pwc-archive/papers-with-abstracts huggingface dataset:
Predicting Lung Nodule Malignancies by Combining Deep Convolutional Neural Network and Handcrafted Features
Authors: Shulong Li, Panpan Xu, Bin Li, Liyuan Chen, Zhiguo Zhou, Hongxia Hao, Yingying Duan, Michael Folkert, Jianhua Ma, Steve Jiang, Jing Wang
Date: 2018-09-07 00:00:00
Tasks: feature selection, Sensitivity, Specificity

To predict lung nodule malignancy with a high sensitivity and specificity, we
propose a fusion algorithm that combines handcrafted features (HF) into the
features learned at the output layer of a 3D deep convolutional neural network
(CNN). First, we extracted twenty-nine handcrafted features, including nine
intensity features, eight geometric features, and twelve texture features based
on grey-level co-occurrence matrix (GLCM) averaged from thirteen directions. We
then trained 3D CNNs modified from three state-of-the-art 2D CNN architectures
(AlexNet, VGG-16 Net and Multi-crop Net) to extract the CNN features learned at
the output layer. For each 3D CNN, the CNN features combined with the 29
handcrafted features were used as the input for the support vector machine
(SVM) coupled with the sequential forward feature selection (SFS) method to
select the optimal feature subset and construct the classifiers. The fusion
algorithm takes full advantage of the handcrafted features and the highest
level CNN features learned at the output layer. It can overcome the
disadvantage of the handcrafted features that may not fully reflect the unique
characteristics of a particular lesion by combining the intrinsic CNN features.
Meanwhile, it also alleviates the requirement of a large scale annotated
dataset for the CNNs based on the complementary of handcrafted features. The
patient cohort includes 431 malignant nodules and 795 benign nodules extracted
from the LIDC/IDRI database. For each investigated CNN architecture, the
proposed fusion algorithm achieved the highest AUC, accuracy, sensitivity, and
specificity scores among all competitive classification models.
Beyond Gradient Descent for Regularized Segmentation Losses
Authors: Dmitrii Marin, Meng Tang, Ismail Ben Ayed, Yuri Boykov
Date: 2018-09-07 00:00:00
Proceeding: CVPR 2019 6
Tasks: Segmentation

The simplicity of gradient descent (GD) made it the default method for
training ever-deeper and complex neural networks. Both loss functions and
architectures are often explicitly tuned to be amenable to this basic local
optimization. In the context of weakly-supervised CNN segmentation, we
demonstrate a well-motivated loss function where an alternative optimizer (ADM)
achieves the state-of-the-art while GD performs poorly. Interestingly, GD
obtains its best result for a "smoother" tuning of the loss function. The
results are consistent across different network architectures. Our loss is
motivated by well-understood MRF/CRF regularization models in "shallow"
segmentation and their known global solvers. Our work suggests that network
design/training should pay more attention to optimization methods.
Scaling Video Analytics Systems to Large Camera Deployments
Authors: Samvit Jain, Ganesh Ananthanarayanan, Junchen Jiang, Yuanchao Shu, Joseph E. Gonzalez
Date: 2018-09-07 00:00:00

Driven by advances in computer vision and the falling costs of camera hardware, organizations are deploying video cameras en masse for the spatial monitoring of their physical premises. Scaling video analytics to massive camera deployments, however, presents a new and mounting challenge, as compute cost grows proportionally to the number of camera feeds. This paper is driven by a simple question: can we scale video analytics in such a way that cost grows sublinearly, or even remains constant, as we deploy more cameras, while inference accuracy remains stable, or even improves. We believe the answer is yes. Our key observation is that video feeds from wide-area camera deployments demonstrate significant content correlations (e.g. to other geographically proximate feeds), both in space and over time. These spatio-temporal correlations can be harnessed to dramatically reduce the size of the inference search space, decreasing both workload and false positive rates in multi-camera video analytics. By discussing use-cases and technical challenges, we propose a roadmap for scaling video analytics to large camera networks, and outline a plan for its realization.
QoS aware Automatic Web Service Composition with Multiple objectives
Authors: Soumi Chattopadhyay, Ansuman Banerjee
Date: 2018-09-07 00:00:00
Tasks: Service Composition

With an increasing number of web services, providing an end-to-end Quality of
Service (QoS) guarantee in responding to user queries is becoming an important
concern. Multiple QoS parameters (e.g., response time, latency, throughput,
reliability, availability, success rate) are associated with a service,
thereby, service composition with a large number of candidate services is a
challenging multi-objective optimization problem. In this paper, we study the
multi-constrained multi-objective QoS aware web service composition problem and
propose three different approaches to solve the same, one optimal, based on
Pareto front construction and two other based on heuristically traversing the
solution space. We compare the performance of the heuristics against the
optimal, and show the effectiveness of our proposals over other classical
approaches for the same problem setting, with experiments on WSC-2009 and
ICEBE-2005 datasets.
Fast greedy algorithms for dictionary selection with generalized sparsity constraints
Authors: Kaito Fujii, Tasuku Soma
Date: 2018-09-07 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Dictionary Learning

In dictionary selection, several atoms are selected from finite candidates
that successfully approximate given data points in the sparse representation.
We propose a novel efficient greedy algorithm for dictionary selection. Not
only does our algorithm work much faster than the known methods, but it can
also handle more complex sparsity constraints, such as average sparsity. Using
numerical experiments, we show that our algorithm outperforms the known methods
for dictionary selection, achieving competitive performances with dictionary
learning algorithms in a smaller running time.
Unsupervised Cross-lingual Word Embedding by Multilingual Neural Language Models
Authors: Takashi Wada, Tomoharu Iwata
Date: 2018-09-07 00:00:00
Tasks: Cross-Lingual Word Embeddings, Sentence, Word Alignment, Word Embeddings

We propose an unsupervised method to obtain cross-lingual embeddings without
any parallel data or pre-trained word embeddings. The proposed model, which we
call multilingual neural language models, takes sentences of multiple languages
as an input. The proposed model contains bidirectional LSTMs that perform as
forward and backward language models, and these networks are shared among all
the languages. The other parameters, i.e. word embeddings and linear
transformation between hidden states and outputs, are specific to each
language. The shared LSTMs can capture the common sentence structure among all
languages. Accordingly, word embeddings of each language are mapped into a
common latent space, making it possible to measure the similarity of words
across multiple languages. We evaluate the quality of the cross-lingual word
embeddings on a word alignment task. Our experiments demonstrate that our model
can obtain cross-lingual embeddings of much higher quality than existing
unsupervised models when only a small amount of monolingual data (i.e. 50k
sentences) are available, or the domains of monolingual data are different
across languages.
Data Augmentation for Spoken Language Understanding via Joint Variational Generation
Authors: Kang Min Yoo, Youhyun Shin, Sang-goo Lee
Date: 2018-09-07 00:00:00
Tasks: Data Augmentation, Domain Adaptation, Spoken Language Understanding

Data scarcity is one of the main obstacles of domain adaptation in spoken
language understanding (SLU) due to the high cost of creating manually tagged
SLU datasets. Recent works in neural text generative models, particularly
latent variable models such as variational autoencoder (VAE), have shown
promising results in regards to generating plausible and natural sentences. In
this paper, we propose a novel generative architecture which leverages the
generative power of latent variable models to jointly synthesize fully
annotated utterances. Our experiments show that existing SLU models trained on
the additional synthetic examples achieve performance gains. Our approach not
only helps alleviate the data scarcity issue in the SLU task for many datasets
but also indiscriminately improves language understanding performances for
various SLU models, supported by extensive experiments and rigorous statistical
testing.
Neurons Merging Layer: Towards Progressive Redundancy Reduction for Deep Supervised Hashing
Authors: Chaoyou Fu, Liangchen Song, Xiang Wu, Guoli Wang, Ran He
Date: 2018-09-07 00:00:00
Tasks: Deep Hashing, Information Retrieval, Retrieval

Deep supervised hashing has become an active topic in information retrieval. It generates hashing bits by the output neurons of a deep hashing network. During binary discretization, there often exists much redundancy between hashing bits that degenerates retrieval performance in terms of both storage and accuracy. This paper proposes a simple yet effective Neurons Merging Layer (NMLayer) for deep supervised hashing. A graph is constructed to represent the redundancy relationship between hashing bits that is used to guide the learning of a hashing network. Specifically, it is dynamically learned by a novel mechanism defined in our active and frozen phases. According to the learned relationship, the NMLayer merges the redundant neurons together to balance the importance of each output neuron. Moreover, multiple NMLayers are progressively trained for a deep hashing network to learn a more compact hashing code from a long redundant code. Extensive experiments on four datasets demonstrate that our proposed method outperforms state-of-the-art hashing methods.
A Block Coordinate Ascent Algorithm for Mean-Variance Optimization
Authors: Bo Liu, Tengyang Xie, Yangyang Xu, Mohammad Ghavamzadeh, Yin-Lam Chow, Daoming Lyu, Daesub Yoon
Date: 2018-09-07 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Autonomous Driving, Management

Risk management in dynamic decision problems is a primary concern in many
fields, including financial investment, autonomous driving, and healthcare. The
mean-variance function is one of the most widely used objective functions in
risk management due to its simplicity and interpretability. Existing algorithms
for mean-variance optimization are based on multi-time-scale stochastic
approximation, whose learning rate schedules are often hard to tune, and have
only asymptotic convergence proof. In this paper, we develop a model-free
policy search framework for mean-variance optimization with finite-sample error
bound analysis (to local optima). Our starting point is a reformulation of the
original mean-variance function with its Fenchel dual, from which we propose a
stochastic block coordinate ascent policy search algorithm. Both the asymptotic
convergence guarantee of the last iteration's solution and the convergence rate
of the randomly picked solution are provided, and their applicability is
demonstrated on several benchmark domains.
Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion
Authors: Longhao Yuan, Chao Li, Danilo Mandic, Jianting Cao, Qibin Zhao
Date: 2018-09-07 00:00:00
Tasks: Model Selection, Tensor Decomposition

In tensor completion tasks, the traditional low-rank tensor decomposition
models suffer from the laborious model selection problem due to their high
model sensitivity. In particular, for tensor ring (TR) decomposition, the
number of model possibilities grows exponentially with the tensor order, which
makes it rather challenging to find the optimal TR decomposition. In this
paper, by exploiting the low-rank structure of the TR latent space, we propose
a novel tensor completion method which is robust to model selection. In
contrast to imposing the low-rank constraint on the data space, we introduce
nuclear norm regularization on the latent TR factors, resulting in the
optimization step using singular value decomposition (SVD) being performed at a
much smaller scale. By leveraging the alternating direction method of
multipliers (ADMM) scheme, the latent TR factors with optimal rank and the
recovered tensor can be obtained simultaneously. Our proposed algorithm is
shown to effectively alleviate the burden of TR-rank selection, thereby greatly
reducing the computational cost. The extensive experimental results on both
synthetic and real-world data demonstrate the superior performance and
efficiency of the proposed approach against the state-of-the-art algorithms.
Dynamic Compositionality in Recursive Neural Networks with Structure-aware Tag Representations
Authors: Taeuk Kim, Jihun Choi, Daniel Edmiston, Sanghwan Bae, Sang-goo Lee
Date: 2018-09-07 00:00:00
Tasks: Natural Language Inference, Sentence, Sentiment Analysis, TAG

Most existing recursive neural network (RvNN) architectures utilize only the
structure of parse trees, ignoring syntactic tags which are provided as
by-products of parsing. We present a novel RvNN architecture that can provide
dynamic compositionality by considering comprehensive syntactic information
derived from both the structure and linguistic tags. Specifically, we introduce
a structure-aware tag representation constructed by a separate tag-level
tree-LSTM. With this, we can control the composition function of the existing
word-level tree-LSTM by augmenting the representation as a supplementary input
to the gate functions of the tree-LSTM. In extensive experiments, we show that
models built upon the proposed architecture obtain superior or competitive
performance on several sentence-level tasks such as sentiment analysis and
natural language inference when compared against previous tree-structured
models and other sophisticated neural models.
Cell-aware Stacked LSTMs for Modeling Sentences
Authors: Jihun Choi, Taeuk Kim, Sang-goo Lee
Date: 2018-09-07 00:00:00
Tasks: Machine Translation, Natural Language Inference, Paraphrase Identification, Sentiment Analysis, Sentiment Classification, Translation

We propose a method of stacking multiple long short-term memory (LSTM) layers for modeling sentences. In contrast to the conventional stacked LSTMs where only hidden states are fed as input to the next layer, the suggested architecture accepts both hidden and memory cell states of the preceding layer and fuses information from the left and the lower context using the soft gating mechanism of LSTMs. Thus the architecture modulates the amount of information to be delivered not only in horizontal recurrence but also in vertical connections, from which useful features extracted from lower layers are effectively conveyed to upper layers. We dub this architecture Cell-aware Stacked LSTM (CAS-LSTM) and show from experiments that our models bring significant performance gain over the standard LSTMs on benchmark datasets for natural language inference, paraphrase detection, sentiment classification, and machine translation. We also conduct extensive qualitative analysis to understand the internal behavior of the suggested approach.
Learning Embeddings of Directed Networks with Text-Associated Nodes---with Applications in Software Package Dependency Networks
Authors: Kexuan Sun, Shudan Zhong, Hong Xu
Date: 2018-09-07 00:00:00
Tasks: Network Embedding, Node Classification, Word Embeddings

A network embedding consists of a vector representation for each node in the network. Its usefulness has been shown in many real-world application domains, such as social networks and web networks. Directed networks with text associated with each node, such as software package dependency networks, are commonplace. However, to the best of our knowledge, their embeddings have hitherto not been specifically studied. In this paper, we propose PCTADW-1 and PCTADW-2, two algorithms based on neural networks that learn embeddings of directed networks with text associated with each node. We create two new node-labeled such networks: The package dependency networks in two popular GNU/Linux distributions, Debian and Fedora. We experimentally demonstrate that the embeddings produced by our algorithms resulted in node classification with better quality than those of various baselines on these two networks. We observe that there exist systematic presence of analogies (similar to those in word embeddings) in the network embeddings of software package dependency networks. To the best of our knowledge, this is the first time that such systematic presence of analogies is observed in network and document embeddings. We further demonstrate that these network embeddings can be novelly used for better understanding software attributes, such as the development process and user interface of software, etc.
Computation of Total Kidney Volume from CT images in Autosomal Dominant Polycystic Kidney Disease using Multi-Task 3D Convolutional Neural Networks
Authors: Deepak Keshwani, Yoshiro Kitamura, Yuanzhong Li
Date: 2018-09-07 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Semantic Segmentation

Autosomal Dominant Polycystic Kidney Disease (ADPKD) characterized by
progressive growth of renal cysts is the most prevalent and potentially lethal
monogenic renal disease, affecting one in every 500-100 people. Total Kidney
Volume (TKV) and its growth computed from Computed Tomography images has been
accepted as an essential prognostic marker for renal function loss. Due to
large variation in shape and size of kidney in ADPKD, existing methods to
compute TKV (i.e. to segment ADKP) including those based on 2D convolutional
neural networks are not accurate enough to be directly useful in clinical
practice. In this work, we propose multi-task 3D Convolutional Neural Networks
to segment ADPK and achieve a mean DICE score of 0.95 and mean absolute
percentage TKV error of 3.86. Additionally, to solve the challenge of class
imbalance, we propose to simply bootstrap cross entropy loss and compare
results with recently prevalent dice loss in medical image segmentation
community.
BubGAN: Bubble Generative Adversarial Networks for Synthesizing Realistic Bubbly Flow Images
Authors: Yucheng Fu, Yang Liu
Date: 2018-09-07 00:00:00
Tasks: Benchmarking

Bubble segmentation and size detection algorithms have been developed in
recent years for their high efficiency and accuracy in measuring bubbly
two-phase flows. In this work, we proposed an architecture called bubble
generative adversarial networks (BubGAN) for the generation of realistic
synthetic images which could be further used as training or benchmarking data
for the development of advanced image processing algorithms. The BubGAN is
trained initially on a labeled bubble dataset consisting of ten thousand
images. By learning the distribution of these bubbles, the BubGAN can generate
more realistic bubbles compared to the conventional models used in the
literature. The trained BubGAN is conditioned on bubble feature parameters and
has full control of bubble properties in terms of aspect ratio, rotation angle,
circularity and edge ratio. A million bubble dataset is pre-generated using the
trained BubGAN. One can then assemble realistic bubbly flow images using this
dataset and associated image processing tool. These images contain detailed
bubble information, therefore do not require additional manual labeling. This
is more useful compared with the conventional GAN which generates images
without labeling information. The tool could be used to provide benchmarking
and training data for existing image processing algorithms and to guide the
future development of bubble detecting algorithms.
Logistic Regression Augmented Community Detection for Network Data with Application in Identifying Autism-Related Gene Pathways
Authors: Yunpeng Zhao, Qing Pan, Chengan Du
Date: 2018-09-07 00:00:00
Tasks: Community Detection

When searching for gene pathways leading to specific disease outcomes,
additional information on gene characteristics is often available that may
facilitate to differentiate genes related to the disease from irrelevant
background when connections involving both types of genes are observed and
their relationships to the disease are unknown. We propose method to single out
irrelevant background genes with the help of auxiliary information through a
logistic regression, and cluster relevant genes into cohesive groups using the
adjacency matrix. Expectation-maximization algorithm is modified to maximize a
joint pseudo-likelihood assuming latent indicators for relevance to the disease
and latent group memberships as well as Poisson or multinomial distributed link
numbers within and between groups. A robust version allowing arbitrary linkage
patterns within the background is further derived. Asymptotic consistency of
label assignments under the stochastic blockmodel is proven. Superior
performance and robustness in finite samples are observed in simulation
studies. The proposed robust method identifies previously missed gene sets
underlying autism related neurological diseases using diverse data sources
including de novo mutations, gene expressions and protein-protein interactions.
The Force of Proof by Which Any Argument Prevails
Authors: Brian Shay, Patrick Brazil
Date: 2018-09-07 00:00:00

Jakob Bernoulli, working in the late 17th century, identified a gap in
contemporary probability theory. He cautioned that it was inadequate to specify
force of proof (probability of provability) for some kinds of uncertain
arguments. After 300 years, this gap remains in present-day probability theory.
We present axioms analogous to Kolmogorov's axioms for probability, specifying
uncertainty that lies in an argument's inference/implication itself rather than
in its premise and conclusion. The axioms focus on arguments spanning two
Boolean algebras, but generalize the obligatory: "force of proof of A implies B
is the probability of B or not A" in the case that the Boolean algebras are
identical. We propose a categorical framework that relies on generalized
probabilities (objects) to express uncertainty in premises, to mix with
arguments (morphisms) to express uncertainty embedded directly in
inference/implication. There is a direct application to Shafer's evidence
theory (Dempster-Shafer theory), greatly expanding its scope for applications.
Therefore, we can offer this framework not only as an optimal solution to a
difficult historical puzzle, but also to advance the frontiers of contemporary
artificial intelligence.
  Keywords: force of proof, probability of provability, Ars Conjectandi, non
additive probabilities, evidence theory.
Representing Images in 200 Bytes: Compression via Triangulation
Authors: David Marwood, Pascal Massimino, Michele Covell, Shumeet Baluja
Date: 2018-09-07 00:00:00
Tasks: Image Compression, SSIM

A rapidly increasing portion of internet traffic is dominated by requests
from mobile devices with limited and metered bandwidth constraints. To satisfy
these requests, it has become standard practice for websites to transmit small
and extremely compressed image previews as part of the initial page load
process to improve responsiveness. Increasing thumbnail compression beyond the
capabilities of existing codecs is therefore an active research direction. In
this work, we concentrate on extreme compression rates, where the size of the
image is typically 200 bytes or less. First, we propose a novel approach for
image compression that, unlike commonly used methods, does not rely on
block-based statistics. We use an approach based on an adaptive triangulation
of the target image, devoting more triangles to high entropy regions of the
image. Second, we present a novel algorithm for encoding the triangles. The
results show favorable statistics, in terms of PSNR and SSIM, over both the
JPEG and the WebP standards.