pwc-archive/papers-with-abstracts huggingface dataset:
Modeling the Mitral Valve
Authors: Alexander D. Kaiser, David M. McQueen, Charles S. Peskin
Date: 2019-01-31 00:00:00
Tasks: Anatomy, Stress-Strain Relation

This work is concerned with modeling and simulation of the mitral valve, one
of the four valves in the human heart. The valve is composed of leaflets, the
free edges of which are supported by a system of chordae, which themselves are
anchored to the papillary muscles inside the left ventricle. First, we examine
valve anatomy and present the results of original dissections. These display
the gross anatomy and information on fiber structure of the mitral valve. Next,
we build a model valve following a design-based methodology, meaning that we
derive the model geometry and the forces that are needed to support a given
load, and construct the model accordingly. We incorporate information from the
dissections to specify the fiber topology of this model. We assume the valve
achieves mechanical equilibrium while supporting a static pressure load. The
solution to the resulting differential equations determines the pressurized
configuration of the valve model. To complete the model we then specify a
constitutive law based on a stress-strain relation consistent with experimental
data that achieves the necessary forces computed in previous steps. Finally,
using the immersed boundary method, we simulate the model valve in fluid in a
computer test chamber. The model opens easily and closes without leak when
driven by physiological pressures over multiple beats. Further, its closure is
robust to driving pressures that lack atrial systole or are much lower or
higher than normal.
Verifying the existence of maximum likelihood estimates for generalized linear models
Authors: Sergio Correia, Paulo Guimarães, Thomas Zylkin
Date: 2019-03-05 00:00:00

A fundamental problem with nonlinear models is that maximum likelihood estimates are not guaranteed to exist. Though nonexistence is a well known problem in the binary choice literature, it presents significant challenges for other models as well and is not as well understood in more general settings. These challenges are only magnified for models that feature many fixed effects and other high-dimensional parameters. We address the current ambiguity surrounding this topic by studying the conditions that govern the existence of estimates for (pseudo-)maximum likelihood estimators used to estimate a wide class of generalized linear models (GLMs). We show that some, but not all, of these GLM estimators can still deliver consistent estimates of at least some of the linear parameters when these conditions fail to hold. We also demonstrate how to verify these conditions in models with high-dimensional parameters, such as panel data models with multiple levels of fixed effects.
Kernel-based Conditional Independence Test and Application in Causal Discovery
Authors: Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schoelkopf
Date: 2012-02-14 00:00:00
Tasks: Causal Discovery

Conditional independence testing is an important problem, especially in
Bayesian network learning and causal discovery. Due to the curse of
dimensionality, testing for conditional independence of continuous variables is
particularly challenging. We propose a Kernel-based Conditional Independence
test (KCI-test), by constructing an appropriate test statistic and deriving its
asymptotic distribution under the null hypothesis of conditional independence.
The proposed method is computationally efficient and easy to implement.
Experimental results show that it outperforms other methods, especially when
the conditioning set is large or the sample size is not very large, in which
case other methods encounter difficulties.
Five Modulus Method For Image Compression
Authors: Firas A. Jassim, Hind E. Qassim
Date: 2012-11-19 00:00:00
Tasks: Image Compression

Data is compressed by reducing its redundancy, but this also makes the data
less reliable, more prone to errors. In this paper a novel approach of image
compression based on a new method that has been created for image compression
which is called Five Modulus Method (FMM). The new method consists of
converting each pixel value in an 8-by-8 block into a multiple of 5 for each of
the R, G and B arrays. After that, the new values could be divided by 5 to get
new values which are 6-bit length for each pixel and it is less in storage
space than the original value which is 8-bits. Also, a new protocol for
compression of the new values as a stream of bits has been presented that gives
the opportunity to store and transfer the new compressed image easily.
Decoders Matter for Semantic Segmentation: Data-Dependent Decoding Enables Flexible Feature Aggregation
Authors: Zhi Tian, Tong He, Chunhua Shen, Youliang Yan
Date: 2019-03-05 00:00:00
Proceeding: CVPR 2019 6
Tasks: Decoder, Segmentation, Semantic Segmentation

Recent semantic segmentation methods exploit encoder-decoder architectures to
produce the desired pixel-wise segmentation prediction. The last layer of the
decoders is typically a bilinear upsampling procedure to recover the final
pixel-wise prediction. We empirically show that this oversimple and
data-independent bilinear upsampling may lead to sub-optimal results.
  In this work, we propose a data-dependent upsampling (DUpsampling) to replace
bilinear, which takes advantages of the redundancy in the label space of
semantic segmentation and is able to recover the pixel-wise prediction from
low-resolution outputs of CNNs. The main advantage of the new upsampling layer
lies in that with a relatively lower-resolution feature map such as
$\frac{1}{16}$ or $\frac{1}{32}$ of the input size, we can achieve even better
segmentation accuracy, significantly reducing computation complexity. This is
made possible by 1) the new upsampling layer's much improved reconstruction
capability; and more importantly 2) the DUpsampling based decoder's flexibility
in leveraging almost arbitrary combinations of the CNN encoders' features.
Experiments demonstrate that our proposed decoder outperforms the
state-of-the-art decoder, with only $\sim$20\% of computation. Finally, without
any post-processing, the framework equipped with our proposed decoder achieves
new state-of-the-art performance on two datasets: 88.1\% mIOU on PASCAL VOC
with 30\% computation of the previously best model; and 52.5\% mIOU on PASCAL
Context.
Deep Transfer Learning for Multiple Class Novelty Detection
Authors: Pramuditha Perera, Vishal M. Patel
Date: 2019-03-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Novelty Detection, Transfer Learning

We propose a transfer learning-based solution for the problem of multiple
class novelty detection. In particular, we propose an end-to-end deep-learning
based approach in which we investigate how the knowledge contained in an
external, out-of-distributional dataset can be used to improve the performance
of a deep network for visual novelty detection. Our solution differs from the
standard deep classification networks on two accounts. First, we use a novel
loss function, membership loss, in addition to the classical cross-entropy loss
for training networks. Secondly, we use the knowledge from the external dataset
more effectively to learn globally negative filters, filters that respond to
generic objects outside the known class set. We show that thresholding the
maximal activation of the proposed network can be used to identify novel
objects effectively. Extensive experiments on four publicly available novelty
detection datasets show that the proposed method achieves significant
improvements over the state-of-the-art methods.
Self-Supervised Learning of 3D Human Pose using Multi-view Geometry
Authors: Muhammed Kocabas, Salih Karagoz, Emre Akbas
Date: 2019-03-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Human Pose Estimation, Pose Estimation, Self-Supervised Learning, Weakly-supervised 3D Human Pose Estimation

Training accurate 3D human pose estimators requires large amount of 3D
ground-truth data which is costly to collect. Various weakly or self supervised
pose estimation methods have been proposed due to lack of 3D data.
Nevertheless, these methods, in addition to 2D ground-truth poses, require
either additional supervision in various forms (e.g. unpaired 3D ground truth
data, a small subset of labels) or the camera parameters in multiview settings.
To address these problems, we present EpipolarPose, a self-supervised learning
method for 3D human pose estimation, which does not need any 3D ground-truth
data or camera extrinsics. During training, EpipolarPose estimates 2D poses
from multi-view images, and then, utilizes epipolar geometry to obtain a 3D
pose and camera geometry which are subsequently used to train a 3D pose
estimator. We demonstrate the effectiveness of our approach on standard
benchmark datasets i.e. Human3.6M and MPI-INF-3DHP where we set the new
state-of-the-art among weakly/self-supervised methods. Furthermore, we propose
a new performance measure Pose Structure Score (PSS) which is a scale
invariant, structure aware measure to evaluate the structural plausibility of a
pose with respect to its ground truth. Code and pretrained models are available
at https://github.com/mkocabas/EpipolarPose
CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning
Authors: Chi Zhang, Guosheng Lin, Fayao Liu, Rui Yao, Chunhua Shen
Date: 2019-03-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Few-Shot Learning, Few-Shot Semantic Segmentation, Segmentation, Semantic Segmentation

Recent progress in semantic segmentation is driven by deep Convolutional
Neural Networks and large-scale labeled image datasets. However, data labeling
for pixel-wise segmentation is tedious and costly. Moreover, a trained model
can only make predictions within a set of pre-defined classes. In this paper,
we present CANet, a class-agnostic segmentation network that performs few-shot
segmentation on new classes with only a few annotated images available. Our
network consists of a two-branch dense comparison module which performs
multi-level feature comparison between the support image and the query image,
and an iterative optimization module which iteratively refines the predicted
results. Furthermore, we introduce an attention mechanism to effectively fuse
information from multiple support examples under the setting of k-shot
learning. Experiments on PASCAL VOC 2012 show that our method achieves a mean
Intersection-over-Union score of 55.4% for 1-shot segmentation and 57.1% for
5-shot segmentation, outperforming state-of-the-art methods by a large margin
of 14.6% and 13.2%, respectively.
Object Counting and Instance Segmentation with Image-level Supervision
Authors: Hisham Cholakkal, Guolei Sun, Fahad Shahbaz Khan, Ling Shao
Date: 2019-03-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image-level Supervised Instance Segmentation, Instance Segmentation, Object, Object Counting, Semantic Segmentation

Common object counting in a natural scene is a challenging problem in computer vision with numerous real-world applications. Existing image-level supervised common object counting approaches only predict the global object count and rely on additional instance-level supervision to also determine object locations. We propose an image-level supervised approach that provides both the global object count and the spatial distribution of object instances by constructing an object category density map. Motivated by psychological studies, we further reduce image-level supervision using a limited object count information (up to four). To the best of our knowledge, we are the first to propose image-level supervised density map estimation for common object counting and demonstrate its effectiveness in image-level supervised instance segmentation. Comprehensive experiments are performed on the PASCAL VOC and COCO datasets. Our approach outperforms existing methods, including those using instance-level supervision, on both datasets for common object counting. Moreover, our approach improves state-of-the-art image-level supervised instance segmentation with a relative gain of 17.8% in terms of average best overlap, on the PASCAL VOC 2012 dataset. Code link: https://github.com/GuoleiSun/CountSeg
Understanding and Visualizing Deep Visual Saliency Models
Authors: Sen He, Hamed R. -Tavakoli, Ali Borji, Yang Mi, Nicolas Pugeault
Date: 2019-03-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Object Recognition, Saliency Prediction, Scene Classification

Recently, data-driven deep saliency models have achieved high performance and
have outperformed classical saliency models, as demonstrated by results on
datasets such as the MIT300 and SALICON. Yet, there remains a large gap between
the performance of these models and the inter-human baseline. Some outstanding
questions include what have these models learned, how and where they fail, and
how they can be improved. This article attempts to answer these questions by
analyzing the representations learned by individual neurons located at the
intermediate layers of deep saliency models. To this end, we follow the steps
of existing deep saliency models, that is borrowing a pre-trained model of
object recognition to encode the visual features and learning a decoder to
infer the saliency. We consider two cases when the encoder is used as a fixed
feature extractor and when it is fine-tuned, and compare the inner
representations of the network. To study how the learned representations depend
on the task, we fine-tune the same network using the same image set but for two
different tasks: saliency prediction versus scene classification. Our analyses
reveal that: 1) some visual regions (e.g. head, text, symbol, vehicle) are
already encoded within various layers of the network pre-trained for object
recognition, 2) using modern datasets, we find that fine-tuning pre-trained
models for saliency prediction makes them favor some categories (e.g. head)
over some others (e.g. text), 3) although deep models of saliency outperform
classical models on natural images, the converse is true for synthetic stimuli
(e.g. pop-out search arrays), an evidence of significant difference between
human and data-driven saliency models, and 4) we confirm that, after-fine
tuning, the change in inner-representations is mostly due to the task and not
the domain shift in the data.
Tactical Rewind: Self-Correction via Backtracking in Vision-and-Language Navigation
Authors: Liyiming Ke, Xiujun Li, Yonatan Bisk, Ari Holtzman, Zhe Gan, Jingjing Liu, Jianfeng Gao, Yejin Choi, Siddhartha Srinivasa
Date: 2019-03-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Vision and Language Navigation, Vision-Language Navigation

We present the Frontier Aware Search with backTracking (FAST) Navigator, a
general framework for action decoding, that achieves state-of-the-art results
on the Room-to-Room (R2R) Vision-and-Language navigation challenge of Anderson
et. al. (2018). Given a natural language instruction and photo-realistic image
views of a previously unseen environment, the agent was tasked with navigating
from source to target location as quickly as possible. While all current
approaches make local action decisions or score entire trajectories using beam
search, ours balances local and global signals when exploring an unobserved
environment. Importantly, this lets us act greedily but use global signals to
backtrack when necessary. Applying FAST framework to existing state-of-the-art
models achieved a 17% relative gain, an absolute 6% gain on Success rate
weighted by Path Length (SPL).
SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
Authors: Sajad Mousavi, Fatemeh Afghah, U. Rajendra Acharya
Date: 2019-03-05 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Sleep Stage Detection

Electroencephalogram (EEG) is a common base signal used to monitor brain
activity and diagnose sleep disorders. Manual sleep stage scoring is a
time-consuming task for sleep experts and is limited by inter-rater
reliability. In this paper, we propose an automatic sleep stage annotation
method called SleepEEGNet using a single-channel EEG signal. The SleepEEGNet is
composed of deep convolutional neural networks (CNNs) to extract time-invariant
features, frequency information, and a sequence to sequence model to capture
the complex and long short-term context dependencies between sleep epochs and
scores. In addition, to reduce the effect of the class imbalance problem
presented in the available sleep datasets, we applied novel loss functions to
have an equal misclassified error for each sleep stage while training the
network. We evaluated the proposed method on different single-EEG channels
(i.e., Fpz-Cz and Pz-Oz EEG channels) from the Physionet Sleep-EDF datasets
published in 2013 and 2018. The evaluation results demonstrate that the
proposed method achieved the best annotation performance compared to current
literature, with an overall accuracy of 84.26%, a macro F1-score of 79.66% and
Cohen's Kappa coefficient = 0.79. Our developed model is ready to test with
more sleep EEG signals and aid the sleep specialists to arrive at an accurate
diagnosis. The source code is available at
https://github.com/SajadMo/SleepEEGNet.
Relational Pooling for Graph Representations
Authors: Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro
Date: 2019-03-06 00:00:00
Tasks: General Classification, Graph Classification

This work generalizes graph neural networks (GNNs) beyond those based on the Weisfeiler-Lehman (WL) algorithm, graph Laplacians, and diffusions. Our approach, denoted Relational Pooling (RP), draws from the theory of finite partial exchangeability to provide a framework with maximal representation power for graphs. RP can work with existing graph representation models and, somewhat counterintuitively, can make them even more powerful than the original WL isomorphism test. Additionally, RP allows architectures like Recurrent Neural Networks and Convolutional Neural Networks to be used in a theoretically sound approach for graph classification. We demonstrate improved performance of RP-based graph representations over state-of-the-art methods on a number of tasks.
Autoregressive Convolutional Recurrent Neural Network for Univariate and Multivariate Time Series Prediction
Authors: Matteo Maggiolo, Gerasimos Spanakis
Date: 2019-03-06 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting, Time Series Prediction

Time Series forecasting (univariate and multivariate) is a problem of high
complexity due the different patterns that have to be detected in the input,
ranging from high to low frequencies ones. In this paper we propose a new model
for timeseries prediction that utilizes convolutional layers for feature
extraction, a recurrent encoder and a linear autoregressive component. We
motivate the model and we test and compare it against a baseline of widely used
existing architectures for univariate and multivariate timeseries. The proposed
model appears to outperform the baselines in almost every case of the
multivariate timeseries datasets, in some cases even with 50% improvement which
shows the strengths of such a hybrid architecture in complex timeseries.
Combining Optimal Control and Learning for Visual Navigation in Novel Environments
Authors: Somil Bansal, Varun Tolani, Saurabh Gupta, Jitendra Malik, Claire Tomlin
Date: 2019-03-06 00:00:00
Tasks: Robot Navigation, Visual Navigation

Model-based control is a popular paradigm for robot navigation because it can leverage a known dynamics model to efficiently plan robust robot trajectories. However, it is challenging to use model-based methods in settings where the environment is a priori unknown and can only be observed partially through on-board sensors on the robot. In this work, we address this short-coming by coupling model-based control with learning-based perception. The learning-based perception module produces a series of waypoints that guide the robot to the goal via a collision-free path. These waypoints are used by a model-based planner to generate a smooth and dynamically feasible trajectory that is executed on the physical system using feedback control. Our experiments in simulated real-world cluttered environments and on an actual ground vehicle demonstrate that the proposed approach can reach goal locations more reliably and efficiently in novel environments as compared to purely geometric mapping-based or end-to-end learning-based alternatives. Our approach does not rely on detailed explicit 3D maps of the environment, works well with low frame rates, and generalizes well from simulation to the real world. Videos describing our approach and experiments are available on the project website.
Safety-Guided Deep Reinforcement Learning via Online Gaussian Process Estimation
Authors: Jiameng Fan, Wenchao Li
Date: 2019-03-06 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Exploration

An important facet of reinforcement learning (RL) has to do with how the
agent goes about exploring the environment. Traditional exploration strategies
typically focus on efficiency and ignore safety. However, for practical
applications, ensuring safety of the agent during exploration is crucial since
performing an unsafe action or reaching an unsafe state could result in
irreversible damage to the agent. The main challenge of safe exploration is
that characterizing the unsafe states and actions is difficult for large
continuous state or action spaces and unknown environments. In this paper, we
propose a novel approach to incorporate estimations of safety to guide
exploration and policy search in deep reinforcement learning. By using a cost
function to capture trajectory-based safety, our key idea is to formulate the
state-action value function of this safety cost as a candidate Lyapunov
function and extend control-theoretic results to approximate its derivative
using online Gaussian Process (GP) estimation. We show how to use these
statistical models to guide the agent in unknown environments to obtain
high-performance control policies with provable stability certificates.
Learning multimodal representations for sample-efficient recognition of human actions
Authors: Miguel Vasco, Francisco S. Melo, David Martins de Matos, Ana Paiva, Tetsunari Inamura
Date: 2019-03-06 00:00:00

Humans interact in rich and diverse ways with the environment. However, the
representation of such behavior by artificial agents is often limited. In this
work we present \textit{motion concepts}, a novel multimodal representation of
human actions in a household environment. A motion concept encompasses a
probabilistic description of the kinematics of the action along with its
contextual background, namely the location and the objects held during the
performance. Furthermore, we present Online Motion Concept Learning (OMCL), a
new algorithm which learns novel motion concepts from action demonstrations and
recognizes previously learned motion concepts. The algorithm is evaluated on a
virtual-reality household environment with the presence of a human avatar. OMCL
outperforms standard motion recognition algorithms on an one-shot recognition
task, attesting to its potential for sample-efficient recognition of human
actions.