Paper Group ANR 218
Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation. Independence clustering (without a matrix). SUNNY-CP and the MiniZinc Challenge. Mondrian Processes for Flow Cytometry Analysis. A Framework for Dynamic Image Sampling Based on Supervised Learning (SLADS). Long Short-Term Memory (LSTM) networks with jet const …
Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
| Title | Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation |
| Authors | Lin Yang, Yizhe Zhang, Jianxu Chen, Siyuan Zhang, Danny Z. Chen |
| Abstract | Image segmentation is a fundamental problem in biomedical image analysis. Recent advances in deep learning have achieved promising results on many biomedical image segmentation benchmarks. However, due to large variations in biomedical images (different modalities, image settings, objects, noise, etc), to utilize deep learning on a new application, it usually needs a new set of training data. This can incur a great deal of annotation effort and cost, because only biomedical experts can annotate effectively, and often there are too many instances in images (e.g., cells) to annotate. In this paper, we aim to address the following question: With limited effort (e.g., time) for annotation, what instances should be annotated in order to attain the best performance? We present a deep active learning framework that combines fully convolutional network (FCN) and active learning to significantly reduce annotation effort by making judicious suggestions on the most effective annotation areas. We utilize uncertainty and similarity information provided by FCN and formulate a generalized version of the maximum set cover problem to determine the most representative and uncertain areas for annotation. Extensive experiments using the 2015 MICCAI Gland Challenge dataset and a lymph node ultrasound image segmentation dataset show that, using annotation suggestions by our method, state-of-the-art segmentation performance can be achieved by using only 50% of training data. |
| Tasks | Active Learning, Semantic Segmentation |
| Published | 2017-06-15 |
| URL | http://arxiv.org/abs/1706.04737v1 |
| http://arxiv.org/pdf/1706.04737v1.pdf | |
| PWC | https://paperswithcode.com/paper/suggestive-annotation-a-deep-active-learning |
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Independence clustering (without a matrix)
| Title | Independence clustering (without a matrix) |
| Authors | Daniil Ryabko |
| Abstract | The independence clustering problem is considered in the following formulation: given a set $S$ of random variables, it is required to find the finest partitioning ${U_1,\dots,U_k}$ of $S$ into clusters such that the clusters $U_1,\dots,U_k$ are mutually independent. Since mutual independence is the target, pairwise similarity measurements are of no use, and thus traditional clustering algorithms are inapplicable. The distribution of the random variables in $S$ is, in general, unknown, but a sample is available. Thus, the problem is cast in terms of time series. Two forms of sampling are considered: i.i.d.\ and stationary time series, with the main emphasis being on the latter, more general, case. A consistent, computationally tractable algorithm for each of the settings is proposed, and a number of open directions for further research are outlined. |
| Tasks | Time Series |
| Published | 2017-03-20 |
| URL | http://arxiv.org/abs/1703.06700v1 |
| http://arxiv.org/pdf/1703.06700v1.pdf | |
| PWC | https://paperswithcode.com/paper/independence-clustering-without-a-matrix |
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SUNNY-CP and the MiniZinc Challenge
| Title | SUNNY-CP and the MiniZinc Challenge |
| Authors | Roberto Amadini, Maurizio Gabbrielli, Jacopo Mauro |
| Abstract | In Constraint Programming (CP) a portfolio solver combines a variety of different constraint solvers for solving a given problem. This fairly recent approach enables to significantly boost the performance of single solvers, especially when multicore architectures are exploited. In this work we give a brief overview of the portfolio solver sunny-cp, and we discuss its performance in the MiniZinc Challenge—the annual international competition for CP solvers—where it won two gold medals in 2015 and 2016. Under consideration in Theory and Practice of Logic Programming (TPLP) |
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| Published | 2017-06-26 |
| URL | http://arxiv.org/abs/1706.08627v3 |
| http://arxiv.org/pdf/1706.08627v3.pdf | |
| PWC | https://paperswithcode.com/paper/sunny-cp-and-the-minizinc-challenge |
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Mondrian Processes for Flow Cytometry Analysis
| Title | Mondrian Processes for Flow Cytometry Analysis |
| Authors | Disi Ji, Eric Nalisnick, Padhraic Smyth |
| Abstract | Analysis of flow cytometry data is an essential tool for clinical diagnosis of hematological and immunological conditions. Current clinical workflows rely on a manual process called gating to classify cells into their canonical types. This dependence on human annotation limits the rate, reproducibility, and complexity of flow cytometry analysis. In this paper, we propose using Mondrian processes to perform automated gating by incorporating prior information of the kind used by gating technicians. The method segments cells into types via Bayesian nonparametric trees. Examining the posterior over trees allows for interpretable visualizations and uncertainty quantification - two vital qualities for implementation in clinical practice. |
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| Published | 2017-11-21 |
| URL | http://arxiv.org/abs/1711.07673v2 |
| http://arxiv.org/pdf/1711.07673v2.pdf | |
| PWC | https://paperswithcode.com/paper/mondrian-processes-for-flow-cytometry |
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A Framework for Dynamic Image Sampling Based on Supervised Learning (SLADS)
| Title | A Framework for Dynamic Image Sampling Based on Supervised Learning (SLADS) |
| Authors | G. M. Dilshan P. Godaliyadda, Dong Hye Ye, Michael D. Uchic, Michael A. Groeber, Gregery T. Buzzard, Charles A. Bouman |
| Abstract | Sparse sampling schemes have the potential to dramatically reduce image acquisition time while simultaneously reducing radiation damage to samples. However, for a sparse sampling scheme to be useful it is important that we are able to reconstruct the underlying object with sufficient clarity using the sparse measurements. In dynamic sampling, each new measurement location is selected based on information obtained from previous measurements. Therefore, dynamic sampling schemes have the potential to dramatically reduce the number of measurements needed for high fidelity reconstructions. However, most existing dynamic sampling methods for point-wise measurement acquisition tend to be computationally expensive and are therefore too slow for practical applications. In this paper, we present a framework for dynamic sampling based on machine learning techniques, which we call a supervised learning approach for dynamic sampling (SLADS). In each step of SLADS, the objective is to find the pixel that maximizes the expected reduction in distortion (ERD) given previous measurements. SLADS is fast because we use a simple regression function to compute the ERD, and it is accurate because the regression function is trained using data sets that are representative of the specific application. In addition, we introduce a method to terminate dynamic sampling at a desired level of distortion, and we extended the SLADS methodology to sample groups of pixels at each step. Finally, we present results on computationally-generated synthetic data and experimentally-collected data to demonstrate a dramatic improvement over state-of-the-art static sampling methods. |
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| Published | 2017-03-14 |
| URL | http://arxiv.org/abs/1703.04653v1 |
| http://arxiv.org/pdf/1703.04653v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-framework-for-dynamic-image-sampling-based |
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Long Short-Term Memory (LSTM) networks with jet constituents for boosted top tagging at the LHC
| Title | Long Short-Term Memory (LSTM) networks with jet constituents for boosted top tagging at the LHC |
| Authors | Shannon Egan, Wojciech Fedorko, Alison Lister, Jannicke Pearkes, Colin Gay |
| Abstract | Multivariate techniques based on engineered features have found wide adoption in the identification of jets resulting from hadronic top decays at the Large Hadron Collider (LHC). Recent Deep Learning developments in this area include the treatment of the calorimeter activation as an image or supplying a list of jet constituent momenta to a fully connected network. This latter approach lends itself well to the use of Recurrent Neural Networks. In this work the applicability of architectures incorporating Long Short-Term Memory (LSTM) networks is explored. Several network architectures, methods of ordering of jet constituents, and input pre-processing are studied. The best performing LSTM network achieves a background rejection of 100 for 50% signal efficiency. This represents more than a factor of two improvement over a fully connected Deep Neural Network (DNN) trained on similar types of inputs. |
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| Published | 2017-11-24 |
| URL | http://arxiv.org/abs/1711.09059v1 |
| http://arxiv.org/pdf/1711.09059v1.pdf | |
| PWC | https://paperswithcode.com/paper/long-short-term-memory-lstm-networks-with-jet |
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A Survey of Question Answering for Math and Science Problem
| Title | A Survey of Question Answering for Math and Science Problem |
| Authors | Arindam Bhattacharya |
| Abstract | Turing test was long considered the measure for artificial intelligence. But with the advances in AI, it has proved to be insufficient measure. We can now aim to mea- sure machine intelligence like we measure human intelligence. One of the widely accepted measure of intelligence is standardized math and science test. In this paper, we explore the progress we have made towards the goal of making a machine smart enough to pass the standardized test. We see the challenges and opportunities posed by the domain, and note that we are quite some ways from actually making a system as smart as a even a middle school scholar. |
| Tasks | Question Answering |
| Published | 2017-05-10 |
| URL | http://arxiv.org/abs/1705.04530v1 |
| http://arxiv.org/pdf/1705.04530v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-survey-of-question-answering-for-math-and |
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Learning Semantic Concepts and Order for Image and Sentence Matching
| Title | Learning Semantic Concepts and Order for Image and Sentence Matching |
| Authors | Yan Huang, Qi Wu, Liang Wang |
| Abstract | Image and sentence matching has made great progress recently, but it remains challenging due to the large visual-semantic discrepancy. This mainly arises from that the representation of pixel-level image usually lacks of high-level semantic information as in its matched sentence. In this work, we propose a semantic-enhanced image and sentence matching model, which can improve the image representation by learning semantic concepts and then organizing them in a correct semantic order. Given an image, we first use a multi-regional multi-label CNN to predict its semantic concepts, including objects, properties, actions, etc. Then, considering that different orders of semantic concepts lead to diverse semantic meanings, we use a context-gated sentence generation scheme for semantic order learning. It simultaneously uses the image global context containing concept relations as reference and the groundtruth semantic order in the matched sentence as supervision. After obtaining the improved image representation, we learn the sentence representation with a conventional LSTM, and then jointly perform image and sentence matching and sentence generation for model learning. Extensive experiments demonstrate the effectiveness of our learned semantic concepts and order, by achieving the state-of-the-art results on two public benchmark datasets. |
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| Published | 2017-12-06 |
| URL | http://arxiv.org/abs/1712.02036v1 |
| http://arxiv.org/pdf/1712.02036v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-semantic-concepts-and-order-for |
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Intrusion Prevention and Detection in Grid Computing - The ALICE Case
| Title | Intrusion Prevention and Detection in Grid Computing - The ALICE Case |
| Authors | Andres Gomez, Camilo Lara, Udo Kebschull |
| Abstract | Grids allow users flexible on-demand usage of computing resources through remote communication networks. A remarkable example of a Grid in High Energy Physics (HEP) research is used in the ALICE experiment at European Organization for Nuclear Research CERN. Physicists can submit jobs used to process the huge amount of particle collision data produced by the Large Hadron Collider (LHC). Grids face complex security challenges. They are interesting targets for attackers seeking for huge computational resources. Since users can execute arbitrary code in the worker nodes on the Grid sites, special care should be put in this environment. Automatic tools to harden and monitor this scenario are required. Currently, there is no integrated solution for such requirement. This paper describes a new security framework to allow execution of job payloads in a sandboxed context. It also allows process behavior monitoring to detect intrusions, even when new attack methods or zero day vulnerabilities are exploited, by a Machine Learning approach. We plan to implement the proposed framework as a software prototype that will be tested as a component of the ALICE Grid middleware. |
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| Published | 2017-04-20 |
| URL | http://arxiv.org/abs/1704.06193v1 |
| http://arxiv.org/pdf/1704.06193v1.pdf | |
| PWC | https://paperswithcode.com/paper/intrusion-prevention-and-detection-in-grid |
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Colorimetric Calibration of a Digital Camera
| Title | Colorimetric Calibration of a Digital Camera |
| Authors | Renata Rychtarikova, Pavel Soucek, Dalibor Stys |
| Abstract | In this paper, we introduce a novel - physico-chemical - approach for calibration of a digital camera chip. This approach utilizes results of measurement of incident light spectra of calibration films of different levels of gray for construction of calibration curve (number of incident photons vs. image pixel intensity) for each camera pixel. We show spectral characteristics of such corrected digital raw image files (a primary camera signal) and demonstrate their suitability for next image processing and analysis. |
| Tasks | Calibration |
| Published | 2017-08-14 |
| URL | http://arxiv.org/abs/1708.04685v1 |
| http://arxiv.org/pdf/1708.04685v1.pdf | |
| PWC | https://paperswithcode.com/paper/colorimetric-calibration-of-a-digital-camera |
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Learning to update Auto-associative Memory in Recurrent Neural Networks for Improving Sequence Memorization
| Title | Learning to update Auto-associative Memory in Recurrent Neural Networks for Improving Sequence Memorization |
| Authors | Wei Zhang, Bowen Zhou |
| Abstract | Learning to remember long sequences remains a challenging task for recurrent neural networks. Register memory and attention mechanisms were both proposed to resolve the issue with either high computational cost to retain memory differentiability, or by discounting the RNN representation learning towards encoding shorter local contexts than encouraging long sequence encoding. Associative memory, which studies the compression of multiple patterns in a fixed size memory, were rarely considered in recent years. Although some recent work tries to introduce associative memory in RNN and mimic the energy decay process in Hopfield nets, it inherits the shortcoming of rule-based memory updates, and the memory capacity is limited. This paper proposes a method to learn the memory update rule jointly with task objective to improve memory capacity for remembering long sequences. Also, we propose an architecture that uses multiple such associative memory for more complex input encoding. We observed some interesting facts when compared to other RNN architectures on some well-studied sequence learning tasks. |
| Tasks | Representation Learning |
| Published | 2017-09-19 |
| URL | http://arxiv.org/abs/1709.06493v3 |
| http://arxiv.org/pdf/1709.06493v3.pdf | |
| PWC | https://paperswithcode.com/paper/learning-to-update-auto-associative-memory-in |
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Convergence Analysis of Backpropagation Algorithm for Designing an Intelligent System for Sensing Manhole Gases
| Title | Convergence Analysis of Backpropagation Algorithm for Designing an Intelligent System for Sensing Manhole Gases |
| Authors | Varun Kumar Ojha, Paramartha Dutta, Atal Chaudhuri, Hiranmay Saha |
| Abstract | Human fatalities are reported due to the excessive proportional presence of hazardous gas components in the manhole, such as Hydrogen Sulfide, Ammonia, Methane, Carbon Dioxide, Nitrogen Oxide, Carbon Monoxide, etc. Hence, predetermination of these gases is imperative. A neural network (NN) based intelligent sensory system is proposed for the avoidance of such fatalities. Backpropagation (BP) was applied for the supervised training of the neural network. A Gas sensor array consists of many sensor elements was employed for the sensing manhole gases. Sensors in the sensor array are responsible for sensing their target gas components only. Therefore, the presence of multiple gases results in cross sensitivity. The cross sensitivity is a crucial issue to this problem and it is viewed as pattern recognition and noise reduction problem. Various performance parameters and complexity of the problem influences NN training. In present chapter the performance of BP algorithm on such a real life application problem was comprehensively studied, compared and contrasted with the several other hybrid intelligent approaches both, in theoretical and in the statistical sense. |
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| Published | 2017-07-06 |
| URL | http://arxiv.org/abs/1707.01821v1 |
| http://arxiv.org/pdf/1707.01821v1.pdf | |
| PWC | https://paperswithcode.com/paper/convergence-analysis-of-backpropagation |
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An Online Hierarchical Algorithm for Extreme Clustering
| Title | An Online Hierarchical Algorithm for Extreme Clustering |
| Authors | Ari Kobren, Nicholas Monath, Akshay Krishnamurthy, Andrew McCallum |
| Abstract | Many modern clustering methods scale well to a large number of data items, N, but not to a large number of clusters, K. This paper introduces PERCH, a new non-greedy algorithm for online hierarchical clustering that scales to both massive N and K–a problem setting we term extreme clustering. Our algorithm efficiently routes new data points to the leaves of an incrementally-built tree. Motivated by the desire for both accuracy and speed, our approach performs tree rotations for the sake of enhancing subtree purity and encouraging balancedness. We prove that, under a natural separability assumption, our non-greedy algorithm will produce trees with perfect dendrogram purity regardless of online data arrival order. Our experiments demonstrate that PERCH constructs more accurate trees than other tree-building clustering algorithms and scales well with both N and K, achieving a higher quality clustering than the strongest flat clustering competitor in nearly half the time. |
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| Published | 2017-04-06 |
| URL | http://arxiv.org/abs/1704.01858v1 |
| http://arxiv.org/pdf/1704.01858v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-online-hierarchical-algorithm-for-extreme |
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Tracking the Diffusion of Named Entities
| Title | Tracking the Diffusion of Named Entities |
| Authors | Leon Derczynski, Matthew Rowe |
| Abstract | Existing studies of how information diffuses across social networks have thus far concentrated on analysing and recovering the spread of deterministic innovations such as URLs, hashtags, and group membership. However investigating how mentions of real-world entities appear and spread has yet to be explored, largely due to the computationally intractable nature of performing large-scale entity extraction. In this paper we present, to the best of our knowledge, one of the first pieces of work to closely examine the diffusion of named entities on social media, using Reddit as our case study platform. We first investigate how named entities can be accurately recognised and extracted from discussion posts. We then use these extracted entities to study the patterns of entity cascades and how the probability of a user adopting an entity (i.e. mentioning it) is associated with exposures to the entity. We put these pieces together by presenting a parallelised diffusion model that can forecast the probability of entity adoption, finding that the influence of adoption between users can be characterised by their prior interactions – as opposed to whether the users propagated entity-adoptions beforehand. Our findings have important implications for researchers studying influence and language, and for community analysts who wish to understand entity-level influence dynamics. |
| Tasks | Entity Extraction |
| Published | 2017-12-22 |
| URL | http://arxiv.org/abs/1712.08349v2 |
| http://arxiv.org/pdf/1712.08349v2.pdf | |
| PWC | https://paperswithcode.com/paper/tracking-the-diffusion-of-named-entities |
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Dialog Context Language Modeling with Recurrent Neural Networks
| Title | Dialog Context Language Modeling with Recurrent Neural Networks |
| Authors | Bing Liu, Ian Lane |
| Abstract | In this work, we propose contextual language models that incorporate dialog level discourse information into language modeling. Previous works on contextual language model treat preceding utterances as a sequence of inputs, without considering dialog interactions. We design recurrent neural network (RNN) based contextual language models that specially track the interactions between speakers in a dialog. Experiment results on Switchboard Dialog Act Corpus show that the proposed model outperforms conventional single turn based RNN language model by 3.3% on perplexity. The proposed models also demonstrate advantageous performance over other competitive contextual language models. |
| Tasks | Language Modelling |
| Published | 2017-01-15 |
| URL | http://arxiv.org/abs/1701.04056v1 |
| http://arxiv.org/pdf/1701.04056v1.pdf | |
| PWC | https://paperswithcode.com/paper/dialog-context-language-modeling-with |
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