OpenRaiser/Intern-Atlas huggingface dataset:
Prediction of Progression to Alzheimer's disease with Deep InfoMax
Venue: arXiv cs.LG 2019
Year: 2019

Arguably, unsupervised learning plays a crucial role in the majority of algorithms for processing brain imaging. A recently introduced unsupervised approach Deep InfoMax (DIM) is a promising tool for exploring brain structure in a flexible non-linear way. In this paper, we investigate the use of variants of DIM in a setting of progression to Alzheimer's disease in comparison with supervised AlexNet and ResNet inspired convolutional neural networks. As a benchmark, we use a classification task between four groups: patients with stable, and progressive mild cognitive impairment (MCI), with Alzheimer's disease, and healthy controls. Our dataset is comprised of 828 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our experiments highlight encouraging evidence of the high potential utility of DIM in future neuroimaging studies.
Assessing the Tolerance of Neural Machine Translation Systems Against Speech Recognition Errors
Venue: arXiv cs.CL 2019
Year: 2019

Machine translation systems are conventionally trained on textual resources that do not model phenomena that occur in spoken language. While the evaluation of neural machine translation systems on textual inputs is actively researched in the literature , little has been discovered about the complexities of translating spoken language data with neural models. We introduce and motivate interesting problems one faces when considering the translation of automatic speech recognition (ASR) outputs on neural machine translation (NMT) systems. We test the robustness of sentence encoding approaches for NMT encoder-decoder modeling, focusing on word-based over byte-pair encoding. We compare the translation of utterances containing ASR errors in state-of-the-art NMT encoder-decoder systems against a strong phrase-based machine translation baseline in order to better understand which phenomena present in ASR outputs are better represented under the NMT framework than approaches that represent translation as a linear model.
A Simple Local Variational Iteration Method and Related Algorithm for Nonlinear Science and Engineering
Venue: arXiv cs.NA 2019
Year: 2019

A very simple and efficient local variational iteration method for solving problems of nonlinear science is proposed in this paper. The analytical iteration formula of this method is derived first using a general form of first order nonlinear differential equations, followed by straightforward discretization using Chebyshev polynomials and collocation method. The resulting numerical algorithm is very concise and easy to use, only involving highly sparse matrix operations of addition and multiplication, and no inversion of the Jacobian in nonlinear problems. Apart from the simple yet efficient iteration formula, another extraordinary feature of LVIM is that in each local domain, all the collocation nodes participate in the calculation simultaneously, thus each local domain can be regarded as one node in calculation through GPU acceleration and parallel processing. For illustration, the proposed algorithm of LVIM is applied to various nonlinear problems including Blasius equations in fluid mechanics, buckled bar equations in solid mechanics, the Chandrasekhar equation in astrophysics, the low-Earth-orbit equation in orbital mechanics, etc. Using the built-in highly optimized ode45 function of MATLAB as a comparison, it is found that the LVIM is not only very accurate, but also much faster by an order of magnitude than ode45 in all the numerical examples, especially when the nonlinear terms are very complicated and difficult to evaluate.
Equivalence classes of small tilings of the Hamming cube
Venue: arXiv cs.IT 2019
Year: 2019

The study of tilings is a major problem in many mathematical instances, which is studied in two main different approaches: when considering the existence (or obstructions to the existence) of a tiling with a given tile and the other considering classification of tilings. Considering the Hamming cube $\mathbb{F}_2^n$, the small tilings, that is, tilings considering tiles with $8=2^3$ elements, were classified in \cite{vardy}. The authors list a total of $193$ different tiles. As the authors noted, many of those tiles can be obtained one from the other by a linear map. In this work, we are concerned with a particular class of linear maps, the class of permutations of coordinates. This is of interest since a permutation is an isometry of the Hamming cube, considering the Hamming metric. We show here that, up to an isometry, all those $193$ tiles can be reduced to $15$ classes. The proof is done by explicitly showing the permutation (represented in cycles) that identify each tile with a given representative.
Risky Business: Assessing Security with External Measurements
Venue: arXiv cs.CR 2019
Year: 2019

Security practices in large organizations are notoriously difficult to assess. The challenge only increases when organizations turn to third parties to provide technology and business services, which typically require tight network integration and sharing of confidential data, potentially increasing the organization's attack surface. The security maturity of an organization describes how well it mitigates known risks and responds to new threats. Today, maturity is typically assessed with audits and questionnaires, which are difficult to quantify, lack objectivity, and may not reflect current threats. This paper demonstrates how external measurement of an organization can be used to assess the relative quality of security among organizations. Using a large dataset from BitSight(www.bitsight.com), a cybersecurity ratings company, containing 3.2 billion measurements spanning nearly 37,000 organizations collected during calendar year 2015, we show how per-organizational "risk vectors" can be constructed that may be related to an organization's overall security posture, or maturity. Using statistical analysis, we then study the correlation between the risk vectors and botnet infections. For example, we find that misconfigured TLS services, publicly available unsecured protocols, and the use of peer-to-peer file sharing correlate with organizations that have increased rates of botnet infections. We argue that the methodology used to identify these correlations can easily be applied to other data to provide a growing picture of organizational security using external measurement.
Comparing machine learning models to choose the variable ordering for cylindrical algebraic decomposition
Venue: arXiv cs.SC 2019
Year: 2019

There has been recent interest in the use of machine learning (ML) approaches within mathematical software to make choices that impact on the computing performance without affecting the mathematical correctness of the result. We address the problem of selecting the variable ordering for cylindrical algebraic decomposition (CAD), an important algorithm in Symbolic Computation. Prior work to apply ML on this problem implemented a Support Vector Machine (SVM) to select between three existing human-made heuristics, which did better than anyone heuristic alone. The present work extends to have ML select the variable ordering directly, and to try a wider variety of ML techniques. We experimented with the NLSAT dataset and the Regular Chains Library CAD function for Maple 2018. For each problem, the variable ordering leading to the shortest computing time was selected as the target class for ML. Features were generated from the polynomial input and used to train the following ML models: k-nearest neighbours (KNN) classifier, multi-layer perceptron (MLP), decision tree (DT) and SVM, as implemented in the Python scikit-learn package. We also compared these with the two leading human constructed heuristics for the problem: Brown's heuristic and sotd. On this dataset all of the ML approaches outperformed the human made heuristics, some by a large margin.
Decision Problems for Restricted Variants of Two-Dimensional Automata
Venue: arXiv cs.FL 2019
Year: 2019

A two-dimensional finite automaton has a read-only input head that moves in four directions on a finite array of cells labelled by symbols of the input alphabet. A three-way two-dimensional automaton is prohibited from making upward moves, while a two-way two-dimensional automaton can only move downward and rightward. We show that the language emptiness problem for unary three-way nondeterministic two-dimensional automata is NP-complete, and is in P for general-alphabet two-way nondeterministic two-dimensional automata. We show that the language equivalence problem for two-way deterministic two-dimensional automata is decidable, while both the equivalence and universality problems for two-way nondeterministic two-dimensional automata are undecidable. The deterministic case is the first known positive decidability result for the equivalence problem on two-dimensional automata over a general alphabet. We show that there exists a unary three-way deterministic two-dimensional automaton with a nonregular column projection, and we show that the row projection of a unary three-way nondeterministic two-dimensional automaton is always regular.
A Multi-Authority Attribute-Based Signcryption Scheme with Efficient Revocation for Smart Grid Downlink Communication
Venue: arXiv cs.CR 2019
Year: 2019

In this paper, we propose a multi-authority attribute-based signcryption scheme with efficient revocation for smart grid downlink communications. In the proposed scheme, grid operators and electricity vendors can send multicast messages securely to different groups of consumers which is required in different applications such as firmware update distribution and sending direct load control messages. Our scheme can ensure the confidentiality and the integrity of the multicasted messages, allows consumers to authenticate the source of the multicasted messages, achieves and non-repudiation property, and allows prompt revocation, simultaneously which are required for the smart grid downlink communications. Our security analysis demonstrates that the proposed scheme can thwart various security threats to the smart grid. Our experiments conducted on an advanced metering infrastructure (AMI) testbed confirm that the proposed scheme has low computational overhead.
Deep Reinforcement Learning for Optimal Critical Care Pain Management with Morphine using Dueling Double-Deep Q Networks
Venue: arXiv cs.LG 2019
Year: 2019

Opioids are the preferred medications for the treatment of pain in the intensive care unit. While undertreatment leads to unrelieved pain and poor clinical outcomes, excessive use of opioids puts patients at risk of experiencing multiple adverse effects. In this work, we present a sequential decision making framework for opioid dosing based on deep reinforcement learning. It provides real-time clinically interpretable dosing recommendations, personalized according to each patient's evolving pain and physiological condition. We focus on morphine, one of the most commonly prescribed opioids. To train and evaluate the model, we used retrospective data from the publicly available MIMIC-3 database. Our results demonstrate that reinforcement learning may be used to aid decision making in the intensive care setting by providing personalized pain management interventions.
TwitterMancer: Predicting Interactions on Twitter Accurately
Venue: arXiv cs.SI 2019
Year: 2019

This paper investigates the interplay between different types of user interactions on Twitter, with respect to predicting missing or unseen interactions. For example, given a set of retweet interactions between Twitter users, how accurately can we predict reply interactions? Is it more difficult to predict retweet or quote interactions between a pair of accounts? Also, how important is time locality, and which features of interaction patterns are most important to enable accurate prediction of specific Twitter interactions? Our empirical study of Twitter interactions contributes initial answers to these questions. We have crawled an extensive dataset of Greek-speaking Twitter accounts and their follow, quote, retweet, reply interactions over a period of a month. We find we can accurately predict many interactions of Twitter users. Interestingly, the most predictive features vary with the user profiles, and are not the same across all users. For example, for a pair of users that interact with a large number of other Twitter users, we find that certain "higher-dimensional" triads, i.e., triads that involve multiple types of interactions, are very informative, whereas for less active Twitter users, certain in-degrees and out-degrees play a major role. Finally, we provide various other insights on Twitter user behavior. Our code and data are available at https://github.com/twittermancer/. Keywords: Graph mining, machine learning, social media, social networks
HAR-Net: Joint Learning of Hybrid Attention for Single-stage Object Detection
Venue: arXiv cs.CV 2019
Year: 2019

Object detection has been a challenging task in computer vision. Although significant progress has been made in object detection with deep neural networks, the attention mechanism is far from development. In this paper, we propose the hybrid attention mechanism for single-stage object detection. First, we present the modules of spatial attention, channel attention and aligned attention for single-stage object detection. In particular, stacked dilated convolution layers with symmetrically fixed rates are constructed to learn spatial attention. The channel attention is proposed with the cross-level group normalization and squeeze-and-excitation module. Aligned attention is constructed with organized deformable filters. Second, the three kinds of attention are unified to construct the hybrid attention mechanism. We then embed the hybrid attention into Retina-Net and propose the efficient single-stage HAR-Net for object detection. The attention modules and the proposed HAR-Net are evaluated on the COCO detection dataset. Experiments demonstrate that hybrid attention can significantly improve the detection accuracy and the HAR-Net can achieve the state-of-the-art 45.8\% mAP, outperform existing single-stage object detectors.
A Deeper Look at Facial Expression Dataset Bias
Venue: arXiv cs.CV 2019
Year: 2019

Datasets play an important role in the progress of facial expression recognition algorithms, but they may suffer from obvious biases caused by different cultures and collection conditions. To look deeper into this bias, we first conduct comprehensive experiments on dataset recognition and crossdataset generalization tasks, and for the first time explore the intrinsic causes of the dataset discrepancy. The results quantitatively verify that current datasets have a strong buildin bias and corresponding analyses indicate that the conditional probability distributions between source and target datasets are different. However, previous researches are mainly based on shallow features with limited discriminative ability under the assumption that the conditional distribution remains unchanged across domains. To address these issues, we further propose a novel deep Emotion-Conditional Adaption Network (ECAN) to learn domain-invariant and discriminative feature representations, which can match both the marginal and the conditional distributions across domains simultaneously. In addition, the largely ignored expression class distribution bias is also addressed by a learnable re-weighting parameter, so that the training and testing domains can share similar class distribution. Extensive cross-database experiments on both lab-controlled datasets (CK+, JAFFE, MMI and Oulu-CASIA) and real-world databases (AffectNet, FER2013, RAF-DB 2.0 and SFEW 2.0) demonstrate that our ECAN can yield competitive performances across various facial expression transfer tasks and outperform the state-of-theart methods.
A Conditional Adversarial Network for Scene Flow Estimation
Venue: arXiv cs.CV 2019
Year: 2019

The problem of Scene flow estimation in depth videos has been attracting attention of researchers of robot vision, due to its potential application in various areas of robotics. The conventional scene flow methods are difficult to use in reallife applications due to their long computational overhead. We propose a conditional adversarial network SceneFlowGAN for scene flow estimation. The proposed SceneFlowGAN uses loss function at two ends: both generator and descriptor ends. The proposed network is the first attempt to estimate scene flow using generative adversarial networks, and is able to estimate both the optical flow and disparity from the input stereo images simultaneously. The proposed method is experimented on a large RGB-D benchmark sceneflow dataset.
Weak-Noise Modulation-Estimation of Vector Parameters
Venue: arXiv cs.IT 2019
Year: 2019

We address the problem of modulating a parameter onto a power-limited signal, transmitted over a discrete-time Gaussian channel and estimating this parameter at the receiver. Continuing an earlier work, where the optimal trade-off between the weak-noise estimation performance and the outage probability (threshold-effect breakdown) was studied for a single (scalar) parameter, here we extend the derivation of the weak-noise estimation performance to the case of a multi-dimensional vector parameter. This turns out to be a non-trivial extension, that provides a few insights and it has some interesting implications, which are discussed in depth. Several modifications and extensions of the basic setup are also studied and discussed.
Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders
Venue: arXiv cs.LG 2019
Year: 2019

In line with recent advances in neural drug design and sensitivity prediction, we propose a novel architecture for interpretable prediction of anticancer compound sensitivity using a multimodal attention-based convolutional encoder. Our model is based on the three key pillars of drug sensitivity: compounds' structure in the form of a SMILES sequence, gene expression profiles of tumors and prior knowledge on intracellular interactions from protein-protein interaction networks. We demonstrate that our multiscale convolutional attention-based (MCA) encoder significantly outperforms a baseline model trained on Morgan fingerprints, a selection of encoders based on SMILES as well as previously reported state of the art for multimodal drug sensitivity prediction (R2 = 0.86 and RMSE = 0.89). Moreover, the explainability of our approach is demonstrated by a thorough analysis of the attention weights. We show that the attended genes significantly enrich apoptotic processes and that the drug attention is strongly correlated with a standard chemical structure similarity index. Finally, we report a case study of two receptor tyrosine kinase (RTK) inhibitors acting on a leukemia cell line, showcasing the ability of the model to focus on informative genes and submolecular regions of the two compounds. The demonstrated generalizability and the interpretability of our model testify its potential for in-silico prediction of anticancer compound efficacy on unseen cancer cells, positioning it as a valid solution for the development of personalized therapies as well as for the evaluation of candidate compounds in de novo drug design.
Online Abuse of UK MPs from 2015 to 2019: Working Paper
Venue: arXiv cs.CY 2019
Year: 2019

We extend previous work about general election-related abuse of UK MPs with two new time periods, one in late 2018 and the other in early 2019, allowing previous observations to be extended to new data and the impact of key stages in the UK withdrawal from the European Union on patterns of abuse to be explored. The topics that draw abuse evolve over the four time periods are reviewed, with topics relevant to the Brexit debate and campaign tone showing a varying pattern as events unfold, and a suggestion of a "bubble" of topics emphasized in the run-up to the highly Brexit-focused 2017 general election. Brexit stance shows a variable relationship with abuse received. We find, as previously, that in quantitative terms, Conservatives and male politicians receive more abuse. Gender difference remains significant even when accounting for prominence, as gauged from Google Trends data, but prominence, or other factors related to being in power, as well as gender, likely account for the difference associated with party membership. No clear relationship between ethnicity and abuse is found in what remains a very small sample (BAME and mixed heritage MPs). Differences are found in the choice of abuse terms levelled at female vs. male MPs.
Complexity of URLLC Scheduling and Efficient Approximation Schemes
Venue: arXiv cs.IT 2019
Year: 2019

In this paper we address the problem of joint admission control and resource scheduling for \emph{Ultra Reliable Low Latency Communications} (URLLC). We examine two models: (i) the \emph{continuous}, where all allocated resource blocks contribute to the success probability, and (ii) a \emph{binary}, where only resource blocks with strong signal are "active" for each user, and user $k$ needs $d_k$ active resource blocks for a successful URLLC transmission. In situations of congestion, we are interested in finding a subset of users that can be scheduled simultaneously. We show that finding a feasible schedule for at least $m$ URLLC users is NP-complete in the (easier) binary SNR model, hence also in the continuous. Maximizing the reward obtained from a feasible set of URLLC users is NP-hard and inapproximable to within ${(\log_2d)^2}/{d}$ of the optimal, where $d\doteq \max_kd_k$. On the other hand, we prove that checking a candidate set of users for feasibility and finding the corresponding schedule (when feasible) can be done in polynomial time, which we exploit to design an efficient heuristic algorithm for the general continuous SNR model. We complement our theoretical contributions with a numerical evaluation of our proposed schemes.
Multi-scale Cross-form Pyramid Network for Stereo Matching
Venue: arXiv cs.CV 2019
Year: 2019

Stereo matching plays an indispensable part in autonomous driving, robotics and 3D scene reconstruction. We propose a novel deep learning architecture, which called CFP-Net, a Cross-Form Pyramid stereo matching network for regressing disparity from a rectified pair of stereo images. The network consists of three modules: Multi-Scale 2D local feature extraction module, Cross-form spatial pyramid module and Multi-Scale 3D Feature Matching and Fusion module. The Multi-Scale 2D local feature extraction module can extract enough multi-scale features. The Cross-form spatial pyramid module aggregates the context information in different scales and locations to form a cost volume. Moreover, it is proved to be more effective than SPP and ASPP in ill-posed regions. The Multi-Scale 3D feature matching and fusion module is proved to regularize the cost volume using two parallel 3D deconvolution structure with two different receptive fields. Our proposed method has been evaluated on the Scene Flow and KITTI datasets. It achieves state-of-the-art performance on the KITTI 2012 and 2015 benchmarks.