pwc-archive/papers-with-abstracts huggingface dataset:
DLIMD: Dictionary Learning based Image-domain Material Decomposition for spectral CT
Authors: Weiwen Wu, Haijun Yu, Peijun Chen, Fulin Luo, Fenglin Liu, Qian Wang, Yining Zhu, Yanbo Zhang, Jian Feng, Hengyong Yu
Date: 2019-05-06 00:00:00
Tasks: Computed Tomography (CT), Dictionary Learning, Image Reconstruction

The potential huge advantage of spectral computed tomography (CT) is its capability to provide accuracy material identification and quantitative tissue information. This can benefit clinical applications, such as brain angiography, early tumor recognition, etc. To achieve more accurate material components with higher material image quality, we develop a dictionary learning based image-domain material decomposition (DLIMD) for spectral CT in this paper. First, we reconstruct spectral CT image from projections and calculate material coefficients matrix by selecting uniform regions of basis materials from image reconstruction results. Second, we employ the direct inversion (DI) method to obtain initial material decomposition results, and a set of image patches are extracted from the mode-1 unfolding of normalized material image tensor to train a united dictionary by the K-SVD technique. Third, the trained dictionary is employed to explore the similarities from decomposed material images by constructing the DLIMD model. Fourth, more constraints (i.e., volume conservation and the bounds of each pixel within material maps) are further integrated into the model to improve the accuracy of material decomposition. Finally, both physical phantom and preclinical experiments are employed to evaluate the performance of the proposed DLIMD in material decomposition accuracy, material image edge preservation and feature recovery.
Nonlinear Approximation and (Deep) ReLU Networks
Authors: I. Daubechies, R. DeVore, S. Foucart, B. Hanin, G. Petrova
Date: 2019-05-05 00:00:00

This article is concerned with the approximation and expressive powers of deep neural networks. This is an active research area currently producing many interesting papers. The results most commonly found in the literature prove that neural networks approximate functions with classical smoothness to the same accuracy as classical linear methods of approximation, e.g. approximation by polynomials or by piecewise polynomials on prescribed partitions. However, approximation by neural networks depending on n parameters is a form of nonlinear approximation and as such should be compared with other nonlinear methods such as variable knot splines or n-term approximation from dictionaries. The performance of neural networks in targeted applications such as machine learning indicate that they actually possess even greater approximation power than these traditional methods of nonlinear approximation. The main results of this article prove that this is indeed the case. This is done by exhibiting large classes of functions which can be efficiently captured by neural networks where classical nonlinear methods fall short of the task. The present article purposefully limits itself to studying the approximation of univariate functions by ReLU networks. Many generalizations to functions of several variables and other activation functions can be envisioned. However, even in this simplest of settings considered here, a theory that completely quantifies the approximation power of neural networks is still lacking.
Towards Big data processing in IoT: network management for online edge data processing
Authors: Shuo Wan, Jiaxun Lu, Pingyi Fan, Khaled B. Letaief
Date: 2019-05-05 00:00:00
Tasks: Edge-computing, Management

Heavy data load and wide cover range have always been crucial problems for internet of things (IoT). However, in mobile-edge computing (MEC) network, the huge data can be partly processed at the edge. In this paper, a MEC-based big data analysis network is discussed. The raw data generated by distributed network terminals are collected and processed by edge servers. The edge servers split out a large sum of redundant data and transmit extracted information to the center cloud for further analysis. However, for consideration of limited edge computation ability, part of the raw data in huge data sources may be directly transmitted to the cloud. To manage limited resources online, we propose an algorithm based on Lyapunov optimization to jointly optimize the policy of edge processor frequency, transmission power and bandwidth allocation. The algorithm aims at stabilizing data processing delay and saving energy without knowing probability distributions of data sources. The proposed network management algorithm may contribute to big data processing in future IoT.
Back to the Future: Predicting Traffic Shockwave Formation and Propagation Using a Convolutional Encoder-Decoder Network
Authors: Mohammadreza Khajeh-Hosseini, Alireza Talebpour
Date: 2019-05-04 00:00:00
Tasks: Decoder

This study proposes a deep learning methodology to predict the propagation of traffic shockwaves. The input to the deep neural network is time-space diagram of the study segment, and the output of the network is the predicted (future) propagation of the shockwave on the study segment in the form of time-space diagram. The main feature of the proposed methodology is the ability to extract the features embedded in the time-space diagram to predict the propagation of traffic shockwaves.
Mapping Missing Population in Rural India: A Deep Learning Approach with Satellite Imagery
Authors: Wenjie Hu, Jay Harshadbhai Patel, Zoe-Alanah Robert, Paul Novosad, Samuel Asher, Zhongyi Tang, Marshall Burke, David Lobell, Stefano Ermon
Date: 2019-05-04 00:00:00

Millions of people worldwide are absent from their country's census. Accurate, current, and granular population metrics are critical to improving government allocation of resources, to measuring disease control, to responding to natural disasters, and to studying any aspect of human life in these communities. Satellite imagery can provide sufficient information to build a population map without the cost and time of a government census. We present two Convolutional Neural Network (CNN) architectures which efficiently and effectively combine satellite imagery inputs from multiple sources to accurately predict the population density of a region. In this paper, we use satellite imagery from rural villages in India and population labels from the 2011 SECC census. Our best model achieves better performance than previous papers as well as LandScan, a community standard for global population distribution.
Regression Equilibrium
Authors: Omer Ben-Porat, Moshe Tennenholtz
Date: 2019-05-04 00:00:00
Tasks: PAC learning, Prediction, regression

Prediction is a well-studied machine learning task, and prediction algorithms are core ingredients in online products and services. Despite their centrality in the competition between online companies who offer prediction-based products, the \textit{strategic} use of prediction algorithms remains unexplored. The goal of this paper is to examine strategic use of prediction algorithms. We introduce a novel game-theoretic setting that is based on the PAC learning framework, where each player (aka a prediction algorithm aimed at competition) seeks to maximize the sum of points for which it produces an accurate prediction and the others do not. We show that algorithms aiming at generalization may wittingly mispredict some points to perform better than others on expectation. We analyze the empirical game, i.e., the game induced on a given sample, prove that it always possesses a pure Nash equilibrium, and show that every better-response learning process converges. Moreover, our learning-theoretic analysis suggests that players can, with high probability, learn an approximate pure Nash equilibrium for the whole population using a small number of samples.
Deep Learning Based on Orthogonal Approximate Message Passing for CP-Free OFDM
Authors: Jing Zhang, Hengtao He, Chao-Kai Wen, Shi Jin, Geoffrey Ye Li
Date: 2019-05-04 00:00:00

Channel estimation and signal detection are very challenging for an orthogonal frequency division multiplexing (OFDM) system without cyclic prefix (CP). In this article, deep learning based on orthogonal approximate message passing (DL-OAMP) is used to address these problems. The DL-OAMP receiver includes a channel estimation neural network (CE-Net) and a signal detection neural network based on OAMP, called OAMP-Net. The CE-Net is initialized by the least square channel estimation algorithm and refined by minimum mean-squared error (MMSE) neural network. The OAMP-Net is established by unfolding the iterative OAMP algorithm and adding some trainable parameters to improve the detection performance. The DL-OAMP receiver is with low complexity and can estimate time-varying channels with only a single training. Simulation results demonstrate that the bit-error rate (BER) of the proposed scheme is lower than those of competitive algorithms for high-order modulation.
The method of automatic summarization from different sources
Authors: Nataliya Shakhovska, Taras Cherna
Date: 2019-05-04 00:00:00
Tasks: Articles

In this article is analyzed technology of automatic text abstracting and annotation. The role of annotation in automatic search and classification for different scientific articles is described. The algorithm of summarization of natural language documents using the concept of importance coefficients is developed. Such concept allows considering the peculiarity of subject areas and topics that could be found in different kinds of documents. Method for generating abstracts of single document based on frequency analysis is developed. The recognition elements for unstructured text analysis are given. The method of pre-processing analysis of several documents is developed. This technique simultaneously considers both statistical approaches to abstracting and the importance of terms in a particular subject domain. The quality of generated abstract is evaluated. For the developed system there was conducted experts evaluation. It was held only for texts in Ukrainian. The developed system concluding essay has higher aggregate score on all criteria. The summarization system architecture is building. To build an information system model there is used CASE-tool AllFusion ERwin Data Modeler. The database scheme for information saving was built. The system is designed to work primarily with Ukrainian texts, which gives a significant advantage, since most modern systems still oriented to English texts
Learning Spatio-Temporal Features with Two-Stream Deep 3D CNNs for Lipreading
Authors: Xinshuo Weng, Kris Kitani
Date: 2019-05-04 00:00:00
Tasks: General Classification, Lipreading, Optical Flow Estimation

We focus on the word-level visual lipreading, which requires recognizing the word being spoken, given only the video but not the audio. State-of-the-art methods explore the use of end-to-end neural networks, including a shallow (up to three layers) 3D convolutional neural network (CNN) + a deep 2D CNN (e.g., ResNet) as the front-end to extract visual features, and a recurrent neural network (e.g., bidirectional LSTM) as the back-end for classification. In this work, we propose to replace the shallow 3D CNNs + deep 2D CNNs front-end with recent successful deep 3D CNNs --- two-stream (i.e., grayscale video and optical flow streams) I3D. We evaluate different combinations of front-end and back-end modules with the grayscale video and optical flow inputs on the LRW dataset. The experiments show that, compared to the shallow 3D CNNs + deep 2D CNNs front-end, the deep 3D CNNs front-end with pre-training on the large-scale image and video datasets (e.g., ImageNet and Kinetics) can improve the classification accuracy. Also, we demonstrate that using the optical flow input alone can achieve comparable performance as using the grayscale video as input. Moreover, the two-stream network using both the grayscale video and optical flow inputs can further improve the performance. Overall, our two-stream I3D front-end with a Bi-LSTM back-end results in an absolute improvement of 5.3% over the previous art on the LRW dataset.
Oriented Point Sampling for Plane Detection in Unorganized Point Clouds
Authors: Bo Sun, Philippos Mordohai
Date: 2019-05-04 00:00:00
Tasks: Point Cloud Segmentation

Plane detection in 3D point clouds is a crucial pre-processing step for applications such as point cloud segmentation, semantic mapping and SLAM. In contrast to many recent plane detection methods that are only applicable on organized point clouds, our work is targeted to unorganized point clouds that do not permit a 2D parametrization. We compare three methods for detecting planes in point clouds efficiently. One is a novel method proposed in this paper that generates plane hypotheses by sampling from a set of points with estimated normals. We named this method Oriented Point Sampling (OPS) to contrast with more conventional techniques that require the sampling of three unoriented points to generate plane hypotheses. We also implemented an efficient plane detection method based on local sampling of three unoriented points and compared it with OPS and the 3D-KHT algorithm, which is based on octrees, on the detection of planes on 10,000 point clouds from the SUN RGB-D dataset.
Meeting Transcription Using Virtual Microphone Arrays
Authors: Takuya Yoshioka, Zhuo Chen, Dimitrios Dimitriadis, William Hinthorn, Xuedong Huang, Andreas Stolcke, Michael Zeng
Date: 2019-05-03 00:00:00
Tasks: speaker-diarization, Speaker Diarization, speech-recognition, Speech Recognition

We describe a system that generates speaker-annotated transcripts of meetings by using a virtual microphone array, a set of spatially distributed asynchronous recording devices such as laptops and mobile phones. The system is composed of continuous audio stream alignment, blind beamforming, speech recognition, speaker diarization using prior speaker information, and system combination. When utilizing seven input audio streams, our system achieves a word error rate (WER) of 22.3% and comes within 3% of the close-talking microphone WER on the non-overlapping speech segments. The speaker-attributed WER (SAWER) is 26.7%. The relative gains in SAWER over the single-device system are 14.8%, 20.3%, and 22.4% for three, five, and seven microphones, respectively. The presented system achieves a 13.6% diarization error rate when 10% of the speech duration contains more than one speaker. The contribution of each component to the overall performance is also investigated, and we validate the system with experiments on the NIST RT-07 conference meeting test set.
Learning Algebraic Structures: Preliminary Investigations
Authors: Yang-Hui He, Minhyong Kim
Date: 2019-05-02 00:00:00
Tasks: BIG-bench Machine Learning

We employ techniques of machine-learning, exemplified by support vector machines and neural classifiers, to initiate the study of whether AI can "learn" algebraic structures. Using finite groups and finite rings as a concrete playground, we find that questions such as identification of simple groups by "looking" at the Cayley table or correctly matching addition and multiplication tables for finite rings can, at least for structures of small size, be performed by the AI, even after having been trained only on small number of cases. These results are in tandem with recent investigations on whether AI can solve certain classes of problems in algebraic geometry.
Deep Learning to Predict Student Outcomes
Authors: Byung-Hak Kim
Date: 2019-04-27 00:00:00
Tasks: Deep Learning, Domain Adaptation, Unsupervised Domain Adaptation

The increasingly fast development cycle for online course contents, along
with the diverse student demographics in each online classroom, make real-time
student outcomes prediction an interesting topic for both industrial research
and practical needs. In this paper, we tackle the problem of real-time student
performance prediction in an on-going course using a domain adaptation
framework. This framework is a system trained on labeled student outcome data
from previous coursework but is meant to be deployed on another course. In
particular, we introduce a GritNet architecture, and develop an unsupervised
domain adaptation method to transfer a GritNet trained on a past course to a
new course without any student outcome label. Our results for real Udacity
student graduation predictions show that the GritNet not only generalizes well
from one course to another across different Nanodegree programs, but also
enhances real-time predictions explicitly in the first few weeks when accurate
predictions are most challenging.
Many-to-Many Voice Conversion with Out-of-Dataset Speaker Support
Authors: Gokce Keskin, Tyler Lee, Cory Stephenson, Oguz H. Elibol
Date: 2019-04-30 00:00:00
Tasks: Speaker Identification, Voice Conversion

We present a Cycle-GAN based many-to-many voice conversion method that can
convert between speakers that are not in the training set. This property is
enabled through speaker embeddings generated by a neural network that is
jointly trained with the Cycle-GAN. In contrast to prior work in this domain,
our method enables conversion between an out-of-dataset speaker and a target
speaker in either direction and does not require re-training. Out-of-dataset
speaker conversion quality is evaluated using an independently trained speaker
identification model, and shows good style conversion characteristics for
previously unheard speakers. Subjective tests on human listeners show style
conversion quality for in-dataset speakers is comparable to the
state-of-the-art baseline model.
Learning from the Syndrome
Authors: Loren Lugosch, Warren J. Gross
Date: 2018-10-23 00:00:00
Tasks: Decoder, valid

In this paper, we introduce the syndrome loss, an alternative loss function
for neural error-correcting decoders based on a relaxation of the syndrome. The
syndrome loss penalizes the decoder for producing outputs that do not
correspond to valid codewords. We show that training with the syndrome loss
yields decoders with consistently lower frame error rate for a number of short
block codes, at little additional cost during training and no additional cost
during inference. The proposed method does not depend on knowledge of the
transmitted codeword, making it a promising tool for online adaptation to
changing channel conditions.
Zero-Shot Learning Through Cross-Modal Transfer
Authors: Richard Socher, Milind Ganjoo, Hamsa Sridhar, Osbert Bastani, Christopher D. Manning, Andrew Y. Ng
Date: 2013-01-16 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Outlier Detection, Zero-Shot Learning

This work introduces a model that can recognize objects in images even if no
training data is available for the objects. The only necessary knowledge about
the unseen categories comes from unsupervised large text corpora. In our
zero-shot framework distributional information in language can be seen as
spanning a semantic basis for understanding what objects look like. Most
previous zero-shot learning models can only differentiate between unseen
classes. In contrast, our model can both obtain state of the art performance on
classes that have thousands of training images and obtain reasonable
performance on unseen classes. This is achieved by first using outlier
detection in the semantic space and then two separate recognition models.
Furthermore, our model does not require any manually defined semantic features
for either words or images.
Learning Cascaded Siamese Networks for High Performance Visual Tracking
Authors: Peng Gao, Yipeng Ma, Ruyue Yuan, Liyi Xiao, Fei Wang
Date: 2019-05-08 00:00:00
Tasks: Classification, General Classification, Visual Tracking, Vocal Bursts Intensity Prediction

Visual tracking is one of the most challenging computer vision problems. In order to achieve high performance visual tracking in various negative scenarios, a novel cascaded Siamese network is proposed and developed based on two different deep learning networks: a matching subnetwork and a classification subnetwork. The matching subnetwork is a fully convolutional Siamese network. According to the similarity score between the exemplar image and the candidate image, it aims to search possible object positions and crop scaled candidate patches. The classification subnetwork is designed to further evaluate the cropped candidate patches and determine the optimal tracking results based on the classification score. The matching subnetwork is trained offline and fixed online, while the classification subnetwork performs stochastic gradient descent online to learn more target-specific information. To improve the tracking performance further, an effective classification subnetwork update method based on both similarity and classification scores is utilized for updating the classification subnetwork. Extensive experimental results demonstrate that our proposed approach achieves state-of-the-art performance in recent benchmarks.
End-to-End Wireframe Parsing
Authors: Yichao Zhou, Haozhi Qi, Yi Ma
Date: 2019-05-08 00:00:00
Proceeding: ICCV 2019 10
Tasks: Line Segment Detection, Wireframe Parsing

We present a conceptually simple yet effective algorithm to detect wireframes in a given image. Compared to the previous methods which first predict an intermediate heat map and then extract straight lines with heuristic algorithms, our method is end-to-end trainable and can directly output a vectorized wireframe that contains semantically meaningful and geometrically salient junctions and lines. To better understand the quality of the outputs, we propose a new metric for wireframe evaluation that penalizes overlapped line segments and incorrect line connectivities. We conduct extensive experiments and show that our method significantly outperforms the previous state-of-the-art wireframe and line extraction algorithms. We hope our simple approach can be served as a baseline for future wireframe parsing studies. Code has been made publicly available at https://github.com/zhou13/lcnn.
Convolutional Mesh Regression for Single-Image Human Shape Reconstruction
Authors: Nikos Kolotouros, Georgios Pavlakos, Kostas Daniilidis
Date: 2019-05-08 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D geometry, 3D Hand Pose Estimation, 3D human pose and shape estimation, 3D Human Pose Estimation, Monocular 3D Human Pose Estimation, Pose Estimation, regression

This paper addresses the problem of 3D human pose and shape estimation from a single image. Previous approaches consider a parametric model of the human body, SMPL, and attempt to regress the model parameters that give rise to a mesh consistent with image evidence. This parameter regression has been a very challenging task, with model-based approaches underperforming compared to nonparametric solutions in terms of pose estimation. In our work, we propose to relax this heavy reliance on the model's parameter space. We still retain the topology of the SMPL template mesh, but instead of predicting model parameters, we directly regress the 3D location of the mesh vertices. This is a heavy task for a typical network, but our key insight is that the regression becomes significantly easier using a Graph-CNN. This architecture allows us to explicitly encode the template mesh structure within the network and leverage the spatial locality the mesh has to offer. Image-based features are attached to the mesh vertices and the Graph-CNN is responsible to process them on the mesh structure, while the regression target for each vertex is its 3D location. Having recovered the complete 3D geometry of the mesh, if we still require a specific model parametrization, this can be reliably regressed from the vertices locations. We demonstrate the flexibility and the effectiveness of our proposed graph-based mesh regression by attaching different types of features on the mesh vertices. In all cases, we outperform the comparable baselines relying on model parameter regression, while we also achieve state-of-the-art results among model-based pose estimation approaches.