Paper Group ANR 738
Deep Learning for Robotic Mass Transport Cloaking. Harmonic Recomposition using Conditional Autoregressive Modeling. Measuring and Computing Database Inconsistency via Repairs. Reinforcement Learning from Imperfect Demonstrations. Automated Mouse Organ Segmentation: A Deep Learning Based Solution. Augmented Reality needle ablation guidance tool for …
Deep Learning for Robotic Mass Transport Cloaking
| Title | Deep Learning for Robotic Mass Transport Cloaking |
| Authors | Reza Khodayi-mehr, Michael M. Zavlanos |
| Abstract | We consider the problem of mass transport cloaking using mobile robots. The robots move along a predefined curve that encloses a safe zone and carry sources that collectively counteract a chemical agent released in the environment. The goal is to steer the mass flux around a desired region so that it remains unaffected by the external concentration. We formulate the problem of controlling the robot positions and release rates as a PDE-constrained optimization, where the propagation of the chemical is modeled by the advection-diffusion (AD) PDE. We use a neural network (NN) to approximate the solution of the PDE. Particularly, we propose a novel loss function for the NN that utilizes the variational form of the AD-PDE and allows us to reformulate the planning problem as an unsupervised model-based learning problem. Our loss function is discretization-free and highly parallelizable. Unlike passive cloaking methods that use metamaterials to steer the mass flux, our method is the first to use mobile robots to actively control the concentration levels and create safe zones independent of environmental conditions. We demonstrate the performance of our method in simulations. |
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| Published | 2018-12-11 |
| URL | https://arxiv.org/abs/1812.04157v3 |
| https://arxiv.org/pdf/1812.04157v3.pdf | |
| PWC | https://paperswithcode.com/paper/deep-learning-for-robotic-mass-transport |
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Harmonic Recomposition using Conditional Autoregressive Modeling
| Title | Harmonic Recomposition using Conditional Autoregressive Modeling |
| Authors | Kyle Kastner, Rithesh Kumar, Tim Cooijmans, Aaron Courville |
| Abstract | We demonstrate a conditional autoregressive pipeline for efficient music recomposition, based on methods presented in van den Oord et al.(2017). Recomposition (Casal & Casey, 2010) focuses on reworking existing musical pieces, adhering to structure at a high level while also re-imagining other aspects of the work. This can involve reuse of pre-existing themes or parts of the original piece, while also requiring the flexibility to generate new content at different levels of granularity. Applying the aforementioned modeling pipeline to recomposition, we show diverse and structured generation conditioned on chord sequence annotations. |
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| Published | 2018-11-18 |
| URL | http://arxiv.org/abs/1811.07426v1 |
| http://arxiv.org/pdf/1811.07426v1.pdf | |
| PWC | https://paperswithcode.com/paper/harmonic-recomposition-using-conditional |
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Measuring and Computing Database Inconsistency via Repairs
| Title | Measuring and Computing Database Inconsistency via Repairs |
| Authors | Leopoldo Bertossi |
| Abstract | We propose a generic numerical measure of inconsistency of a database with respect to a set of integrity constraints. It is based on an abstract repair semantics. A particular inconsistency measure associated to cardinality-repairs is investigated; and we show that it can be computed via answer-set programs. Keywords: Integrity constraints in databases, inconsistent databases, database repairs, inconsistency measure. |
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| Published | 2018-04-24 |
| URL | http://arxiv.org/abs/1804.08834v3 |
| http://arxiv.org/pdf/1804.08834v3.pdf | |
| PWC | https://paperswithcode.com/paper/measuring-and-computing-database |
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Reinforcement Learning from Imperfect Demonstrations
| Title | Reinforcement Learning from Imperfect Demonstrations |
| Authors | Yang Gao, Huazhe Xu, Ji Lin, Fisher Yu, Sergey Levine, Trevor Darrell |
| Abstract | Robust real-world learning should benefit from both demonstrations and interactions with the environment. Current approaches to learning from demonstration and reward perform supervised learning on expert demonstration data and use reinforcement learning to further improve performance based on the reward received from the environment. These tasks have divergent losses which are difficult to jointly optimize and such methods can be very sensitive to noisy demonstrations. We propose a unified reinforcement learning algorithm, Normalized Actor-Critic (NAC), that effectively normalizes the Q-function, reducing the Q-values of actions unseen in the demonstration data. NAC learns an initial policy network from demonstrations and refines the policy in the environment, surpassing the demonstrator’s performance. Crucially, both learning from demonstration and interactive refinement use the same objective, unlike prior approaches that combine distinct supervised and reinforcement losses. This makes NAC robust to suboptimal demonstration data since the method is not forced to mimic all of the examples in the dataset. We show that our unified reinforcement learning algorithm can learn robustly and outperform existing baselines when evaluated on several realistic driving games. |
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| Published | 2018-02-14 |
| URL | https://arxiv.org/abs/1802.05313v2 |
| https://arxiv.org/pdf/1802.05313v2.pdf | |
| PWC | https://paperswithcode.com/paper/reinforcement-learning-from-imperfect |
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Automated Mouse Organ Segmentation: A Deep Learning Based Solution
| Title | Automated Mouse Organ Segmentation: A Deep Learning Based Solution |
| Authors | Naveen Ashish, Mi-Youn Brusniak |
| Abstract | The analysis of animal cross section images, such as cross sections of laboratory mice, is critical in assessing the effect of experimental drugs such as the biodistribution of candidate compounds in preclinical drug development stage. Tissue distribution of radiolabeled candidate therapeutic compounds can be quantified using techniques like Quantitative Whole-Body Autoradiography (QWBA).QWBA relies, among other aspects, on the accurate segmentation or identification of key organs of interest in the animal cross section image such as the brain, spine, heart, liver and others. We present a deep learning based organ segmentation solution to this problem, using which we can achieve automated organ segmentation with high precision (dice coefficient in the 0.83-0.95 range depending on organ) for the key organs of interest. |
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| Published | 2018-04-24 |
| URL | http://arxiv.org/abs/1804.09205v2 |
| http://arxiv.org/pdf/1804.09205v2.pdf | |
| PWC | https://paperswithcode.com/paper/automated-mouse-organ-segmentation-a-deep |
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Augmented Reality needle ablation guidance tool for Irreversible Electroporation in the pancreas
| Title | Augmented Reality needle ablation guidance tool for Irreversible Electroporation in the pancreas |
| Authors | Timur Kuzhagaliyev, Neil T. Clancy, Mirek Janatka, Kevin Tchaka, Francisco Vasconcelos, Matthew J. Clarkson, Kurinchi Gurusamy, David J. Hawkes, Brian Davidson, Danail Stoyanov |
| Abstract | Irreversible electroporation (IRE) is a soft tissue ablation technique suitable for treatment of inoperable tumours in the pancreas. The process involves applying a high voltage electric field to the tissue containing the mass using needle electrodes, leaving cancerous cells irreversibly damaged and vulnerable to apoptosis. Efficacy of the treatment depends heavily on the accuracy of needle placement and requires a high degree of skill from the operator. In this paper, we describe an Augmented Reality (AR) system designed to overcome the challenges associated with planning and guiding the needle insertion process. Our solution, based on the HoloLens (Microsoft, USA) platform, tracks the position of the headset, needle electrodes and ultrasound (US) probe in space. The proof of concept implementation of the system uses this tracking data to render real-time holographic guides on the HoloLens, giving the user insight into the current progress of needle insertion and an indication of the target needle trajectory. The operator’s field of view is augmented using visual guides and real-time US feed rendered on a holographic plane, eliminating the need to consult external monitors. Based on these early prototypes, we are aiming to develop a system that will lower the skill level required for IRE while increasing overall accuracy of needle insertion and, hence, the likelihood of successful treatment. |
| Tasks | |
| Published | 2018-02-09 |
| URL | http://arxiv.org/abs/1802.03274v1 |
| http://arxiv.org/pdf/1802.03274v1.pdf | |
| PWC | https://paperswithcode.com/paper/augmented-reality-needle-ablation-guidance |
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A Data-Driven Approach for Predicting Vegetation-Related Outages in Power Distribution Systems
| Title | A Data-Driven Approach for Predicting Vegetation-Related Outages in Power Distribution Systems |
| Authors | Milad Doostan, Reza Sohrabi, Badrul Chowdhury |
| Abstract | This paper presents a novel data-driven approach for predicting the number of vegetation-related outages that occur in power distribution systems on a monthly basis. In order to develop an approach that is able to successfully fulfill this objective, there are two main challenges that ought to be addressed. The first challenge is to define the extent of the target area. An unsupervised machine learning approach is proposed to overcome this difficulty. The second challenge is to correctly identify the main causes of vegetation-related outages and to thoroughly investigate their nature. In this paper, these outages are categorized into two main groups: growth-related and weather-related outages, and two types of models, namely time series and non-linear machine learning regression models are proposed to conduct the prediction tasks, respectively. Moreover, various features that can explain the variability in vegetation-related outages are engineered and employed. Actual outage data, obtained from a major utility in the U.S., in addition to different types of weather and geographical data are utilized to build the proposed approach. Finally, by utilizing various time series models and machine learning methods, a comprehensive case study is carried out to demonstrate how the proposed approach can be used to successfully predict the number of vegetation-related outages and to help decision-makers to detect vulnerable zones in their systems. |
| Tasks | Time Series |
| Published | 2018-07-17 |
| URL | http://arxiv.org/abs/1807.06180v2 |
| http://arxiv.org/pdf/1807.06180v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-data-driven-approach-for-predicting |
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Polyglot Semantic Role Labeling
| Title | Polyglot Semantic Role Labeling |
| Authors | Phoebe Mulcaire, Swabha Swayamdipta, Noah Smith |
| Abstract | Previous approaches to multilingual semantic dependency parsing treat languages independently, without exploiting the similarities between semantic structures across languages. We experiment with a new approach where we combine resources from a pair of languages in the CoNLL 2009 shared task to build a polyglot semantic role labeler. Notwithstanding the absence of parallel data, and the dissimilarity in annotations between languages, our approach results in an improvement in SRL performance on multiple languages over a monolingual baseline. Analysis of the polyglot model shows it to be advantageous in lower-resource settings. |
| Tasks | Dependency Parsing, Semantic Dependency Parsing, Semantic Role Labeling |
| Published | 2018-05-29 |
| URL | http://arxiv.org/abs/1805.11598v1 |
| http://arxiv.org/pdf/1805.11598v1.pdf | |
| PWC | https://paperswithcode.com/paper/polyglot-semantic-role-labeling |
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Panchromatic Sharpening of Remote Sensing Images Using a Multi-scale Approach
| Title | Panchromatic Sharpening of Remote Sensing Images Using a Multi-scale Approach |
| Authors | Hamid Reza Shahdoosti |
| Abstract | An ideal fusion method preserves the Spectral information in fused image and adds spatial information to it with no spectral distortion. Recently wavelet kalman filter method is proposed which uses ARSIS concept to fuses MS and PAN images. This method is applied in a multiscale version, i.e. the variable index is scale instead of time. With the aim of fusion we present a more detailed study on this model and discuss about rationality of its assumptions such as first order markov model and Gaussian distribution of the posterior density. Finally, we propose a method using wavelet Kalman Particle filter to improve the spectral and spatial quality of the fused image. We show that our model is more consistent with natural MS and PAN images. Visual and statistical analyzes show that the proposed algorithm clearly improves the fusion quality in terms of: correlation coefficient, ERGAS, UIQI, and Q4; compared to other methods including IHS, HMP, PCA, A`trous, udWI, udWPC, Adaptive IHS, Improved Adaptive PCA and wavelet kalman filter. | |
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| Published | 2018-07-24 |
| URL | http://arxiv.org/abs/1807.08917v1 |
| http://arxiv.org/pdf/1807.08917v1.pdf | |
| PWC | https://paperswithcode.com/paper/panchromatic-sharpening-of-remote-sensing |
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Multimodal deep learning for short-term stock volatility prediction
| Title | Multimodal deep learning for short-term stock volatility prediction |
| Authors | Marcelo Sardelich, Suresh Manandhar |
| Abstract | Stock market volatility forecasting is a task relevant to assessing market risk. We investigate the interaction between news and prices for the one-day-ahead volatility prediction using state-of-the-art deep learning approaches. The proposed models are trained either end-to-end or using sentence encoders transfered from other tasks. We evaluate a broad range of stock market sectors, namely Consumer Staples, Energy, Utilities, Heathcare, and Financials. Our experimental results show that adding news improves the volatility forecasting as compared to the mainstream models that rely only on price data. In particular, our model outperforms the widely-recognized GARCH(1,1) model for all sectors in terms of coefficient of determination $R^2$, $MSE$ and $MAE$, achieving the best performance when training from both news and price data. |
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| Published | 2018-12-25 |
| URL | http://arxiv.org/abs/1812.10479v1 |
| http://arxiv.org/pdf/1812.10479v1.pdf | |
| PWC | https://paperswithcode.com/paper/multimodal-deep-learning-for-short-term-stock |
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Motif and Hypergraph Correlation Clustering
| Title | Motif and Hypergraph Correlation Clustering |
| Authors | Pan Li, Gregory J. Puleo, Olgica Milenkovic |
| Abstract | Motivated by applications in social and biological network analysis, we introduce a new form of agnostic clustering termed~\emph{motif correlation clustering}, which aims to minimize the cost of clustering errors associated with both edges and higher-order network structures. The problem may be succinctly described as follows: Given a complete graph $G$, partition the vertices of the graph so that certain predetermined important' subgraphs mostly lie within the same cluster, while less relevant’ subgraphs are allowed to lie across clusters. Our contributions are as follows: We first introduce several variants of motif correlation clustering and then show that these clustering problems are NP-hard. We then proceed to describe polynomial-time clustering algorithms that provide constant approximation guarantees for the problems at hand. Despite following the frequently used LP relaxation and rounding procedure, the algorithms involve a sophisticated and carefully designed neighborhood growing step that combines information about both edge and motif structures. We conclude with several examples illustrating the performance of the developed algorithms on synthetic and real networks. |
| Tasks | |
| Published | 2018-11-05 |
| URL | http://arxiv.org/abs/1811.02089v1 |
| http://arxiv.org/pdf/1811.02089v1.pdf | |
| PWC | https://paperswithcode.com/paper/motif-and-hypergraph-correlation-clustering |
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Class-Aware Fully-Convolutional Gaussian and Poisson Denoising
| Title | Class-Aware Fully-Convolutional Gaussian and Poisson Denoising |
| Authors | Tal Remez, Or Litany, Raja Giryes, Alex M. Bronstein |
| Abstract | We propose a fully-convolutional neural-network architecture for image denoising which is simple yet powerful. Its structure allows to exploit the gradual nature of the denoising process, in which shallow layers handle local noise statistics, while deeper layers recover edges and enhance textures. Our method advances the state-of-the-art when trained for different noise levels and distributions (both Gaussian and Poisson). In addition, we show that making the denoiser class-aware by exploiting semantic class information boosts performance, enhances textures and reduces artifacts. |
| Tasks | Denoising, Image Denoising |
| Published | 2018-08-20 |
| URL | http://arxiv.org/abs/1808.06562v1 |
| http://arxiv.org/pdf/1808.06562v1.pdf | |
| PWC | https://paperswithcode.com/paper/class-aware-fully-convolutional-gaussian-and |
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Physics Informed Topology Learning in Networks of Linear Dynamical Systems
| Title | Physics Informed Topology Learning in Networks of Linear Dynamical Systems |
| Authors | Saurav Talukdar, Deepjyoti Deka, Harish Doddi, Donatello Materassi, Misha Chertkov, Murti V. Salapaka |
| Abstract | Learning influence pathways of a network of dynamically related processes from observations is of considerable importance in many disciplines. In this article, influence networks of agents which interact dynamically via linear dependencies are considered. An algorithm for the reconstruction of the topology of interaction based on multivariate Wiener filtering is analyzed. It is shown that for a vast and important class of interactions, that respect flow conservation, the topology of the interactions can be exactly recovered. The class of problems where reconstruction is guaranteed to be exact includes power distribution networks, dynamic thermal networks and consensus networks. The efficacy of the approach is illustrated through simulation and experiments on consensus networks, IEEE power distribution networks and thermal dynamics of buildings. |
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| Published | 2018-09-27 |
| URL | http://arxiv.org/abs/1809.10535v1 |
| http://arxiv.org/pdf/1809.10535v1.pdf | |
| PWC | https://paperswithcode.com/paper/physics-informed-topology-learning-in |
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Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning
| Title | Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning |
| Authors | Felix J. S. Bragman, Ryutaro Tanno, Zach Eaton-Rosen, Wenqi Li, David J. Hawkes, Sebastien Ourselin, Daniel C. Alexander, Jamie R. McClelland, M. Jorge Cardoso |
| Abstract | Multi-task neural network architectures provide a mechanism that jointly integrates information from distinct sources. It is ideal in the context of MR-only radiotherapy planning as it can jointly regress a synthetic CT (synCT) scan and segment organs-at-risk (OAR) from MRI. We propose a probabilistic multi-task network that estimates: 1) intrinsic uncertainty through a heteroscedastic noise model for spatially-adaptive task loss weighting and 2) parameter uncertainty through approximate Bayesian inference. This allows sampling of multiple segmentations and synCTs that share their network representation. We test our model on prostate cancer scans and show that it produces more accurate and consistent synCTs with a better estimation in the variance of the errors, state of the art results in OAR segmentation and a methodology for quality assurance in radiotherapy treatment planning. |
| Tasks | Bayesian Inference |
| Published | 2018-06-18 |
| URL | http://arxiv.org/abs/1806.06595v1 |
| http://arxiv.org/pdf/1806.06595v1.pdf | |
| PWC | https://paperswithcode.com/paper/uncertainty-in-multitask-learning-joint |
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Quantitative Evaluation of Style Transfer
| Title | Quantitative Evaluation of Style Transfer |
| Authors | Mao-Chuang Yeh, Shuai Tang, Anand Bhattad, D. A. Forsyth |
| Abstract | Style transfer methods produce a transferred image which is a rendering of a content image in the manner of a style image. There is a rich literature of variant methods. However, evaluation procedures are qualitative, mostly involving user studies. We describe a novel quantitative evaluation procedure. One plots effectiveness (a measure of the extent to which the style was transferred) against coherence (a measure of the extent to which the transferred image decomposes into objects in the same way that the content image does) to obtain an EC plot. We construct EC plots comparing a number of recent style transfer methods. Most methods control within-layer gram matrices, but we also investigate a method that controls cross-layer gram matrices. These EC plots reveal a number of intriguing properties of recent style transfer methods. The style used has a strong effect on the outcome, for all methods. Using large style weights does not necessarily improve effectiveness, and can produce worse results. Cross-layer gram matrices easily beat all other methods, but some styles remain difficult for all methods. Ensemble methods show real promise. It is likely that, for current methods, each style requires a different choice of weights to obtain the best results, so that automated weight setting methods are desirable. Finally, we show evidence comparing our EC evaluations to human evaluations. |
| Tasks | Style Transfer |
| Published | 2018-03-31 |
| URL | http://arxiv.org/abs/1804.00118v1 |
| http://arxiv.org/pdf/1804.00118v1.pdf | |
| PWC | https://paperswithcode.com/paper/quantitative-evaluation-of-style-transfer |
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