pwc-archive/papers-with-abstracts huggingface dataset:
Remote Sensing Image Fusion Based on Two-stream Fusion Network
Authors: Xiangyu Liu, Qingjie Liu, Yunhong Wang
Date: 2017-11-07 00:00:00
Tasks: Image Reconstruction, Super-Resolution, Vocal Bursts Valence Prediction

Remote sensing image fusion (also known as pan-sharpening) aims at generating
high resolution multi-spectral (MS) image from inputs of a high spatial
resolution single band panchromatic (PAN) image and a low spatial resolution
multi-spectral image. Inspired by the astounding achievements of convolutional
neural networks (CNNs) in a variety of computer vision tasks, in this paper, we
propose a two-stream fusion network (TFNet) to address the problem of
pan-sharpening. Unlike previous CNN based methods that consider pan-sharpening
as a super resolution problem and perform pan-sharpening in pixel level, the
proposed TFNet aims to fuse PAN and MS images in feature level and reconstruct
the pan-sharpened image from the fused features. The TFNet mainly consists of
three parts. The first part is comprised of two networks extracting features
from PAN and MS images, respectively. The subsequent network fuses them
together to form compact features that represent both spatial and spectral
information of PAN and MS images, simultaneously. Finally, the desired high
spatial resolution MS image is recovered from the fused features through an
image reconstruction network. Experiments on Quickbird and \mbox{GaoFen-1}
satellite images demonstrate that the proposed TFNet can fuse PAN and MS
images, effectively, and produce pan-sharpened images competitive with even
superior to state of the arts.
Efficient Parallel Connected Components Labeling with a Coarse-to-fine Strategy
Authors: Jun Chen, Keisuke Nonaka, Ryosuke Watanabe, Hiroshi Sankoh, Houari Sabirin, Sei Naito
Date: 2017-12-28 00:00:00

This paper proposes a new parallel approach to solve connected components on
a 2D binary image implemented with CUDA. We employ the following strategies to
accelerate neighborhood exploration after dividing an input image into
independent blocks. In the local labeling stage, a coarse-labeling algorithm,
including row-column connection and label-equivalence list unification, is
applied first to sort out the mess of an initialized local label map; a
refinement algorithm is then introduced to merge separated sub-regions from a
single component. In the block merge stage, we scan the pixels located on the
boundary of each block instead of solving the connectivity of all the pixels.
With the proposed method, the length of label-equivalence lists is compressed,
and the number of memory accesses is reduced. Thus, the efficiency of connected
components labeling is improved. Experimental results show that our method
outperforms the other approaches between $29\%$ and $80\%$ on average.
Cloud Detection From RGB Color Remote Sensing Images With Deep Pyramid Networks
Authors: Savas Ozkan, Mehmet Efendioglu, Caner Demirpolat
Date: 2018-01-26 00:00:00
Tasks: Cloud Detection

Cloud detection from remotely observed data is a critical pre-processing step
for various remote sensing applications. In particular, this problem becomes
even harder for RGB color images, since there is no distinct spectral pattern
for clouds, which is directly separable from the Earth surface. In this paper,
we adapt a deep pyramid network (DPN) to tackle this problem. For this purpose,
the network is enhanced with a pre-trained parameter model at the encoder
layer. Moreover, the method is able to obtain accurate pixel-level segmentation
and classification results from a set of noisy labeled RGB color images. In
order to demonstrate the superiority of the method, we collect and label data
with the corresponding cloud/non-cloudy masks acquired from low-orbit Gokturk-2
and RASAT satellites. The experimental results validates that the proposed
method outperforms several baselines even for hard cases (e.g. snowy mountains)
that are perceptually difficult to distinguish by human eyes.
Improving Bi-directional Generation between Different Modalities with Variational Autoencoders
Authors: Masahiro Suzuki, Kotaro Nakayama, Yutaka Matsuo
Date: 2018-01-26 00:00:00

We investigate deep generative models that can exchange multiple modalities
bi-directionally, e.g., generating images from corresponding texts and vice
versa. A major approach to achieve this objective is to train a model that
integrates all the information of different modalities into a joint
representation and then to generate one modality from the corresponding other
modality via this joint representation. We simply applied this approach to
variational autoencoders (VAEs), which we call a joint multimodal variational
autoencoder (JMVAE). However, we found that when this model attempts to
generate a large dimensional modality missing at the input, the joint
representation collapses and this modality cannot be generated successfully.
Furthermore, we confirmed that this difficulty cannot be resolved even using a
known solution. Therefore, in this study, we propose two models to prevent this
difficulty: JMVAE-kl and JMVAE-h. Results of our experiments demonstrate that
these methods can prevent the difficulty above and that they generate
modalities bi-directionally with equal or higher likelihood than conventional
VAE methods, which generate in only one direction. Moreover, we confirm that
these methods can obtain the joint representation appropriately, so that they
can generate various variations of modality by moving over the joint
representation or changing the value of another modality.
Syntactic Enhancement to VSIMM for Roadmap Based Anomalous Trajectory Detection: A Natural Language Processing Approach
Authors: Vikram Krishnamurthy, Sijia Gao
Date: 2016-11-10 00:00:00

The aim of syntactic tracking is to classify spatio-temporal patterns of a
target's motion using natural language processing models. In this paper, we
generalize earlier work by considering a constrained stochastic context free
grammar (CSCFG) for modeling patterns confined to a roadmap. The constrained
grammar facilitates modeling specific directions and road names in a roadmap.
We present a novel particle filtering algorithm that exploits the CSCFG model
for estimating the target's patterns. This meta-level algorithm operates in
conjunction with a base-level tracking algorithm. Extensive numerical results
using simulated ground moving target indicator (GMTI) radar measurements show
substantial improvement in target tracking accuracy.
Neural Algebra of Classifiers
Authors: Rodrigo Santa Cruz, Basura Fernando, Anoop Cherian, Stephen Gould
Date: 2018-01-26 00:00:00

The world is fundamentally compositional, so it is natural to think of visual
recognition as the recognition of basic visually primitives that are composed
according to well-defined rules. This strategy allows us to recognize unseen
complex concepts from simple visual primitives. However, the current trend in
visual recognition follows a data greedy approach where huge amounts of data
are required to learn models for any desired visual concept. In this paper, we
build on the compositionality principle and develop an "algebra" to compose
classifiers for complex visual concepts. To this end, we learn neural network
modules to perform boolean algebra operations on simple visual classifiers.
Since these modules form a complete functional set, a classifier for any
complex visual concept defined as a boolean expression of primitives can be
obtained by recursively applying the learned modules, even if we do not have a
single training sample. As our experiments show, using such a framework, we can
compose classifiers for complex visual concepts outperforming standard
baselines on two well-known visual recognition benchmarks. Finally, we present
a qualitative analysis of our method and its properties.
21 Million Opportunities: A 19 Facility Investigation of Factors Affecting Hand Hygiene Compliance via Linear Predictive Models
Authors: Michael T. Lash, Jason Slater, Philip M. Polgreen, Alberto M. Segre
Date: 2018-01-26 00:00:00

This large-scale study, consisting of 21.3 million hand hygiene opportunities
from 19 distinct facilities in 10 different states, uses linear predictive
models to expose factors that may affect hand hygiene compliance. We examine
the use of features such as temperature, relative humidity, influenza severity,
day/night shift, federal holidays and the presence of new medical residents in
predicting daily hand hygiene compliance; the investigation is undertaken using
both a "global" model to glean general trends, and facility-specific models to
elicit facility-specific insights. The results suggest that colder temperatures
and federal holidays have an adverse effect on hand hygiene compliance rates,
and that individual cultures and attitudes regarding hand hygiene exist among
facilities.
Finding ReMO (Related Memory Object): A Simple Neural Architecture for Text based Reasoning
Authors: Jihyung Moon, Hyochang Yang, Sungzoon Cho
Date: 2018-01-25 00:00:00
Proceeding: ICLR 2018 1
Tasks: Question Answering, Relation, Relational Reasoning, Relation Network

To solve the text-based question and answering task that requires relational
reasoning, it is necessary to memorize a large amount of information and find
out the question relevant information from the memory. Most approaches were
based on external memory and four components proposed by Memory Network. The
distinctive component among them was the way of finding the necessary
information and it contributes to the performance. Recently, a simple but
powerful neural network module for reasoning called Relation Network (RN) has
been introduced. We analyzed RN from the view of Memory Network, and realized
that its MLP component is able to reveal the complicate relation between
question and object pair. Motivated from it, we introduce which uses MLP to
find out relevant information on Memory Network architecture. It shows new
state-of-the-art results in jointly trained bAbI-10k story-based question
answering tasks and bAbI dialog-based question answering tasks.
Context Models for OOV Word Translation in Low-Resource Languages
Authors: Angli Liu, Katrin Kirchhoff
Date: 2018-01-26 00:00:00
Proceeding: WS 2018 3
Tasks: Machine Translation, Sentence, Translation, Word Translation

Out-of-vocabulary word translation is a major problem for the translation of
low-resource languages that suffer from a lack of parallel training data. This
paper evaluates the contributions of target-language context models towards the
translation of OOV words, specifically in those cases where OOV translations
are derived from external knowledge sources, such as dictionaries. We develop
both neural and non-neural context models and evaluate them within both
phrase-based and self-attention based neural machine translation systems. Our
results show that neural language models that integrate additional context
beyond the current sentence are the most effective in disambiguating possible
OOV word translations. We present an efficient second-pass lattice-rescoring
method for wide-context neural language models and demonstrate performance
improvements over state-of-the-art self-attention based neural MT systems in
five out of six low-resource language pairs.
PointCNN: Convolution On $\mathcal{X}$-Transformed Points
Authors: Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, Baoquan Chen
Date: 2018-01-23 00:00:00
Proceeding: NeurIPS 2018
Tasks: 3D Instance Segmentation, 3D Part Segmentation, 3D Point Cloud Classification, Few-Shot 3D Point Cloud Classification

We present a simple and general framework for feature learning from point
clouds. The key to the success of CNNs is the convolution operator that is
capable of leveraging spatially-local correlation in data represented densely
in grids (e.g. images). However, point clouds are irregular and unordered, thus
directly convolving kernels against features associated with the points, will
result in desertion of shape information and variance to point ordering. To
address these problems, we propose to learn an $\mathcal{X}$-transformation
from the input points, to simultaneously promote two causes. The first is the
weighting of the input features associated with the points, and the second is
the permutation of the points into a latent and potentially canonical order.
Element-wise product and sum operations of the typical convolution operator are
subsequently applied on the $\mathcal{X}$-transformed features. The proposed
method is a generalization of typical CNNs to feature learning from point
clouds, thus we call it PointCNN. Experiments show that PointCNN achieves on
par or better performance than state-of-the-art methods on multiple challenging
benchmark datasets and tasks.
Ontology-based Fuzzy Markup Language Agent for Student and Robot Co-Learning
Authors: Chang-Shing Lee, Mei-Hui Wang, Tzong-Xiang Huang, Li-Chung Chen, Yung-Ching Huang, Sheng-Chi Yang, Chien-Hsun Tseng, Pi-Hsia Hung, Naoyuki Kubota
Date: 2018-01-26 00:00:00
Tasks: BIG-bench Machine Learning

An intelligent robot agent based on domain ontology, machine learning
mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning
is presented in this paper. The machine-human co-learning model is established
to help various students learn the mathematical concepts based on their
learning ability and performance. Meanwhile, the robot acts as a teacher's
assistant to co-learn with children in the class. The FML-based knowledge base
and rule base are embedded in the robot so that the teachers can get feedback
from the robot on whether students make progress or not. Next, we inferred
students' learning performance based on learning content's difficulty and
students' ability, concentration level, as well as teamwork sprit in the class.
Experimental results show that learning with the robot is helpful for
disadvantaged and below-basic children. Moreover, the accuracy of the
intelligent FML-based agent for student learning is increased after machine
learning mechanism.
Knowledge Graph Embedding with Multiple Relation Projections
Authors: Kien Do, Truyen Tran, Svetha Venkatesh
Date: 2018-01-26 00:00:00
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graphs, Link Prediction, Relation, Translation

Knowledge graphs contain rich relational structures of the world, and thus
complement data-driven machine learning in heterogeneous data. One of the most
effective methods in representing knowledge graphs is to embed symbolic
relations and entities into continuous spaces, where relations are
approximately linear translation between projected images of entities in the
relation space. However, state-of-the-art relation projection methods such as
TransR, TransD or TransSparse do not model the correlation between relations,
and thus are not scalable to complex knowledge graphs with thousands of
relations, both in computational demand and in statistical robustness. To this
end we introduce TransF, a novel translation-based method which mitigates the
burden of relation projection by explicitly modeling the basis subspaces of
projection matrices. As a result, TransF is far more light weight than the
existing projection methods, and is robust when facing a high number of
relations. Experimental results on the canonical link prediction task show that
our proposed model outperforms competing rivals by a large margin and achieves
state-of-the-art performance. Especially, TransF improves by 9%/5% in the
head/tail entity prediction task for N-to-1/1-to-N relations over the best
performing translation-based method.
Considerations When Learning Additive Explanations for Black-Box Models
Authors: Sarah Tan, Giles Hooker, Paul Koch, Albert Gordo, Rich Caruana
Date: 2018-01-26 00:00:00
Proceeding: ICLR 2019 5
Tasks: Additive models

Many methods to explain black-box models, whether local or global, are additive. In this paper, we study global additive explanations for non-additive models, focusing on four explanation methods: partial dependence, Shapley explanations adapted to a global setting, distilled additive explanations, and gradient-based explanations. We show that different explanation methods characterize non-additive components in a black-box model's prediction function in different ways. We use the concepts of main and total effects to anchor additive explanations, and quantitatively evaluate additive and non-additive explanations. Even though distilled explanations are generally the most accurate additive explanations, non-additive explanations such as tree explanations that explicitly model non-additive components tend to be even more accurate. Despite this, our user study showed that machine learning practitioners were better able to leverage additive explanations for various tasks. These considerations should be taken into account when considering which explanation to trust and use to explain black-box models.
Fast binary embeddings, and quantized compressed sensing with structured matrices
Authors: Thang Huynh, Rayan Saab
Date: 2018-01-26 00:00:00
Tasks: compressed sensing, Quantization

This paper deals with two related problems, namely distance-preserving binary
embeddings and quantization for compressed sensing . First, we propose fast
methods to replace points from a subset $\mathcal{X} \subset \mathbb{R}^n$,
associated with the Euclidean metric, with points in the cube $\{\pm 1\}^m$ and
we associate the cube with a pseudo-metric that approximates Euclidean distance
among points in $\mathcal{X}$. Our methods rely on quantizing fast
Johnson-Lindenstrauss embeddings based on bounded orthonormal systems and
partial circulant ensembles, both of which admit fast transforms. Our
quantization methods utilize noise-shaping, and include Sigma-Delta schemes and
distributed noise-shaping schemes. The resulting approximation errors decay
polynomially and exponentially fast in $m$, depending on the embedding method.
This dramatically outperforms the current decay rates associated with binary
embeddings and Hamming distances. Additionally, it is the first such binary
embedding result that applies to fast Johnson-Lindenstrauss maps while
preserving $\ell_2$ norms.
  Second, we again consider noise-shaping schemes, albeit this time to quantize
compressed sensing measurements arising from bounded orthonormal ensembles and
partial circulant matrices. We show that these methods yield a reconstruction
error that again decays with the number of measurements (and bits), when using
convex optimization for reconstruction. Specifically, for Sigma-Delta schemes,
the error decays polynomially in the number of measurements, and it decays
exponentially for distributed noise-shaping schemes based on beta encoding.
These results are near optimal and the first of their kind dealing with bounded
orthonormal systems.
Generative Adversarial Networks using Adaptive Convolution
Authors: Nhat M. Nguyen, Nilanjan Ray
Date: 2018-01-25 00:00:00
Proceeding: ICLR 2018 1

Most existing GANs architectures that generate images use transposed
convolution or resize-convolution as their upsampling algorithm from lower to
higher resolution feature maps in the generator. We argue that this kind of
fixed operation is problematic for GANs to model objects that have very
different visual appearances. We propose a novel adaptive convolution method
that learns the upsampling algorithm based on the local context at each
location to address this problem. We modify a baseline GANs architecture by
replacing normal convolutions with adaptive convolutions in the generator.
Experiments on CIFAR-10 dataset show that our modified models improve the
baseline model by a large margin. Furthermore, our models achieve
state-of-the-art performance on CIFAR-10 and STL-10 datasets in the
unsupervised setting.
ConvSCCS: convolutional self-controlled case series model for lagged adverse event detection
Authors: Maryan Morel, Emmanuel Bacry, Stéphane Gaïffas, Agathe Guilloux, Fanny Leroy
Date: 2017-12-21 00:00:00
Tasks: Event Detection, Marketing

With the increased availability of large databases of electronic health
records (EHRs) comes the chance of enhancing health risks screening. Most
post-marketing detections of adverse drug reaction (ADR) rely on physicians'
spontaneous reports, leading to under reporting. To take up this challenge, we
develop a scalable model to estimate the effect of multiple longitudinal
features (drug exposures) on a rare longitudinal outcome. Our procedure is
based on a conditional Poisson model also known as self-controlled case series
(SCCS). We model the intensity of outcomes using a convolution between
exposures and step functions, that are penalized using a combination of
group-Lasso and total-variation. This approach does not require the
specification of precise risk periods, and allows to study in the same model
several exposures at the same time. We illustrate the fact that this approach
improves the state-of-the-art for the estimation of the relative risks both on
simulations and on a cohort of diabetic patients, extracted from the large
French national health insurance database (SNIIRAM), a SQL database built
around medical reimbursements of more than 65 million people. This work has
been done in the context of a research partnership between Ecole Polytechnique
and CNAMTS (in charge of SNIIRAM).
DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification
Authors: Wentao Zhu, Chaochun Liu, Wei Fan, Xiaohui Xie
Date: 2018-01-25 00:00:00
Tasks: Classification, Computed Tomography (CT), Decoder, General Classification, Lung Nodule Classification

In this work, we present a fully automated lung computed tomography (CT)
cancer diagnosis system, DeepLung. DeepLung consists of two components, nodule
detection (identifying the locations of candidate nodules) and classification
(classifying candidate nodules into benign or malignant). Considering the 3D
nature of lung CT data and the compactness of dual path networks (DPN), two
deep 3D DPN are designed for nodule detection and classification respectively.
Specifically, a 3D Faster Regions with Convolutional Neural Net (R-CNN) is
designed for nodule detection with 3D dual path blocks and a U-net-like
encoder-decoder structure to effectively learn nodule features. For nodule
classification, gradient boosting machine (GBM) with 3D dual path network
features is proposed. The nodule classification subnetwork was validated on a
public dataset from LIDC-IDRI, on which it achieved better performance than
state-of-the-art approaches and surpassed the performance of experienced
doctors based on image modality. Within the DeepLung system, candidate nodules
are detected first by the nodule detection subnetwork, and nodule diagnosis is
conducted by the classification subnetwork. Extensive experimental results
demonstrate that DeepLung has performance comparable to experienced doctors
both for the nodule-level and patient-level diagnosis on the LIDC-IDRI
dataset.\footnote{https://github.com/uci-cbcl/DeepLung.git}
Generating Handwritten Chinese Characters using CycleGAN
Authors: Bo Chang, Qiong Zhang, Shenyi Pan, Lili Meng
Date: 2018-01-25 00:00:00

Handwriting of Chinese has long been an important skill in East Asia.
However, automatic generation of handwritten Chinese characters poses a great
challenge due to the large number of characters. Various machine learning
techniques have been used to recognize Chinese characters, but few works have
studied the handwritten Chinese character generation problem, especially with
unpaired training data. In this work, we formulate the Chinese handwritten
character generation as a problem that learns a mapping from an existing
printed font to a personalized handwritten style. We further propose DenseNet
CycleGAN to generate Chinese handwritten characters. Our method is applied not
only to commonly used Chinese characters but also to calligraphy work with
aesthetic values. Furthermore, we propose content accuracy and style
discrepancy as the evaluation metrics to assess the quality of the handwritten
characters generated. We then use our proposed metrics to evaluate the
generated characters from CASIA dataset as well as our newly introduced Lanting
calligraphy dataset.