Paper Group ANR 828
Spoofing PRNU Patterns of Iris Sensors while Preserving Iris Recognition. Evolving Graphs with Semantic Neutral Drift. Ensemble Sequence Level Training for Multimodal MT: OSU-Baidu WMT18 Multimodal Machine Translation System Report. Perception of Image Features in Post-Mortem Iris Recognition: Humans vs Machines. Spatiotemporal Manifold Prediction …
Spoofing PRNU Patterns of Iris Sensors while Preserving Iris Recognition
| Title | Spoofing PRNU Patterns of Iris Sensors while Preserving Iris Recognition |
| Authors | Sudipta Banerjee, Vahid Mirjalili, Arun Ross |
| Abstract | The principle of Photo Response Non-Uniformity (PRNU) is used to link an image with its source, i.e., the sensor that produced it. In this work, we investigate if it is possible to modify an iris image acquired using one sensor in order to spoof the PRNU noise pattern of a different sensor. In this regard, we develop an image perturbation routine that iteratively modifies blocks of pixels in the original iris image such that its PRNU pattern approaches that of a target sensor. Experiments indicate the efficacy of the proposed perturbation method in spoofing PRNU patterns present in an iris image whilst still retaining its biometric content. |
| Tasks | Iris Recognition |
| Published | 2018-08-31 |
| URL | http://arxiv.org/abs/1808.10765v2 |
| http://arxiv.org/pdf/1808.10765v2.pdf | |
| PWC | https://paperswithcode.com/paper/spoofing-prnu-patterns-of-iris-sensors-while |
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Evolving Graphs with Semantic Neutral Drift
| Title | Evolving Graphs with Semantic Neutral Drift |
| Authors | Timothy Atkinson, Detlef Plump, Susan Stepney |
| Abstract | We introduce the concept of Semantic Neutral Drift (SND) for genetic programming (GP), where we exploit equivalence laws to design semantics preserving mutations guaranteed to preserve individuals’ fitness scores. A number of digital circuit benchmark problems have been implemented with rule-based graph programs and empirically evaluated, demonstrating quantitative improvements in evolutionary performance. Analysis reveals that the benefits of the designed SND reside in more complex processes than simple growth of individuals, and that there are circumstances where it is beneficial to choose otherwise detrimental parameters for a GP system if that facilitates the inclusion of SND. |
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| Published | 2018-10-24 |
| URL | https://arxiv.org/abs/1810.10453v2 |
| https://arxiv.org/pdf/1810.10453v2.pdf | |
| PWC | https://paperswithcode.com/paper/semantic-neutral-drift |
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Ensemble Sequence Level Training for Multimodal MT: OSU-Baidu WMT18 Multimodal Machine Translation System Report
| Title | Ensemble Sequence Level Training for Multimodal MT: OSU-Baidu WMT18 Multimodal Machine Translation System Report |
| Authors | Renjie Zheng, Yilin Yang, Mingbo Ma, Liang Huang |
| Abstract | This paper describes multimodal machine translation systems developed jointly by Oregon State University and Baidu Research for WMT 2018 Shared Task on multimodal translation. In this paper, we introduce a simple approach to incorporate image information by feeding image features to the decoder side. We also explore different sequence level training methods including scheduled sampling and reinforcement learning which lead to substantial improvements. Our systems ensemble several models using different architectures and training methods and achieve the best performance for three subtasks: En-De and En-Cs in task 1 and (En+De+Fr)-Cs task 1B. |
| Tasks | Machine Translation, Multimodal Machine Translation |
| Published | 2018-08-31 |
| URL | http://arxiv.org/abs/1808.10592v1 |
| http://arxiv.org/pdf/1808.10592v1.pdf | |
| PWC | https://paperswithcode.com/paper/ensemble-sequence-level-training-for |
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Perception of Image Features in Post-Mortem Iris Recognition: Humans vs Machines
| Title | Perception of Image Features in Post-Mortem Iris Recognition: Humans vs Machines |
| Authors | Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz |
| Abstract | Post-mortem iris recognition can offer an additional forensic method of personal identification. However, in contrary to already well-established human examination of fingerprints, making iris recognition human-interpretable is harder, and therefore it has never been applied in forensic proceedings. There is no strong consensus among biometric experts which iris features, especially those in iris images acquired post-mortem, are the most important for human experts solving an iris recognition task. This paper explores two ways of broadening this knowledge: (a) with an eye tracker, the salient features used by humans comparing iris images on a screen are extracted, and (b) class-activation maps produced by the convolutional neural network solving the iris recognition task are analyzed. Both humans and deep learning-based solutions were examined with the same set of iris image pairs. This made it possible to compare the attention maps and conclude that (a) deep learning-based method can offer human-interpretable decisions backed by visual explanations pointing a human examiner to salient regions, and (b) in many cases humans and a machine used different features, what means that a deep learning-based method can offer a complementary support to human experts. This paper offers the first known to us human-interpretable comparison of machine-based and human-based post-mortem iris recognition, and the trained models annotating salient iris image regions. |
| Tasks | Iris Recognition |
| Published | 2018-07-11 |
| URL | https://arxiv.org/abs/1807.04049v3 |
| https://arxiv.org/pdf/1807.04049v3.pdf | |
| PWC | https://paperswithcode.com/paper/dcnn-based-human-interpretable-post-mortem |
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Spatiotemporal Manifold Prediction Model for Anterior Vertebral Body Growth Modulation Surgery in Idiopathic Scoliosis
| Title | Spatiotemporal Manifold Prediction Model for Anterior Vertebral Body Growth Modulation Surgery in Idiopathic Scoliosis |
| Authors | William Mandel, Olivier Turcot, Dejan Knez, Stefan Parent, Samuel Kadoury |
| Abstract | Anterior Vertebral Body Growth Modulation (AVBGM) is a minimally invasive surgical technique that gradually corrects spine deformities while preserving lumbar motion. However the selection of potential surgical patients is currently based on clinical judgment and would be facilitated by the identification of patients responding to AVBGM prior to surgery. We introduce a statistical framework for predicting the surgical outcomes following AVBGM in adolescents with idiopathic scoliosis. A discriminant manifold is first constructed to maximize the separation between responsive and non-responsive groups of patients treated with AVBGM for scoliosis. The model then uses subject-specific correction trajectories based on articulated transformations in order to map spine correction profiles to a group-average piecewise-geodesic path. Spine correction trajectories are described in a piecewise-geodesic fashion to account for varying times at follow-up exams, regressing the curve via a quadratic optimization process. To predict the evolution of correction, a baseline reconstruction is projected onto the manifold, from which a spatiotemporal regression model is built from parallel transport curves inferred from neighboring exemplars. The model was trained on 438 reconstructions and tested on 56 subjects using 3D spine reconstructions from follow-up exams, with the probabilistic framework yielding accurate results with differences of 2.1 +/- 0.6deg in main curve angulation, and generating models similar to biomechanical simulations. |
| Tasks | |
| Published | 2018-06-06 |
| URL | http://arxiv.org/abs/1806.02285v1 |
| http://arxiv.org/pdf/1806.02285v1.pdf | |
| PWC | https://paperswithcode.com/paper/spatiotemporal-manifold-prediction-model-for |
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Is Generator Conditioning Causally Related to GAN Performance?
| Title | Is Generator Conditioning Causally Related to GAN Performance? |
| Authors | Augustus Odena, Jacob Buckman, Catherine Olsson, Tom B. Brown, Christopher Olah, Colin Raffel, Ian Goodfellow |
| Abstract | Recent work (Pennington et al, 2017) suggests that controlling the entire distribution of Jacobian singular values is an important design consideration in deep learning. Motivated by this, we study the distribution of singular values of the Jacobian of the generator in Generative Adversarial Networks (GANs). We find that this Jacobian generally becomes ill-conditioned at the beginning of training. Moreover, we find that the average (with z from p(z)) conditioning of the generator is highly predictive of two other ad-hoc metrics for measuring the ‘quality’ of trained GANs: the Inception Score and the Frechet Inception Distance (FID). We test the hypothesis that this relationship is causal by proposing a ‘regularization’ technique (called Jacobian Clamping) that softly penalizes the condition number of the generator Jacobian. Jacobian Clamping improves the mean Inception Score and the mean FID for GANs trained on several datasets. It also greatly reduces inter-run variance of the aforementioned scores, addressing (at least partially) one of the main criticisms of GANs. |
| Tasks | |
| Published | 2018-02-23 |
| URL | http://arxiv.org/abs/1802.08768v2 |
| http://arxiv.org/pdf/1802.08768v2.pdf | |
| PWC | https://paperswithcode.com/paper/is-generator-conditioning-causally-related-to |
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Seeking Open-Ended Evolution in Swarm Chemistry II: Analyzing Long-Term Dynamics via Automated Object Harvesting
| Title | Seeking Open-Ended Evolution in Swarm Chemistry II: Analyzing Long-Term Dynamics via Automated Object Harvesting |
| Authors | Hiroki Sayama |
| Abstract | We studied the long-term dynamics of evolutionary Swarm Chemistry by extending the simulation length ten-fold compared to earlier work and by developing and using a new automated object harvesting method. Both macroscopic dynamics and microscopic object features were characterized and tracked using several measures. Results showed that the evolutionary dynamics tended to settle down into a stable state after the initial transient period, and that the extent of environmental perturbations also affected the evolutionary trends substantially. In the meantime, the automated harvesting method successfully produced a huge collection of spontaneously evolved objects, revealing the system’s autonomous creativity at an unprecedented scale. |
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| Published | 2018-04-10 |
| URL | http://arxiv.org/abs/1804.03304v2 |
| http://arxiv.org/pdf/1804.03304v2.pdf | |
| PWC | https://paperswithcode.com/paper/seeking-open-ended-evolution-in-swarm |
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Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applications
| Title | Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applications |
| Authors | Guy Hadash, Einat Kermany, Boaz Carmeli, Ofer Lavi, George Kour, Alon Jacovi |
| Abstract | Existing applications include a huge amount of knowledge that is out of reach for deep neural networks. This paper presents a novel approach for integrating calls to existing applications into deep learning architectures. Using this approach, we estimate each application’s functionality with an estimator, which is implemented as a deep neural network (DNN). The estimator is then embedded into a base network that we direct into complying with the application’s interface during an end-to-end optimization process. At inference time, we replace each estimator with its existing application counterpart and let the base network solve the task by interacting with the existing application. Using this ‘Estimate and Replace’ method, we were able to train a DNN end-to-end with less data and outperformed a matching DNN that did not interact with the external application. |
| Tasks | |
| Published | 2018-04-24 |
| URL | http://arxiv.org/abs/1804.09028v1 |
| http://arxiv.org/pdf/1804.09028v1.pdf | |
| PWC | https://paperswithcode.com/paper/estimate-and-replace-a-novel-approach-to |
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Undersampling and Bagging of Decision Trees in the Analysis of Cardiorespiratory Behavior for the Prediction of Extubation Readiness in Extremely Preterm Infants
| Title | Undersampling and Bagging of Decision Trees in the Analysis of Cardiorespiratory Behavior for the Prediction of Extubation Readiness in Extremely Preterm Infants |
| Authors | Lara J. Kanbar, Charles C. Onu, Wissam Shalish, Karen A. Brown, Guilherme M. Sant’Anna, Robert E. Kearney, Doina Precup |
| Abstract | Extremely preterm infants often require endotracheal intubation and mechanical ventilation during the first days of life. Due to the detrimental effects of prolonged invasive mechanical ventilation (IMV), clinicians aim to extubate infants as soon as they deem them ready. Unfortunately, existing strategies for prediction of extubation readiness vary across clinicians and institutions, and lead to high reintubation rates. We present an approach using Random Forest classifiers for the analysis of cardiorespiratory variability to predict extubation readiness. We address the issue of data imbalance by employing random undersampling of examples from the majority class before training each Decision Tree in a bag. By incorporating clinical domain knowledge, we further demonstrate that our classifier could have identified 71% of infants who failed extubation, while maintaining a success detection rate of 78%. |
| Tasks | |
| Published | 2018-08-24 |
| URL | http://arxiv.org/abs/1808.07992v1 |
| http://arxiv.org/pdf/1808.07992v1.pdf | |
| PWC | https://paperswithcode.com/paper/undersampling-and-bagging-of-decision-trees |
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Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem
| Title | Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem |
| Authors | Andre Wibisono |
| Abstract | We study sampling as optimization in the space of measures. We focus on gradient flow-based optimization with the Langevin dynamics as a case study. We investigate the source of the bias of the unadjusted Langevin algorithm (ULA) in discrete time, and consider how to remove or reduce the bias. We point out the difficulty is that the heat flow is exactly solvable, but neither its forward nor backward method is implementable in general, except for Gaussian data. We propose the symmetrized Langevin algorithm (SLA), which should have a smaller bias than ULA, at the price of implementing a proximal gradient step in space. We show SLA is in fact consistent for Gaussian target measure, whereas ULA is not. We also illustrate various algorithms explicitly for Gaussian target measure, including gradient descent, proximal gradient, and Forward-Backward, and show they are all consistent. |
| Tasks | |
| Published | 2018-02-22 |
| URL | http://arxiv.org/abs/1802.08089v2 |
| http://arxiv.org/pdf/1802.08089v2.pdf | |
| PWC | https://paperswithcode.com/paper/sampling-as-optimization-in-the-space-of |
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Latent Space Optimal Transport for Generative Models
| Title | Latent Space Optimal Transport for Generative Models |
| Authors | Huidong Liu, Yang Guo, Na Lei, Zhixin Shu, Shing-Tung Yau, Dimitris Samaras, Xianfeng Gu |
| Abstract | Variational Auto-Encoders enforce their learned intermediate latent-space data distribution to be a simple distribution, such as an isotropic Gaussian. However, this causes the posterior collapse problem and loses manifold structure which can be important for datasets such as facial images. A GAN can transform a simple distribution to a latent-space data distribution and thus preserve the manifold structure, but optimizing a GAN involves solving a Min-Max optimization problem, which is difficult and not well understood so far. Therefore, we propose a GAN-like method to transform a simple distribution to a data distribution in the latent space by solving only a minimization problem. This minimization problem comes from training a discriminator between a simple distribution and a latent-space data distribution. Then, we can explicitly formulate an Optimal Transport (OT) problem that computes the desired mapping between the two distributions. This means that we can transform a distribution without solving the difficult Min-Max optimization problem. Experimental results on an eight-Gaussian dataset show that the proposed OT can handle multi-cluster distributions. Results on the MNIST and the CelebA datasets validate the effectiveness of the proposed method. |
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| Published | 2018-09-16 |
| URL | http://arxiv.org/abs/1809.05964v1 |
| http://arxiv.org/pdf/1809.05964v1.pdf | |
| PWC | https://paperswithcode.com/paper/latent-space-optimal-transport-for-generative |
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Interpretable Patient Mortality Prediction with Multi-value Rule Sets
| Title | Interpretable Patient Mortality Prediction with Multi-value Rule Sets |
| Authors | Tong Wang, Veerajalandhar Allareddy, Sankeerth Rampa, Veerasathpurush Allareddy |
| Abstract | We propose a Multi-vAlue Rule Set (MRS) model for in-hospital predicting patient mortality. Compared to rule sets built from single-valued rules, MRS adopts a more generalized form of association rules that allows multiple values in a condition. Rules of this form are more concise than classical single-valued rules in capturing and describing patterns in data. Our formulation also pursues a higher efficiency of feature utilization, which reduces possible cost in data collection and storage. We propose a Bayesian framework for formulating a MRS model and propose an efficient inference method for learning a maximum \emph{a posteriori}, incorporating theoretically grounded bounds to iteratively reduce the search space and improve the search efficiency. Experiments show that our model was able to achieve better performance than baseline method including the current system used by the hospital. |
| Tasks | Mortality Prediction |
| Published | 2018-07-06 |
| URL | http://arxiv.org/abs/1807.03633v2 |
| http://arxiv.org/pdf/1807.03633v2.pdf | |
| PWC | https://paperswithcode.com/paper/interpretable-patient-mortality-prediction |
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Shape-from-Mask: A Deep Learning Based Human Body Shape Reconstruction from Binary Mask Images
| Title | Shape-from-Mask: A Deep Learning Based Human Body Shape Reconstruction from Binary Mask Images |
| Authors | Zhongping Ji, Xiao Qi, Yigang Wang, Gang Xu, Peng Du, Qing Wu |
| Abstract | 3D content creation is referred to as one of the most fundamental tasks of computer graphics. And many 3D modeling algorithms from 2D images or curves have been developed over the past several decades. Designers are allowed to align some conceptual images or sketch some suggestive curves, from front, side, and top views, and then use them as references in constructing a 3D model automatically or manually. However, to the best of our knowledge, no studies have investigated on 3D human body reconstruction in a similar manner. In this paper, we propose a deep learning based reconstruction of 3D human body shape from 2D orthographic views. A novel CNN-based regression network, with two branches corresponding to frontal and lateral views respectively, is designed for estimating 3D human body shape from 2D mask images. We train our networks separately to decouple the feature descriptors which encode the body parameters from different views, and fuse them to estimate an accurate human body shape. In addition, to overcome the shortage of training data required for this purpose, we propose some significantly data augmentation schemes for 3D human body shapes, which can be used to promote further research on this topic. Extensive experimen- tal results demonstrate that visually realistic and accurate reconstructions can be achieved effectively using our algorithm. Requiring only binary mask images, our method can help users create their own digital avatars quickly, and also make it easy to create digital human body for 3D game, virtual reality, online fashion shopping. |
| Tasks | Data Augmentation |
| Published | 2018-06-22 |
| URL | http://arxiv.org/abs/1806.08485v1 |
| http://arxiv.org/pdf/1806.08485v1.pdf | |
| PWC | https://paperswithcode.com/paper/shape-from-mask-a-deep-learning-based-human |
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The IFF Foundation for Ontological Knowledge Organization
| Title | The IFF Foundation for Ontological Knowledge Organization |
| Authors | Robert E. Kent |
| Abstract | This paper discusses an axiomatic approach for the integration of ontologies, an approach that extends to first order logic a previous approach (Kent 2000) based on information flow. This axiomatic approach is represented in the Information Flow Framework (IFF), a metalevel framework for organizing the information that appears in digital libraries, distributed databases and ontologies (Kent 2001). The paper argues that the integration of ontologies is the two-step process of alignment and unification. Ontological alignment consists of the sharing of common terminology and semantics through a mediating ontology. Ontological unification, concentrated in a virtual ontology of community connections, is fusion of the alignment diagram of participant community ontologies - the quotient of the sum of the participant portals modulo the ontological alignment structure. |
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| Published | 2018-10-10 |
| URL | http://arxiv.org/abs/1810.04773v1 |
| http://arxiv.org/pdf/1810.04773v1.pdf | |
| PWC | https://paperswithcode.com/paper/the-iff-foundation-for-ontological-knowledge |
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A novel database of Children’s Spontaneous Facial Expressions (LIRIS-CSE)
| Title | A novel database of Children’s Spontaneous Facial Expressions (LIRIS-CSE) |
| Authors | Rizwan Ahmed Khan, Crenn Arthur, Alexandre Meyer, Saida Bouakaz |
| Abstract | Computing environment is moving towards human-centered designs instead of computer centered designs and human’s tend to communicate wealth of information through affective states or expressions. Traditional Human Computer Interaction (HCI) based systems ignores bulk of information communicated through those affective states and just caters for user’s intentional input. Generally, for evaluating and benchmarking different facial expression analysis algorithms, standardized databases are needed to enable a meaningful comparison. In the absence of comparative tests on such standardized databases it is difficult to find relative strengths and weaknesses of different facial expression recognition algorithms. In this article we present a novel video database for Children’s Spontaneous facial Expressions (LIRIS-CSE). Proposed video database contains six basic spontaneous facial expressions shown by 12 ethnically diverse children between the ages of 6 and 12 years with mean age of 7.3 years. To the best of our knowledge, this database is first of its kind as it records and shows spontaneous facial expressions of children. Previously there were few database of children expressions and all of them show posed or exaggerated expressions which are different from spontaneous or natural expressions. Thus, this database will be a milestone for human behavior researchers. This database will be a excellent resource for vision community for benchmarking and comparing results. In this article, we have also proposed framework for automatic expression recognition based on convolutional neural network (CNN) architecture with transfer learning approach. Proposed architecture achieved average classification accuracy of 75% on our proposed database i.e. LIRIS-CSE. |
| Tasks | Facial Expression Recognition, Transfer Learning |
| Published | 2018-12-04 |
| URL | http://arxiv.org/abs/1812.01555v2 |
| http://arxiv.org/pdf/1812.01555v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-novel-database-of-childrens-spontaneous |
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