pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Tap Setting of Voltage Regulation Transformers Using Batch Reinforcement Learning
Authors: Hanchen Xu, Alejandro D. Domínguez-García, Peter W. Sauer
Date: 2018-07-29 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we address the problem of setting the tap positions of load
tap changers (LTCs) for voltage regulation in radial power distribution systems
under uncertain load dynamics. The objective is to find a policy to determine
the tap positions that only uses measurements of voltage magnitudes and
topology information so as to minimize the voltage deviation across the system.
We formulate this problem as a Markov decision process (MDP), and propose a
batch reinforcement learning (RL) algorithm to solve it. By taking advantage of
a linearized power flow model, we propose an effective algorithm to estimate
the voltage magnitudes under different tap settings, which allows the RL
algorithm to explore the state and action spaces freely offline without
impacting the system operation. To circumvent the "curse of dimensionality"
resulted from the large state and action spaces, we propose a sequential
learning algorithm to learn an action-value function for each LTC, based on
which the optimal tap positions can be directly determined. The effectiveness
of the proposed algorithm is validated via numerical simulations on the IEEE
13-bus and 123-bus distribution test feeders.
Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model
Authors: Chen Li, Mai Xu, Xinzhe Du, Zulin Wang
Date: 2018-07-29 00:00:00
Tasks: Visual Question Answering (VQA)

Omnidirectional video enables spherical stimuli with the $360 \times 180^
\circ$ viewing range. Meanwhile, only the viewport region of omnidirectional
video can be seen by the observer through head movement (HM), and an even
smaller region within the viewport can be clearly perceived through eye
movement (EM). Thus, the subjective quality of omnidirectional video may be
correlated with HM and EM of human behavior. To fill in the gap between
subjective quality and human behavior, this paper proposes a large-scale visual
quality assessment (VQA) dataset of omnidirectional video, called VQA-OV, which
collects 60 reference sequences and 540 impaired sequences. Our VQA-OV dataset
provides not only the subjective quality scores of sequences but also the HM
and EM data of subjects. By mining our dataset, we find that the subjective
quality of omnidirectional video is indeed related to HM and EM. Hence, we
develop a deep learning model, which embeds HM and EM, for objective VQA on
omnidirectional video. Experimental results show that our model significantly
improves the state-of-the-art performance of VQA on omnidirectional video.
Domain Robust Feature Extraction for Rapid Low Resource ASR Development
Authors: Siddharth Dalmia, Xinjian Li, Florian Metze, Alan W. black
Date: 2018-07-28 00:00:00

Developing a practical speech recognizer for a low resource language is
challenging, not only because of the (potentially unknown) properties of the
language, but also because test data may not be from the same domain as the
available training data. In this paper, we focus on the latter challenge, i.e.
domain mismatch, for systems trained using a sequence-based criterion. We
demonstrate the effectiveness of using a pre-trained English recognizer, which
is robust to such mismatched conditions, as a domain normalizing feature
extractor on a low resource language. In our example, we use Turkish
Conversational Speech and Broadcast News data. This enables rapid development
of speech recognizers for new languages which can easily adapt to any domain.
Testing in various cross-domain scenarios, we achieve relative improvements of
around 25% in phoneme error rate, with improvements being around 50% for some
domains.
Actor-Centric Relation Network
Authors: Chen Sun, Abhinav Shrivastava, Carl Vondrick, Kevin Murphy, Rahul Sukthankar, Cordelia Schmid
Date: 2018-07-28 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Classification, Action Detection, Action Localization, Action Recognition, Relation, Relation Network, Spatio-Temporal Action Localization, Temporal Action Localization

Current state-of-the-art approaches for spatio-temporal action localization
rely on detections at the frame level and model temporal context with 3D
ConvNets. Here, we go one step further and model spatio-temporal relations to
capture the interactions between human actors, relevant objects and scene
elements essential to differentiate similar human actions. Our approach is
weakly supervised and mines the relevant elements automatically with an
actor-centric relational network (ACRN). ACRN computes and accumulates
pair-wise relation information from actor and global scene features, and
generates relation features for action classification. It is implemented as
neural networks and can be trained jointly with an existing action detection
system. We show that ACRN outperforms alternative approaches which capture
relation information, and that the proposed framework improves upon the
state-of-the-art performance on JHMDB and AVA. A visualization of the learned
relation features confirms that our approach is able to attend to the relevant
relations for each action.
RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours
Authors: Raghav Mehta, Tal Arbel
Date: 2018-07-28 00:00:00

Accurate synthesis of a full 3D MR image containing tumours from available
MRI (e.g. to replace an image that is currently unavailable or corrupted) would
provide a clinician as well as downstream inference methods with important
complementary information for disease analysis. In this paper, we present an
end-to-end 3D convolution neural network that takes a set of acquired MR image
sequences (e.g. T1, T2, T1ce) as input and concurrently performs (1) regression
of the missing full resolution 3D MRI (e.g. FLAIR) and (2) segmentation of the
tumour into subtypes (e.g. enhancement, core). The hypothesis is that this
would focus the network to perform accurate synthesis in the area of the
tumour. Experiments on the BraTS 2015 and 2017 datasets [1] show that: (1) the
proposed method gives better performance than state-of-the-art methods in terms
of established global evaluation metrics (e.g. PSNR), (2) replacing real MR
volumes with the synthesized MRI does not lead to significant degradation in
tumour and sub-structure segmentation accuracy. The system further provides
uncertainty estimates based on Monte Carlo (MC) dropout [11] for the
synthesized volume at each voxel, permitting quantification of the system's
confidence in the output at each location.
Ontology-Grounded Topic Modeling for Climate Science Research
Authors: Jennifer Sleeman, Tim Finin, Milton Halem
Date: 2018-07-28 00:00:00

In scientific disciplines where research findings have a strong impact on
society, reducing the amount of time it takes to understand, synthesize and
exploit the research is invaluable. Topic modeling is an effective technique
for summarizing a collection of documents to find the main themes among them
and to classify other documents that have a similar mixture of co-occurring
words. We show how grounding a topic model with an ontology, extracted from a
glossary of important domain phrases, improves the topics generated and makes
them easier to understand. We apply and evaluate this method to the climate
science domain. The result improves the topics generated and supports faster
research understanding, discovery of social networks among researchers, and
automatic ontology generation.
Improving Sequential Determinantal Point Processes for Supervised Video Summarization
Authors: Aidean Sharghi, Ali Borji, Chengtao Li, Tianbao Yang, Boqing Gong
Date: 2018-07-28 00:00:00
Proceeding: ECCV 2018 9
Tasks: Diversity, Point Processes, Supervised Video Summarization, Video Summarization

It is now much easier than ever before to produce videos. While the
ubiquitous video data is a great source for information discovery and
extraction, the computational challenges are unparalleled. Automatically
summarizing the videos has become a substantial need for browsing, searching,
and indexing visual content. This paper is in the vein of supervised video
summarization using sequential determinantal point process (SeqDPP), which
models diversity by a probabilistic distribution. We improve this model in two
folds. In terms of learning, we propose a large-margin algorithm to address the
exposure bias problem in SeqDPP. In terms of modeling, we design a new
probabilistic distribution such that, when it is integrated into SeqDPP, the
resulting model accepts user input about the expected length of the summary.
Moreover, we also significantly extend a popular video summarization dataset by
1) more egocentric videos, 2) dense user annotations, and 3) a refined
evaluation scheme. We conduct extensive experiments on this dataset (about 60
hours of videos in total) and compare our approach to several competitive
baselines.
Group-sparse SVD Models and Their Applications in Biological Data
Authors: Wenwen Min, Juan Liu, Shihua Zhang
Date: 2018-07-28 00:00:00
Tasks: Variable Selection

Sparse Singular Value Decomposition (SVD) models have been proposed for
biclustering high dimensional gene expression data to identify block patterns
with similar expressions. However, these models do not take into account prior
group effects upon variable selection. To this end, we first propose
group-sparse SVD models with group Lasso (GL1-SVD) and group L0-norm penalty
(GL0-SVD) for non-overlapping group structure of variables. However, such
group-sparse SVD models limit their applicability in some problems with
overlapping structure. Thus, we also propose two group-sparse SVD models with
overlapping group Lasso (OGL1-SVD) and overlapping group L0-norm penalty
(OGL0-SVD). We first adopt an alternating iterative strategy to solve GL1-SVD
based on a block coordinate descent method, and GL0-SVD based on a projection
method. The key of solving OGL1-SVD is a proximal operator with overlapping
group Lasso penalty. We employ an alternating direction method of multipliers
(ADMM) to solve the proximal operator. Similarly, we develop an approximate
method to solve OGL0-SVD. Applications of these methods and comparison with
competing ones using simulated data demonstrate their effectiveness. Extensive
applications of them onto several real gene expression data with gene prior
group knowledge identify some biologically interpretable gene modules.
Building a Unified Code-Switching ASR System for South African Languages
Authors: Emre Yilmaz, Astik Biswas, Ewald van der Westhuizen, Febe De Wet, Thomas Niesler
Date: 2018-07-28 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

We present our first efforts towards building a single multilingual automatic
speech recognition (ASR) system that can process code-switching (CS) speech in
five languages spoken within the same population. This contrasts with related
prior work which focuses on the recognition of CS speech in bilingual
scenarios. Recently, we have compiled a small five-language corpus of South
African soap opera speech which contains examples of CS between 5 languages
occurring in various contexts such as using English as the matrix language and
switching to other indigenous languages. The ASR system presented in this work
is trained on 4 corpora containing English-isiZulu, English-isiXhosa,
English-Setswana and English-Sesotho CS speech. The interpolation of multiple
language models trained on these language pairs enables the ASR system to
hypothesize mixed word sequences from these 5 languages. We evaluate various
state-of-the-art acoustic models trained on this 5-lingual training data and
report ASR accuracy and language recognition performance on the development and
test sets of the South African multilingual soap opera corpus.
Articulatory Features for ASR of Pathological Speech
Authors: Emre Yilmaz, Vikramjit Mitra, Chris Bartels, Horacio Franco
Date: 2018-07-28 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

In this work, we investigate the joint use of articulatory and acoustic
features for automatic speech recognition (ASR) of pathological speech. Despite
long-lasting efforts to build speaker- and text-independent ASR systems for
people with dysarthria, the performance of state-of-the-art systems is still
considerably lower on this type of speech than on normal speech. The most
prominent reason for the inferior performance is the high variability in
pathological speech that is characterized by the spectrotemporal deviations
caused by articulatory impairments due to various etiologies. To cope with this
high variation, we propose to use speech representations which utilize
articulatory information together with the acoustic properties. A designated
acoustic model, namely a fused-feature-map convolutional neural network (fCNN),
which performs frequency convolution on acoustic features and time convolution
on articulatory features is trained and tested on a Dutch and a Flemish
pathological speech corpus. The ASR performance of fCNN-based ASR system using
joint features is compared to other neural network architectures such
conventional CNNs and time-frequency convolutional networks (TFCNNs) in several
training scenarios.
Acoustic and Textual Data Augmentation for Improved ASR of Code-Switching Speech
Authors: Emre Yilmaz, Henk van den Heuvel, David A. van Leeuwen
Date: 2018-07-28 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

In this paper, we describe several techniques for improving the acoustic and
language model of an automatic speech recognition (ASR) system operating on
code-switching (CS) speech. We focus on the recognition of Frisian-Dutch radio
broadcasts where one of the mixed languages, namely Frisian, is an
under-resourced language. In previous work, we have proposed several automatic
transcription strategies for CS speech to increase the amount of available
training speech data. In this work, we explore how the acoustic modeling (AM)
can benefit from monolingual speech data belonging to the high-resourced mixed
language. For this purpose, we train state-of-the-art AMs, which were
ineffective due to lack of training data, on a significantly increased amount
of CS speech and monolingual Dutch speech. Moreover, we improve the language
model (LM) by creating code-switching text, which is in practice almost
non-existent, by (1) generating text using recurrent LMs trained on the
transcriptions of the training CS speech data, (2) adding the transcriptions of
the automatically transcribed CS speech data and (3) translating Dutch text
extracted from the transcriptions of a large Dutch speech corpora. We report
significantly improved CS ASR performance due to the increase in the acoustic
and textual training data.
PROPEL: Probabilistic Parametric Regression Loss for Convolutional Neural Networks
Authors: Muhammad Asad, Rilwan Basaru, S M Masudur Rahman Al Arif, Greg Slabaugh
Date: 2018-07-28 00:00:00
Tasks: Head Pose Estimation, regression

In recent years, Convolutional Neural Networks (CNNs) have enabled significant advancements to the state-of-the-art in computer vision. For classification tasks, CNNs have widely employed probabilistic output and have shown the significance of providing additional confidence for predictions. However, such probabilistic methodologies are not widely applicable for addressing regression problems using CNNs, as regression involves learning unconstrained continuous and, in many cases, multi-variate target variables. We propose a PRObabilistic Parametric rEgression Loss (PROPEL) that facilitates CNNs to learn parameters of probability distributions for addressing probabilistic regression problems. PROPEL is fully differentiable and, hence, can be easily incorporated for end-to-end training of existing CNN regression architectures using existing optimization algorithms. The proposed method is flexible as it enables learning complex unconstrained probabilities while being generalizable to higher dimensional multi-variate regression problems. We utilize a PROPEL-based CNN to address the problem of learning hand and head orientation from uncalibrated color images. Our experimental validation and comparison with existing CNN regression loss functions show that PROPEL improves the accuracy of a CNN by enabling probabilistic regression, while significantly reducing required model parameters by $10 \times$, resulting in improved generalization as compared to the existing state-of-the-art.
Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception
Authors: Federico Paredes-Vallés, Kirk Y. W. Scheper, Guido C. H. E. de Croon
Date: 2018-07-28 00:00:00
Tasks: Event-based vision, GPU, Optical Flow Estimation

The combination of spiking neural networks and event-based vision sensors
holds the potential of highly efficient and high-bandwidth optical flow
estimation. This paper presents the first hierarchical spiking architecture in
which motion (direction and speed) selectivity emerges in an unsupervised
fashion from the raw stimuli generated with an event-based camera. A novel
adaptive neuron model and stable spike-timing-dependent plasticity formulation
are at the core of this neural network governing its spike-based processing and
learning, respectively. After convergence, the neural architecture exhibits the
main properties of biological visual motion systems, namely feature extraction
and local and global motion perception. Convolutional layers with input
synapses characterized by single and multiple transmission delays are employed
for feature and local motion perception, respectively; while global motion
selectivity emerges in a final fully-connected layer. The proposed solution is
validated using synthetic and real event sequences. Along with this paper, we
provide the cuSNN library, a framework that enables GPU-accelerated simulations
of large-scale spiking neural networks. Source code and samples are available
at https://github.com/tudelft/cuSNN.
Towards Explainable Inference about Object Motion using Qualitative Reasoning
Authors: Xiaoyu Ge, Jochen Renz, Hua Hua
Date: 2018-07-28 00:00:00

The capability of making explainable inferences regarding physical processes
has long been desired. One fundamental physical process is object motion.
Inferring what causes the motion of a group of objects can even be a
challenging task for experts, e.g., in forensics science. Most of the work in
the literature relies on physics simulation to draw such infer- ences. The
simulation requires a precise model of the under- lying domain to work well and
is essentially a black-box from which one can hardly obtain any useful
explanation. By contrast, qualitative reasoning methods have the advan- tage in
making transparent inferences with ambiguous infor- mation, which makes it
suitable for this task. However, there has been no suitable qualitative theory
proposed for object motion in three-dimensional space. In this paper, we take
this challenge and develop a qualitative theory for the motion of rigid
objects. Based on this theory, we develop a reasoning method to solve a very
interesting problem: Assuming there are several objects that were initially at
rest and now have started to move. We want to infer what action causes the
movement of these objects.
Bike Flow Prediction with Multi-Graph Convolutional Networks
Authors: Di Chai, Leye Wang, Qiang Yang
Date: 2018-07-28 00:00:00
Tasks: Management, Prediction

One fundamental issue in managing bike sharing systems is the bike flow
prediction. Due to the hardness of predicting the flow for a single station,
recent research works often predict the bike flow at cluster-level. While such
studies gain satisfactory prediction accuracy, they cannot directly guide some
fine-grained bike sharing system management issues at station-level. In this
paper, we revisit the problem of the station-level bike flow prediction, aiming
to boost the prediction accuracy leveraging the breakthroughs of deep learning
techniques. We propose a new multi-graph convolutional neural network model to
predict the bike flow at station-level, where the key novelty is viewing the
bike sharing system from the graph perspective. More specifically, we construct
multiple inter-station graphs for a bike sharing system. In each graph, nodes
are stations, and edges are a certain type of relations between stations. Then,
multiple graphs are constructed to reflect heterogeneous relationships (e.g.,
distance, ride record correlation). Afterward, we fuse the multiple graphs and
then apply the convolutional layers on the fused graph to predict station-level
future bike flow. In addition to the estimated bike flow value, our model also
gives the prediction confidence interval so as to help the bike sharing system
managers make decisions. Using New York City and Chicago bike sharing data for
experiments, our model can outperform state-of-the-art station-level prediction
models by reducing 25.1% and 17.0% of prediction error in New York City and
Chicago, respectively.
Deep Leaf Segmentation Using Synthetic Data
Authors: Daniel Ward, Peyman Moghadam, Nicolas Hudson
Date: 2018-07-28 00:00:00
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

Automated segmentation of individual leaves of a plant in an image is a
prerequisite to measure more complex phenotypic traits in high-throughput
phenotyping. Applying state-of-the-art machine learning approaches to tackle
leaf instance segmentation requires a large amount of manually annotated
training data. Currently, the benchmark datasets for leaf segmentation contain
only a few hundred labeled training images. In this paper, we propose a
framework for leaf instance segmentation by augmenting real plant datasets with
generated synthetic images of plants inspired by domain randomisation. We train
a state-of-the-art deep learning segmentation architecture (Mask-RCNN) with a
combination of real and synthetic images of Arabidopsis plants. Our proposed
approach achieves 90% leaf segmentation score on the A1 test set outperforming
the-state-of-the-art approaches for the CVPPP Leaf Segmentation Challenge
(LSC). Our approach also achieves 81% mean performance over all five test
datasets.
Fine-Grained Visual Categorization using Meta-Learning Optimization with Sample Selection of Auxiliary Data
Authors: Yabin Zhang, Hui Tang, Kui Jia
Date: 2018-07-28 00:00:00
Proceeding: ECCV 2018 9
Tasks: Fine-Grained Visual Categorization, Meta-Learning

Fine-grained visual categorization (FGVC) is challenging due in part to the
fact that it is often difficult to acquire an enough number of training
samples. To employ large models for FGVC without suffering from overfitting,
existing methods usually adopt a strategy of pre-training the models using a
rich set of auxiliary data, followed by fine-tuning on the target FGVC task.
However, the objective of pre-training does not take the target task into
account, and consequently such obtained models are suboptimal for fine-tuning.
To address this issue, we propose in this paper a new deep FGVC model termed
MetaFGNet. Training of MetaFGNet is based on a novel regularized meta-learning
objective, which aims to guide the learning of network parameters so that they
are optimal for adapting to the target FGVC task. Based on MetaFGNet, we also
propose a simple yet effective scheme for selecting more useful samples from
the auxiliary data. Experiments on benchmark FGVC datasets show the efficacy of
our proposed method.