pwc-archive/papers-with-abstracts huggingface dataset:
Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks
Authors: Davood Karimi, Septimiu E. Salcudean
Date: 2019-04-22 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

The Hausdorff Distance (HD) is widely used in evaluating medical image
segmentation methods. However, existing segmentation methods do not attempt to
reduce HD directly. In this paper, we present novel loss functions for training
convolutional neural network (CNN)-based segmentation methods with the goal of
reducing HD directly. We propose three methods to estimate HD from the
segmentation probability map produced by a CNN. One method makes use of the
distance transform of the segmentation boundary. Another method is based on
applying morphological erosion on the difference between the true and estimated
segmentation maps. The third method works by applying circular/spherical
convolution kernels of different radii on the segmentation probability maps.
Based on these three methods for estimating HD, we suggest three loss functions
that can be used for training to reduce HD. We use these loss functions to
train CNNs for segmentation of the prostate, liver, and pancreas in ultrasound,
magnetic resonance, and computed tomography images and compare the results with
commonly-used loss functions. Our results show that the proposed loss functions
can lead to approximately 18-45 % reduction in HD without degrading other
segmentation performance criteria such as the Dice similarity coefficient. The
proposed loss functions can be used for training medical image segmentation
methods in order to reduce the large segmentation errors.
Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence
Authors: Vasileios Charisopoulos, Yudong Chen, Damek Davis, Mateo Díaz, Lijun Ding, Dmitriy Drusvyatskiy
Date: 2019-04-22 00:00:00
Tasks: Matrix Completion, Retrieval

The task of recovering a low-rank matrix from its noisy linear measurements
plays a central role in computational science. Smooth formulations of the
problem often exhibit an undesirable phenomenon: the condition number,
classically defined, scales poorly with the dimension of the ambient space. In
contrast, we here show that in a variety of concrete circumstances, nonsmooth
penalty formulations do not suffer from the same type of ill-conditioning.
Consequently, standard algorithms for nonsmooth optimization, such as
subgradient and prox-linear methods, converge at a rapid dimension-independent
rate when initialized within constant relative error of the solution. Moreover,
nonsmooth formulations are naturally robust against outliers. Our framework
subsumes such important computational tasks as phase retrieval, blind
deconvolution, quadratic sensing, matrix completion, and robust PCA. Numerical
experiments on these problems illustrate the benefits of the proposed approach.
The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism
Authors: Jake Goldenfein
Date: 2019-04-22 00:00:00

Computer vision and other biometrics data science applications have commenced
a new project of profiling people. Rather than using 'transaction generated
information', these systems measure the 'real world' and produce an assessment
of the 'world state' - in this case an assessment of some individual trait.
Instead of using proxies or scores to evaluate people, they increasingly deploy
a logic of revealing the truth about reality and the people within it. While
these profiling knowledge claims are sometimes tentative, they increasingly
suggest that only through computation can these excesses of reality be captured
and understood. This article explores the bases of those claims in the systems
of measurement, representation, and classification deployed in computer vision.
It asks if there is something new in this type of knowledge claim, sketches an
account of a new form of computational empiricism being operationalised, and
questions what kind of human subject is being constructed by these
technological systems and practices. Finally, the article explores legal
mechanisms for contesting the emergence of computational empiricism as the
dominant knowledge platform for understanding the world and the people within
it.
Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features
Authors: Kuangen Zhang, Ming Hao, Jing Wang, Clarence W. de Silva, Chenglong Fu
Date: 2019-04-22 00:00:00

Learning on point cloud is eagerly in demand because the point cloud is a common type of geometric data and can aid robots to understand environments robustly. However, the point cloud is sparse, unstructured, and unordered, which cannot be recognized accurately by a traditional convolutional neural network (CNN) nor a recurrent neural network (RNN). Fortunately, a graph convolutional neural network (Graph CNN) can process sparse and unordered data. Hence, we propose a linked dynamic graph CNN (LDGCNN) to classify and segment point cloud directly in this paper. We remove the transformation network, link hierarchical features from dynamic graphs, freeze feature extractor, and retrain the classifier to increase the performance of LDGCNN. We explain our network using theoretical analysis and visualization. Through experiments, we show that the proposed LDGCNN achieves state-of-art performance on two standard datasets: ModelNet40 and ShapeNet.
MinCall - MinION end2end convolutional deep learning basecaller
Authors: Neven Miculinić, Marko Ratković, Mile Šikić
Date: 2019-04-22 00:00:00
Tasks: Deep Learning, General Classification

The Oxford Nanopore Technologies's MinION is the first portable DNA
sequencing device. It is capable of producing long reads, over 100 kBp were
reported. However, it has significantly higher error rate than other methods.
In this study, we present MinCall, an end2end basecaller model for the MinION.
The model is based on deep learning and uses convolutional neural networks
(CNN) in its implementation. For extra performance, it uses cutting edge deep
learning techniques and architectures, batch normalization and Connectionist
Temporal Classification (CTC) loss. The best performing deep learning model
achieves 91.4% median match rate on E. Coli dataset using R9 pore chemistry and
1D reads.
GraphNAS: Graph Neural Architecture Search with Reinforcement Learning
Authors: Yang Gao, Hong Yang, Peng Zhang, Chuan Zhou, Yue Hu
Date: 2019-04-22 00:00:00
Tasks: General Classification, Inductive Learning, Neural Architecture Search, Node Classification, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Graph Neural Networks (GNNs) have been popularly used for analyzing non-Euclidean data such as social network data and biological data. Despite their success, the design of graph neural networks requires a lot of manual work and domain knowledge. In this paper, we propose a Graph Neural Architecture Search method (GraphNAS for short) that enables automatic search of the best graph neural architecture based on reinforcement learning. Specifically, GraphNAS first uses a recurrent network to generate variable-length strings that describe the architectures of graph neural networks, and then trains the recurrent network with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation data set. Extensive experimental results on node classification tasks in both transductive and inductive learning settings demonstrate that GraphNAS can achieve consistently better performance on the Cora, Citeseer, Pubmed citation network, and protein-protein interaction network. On node classification tasks, GraphNAS can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy.
Estimating Forces of Robotic Pouring Using a LSTM RNN
Authors: Kyle Mott
Date: 2019-04-21 00:00:00

In machine learning, it is very important for a robot to be able to estimate
dynamics from sequences of input data. This problem can be solved using a
recurrent neural network. In this paper, we will discuss the preprocessing of
10 states of the dataset, then the use of a LSTM recurrent neural network to
estimate one output state (dynamics) from the other 9 input states. We will
discuss the architecture of the recurrent neural network, the data collection
and preprocessing, the loss function, the results of the test data, and the
discussion of changes that could improve the network. The results of this paper
will be used for artificial intelligence research and identify the capabilities
of a LSTM recurrent neural network architecture to estimate dynamics of a
system.
Android Malicious Application Classification Using Clustering
Authors: Hemant Rathore, Sanjay K. Sahay, Palash Chaturvedi, Mohit Sewak
Date: 2019-04-21 00:00:00
Tasks: Classification, Clustering, General Classification

Android malware have been growing at an exponential pace and becomes a
serious threat to mobile users. It appears that most of the anti-malware still
relies on the signature-based detection system which is generally slow and
often not able to detect advanced obfuscated malware. Hence time-to-time
various authors have proposed different machine learning solutions to identify
sophisticated malware. However, it appears that detection accuracy can be
improved by using the clustering method. Therefore in this paper, we propose a
novel scalable and effective clustering method to improve the detection
accuracy of the malicious android application and obtained a better overall
accuracy (98.34%) by random forest classifier compared to regular method, i.e.,
taking the data altogether to detect the malware. However, as far as true
positive and true negative are concerned, by clustering method, true positive
is best obtained by decision tree (97.59%) and true negative by support vector
machine (99.96%) which is the almost same result obtained by the random forest
true positive (97.30%) and true negative (99.38%) respectively. The reason that
overall accuracy of random forest is high because the true positive of support
vector machine and true negative of the decision tree is significantly less
than the random forest.
A Fast, Semi-Automatic Brain Structure Segmentation Algorithm for Magnetic Resonance Imaging
Authors: Kevin Karsch, Qing He, Ye Duan
Date: 2019-04-21 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Medical image segmentation has become an essential technique in clinical and
research-oriented applications. Because manual segmentation methods are
tedious, and fully automatic segmentation lacks the flexibility of human
intervention or correction, semi-automatic methods have become the preferred
type of medical image segmentation. We present a hybrid, semi-automatic
segmentation method in 3D that integrates both region-based and boundary-based
procedures. Our method differs from previous hybrid methods in that we perform
region-based and boundary-based approaches separately, which allows for more
efficient segmentation. A region-based technique is used to generate an initial
seed contour that roughly represents the boundary of a target brain structure,
alleviating the local minima problem in the subsequent model deformation phase.
The contour is deformed under a unique force equation independent of image
edges. Experiments on MRI data show that this method can achieve high accuracy
and efficiency primarily due to the unique seed initialization technique.
Web Based Brain Volume Calculation for Magnetic Resonance Images
Authors: Kevin Karsch, Brian Grinstead, Qing He, Ye Duan
Date: 2019-04-21 00:00:00

Brain volume calculations are crucial in modern medical research, especially
in the study of neurodevelopmental disorders. In this paper, we present an
algorithm for calculating two classifications of brain volume, total brain
volume (TBV) and intracranial volume (ICV). Our algorithm takes MRI data as
input, performs several preprocessing and intermediate steps, and then returns
each of the two calculated volumes. To simplify this process and make our
algorithm publicly accessible to anyone, we have created a web-based interface
that allows users to upload their own MRI data and calculate the TBV and ICV
for the given data. This interface provides a simple and efficient method for
calculating these two classifications of brain volume, and it also removes the
need for the user to download or install any applications.
A Neural Influence Diffusion Model for Social Recommendation
Authors: Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang, Meng Wang
Date: 2019-04-20 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Precise user and item embedding learning is the key to building a successful
recommender system. Traditionally, Collaborative Filtering(CF) provides a way
to learn user and item embeddings from the user-item interaction history.
However, the performance is limited due to the sparseness of user behavior
data. With the emergence of online social networks, social recommender systems
have been proposed to utilize each user's local neighbors' preferences to
alleviate the data sparsity for better user embedding modeling. We argue that,
for each user of a social platform, her potential embedding is influenced by
her trusted users. As social influence recursively propagates and diffuses in
the social network, each user's interests change in the recursive process.
Nevertheless, the current social recommendation models simply developed static
models by leveraging the local neighbors of each user without simulating the
recursive diffusion in the global social network, leading to suboptimal
recommendation performance. In this paper, we propose a deep influence
propagation model to stimulate how users are influenced by the recursive social
diffusion process for social recommendation. For each user, the diffusion
process starts with an initial embedding that fuses the related features and a
free user latent vector that captures the latent behavior preference. The key
idea of our proposed model is that we design a layer-wise influence propagation
structure to model how users' latent embeddings evolve as the social diffusion
process continues. We further show that our proposed model is general and could
be applied when the user~(item) attributes or the social network structure is
not available. Finally, extensive experimental results on two real-world
datasets clearly show the effectiveness of our proposed model, with more than
13% performance improvements over the best baselines.
Three dimensional blind image deconvolution for fluorescence microscopy using generative adversarial networks
Authors: Soonam Lee, Shuo Han, Paul Salama, Kenneth W. Dunn, Edward J. Delp
Date: 2019-04-19 00:00:00
Tasks: Denoising, Image Deconvolution

Due to image blurring image deconvolution is often used for studying
biological structures in fluorescence microscopy. Fluorescence microscopy image
volumes inherently suffer from intensity inhomogeneity, blur, and are corrupted
by various types of noise which exacerbate image quality at deeper tissue
depth. Therefore, quantitative analysis of fluorescence microscopy in deeper
tissue still remains a challenge. This paper presents a three dimensional blind
image deconvolution method for fluorescence microscopy using 3-way spatially
constrained cycle-consistent adversarial networks. The restored volumes of the
proposed deconvolution method and other well-known deconvolution methods,
denoising methods, and an inhomogeneity correction method are visually and
numerically evaluated. Experimental results indicate that the proposed method
can restore and improve the quality of blurred and noisy deep depth microscopy
image visually and quantitatively.
Is coding a relevant metaphor for building AI? A commentary on "Is coding a relevant metaphor for the brain?", by Romain Brette
Authors: Adam Santoro, Felix Hill, David Barrett, David Raposo, Matthew Botvinick, Timothy Lillicrap
Date: 2019-04-18 00:00:00

Brette contends that the neural coding metaphor is an invalid basis for
theories of what the brain does. Here, we argue that it is an insufficient
guide for building an artificial intelligence that learns to accomplish short-
and long-term goals in a complex, changing environment.
Efficient single input-output layer spiking neural classifier with time-varying weight model
Authors: Abeegithan Jeyasothy, Savitha Ramasamy, Suresh Sundaram
Date: 2019-03-21 00:00:00

This paper presents a supervised learning algorithm, namely, the Synaptic
Efficacy Function with Meta-neuron based learning algorithm (SEF-M) for a
spiking neural network with a time-varying weight model. For a given pattern,
SEF-M uses the learning algorithm derived from meta-neuron based learning
algorithm to determine the change in weights corresponding to each presynaptic
spike times. The changes in weights modulate the amplitude of a Gaussian
function centred at the same presynaptic spike times. The sum of amplitude
modulated Gaussian functions represents the synaptic efficacy functions (or
time-varying weight models). The performance of SEF-M is evaluated against
state-of-the-art spiking neural network learning algorithms on 10 benchmark
datasets from UCI machine learning repository. Performance studies show
superior generalization ability of SEF-M. An ablation study on time-varying
weight model is conducted using JAFFE dataset. The results of the ablation
study indicate that using a time-varying weight model instead of single weight
model improves the classification accuracy by 14%. Thus, it can be inferred
that a single input-output layer spiking neural network with time-varying
weight model is computationally more efficient than a multi-layer spiking
neural network with long-term or short-term weight model.
Automatic Spelling Correction with Transformer for CTC-based End-to-End Speech Recognition
Authors: Shiliang Zhang, Ming Lei, Zhijie Yan
Date: 2019-03-27 00:00:00
Tasks: Decoder, Language Modeling, Language Modelling, Machine Translation, speech-recognition, Speech Recognition, Spelling Correction, Translation

Connectionist Temporal Classification (CTC) based end-to-end speech
recognition system usually need to incorporate an external language model by
using WFST-based decoding in order to achieve promising results. This is more
essential to Mandarin speech recognition since it owns a special phenomenon,
namely homophone, which causes a lot of substitution errors. The linguistic
information introduced by language model will help to distinguish these
substitution errors. In this work, we propose a transformer based spelling
correction model to automatically correct errors especially the substitution
errors made by CTC-based Mandarin speech recognition system. Specifically, we
investigate using the recognition results generated by CTC-based systems as
input and the ground-truth transcriptions as output to train a transformer with
encoder-decoder architecture, which is much similar to machine translation.
Results in a 20,000 hours Mandarin speech recognition task show that the
proposed spelling correction model can achieve a CER of 3.41%, which results in
22.9% and 53.2% relative improvement compared to the baseline CTC-based systems
decoded with and without language model respectively.
Generating Responses Expressing Emotion in an Open-domain Dialogue System
Authors: Chenyang Huang, Osmar R. Zaïane
Date: 2018-11-15 00:00:00
Tasks: Decoder, Sentence

Neural network-based Open-ended conversational agents automatically generate
responses based on predictive models learned from a large number of pairs of
utterances. The generated responses are typically acceptable as a sentence but
are often dull, generic, and certainly devoid of any emotion. In this paper, we
present neural models that learn to express a given emotion in the generated
response. We propose four models and evaluate them against 3 baselines. An
encoder-decoder framework-based model with multiple attention layers provides
the best overall performance in terms of expressing the required emotion. While
it does not outperform other models on all emotions, it presents promising
results in most cases.
A Survey on Practical Applications of Multi-Armed and Contextual Bandits
Authors: Djallel Bouneffouf, Irina Rish
Date: 2019-04-02 00:00:00
Tasks: Information Retrieval, Multi-Armed Bandits, Recommendation Systems, Retrieval

In recent years, multi-armed bandit (MAB) framework has attracted a lot of
attention in various applications, from recommender systems and information
retrieval to healthcare and finance, due to its stellar performance combined
with certain attractive properties, such as learning from less feedback. The
multi-armed bandit field is currently flourishing, as novel problem settings
and algorithms motivated by various practical applications are being
introduced, building on top of the classical bandit problem. This article aims
to provide a comprehensive review of top recent developments in multiple
real-life applications of the multi-armed bandit. Specifically, we introduce a
taxonomy of common MAB-based applications and summarize state-of-art for each
of those domains. Furthermore, we identify important current trends and provide
new perspectives pertaining to the future of this exciting and fast-growing
field.
Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks
Authors: Gabriel de Souza Pereira Moreira, Dietmar Jannach, Adilson Marques da Cunha
Date: 2019-04-15 00:00:00
Tasks: News Recommendation, Recommendation Systems, Session-Based Recommendations

Recommender systems help users deal with information overload by providing tailored item suggestions to them. The recommendation of news is often considered to be challenging, since the relevance of an article for a user can depend on a variety of factors, including the user's short-term reading interests, the reader's context, or the recency or popularity of an article. Previous work has shown that the use of Recurrent Neural Networks is promising for the next-in-session prediction task, but has certain limitations when only recorded item click sequences are used as input. In this work, we present a contextual hybrid, deep learning based approach for session-based news recommendation that is able to leverage a variety of information types. We evaluated our approach on two public datasets, using a temporal evaluation protocol that simulates the dynamics of a news portal in a realistic way. Our results confirm the benefits of considering additional types of information, including article popularity and recency, in the proposed way, resulting in significantly higher recommendation accuracy and catalog coverage than other session-based algorithms. Additional experiments show that the proposed parameterizable loss function used in our method also allows us to balance two usually conflicting quality factors, accuracy and novelty. Keywords: Artificial Neural Networks, Context-Aware Recommender Systems, Hybrid Recommender Systems, News Recommender Systems, Session-based Recommendation