Francisco A. Rodrigues
Date: 2017-12-08 00:00:00
Tasks: Crime Prediction, Prediction

Understanding the causes of crime is a longstanding issue in researcher's
agenda. While it is a hard task to extract causality from data, several linear
models have been proposed to predict crime through the existing correlations
between crime and urban metrics. However, because of non-Gaussian distributions
and multicollinearity in urban indicators, it is common to find controversial
conclusions about the influence of some urban indicators on crime. Machine
learning ensemble-based algorithms can handle well such problems. Here, we use
a random forest regressor to predict crime and quantify the influence of urban
indicators on homicides. Our approach can have up to 97% of accuracy on crime
prediction, and the importance of urban indicators is ranked and clustered in
groups of equal influence, which are robust under slightly changes in the data
sample analyzed. Our results determine the rank of importance of urban
indicators to predict crime, unveiling that unemployment and illiteracy are the
most important variables for describing homicides in Brazilian cities. We
further believe that our approach helps in producing more robust conclusions
regarding the effects of urban indicators on crime, having potential
applications for guiding public policies for crime control.
Abdominal Aortic Aneurysm Segmentation with a Small Number of Training Subjects
Authors: Jian-Qing Zheng, Xiao-Yun Zhou, Qing-Biao Li, Celia Riga, Guang-Zhong Yang
Date: 2018-04-09 00:00:00
Tasks: Segmentation, Translation

Pre-operative Abdominal Aortic Aneurysm (AAA) 3D shape is critical for
customized stent-graft design in Fenestrated Endovascular Aortic Repair
(FEVAR). Traditional segmentation approaches implement expert-designed feature
extractors while recent deep neural networks extract features automatically
with multiple non-linear modules. Usually, a large training dataset is
essential for applying deep learning on AAA segmentation. In this paper, the
AAA was segmented using U-net with a small number (two) of training subjects.
Firstly, Computed Tomography Angiography (CTA) slices were augmented with gray
value variation and translation to avoid the overfitting caused by the small
number of training subjects. Then, U-net was trained to segment the AAA. Dice
Similarity Coefficients (DSCs) over 0.8 were achieved on the testing subjects.
The PLZ, DLZ and aortic branches are all reconstructed reasonably, which will
facilitate stent graft customization and help shape instantiation for
intra-operative surgery navigation in FEVAR.