OpenRaiser/Intern-Atlas huggingface dataset:
Detecting English Writing Styles For Non Native Speakers
Venue: arXiv cs.CL 2017
Year: 2017

This paper presents the first attempt, up to our knowledge, to classify English writing styles on this scale with the challenge of classifying day to day language written by writers with different backgrounds covering various areas of topics.The paper proposes simple machine learning algorithms and simple to generate features to solve hard problems. Relying on the scale of the data available from large sources of knowledge like Wikipedia. We believe such sources of data are crucial to generate robust solutions for the web with high accuracy and easy to deploy in practice. The paper achieves 74\% accuracy classifying native versus non native speakers writing styles. Moreover, the paper shows some interesting observations on the similarity between different languages measured by the similarity of their users English writing styles. This technique could be used to show some well known facts about languages as in grouping them into families, which our experiments support.
A Context Aware and Video-Based Risk Descriptor for Cyclists
Venue: arXiv cs.CV 2017
Year: 2017

Aiming to reduce pollutant emissions, bicycles are regaining popularity specially in urban areas. However, the number of cyclists' fatalities is not showing the same decreasing trend as the other traffic groups. Hence, monitoring cyclists' data appears as a keystone to foster urban cyclists' safety by helping urban planners to design safer cyclist routes. In this work, we propose a fully image-based framework to assess the rout risk from the cyclist perspective. From smartphone sequences of images, this generic framework is able to automatically identify events considering different risk criteria based on the cyclist's motion and object detection. Moreover, since it is entirely based on images, our method provides context on the situation and is independent from the expertise level of the cyclist. Additionally, we build on an existing platform and introduce several improvements on its mobile app to acquire smartphone sensor data, including video. From the inertial sensor data, we automatically detect the route segments performed by bicycle, applying behavior analysis techniques. We test our methods on real data, attaining very promising results in terms of risk classification, according to two different criteria, and behavior analysis accuracy.
How Close Can I Be? - A Comprehensive Analysis of Cellular Interference on ATC Radar
Venue: arXiv cs.NI 2017
Year: 2017

Increasing data traffic demands over wireless spectrum have necessitated spectrum sharing and coexistence between heterogeneous systems such as radar and cellular communications systems. In this context, we specifically investigate the co-channel coexistence between an air traffic control (ATC) radar and a wide area cellular communication (comms) system. We present a comprehensive characterization and analysis of interference caused by the comms system on the ATC radar with respect to multiple parameters such as radar range, protection radius around the radar, and radar antenna elevation angle. The analysis suggests that maintaining a protection radius of 50 km around the radar will ensure the required INR protection criterion of -10 dB at the radar receiver with ~0.9 probability, even when the radar beam is in the same horizon as the comms BS. Detailed evaluations of the radar target detection performance provide a framework to choose appropriate protection radii around the radar to meet specific performance requirements.
Deep Over-sampling Framework for Classifying Imbalanced Data
Venue: arXiv cs.LG 2017
Year: 2017

Class imbalance is a challenging issue in practical classification problems for deep learning models as well as traditional models. Traditionally successful countermeasures such as synthetic over-sampling have had limited success with complex, structured data handled by deep learning models. In this paper, we propose Deep Over-sampling (DOS), a framework for extending the synthetic over-sampling method to exploit the deep feature space acquired by a convolutional neural network (CNN). Its key feature is an explicit, supervised representation learning, for which the training data presents each raw input sample with a synthetic embedding target in the deep feature space, which is sampled from the linear subspace of in-class neighbors. We implement an iterative process of training the CNN and updating the targets, which induces smaller in-class variance among the embeddings, to increase the discriminative power of the deep representation. We present an empirical study using public benchmarks, which shows that the DOS framework not only counteracts class imbalance better than the existing method, but also improves the performance of the CNN in the standard, balanced settings.
Molecular De Novo Design through Deep Reinforcement Learning
Venue: arXiv cs.AI 2017
Year: 2017

This work introduces a method to tune a sequence-based generative model for molecular de novo design that through augmented episodic likelihood can learn to generate structures with certain specified desirable properties. We demonstrate how this model can execute a range of tasks such as generating analogues to a query structure and generating compounds predicted to be active against a biological target. As a proof of principle, the model is first trained to generate molecules that do not contain sulphur. As a second example, the model is trained to generate analogues to the drug Celecoxib, a technique that could be used for scaffold hopping or library expansion starting from a single molecule. Finally, when tuning the model towards generating compounds predicted to be active against the dopamine receptor type 2, the model generates structures of which more than 95% are predicted to be active, including experimentally confirmed actives that have not been included in either the generative model nor the activity prediction model.
Skeleton-based Action Recognition with Convolutional Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

Current state-of-the-art approaches to skeleton-based action recognition are mostly based on recurrent neural networks (RNN). In this paper, we propose a novel convolutional neural networks (CNN) based framework for both action classification and detection. Raw skeleton coordinates as well as skeleton motion are fed directly into CNN for label prediction. A novel skeleton transformer module is designed to rearrange and select important skeleton joints automatically. With a simple 7-layer network, we obtain 89.3% accuracy on validation set of the NTU RGB+D dataset. For action detection in untrimmed videos, we develop a window proposal network to extract temporal segment proposals, which are further classified within the same network. On the recent PKU-MMD dataset, we achieve 93.7% mAP, surpassing the baseline by a large margin.
Optimal Demand-Side Management for Joint Privacy-Cost Optimization with Energy Storage
Venue: arXiv cs.IT 2017
Year: 2017

The smart meter (SM) privacy problem is addressed together with the cost of energy for the user. It is assumed that a storage device, e.g., an electrical battery, is available to the user, which can be utilized both to achieve privacy and to reduce the energy cost by modifying the energy consumption profile. Privacy is measured via the mean squared-error between the SM readings, which are reported to the utility provider (UP), and a target load; while time-of-use pricing is considered for energy cost calculation. The optimal trade-off between the achievable privacy and the energy cost is characterized by taking into account the limited capacity of the battery as well as the capability to sell energy to the UP. Extensive numerical simulations are presented to evaluate the performance of the proposed strategy for different system settings.
Asynchronous Early Output Dual-Bit Full Adders Based on Homogeneous and Heterogeneous Delay-Insensitive Data Encoding
Venue: arXiv cs.AR 2017
Year: 2017

This paper presents the designs of asynchronous early output dual-bit full adders without and with redundant logic (implicit) corresponding to homogeneous and heterogeneous delay-insensitive data encoding. For homogeneous delay-insensitive data encoding only dual-rail i.e. 1-of-2 code is used, and for heterogeneous delay-insensitive data encoding 1-of-2 and 1-of-4 codes are used. The 4-phase return-to-zero protocol is used for handshaking. To demonstrate the merits of the proposed dual-bit full adder designs, 32-bit ripple carry adders (RCAs) are constructed comprising dual-bit full adders. The proposed dual-bit full adders based 32-bit RCAs incorporating redundant logic feature reduced latency and area compared to their non-redundant counterparts with no accompanying power penalty. In comparison with the weakly indicating 32-bit RCA constructed using homogeneously encoded dual-bit full adders containing redundant logic, the early output 32-bit RCA comprising the proposed homogeneously encoded dual-bit full adders with redundant logic reports corresponding reductions in latency and area by 22.2% and 15.1% with no associated power penalty. On the other hand, the early output 32-bit RCA constructed using the proposed heterogeneously encoded dual-bit full adder which incorporates redundant logic reports respective decreases in latency and area than the weakly indicating 32-bit RCA that consists of heterogeneously encoded dual-bit full adders with redundant logic by 21.5% and 21.3% with nil power overhead. The simulation results obtained are based on a 32/28nm CMOS process technology.
Decision Stream: Cultivating Deep Decision Trees
Venue: arXiv cs.LG 2017
Year: 2017

Various modifications of decision trees have been extensively used during the past years due to their high efficiency and interpretability. Tree node splitting based on relevant feature selection is a key step of decision tree learning, at the same time being their major shortcoming: the recursive nodes partitioning leads to geometric reduction of data quantity in the leaf nodes, which causes an excessive model complexity and data overfitting. In this paper, we present a novel architecture - a Decision Stream, - aimed to overcome this problem. Instead of building a tree structure during the learning process, we propose merging nodes from different branches based on their similarity that is estimated with two-sample test statistics, which leads to generation of a deep directed acyclic graph of decision rules that can consist of hundreds of levels. To evaluate the proposed solution, we test it on several common machine learning problems - credit scoring, twitter sentiment analysis, aircraft flight control, MNIST and CIFAR image classification, synthetic data classification and regression. Our experimental results reveal that the proposed approach significantly outperforms the standard decision tree learning methods on both regression and classification tasks, yielding a prediction error decrease up to 35%.
Perivascular Spaces Segmentation in Brain MRI Using Optimal 3D Filtering
Venue: arXiv cs.CV 2017
Year: 2017

Perivascular Spaces (PVS) are a recently recognised feature of Small Vessel Disease (SVD), also indicating neuroinflammation, and are an important part of the brain's circulation and glymphatic drainage system. Quantitative analysis of PVS on Magnetic Resonance Images (MRI) is important for understanding their relationship with neurological diseases. In this work, we propose a segmentation technique based on the 3D Frangi filtering for extraction of PVS from MRI. Based on prior knowledge from neuroradiological ratings of PVS, we used ordered logit models to optimise Frangi filter parameters in response to the variability in the scanner's parameters and study protocols. We optimized and validated our proposed models on two independent cohorts, a dementia sample (N=20) and patients who previously had mild to moderate stroke (N=48). Results demonstrate the robustness and generalisability of our segmentation method. Segmentation-based PVS burden estimates correlated with neuroradiological assessments (Spearman's $ρ$ = 0.74, p $<$ 0.001), suggesting the great potential of our proposed method
A lower bound on the differential entropy of log-concave random vectors with applications
Venue: arXiv cs.IT 2017
Year: 2017

We derive a lower bound on the differential entropy of a log-concave random variable $X$ in terms of the $p$-th absolute moment of $X$. The new bound leads to a reverse entropy power inequality with an explicit constant, and to new bounds on the rate-distortion function and the channel capacity. Specifically, we study the rate-distortion function for log-concave sources and distortion measure $| x - \hat x|^r$, and we establish that the difference between the rate distortion function and the Shannon lower bound is at most $\log(\sqrt{πe}) \approx 1.5$ bits, independently of $r$ and the target distortion $d$. For mean-square error distortion, the difference is at most $\log (\sqrt{\frac{πe}{2}}) \approx 1$ bits, regardless of $d$. We also provide bounds on the capacity of memoryless additive noise channels when the noise is log-concave. We show that the difference between the capacity of such channels and the capacity of the Gaussian channel with the same noise power is at most $\log (\sqrt{\frac{πe}{2}}) \approx 1$ bits. Our results generalize to the case of vector $X$ with possibly dependent coordinates, and to $γ$-concave random variables. Our proof technique leverages tools from convex geometry.
FWDA: a Fast Wishart Discriminant Analysis with its Application to Electronic Health Records Data Classification
Venue: arXiv cs.LG 2017
Year: 2017

Linear Discriminant Analysis (LDA) on Electronic Health Records (EHR) data is widely-used for early detection of diseases. Classical LDA for EHR data classification, however, suffers from two handicaps: the ill-posed estimation of LDA parameters (e.g., covariance matrix), and the "linear inseparability" of EHR data. To handle these two issues, in this paper, we propose a novel classifier FWDA -- Fast Wishart Discriminant Analysis, that makes predictions in an ensemble way. Specifically, FWDA first surrogates the distribution of inverse covariance matrices using a Wishart distribution estimated from the training data, then "weighted-averages" the classification results of multiple LDA classifiers parameterized by the sampled inverse covariance matrices via a Bayesian Voting scheme. The weights for voting are optimally updated to adapt each new input data, so as to enable the nonlinear classification. Theoretical analysis indicates that FWDA possesses a fast convergence rate and a robust performance on high dimensional data. Extensive experiments on large-scale EHR dataset show that our approach outperforms state-of-the-art algorithms by a large margin.
Predicting Financial Crime: Augmenting the Predictive Policing Arsenal
Venue: arXiv cs.CY 2017
Year: 2017

Financial crime is a rampant but hidden threat. In spite of this, predictive policing systems disproportionately target "street crime" rather than white collar crime. This paper presents the White Collar Crime Early Warning System (WCCEWS), a white collar crime predictive model that uses random forest classifiers to identify high risk zones for incidents of financial crime.
Grouping-Based Random Access Collision Control for Massive Machine-Type Communication
Venue: arXiv cs.NI 2017
Year: 2017

Massive Machine-Type Communication (mMTC) is expected to be strongly supported by future 5G wireless networks. Its deployment, however, is seriously challenged by the high risk of random access (RA) collision. A popular concept is to reduce RA collisions by clustering mMTC devices into groups, and to connect group members with device-to-device (D2D) links. However, analytical models of this method and discussions about the reliability of D2D links are still absent. In this paper, existing grouping-based solutions are reviewed, an analytical model of grouped RA collision is proposed. Based on the analytical model, the impact of D2D reliability on the collision rate is also investigated. Afterwards, an efficient grouped RA procedure is designed to extend the state-of-the-art with an efficient local group update mechanism against D2D link exceptions.
Anisotropic twicing for single particle reconstruction using autocorrelation analysis
Venue: arXiv cs.CV 2017
Year: 2017

The missing phase problem in X-ray crystallography is commonly solved using the technique of molecular replacement, which borrows phases from a previously solved homologous structure, and appends them to the measured Fourier magnitudes of the diffraction patterns of the unknown structure. More recently, molecular replacement has been proposed for solving the missing orthogonal matrices problem arising in Kam's autocorrelation analysis for single particle reconstruction using X-ray free electron lasers and cryo-EM. In classical molecular replacement, it is common to estimate the magnitudes of the unknown structure as twice the measured magnitudes minus the magnitudes of the homologous structure, a procedure known as `twicing'. Mathematically, this is equivalent to finding an unbiased estimator for a complex-valued scalar. We generalize this scheme for the case of estimating real or complex valued matrices arising in single particle autocorrelation analysis. We name this approach "Anisotropic Twicing" because unlike the scalar case, the unbiased estimator is not obtained by a simple magnitude isotropic correction. We compare the performance of the least squares, twicing and anisotropic twicing estimators on synthetic and experimental datasets. We demonstrate 3D homology modeling in cryo-EM directly from experimental data without iterative refinement or class averaging, for the first time.
Hybrid Precoder and Combiner Design with One-Bit Quantized Phase Shifters in mmWave MIMO Systems
Venue: arXiv cs.IT 2017
Year: 2017

Analog/digital hybrid precoder and combiner have been widely used in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems due to its energy-efficient and economic superiorities. Infinite resolution of phase shifters (PSs) for the analog beamformer can achieve very close performance compared to the full-digital scheme but will result in high complexity and intensive power consumption. Thus, more cost effective and energy efficient low resolution PSs are typically used in practical mmWave MIMO systems. In this paper, we consider the joint hybrid precoder and combiner design with one-bit quantized PSs in mmWave MIMO systems. We propose to firstly design the analog precoder and combiner pair for each data stream successively, aiming at conditionally maximizing the spectral efficiency. We present a novel binary analog precoder and combiner optimization algorithm under a Rank-1 approximation of the interference-included equivalent channel with lower than quadratic complexity. Then the digital precoder and combiner are computed based on the obtained baseband effective channel to further enhance the spectral efficiency. Simulation results demonstrate that the proposed algorithm outperforms the existing one-bit PSs based hybrid beamforming scheme.
Airway segmentation from 3D chest CT volumes based on volume of interest using gradient vector flow
Venue: arXiv cs.CV 2017
Year: 2017

Some lung diseases are related to bronchial airway structures and morphology. Although airway segmentation from chest CT volumes is an important task in the computer-aided diagnosis and surgery assistance systems for the chest, complete 3-D airway structure segmentation is a quite challenging task due to its complex tree-like structure. In this paper, we propose a new airway segmentation method from 3D chest CT volumes based on volume of interests (VOI) using gradient vector flow (GVF). This method segments the bronchial regions by applying the cavity enhancement filter (CEF) to trace the bronchial tree structure from the trachea. It uses the CEF in the VOI to segment each branch. And a tube-likeness function based on GVF and the GVF magnitude map in each VOI are utilized to assist predicting the positions and directions of child branches. By calculating the tube-likeness function based on GVF and the GVF magnitude map, the airway-like candidate structures are identified and their centrelines are extracted. Based on the extracted centrelines, we can detect the branch points of the bifurcations and directions of the airway branches in the next level. At the same time, a leakage detection is performed to avoid the leakage by analysing the pixel information and the shape information of airway candidate regions extracted in the VOI. Finally, we unify all of the extracted bronchial regions to form an integrated airway tree. Preliminary experiments using four cases of chest CT volumes demonstrated that the proposed method can extract more bronchial branches in comparison with other methods.
A Faster Patch Ordering Method for Image Denoising
Venue: arXiv cs.CV 2017
Year: 2017

Among the patch-based image denoising processing methods, smooth ordering of local patches (patch ordering) has been shown to give state-of-art results. For image denoising the patch ordering method forms two large TSPs (Traveling Salesman Problem) comprised of nodes in N-dimensional space. Ten approximate solutions of the two large TSPs are then used in a filtering process to form the reconstructed image. Use of large TSPs makes patch ordering a computationally intensive method. A modified patch ordering method for image denoising is proposed. In the proposed method, several smaller-sized TSPs are formed and the filtering process varied to work with solutions of these smaller TSPs. In terms of PSNR, denoising results of the proposed method differed by 0.032 dB to 0.016 dB on average. In original method, solving TSPs was observed to consume 85% of execution time. In proposed method, the time for solving TSPs can be reduced to half of the time required in original method. The proposed method can denoise images in 40% less time.
Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we propose a novel learning based method for automated segmenta-tion of brain tumor in multimodal MRI images. The machine learned features from fully convolutional neural network (FCN) and hand-designed texton fea-tures are used to classify the MRI image voxels. The score map with pixel-wise predictions is used as a feature map which is learned from multimodal MRI train-ing dataset using the FCN. The learned features are then applied to random for-ests to classify each MRI image voxel into normal brain tissues and different parts of tumor. The method was evaluated on BRATS 2013 challenge dataset. The results show that the application of the random forest classifier to multimodal MRI images using machine-learned features based on FCN and hand-designed features based on textons provides promising segmentations. The Dice overlap measure for automatic brain tumor segmentation against ground truth is 0.88, 080 and 0.73 for complete tumor, core and enhancing tumor, respectively.