May 6, 2019

3346 words 16 mins read

Paper Group ANR 293

Identifying Diabetic Patients with High Risk of Readmission. When was that made?. Reliable Prediction Intervals for Local Linear Regression. Distributed Asynchronous Dual Free Stochastic Dual Coordinate Ascent. Leveraging Environmental Correlations: The Thermodynamics of Requisite Variety. Inferring unknown biological function by integration of GO …

Identifying Diabetic Patients with High Risk of Readmission


Title	Identifying Diabetic Patients with High Risk of Readmission
Authors	Malladihalli S Bhuvan, Ankit Kumar, Adil Zafar, Vinith Kishore
Abstract	Hospital readmissions are expensive and reflect the inadequacies in healthcare system. In the United States alone, treatment of readmitted diabetic patients exceeds 250 million dollars per year. Early identification of patients facing a high risk of readmission can enable healthcare providers to to conduct additional investigations and possibly prevent future readmissions. This not only improves the quality of care but also reduces the medical expenses on readmission. Machine learning methods have been leveraged on public health data to build a system for identifying diabetic patients facing a high risk of future readmission. Number of inpatient visits, discharge disposition and admission type were identified as strong predictors of readmission. Further, it was found that the number of laboratory tests and discharge disposition together predict whether the patient will be readmitted shortly after being discharged from the hospital (i.e. <30 days) or after a longer period of time (i.e. >30 days). These insights can help healthcare providers to improve inpatient diabetic care. Finally, the cost analysis suggests that $252.76 million can be saved across 98,053 diabetic patient encounters by incorporating the proposed cost sensitive analysis model.
Tasks
Published	2016-02-12
URL	http://arxiv.org/abs/1602.04257v1
PDF	http://arxiv.org/pdf/1602.04257v1.pdf
PWC	https://paperswithcode.com/paper/identifying-diabetic-patients-with-high-risk
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When was that made?


Title	When was that made?
Authors	Sirion Vittayakorn, Alexander C. Berg, Tamara L. Berg
Abstract	In this paper, we explore deep learning methods for estimating when objects were made. Automatic methods for this task could potentially be useful for historians, collectors, or any individual interested in estimating when their artifact was created. Direct applications include large-scale data organization or retrieval. Toward this goal, we utilize features from existing deep networks and also fine-tune new networks for temporal estimation. In addition, we create two new datasets of 67,771 dated clothing items from Flickr and museum collections. Our method outperforms both a color-based baseline and previous state of the art methods for temporal estimation. We also provide several analyses of what our networks have learned, and demonstrate applications to identifying temporal inspiration in fashion collections.
Tasks
Published	2016-08-12
URL	http://arxiv.org/abs/1608.03914v1
PDF	http://arxiv.org/pdf/1608.03914v1.pdf
PWC	https://paperswithcode.com/paper/when-was-that-made
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Reliable Prediction Intervals for Local Linear Regression


Title	Reliable Prediction Intervals for Local Linear Regression
Authors	Mohammad Ghasemi Hamed, Masoud Ebadi Kivaj
Abstract	This paper introduces two methods for estimating reliable prediction intervals for local linear least-squares regressions, named Bounded Oscillation Prediction Intervals (BOPI). It also proposes a new measure for comparing interval prediction models named Equivalent Gaussian Standard Deviation (EGSD). The experimental results compare BOPI to other methods using coverage probability, Mean Interval Size and the introduced EGSD measure. The results were generally in favor of the BOPI on considered benchmark regression datasets. It also, reports simulation studies validating the BOPI method’s reliability.
Tasks
Published	2016-03-17
URL	http://arxiv.org/abs/1603.05587v5
PDF	http://arxiv.org/pdf/1603.05587v5.pdf
PWC	https://paperswithcode.com/paper/reliable-prediction-intervals-for-local
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Distributed Asynchronous Dual Free Stochastic Dual Coordinate Ascent


Title	Distributed Asynchronous Dual Free Stochastic Dual Coordinate Ascent
Authors	Zhouyuan Huo, Heng Huang
Abstract	The primal-dual distributed optimization methods have broad large-scale machine learning applications. Previous primal-dual distributed methods are not applicable when the dual formulation is not available, e.g. the sum-of-non-convex objectives. Moreover, these algorithms and theoretical analysis are based on the fundamental assumption that the computing speeds of multiple machines in a cluster are similar. However, the straggler problem is an unavoidable practical issue in the distributed system because of the existence of slow machines. Therefore, the total computational time of the distributed optimization methods is highly dependent on the slowest machine. In this paper, we address these two issues by proposing distributed asynchronous dual free stochastic dual coordinate ascent algorithm for distributed optimization. Our method does not need the dual formulation of the target problem in the optimization. We tackle the straggler problem through asynchronous communication and the negative effect of slow machines is significantly alleviated. We also analyze the convergence rate of our method and prove the linear convergence rate even if the individual functions in objective are non-convex. Experiments on both convex and non-convex loss functions are used to validate our statements.
Tasks	Distributed Optimization
Published	2016-05-29
URL	http://arxiv.org/abs/1605.09066v4
PDF	http://arxiv.org/pdf/1605.09066v4.pdf
PWC	https://paperswithcode.com/paper/distributed-asynchronous-dual-free-stochastic
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Leveraging Environmental Correlations: The Thermodynamics of Requisite Variety


Title	Leveraging Environmental Correlations: The Thermodynamics of Requisite Variety
Authors	Alexander B. Boyd, Dibyendu Mandal, James P. Crutchfield
Abstract	Key to biological success, the requisite variety that confronts an adaptive organism is the set of detectable, accessible, and controllable states in its environment. We analyze its role in the thermodynamic functioning of information ratchets—a form of autonomous Maxwellian Demon capable of exploiting fluctuations in an external information reservoir to harvest useful work from a thermal bath. This establishes a quantitative paradigm for understanding how adaptive agents leverage structured thermal environments for their own thermodynamic benefit. General ratchets behave as memoryful communication channels, interacting with their environment sequentially and storing results to an output. The bulk of thermal ratchets analyzed to date, however, assume memoryless environments that generate input signals without temporal correlations. Employing computational mechanics and a new information-processing Second Law of Thermodynamics (IPSL) we remove these restrictions, analyzing general finite-state ratchets interacting with structured environments that generate correlated input signals. On the one hand, we demonstrate that a ratchet need not have memory to exploit an uncorrelated environment. On the other, and more appropriate to biological adaptation, we show that a ratchet must have memory to most effectively leverage structure and correlation in its environment. The lesson is that to optimally harvest work a ratchet’s memory must reflect the input generator’s memory. Finally, we investigate achieving the IPSL bounds on the amount of work a ratchet can extract from its environment, discovering that finite-state, optimal ratchets are unable to reach these bounds. In contrast, we show that infinite-state ratchets can go well beyond these bounds by utilizing their own infinite “negentropy”. We conclude with an outline of the collective thermodynamics of information-ratchet swarms.
Tasks
Published	2016-09-17
URL	http://arxiv.org/abs/1609.05353v1
PDF	http://arxiv.org/pdf/1609.05353v1.pdf
PWC	https://paperswithcode.com/paper/leveraging-environmental-correlations-the
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Inferring unknown biological function by integration of GO annotations and gene expression data


Title	Inferring unknown biological function by integration of GO annotations and gene expression data
Authors	Guillermo Leale, Ariel Bayá, Diego Milone, Pablo Granitto, Georgina Stegmayer
Abstract	Characterizing genes with semantic information is an important process regarding the description of gene products. In spite that complete genomes of many organisms have been already sequenced, the biological functions of all of their genes are still unknown. Since experimentally studying the functions of those genes, one by one, would be unfeasible, new computational methods for gene functions inference are needed. We present here a novel computational approach for inferring biological function for a set of genes with previously unknown function, given a set of genes with well-known information. This approach is based on the premise that genes with similar behaviour should be grouped together. This is known as the guilt-by-association principle. Thus, it is possible to take advantage of clustering techniques to obtain groups of unknown genes that are co-clustered with genes that have well-known semantic information (GO annotations). Meaningful knowledge to infer unknown semantic information can therefore be provided by these well-known genes. We provide a method to explore the potential function of new genes according to those currently annotated. The results obtained indicate that the proposed approach could be a useful and effective tool when used by biologists to guide the inference of biological functions for recently discovered genes. Our work sets an important landmark in the field of identifying unknown gene functions through clustering, using an external source of biological input. A simple web interface to this proposal can be found at http://fich.unl.edu.ar/sinc/webdemo/gamma-am/.
Tasks
Published	2016-08-12
URL	http://arxiv.org/abs/1608.03672v1
PDF	http://arxiv.org/pdf/1608.03672v1.pdf
PWC	https://paperswithcode.com/paper/inferring-unknown-biological-function-by
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Multilayer Spectral Graph Clustering via Convex Layer Aggregation


Title	Multilayer Spectral Graph Clustering via Convex Layer Aggregation
Authors	Pin-Yu Chen, Alfred O. Hero III
Abstract	Multilayer graphs are commonly used for representing different relations between entities and handling heterogeneous data processing tasks. New challenges arise in multilayer graph clustering for assigning clusters to a common multilayer node set and for combining information from each layer. This paper presents a theoretical framework for multilayer spectral graph clustering of the nodes via convex layer aggregation. Under a novel multilayer signal plus noise model, we provide a phase transition analysis that establishes the existence of a critical value on the noise level that permits reliable cluster separation. The analysis also specifies analytical upper and lower bounds on the critical value, where the bounds become exact when the clusters have identical sizes. Numerical experiments on synthetic multilayer graphs are conducted to validate the phase transition analysis and study the effect of layer weights and noise levels on clustering reliability.
Tasks	Graph Clustering, Spectral Graph Clustering
Published	2016-09-23
URL	http://arxiv.org/abs/1609.07200v1
PDF	http://arxiv.org/pdf/1609.07200v1.pdf
PWC	https://paperswithcode.com/paper/multilayer-spectral-graph-clustering-via
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Distributed Graph Clustering by Load Balancing


Title	Distributed Graph Clustering by Load Balancing
Authors	He Sun, Luca Zanetti
Abstract	Graph clustering is a fundamental computational problem with a number of applications in algorithm design, machine learning, data mining, and analysis of social networks. Over the past decades, researchers have proposed a number of algorithmic design methods for graph clustering. However, most of these methods are based on complicated spectral techniques or convex optimisation, and cannot be applied directly for clustering many networks that occur in practice, whose information is often collected on different sites. Designing a simple and distributed clustering algorithm is of great interest, and has wide applications for processing big datasets. In this paper we present a simple and distributed algorithm for graph clustering: for a wide class of graphs that are characterised by a strong cluster-structure, our algorithm finishes in a poly-logarithmic number of rounds, and recovers a partition of the graph close to an optimal partition. The main component of our algorithm is an application of the random matching model of load balancing, which is a fundamental protocol in distributed computing and has been extensively studied in the past 20 years. Hence, our result highlights an intrinsic and interesting connection between graph clustering and load balancing. At a technical level, we present a purely algebraic result characterising the early behaviours of load balancing processes for graphs exhibiting a cluster-structure. We believe that this result can be further applied to analyse other gossip processes, such as rumour spreading and averaging processes.
Tasks	Graph Clustering
Published	2016-07-18
URL	http://arxiv.org/abs/1607.04984v3
PDF	http://arxiv.org/pdf/1607.04984v3.pdf
PWC	https://paperswithcode.com/paper/distributed-graph-clustering-by-load
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Standard State Space Models of Unawareness (Extended Abstract)


Title	Standard State Space Models of Unawareness (Extended Abstract)
Authors	Peter Fritz, Harvey Lederman
Abstract	The impossibility theorem of Dekel, Lipman and Rustichini has been thought to demonstrate that standard state-space models cannot be used to represent unawareness. We first show that Dekel, Lipman and Rustichini do not establish this claim. We then distinguish three notions of awareness, and argue that although one of them may not be adequately modeled using standard state spaces, there is no reason to think that standard state spaces cannot provide models of the other two notions. In fact, standard space models of these forms of awareness are attractively simple. They allow us to prove completeness and decidability results with ease, to carry over standard techniques from decision theory, and to add propositional quantifiers straightforwardly.
Tasks
Published	2016-06-24
URL	http://arxiv.org/abs/1606.07520v1
PDF	http://arxiv.org/pdf/1606.07520v1.pdf
PWC	https://paperswithcode.com/paper/standard-state-space-models-of-unawareness
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Find your Way by Observing the Sun and Other Semantic Cues


Title	Find your Way by Observing the Sun and Other Semantic Cues
Authors	Wei-Chiu Ma, Shenlong Wang, Marcus A. Brubaker, Sanja Fidler, Raquel Urtasun
Abstract	In this paper we present a robust, efficient and affordable approach to self-localization which does not require neither GPS nor knowledge about the appearance of the world. Towards this goal, we utilize freely available cartographic maps and derive a probabilistic model that exploits semantic cues in the form of sun direction, presence of an intersection, road type, speed limit as well as the ego-car trajectory in order to produce very reliable localization results. Our experimental evaluation shows that our approach can localize much faster (in terms of driving time) with less computation and more robustly than competing approaches, which ignore semantic information.
Tasks
Published	2016-06-23
URL	http://arxiv.org/abs/1606.07415v1
PDF	http://arxiv.org/pdf/1606.07415v1.pdf
PWC	https://paperswithcode.com/paper/find-your-way-by-observing-the-sun-and-other
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A Holistic Approach for Data-Driven Object Cutout


Title	A Holistic Approach for Data-Driven Object Cutout
Authors	Huayong Xu, Yangyan Li, Wenzheng Chen, Dani Lischinski, Daniel Cohen-Or, Baoquan Chen
Abstract	Object cutout is a fundamental operation for image editing and manipulation, yet it is extremely challenging to automate it in real-world images, which typically contain considerable background clutter. In contrast to existing cutout methods, which are based mainly on low-level image analysis, we propose a more holistic approach, which considers the entire shape of the object of interest by leveraging higher-level image analysis and learnt global shape priors. Specifically, we leverage a deep neural network (DNN) trained for objects of a particular class (chairs) for realizing this mechanism. Given a rectangular image region, the DNN outputs a probability map (P-map) that indicates for each pixel inside the rectangle how likely it is to be contained inside an object from the class of interest. We show that the resulting P-maps may be used to evaluate how likely a rectangle proposal is to contain an instance of the class, and further process good proposals to produce an accurate object cutout mask. This amounts to an automatic end-to-end pipeline for catergory-specific object cutout. We evaluate our approach on segmentation benchmark datasets, and show that it significantly outperforms the state-of-the-art on them.
Tasks
Published	2016-08-18
URL	http://arxiv.org/abs/1608.05180v2
PDF	http://arxiv.org/pdf/1608.05180v2.pdf
PWC	https://paperswithcode.com/paper/a-holistic-approach-for-data-driven-object
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Partial Procedural Geometric Model Fitting for Point Clouds


Title	Partial Procedural Geometric Model Fitting for Point Clouds
Authors	Zongliang Zhang, Jonathan Li, Yulan Guo, Yangbin Lin, Ming Cheng, Cheng Wang
Abstract	Geometric model fitting is a fundamental task in computer graphics and computer vision. However, most geometric model fitting methods are unable to fit an arbitrary geometric model (e.g. a surface with holes) to incomplete data, due to that the similarity metrics used in these methods are unable to measure the rigid partial similarity between arbitrary models. This paper hence proposes a novel rigid geometric similarity metric, which is able to measure both the full similarity and the partial similarity between arbitrary geometric models. The proposed metric enables us to perform partial procedural geometric model fitting (PPGMF). The task of PPGMF is to search a procedural geometric model space for the model rigidly similar to a query of non-complete point set. Models in the procedural model space are generated according to a set of parametric modeling rules. A typical query is a point cloud. PPGMF is very useful as it can be used to fit arbitrary geometric models to non-complete (incomplete, over-complete or hybrid-complete) point cloud data. For example, most laser scanning data is non-complete due to occlusion. Our PPGMF method uses Markov chain Monte Carlo technique to optimize the proposed similarity metric over the model space. To accelerate the optimization process, the method also employs a novel coarse-to-fine model dividing strategy to reject dissimilar models in advance. Our method has been demonstrated on a variety of geometric models and non-complete data. Experimental results show that the PPGMF method based on the proposed metric is able to fit non-complete data, while the method based on other metrics is unable. It is also shown that our method can be accelerated by several times via early rejection.
Tasks
Published	2016-10-17
URL	http://arxiv.org/abs/1610.04936v1
PDF	http://arxiv.org/pdf/1610.04936v1.pdf
PWC	https://paperswithcode.com/paper/partial-procedural-geometric-model-fitting
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Benchmarking Quantum Hardware for Training of Fully Visible Boltzmann Machines


Title	Benchmarking Quantum Hardware for Training of Fully Visible Boltzmann Machines
Authors	Dmytro Korenkevych, Yanbo Xue, Zhengbing Bian, Fabian Chudak, William G. Macready, Jason Rolfe, Evgeny Andriyash
Abstract	Quantum annealing (QA) is a hardware-based heuristic optimization and sampling method applicable to discrete undirected graphical models. While similar to simulated annealing, QA relies on quantum, rather than thermal, effects to explore complex search spaces. For many classes of problems, QA is known to offer computational advantages over simulated annealing. Here we report on the ability of recent QA hardware to accelerate training of fully visible Boltzmann machines. We characterize the sampling distribution of QA hardware, and show that in many cases, the quantum distributions differ significantly from classical Boltzmann distributions. In spite of this difference, training (which seeks to match data and model statistics) using standard classical gradient updates is still effective. We investigate the use of QA for seeding Markov chains as an alternative to contrastive divergence (CD) and persistent contrastive divergence (PCD). Using $k=50$ Gibbs steps, we show that for problems with high-energy barriers between modes, QA-based seeds can improve upon chains with CD and PCD initializations. For these hard problems, QA gradient estimates are more accurate, and allow for faster learning. Furthermore, and interestingly, even the case of raw QA samples (that is, $k=0$) achieved similar improvements. We argue that this relates to the fact that we are training a quantum rather than classical Boltzmann distribution in this case. The learned parameters give rise to hardware QA distributions closely approximating classical Boltzmann distributions that are hard to train with CD/PCD.
Tasks
Published	2016-11-14
URL	http://arxiv.org/abs/1611.04528v1
PDF	http://arxiv.org/pdf/1611.04528v1.pdf
PWC	https://paperswithcode.com/paper/benchmarking-quantum-hardware-for-training-of
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Personalizing a Dialogue System with Transfer Reinforcement Learning


Title	Personalizing a Dialogue System with Transfer Reinforcement Learning
Authors	Kaixiang Mo, Shuangyin Li, Yu Zhang, Jiajun Li, Qiang Yang
Abstract	It is difficult to train a personalized task-oriented dialogue system because the data collected from each individual is often insufficient. Personalized dialogue systems trained on a small dataset can overfit and make it difficult to adapt to different user needs. One way to solve this problem is to consider a collection of multiple users’ data as a source domain and an individual user’s data as a target domain, and to perform a transfer learning from the source to the target domain. By following this idea, we propose “PETAL”(PErsonalized Task-oriented diALogue), a transfer-learning framework based on POMDP to learn a personalized dialogue system. The system first learns common dialogue knowledge from the source domain and then adapts this knowledge to the target user. This framework can avoid the negative transfer problem by considering differences between source and target users. The policy in the personalized POMDP can learn to choose different actions appropriately for different users. Experimental results on a real-world coffee-shopping data and simulation data show that our personalized dialogue system can choose different optimal actions for different users, and thus effectively improve the dialogue quality under the personalized setting.
Tasks	Transfer Learning, Transfer Reinforcement Learning
Published	2016-10-10
URL	http://arxiv.org/abs/1610.02891v3
PDF	http://arxiv.org/pdf/1610.02891v3.pdf
PWC	https://paperswithcode.com/paper/personalizing-a-dialogue-system-with-transfer
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TRIM: Triangulating Images for Efficient Registration


Title	TRIM: Triangulating Images for Efficient Registration
Authors	Chun Pang Yung, Gary Pui-Tung Choi, Ke Chen, Lok Ming Lui
Abstract	With the advancement in the digital camera technology, the use of high resolution images and videos has been widespread in the modern society. In particular, image and video frame registration is frequently applied in computer graphics and film production. However, the conventional registration approaches usually require long computational time for high quality images and video frames. This hinders the applications of the registration approaches in the modern industries. In this work, we propose a novel approach called {\em TRIM} to accelerate the computations of the registration by triangulating the images. More specifically, given a high resolution image or video frame, we compute an optimal coarse triangulation which captures the important features of the image. Then, the computation of the registration can be simplified with the aid of the coarse triangulation. Experimental results suggest that the computational time of the registration is significantly reduced using our triangulation-based approach, meanwhile the accuracy of the registration is well retained when compared with the conventional grid-based approach.
Tasks
Published	2016-05-20
URL	http://arxiv.org/abs/1605.06215v1
PDF	http://arxiv.org/pdf/1605.06215v1.pdf
PWC	https://paperswithcode.com/paper/trim-triangulating-images-for-efficient
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