Paper Group ANR 532
Counting Cells in Time-Lapse Microscopy using Deep Neural Networks. Visibility graphs for image processing. Inferring short-term volatility indicators from Bitcoin blockchain. Improved Oracle Complexity of Variance Reduced Methods for Nonsmooth Convex Stochastic Composition Optimization. CHET: Compiler and Runtime for Homomorphic Evaluation of Tens …
Counting Cells in Time-Lapse Microscopy using Deep Neural Networks
| Title | Counting Cells in Time-Lapse Microscopy using Deep Neural Networks |
| Authors | Alexander Gomez Villa, Augusto Salazar, Igor Stefanini |
| Abstract | An automatic approach to counting any kind of cells could alleviate work of the experts and boost the research in fields such as regenerative medicine. In this paper, a method for microscopy cell counting using multiple frames (hence temporal information) is proposed. Unlike previous approaches where the cell counting is done independently in each frame (static cell counting), in this work the cell counting prediction is done using multiple frames (dynamic cell counting). A spatiotemporal model using ConvNets and long short term memory (LSTM) recurrent neural networks is proposed to overcome temporal variations. The model outperforms static cell counting in a publicly available dataset of stem cells. The advantages, working conditions and limitations of the ConvNet-LSTM method are discussed. Although our method is tested in cell counting, it can be extrapolated to quantify in video (or correlated image series) any kind of objects or volumes. |
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| Published | 2018-01-31 |
| URL | http://arxiv.org/abs/1801.10443v1 |
| http://arxiv.org/pdf/1801.10443v1.pdf | |
| PWC | https://paperswithcode.com/paper/counting-cells-in-time-lapse-microscopy-using |
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Visibility graphs for image processing
| Title | Visibility graphs for image processing |
| Authors | Jacopo Iacovacci, Lucas Lacasa |
| Abstract | The family of image visibility graphs (IVGs) have been recently introduced as simple algorithms by which scalar fields can be mapped into graphs. Here we explore the usefulness of such operator in the scenario of image processing and image classification. We demonstrate that the link architecture of the image visibility graphs encapsulates relevant information on the structure of the images and we explore their potential as image filters and compressors. We introduce several graph features, including the novel concept of Visibility Patches, and show through several examples that these features are highly informative, computationally efficient and universally applicable for general pattern recognition and image classification tasks. |
| Tasks | Image Classification |
| Published | 2018-04-19 |
| URL | http://arxiv.org/abs/1804.07125v1 |
| http://arxiv.org/pdf/1804.07125v1.pdf | |
| PWC | https://paperswithcode.com/paper/visibility-graphs-for-image-processing |
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Inferring short-term volatility indicators from Bitcoin blockchain
| Title | Inferring short-term volatility indicators from Bitcoin blockchain |
| Authors | Nino Antulov-Fantulin, Dijana Tolic, Matija Piskorec, Zhang Ce, Irena Vodenska |
| Abstract | In this paper, we study the possibility of inferring early warning indicators (EWIs) for periods of extreme bitcoin price volatility using features obtained from Bitcoin daily transaction graphs. We infer the low-dimensional representations of transaction graphs in the time period from 2012 to 2017 using Bitcoin blockchain, and demonstrate how these representations can be used to predict extreme price volatility events. Our EWI, which is obtained with a non-negative decomposition, contains more predictive information than those obtained with singular value decomposition or scalar value of the total Bitcoin transaction volume. |
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| Published | 2018-09-19 |
| URL | http://arxiv.org/abs/1809.07856v1 |
| http://arxiv.org/pdf/1809.07856v1.pdf | |
| PWC | https://paperswithcode.com/paper/inferring-short-term-volatility-indicators |
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Improved Oracle Complexity of Variance Reduced Methods for Nonsmooth Convex Stochastic Composition Optimization
| Title | Improved Oracle Complexity of Variance Reduced Methods for Nonsmooth Convex Stochastic Composition Optimization |
| Authors | Tianyi Lin, Chenyou Fan, Mengdi Wang |
| Abstract | We consider the nonsmooth convex composition optimization problem where the objective is a composition of two finite-sum functions and analyze stochastic compositional variance reduced gradient (SCVRG) methods for them. SCVRG and its variants have recently drawn much attention given their edge over stochastic compositional gradient descent (SCGD); but the theoretical analysis exclusively assumes strong convexity of the objective, which excludes several important examples such as Lasso, logistic regression, principle component analysis and deep neural nets. In contrast, we prove non-asymptotic incremental first-order oracle (IFO) complexity of SCVRG or its novel variants for nonsmooth convex composition optimization and show that they are provably faster than SCGD and gradient descent. More specifically, our method achieves the total IFO complexity of $O\left((m+n)\log\left(1/\epsilon\right)+1/\epsilon^3\right)$ which improves that of $O\left(1/\epsilon^{3.5}\right)$ and $O\left((m+n)/\sqrt{\epsilon}\right)$ obtained by SCGD and accelerated gradient descent (AGD) respectively. Experimental results confirm that our methods outperform several existing methods, e.g., SCGD and AGD, on sparse mean-variance optimization problem. |
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| Published | 2018-02-07 |
| URL | https://arxiv.org/abs/1802.02339v7 |
| https://arxiv.org/pdf/1802.02339v7.pdf | |
| PWC | https://paperswithcode.com/paper/improved-oracle-complexity-of-variance |
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CHET: Compiler and Runtime for Homomorphic Evaluation of Tensor Programs
| Title | CHET: Compiler and Runtime for Homomorphic Evaluation of Tensor Programs |
| Authors | Roshan Dathathri, Olli Saarikivi, Hao Chen, Kim Laine, Kristin Lauter, Saeed Maleki, Madanlal Musuvathi, Todd Mytkowicz |
| Abstract | Fully Homomorphic Encryption (FHE) refers to a set of encryption schemes that allow computations to be applied directly on encrypted data without requiring a secret key. This enables novel application scenarios where a client can safely offload storage and computation to a third-party cloud provider without having to trust the software and the hardware vendors with the decryption keys. Recent advances in both FHE schemes and implementations have moved such applications from theoretical possibilities into the realm of practicalities. This paper proposes a compact and well-reasoned interface called the Homomorphic Instruction Set Architecture (HISA) for developing FHE applications. Just as the hardware ISA interface enabled hardware advances to proceed independent of software advances in the compiler and language runtimes, HISA decouples compiler optimizations and runtimes for supporting FHE applications from advancements in the underlying FHE schemes. This paper demonstrates the capabilities of HISA by building an end-to-end software stack for evaluating neural network models on encrypted data. Our stack includes an end-to-end compiler, runtime, and a set of optimizations. Our approach shows generated code, on a set of popular neural network architectures, is faster than hand-optimized implementations. |
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| Published | 2018-10-01 |
| URL | http://arxiv.org/abs/1810.00845v1 |
| http://arxiv.org/pdf/1810.00845v1.pdf | |
| PWC | https://paperswithcode.com/paper/chet-compiler-and-runtime-for-homomorphic |
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Spelling Error Correction Using a Nested RNN Model and Pseudo Training Data
| Title | Spelling Error Correction Using a Nested RNN Model and Pseudo Training Data |
| Authors | Hao Li, Yang Wang, Xinyu Liu, Zhichao Sheng, Si Wei |
| Abstract | We propose a nested recurrent neural network (nested RNN) model for English spelling error correction and generate pseudo data based on phonetic similarity to train it. The model fuses orthographic information and context as a whole and is trained in an end-to-end fashion. This avoids feature engineering and does not rely on a noisy channel model as in traditional methods. Experiments show that the proposed method is superior to existing systems in correcting spelling errors. |
| Tasks | Feature Engineering |
| Published | 2018-11-01 |
| URL | http://arxiv.org/abs/1811.00238v1 |
| http://arxiv.org/pdf/1811.00238v1.pdf | |
| PWC | https://paperswithcode.com/paper/spelling-error-correction-using-a-nested-rnn |
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Natural Language Generation with Neural Variational Models
| Title | Natural Language Generation with Neural Variational Models |
| Authors | Hareesh Bahuleyan |
| Abstract | In this thesis, we explore the use of deep neural networks for generation of natural language. Specifically, we implement two sequence-to-sequence neural variational models - variational autoencoders (VAE) and variational encoder-decoders (VED). VAEs for text generation are difficult to train due to issues associated with the Kullback-Leibler (KL) divergence term of the loss function vanishing to zero. We successfully train VAEs by implementing optimization heuristics such as KL weight annealing and word dropout. We also demonstrate the effectiveness of this continuous latent space through experiments such as random sampling, linear interpolation and sampling from the neighborhood of the input. We argue that if VAEs are not designed appropriately, it may lead to bypassing connections which results in the latent space being ignored during training. We show experimentally with the example of decoder hidden state initialization that such bypassing connections degrade the VAE into a deterministic model, thereby reducing the diversity of generated sentences. We discover that the traditional attention mechanism used in sequence-to-sequence VED models serves as a bypassing connection, thereby deteriorating the model’s latent space. In order to circumvent this issue, we propose the variational attention mechanism where the attention context vector is modeled as a random variable that can be sampled from a distribution. We show empirically using automatic evaluation metrics, namely entropy and distinct measures, that our variational attention model generates more diverse output sentences than the deterministic attention model. A qualitative analysis with human evaluation study proves that our model simultaneously produces sentences that are of high quality and equally fluent as the ones generated by the deterministic attention counterpart. |
| Tasks | Text Generation |
| Published | 2018-08-27 |
| URL | http://arxiv.org/abs/1808.09012v1 |
| http://arxiv.org/pdf/1808.09012v1.pdf | |
| PWC | https://paperswithcode.com/paper/natural-language-generation-with-neural |
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Comments on “Momentum fractional LMS for power signal parameter estimation”
| Title | Comments on “Momentum fractional LMS for power signal parameter estimation” |
| Authors | Shujaat Khan, Imran Naseem, Alishba Sadiq, Jawwad Ahmad, Muhammad Moinuddin |
| Abstract | The purpose of this paper is to indicate that the recently proposed Momentum fractional least mean squares (mFLMS) algorithm has some serious flaws in its design and analysis. Our apprehensions are based on the evidence we found in the derivation and analysis in the paper titled: \textquotedblleft \textit{Momentum fractional LMS for power signal parameter estimation}\textquotedblright. In addition to the theoretical bases our claims are also verified through extensive simulation results. The experiments clearly show that the new method does not have any advantage over the classical least mean square (LMS) method. |
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| Published | 2018-05-19 |
| URL | http://arxiv.org/abs/1805.07640v1 |
| http://arxiv.org/pdf/1805.07640v1.pdf | |
| PWC | https://paperswithcode.com/paper/comments-on-momentum-fractional-lms-for-power |
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Predicting Graph Categories from Structural Properties
| Title | Predicting Graph Categories from Structural Properties |
| Authors | James P. Canning, Emma E. Ingram, Sammantha Nowak-Wolff, Adriana M. Ortiz, Nesreen K. Ahmed, Ryan A. Rossi, Karl R. B. Schmitt, Sucheta Soundarajan |
| Abstract | This paper has been withdrawn from arXiv.org due to a disagreement among the authors related to several peer-review comments received prior to submission on arXiv.org. Even though the current version of this paper is withdrawn, there was no disagreement between authors on the novel work in this paper. One specific issue was the discussion of related work by Ikehara & Clauset (found on page 8 of the previously posted version). Peer-review comments on a similar version made ALL authors aware that the discussion misrepresented their work prior to submission to arXiv.org. However, some authors choose to post to arXiv a minimally updated version without the consent of all authors or properly addressing this attribution issue. ================ Original Paper Abstract: Complex networks are often categorized according to the underlying phenomena that they represent such as molecular interactions, re-tweets, and brain activity. In this work, we investigate the problem of predicting the category (domain) of arbitrary networks. This includes complex networks from different domains as well as synthetically generated graphs from five different network models. A classification accuracy of $96.6%$ is achieved using a random forest classifier with both real and synthetic networks. This work makes two important findings. First, our results indicate that complex networks from various domains have distinct structural properties that allow us to predict with high accuracy the category of a new previously unseen network. Second, synthetic graphs are trivial to classify as the classification model can predict with near-certainty the network model used to generate it. Overall, the results demonstrate that networks drawn from different domains (and network models) are trivial to distinguish using only a handful of simple structural properties. |
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| Published | 2018-05-07 |
| URL | https://arxiv.org/abs/1805.02682v2 |
| https://arxiv.org/pdf/1805.02682v2.pdf | |
| PWC | https://paperswithcode.com/paper/predicting-graph-categories-from-structural |
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DeepHTTP: Semantics-Structure Model with Attention for Anomalous HTTP Traffic Detection and Pattern Mining
| Title | DeepHTTP: Semantics-Structure Model with Attention for Anomalous HTTP Traffic Detection and Pattern Mining |
| Authors | Yuqi Yu, Hanbing Yan, Hongchao Guan, Hao Zhou |
| Abstract | In the Internet age, cyber-attacks occur frequently with complex types. Traffic generated by access activities can record website status and user request information, which brings a great opportunity for network attack detection. Among diverse network protocols, Hypertext Transfer Protocol (HTTP) is widely used in government, organizations and enterprises. In this work, we propose DeepHTTP, a semantics structure integration model utilizing Bidirectional Long Short-Term Memory (Bi-LSTM) with attention mechanism to model HTTP traffic as a natural language sequence. In addition to extracting traffic content information, we integrate structural information to enhance the generalization capabilities of the model. Moreover, the application of attention mechanism can assist in discovering critical parts of anomalous traffic and further mining attack patterns. Additionally, we demonstrate how to incrementally update the data set and retrain model so that it can be adapted to new anomalous traffic. Extensive experimental evaluations over large traffic data have illustrated that DeepHTTP has outstanding performance in traffic detection and pattern discovery. |
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| Published | 2018-10-30 |
| URL | http://arxiv.org/abs/1810.12751v1 |
| http://arxiv.org/pdf/1810.12751v1.pdf | |
| PWC | https://paperswithcode.com/paper/deephttp-semantics-structure-model-with |
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Best sources forward: domain generalization through source-specific nets
| Title | Best sources forward: domain generalization through source-specific nets |
| Authors | Massimiliano Mancini, Samuel Rota Bulò, Barbara Caputo, Elisa Ricci |
| Abstract | A long standing problem in visual object categorization is the ability of algorithms to generalize across different testing conditions. The problem has been formalized as a covariate shift among the probability distributions generating the training data (source) and the test data (target) and several domain adaptation methods have been proposed to address this issue. While these approaches have considered the single source-single target scenario, it is plausible to have multiple sources and require adaptation to any possible target domain. This last scenario, named Domain Generalization (DG), is the focus of our work. Differently from previous DG methods which learn domain invariant representations from source data, we design a deep network with multiple domain-specific classifiers, each associated to a source domain. At test time we estimate the probabilities that a target sample belongs to each source domain and exploit them to optimally fuse the classifiers predictions. To further improve the generalization ability of our model, we also introduced a domain agnostic component supporting the final classifier. Experiments on two public benchmarks demonstrate the power of our approach. |
| Tasks | Domain Adaptation, Domain Generalization |
| Published | 2018-06-15 |
| URL | http://arxiv.org/abs/1806.05810v1 |
| http://arxiv.org/pdf/1806.05810v1.pdf | |
| PWC | https://paperswithcode.com/paper/best-sources-forward-domain-generalization |
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An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Loss
| Title | An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Loss |
| Authors | Peixiang Zhong, Di Wang, Chunyan Miao |
| Abstract | Affect conveys important implicit information in human communication. Having the capability to correctly express affect during human-machine conversations is one of the major milestones in artificial intelligence. In recent years, extensive research on open-domain neural conversational models has been conducted. However, embedding affect into such models is still under explored. In this paper, we propose an end-to-end affect-rich open-domain neural conversational model that produces responses not only appropriate in syntax and semantics, but also with rich affect. Our model extends the Seq2Seq model and adopts VAD (Valence, Arousal and Dominance) affective notations to embed each word with affects. In addition, our model considers the effect of negators and intensifiers via a novel affective attention mechanism, which biases attention towards affect-rich words in input sentences. Lastly, we train our model with an affect-incorporated objective function to encourage the generation of affect-rich words in the output responses. Evaluations based on both perplexity and human evaluations show that our model outperforms the state-of-the-art baseline model of comparable size in producing natural and affect-rich responses. |
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| Published | 2018-11-17 |
| URL | http://arxiv.org/abs/1811.07078v1 |
| http://arxiv.org/pdf/1811.07078v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-affect-rich-neural-conversational-model |
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Dilatation of Lateral Ventricles with Brain Volumes in Infants with 3D Transfontanelle US
| Title | Dilatation of Lateral Ventricles with Brain Volumes in Infants with 3D Transfontanelle US |
| Authors | Marc-Antoine Boucher, Sarah Lippe, Amelie Damphousse, Ramy El-Jalbout, Samuel Kadoury |
| Abstract | Ultrasound (US) can be used to assess brain development in newborns, as MRI is challenging due to immobilization issues, and may require sedation. Dilatation of the lateral ventricles in the brain is a risk factor for poorer neurodevelopment outcomes in infants. Hence, 3D US has the ability to assess the volume of the lateral ventricles similar to clinically standard MRI, but manual segmentation is time consuming. The objective of this study is to develop an approach quantifying the ratio of lateral ventricular dilatation with respect to total brain volume using 3D US, which can assess the severity of macrocephaly. Automatic segmentation of the lateral ventricles is achieved with a multi-atlas deformable registration approach using locally linear correlation metrics for US-MRI fusion, followed by a refinement step using deformable mesh models. Total brain volume is estimated using a 3D ellipsoid modeling approach. Validation was performed on a cohort of 12 infants, ranging from 2 to 8.5 months old, where 3D US and MRI were used to compare brain volumes and segmented lateral ventricles. Automatically extracted volumes from 3D US show a high correlation and no statistically significant difference when compared to ground truth measurements. Differences in volume ratios was 6.0 +/- 4.8% compared to MRI, while lateral ventricular segmentation yielded a mean Dice coefficient of 70.8 +/- 3.6% and a mean absolute distance (MAD) of 0.88 +/- 0.2mm, demonstrating the clinical benefit of this tool in paediatric ultrasound. |
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| Published | 2018-06-06 |
| URL | http://arxiv.org/abs/1806.02305v1 |
| http://arxiv.org/pdf/1806.02305v1.pdf | |
| PWC | https://paperswithcode.com/paper/dilatation-of-lateral-ventricles-with-brain |
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Models for Predicting Community-Specific Interest in News Articles
| Title | Models for Predicting Community-Specific Interest in News Articles |
| Authors | Benjamin D. Horne, William Dron, Sibel Adali |
| Abstract | In this work, we ask two questions: 1. Can we predict the type of community interested in a news article using only features from the article content? and 2. How well do these models generalize over time? To answer these questions, we compute well-studied content-based features on over 60K news articles from 4 communities on reddit.com. We train and test models over three different time periods between 2015 and 2017 to demonstrate which features degrade in performance the most due to concept drift. Our models can classify news articles into communities with high accuracy, ranging from 0.81 ROC AUC to 1.0 ROC AUC. However, while we can predict the community-specific popularity of news articles with high accuracy, practitioners should approach these models carefully. Predictions are both community-pair dependent and feature group dependent. Moreover, these feature groups generalize over time differently, with some only degrading slightly over time, but others degrading greatly. Therefore, we recommend that community-interest predictions are done in a hierarchical structure, where multiple binary classifiers can be used to separate community pairs, rather than a traditional multi-class model. Second, these models should be retrained over time based on accuracy goals and the availability of training data. |
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| Published | 2018-08-27 |
| URL | http://arxiv.org/abs/1808.09270v1 |
| http://arxiv.org/pdf/1808.09270v1.pdf | |
| PWC | https://paperswithcode.com/paper/models-for-predicting-community-specific |
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Asynchronous Spatial Image Convolutions for Event Cameras
| Title | Asynchronous Spatial Image Convolutions for Event Cameras |
| Authors | Cedric Scheerlinck, Nick Barnes, Robert Mahony |
| Abstract | Spatial convolution is arguably the most fundamental of 2D image processing operations. Conventional spatial image convolution can only be applied to a conventional image, that is, an array of pixel values (or similar image representation) that are associated with a single instant in time. Event cameras have serial, asynchronous output with no natural notion of an image frame, and each event arrives with a different timestamp. In this paper, we propose a method to compute the convolution of a linear spatial kernel with the output of an event camera. The approach operates on the event stream output of the camera directly without synthesising pseudo-image frames as is common in the literature. The key idea is the introduction of an internal state that directly encodes the convolved image information, which is updated asynchronously as each event arrives from the camera. The state can be read-off as-often-as and whenever required for use in higher level vision algorithms for real-time robotic systems. We demonstrate the application of our method to corner detection, providing an implementation of a Harris corner-response “state” that can be used in real-time for feature detection and tracking on robotic systems. |
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| Published | 2018-12-02 |
| URL | http://arxiv.org/abs/1812.00438v3 |
| http://arxiv.org/pdf/1812.00438v3.pdf | |
| PWC | https://paperswithcode.com/paper/asynchronous-spatial-image-convolutions-for |
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