pwc-archive/papers-with-abstracts huggingface dataset:
Machine Vision for Natural Gas Methane Emissions Detection Using an Infrared Camera
Authors: Jingfan Wang, Lyne P. Tchapmi, Arvind P. Ravikumara, Mike McGuire, Clay S. Bell, Daniel Zimmerle, Silvio Savarese, Adam R. Brandt
Date: 2019-04-01 00:00:00
Tasks: Change Detection, Optical Flow Estimation

It is crucial to reduce natural gas methane emissions, which can potentially
offset the climate benefits of replacing coal with gas. Optical gas imaging
(OGI) is a widely-used method to detect methane leaks, but is labor-intensive
and cannot provide leak detection results without operators' judgment. In this
paper, we develop a computer vision approach to OGI-based leak detection using
convolutional neural networks (CNN) trained on methane leak images to enable
automatic detection. First, we collect ~1 M frames of labeled video of methane
leaks from different leaking equipment for building CNN model, covering a wide
range of leak sizes (5.3-2051.6 gCH4/h) and imaging distances (4.6-15.6 m).
Second, we examine different background subtraction methods to extract the
methane plume in the foreground. Third, we then test three CNN model variants,
collectively called GasNet, to detect plumes in videos taken at other pieces of
leaking equipment. We assess the ability of GasNet to perform leak detection by
comparing it to a baseline method that uses optical-flow based change detection
algorithm. We explore the sensitivity of results to the CNN structure, with a
moderate-complexity variant performing best across distances. We find that the
detection accuracy can reach as high as 99%, the overall detection accuracy can
exceed 95% for a case across all leak sizes and imaging distances. Binary
detection accuracy exceeds 97% for large leaks (~710 gCH4/h) imaged closely
(~5-7 m). At closer imaging distances (~5-10 m), CNN-based models have greater
than 94% accuracy across all leak sizes. At farthest distances (~13-16 m),
performance degrades rapidly, but it can achieve above 95% accuracy to detect
large leaks (>950 gCH4/h). The GasNet-based computer vision approach could be
deployed in OGI surveys to allow automatic vigilance of methane leak detection
with high detection accuracy in the real world.
A new approach for measuring semantic similarity of ontology concepts using dynamic programming
Authors: Noreddine Gherabi, Abdelhadi Daoui, Abderrahim Marzouk
Date: 2019-04-04 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

Today, with the emergence of semantic web technologies and increasing of information quantity, searching for information based on the semantic web has become a fertile area of research. For this reason, a large number of studies are performed based on the measure of semantic similarity. Therefore, in this paper, we propose a new method of semantic similarity measuring which uses the dynamic programming to compute the semantic distance between any two concepts defined in the same hierarchy of ontology. Then, we base on this result to compute the semantic similarity. Finally, we present an experimental comparison between our method and other methods of similarity measuring. Where we will show the limits of these methods and how we avoid them with our method. This one bases on a function of weight allocation, which allows finding different rate of semantic similarity between a given concept and two other sibling concepts which is impossible using the other methods.
Dynamic Gesture Recognition by Using CNNs and Star RGB: a Temporal Information Condensation
Authors: Clebeson Canuto dos Santos, Jorge Leonid Aching Samatelo, Raquel Frizera Vassallo
Date: 2019-04-10 00:00:00
Tasks: Gesture Recognition

Due to the advance of technologies, machines are increasingly present in people's daily lives. Thus, there has been more and more effort to develop interfaces, such as dynamic gestures, that provide an intuitive way of interaction. Currently, the most common trend is to use multimodal data, as depth and skeleton information, to enable dynamic gesture recognition. However, using only color information would be more interesting, since RGB cameras are usually available in almost every public place, and could be used for gesture recognition without the need of installing other equipment. The main problem with such approach is the difficulty of representing spatio-temporal information using just color. With this in mind, we propose a technique capable of condensing a dynamic gesture, shown in a video, in just one RGB image. We call this technique star RGB. This image is then passed to a classifier formed by two Resnet CNNs, a soft-attention ensemble, and a fully connected layer, which indicates the class of the gesture present in the input video. Experiments were carried out using both Montalbano and GRIT datasets. For Montalbano dataset, the proposed approach achieved an accuracy of 94.58%. Such result reaches the state-of-the-art when considering this dataset and only color information. Regarding the GRIT dataset, our proposal achieves more than 98% of accuracy, recall, precision, and F1-score, outperforming the reference approach by more than 6%.
Adaptive Hierarchical Down-Sampling for Point Cloud Classification
Authors: Ehsan Nezhadarya, Ehsan Taghavi, Ryan Razani, Bingbing Liu, Jun Luo
Date: 2019-04-11 00:00:00
Proceeding: CVPR 2020 6
Tasks: Classification, General Classification, Point Cloud Classification

While several convolution-like operators have recently been proposed for extracting features out of point clouds, down-sampling an unordered point cloud in a deep neural network has not been rigorously studied. Existing methods down-sample the points regardless of their importance for the output. As a result, some important points in the point cloud may be removed, while less valuable points may be passed to the next layers. In contrast, adaptive down-sampling methods sample the points by taking into account the importance of each point, which varies based on the application, task and training data. In this paper, we propose a permutation-invariant learning-based adaptive down-sampling layer, called Critical Points Layer (CPL), which reduces the number of points in an unordered point cloud while retaining the important points. Unlike most graph-based point cloud down-sampling methods that use $k$-NN search algorithm to find the neighbouring points, CPL is a global down-sampling method, rendering it computationally very efficient. The proposed layer can be used along with any graph-based point cloud convolution layer to form a convolutional neural network, dubbed CP-Net in this paper. We introduce a CP-Net for $3$D object classification that achieves the best accuracy for the ModelNet$40$ dataset among point cloud-based methods, which validates the effectiveness of the CPL.
Cursive Multilingual Characters Recognition Based on Hard Geometric Features
Authors: Amjad Rehman, Majid Harouni, Tanzila Saba
Date: 2019-04-07 00:00:00
Tasks: General Classification, Segmentation

The cursive nature of multilingual characters segmentation and recognition of
Arabic, Persian, Urdu languages have attracted researchers from academia and
industry. However, despite several decades of research, still multilingual
characters classification accuracy is not up to the mark. This paper presents
an automated approach for multilingual characters segmentation and recognition.
The proposed methodology explores character based on their geometric features.
However, due to uncertainty and without dictionary support few characters are
over-divided. To expand the productivity of the proposed methodology a BPN is
prepared with countless division focuses for cursive multilingual characters.
Prepared BPN separates off base portioned indicates effectively with rapid
upgrade character acknowledgment precision. For reasonable examination, only
benchmark dataset is utilized.
Artificial Intelligence for Pediatric Ophthalmology
Authors: Julia E. Reid, Eric Eaton
Date: 2019-04-06 00:00:00
Tasks: BIG-bench Machine Learning, Image Generation, scientific discovery

PURPOSE OF REVIEW: Despite the impressive results of recent artificial
intelligence (AI) applications to general ophthalmology, comparatively less
progress has been made toward solving problems in pediatric ophthalmology using
similar techniques. This article discusses the unique needs of pediatric
ophthalmology patients and how AI techniques can address these challenges,
surveys recent applications of AI to pediatric ophthalmology, and discusses
future directions in the field.
  RECENT FINDINGS: The most significant advances involve the automated
detection of retinopathy of prematurity (ROP), yielding results that rival
experts. Machine learning (ML) has also been successfully applied to the
classification of pediatric cataracts, prediction of post-operative
complications following cataract surgery, detection of strabismus and
refractive error, prediction of future high myopia, and diagnosis of reading
disability via eye tracking. In addition, ML techniques have been used for the
study of visual development, vessel segmentation in pediatric fundus images,
and ophthalmic image synthesis.
  SUMMARY: AI applications could significantly benefit clinical care for
pediatric ophthalmology patients by optimizing disease detection and grading,
broadening access to care, furthering scientific discovery, and improving
clinical efficiency. These methods need to match or surpass physician
performance in clinical trials before deployment with patients. Due to
widespread use of closed-access data sets and software implementations, it is
difficult to directly compare the performance of these approaches, and
reproducibility is poor. Open-access data sets and software implementations
could alleviate these issues, and encourage further AI applications to
pediatric ophthalmology.
  KEYWORDS: pediatric ophthalmology, machine learning, artificial intelligence,
deep learning
Multi-branch fusion network for hyperspectral image classification
Authors: Hongmin Gao a, Yao Yang a, 1, Sheng Lei b, Chenming Li a, ∗, Hui Zhou a, Xiaoyu Qu a
Date: 2019-01-18 00:00:00
Proceeding: Knowledge-Based Systems 2019 1
Tasks: Classification, General Classification, Hyperspectral Image Classification, image-classification, Image Classification, L2 Regularization

Hyperspectral remote sensing image (HSI) has the characteristics of large data volume and high spectral
resolution. It contains abundant spectral information and has tremendous applicable value. Convolutional
neural network (CNN) has been successfully applied to HSI classification. However, the limited labeled
samples of the HSI make the existing CNN based HSI classification methods generally be plagued by
small sample size problem and class imbalance, which cause great challenges for HSI classification. This
work proposes a novel CNN architecture for HSI classification. The proposed CNN is a multi-branch fusion
network, which is formed by merging multiple branches on an ordinary CNN. It can effectively extract
features of HSIs. In addition, the 1 × 1 convolutional layer is introduced into the branches to reduce the
number of parameters and then improve the classification efficiency. Furthermore, the L2 regularization
is introduced into this work to improve the generalization performance of the proposed model under
small sample set. Experimental results on three benchmark hyperspectral images demonstrate that the
proposed CNN can provide excellent classification performance under small training set.
A consensus layer v pyramidal neuron can sustain interpulse-interval coding
Authors: Chandan Singh, William B. Levy
Date: 2017-07-13 00:00:00
Proceeding: PLOS ONE 2017 7

In terms of a single neuron’s long-distance communication, interpulse intervals (IPIs) are an attractive alternative to rate and binary codes. As a proxy for an IPI, a neuron’s time-to-spike can be found in the biophysical and experimental intracellular literature. Using the current, consensus layer V pyramidal neuron, the present study examines the feasibility of IPI-coding and examines the noise sources that limit the information rate of such an encoding. In descending order of importance, the noise sources are (i) synaptic variability, (ii) sodium channel shot-noise, followed by (iii) thermal noise. The biophysical simulations allow the calculation of mutual information, which is about 3.0 bits/spike. More importantly, while, by any conventional definition, the biophysical model is highly nonlinear, the underlying function that relates input intensity to the defined output variable is linear. When one assumes the perspective of a neuron coding via first hitting-time, this result justifies a pervasive and simplifying assumption of computational modelers—that a class of cortical neurons can be treated as linearly additive, computational devices.
Linearization of excitatory synaptic integration at no extra cost
Authors: Danielle Morel, Chandan Singh, William B. Levy
Date: 2018-01-25 00:00:00
Proceeding: Journal of Computational Neuroscience 2018 1

In many theories of neural computation, linearly summed synaptic activation is a pervasive assumption for the computations performed by individual neurons. Indeed, for certain nominally optimal models, linear summation is required. However, the biophysical mechanisms needed to produce linear summation may add to the energy-cost of neural processing. Thus, the benefits provided by linear summation may be outweighed by the energy-costs. Using voltage-gated conductances in a relatively simple neuron model, this paper quantifies the cost of linearizing dendritically localized synaptic activation. Different combinations of voltage-gated conductances were examined, and many are found to produce linearization; here, four of these models are presented. Comparing the energy-costs to a purely passive model, reveals minimal or even no additional costs in some cases.
Entity Set Search of Scientific Literature: An Unsupervised Ranking Approach
Authors: Jiaming Shen, Jinfeng Xiao, Xinwei He, Jingbo Shang, Saurabh Sinha, Jiawei Han
Date: 2018-04-29 00:00:00
Tasks: Model Selection

Literature search is critical for any scientific research. Different from Web
or general domain search, a large portion of queries in scientific literature
search are entity-set queries, that is, multiple entities of possibly different
types. Entity-set queries reflect user's need for finding documents that
contain multiple entities and reveal inter-entity relationships and thus pose
non-trivial challenges to existing search algorithms that model each entity
separately. However, entity-set queries are usually sparse (i.e., not so
repetitive), which makes ineffective many supervised ranking models that rely
heavily on associated click history. To address these challenges, we introduce
SetRank, an unsupervised ranking framework that models inter-entity
relationships and captures entity type information. Furthermore, we develop a
novel unsupervised model selection algorithm, based on the technique of
weighted rank aggregation, to automatically choose the parameter settings in
SetRank without resorting to a labeled validation set. We evaluate our proposed
unsupervised approach using datasets from TREC Genomics Tracks and Semantic
Scholar's query log. The experiments demonstrate that SetRank significantly
outperforms the baseline unsupervised models, especially on entity-set queries,
and our model selection algorithm effectively chooses suitable parameter
settings.
Baseline wander removal methods for ECG signals: A comparative study
Authors: Francisco Perdigon Romero, Liset Vazquez Romaguera, Carlos Román Vázquez-Seisdedos, Cícero Ferreira Fernandes Costa Filho, Marly Guimarães Fernandes Costa, João Evangelista Neto
Date: 2018-07-30 00:00:00
Tasks: Diagnostic

Cardiovascular diseases are the leading cause of death worldwide, accounting
for 17.3 million deaths per year. The electrocardiogram (ECG) is a non-invasive
technique widely used for the detection of cardiac diseases. To increase
diagnostic sensitivity, ECG is acquired during exercise stress tests or in an
ambulatory way. Under these acquisition conditions, the ECG is strongly
affected by some types of noise, mainly by baseline wander (BLW). In this work
were implemented nine methods widely used for the elimination of BLW, which
are: interpolation using cubic splines, FIR filter, IIR filter, least mean
square adaptive filtering, moving-average filter, independent component
analysis, interpolation and successive subtraction of median values in RR
interval, empirical mode decomposition and wavelet filtering. For the
quantitative evaluation, the following similarity metrics were used: absolute
maximum distance, the sum of squares of distances and percentage
root-mean-square difference. Several experiments were performed using synthetic
ECG signals generated by ECGSYM software, real ECG signals from QT Database,
artificial BLW generated by software and real BLW from the Noise Stress Test
Database. The best results were obtained by the method based on FIR high-pass
filter with a cut-off frequency of 0.67 Hz.
Localization and Perception for Control and Decision Making of a Low Speed Autonomous Shuttle in a Campus Pilot Deployment
Authors: Bowen Wen, Sukru Yaren Gelbal, Bilin Aksun Guvenc, Levent Guvenc
Date: 2018-11-01 00:00:00
Proceeding: journal 2018 11
Tasks: Autonomous Driving, Autonomous Vehicles, Decision Making, Self-Driving Cars, Simultaneous Localization and Mapping

Future SAE Level 4 and Level 5 autonomous vehicles will require novel applications of localization, perception, control and artificial intelligence technology in order to offer innovative and disruptive solutions to current mobility problems. This paper concentrates on low speed autonomous shuttles that are transitioning from being tested in limited traffic, dedicated routes to being deployed as SAE Level 4 automated driving vehicles in urban environments like college campuses and outdoor shopping centers within smart cities. The Ohio State University has designated a small segment in an underserved area of campus as an initial autonomous vehicle (AV) pilot test route for the deployment of low speed autonomous shuttles. This paper presents initial results of ongoing work on developing solutions to the localization and perception challenges of this planned pilot deployment. The paper treats autonomous driving with real time kinematics GPS (Global Positioning Systems) with an inertial measurement unit (IMU), combined with simultaneous localization and mapping (SLAM) with three-dimensional light detection and ranging (LIDAR) sensor, which provides solutions to scenarios where GPS is not available or a lower cost and hence lower accuracy GPS is desirable. Our in-house automated low speed electric vehicle is used in experimental evaluation and verification. In addition, the experimental vehicle has vehicle to everything (V2X) communication capability and utilizes a dedicated short-range communication (DSRC) modem. It is able to communicate with instrumented traffic lights and with pedestrians and bicyclists with DSRC enabled smartphones. Before real-world experiments, our connected and automated driving hardware in the loop (HiL) simulator with real DSRC modems is used for extensive testing of the algorithms and the low level longitudinal and lateral controllers. Real-world experiments that are reported here have been conducted in a small test area close to the Ohio State University AV pilot test route. Model-in-the-loop simulation, HiL simulation and experimental testing are used for demonstrating the feasibility and robustness of this approach to developing and evaluating low speed autonomous shuttle localization and perception algorithms for control and decision making.
STEP: Spatio-Temporal Progressive Learning for Video Action Detection
Authors: Xitong Yang, Xiaodong Yang, Ming-Yu Liu, Fanyi Xiao, Larry Davis, Jan Kautz
Date: 2019-04-19 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Detection, Action Recognition, Video Action Detection

In this paper, we propose Spatio-TEmporal Progressive (STEP) action
detector---a progressive learning framework for spatio-temporal action
detection in videos. Starting from a handful of coarse-scale proposal cuboids,
our approach progressively refines the proposals towards actions over a few
steps. In this way, high-quality proposals (i.e., adhere to action movements)
can be gradually obtained at later steps by leveraging the regression outputs
from previous steps. At each step, we adaptively extend the proposals in time
to incorporate more related temporal context. Compared to the prior work that
performs action detection in one run, our progressive learning framework is
able to naturally handle the spatial displacement within action tubes and
therefore provides a more effective way for spatio-temporal modeling. We
extensively evaluate our approach on UCF101 and AVA, and demonstrate superior
detection results. Remarkably, we achieve mAP of 75.0% and 18.6% on the two
datasets with 3 progressive steps and using respectively only 11 and 34 initial
proposals.
Unifying Question Answering, Text Classification, and Regression via Span Extraction
Authors: Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, Richard Socher
Date: 2019-04-19 00:00:00
Tasks: Classification, General Classification, Multi-Task Learning, Question Answering, regression, text-classification, Text Classification

Even as pre-trained language encoders such as BERT are shared across many tasks, the output layers of question answering, text classification, and regression models are significantly different. Span decoders are frequently used for question answering, fixed-class, classification layers for text classification, and similarity-scoring layers for regression tasks, We show that this distinction is not necessary and that all three can be unified as span extraction. A unified, span-extraction approach leads to superior or comparable performance in supplementary supervised pre-trained, low-data, and multi-task learning experiments on several question answering, text classification, and regression benchmarks.
SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points
Authors: Zhize Li
Date: 2019-04-19 00:00:00
Proceeding: NeurIPS 2019 12

We analyze stochastic gradient algorithms for optimizing nonconvex problems. In particular, our goal is to find local minima (second-order stationary points) instead of just finding first-order stationary points which may be some bad unstable saddle points. We show that a simple perturbed version of stochastic recursive gradient descent algorithm (called SSRGD) can find an $(\epsilon,\delta)$-second-order stationary point with $\widetilde{O}(\sqrt{n}/\epsilon^2 + \sqrt{n}/\delta^4 + n/\delta^3)$ stochastic gradient complexity for nonconvex finite-sum problems. As a by-product, SSRGD finds an $\epsilon$-first-order stationary point with $O(n+\sqrt{n}/\epsilon^2)$ stochastic gradients. These results are almost optimal since Fang et al. [2018] provided a lower bound $\Omega(\sqrt{n}/\epsilon^2)$ for finding even just an $\epsilon$-first-order stationary point. We emphasize that SSRGD algorithm for finding second-order stationary points is as simple as for finding first-order stationary points just by adding a uniform perturbation sometimes, while all other algorithms for finding second-order stationary points with similar gradient complexity need to combine with a negative-curvature search subroutine (e.g., Neon2 [Allen-Zhu and Li, 2018]). Moreover, the simple SSRGD algorithm gets a simpler analysis. Besides, we also extend our results from nonconvex finite-sum problems to nonconvex online (expectation) problems, and prove the corresponding convergence results.