pwc-archive/papers-with-abstracts huggingface dataset:
Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data
Authors: Biwei Huang, Kun Zhang, Ruben Sanchez-Romero, Joseph Ramsey, Madelyn Glymour, Clark Glymour
Date: 2019-01-27 00:00:00
Tasks: Causal Discovery, Diagnostic, feature selection

Autism spectrum disorder (ASD) is one of the major developmental disorders
affecting children. Recently, it has been hypothesized that ASD is associated
with atypical brain connectivities. A substantial body of researches use
Pearson's correlation coefficients, mutual information, or partial correlation
to investigate the differences in brain connectivities between ASD and typical
controls from functional Magnetic Resonance Imaging (fMRI). However,
correlation or partial correlation does not directly reveal causal influences -
the information flow - between brain regions. Comparing to correlation,
causality pinpoints the key connectivity characteristics and removes redundant
features for diagnosis.
  In this paper, we propose a two-step method for large-scale and cyclic causal
discovery from fMRI. It can identify brain causal structures without doing
interventional experiments. The learned causal structure, as well as the causal
influence strength, provides us the path and effectiveness of information flow.
With the recovered causal influence strength as candidate features, we then
perform ASD diagnosis by further doing feature selection and classification. We
apply our methods to three datasets from Autism Brain Imaging Data Exchange
(ABIDE).
  From experimental results, it shows that with causal connectivities, the
diagnostic accuracy largely improves. A closer examination shows that
information flows starting from the superior front gyrus to default mode
network and posterior areas are largely reduced. Moreover, all enhanced
information flows are from posterior to anterior or in local areas. Overall, it
shows that long-range influences have a larger proportion of reductions than
local ones, while local influences have a larger proportion of increases than
long-range ones. By examining the graph properties of brain causal structure,
the group of ASD shows reduced small-worldness.
A Neural-Network-Based Model Predictive Control of Three-Phase Inverter With an Output LC Filter
Authors: Ihab S. Mohamed, Stefano Rovetta, Ton Duc Do, Tomislav Dragicevic, Ahmed A. Zaki Diab
Date: 2019-02-22 00:00:00
Tasks: Model Predictive Control

Model predictive control (MPC) has become one of the well-established modern control methods for three-phase inverters with an output LC filter, where a high-quality voltage with low total harmonic distortion (THD) is needed. Although it is an intuitive controller, easy to understand and implement, it has the significant disadvantage of requiring a large number of online calculations for solving the optimization problem. On the other hand, the application of model-free approaches such as those based on artificial neural networks approaches is currently growing rapidly in the area of power electronics and drives. This paper presents a new control scheme for a two-level converter based on combining MPC and feed-forward ANN, with the aim of getting lower THD and improving the steady and dynamic performance of the system for different types of loads. First, MPC is used, as an expert, in the training phase to generate data required for training the proposed neural network. Then, once the neural network is fine-tuned, it can be successfully used online for voltage tracking purpose, without the need of using MPC. The proposed ANN-based control strategy is validated through simulation, using MATLAB/Simulink tools, taking into account different loads conditions. Moreover, the performance of the ANN-based controller is evaluated, on several samples of linear and non-linear loads under various operating conditions, and compared to that of MPC, demonstrating the excellent steady-state and dynamic performance of the proposed ANN-based control strategy.
Performance Analysis of Machine Learning Techniques to Predict Diabetes Mellitus
Authors: Md. Faisal Faruque, Asaduzzaman, Iqbal H. Sarker
Date: 2019-01-10 00:00:00
Tasks: BIG-bench Machine Learning, Diagnostic

Diabetes mellitus is a common disease of human body caused by a group of
metabolic disorders where the sugar levels over a prolonged period is very
high. It affects different organs of the human body which thus harm a large
number of the body's system, in particular the blood veins and nerves. Early
prediction in such disease can be controlled and save human life. To achieve
the goal, this research work mainly explores various risk factors related to
this disease using machine learning techniques. Machine learning techniques
provide efficient result to extract knowledge by constructing predicting models
from diagnostic medical datasets collected from the diabetic patients.
Extracting knowledge from such data can be useful to predict diabetic patients.
In this work, we employ four popular machine learning algorithms, namely
Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbor (KNN) and
C4.5 Decision Tree, on adult population data to predict diabetic mellitus. Our
experimental results show that C4.5 decision tree achieved higher accuracy
compared to other machine learning techniques.
Supervised learning improves disease outbreak detection
Authors: Benedikt Zacher, Irina Czogiel
Date: 2019-02-06 00:00:00

The early detection of infectious disease outbreaks is a crucial task to
protect population health. To this end, public health surveillance systems have
been established to systematically collect and analyse infectious disease data.
A variety of statistical tools are available, which detect potential outbreaks
as abberations from an expected endemic level using these data. Here, we
develop the first supervised learning approach based on hidden Markov models
for disease outbreak detection, which leverages data that is routinely
collected within a public health surveillance system. We evaluate our model
using real Salmonella and Campylobacter data, as well as simulations. In
comparison to a state-of-the-art approach, which is applied in multiple
European countries including Germany, our proposed model reduces the false
positive rate by up to 50% while retaining the same sensitivity. We see our
supervised learning approach as a significant step to further develop machine
learning applications for disease outbreak detection, which will be
instrumental to improve public health surveillance systems.
A General Framework for Structured Learning of Mechanical Systems
Authors: Jayesh K. Gupta, Kunal Menda, Zachary Manchester, Mykel J. Kochenderfer
Date: 2019-02-22 00:00:00
Tasks: Model-based Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

Learning accurate dynamics models is necessary for optimal, compliant control
of robotic systems. Current approaches to white-box modeling using analytic
parameterizations, or black-box modeling using neural networks, can suffer from
high bias or high variance. We address the need for a flexible, gray-box model
of mechanical systems that can seamlessly incorporate prior knowledge where it
is available, and train expressive function approximators where it is not. We
propose to parameterize a mechanical system using neural networks to model its
Lagrangian and the generalized forces that act on it. We test our method on a
simulated, actuated double pendulum. We show that our method outperforms a
naive, black-box model in terms of data-efficiency, as well as performance in
model-based reinforcement learning. We also conduct a systematic study of our
method's ability to incorporate available prior knowledge about the system to
improve data efficiency.
Still a Pain in the Neck: Evaluating Text Representations on Lexical Composition
Authors: Vered Shwartz, Ido Dagan
Date: 2019-02-27 00:00:00
Proceeding: TACL 2019 3
Tasks: Word Embeddings

Building meaningful phrase representations is challenging because phrase meanings are not simply the sum of their constituent meanings. Lexical composition can shift the meanings of the constituent words and introduce implicit information. We tested a broad range of textual representations for their capacity to address these issues. We found that as expected, contextualized word representations perform better than static word embeddings, more so on detecting meaning shift than in recovering implicit information, in which their performance is still far from that of humans. Our evaluation suite, including 5 tasks related to lexical composition effects, can serve future research aiming to improve such representations.
Efficient Video Classification Using Fewer Frames
Authors: Shweta Bhardwaj, Mukundhan Srinivasan, Mitesh M. Khapra
Date: 2019-02-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: Classification, Clustering, General Classification, Video Classification

Recently,there has been a lot of interest in building compact models for
video classification which have a small memory footprint (<1 GB). While these
models are compact, they typically operate by repeated application of a small
weight matrix to all the frames in a video. E.g. recurrent neural network based
methods compute a hidden state for every frame of the video using a recurrent
weight matrix. Similarly, cluster-and-aggregate based methods such as NetVLAD,
have a learnable clustering matrix which is used to assign soft-clusters to
every frame in the video. Since these models look at every frame in the video,
the number of floating point operations (FLOPs) is still large even though the
memory footprint is small. We focus on building compute-efficient video
classification models which process fewer frames and hence have less number of
FLOPs. Similar to memory efficient models, we use the idea of distillation
albeit in a different setting. Specifically, in our case, a compute-heavy
teacher which looks at all the frames in the video is used to train a
compute-efficient student which looks at only a small fraction of frames in the
video. This is in contrast to a typical memory efficient Teacher-Student
setting, wherein both the teacher and the student look at all the frames in the
video but the student has fewer parameters. Our work thus complements the
research on memory efficient video classification. We do an extensive
evaluation with three types of models for video classification,viz.(i)
recurrent models (ii) cluster-and-aggregate models and (iii) memory-efficient
cluster-and-aggregate models and show that in each of these cases, a see-it-all
teacher can be used to train a compute efficient see-very-little student. We
show that the proposed student network can reduce the inference time by 30% and
the number of FLOPs by approximately 90% with a negligible drop in the
performance.
Recurrent MVSNet for High-resolution Multi-view Stereo Depth Inference
Authors: Yao Yao, Zixin Luo, Shiwei Li, Tianwei Shen, Tian Fang, Long Quan
Date: 2019-02-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: Vocal Bursts Intensity Prediction

Deep learning has recently demonstrated its excellent performance for
multi-view stereo (MVS). However, one major limitation of current learned MVS
approaches is the scalability: the memory-consuming cost volume regularization
makes the learned MVS hard to be applied to high-resolution scenes. In this
paper, we introduce a scalable multi-view stereo framework based on the
recurrent neural network. Instead of regularizing the entire 3D cost volume in
one go, the proposed Recurrent Multi-view Stereo Network (R-MVSNet)
sequentially regularizes the 2D cost maps along the depth direction via the
gated recurrent unit (GRU). This reduces dramatically the memory consumption
and makes high-resolution reconstruction feasible. We first show the
state-of-the-art performance achieved by the proposed R-MVSNet on the recent
MVS benchmarks. Then, we further demonstrate the scalability of the proposed
method on several large-scale scenarios, where previous learned approaches
often fail due to the memory constraint. Code is available at
https://github.com/YoYo000/MVSNet.
Single-frame Regularization for Temporally Stable CNNs
Authors: Gabriel Eilertsen, Rafał K. Mantiuk, Jonas Unger
Date: 2019-02-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: Motion Estimation, Optical Flow Estimation

Convolutional neural networks (CNNs) can model complicated non-linear relations between images. However, they are notoriously sensitive to small changes in the input. Most CNNs trained to describe image-to-image mappings generate temporally unstable results when applied to video sequences, leading to flickering artifacts and other inconsistencies over time. In order to use CNNs for video material, previous methods have relied on estimating dense frame-to-frame motion information (optical flow) in the training and/or the inference phase, or by exploring recurrent learning structures. We take a different approach to the problem, posing temporal stability as a regularization of the cost function. The regularization is formulated to account for different types of motion that can occur between frames, so that temporally stable CNNs can be trained without the need for video material or expensive motion estimation. The training can be performed as a fine-tuning operation, without architectural modifications of the CNN. Our evaluation shows that the training strategy leads to large improvements in temporal smoothness. Moreover, for small datasets the regularization can help in boosting the generalization performance to a much larger extent than what is possible with na\"ive augmentation strategies.
FickleNet: Weakly and Semi-supervised Semantic Image Segmentation using Stochastic Inference
Authors: Jungbeom Lee, Eunji Kim, Sungmin Lee, Jangho Lee, Sungroh Yoon
Date: 2019-02-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: image-classification, Image Classification, Image Segmentation, Semantic Segmentation, Weakly-Supervised Semantic Segmentation

The main obstacle to weakly supervised semantic image segmentation is the
difficulty of obtaining pixel-level information from coarse image-level
annotations. Most methods based on image-level annotations use localization
maps obtained from the classifier, but these only focus on the small
discriminative parts of objects and do not capture precise boundaries.
FickleNet explores diverse combinations of locations on feature maps created by
generic deep neural networks. It selects hidden units randomly and then uses
them to obtain activation scores for image classification. FickleNet implicitly
learns the coherence of each location in the feature maps, resulting in a
localization map which identifies both discriminative and other parts of
objects. The ensemble effects are obtained from a single network by selecting
random hidden unit pairs, which means that a variety of localization maps are
generated from a single image. Our approach does not require any additional
training steps and only adds a simple layer to a standard convolutional neural
network; nevertheless it outperforms recent comparable techniques on the Pascal
VOC 2012 benchmark in both weakly and semi-supervised settings.
Spatio-Temporal Dynamics and Semantic Attribute Enriched Visual Encoding for Video Captioning
Authors: Nayyer Aafaq, Naveed Akhtar, Wei Liu, Syed Zulqarnain Gilani, Ajmal Mian
Date: 2019-02-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: Attribute, Caption Generation, Language Modeling, Language Modelling, Video Captioning

Automatic generation of video captions is a fundamental challenge in computer
vision. Recent techniques typically employ a combination of Convolutional
Neural Networks (CNNs) and Recursive Neural Networks (RNNs) for video
captioning. These methods mainly focus on tailoring sequence learning through
RNNs for better caption generation, whereas off-the-shelf visual features are
borrowed from CNNs. We argue that careful designing of visual features for this
task is equally important, and present a visual feature encoding technique to
generate semantically rich captions using Gated Recurrent Units (GRUs). Our
method embeds rich temporal dynamics in visual features by hierarchically
applying Short Fourier Transform to CNN features of the whole video. It
additionally derives high level semantics from an object detector to enrich the
representation with spatial dynamics of the detected objects. The final
representation is projected to a compact space and fed to a language model. By
learning a relatively simple language model comprising two GRU layers, we
establish new state-of-the-art on MSVD and MSR-VTT datasets for METEOR and
ROUGE_L metrics.
Attributes-aided Part Detection and Refinement for Person Re-identification
Authors: Shuzhao Li, Huimin Yu, Wei Huang, Jing Zhang
Date: 2019-02-27 00:00:00
Tasks: Attribute, Person Re-Identification

Person attributes are often exploited as mid-level human semantic information
to help promote the performance of person re-identification task. In this
paper, unlike most existing methods simply taking attribute learning as a
classification problem, we perform it in a different way with the motivation
that attributes are related to specific local regions, which refers to the
perceptual ability of attributes. We utilize the process of attribute detection
to generate corresponding attribute-part detectors, whose invariance to many
influences like poses and camera views can be guaranteed. With detected local
part regions, our model extracts local features to handle the body part
misalignment problem, which is another major challenge for person
re-identification. The local descriptors are further refined by fused attribute
information to eliminate interferences caused by detection deviation. Extensive
experiments on two popular benchmarks with attribute annotations demonstrate
the effectiveness of our model and competitive performance compared with
state-of-the-art algorithms.
Analyzing the Perceived Severity of Cybersecurity Threats Reported on Social Media
Authors: Shi Zong, Alan Ritter, Graham Mueller, Evan Wright
Date: 2019-02-27 00:00:00
Proceeding: NAACL 2019 6

Breaking cybersecurity events are shared across a range of websites, including security blogs (FireEye, Kaspersky, etc.), in addition to social media platforms such as Facebook and Twitter. In this paper, we investigate methods to analyze the severity of cybersecurity threats based on the language that is used to describe them online. A corpus of 6,000 tweets describing software vulnerabilities is annotated with authors' opinions toward their severity. We show that our corpus supports the development of automatic classifiers with high precision for this task. Furthermore, we demonstrate the value of analyzing users' opinions about the severity of threats reported online as an early indicator of important software vulnerabilities. We present a simple, yet effective method for linking software vulnerabilities reported in tweets to Common Vulnerabilities and Exposures (CVEs) in the National Vulnerability Database (NVD). Using our predicted severity scores, we show that it is possible to achieve a Precision@50 of 0.86 when forecasting high severity vulnerabilities, significantly outperforming a baseline that is based on tweet volume. Finally we show how reports of severe vulnerabilities online are predictive of real-world exploits.
On Constrained Open-World Probabilistic Databases
Authors: Tal Friedman, Guy Van Den Broeck
Date: 2019-02-27 00:00:00

Increasing amounts of available data have led to a heightened need for
representing large-scale probabilistic knowledge bases. One approach is to use
a probabilistic database, a model with strong assumptions that allow for
efficiently answering many interesting queries. Recent work on open-world
probabilistic databases strengthens the semantics of these probabilistic
databases by discarding the assumption that any information not present in the
data must be false. While intuitive, these semantics are not sufficiently
precise to give reasonable answers to queries. We propose overcoming these
issues by using constraints to restrict this open world. We provide an
algorithm for one class of queries, and establish a basic hardness result for
another. Finally, we propose an efficient and tight approximation for a large
class of queries.
High-dimensional Bayesian optimization using low-dimensional feature spaces
Authors: Riccardo Moriconi, Marc P. Deisenroth, K. S. Sesh Kumar
Date: 2019-02-27 00:00:00
Tasks: Bayesian Optimization, Dimensionality Reduction, Vocal Bursts Intensity Prediction

Bayesian optimization (BO) is a powerful approach for seeking the global optimum of expensive black-box functions and has proven successful for fine tuning hyper-parameters of machine learning models. However, BO is practically limited to optimizing 10--20 parameters. To scale BO to high dimensions, we usually make structural assumptions on the decomposition of the objective and\slash or exploit the intrinsic lower dimensionality of the problem, e.g. by using linear projections. We could achieve a higher compression rate with nonlinear projections, but learning these nonlinear embeddings typically requires much data. This contradicts the BO objective of a relatively small evaluation budget. To address this challenge, we propose to learn a low-dimensional feature space jointly with (a) the response surface and (b) a reconstruction mapping. Our approach allows for optimization of BO's acquisition function in the lower-dimensional subspace, which significantly simplifies the optimization problem. We reconstruct the original parameter space from the lower-dimensional subspace for evaluating the black-box function. For meaningful exploration, we solve a constrained optimization problem.
The Best Defense Is a Good Offense: Adversarial Attacks to Avoid Modulation Detection
Authors: Muhammad Zaid Hameed, Andras Gyorgy, Deniz Gunduz
Date: 2019-02-27 00:00:00
Tasks: image-classification, Image Classification

We consider a communication scenario, in which an intruder tries to determine the modulation scheme of the intercepted signal. Our aim is to minimize the accuracy of the intruder, while guaranteeing that the intended receiver can still recover the underlying message with the highest reliability. This is achieved by perturbing channel input symbols at the encoder, similarly to adversarial attacks against classifiers in machine learning. In image classification, the perturbation is limited to be imperceptible to a human observer, while in our case the perturbation is constrained so that the message can still be reliably decoded by the legitimate receiver, which is oblivious to the perturbation. Simulation results demonstrate the viability of our approach to make wireless communication secure against state-of-the-art intruders (using deep learning or decision trees) with minimal sacrifice in the communication performance. On the other hand, we also demonstrate that using diverse training data and curriculum learning can significantly boost the accuracy of the intruder.
Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions
Authors: Omid Rohanian, Shiva Taslimipoor, Samaneh Kouchaki, Le An Ha, Ruslan Mitkov
Date: 2019-02-27 00:00:00
Proceeding: NAACL 2019 6
Tasks: TAG

We introduce a new method to tag Multiword Expressions (MWEs) using a
linguistically interpretable language-independent deep learning architecture.
We specifically target discontinuity, an under-explored aspect that poses a
significant challenge to computational treatment of MWEs. Two neural
architectures are explored: Graph Convolutional Network (GCN) and multi-head
self-attention. GCN leverages dependency parse information, and self-attention
attends to long-range relations. We finally propose a combined model that
integrates complementary information from both through a gating mechanism. The
experiments on a standard multilingual dataset for verbal MWEs show that our
model outperforms the baselines not only in the case of discontinuous MWEs but
also in overall F-score.