OpenRaiser/Intern-Atlas huggingface dataset:
The Development of Darwin's Origin of Species
Venue: arXiv cs.CL 2018
Year: 2018

From 1837, when he returned to England aboard the $\textit{HMS Beagle}$, to 1860, just after publication of $\textit{The Origin of Species}$, Charles Darwin kept detailed notes of each book he read or wanted to read. His notes and manuscripts provide information about decades of individual scientific practice. Previously, we trained topic models on the full texts of each reading, and applied information-theoretic measures to detect that changes in his reading patterns coincided with the boundaries of his three major intellectual projects in the period 1837-1860. In this new work we apply the reading model to five additional documents, four of them by Darwin: the first edition of $\textit{The Origin of Species}$, two private essays stating intermediate forms of his theory in 1842 and 1844, a third essay of disputed dating, and Alfred Russel Wallace's essay, which Darwin received in 1858. We address three historical inquiries, previously treated qualitatively: 1) the mythology of "Darwin's Delay," that despite completing an extensive draft in 1844, Darwin waited until 1859 to publish $\textit{The Origin of Species}$ due to external pressures; 2) the relationship between Darwin and Wallace's contemporaneous theories, especially in light of their joint presentation; and 3) dating of the "Outline and Draft" which was rediscovered in 1975 and postulated first as an 1839 draft preceding the Sketch of 1842, then as an interstitial draft between the 1842 and 1844 essays.
Convolutional Neural Networks for Toxic Comment Classification
Venue: arXiv cs.CL 2018
Year: 2018

Flood of information is produced in a daily basis through the global Internet usage arising from the on-line interactive communications among users. While this situation contributes significantly to the quality of human life, unfortunately it involves enormous dangers, since on-line texts with high toxicity can cause personal attacks, on-line harassment and bullying behaviors. This has triggered both industrial and research community in the last few years while there are several tries to identify an efficient model for on-line toxic comment prediction. However, these steps are still in their infancy and new approaches and frameworks are required. On parallel, the data explosion that appears constantly, makes the construction of new machine learning computational tools for managing this information, an imperative need. Thankfully advances in hardware, cloud computing and big data management allow the development of Deep Learning approaches appearing very promising performance so far. For text classification in particular the use of Convolutional Neural Networks (CNN) have recently been proposed approaching text analytics in a modern manner emphasizing in the structure of words in a document. In this work, we employ this approach to discover toxic comments in a large pool of documents provided by a current Kaggle's competition regarding Wikipedia's talk page edits. To justify this decision we choose to compare CNNs against the traditional bag-of-words approach for text analysis combined with a selection of algorithms proven to be very effective in text classification. The reported results provide enough evidence that CNN enhance toxic comment classification reinforcing research interest towards this direction.
Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of Emotions
Venue: arXiv cs.CL 2018
Year: 2018

We describe an algorithm for automatic classification of idiomatic and literal expressions. Our starting point is that words in a given text segment, such as a paragraph, that are highranking representatives of a common topic of discussion are less likely to be a part of an idiomatic expression. Our additional hypothesis is that contexts in which idioms occur, typically, are more affective and therefore, we incorporate a simple analysis of the intensity of the emotions expressed by the contexts. We investigate the bag of words topic representation of one to three paragraphs containing an expression that should be classified as idiomatic or literal (a target phrase). We extract topics from paragraphs containing idioms and from paragraphs containing literals using an unsupervised clustering method, Latent Dirichlet Allocation (LDA) (Blei et al., 2003). Since idiomatic expressions exhibit the property of non-compositionality, we assume that they usually present different semantics than the words used in the local topic. We treat idioms as semantic outliers, and the identification of a semantic shift as outlier detection. Thus, this topic representation allows us to differentiate idioms from literals using local semantic contexts. Our results are encouraging.
Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filtering
Venue: arXiv cs.CV 2018
Year: 2018

Monocular cameras are one of the most commonly used sensors in the automotive industry for autonomous vehicles. One major drawback using a monocular camera is that it only makes observations in the two dimensional image plane and can not directly measure the distance to objects. In this paper, we aim at filling this gap by developing a multi-object tracking algorithm that takes an image as input and produces trajectories of detected objects in a world coordinate system. We solve this by using a deep neural network trained to detect and estimate the distance to objects from a single input image. The detections from a sequence of images are fed in to a state-of-the art Poisson multi-Bernoulli mixture tracking filter. The combination of the learned detector and the PMBM filter results in an algorithm that achieves 3D tracking using only mono-camera images as input. The performance of the algorithm is evaluated both in 3D world coordinates, and 2D image coordinates, using the publicly available KITTI object tracking dataset. The algorithm shows the ability to accurately track objects, correctly handle data associations, even when there is a big overlap of the objects in the image, and is one of the top performing algorithms on the KITTI object tracking benchmark. Furthermore, the algorithm is efficient, running on average close to 20 frames per second.
Simultaneous Traffic Sign Detection and Boundary Estimation using Convolutional Neural Network
Venue: arXiv cs.CV 2018
Year: 2018

We propose a novel traffic sign detection system that simultaneously estimates the location and precise boundary of traffic signs using convolutional neural network (CNN). Estimating the precise boundary of traffic signs is important in navigation systems for intelligent vehicles where traffic signs can be used as 3D landmarks for road environment. Previous traffic sign detection systems, including recent methods based on CNN, only provide bounding boxes of traffic signs as output, and thus requires additional processes such as contour estimation or image segmentation to obtain the precise sign boundary. In this work, the boundary estimation of traffic signs is formulated as a 2D pose and shape class prediction problem, and this is effectively solved by a single CNN. With the predicted 2D pose and the shape class of a target traffic sign in an input image, we estimate the actual boundary of the target sign by projecting the boundary of a corresponding template sign image into the input image plane. By formulating the boundary estimation problem as a CNN-based pose and shape prediction task, our method is end-to-end trainable, and more robust to occlusion and small targets than other boundary estimation methods that rely on contour estimation or image segmentation. The proposed method with architectural optimization provides an accurate traffic sign boundary estimation which is also efficient in compute, showing a detection frame rate higher than 7 frames per second on low-power mobile platforms.
Generating High Quality Visible Images from SAR Images Using CNNs
Venue: arXiv cs.CV 2018
Year: 2018

We propose a novel approach for generating high quality visible-like images from Synthetic Aperture Radar (SAR) images using Deep Convolutional Generative Adversarial Network (GAN) architectures. The proposed approach is based on a cascaded network of convolutional neural nets (CNNs) for despeckling and image colorization. The cascaded structure results in faster convergence during training and produces high quality visible images from the corresponding SAR images. Experimental results on both simulated and real SAR images show that the proposed method can produce visible-like images better compared to the recent state-of-the-art deep learning-based methods.
Effect of Transducer Positioning in Active Noise Control
Venue: arXiv cs.SD 2018
Year: 2018

Research in traditional Active Noise Control(ANC) often abstracts acoustic channels with band-limited filter coefficients. This is a limitation in exploring structural and positional aspects of ANC. As a solution to this, we propose the use of room acoustic models in ANC research. As a use case, we demonstrate anti-noise source position optimization using room acoustics models in achieving better noise control. Using numerical simulations, we show that level of cancellation can be improved up to 7.34 dB. All the codes and results are available in the Github repository https://github.com/cksajil/ancram in the spirit of reproducible research.
Generalized Byzantine-tolerant SGD
Venue: arXiv cs.DC 2018
Year: 2018

We propose three new robust aggregation rules for distributed synchronous Stochastic Gradient Descent~(SGD) under a general Byzantine failure model. The attackers can arbitrarily manipulate the data transferred between the servers and the workers in the parameter server~(PS) architecture. We prove the Byzantine resilience properties of these aggregation rules. Empirical analysis shows that the proposed techniques outperform current approaches for realistic use cases and Byzantine attack scenarios.
PROMETHEUS: PROcedural METhodology for developing HEuristics of USability
Venue: arXiv cs.HC 2018
Year: 2018

Usability is used to assess the effectiveness of a software product from the user point of view. Hence, proper methodologies and techniques to perform this assessment are very relevant. Heuristic evaluation is probably the most commonly used method for usability assessment. Developed by Nielsen and Molich in the '90s, traditional heuristic evaluations rely on Nielsen's 10 usability heuristics. However, recent evidence suggests that such heuristics are not sufficiently complete for dealing with new domains such as interactive television, virtual worlds, and many others. In addition to the lack of suitability of traditional heuristics, in the past years the lack of a robust methodology or process to effectively develop and validate these new domain-specific heuristics has been documented. In this paper we summarize current evidence on the lack of suitability of traditional heuristics, as well as the need to develop new domain-specific heuristics. After identifying and acknowledging existing gaps in heuristics for state-of-the-art technology, we present PROMETHEUS, a PROcedural METhodology for developing HEuristics of USability. PROMETHEUS refines the methodology of Rusu et. al. (2011), and is composed of 8 stages. PROMETHEUS clearly defines the artifacts that are required and produced by each stage, and also presents a set of quality indicators in order to assess the need for further refinement in the development of new heuristics. As an initial validation of PROMETHEUS, we apply a questionnaire to several researchers that have used the methodology of Rusu et. al., and we have also performed a small retrospective study, computing the quality indicators of several previous studies. Our results suggest that PROMETHEUS is a very promising methodology, and that the metrics and indicators are indeed pertinent with respect to the conclusions of previous studies.
Interplay between musical practices and tuning in the marimba de chonta music
Venue: arXiv cs.SD 2018
Year: 2018

In the Pacific Coast of Colombia there is a type of marimba called marimba de chonta, which provides the melodic and harmonic contour for traditional music with characteristic chants and dances. The tunings of this marimba are based on the voice of female singers and allows musical practices, as a transposition that preserves relative distances between bars. Here we show that traditional tunings are consistent with isotonic scales, and that they have changed in the last three decades due to the influence of Western music. Specifically, low octaves have changed into just octaves. Additionally, consonance properties of this instrument include the occurrence of a broad minimum of dissonance that is used in the musical practices, while the narrow local peaks of dissonance are avoided. We found that the main reason for this is the occurrence of uncertainties in the tunings with respect to the mathematical successions of isotonic scales. We conclude that in this music the emergence of tunings and musical practices cannot be considered as separate issues. Consonance, timbre, and musical practices are entangled.
Spectral Efficiency of Mixed-ADC Massive MIMO
Venue: arXiv cs.IT 2018
Year: 2018

We study the spectral efficiency (SE) of a mixed-ADC massive MIMO system in which K single-antenna users communicate with a base station (BS) equipped with M antennas connected to N high-resolution ADCs and M-N one-bit ADCs. This architecture has been proposed as an approach for realizing massive MIMO systems with reasonable power consumption. First, we investigate the effectiveness of mixed-ADC architectures in overcoming the channel estimation error caused by coarse quantization. For the channel estimation phase, we study to what extent one can combat the SE loss by exploiting just N << M pairs of high-resolution ADCs. We extend the round-robin training scheme for mixed-ADC systems to include both high-resolution and one-bit quantized observations. Then, we analyze the impact of the resulting channel estimation error in the data detection phase. We consider random high-resolution ADC assignment and also analyze a simple antenna selection scheme to increase the SE. Analytical expressions are derived for the SE for maximum ratio combining (MRC) and numerical results are presented for zero-forcing (ZF) detection. Performance comparisons are made against systems with uniform ADC resolution and against mixed-ADC systems without round-robin training to illustrate under what conditions each approach provides the greatest benefit.
Solving for high dimensional committor functions using artificial neural networks
Venue: arXiv cs.LG 2018
Year: 2018

In this note we propose a method based on artificial neural network to study the transition between states governed by stochastic processes. In particular, we aim for numerical schemes for the committor function, the central object of transition path theory, which satisfies a high-dimensional Fokker-Planck equation. By working with the variational formulation of such partial differential equation and parameterizing the committor function in terms of a neural network, approximations can be obtained via optimizing the neural network weights using stochastic algorithms. The numerical examples show that moderate accuracy can be achieved for high-dimensional problems.
An Approach to Sparse Continuous-time System Identification from Unevenly Sampled Data
Venue: arXiv cs.SY 2018
Year: 2018

In this work, we address the problem of identifying sparse continuous-time dynamical systems when the spacing between successive samples (the sampling period) is not constant over time. The proposed approach combines the leave-one-sample-out cross-validation error trick from machine learning with an iterative subset growth method to select the subset of basis functions that governs the dynamics of the system. The least-squares solution using only the selected subset of basis functions is then used. The approach is illustrated on two examples: a 6-node feedback ring and the Van der Pol oscillator.
Tensor Decomposition for Compressing Recurrent Neural Network
Venue: arXiv cs.LG 2018
Year: 2018

In the machine learning fields, Recurrent Neural Network (RNN) has become a popular architecture for sequential data modeling. However, behind the impressive performance, RNNs require a large number of parameters for both training and inference. In this paper, we are trying to reduce the number of parameters and maintain the expressive power from RNN simultaneously. We utilize several tensor decompositions method including CANDECOMP/PARAFAC (CP), Tucker decomposition and Tensor Train (TT) to re-parameterize the Gated Recurrent Unit (GRU) RNN. We evaluate all tensor-based RNNs performance on sequence modeling tasks with a various number of parameters. Based on our experiment results, TT-GRU achieved the best results in a various number of parameters compared to other decomposition methods.
A Simple Method to improve Initialization Robustness for Active Contours driven by Local Region Fitting Energy
Venue: arXiv cs.CV 2018
Year: 2018

Active contour models based on local region fitting energy can segment images with intensity inhomogeneity effectively, but their segmentation results are easy to error if the initial contour is inappropriate. In this paper, we present a simple and universal method of improving the robustness of initial contour for these local fitting-based models. The core idea of proposed method is exchanging the fitting values on the two sides of contour, so that the fitting values inside the contour are always larger (or smaller) than the values outside the contour in the process of curve evolution. In this way, the whole curve will evolve along the inner (or outer) boundaries of object, and less likely to be stuck in the object or background. Experimental results have proved that using the proposed method can enhance the robustness of initial contour and meanwhile keep the original advantages in the local fitting-based models.
Symmetric indefinite triangular factorization revealing the rank profile matrix
Venue: arXiv cs.NA 2018
Year: 2018

We present a novel recursive algorithm for reducing a symmetric matrix to a triangular factorization which reveals the rank profile matrix. That is, the algorithm computes a factorization $\mathbf{P}^T\mathbf{A}\mathbf{P} = \mathbf{L}\mathbf{D}\mathbf{L}^T$ where $\mathbf{P}$ is a permutation matrix, $\mathbf{L}$ is lower triangular with a unit diagonal and $\mathbf{D}$ is symmetric block diagonal with $1{\times}1$ and $2{\times}2$ antidiagonal blocks. The novel algorithm requires $O(n^2r^{ω-2})$ arithmetic operations. Furthermore, experimental results demonstrate that our algorithm can even be slightly more than twice as fast as the state of the art unsymmetric Gaussian elimination in most cases, that is it achieves approximately the same computational speed. By adapting the pivoting strategy developed in the unsymmetric case, we show how to recover the rank profile matrix from the permutation matrix and the support of the block-diagonal matrix. There is an obstruction in characteristic $2$ for revealing the rank profile matrix which requires to relax the shape of the block diagonal by allowing the 2-dimensional blocks to have a non-zero bottom-right coefficient. This relaxed decomposition can then be transformed into a standard $\mathbf{P}\mathbf{L}\mathbf{D}\mathbf{L}^T\mathbf{P}^T$ decomposition at a negligible cost.
A High GOPs/Slice Time Series Classifier for Portable and Embedded Biomedical Applications
Venue: arXiv cs.LG 2018
Year: 2018

Nowadays a diverse range of physiological data can be captured continuously for various applications in particular wellbeing and healthcare. Such data require efficient methods for classification and analysis. Deep learning algorithms have shown remarkable potential regarding such analyses, however, the use of these algorithms on low-power wearable devices is challenged by resource constraints such as area and power consumption. Most of the available on-chip deep learning processors contain complex and dense hardware architectures in order to achieve the highest possible throughput. Such a trend in hardware design may not be efficient in applications where on-node computation is required and the focus is more on the area and power efficiency as in the case of portable and embedded biomedical devices. This paper presents an efficient time-series classifier capable of automatically detecting effective features and classifying the input signals in real-time. In the proposed classifier, throughput is traded off with hardware complexity and cost using resource sharing techniques. A Convolutional Neural Network (CNN) is employed to extract input features and then a Long-Short-Term-Memory (LSTM) architecture with ternary weight precision classifies the input signals according to the extracted features. Hardware implementation on a Xilinx FPGA confirm that the proposed hardware can accurately classify multiple complex biomedical time series data with low area and power consumption and outperform all previously presented state-of-the-art records. Most notably, our classifier reaches 1.3$\times$ higher GOPs/Slice than similar state of the art FPGA-based accelerators.
A Novel Asymmetric Coded Placement in Combination Networks with end-user Caches
Venue: arXiv cs.IT 2018
Year: 2018

The tradeoff between the user's memory size and the worst-case download time in the $(H,r,M,N)$ combination network is studied, where a central server communicates with $K$ users through $H$ immediate relays, and each user has local cache of size $M$ files and is connected to a different subset of $r$ relays. The main contribution of this paper is the design of a coded caching scheme with asymmetric coded placement by leveraging coordination among the relays, which was not exploited in past work. Mathematical analysis and numerical results show that the proposed schemes outperform existing schemes.
Thresholds of Braided Convolutional Codes on the AWGN Channel
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we perform a threshold analysis of braided convolutional codes (BCCs) on the additive white Gaussian noise (AWGN) channel. The decoding thresholds are estimated by Monte-Carlo density evolution (MC-DE) techniques and compared with approximate thresholds from an erasure channel prediction. The results show that, with spatial coupling, the predicted thresholds are very accurate and quickly approach capacity if the coupling memory is increased. For uncoupled ensembles with random puncturing, the prediction can be improved with help of the AWGN threshold of the unpunctured ensemble.
Machine learning and genomics: precision medicine vs. patient privacy
Venue: arXiv cs.CY 2018
Year: 2018

Machine learning can have major societal impact in computational biology applications. In particular, it plays a central role in the development of precision medicine, whereby treatment is tailored to the clinical or genetic features of the patient. However, these advances require collecting and sharing among researchers large amounts of genomic data, which generates much concern about privacy. Researchers, study participants and governing bodies should be aware of the ways in which the privacy of participants might be compromised, as well as of the large body of research on technical solutions to these issues. We review how breaches in patient privacy can occur, present recent developments in computational data protection, and discuss how they can be combined with legal and ethical perspectives to provide secure frameworks for genomic data sharing.