OpenRaiser/Intern-Atlas huggingface dataset:
Spectrally Robust Graph Isomorphism
Venue: arXiv cs.DS 2018
Year: 2018

We initiate the study of spectral generalizations of the graph isomorphism problem. (a)The Spectral Graph Dominance (SGD) problem: On input of two graphs $G$ and $H$ does there exist a permutation $π$ such that $G\preceq π(H)$? (b) The Spectrally Robust Graph Isomorphism (SRGI) problem: On input of two graphs $G$ and $H$, find the smallest number $κ$ over all permutations $π$ such that $ π(H) \preceq G\preceq κc π(H)$ for some $c$. SRGI is a natural formulation of the network alignment problem that has various applications, most notably in computational biology. Here $G\preceq c H$ means that for all vectors $x$ we have $x^T L_G x \leq c x^T L_H x$, where $L_G$ is the Laplacian $G$. We prove NP-hardness for SGD. We also present a $κ$-approximation algorithm for SRGI for the case when both $G$ and $H$ are bounded-degree trees. The algorithm runs in polynomial time when $κ$ is a constant.
Fixation Data Analysis for High Resolution Satellite Images
Venue: arXiv cs.CV 2018
Year: 2018

The presented study is an eye tracking experiment for high-resolution satellite (HRS) images. The reported experiment explores the Area Of Interest (AOI) based analysis of eye fixation data for complex HRS images. The study reflects the requisite of reference data for bottom-up saliency-based segmentation and the struggle of eye tracking data analysis for complex satellite images. The intended fixation data analysis aims towards the reference data creation for bottom-up saliency-based segmentation of high-resolution satellite images. The analytical outcome of this experimental study provides a solution for AOI-based analysis for fixation data in the complex environment of satellite images and recommendations for reference data construction which is already an ongoing effort.
Characterizing Efficient Referrals in Social Networks
Venue: arXiv cs.SI 2018
Year: 2018

Users of social networks often focus on specific areas of that network, leading to the well-known "filter bubble" effect. Connecting people to a new area of the network in a way that will cause them to become active in that area could help alleviate this effect and improve social welfare. Here we present preliminary analysis of network referrals, that is, attempts by users to connect peers to other areas of the network. We classify these referrals by their efficiency, i.e., the likelihood that a referral will result in a user becoming active in the new area of the network. We show that by using features describing past experience of the referring author and the content of their messages we are able to predict whether referral will be effective, reaching an AUC of 0.87 for those users most experienced in writing efficient referrals. Our results represent a first step towards algorithmically constructing efficient referrals with the goal of mitigating the "filter bubble" effect pervasive in on line social networks.
Deep Autoassociative Neural Networks for Noise Reduction in Seismic data
Venue: arXiv cs.CE 2018
Year: 2018

Machine learning is currently a trending topic in various science and engineering disciplines, and the field of geophysics is no exception. With the advent of powerful computers, it is now possible to train the machine to learn complex patterns in the data, which may not be easily realized using the traditional methods. Among the various machine learning methods, the artificial neural networks (ANNs) have received enormous attention. A variant of ANNs, autoassociative neural network (autoNN) tries to learn the reconstruction of input itself using backpropagation. In an autoNN, the input and output are the same, and an approximation to the identity mapping is obtained in a nonlinear setting. AutoNNs have primarily been used to extract sparse internal representations of any input and reduce its dimensionality. In this paper, we explore the potential of autoNNs in reducing random noise in geophysical data. In this paper, the first results of this study are presented. The synthetic mathematical example demonstrates the concept of autoNN. For the test seismic data, it is observed that autoNN can significantly remove the vertical time- and frequency-local noise, however, the resolution of the output signal is compromised to a certain extent. Future work includes testing larger examples with several different types of noise, and using deep-stacked-autoNNs to further reduce the noise, ensuring minimal compromise with the resolution of the signal.
I Know How You Feel: Emotion Recognition with Facial Landmarks
Venue: arXiv cs.CV 2018
Year: 2018

Classification of human emotions remains an important and challenging task for many computer vision algorithms, especially in the era of humanoid robots which coexist with humans in their everyday life. Currently proposed methods for emotion recognition solve this task using multi-layered convolutional networks that do not explicitly infer any facial features in the classification phase. In this work, we postulate a fundamentally different approach to solve emotion recognition task that relies on incorporating facial landmarks as a part of the classification loss function. To that end, we extend a recently proposed Deep Alignment Network (DAN), that achieves state-of-the-art results in the recent facial landmark recognition challenge, with a term related to facial features. Thanks to this simple modification, our model called EmotionalDAN is able to outperform state-of-the-art emotion classification methods on two challenging benchmark dataset by up to 5%.
Which Facial Expressions Can Reveal Your Gender? A Study With 3D Faces
Venue: arXiv cs.CV 2018
Year: 2018

Human exhibit rich gender cues in both appearance and behavior. In computer vision domain, gender recognition from facial appearance have been extensively studied, while facial behavior based gender recognition studies remain rare. In this work, we first demonstrate that facial expressions influence the gender patterns presented in 3D face, and gender recognition performance increases when training and testing within the same expression. In further, we design experiments which directly extract the morphological changes resulted from facial expressions as features, for expression-based gender recognition. Experimental results demonstrate that gender can be recognized with considerable accuracy in Happy and Disgust expressions, while Surprise and Sad expressions do not convey much gender related information. This is the first work in the literature which investigates expression-based gender classification with 3D faces, and reveals the strength of gender patterns incorporated in different types of expressions, namely the Happy, the Disgust, the Surprise and the Sad expressions.
What is online citizen science anyway? An educational perspective
Venue: arXiv cs.CY 2018
Year: 2018

In this paper we seek to contribute to the debate about the nature of citizen involvement in real scientific projects by the means of online tools that facilitate crowdsourcing and collaboration. We focus on an understudied area, the impact of online citizen science participation on the science education of school age children. We present a binary tree of online citizen science process flows and the results of an anonymous survey among primary school teachers in New Zealand that are known advocates of science education. Our findings reveal why teachers are interested in using online citizen science in classroom activities and what they are looking for when making their choice for a particular project to use. From these characteristics we derive recommendations for the optimal embedding of online citizen science in education related to the process, the context, and the dissemination of results.
Une approche pour mieux appr{é}hender l'alt{é}rit{é} en SIC
Venue: arXiv cs.CY 2018
Year: 2018

In this paper, we propose a novel approach that aims: to facilitate the building of teams relying on notions of skills, of motivation or of potential; identify the requested upgrade effort to improve his own knowledge and join a team; or create more suitable devices for a target population. The whole forms a toolbox that seems appropriate to facilitate better recognition of otherness.
Skin Tone Emoji and Sentiment on Twitter
Venue: arXiv cs.CY 2018
Year: 2018

In 2015, the Unicode Consortium introduced five skin tone emoji that can be used in combination with emoji representing human figures and body parts. In this study, use of the skin tone emoji is analyzed geographically in a large sample of data from Twitter. It can be shown that values for the skin tone emoji by country correspond approximately to the skin tone of the resident populations, and that a negative correlation exists between tweet sentiment and darker skin tone at the global level. In an era of large-scale migrations and continued sensitivity to questions of skin color and race, understanding how new language elements such as skin tone emoji are used can help frame our understanding of how people represent themselves and others in terms of a salient personal appearance attribute.
Epidemiological data challenges: planning for a more robust future through data standards
Venue: arXiv cs.CY 2018
Year: 2018

Accessible epidemiological data are of great value for emergency preparedness and response, understanding disease progression through a population, and building statistical and mechanistic disease models that enable forecasting. The status quo, however, renders acquiring and using such data difficult in practice. In many cases, a primary way of obtaining epidemiological data is through the internet, but the methods by which the data are presented to the public often differ drastically among institutions. As a result, there is a strong need for better data sharing practices. This paper identifies, in detail and with examples, the three key challenges one encounters when attempting to acquire and use epidemiological data: 1) interfaces, 2) data formatting, and 3) reporting. These challenges are used to provide suggestions and guidance for improvement as these systems evolve in the future. If these suggested data and interface recommendations were adhered to, epidemiological and public health analysis, modeling, and informatics work would be significantly streamlined, which can in turn yield better public health decision-making capabilities.
Probabilistic Stable Functions on Discrete Cones are Power Series (long version)
Venue: arXiv cs.LO 2018
Year: 2018

We study the category Cstabm of measurable cones and measurable stable functions, which is a denotational model of an higher-order language with continuous probabilities and full recursion. We look at Cstabm as a model for discrete probabilities, by showing the existence of a cartesian closed, full and faithful functor which embeds probabilistic coherence spaces (a fully abstract denotational model of an higher-order language with full recursion and discrete probabilities) into Cstabm. The proof is based on a generalization of Bernstein's theorem from real analysis allowing to see stable functions between discrete cones as generalized power series.
Securing Social Media User Data - An Adversarial Approach
Venue: arXiv cs.CR 2018
Year: 2018

Social media users generate tremendous amounts of data. To better serve users, it is required to share the user-related data among researchers, advertisers and application developers. Publishing such data would raise more concerns on user privacy. To encourage data sharing and mitigate user privacy concerns, a number of anonymization and de-anonymization algorithms have been developed to help protect privacy of social media users. In this work, we propose a new adversarial attack specialized for social media data. We further provide a principled way to assess effectiveness of anonymizing different aspects of social media data. Our work sheds light on new privacy risks in social media data due to innate heterogeneity of user-generated data which require striking balance between sharing user data and protecting user privacy.
Reconstruction of Simulation-Based Physical Field by Reconstruction Neural Network Method
Venue: arXiv cs.LG 2018
Year: 2018

A variety of modeling techniques have been developed in the past decade to reduce the computational expense and improve the accuracy of modeling. In this study, a new framework of modeling is suggested. Compared with other popular methods, a distinctive characteristic is "from image based model to analysis based model (e.g. stress, strain, and deformation)". In such a framework, a reconstruction neural network (ReConNN) model designed for simulation-based physical field's reconstruction is proposed. The ReConNN contains two submodels that are convolutional neural network (CNN) and generative adversarial net-work (GAN). The CNN is employed to construct the mapping between contour images of physical field and objective function. Subsequently, the GAN is utilized to generate more images which are similar to the existing contour images. Finally, Lagrange polynomial is applied to complete the reconstruction. However, the existing CNN models are commonly applied to the classification tasks, which seem to be difficult to handle with regression tasks of images. Meanwhile, the existing GAN architectures are insufficient to generate high-accuracy "pseudo contour images". Therefore, a ReConNN model based on a Convolution in Convolution (CIC) and a Convolutional AutoEncoder based on Wasserstein Generative Adversarial Network (WGAN-CAE) is suggested. To evaluate the performance of the proposed model representatively, a classical topology optimization procedure is considered. Then the ReConNN is utilized to the reconstruction of heat transfer process of a pin fin heat sink. It demonstrates that the proposed ReConNN model is proved to be a potential capability to reconstruct physical field for multidisciplinary, such as structural optimization.
Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising
Venue: arXiv cs.CV 2018
Year: 2018

Computed tomography (CT) is a popular medical imaging modality in clinical applications. At the same time, the x-ray radiation dose associated with CT scans raises public concerns due to its potential risks to the patients. Over the past years, major efforts have been dedicated to the development of Low-Dose CT (LDCT) methods. However, the radiation dose reduction compromises the signal-to-noise ratio (SNR), leading to strong noise and artifacts that down-grade CT image quality. In this paper, we propose a novel 3D noise reduction method, called Structure-sensitive Multi-scale Generative Adversarial Net (SMGAN), to improve the LDCT image quality. Specifically, we incorporate three-dimensional (3D) volumetric information to improve the image quality. Also, different loss functions for training denoising models are investigated. Experiments show that the proposed method can effectively preserve structural and texture information from normal-dose CT (NDCT) images, and significantly suppress noise and artifacts. Qualitative visual assessments by three experienced radiologists demonstrate that the proposed method retrieves more detailed information, and outperforms competing methods.
Placement Delivery Array Design via Attention-Based Deep Neural Network
Venue: arXiv cs.IT 2018
Year: 2018

A decentralized coded caching scheme has been proposed by Maddah-Ali and Niesen, and has been shown to alleviate the load of networks. Recently, placement delivery array (PDA) was proposed to characterize the coded caching scheme. In this paper, a neural architecture is first proposed to learn the construction of PDAs. Our model solves the problem of variable size PDAs using mechanism of neural attention and reinforcement learning. It differs from the previous attempts in that, instead of using combined optimization algorithms to get PDAs, it uses sequence-to-sequence model to learn construct PDAs. Numerical results are given to demonstrate that the proposed method can effectively implement coded caching. We also show that the complexity of our method to construct PDAs is low.
The Story of HyperLogLog: How Flajolet Processed Streams with Coin Flips
Venue: arXiv cs.DS 2018
Year: 2018

This article is a historical introduction to data streaming algorithms that was written as a companion piece to the talk "How Philippe Flipped Coins to Count Data", given on December 16th, 2011, in the context of the conference in honor of "Philippe Flajolet and Analytic Combinatorics." The narrative was pieced together through conversations with Philippe Flajolet during my Ph.D. thesis under his supervision, as well as several conversations with collaborators after his death. In particular, I am deeply indebted to Nigel Martin for his archival records. This article is intended to serve as an introductory text presenting Flajolet's data streaming articles in a projected set of complete works.
Deep Perm-Set Net: Learn to predict sets with unknown permutation and cardinality using deep neural networks
Venue: arXiv cs.CV 2018
Year: 2018

Many real-world problems, e.g. object detection, have outputs that are naturally expressed as sets of entities. This creates a challenge for traditional deep neural networks which naturally deal with structured outputs such as vectors, matrices or tensors. We present a novel approach for learning to predict sets with unknown permutation and cardinality using deep neural networks. Specifically, in our formulation we incorporate the permutation as unobservable variable and estimate its distribution during the learning process using alternating optimization. We demonstrate the validity of this new formulation on two relevant vision problems: object detection, for which our formulation outperforms state-of-the-art detectors such as Faster R-CNN and YOLO, and a complex CAPTCHA test, where we observe that, surprisingly, our set based network acquired the ability of mimicking arithmetics without any rules being coded.
Delay-Constrained Rate Control for Real-Time Video Streaming with Bounded Neural Network
Venue: arXiv cs.MM 2018
Year: 2018

Rate control is widely adopted during video streaming to provide both high video qualities and low latency under various network conditions. However, despite that many work have been proposed, they fail to tackle one major problem: previous methods determine a future transmission rate as a single for value which will be used in an entire time-slot, while real-world network conditions, unlike lab setup, often suffer from rapid and stochastic changes, resulting in the failures of predictions. In this paper, we propose a delay-constrained rate control approach based on end-to-end deep learning. The proposed model predicts future bit rate not as a single value, but as possible bit rate ranges using target delay gradient, with which the transmission delay is guaranteed. We collect a large scale of real-world live streaming data to train our model, and as a result, it automatically learns the correlation between throughput and target delay gradient. We build a testbed to evaluate our approach. Compared with the state-of-the-art methods, our approach demonstrates a better performance in bandwidth utilization. In all considered scenarios, a range based rate control approach outperforms the one without range by 19% to 35% in average QoE improvement.
Distributed Big-Data Optimization via Block-Iterative Convexification and Averaging
Venue: arXiv cs.DC 2018
Year: 2018

In this paper, we study distributed big-data nonconvex optimization in multi-agent networks. We consider the (constrained) minimization of the sum of a smooth (possibly) nonconvex function, i.e., the agents' sum-utility, plus a convex (possibly) nonsmooth regularizer. Our interest is in big-data problems wherein there is a large number of variables to optimize. If treated by means of standard distributed optimization algorithms, these large-scale problems may be intractable, due to the prohibitive local computation and communication burden at each node. We propose a novel distributed solution method whereby at each iteration agents optimize and then communicate (in an uncoordinated fashion) only a subset of their decision variables. To deal with non-convexity of the cost function, the novel scheme hinges on Successive Convex Approximation (SCA) techniques coupled with i) a tracking mechanism instrumental to locally estimate gradient averages; and ii) a novel block-wise consensus-based protocol to perform local block-averaging operations and gradient tacking. Asymptotic convergence to stationary solutions of the nonconvex problem is established. Finally, numerical results show the effectiveness of the proposed algorithm and highlight how the block dimension impacts on the communication overhead and practical convergence speed.
Text to Image Synthesis Using Generative Adversarial Networks
Venue: arXiv cs.CV 2018
Year: 2018

Generating images from natural language is one of the primary applications of recent conditional generative models. Besides testing our ability to model conditional, highly dimensional distributions, text to image synthesis has many exciting and practical applications such as photo editing or computer-aided content creation. Recent progress has been made using Generative Adversarial Networks (GANs). This material starts with a gentle introduction to these topics and discusses the existent state of the art models. Moreover, I propose Wasserstein GAN-CLS, a new model for conditional image generation based on the Wasserstein distance which offers guarantees of stability. Then, I show how the novel loss function of Wasserstein GAN-CLS can be used in a Conditional Progressive Growing GAN. In combination with the proposed loss, the model boosts by 7.07% the best Inception Score (on the Caltech birds dataset) of the models which use only the sentence-level visual semantics. The only model which performs better than the Conditional Wasserstein Progressive Growing GAN is the recently proposed AttnGAN which uses word-level visual semantics as well.
On the primal-dual dynamics of Support Vector Machines
Venue: arXiv cs.SY 2018
Year: 2018

The aim of this paper is to study the convergence of the primal-dual dynamics pertaining to Support Vector Machines (SVM). The optimization routine, used for determining an SVM for classification, is first formulated as a dynamical system. The dynamical system is constructed such that its equilibrium point is the solution to the SVM optimization problem. It is then shown, using passivity theory, that the dynamical system is global asymptotically stable. In other words, the dynamical system converges onto the optimal solution asymptotically, irrespective of the initial condition. Simulations and computations are provided for corroboration.
On the Structure of Interlinked Cycle Structures with Interlocked Outer Cycles
Venue: arXiv cs.IT 2018
Year: 2018

For index coding problems with special structure on the side-information graphs called Interlinked Cycle (IC) structures index codes have been proposed in the literature (C. Thapa, L. Ong, and S. Johnson, "Interlinked Cycles for Index Coding: Generalizing Cycles and Cliques", in IEEE Trans. Inf. Theory, vol. 63, no. 6, Jun. 2017, with a correction in "Interlinked Cycles for Index Coding: Generalizing Cycles and Cliques", in arxiv (arxiv:1603.00092v2 [cs.IT] 25 Feb 2018)). Recently (S. Sasi and B.S. Rajan, "On Optimal Index Codes for Interlinked Cycle Structures with Outer Cycles," in arxiv (arXiv:1804.09120v1 [cs.IT]), 24 Apr 2018) for a generalization of IC structures called IC structures with interlocked outer cycles optimal length index codes have been reported and it is shown that the optimal length depends on the maximum number of disjoint outer cycles. In this paper we discuss certain structural properties of IC structures with interlocked outer cycles and provide a simple algorithm to find the maximum number of disjoint outer cycles.