pwc-archive/papers-with-abstracts huggingface dataset:
X-Ray Image Compression Using Convolutional Recurrent Neural Networks
Authors: Asif Shahriyar Sushmit, Shakib Uz Zaman, Ahmed Imtiaz Humayun, Taufiq Hasan, Mohammed Imamul Hassan Bhuiyan
Date: 2019-04-28 00:00:00
Tasks: Image Compression, Retrieval, SSIM, X-Ray Image Compression

In the advent of a digital health revolution, vast amounts of clinical data are being generated, stored and processed on a daily basis. This has made the storage and retrieval of large volumes of health-care data, especially, high-resolution medical images, particularly challenging. Effective image compression for medical images thus plays a vital role in today's healthcare information system, particularly in teleradiology. In this work, an X-ray image compression method based on a Convolutional Recurrent Neural Networks RNN-Conv is presented. The proposed architecture can provide variable compression rates during deployment while it requires each network to be trained only once for a specific dimension of X-ray images. The model uses a multi-level pooling scheme that learns contextualized features for effective compression. We perform our image compression experiments on the National Institute of Health (NIH) ChestX-ray8 dataset and compare the performance of the proposed architecture with a state-of-the-art RNN based technique and JPEG 2000. The experimental results depict improved compression performance achieved by the proposed method in terms of Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) metrics. To the best of our knowledge, this is the first reported evaluation on using a deep convolutional RNN for medical image compression.
Spatio-Temporal Filter Adaptive Network for Video Deblurring
Authors: Shangchen Zhou, Jiawei Zhang, Jinshan Pan, Haozhe Xie, WangMeng Zuo, Jimmy Ren
Date: 2019-04-28 00:00:00
Proceeding: ICCV 2019 10
Tasks: Deblurring, Image Deblurring, Optical Flow Estimation, Video Deblurring

Video deblurring is a challenging task due to the spatially variant blur caused by camera shake, object motions, and depth variations, etc. Existing methods usually estimate optical flow in the blurry video to align consecutive frames or approximate blur kernels. However, they tend to generate artifacts or cannot effectively remove blur when the estimated optical flow is not accurate. To overcome the limitation of separate optical flow estimation, we propose a Spatio-Temporal Filter Adaptive Network (STFAN) for the alignment and deblurring in a unified framework. The proposed STFAN takes both blurry and restored images of the previous frame as well as blurry image of the current frame as input, and dynamically generates the spatially adaptive filters for the alignment and deblurring. We then propose the new Filter Adaptive Convolutional (FAC) layer to align the deblurred features of the previous frame with the current frame and remove the spatially variant blur from the features of the current frame. Finally, we develop a reconstruction network which takes the fusion of two transformed features to restore the clear frames. Both quantitative and qualitative evaluation results on the benchmark datasets and real-world videos demonstrate that the proposed algorithm performs favorably against state-of-the-art methods in terms of accuracy, speed as well as model size.
Non-myopic Planetary Exploration Combining In Situ and Remote Measurements
Authors: Suhit Kodgule, Alberto Candela, David Wettergreen
Date: 2019-04-28 00:00:00

Remote sensing can provide crucial information for planetary rovers. However,
they must validate these orbital observations with in situ measurements.
Typically, this involves validating hyperspectral data using a spectrometer
on-board the field robot. In order to achieve this, the robot must visit
sampling locations that jointly improve a model of the environment while
satisfying sampling constraints. However, current planners follow sub-optimal
greedy strategies that are not scalable to larger regions. We demonstrate how
the problem can be effectively defined in an MDP framework and propose a
planning algorithm based on Monte Carlo Tree Search, which is devoid of the
common drawbacks of existing planners and also provides superior performance.
We evaluate our approach using hyperspectral imagery of a well-studied geologic
site in Cuprite, Nevada.
Translate-to-Recognize Networks for RGB-D Scene Recognition
Authors: Dapeng Du, Li-Min Wang, Huiling Wang, Kai Zhao, Gangshan Wu
Date: 2019-04-28 00:00:00
Proceeding: CVPR 2019 6
Tasks: Decoder, Scene Recognition, Translation

Cross-modal transfer is helpful to enhance modality-specific discriminative
power for scene recognition. To this end, this paper presents a unified
framework to integrate the tasks of cross-modal translation and
modality-specific recognition, termed as Translate-to-Recognize Network
(TRecgNet). Specifically, both translation and recognition tasks share the same
encoder network, which allows to explicitly regularize the training of
recognition task with the help of translation, and thus improve its final
generalization ability. For translation task, we place a decoder module on top
of the encoder network and it is optimized with a new layer-wise semantic loss,
while for recognition task, we use a linear classifier based on the feature
embedding from encoder and its training is guided by the standard cross-entropy
loss. In addition, our TRecgNet allows to exploit large numbers of unlabeled
RGB-D data to train the translation task and thus improve the representation
power of encoder network. Empirically, we verify that this new semi-supervised
setting is able to further enhance the performance of recognition network. We
perform experiments on two RGB-D scene recognition benchmarks: NYU Depth v2 and
SUN RGB-D, demonstrating that TRecgNet achieves superior performance to the
existing state-of-the-art methods, especially for recognition solely based on a
single modality.
Hierarchical Recurrent Neural Network for Video Summarization
Authors: Bin Zhao, Xuelong. Li, Xiaoqiang Lu
Date: 2019-04-28 00:00:00
Tasks: Video Captioning, Video Summarization

Exploiting the temporal dependency among video frames or subshots is very
important for the task of video summarization. Practically, RNN is good at
temporal dependency modeling, and has achieved overwhelming performance in many
video-based tasks, such as video captioning and classification. However, RNN is
not capable enough to handle the video summarization task, since traditional
RNNs, including LSTM, can only deal with short videos, while the videos in the
summarization task are usually in longer duration. To address this problem, we
propose a hierarchical recurrent neural network for video summarization, called
H-RNN in this paper. Specifically, it has two layers, where the first layer is
utilized to encode short video subshots cut from the original video, and the
final hidden state of each subshot is input to the second layer for calculating
its confidence to be a key subshot. Compared to traditional RNNs, H-RNN is more
suitable to video summarization, since it can exploit long temporal dependency
among frames, meanwhile, the computation operations are significantly lessened.
The results on two popular datasets, including the Combined dataset and VTW
dataset, have demonstrated that the proposed H-RNN outperforms the
state-of-the-arts.
Weighted Dark Channel Dehazing
Authors: Zhu Mingzhu, He Bingwei, Liu Jiantao
Date: 2019-04-28 00:00:00

In dark channel based methods, local constant assumption is widely used to
make the algorithms invertible. It inevitably introduces defects since the
assumption can not perfectly avoid depth discontinuities and meanwhile cover
enough pixels. Unfortunately, because of the limitation of the prior, which
only confirms the existence of dark things but does not specify their locations
or likelihood, no fidelity measurement is available in refinement thus the
defects are either under-corrected or over-corrected. In this paper, we go
deeper than the dark channel theory to overcome this problem. We split the
concept of dark channel into dark pixels and local constant assumption, and
then, control the problematic assumption based on a novel weight map. With such
effort, our methods show significant improvement on quality and have
competitive speed. In the last, we show that the method is highly robust to
initial transmission estimates and can be ever-improved by providing better
dark pixel locations.
Enhancement of Power Equipment Management Using Knowledge Graph
Authors: Yachen Tang, Tingting Liu, Guangyi Liu, Jie Li, Renchang Dai, Chen Yuan
Date: 2019-04-28 00:00:00
Tasks: Asset Management, Management, Retrieval

Accurate retrieval of the power equipment information plays an important role
in guiding the full-lifecycle management of power system assets. Because of
data duplication, database decentralization, weak data relations, and sluggish
data updates, the power asset management system eager to adopt a new strategy
to avoid the information losses, bias, and improve the data storage efficiency
and extraction process. Knowledge graph has been widely developed in large part
owing to its schema-less nature. It enables the knowledge graph to grow
seamlessly and allows new relations addition and entities insertion when
needed. This study proposes an approach for constructing power equipment
knowledge graph by merging existing multi-source heterogeneous power equipment
related data. A graph-search method to illustrate exhaustive results to the
desired information based on the constructed knowledge graph is proposed. A
case of a 500 kV station example is then demonstrated to show relevant search
results and to explain that the knowledge graph can improve the efficiency of
power equipment management.
Sistema Sensor para el Monitoreo Ambiental Basado en Redes Neuronales
Authors: Jose de Jesus Rubio, Jose Alberto Hernandez-Aguilar, Francisco Jacob Avila-Camacho, Juan Manuel Stein-Carrillo, Adolfo Melendez-Ramirez
Date: 2019-04-28 00:00:00
Tasks: Management

In the tasks of environmental monitoring is of great importance to have
compact and portable systems able to identify environmental contaminants that
facilitate tasks related to waste management and environmental restoration. In
this paper, a prototype sensor is described to identify contaminants in the
environment. This prototype is made with an array of tin oxide SnO2 gas sensors
used to identify chemical vapors, a step of data acquisition implemented with
ARM (Advanced RISC Machine) low-cost platform (Arduino) and a neural network
able to identify environmental contaminants automatically. The neural network
is used to identify the composition of contaminant census. In the computer
system, the heavy computational load is presented only in the training process,
once the neural network has been trained, the operation is to spread the data
across the network with a much lighter computational load, which consists
mainly of a vector-matrix multiplication and a search table that holds the
activation function to quickly identify unknown samples.
Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without
Authors: Sébastien Bubeck, Yuanzhi Li, Yuval Peres, Mark Sellke
Date: 2019-04-28 00:00:00
Tasks: Multi-Armed Bandits

We consider the non-stochastic version of the (cooperative) multi-player
multi-armed bandit problem. The model assumes no communication at all between
the players, and furthermore when two (or more) players select the same action
this results in a maximal loss. We prove the first $\sqrt{T}$-type regret
guarantee for this problem, under the feedback model where collisions are
announced to the colliding players. Such a bound was not known even for the
simpler stochastic version. We also prove the first sublinear guarantee for the
feedback model where collision information is not available, namely
$T^{1-\frac{1}{2m}}$ where $m$ is the number of players.
Arbitrage of Energy Storage in Electricity Markets with Deep Reinforcement Learning
Authors: Hanchen Xu, Xiao Li, Xiangyu Zhang, Junbo Zhang
Date: 2019-04-28 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this letter, we address the problem of controlling energy storage systems (ESSs) for arbitrage in real-time electricity markets under price uncertainty. We first formulate this problem as a Markov decision process, and then develop a deep reinforcement learning based algorithm to learn a stochastic control policy that maps a set of available information processed by a recurrent neural network to ESSs' charging/discharging actions. Finally, we verify the effectiveness of our algorithm using real-time electricity prices from PJM.
A Deep Generative Model for Graph Layout
Authors: Oh-Hyun Kwon, Kwan-Liu Ma
Date: 2019-04-27 00:00:00
Tasks: 3D Depth Estimation, Decoder, Layout Design, model, Navigate

Different layouts can characterize different aspects of the same graph. Finding a "good" layout of a graph is thus an important task for graph visualization. In practice, users often visualize a graph in multiple layouts by using different methods and varying parameter settings until they find a layout that best suits the purpose of the visualization. However, this trial-and-error process is often haphazard and time-consuming. To provide users with an intuitive way to navigate the layout design space, we present a technique to systematically visualize a graph in diverse layouts using deep generative models. We design an encoder-decoder architecture to learn a model from a collection of example layouts, where the encoder represents training examples in a latent space and the decoder produces layouts from the latent space. In particular, we train the model to construct a two-dimensional latent space for users to easily explore and generate various layouts. We demonstrate our approach through quantitative and qualitative evaluations of the generated layouts. The results of our evaluations show that our model is capable of learning and generalizing abstract concepts of graph layouts, not just memorizing the training examples. In summary, this paper presents a fundamentally new approach to graph visualization where a machine learning model learns to visualize a graph from examples without manually-defined heuristics.
Collage Inference: Using Coded Redundancy for Low Variance Distributed Image Classification
Authors: Krishna Giri Narra, Zhifeng Lin, Ganesh Ananthanarayanan, Salman Avestimehr, Murali Annavaram
Date: 2019-04-27 00:00:00
Tasks: Classification, Cloud Computing, General Classification, image-classification, Image Classification, Object Detection

MLaaS (ML-as-a-Service) offerings by cloud computing platforms are becoming increasingly popular. Hosting pre-trained machine learning models in the cloud enables elastic scalability as the demand grows. But providing low latency and reducing the latency variance is a key requirement. Variance is harder to control in a cloud deployment due to uncertainties in resource allocations across many virtual instances. We propose the collage inference technique which uses a novel convolutional neural network model, collage-cnn, to provide low-cost redundancy. A collage-cnn model takes a collage image formed by combining multiple images and performs multi-image classification in one shot, albeit at slightly lower accuracy. We augment a collection of traditional single image classifier models with a single collage-cnn classifier which acts as their low-cost redundant backup. Collage-cnn provides backup classification results if any single image classification requests experience slowdown. Deploying the collage-cnn models in the cloud, we demonstrate that the 99th percentile tail latency of inference can be reduced by 1.2x to 2x compared to replication based approaches while providing high accuracy. Variation in inference latency can be reduced by 1.8x to 15x.
Analysis of Confident-Classifiers for Out-of-distribution Detection
Authors: Sachin Vernekar, Ashish Gaurav, Taylor Denouden, Buu Phan, Vahdat Abdelzad, Rick Salay, Krzysztof Czarnecki
Date: 2019-04-27 00:00:00
Tasks: General Classification, image-classification, Image Classification, Out-of-Distribution Detection, Out of Distribution (OOD) Detection

Discriminatively trained neural classifiers can be trusted, only when the
input data comes from the training distribution (in-distribution). Therefore,
detecting out-of-distribution (OOD) samples is very important to avoid
classification errors. In the context of OOD detection for image
classification, one of the recent approaches proposes training a classifier
called "confident-classifier" by minimizing the standard cross-entropy loss on
in-distribution samples and minimizing the KL divergence between the predictive
distribution of OOD samples in the low-density regions of in-distribution and
the uniform distribution (maximizing the entropy of the outputs). Thus, the
samples could be detected as OOD if they have low confidence or high entropy.
In this paper, we analyze this setting both theoretically and experimentally.
We conclude that the resulting confident-classifier still yields arbitrarily
high confidence for OOD samples far away from the in-distribution. We instead
suggest training a classifier by adding an explicit "reject" class for OOD
samples.
Graph Kernels: A Survey
Authors: Giannis Nikolentzos, Giannis Siglidis, Michalis Vazirgiannis
Date: 2019-04-27 00:00:00
Tasks: Graph Classification, Survey

Graph kernels have attracted a lot of attention during the last decade, and have evolved into a rapidly developing branch of learning on structured data. During the past 20 years, the considerable research activity that occurred in the field resulted in the development of dozens of graph kernels, each focusing on specific structural properties of graphs. Graph kernels have proven successful in a wide range of domains, ranging from social networks to bioinformatics. The goal of this survey is to provide a unifying view of the literature on graph kernels. In particular, we present a comprehensive overview of a wide range of graph kernels. Furthermore, we perform an experimental evaluation of several of those kernels on publicly available datasets, and provide a comparative study. Finally, we discuss key applications of graph kernels, and outline some challenges that remain to be addressed.
Towards Recognizing Phrase Translation Processes: Experiments on English-French
Authors: Yuming Zhai, Pooyan Safari, Gabriel Illouz, Alexandre Allauzen, Anne Vilnat
Date: 2019-04-27 00:00:00
Tasks: General Classification, Translation

When translating phrases (words or group of words), human translators,
consciously or not, resort to different translation processes apart from the
literal translation, such as Idiom Equivalence, Generalization,
Particularization, Semantic Modulation, etc. Translators and linguists (such as
Vinay and Darbelnet, Newmark, etc.) have proposed several typologies to
characterize the different translation processes. However, to the best of our
knowledge, there has not been effort to automatically classify these
fine-grained translation processes. Recently, an English-French parallel corpus
of TED Talks has been manually annotated with translation process categories,
along with established annotation guidelines. Based on these annotated
examples, we propose an automatic classification of translation processes at
subsentential level. Experimental results show that we can distinguish
non-literal translation from literal translation with an accuracy of 87.09%,
and 55.20% for classifying among five non-literal translation processes. This
work demonstrates that it is possible to automatically classify translation
processes. Even with a small amount of annotated examples, our experiments show
the directions that we can follow in future work. One of our long term
objectives is leveraging this automatic classification to better control
paraphrase extraction from bilingual parallel corpora.
Soft Marginal TransE for Scholarly Knowledge Graph Completion
Authors: Mojtaba Nayyeri, Sahar Vahdati, Jens Lehmann, Hamed Shariat Yazdi
Date: 2019-04-27 00:00:00
Tasks: Knowledge Graph Completion, Knowledge Graphs, Link Prediction, Question Answering

Knowledge graphs (KGs), i.e. representation of information as a semantic
graph, provide a significant test bed for many tasks including question
answering, recommendation, and link prediction. Various amount of scholarly
metadata have been made vailable as knowledge graphs from the diversity of data
providers and agents. However, these high-quantities of data remain far from
quality criteria in terms of completeness while growing at a rapid pace. Most
of the attempts in completing such KGs are following traditional data
digitization, harvesting and collaborative curation approaches. Whereas,
advanced AI-related approaches such as embedding models - specifically designed
for such tasks - are usually evaluated for standard benchmarks such as Freebase
and Wordnet. The tailored nature of such datasets prevents those approaches to
shed the lights on more accurate discoveries. Application of such models on
domain-specific KGs takes advantage of enriched meta-data and provides accurate
results where the underlying domain can enormously benefit. In this work, the
TransE embedding model is reconciled for a specific link prediction task on
scholarly metadata. The results show a significant shift in the accuracy and
performance evaluation of the model on a dataset with scholarly metadata. The
newly proposed version of TransE obtains 99.9% for link prediction task while
original TransE gets 95%. In terms of accuracy and Hit@10, TransE outperforms
other embedding models such as ComplEx, TransH and TransR experimented over
scholarly knowledge graphs
Temporal-Clustering Invariance in Irregular Healthcare Time Series
Authors: Mohammad Taha Bahadori, Zachary Chase Lipton
Date: 2019-04-27 00:00:00
Tasks: Clustering, Data Augmentation, Irregular Time Series, Mortality Prediction, Time Series, Time Series Analysis

Electronic records contain sequences of events, some of which take place all
at once in a single visit, and others that are dispersed over multiple visits,
each with a different timestamp. We postulate that fine temporal detail, e.g.,
whether a series of blood tests are completed at once or in rapid succession
should not alter predictions based on this data. Motivated by this intuition,
we propose models for analyzing sequences of multivariate clinical time series
data that are invariant to this temporal clustering. We propose an efficient
data augmentation technique that exploits the postulated temporal-clustering
invariance to regularize deep neural networks optimized for several clinical
prediction tasks. We introduce two techniques to temporally coarsen
(downsample) irregular time series: (i) grouping the data points based on
regularly-spaced timestamps; and (ii) clustering them, yielding
irregularly-paced timestamps. Moreover, we propose a MultiResolution Ensemble
(MRE) model, improving predictive accuracy by ensembling predictions based on
inputs sequences transformed by different coarsening operators. Our experiments
show that MRE improves the mAP on the benchmark mortality prediction task from
51.53% to 53.92%.