pwc-archive/papers-with-abstracts huggingface dataset:
Image Super-Resolution Using Attention Based DenseNet with Residual Deconvolution
Authors: Zhuangzi Li
Date: 2019-07-03 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Image super-resolution is a challenging task and has attracted increasing attention in research and industrial communities. In this paper, we propose a novel end-to-end Attention-based DenseNet with Residual Deconvolution named as ADRD. In our ADRD, a weighted dense block, in which the current layer receives weighted features from all previous levels, is proposed to capture valuable features rely in dense layers adaptively. And a novel spatial attention module is presented to generate a group of attentive maps for emphasizing informative regions. In addition, we design an innovative strategy to upsample residual information via the deconvolution layer, so that the high-frequency details can be accurately upsampled. Extensive experiments conducted on publicly available datasets demonstrate the promising performance of the proposed ADRD against the state-of-the-arts, both quantitatively and qualitatively.
Human Body Parts Tracking: Applications to Activity Recognition
Authors: Aras R. Dargazany
Date: 2019-07-02 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

As cameras and computers became popular, the applications of computer vision techniques attracted attention enormously. One of the most important applications in the computer vision community is human activity recognition. In order to recognize human activities, we propose a human body parts tracking system that tracks human body parts such as head, torso, arms and legs in order to perform activity recognition tasks in real time. This thesis presents a real-time human body parts tracking system (i.e. HBPT) from video sequences. Our body parts model is mostly represented by body components such as legs, head, torso and arms. The body components are modeled using torso location and size which are obtained by a torso tracking method in each frame. In order to track the torso, we are using a blob tracking module to find the approximate location and size of the torso in each frame. By tracking the torso, we will be able to track other body parts based on their location with respect to the torso on the detected silhouette. In the proposed method for human body part tracking, we are also using a refining module to improve the detected silhouette by refining the foreground mask (i.e. obtained by background subtraction) in order to detect the body parts with respect to torso location and size. Having found the torso size and location, the region of each human body part on the silhouette will be modeled by a 2D-Gaussian blob in each frame in order to show its location, size and pose. The proposed approach described in this thesis tracks accurately the body parts in different illumination conditions and in the presence of partial occlusions. The proposed approach is applied to activity recognition tasks such as approaching an object, carrying an object and opening a box or suitcase.
Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches
Authors: Yuqing Zhang, Neil Walton
Date: 2019-07-02 00:00:00
Tasks: Gaussian Processes, Management, regression

We study the application of dynamic pricing to insurance. We view this as an online revenue management problem where the insurance company looks to set prices to optimize the long-run revenue from selling a new insurance product. We develop two pricing models: an adaptive Generalized Linear Model (GLM) and an adaptive Gaussian Process (GP) regression model. Both balance between exploration, where we choose prices in order to learn the distribution of demands & claims for the insurance product, and exploitation, where we myopically choose the best price from the information gathered so far. The performance of the pricing policies is measured in terms of regret: the expected revenue loss caused by not using the optimal price. As is commonplace in insurance, we model demand and claims by GLMs. In our adaptive GLM design, we use the maximum quasi-likelihood estimation (MQLE) to estimate the unknown parameters. We show that, if prices are chosen with suitably decreasing variability, the MQLE parameters eventually exist and converge to the correct values, which in turn implies that the sequence of chosen prices will also converge to the optimal price. In the adaptive GP regression model, we sample demand and claims from Gaussian Processes and then choose selling prices by the upper confidence bound rule. We also analyze these GLM and GP pricing algorithms with delayed claims. Although similar results exist in other domains, this is among the first works to consider dynamic pricing problems in the field of insurance. We also believe this is the first work to consider Gaussian Process regression in the context of insurance pricing. These initial findings suggest that online machine learning algorithms could be a fruitful area of future investigation and application in insurance.
Vision-based Pedestrian Alert Safety System (PASS) for Signalized Intersections
Authors: Mhafuzul Islam, Mizanur Rahman, Mashrur Chowdhury, Gurcan Comert, Eshaa Deepak Sood, Amy Apon
Date: 2019-07-02 00:00:00

Although Vehicle-to-Pedestrian (V2P) communication can significantly improve pedestrian safety at a signalized intersection, this safety is hindered as pedestrians often do not carry hand-held devices (e.g., Dedicated short-range communication (DSRC) and 5G enabled cell phone) to communicate with connected vehicles nearby. To overcome this limitation, in this study, traffic cameras at a signalized intersection were used to accurately detect and locate pedestrians via a vision-based deep learning technique to generate safety alerts in real-time about possible conflicts between vehicles and pedestrians. The contribution of this paper lies in the development of a system using a vision-based deep learning model that is able to generate personal safety messages (PSMs) in real-time (every 100 milliseconds). We develop a pedestrian alert safety system (PASS) to generate a safety alert of an imminent pedestrian-vehicle crash using generated PSMs to improve pedestrian safety at a signalized intersection. Our approach estimates the location and velocity of a pedestrian more accurately than existing DSRC-enabled pedestrian hand-held devices. A connected vehicle application, the Pedestrian in Signalized Crosswalk Warning (PSCW), was developed to evaluate the vision-based PASS. Numerical analyses show that our vision-based PASS is able to satisfy the accuracy and latency requirements of pedestrian safety applications in a connected vehicle environment.
Visualizing and Describing Fine-grained Categories as Textures
Authors: Tsung-Yu Lin, Mikayla Timm, Chenyun Wu, Subhransu Maji
Date: 2019-07-02 00:00:00

We analyze how categories from recent FGVC challenges can be described by their textural content. The motivation is that subtle differences between species of birds or butterflies can often be described in terms of the texture associated with them and that several top-performing networks are inspired by texture-based representations. These representations are characterized by orderless pooling of second-order filter activations such as in bilinear CNNs and the winner of the iNaturalist 2018 challenge. Concretely, for each category we (i) visualize the "maximal images" by obtaining inputs x that maximize the probability of the particular class according to a texture-based deep network, and (ii) automatically describe the maximal images using a set of texture attributes. The models for texture captioning were trained on our ongoing efforts on collecting a dataset of describable textures building on the DTD dataset. These visualizations indicate what aspects of the texture is most discriminative for each category while the descriptions provide a language-based explanation of the same.
Interactive Topic Modeling with Anchor Words
Authors: Sanjoy Dasgupta, Stefanos Poulis, Christopher Tosh
Date: 2019-06-18 00:00:00
Tasks: Topic Models

The formalism of anchor words has enabled the development of fast topic modeling algorithms with provable guarantees. In this paper, we introduce a protocol that allows users to interact with anchor words to build customized and interpretable topic models. Experimental evidence validating the usefulness of our approach is also presented.
Eigen Artificial Neural Networks
Authors: Francisco Yepes Barrera
Date: 2019-05-22 00:00:00

This work has its origin in intuitive physical and statistical considerations. The problem of optimizing an artificial neural network is treated as a physical system, composed of a conservative vector force field. The derived scalar potential is a measure of the potential energy of the network, a function of the distance between predictions and targets. Starting from some analogies with wave mechanics, the description of the system is justified with an eigenvalue equation that is a variant of the Schr\~odinger equation, in which the potential is defined by the mutual information between inputs and targets. The weights and parameters of the network, as well as those of the state function, are varied so as to minimize energy, using an equivalent of the variational theorem of wave mechanics. The minimum energy thus obtained implies the principle of minimum mutual information (MinMI). We also propose a definition of the potential work produced by the force field to bring a network from an arbitrary probability distribution to the potential-constrained system, which allows to establish a measure of the complexity of the system. At the end of the discussion we expose a recursive procedure that allows to refine the state function and bypass some initial assumptions, as well as a discussion of some topics in quantum mechanics applied to the formalism, such as the uncertainty principle and the temporal evolution of the system. Results demonstrate how the minimization of energy effectively leads to a decrease in the average error between network predictions and targets.
Prediction of Compression Index of Fine-Grained Soils Using a Gene Expression Programming Model
Authors: Danial Mohammadzadeh, Seyed-Farzan Kazemi, Amir Mosavi, Ehsan Nasseralshariati, Joseph H. M. Tah
Date: 2019-05-26 00:00:00

In construction projects, estimation of the settlement of fine-grained soils is of critical importance, and yet is a challenging task. The coefficient of consolidation for the compression index (Cc) is a key parameter in modeling the settlement of fine-grained soil layers. However, the estimation of this parameter is costly, time-consuming, and requires skilled technicians. To overcome these drawbacks, we aimed to predict Cc through other soil parameters, i.e., the liquid limit (LL), plastic limit (PL), and initial void ratio (e0). Using these parameters is more convenient and requires substantially less time and cost compared to the conventional tests to estimate Cc. This study presents a novel prediction model for the Cc of fine-grained soils using gene expression programming (GEP). A database consisting of 108 different data points was used to develop the model. A closed-form equation solution was derived to estimate Cc based on LL, PL, and e0. The performance of the developed GEP-based model was evaluated through the coefficient of determination (R2), the root mean squared error (RMSE), and the mean average error (MAE). The proposed model performed better in terms of R2, RMSE, and MAE compared to the other models.
Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds
Authors: Bei Wang, Jianping An, Jiayan Cao
Date: 2019-06-28 00:00:00
Tasks: 3D Object Detection, Autonomous Driving, Decoder, Object, object-detection, Object Detection, Region Proposal

Object detection in point cloud data is one of the key components in computer vision systems, especially for autonomous driving applications. In this work, we present Voxel-FPN, a novel one-stage 3D object detector that utilizes raw data from LIDAR sensors only. The core framework consists of an encoder network and a corresponding decoder followed by a region proposal network. Encoder extracts multi-scale voxel information in a bottom-up manner while decoder fuses multiple feature maps from various scales in a top-down way. Extensive experiments show that the proposed method has better performance on extracting features from point data and demonstrates its superiority over some baselines on the challenging KITTI-3D benchmark, obtaining good performance on both speed and accuracy in real-world scenarios.
Towards fully automated post-event data collection and analysis: pre-event and post-event information fusion
Authors: Ali Lenjani, Shirley J. Dyke, Ilias Bilionis, Chul Min Yeum, Kenzo Kamiya, Jongseong Choi, Xiaoyu Liu, Arindam G. Chowdhury
Date: 2019-06-30 00:00:00
Tasks: image-classification, Image Classification, Survey

In post-event reconnaissance missions, engineers and researchers collect perishable information about damaged buildings in the affected geographical region to learn from the consequences of the event. A typical post-event reconnaissance mission is conducted by first doing a preliminary survey, followed by a detailed survey. The preliminary survey is typically conducted by driving slowly along a pre-determined route, observing the damage, and noting where further detailed data should be collected. This involves several manual, time-consuming steps that can be accelerated by exploiting recent advances in computer vision and artificial intelligence. The objective of this work is to develop and validate an automated technique to support post-event reconnaissance teams in the rapid collection of reliable and sufficiently comprehensive data, for planning the detailed survey. The technique incorporates several methods designed to automate the process of categorizing buildings based on their key physical attributes, and rapidly assessing their post-event structural condition. It is divided into pre-event and post-event streams, each intending to first extract all possible information about the target buildings using both pre-event and post-event images. Algorithms based on convolutional neural network (CNNs) are implemented for scene (image) classification. A probabilistic approach is developed to fuse the results obtained from analyzing several images to yield a robust decision regarding the attributes and condition of a target building. We validate the technique using post-event images captured during reconnaissance missions that took place after hurricanes Harvey and Irma. The validation data were collected by a structural wind and coastal engineering reconnaissance team, the National Science Foundation (NSF) funded Structural Extreme Events Reconnaissance (StEER) Network.
A Regularized Convolutional Neural Network for Semantic Image Segmentation
Authors: Fan Jia, Jun Liu, Xue-Cheng Tai
Date: 2019-06-28 00:00:00
Tasks: Image Segmentation, object-detection, Object Detection, Segmentation, Semantic Segmentation

Convolutional neural networks (CNNs) show outstanding performance in many image processing problems, such as image recognition, object detection and image segmentation. Semantic segmentation is a very challenging task that requires recognizing, understanding what's in the image in pixel level. Though the state of the art has been greatly improved by CNNs, there is no explicit connections between prediction of neighbouring pixels. That is, spatial regularity of the segmented objects is still a problem for CNNs. In this paper, we propose a method to add spatial regularization to the segmented objects. In our method, the spatial regularization such as total variation (TV) can be easily integrated into CNN network. It can help CNN find a better local optimum and make the segmentation results more robust to noise. We apply our proposed method to Unet and Segnet, which are well established CNNs for image segmentation, and test them on WBC, CamVid and SUN-RGBD datasets, respectively. The results show that the regularized networks not only could provide better segmentation results with regularization effect than the original ones but also have certain robustness to noise.
An algorithm for the selection of route dependent orientation information
Authors: Heinrich Löwen, Angela Schwering
Date: 2019-06-28 00:00:00

Landmarks are important features of spatial cognition. Landmarks are naturally included in human route descriptions and in the past algorithms were developed to select the most salient landmarks at decision points and automatically incorporate them in route instructions. Moreover, it was shown that human route descriptions contain a significant amount of orientation information and that these orientation information support the acquisition of survey knowledge. Thus, there is a need to extend the landmarks selection in order to automatically select orientation information. In this work we present an algorithm for the computational selection of route dependent orientation information, which extends previous algorithms and includes a salience evaluation of orientation information for any location along the route. We implemented the algorithm and demonstrate the functionality on the basis of OpenStreetMap data.
The active geometric shape model: A new robust deformable shape model and its applications
Authors: Quan Wang, Kim L. Boyer
Date: 2012-08-01 00:00:00
Proceeding: journal 2012 8
Tasks: model

We present a novel approach for fitting a geometric shape in images. Similar to active shape models and active contours, a force field is used in our approach. But the object to be detected is described with a geometric shape, represented by parametric equations. Our model associates each parameter of this geometric shape with a combination of integrals (summations in the discrete case) of the force field along the contour. By iteratively updating the shape parameters according to these integrals, we are able to find the optimal fit of the shape in the image. In this paper, we first explore simple cases such as fitting a line, circle, ellipse or cubic spline contour using this approach. Then we employ this technique to detect the cross-sections of subarachnoid spaces containing cerebrospinal fluid (CSF) in phase-contrast magnetic resonance (PC-MR) images, where the object of interest can be described by a distorted ellipse. The detection results can be further used by an s–t graph cut to generate a segmentation of the CSF structure. We demonstrate that, given a properly configured geometric shape model and force field, this approach is robust to noise and defects (disconnections and non-uniform contrast) in the image. By using a geometric shape model, this approach does not rely on large training datasets, and requires no manual labeling of the training images as is needed when using point distribution models.
A review of two decades of correlations, hierarchies, networks and clustering in financial markets
Authors: Gautier Marti, Frank Nielsen, Mikołaj Bińkowski, Philippe Donnat
Date: 2017-03-01 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Econometrics, Time Series, Time Series Analysis

We review the state of the art of clustering financial time series and the study of their correlations alongside other interaction networks. The aim of this review is to gather in one place the relevant material from different fields, e.g. machine learning, information geometry, econophysics, statistical physics, econometrics, behavioral finance. We hope it will help researchers to use more effectively this alternative modeling of the financial time series. Decision makers and quantitative researchers may also be able to leverage its insights. Finally, we also hope that this review will form the basis of an open toolbox to study correlations, hierarchies, networks and clustering in financial markets.
Network Filtering for Big Data: Triangulated Maximally Filtered Graph
Authors: Guido Previde Massara, T. Di Matteo, Tomaso Aste
Date: 2015-05-10 00:00:00
Tasks: Clustering, Community Detection

We propose a network-filtering method, the Triangulated Maximally Filtered Graph (TMFG), that provides an approximate solution to the Weighted Maximal Planar Graph problem. The underlying idea of TMFG consists in building a triangulation that maximizes a score function associated with the amount of information retained by the network. TMFG uses as weights any arbitrary similarity measure to arrange data into a meaningful network structure that can be used for clustering, community detection and modeling. The method is fast, adaptable and scalable to very large datasets, it allows online updating and learning as new data can be inserted and deleted with combinations of local and non-local moves. TMFG permits readjustments of the network in consequence of changes in the strength of the similarity measure. The method is based on local topological moves and can therefore take advantage of parallel and GPUs computing. We discuss how this network-filtering method can be used intuitively and efficiently for big data studies and its significance from an information-theoretic perspective.
Networks of Economic Market Interdependence and Systemic Risk
Authors: Dion Harmon, Blake Stacey, Yavni Bar-Yam, Yaneer Bar-Yam
Date: 2010-11-16 00:00:00

The dynamic network of relationships among corporations underlies cascading economic failures including the current economic crisis, and can be inferred from correlations in market value fluctuations. We analyze the time dependence of the network of correlations to reveal the changing relationships among the financial, technology, and basic materials sectors with rising and falling markets and resource constraints. The financial sector links otherwise weakly coupled economic sectors, particularly during economic declines. Such links increase economic risk and the extent of cascading failures. Our results suggest that firewalls between financial services for different sectors would reduce systemic risk without hampering economic growth.
An Inverse Power Method for Nonlinear Eigenproblems with Applications in 1-Spectral Clustering and Sparse PCA
Authors: Matthias Hein, Thomas Bühler
Date: 2010-12-03 00:00:00
Proceeding: NeurIPS 2010 12
Tasks: Clustering

Many problems in machine learning and statistics can be formulated as (generalized) eigenproblems. In terms of the associated optimization problem, computing linear eigenvectors amounts to finding critical points of a quadratic function subject to quadratic constraints. In this paper we show that a certain class of constrained optimization problems with nonquadratic objective and constraints can be understood as nonlinear eigenproblems. We derive a generalization of the inverse power method which is guaranteed to converge to a nonlinear eigenvector. We apply the inverse power method to 1-spectral clustering and sparse PCA which can naturally be formulated as nonlinear eigenproblems. In both applications we achieve state-of-the-art results in terms of solution quality and runtime. Moving beyond the standard eigenproblem should be useful also in many other applications and our inverse power method can be easily adapted to new problems.