OpenRaiser/Intern-Atlas huggingface dataset:
Disinformation Detection: A review of linguistic feature selection and classification models in news veracity assessments
Venue: arXiv cs.CL 2019
Year: 2019

Over the past couple of years, the topic of "fake news" and its influence over people's opinions has become a growing cause for concern. Although the spread of disinformation on the Internet is not a new phenomenon, the widespread use of social media has exacerbated its effects, providing more channels for dissemination and the potential to "go viral." Nowhere was this more evident than during the 2016 United States Presidential Election. Although the current of disinformation spread via trolls, bots, and hyperpartisan media outlets likely reinforced existing biases rather than sway undecided voters, the effects of this deluge of disinformation are by no means trivial. The consequences range in severity from an overall distrust in news media, to an ill-informed citizenry, and in extreme cases, provocation of violent action. It is clear that human ability to discern lies from truth is flawed at best. As such, greater attention has been given towards applying machine learning approaches to detect deliberately deceptive news articles. This paper looks at the work that has already been done in this area.
Computing the Center Region and Its Variants
Venue: arXiv cs.CG 2019
Year: 2019

We present an $O(n^2\log^4 n)$-time algorithm for computing the center region of a set of $n$ points in the three-dimensional Euclidean space. This improves the previously best known algorithm by Agarwal, Sharir and Welzl, which takes $O(n^{2+ε})$ time for any $ε> 0$. It is known that the combinatorial complexity of the center region is $Ω(n^2)$ in the worst case, thus our algorithm is almost tight. We also consider the problem of computing a colored version of the center region in the two-dimensional Euclidean space and present an $O(n\log^4 n)$-time algorithm.
Neural Network Distiller: A Python Package For DNN Compression Research
Venue: arXiv cs.LG 2019
Year: 2019

This paper presents the philosophy, design and feature-set of Neural Network Distiller, an open-source Python package for DNN compression research. Distiller is a library of DNN compression algorithms implementations, with tools, tutorials and sample applications for various learning tasks. Its target users are both engineers and researchers, and the rich content is complemented by a design-for-extensibility to facilitate new research. Distiller is open-source and is available on Github at https://github.com/NervanaSystems/distiller.
Solving Optimization Problems through Fully Convolutional Networks: an Application to the Travelling Salesman Problem
Venue: arXiv cs.LG 2019
Year: 2019

In the new wave of artificial intelligence, deep learning is impacting various industries. As a closely related area, optimization algorithms greatly contribute to the development of deep learning. But the reverse applications are still insufficient. Is there any efficient way to solve certain optimization problem through deep learning? The key is to convert the optimization to a representation suitable for deep learning. In this paper, a traveling salesman problem (TSP) is studied. Considering that deep learning is good at image processing, an image representation method is proposed to transfer a TSP to an image. Based on samples of a 10 city TSP, a fully convolutional network (FCN) is used to learn the mapping from a feasible region to an optimal solution. The training process is analyzed and interpreted through stages. A visualization method is presented to show how a FCN can understand the training task of a TSP. Once the training is completed, no significant effort is required to solve a new TSP and the prediction is obtained on the scale of milliseconds. The results show good performance in finding the global optimal solution. Moreover, the developed FCN model has been demonstrated on TSP's with different city numbers, proving excellent generalization performance.
Prediction stability as a criterion in active learning
Venue: arXiv cs.CV 2019
Year: 2019

Recent breakthroughs made by deep learning rely heavily on large number of annotated samples. To overcome this shortcoming, active learning is a possible solution. Beside the previous active learning algorithms that only adopted information after training, we propose a new class of method based on the information during training, named sequential-based method. An specific criterion of active learning called prediction stability is proposed to prove the feasibility of sequential-based methods. Experiments are made on CIFAR-10 and CIFAR-100, and the results indicates that prediction stability is effective and works well on fewer-labeled datasets. Prediction stability reaches the accuracy of traditional acquisition functions like entropy on CIFAR-10, and notably outperforms them on CIFAR-100.
Smart Hypothesis Generation for Efficient and Robust Room Layout Estimation
Venue: arXiv cs.CV 2019
Year: 2019

We propose a novel method to efficiently estimate the spatial layout of a room from a single monocular RGB image. As existing approaches based on low-level feature extraction, followed by a vanishing point estimation are very slow and often unreliable in realistic scenarios, we build on semantic segmentation of the input image. To obtain better segmentations, we introduce a robust, accurate and very efficient hypothesize-and-test scheme. The key idea is to use three segmentation hypotheses, each based on a different number of visible walls. For each hypothesis, we predict the image locations of the room corners and select the hypothesis for which the layout estimated from the room corners is consistent with the segmentation. We demonstrate the efficiency and robustness of our method on three challenging benchmark datasets, where we significantly outperform the state-of-the-art.
Compressed Sensing with Probability-based Prior Information
Venue: arXiv cs.LG 2019
Year: 2019

This paper deals with the design of a sensing matrix along with a sparse recovery algorithm by utilizing the probability-based prior information for compressed sensing system. With the knowledge of the probability for each atom of the dictionary being used, a diagonal weighted matrix is obtained and then the sensing matrix is designed by minimizing a weighted function such that the Gram of the equivalent dictionary is as close to the Gram of dictionary as possible. An analytical solution for the corresponding sensing matrix is derived which leads to low computational complexity. We also exploit this prior information through the sparse recovery stage and propose a probability-driven orthogonal matching pursuit algorithm that improves the accuracy of the recovery. Simulations for synthetic data and application scenarios of surveillance video are carried out to compare the performance of the proposed methods with some existing algorithms. The results reveal that the proposed CS system outperforms existing CS systems.
Memeify: A Large-Scale Meme Generation System
Venue: arXiv cs.CL 2019
Year: 2019

Interest in the research areas related to meme propagation and generation has been increasing rapidly in the last couple of years. Meme datasets available online are either specific to a context or contain no class information. Here, we prepare a large-scale dataset of memes with captions and class labels. The dataset consists of 1.1 million meme captions from 128 classes. We also provide reasoning for the existence of broad categories, called "themes" across the meme dataset; each theme consists of multiple meme classes. Our generation system uses a trained state-of-the-art transformer-based model for caption generation by employing an encoder-decoder architecture. We develop a web interface, called Memeify for users to generate memes of their choice, and explain in detail, the working of individual components of the system. We also perform a qualitative evaluation of the generated memes by conducting a user study. A link to the demonstration of the Memeify system is https://youtu.be/P_Tfs0X-czs.
Deep convolutional autoencoder for cryptocurrency market analysis
Venue: arXiv cs.LG 2019
Year: 2019

This study attempts to analyze patterns in cryptocurrency markets using a special type of deep neural networks, namely a convolutional autoencoder. The method extracts the dominant features of market behavior and classifies the 40 studied cryptocurrencies into several classes for twelve 6-month periods starting from 15th May 2013. Transitions from one class to another with time are related to the maturement of cryptocurrencies. In speculative cryptocurrency markets, these findings have potential implications for investment and trading strategies.
Task-Informed Fidelity Management for Speeding Up Robotics Simulation
Venue: arXiv cs.RO 2019
Year: 2019

Simulators are an important tool in robotics that is used to develop robot software and generate synthetic data for machine learning algorithms. Faster simulation can result in better software validation and larger amounts of data. Previous efforts for speeding up simulators have been performed at the level of simulator building blocks, and robot systems. Our key insight, motivating this work, is that further speedups can be obtained at the level of the robot task. Building on the observation that not all parts of a scene need to be simulated in high fidelity at all times, our approach is to toggle between high- and low-fidelity states for scene objects in a task-informed manner. Our contribution is a framework for speeding up robot simulation by exploiting task knowledge. The framework is agnostic to the underlying simulator, and preserves simulation fidelity. As a case study, we consider a complex material-handling task. For the associated simulation, which contains many of the characteristics that make robot simulation slow, we achieve a speedup that can be up to three times faster than high fidelity without compromising on the quality of the results. We also demonstrate that faster simulation allows us to train better policies for performing the task at hand in a short period of time. A video summarizing our contributions can be found at https://youtu.be/PEzypDyqc3o .
Deep Learning Models for Digital Pathology
Venue: arXiv cs.CV 2019
Year: 2019

Histopathology images; microscopy images of stained tissue biopsies contain fundamental prognostic information that forms the foundation of pathological analysis and diagnostic medicine. However, diagnostics from histopathology images generally rely on a visual cognitive assessment of tissue slides which implies an inherent element of interpretation and hence subjectivity. Access to digitized histopathology images enabled the development of computational systems aiming at reducing manual intervention and automating parts of pathologists' workflow. Specifically, applications of deep learning to histopathology image analysis now offer opportunities for better quantitative modeling of disease appearance and hence possibly improved prediction of disease aggressiveness and patient outcome. However digitized histopathology tissue slides are unique in a variety of ways and come with their own set of computational challenges. In this survey, we summarize the different challenges facing computational systems for digital pathology and provide a review of state-of-the-art works that developed deep learning-based solutions for the predictive modeling of histopathology images from a detection, stain normalization, segmentation, and tissue classification perspective. We then discuss the challenges facing the validation and integration of such deep learning-based computational systems in clinical workflow and reflect on future opportunities for histopathology derived image measurements and better predictive modeling.
Neighbor connectivity of $k$-ary $n$-cubes
Venue: arXiv cs.DM 2019
Year: 2019

The neighbor connectivity of a graph $G$ is the least number of vertices such that removing their closed neighborhoods from $G$ results in a graph that is disconnected, complete or empty. If a~graph is used to model the topology of an interconnection network, this means that the failure of a network node causes failures of all its neighbors. We completely determine the neighbor connectivity of $k$-ary $n$-cubes for all $n\ge1$ and $k\ge2$.
Components of Machine Learning: Binding Bits and FLOPS
Venue: arXiv cs.LG 2019
Year: 2019

Many machine learning problems and methods are combinations of three components: data, hypothesis space and loss function. Different machine learning methods are obtained as combinations of different choices for the representation of data, hypothesis space and loss function. After reviewing the mathematical structure of these three components, we discuss intrinsic trade-offs between statistical and computational properties of machine learning methods.
Exploring Kernel Functions in the Softmax Layer for Contextual Word Classification
Venue: arXiv cs.CL 2019
Year: 2019

Prominently used in support vector machines and logistic regressions, kernel functions (kernels) can implicitly map data points into high dimensional spaces and make it easier to learn complex decision boundaries. In this work, by replacing the inner product function in the softmax layer, we explore the use of kernels for contextual word classification. In order to compare the individual kernels, experiments are conducted on standard language modeling and machine translation tasks. We observe a wide range of performances across different kernel settings. Extending the results, we look at the gradient properties, investigate various mixture strategies and examine the disambiguation abilities.
Polynomial Probabilistic Invariants and the Optional Stopping Theorem
Venue: arXiv cs.LO 2019
Year: 2019

In this paper we present methods for the synthesis of polynomial invariants for probabilistic transition systems. Our approach is based on martingale theory. We construct invariants in the form of polynomials over program variables, which give rise to martingales. These polynomials are program invariants in the sense that their expected value upon termination is the same as their value at the start of the computation. In order to guarantee this we apply the Optional Stopping Theorem. Concretely, we present two approaches. The first is restricted to linear systems. In this case under positive almost sure termination there is a reduction to finding linear invariants for deterministic transition systems. Secondly, by exploiting geometric persistence properties we construct martingale invariants for general polynomial transition system. We have implemented this approach and it works on our examples.
Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration
Venue: arXiv cs.LG 2019
Year: 2019

Class probabilities predicted by most multiclass classifiers are uncalibrated, often tending towards over-confidence. With neural networks, calibration can be improved by temperature scaling, a method to learn a single corrective multiplicative factor for inputs to the last softmax layer. On non-neural models the existing methods apply binary calibration in a pairwise or one-vs-rest fashion. We propose a natively multiclass calibration method applicable to classifiers from any model class, derived from Dirichlet distributions and generalising the beta calibration method from binary classification. It is easily implemented with neural nets since it is equivalent to log-transforming the uncalibrated probabilities, followed by one linear layer and softmax. Experiments demonstrate improved probabilistic predictions according to multiple measures (confidence-ECE, classwise-ECE, log-loss, Brier score) across a wide range of datasets and classifiers. Parameters of the learned Dirichlet calibration map provide insights to the biases in the uncalibrated model.
AI Ethics in Industry: A Research Framework
Venue: arXiv cs.CY 2019
Year: 2019

Artificial Intelligence (AI) systems exert a growing influence on our society. As they become more ubiquitous, their potential negative impacts also become evident through various real-world incidents. Following such early incidents, academic and public discussion on AI ethics has highlighted the need for implementing ethics in AI system development. However, little currently exists in the way of frameworks for understanding the practical implementation of AI ethics. In this paper, we discuss a research framework for implementing AI ethics in industrial settings. The framework presents a starting point for empirical studies into AI ethics but is still being developed further based on its practical utilization.
Deep Joint Source-Channel Coding for Wireless Image Retrieval
Venue: arXiv cs.IT 2019
Year: 2019

Motivated by surveillance applications with wireless cameras or drones, we consider the problem of image retrieval over a wireless channel. Conventional systems apply lossy compression on query images to reduce the data that must be transmitted over the bandwidth and power limited wireless link. We first note that reconstructing the original image is not needed for retrieval tasks; hence, we introduce a deep neutral network (DNN) based compression scheme targeting the retrieval task. Then, we completely remove the compression step, and propose another DNN-based communication scheme that directly maps the feature vectors to channel inputs. This joint source-channel coding (JSCC) approach not only improves the end-to-end accuracy, but also simplifies and speeds up the encoding operation which is highly beneficial for power and latency constrained IoT applications.
Evaluating Lottery Tickets Under Distributional Shifts
Venue: arXiv cs.CL 2019
Year: 2019

The Lottery Ticket Hypothesis suggests large, over-parameterized neural networks consist of small, sparse subnetworks that can be trained in isolation to reach a similar (or better) test accuracy. However, the initialization and generalizability of the obtained sparse subnetworks have been recently called into question. Our work focuses on evaluating the initialization of sparse subnetworks under distributional shifts. Specifically, we investigate the extent to which a sparse subnetwork obtained in a source domain can be re-trained in isolation in a dissimilar, target domain. In addition, we examine the effects of different initialization strategies at transfer-time. Our experiments show that sparse subnetworks obtained through lottery ticket training do not simply overfit to particular domains, but rather reflect an inductive bias of deep neural networks that can be exploited in multiple domains.
Efficient QP-ADMM Decoder for Binary LDPC Codes and Its Performance Analysis
Venue: arXiv cs.IT 2019
Year: 2019

This paper presents an efficient quadratic programming (QP) decoder via the alternating direction method of multipliers (ADMM) technique, called QP-ADMM, for binary low-density parity-check (LDPC) codes. Its main contents are as follows: first, we relax maximum likelihood (ML) decoding problem to a non-convex quadratic program. Then, we develop an ADMM solving algorithm for the formulated non-convex QP decoding model. In the proposed QP-ADMM decoder, complex Euclidean projections onto the check polytope are eliminated and variables in each updated step can be solved analytically in parallel. Moreover, it is proved that the proposed ADMM algorithm converges to a stationary point of the non-convex QP problem under the assumption of sequence convergence. We also verify that the proposed decoder satisfies the favorable property of the all-zeros assumption. Furthermore, by exploiting the inside structures of the QP model, the complexity of the proposed algorithm in each iteration is shown to be linear in terms of LDPC code length. Simulation results demonstrate the effectiveness of the proposed QP-ADMM decoder.
A Study of Machine Learning Models in Predicting the Intention of Adolescents to Smoke Cigarettes
Venue: arXiv cs.LG 2019
Year: 2019

The use of electronic cigarette (e-cigarette) is increasing among adolescents. This is problematic since consuming nicotine at an early age can cause harmful effects in developing teenager's brain and health. Additionally, the use of e-cigarette has a possibility of leading to the use of cigarettes, which is more severe. There were many researches about e-cigarette and cigarette that mostly focused on finding and analyzing causes of smoking using conventional statistics. However, there is a lack of research on developing prediction models, which is more applicable to anti-smoking campaign, about e-cigarette and cigarette. In this paper, we research the prediction models that can be used to predict an individual e-cigarette user's (including non-e-cigarette users) intention to smoke cigarettes, so that one can be early informed about the risk of going down the path of smoking cigarettes. To construct the prediction models, five machine learning (ML) algorithms are exploited and tested for their accuracy in predicting the intention to smoke cigarettes among never smokers using data from the 2018 National Youth Tobacco Survey (NYTS). In our investigation, the Gradient Boosting Classifier, one of the prediction models, shows the highest accuracy out of all the other models. Also, with the best prediction model, we made a public website that enables users to input information to predict their intentions of smoking cigarettes.
Blind Robust 3-D Mesh Watermarking based on Mesh Saliency and QIM quantization for Copyright Protection
Venue: arXiv cs.MM 2019
Year: 2019

Due to the recent demand of 3-D models in several applications like medical imaging, video games, among others, the necessity of implementing 3-D mesh watermarking schemes aiming to protect copyright has increased considerably. The majority of robust 3-D watermarking techniques have essentially focused on the robustness against attacks while the imperceptibility of these techniques is still a real issue. In this context, a blind robust 3-D mesh watermarking method based on mesh saliency and Quantization Index Modulation (QIM) for Copyright protection is proposed. The watermark is embedded by quantifying the vertex norms of the 3-D mesh using QIM scheme since it offers a good robustness-capacity tradeoff. The choice of the vertices is adjusted by the mesh saliency to achieve watermark robustness and to avoid visual distortions. The experimental results show the high imperceptibility of the proposed scheme while ensuring a good robustness against a wide range of attacks including additive noise, similarity transformations, smoothing, quantization, etc.