OpenRaiser/Intern-Atlas huggingface dataset:
Relation extraction from clinical texts using domain invariant convolutional neural network
Venue: arXiv cs.CL 2016
Year: 2016

In recent years extracting relevant information from biomedical and clinical texts such as research articles, discharge summaries, or electronic health records have been a subject of many research efforts and shared challenges. Relation extraction is the process of detecting and classifying the semantic relation among entities in a given piece of texts. Existing models for this task in biomedical domain use either manually engineered features or kernel methods to create feature vector. These features are then fed to classifier for the prediction of the correct class. It turns out that the results of these methods are highly dependent on quality of user designed features and also suffer from curse of dimensionality. In this work we focus on extracting relations from clinical discharge summaries. Our main objective is to exploit the power of convolution neural network (CNN) to learn features automatically and thus reduce the dependency on manual feature engineering. We evaluate performance of the proposed model on i2b2-2010 clinical relation extraction challenge dataset. Our results indicate that convolution neural network can be a good model for relation exaction in clinical text without being dependent on expert's knowledge on defining quality features.
Steering a Predator Robot using a Mixed Frame/Event-Driven Convolutional Neural Network
Venue: arXiv cs.RO 2016
Year: 2016

This paper describes the application of a Convolutional Neural Network (CNN) in the context of a predator/prey scenario. The CNN is trained and run on data from a Dynamic and Active Pixel Sensor (DAVIS) mounted on a Summit XL robot (the predator), which follows another one (the prey). The CNN is driven by both conventional image frames and dynamic vision sensor "frames" that consist of a constant number of DAVIS ON and OFF events. The network is thus "data driven" at a sample rate proportional to the scene activity, so the effective sample rate varies from 15 Hz to 240 Hz depending on the robot speeds. The network generates four outputs: steer right, left, center and non-visible. After off-line training on labeled data, the network is imported on the on-board Summit XL robot which runs jAER and receives steering directions in real time. Successful results on closed-loop trials, with accuracies up to 87% or 92% (depending on evaluation criteria) are reported. Although the proposed approach discards the precise DAVIS event timing, it offers the significant advantage of compatibility with conventional deep learning technology without giving up the advantage of data-driven computing.
SnapToGrid: From Statistical to Interpretable Models for Biomedical Information Extraction
Venue: arXiv cs.CL 2016
Year: 2016

We propose an approach for biomedical information extraction that marries the advantages of machine learning models, e.g., learning directly from data, with the benefits of rule-based approaches, e.g., interpretability. Our approach starts by training a feature-based statistical model, then converts this model to a rule-based variant by converting its features to rules, and "snapping to grid" the feature weights to discrete votes. In doing so, our proposal takes advantage of the large body of work in machine learning, but it produces an interpretable model, which can be directly edited by experts. We evaluate our approach on the BioNLP 2009 event extraction task. Our results show that there is a small performance penalty when converting the statistical model to rules, but the gain in interpretability compensates for that: with minimal effort, human experts improve this model to have similar performance to the statistical model that served as starting point.
Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model
Venue: arXiv cs.LG 2016
Year: 2016

Unsupervised learning on imbalanced data is challenging because, when given imbalanced data, current model is often dominated by the major category and ignores the categories with small amount of data. We develop a latent variable model that can cope with imbalanced data by dividing the latent space into a shared space and a private space. Based on Gaussian Process Latent Variable Models, we propose a new kernel formulation that enables the separation of latent space and derives an efficient variational inference method. The performance of our model is demonstrated with an imbalanced medical image dataset.
Fractal Dimension Pattern Based Multiresolution Analysis for Rough Estimator of Person-Dependent Audio Emotion Recognition
Venue: arXiv cs.AI 2016
Year: 2016

As a general means of expression, audio analysis and recognition has attracted much attentions for its wide applications in real-life world. Audio emotion recognition (AER) attempts to understand emotional states of human with the given utterance signals, and has been studied abroad for its further development on friendly human-machine interfaces. Distinguish from other existing works, the person-dependent patterns of audio emotions are conducted, and fractal dimension features are calculated for acoustic feature extraction. Furthermore, it is able to efficiently learn intrinsic characteristics of auditory emotions, while the utterance features are learned from fractal dimensions of each sub-bands. Experimental results show the proposed method is able to provide comparative performance for audio emotion recognition.
A note on extended Euclid's algorithm
Venue: arXiv cs.DM 2016
Year: 2016

Starting with the recursive extended Euclid's algorithm, we apply a systematic approach using matrix notation to transform it into an iterative algorithm. The partial correctness proof derived from the transformation turns out to be very elegant, and easy to follow. The paper provides a connection between recursive and iterative versions of extended Euclid's algorithm.
An Optimal Algorithm for Range Search on Multidimensional Points
Venue: arXiv cs.CG 2016
Year: 2016

This paper proposes an efficient and novel method to address range search on multidimensional points in $θ(t)$ time, where $t$ is the number of points reported in $\Re^k$ space. This is accomplished by introducing a new data structure, called BITS $k$d-tree. This structure also supports fast updation that takes $θ(1)$ time for insertion and $O(\log n)$ time for deletion. The earlier best known algorithm for this problem is $O(\log^k n+t)$ time in the pointer machine model.
Automated 5-year Mortality Prediction using Deep Learning and Radiomics Features from Chest Computed Tomography
Venue: arXiv cs.CV 2016
Year: 2016

We propose new methods for the prediction of 5-year mortality in elderly individuals using chest computed tomography (CT). The methods consist of a classifier that performs this prediction using a set of features extracted from the CT image and segmentation maps of multiple anatomic structures. We explore two approaches: 1) a unified framework based on deep learning, where features and classifier are automatically learned in a single optimisation process; and 2) a multi-stage framework based on the design and selection/extraction of hand-crafted radiomics features, followed by the classifier learning process. Experimental results, based on a dataset of 48 annotated chest CTs, show that the deep learning model produces a mean 5-year mortality prediction accuracy of 68.5%, while radiomics produces a mean accuracy that varies between 56% to 66% (depending on the feature selection/extraction method and classifier). The successful development of the proposed models has the potential to make a profound impact in preventive and personalised healthcare.
Obstructing Visibilities with One Obstacle
Venue: arXiv cs.CG 2016
Year: 2016

Obstacle representations of graphs have been investigated quite intensely over the last few years. We focus on graphs that can be represented by a single obstacle. Given a (topologically open) polygon $C$ and a finite set $P$ of points in general position in the complement of $C$, the visibility graph $G_C(P)$ has a vertex for each point in $P$ and an edge $pq$ for any two points $p$ and $q$ in $P$ that can see each other, that is, $\overline{pq} \cap C=\emptyset$. We draw $G_C(P)$ straight-line. Given a graph $G$, we want to compute an obstacle representation of $G$, that is, an obstacle $C$ and a set of points $P$ such that $G=G_C(P)$. The complexity of this problem is open, even for the case that the points are exactly the vertices of a simple polygon and the obstacle is the complement of the polygon-the simple-polygon visibility graph problem. There are two types of obstacles; an inside obstacle lies in a bounded component of the complement of the visibility drawing, whereas an outside obstacle lies in the unbounded component. We show that the class of graphs with an inside-obstacle representation is incomparable with the class of graphs that have an outside-obstacle representation. We further show that any graph with at most seven vertices or circumference at most 6 has an outside-obstacle representation, which does not hold for a specific graph with 8 vertices and circumference 8. Finally, we consider the outside-obstacle graph sandwich problem: given graphs $G$ and $H$ on the same vertex set, is there a graph $K$ such that $G \subseteq K \subseteq H$ and $K$ has an outside-obstacle representation? We show that this problem is NP-hard even for co-bipartite graphs. With slight modifications, our proof also shows that the inside-obstacle graph sandwich problem, the single-obstacle graph sandwich problem, and the simple-polygon visibility graph sandwich problem are all NP-hard.
CLEX: Yet Another Supercomputer Architecture?
Venue: arXiv cs.DC 2016
Year: 2016

We propose the CLEX supercomputer topology and routing scheme. We prove that CLEX can utilize a constant fraction of the total bandwidth for point-to-point communication, at delays proportional to the sum of the number of intermediate hops and the maximum physical distance between any two nodes. Moreover, % applying an asymmetric bandwidth assignment to the links, all-to-all communication can be realized $(1+o(1))$-optimally both with regard to bandwidth and delays. This is achieved at node degrees of $n^{\varepsilon}$, for an arbitrary small constant $\varepsilon\in (0,1]$. In contrast, these results are impossible in any network featuring constant or polylogarithmic node degrees. Through simulation, we assess the benefits of an implementation of the proposed communication strategy. Our results indicate that, for a million processors, CLEX can increase bandwidth utilization and reduce average routing path length by at least factors $10$ respectively $5$ in comparison to a torus network. Furthermore, the CLEX communication scheme features several other properties, such as deadlock-freedom, inherent fault-tolerance, and canonical partition into smaller subsystems.
A multilevel framework for sparse optimization with application to inverse covariance estimation and logistic regression
Venue: arXiv cs.NA 2016
Year: 2016

Solving l1 regularized optimization problems is common in the fields of computational biology, signal processing and machine learning. Such l1 regularization is utilized to find sparse minimizers of convex functions. A well-known example is the LASSO problem, where the l1 norm regularizes a quadratic function. A multilevel framework is presented for solving such l1 regularized sparse optimization problems efficiently. We take advantage of the expected sparseness of the solution, and create a hierarchy of problems of similar type, which is traversed in order to accelerate the optimization process. This framework is applied for solving two problems: (1) the sparse inverse covariance estimation problem, and (2) l1-regularized logistic regression. In the first problem, the inverse of an unknown covariance matrix of a multivariate normal distribution is estimated, under the assumption that it is sparse. To this end, an l1 regularized log-determinant optimization problem needs to be solved. This task is challenging especially for large-scale datasets, due to time and memory limitations. In the second problem, the l1-regularization is added to the logistic regression classification objective to reduce overfitting to the data and obtain a sparse model. Numerical experiments demonstrate the efficiency of the multilevel framework in accelerating existing iterative solvers for both of these problems.
Want Drugs? Use Python
Venue: arXiv cs.OH 2016
Year: 2016

We describe how Python can be leveraged to streamline the curation, modelling and dissemination of drug discovery data as well as the development of innovative, freely available tools for the related scientific community. We look at various examples, such as chemistry toolkits, machine-learning applications and web frameworks and show how Python can glue it all together to create efficient data science pipelines.
Uniqueness of Normal Forms for Shallow Term Rewrite Systems
Venue: arXiv cs.LO 2016
Year: 2016

Uniqueness of normal forms ($UN^=$) is an important property of term rewrite systems. $UN^=$ is decidable for ground (i.e., variable-free) systems and undecidable in general. Recently it was shown to be decidable for linear, shallow systems. We generalize this previous result and show that this property is decidable for shallow rewrite systems, in contrast to confluence, reachability and other properties, which are all undecidable for flat systems. Our result is also optimal in some sense, since we prove that the $UN^=$ property is undecidable for two classes of linear rewrite systems: left-flat systems in which right-hand sides are of depth at most two and right-flat systems in which left-hand sides are of depth at most two.
Outlier absorbing based on a Bayesian approach
Venue: arXiv cs.LG 2016
Year: 2016

The presence of outliers is prevalent in machine learning applications and may produce misleading results. In this paper a new method for dealing with outliers and anomal samples is proposed. To overcome the outlier issue, the proposed method combines the global and local views of the samples. By combination of these views, our algorithm performs in a robust manner. The experimental results show the capabilities of the proposed method.
A Distributed Deep Representation Learning Model for Big Image Data Classification
Venue: arXiv cs.CV 2016
Year: 2016

This paper describes an effective and efficient image classification framework nominated distributed deep representation learning model (DDRL). The aim is to strike the balance between the computational intensive deep learning approaches (tuned parameters) which are intended for distributed computing, and the approaches that focused on the designed parameters but often limited by sequential computing and cannot scale up. In the evaluation of our approach, it is shown that DDRL is able to achieve state-of-art classification accuracy efficiently on both medium and large datasets. The result implies that our approach is more efficient than the conventional deep learning approaches, and can be applied to big data that is too complex for parameter designing focused approaches. More specifically, DDRL contains two main components, i.e., feature extraction and selection. A hierarchical distributed deep representation learning algorithm is designed to extract image statistics and a nonlinear mapping algorithm is used to map the inherent statistics into abstract features. Both algorithms are carefully designed to avoid millions of parameters tuning. This leads to a more compact solution for image classification of big data. We note that the proposed approach is designed to be friendly with parallel computing. It is generic and easy to be deployed to different distributed computing resources. In the experiments, the largescale image datasets are classified with a DDRM implementation on Hadoop MapReduce, which shows high scalability and resilience.
Reversible Nets of Polyhedra
Venue: arXiv cs.CG 2016
Year: 2016

An example of reversible (or hinge inside-out transformable) figures is the Dudeney's Haberdasher's puzzle in which an equilateral triangle is dissected into four pieces, then hinged like a chain, and then is transformed into a square by rotating the hinged pieces. Furthermore, the entire boundary of each figure goes into the inside of the other figure and becomes the dissection lines of the other figure. Many intriguing results on reversibilities of figures have been found in prior research, but most of them are results on polygons. This paper generalizes those results to a wider range of general connected figures. It is shown that two nets obtained by cutting the surface of an arbitrary convex polyhedron along non-intersecting dissection trees are reversible. Moreover, a condition for two nets of an isotetrahedron to be both reversible and tessellative is given.
Active Object Localization in Visual Situations
Venue: arXiv cs.CV 2016
Year: 2016

We describe a method for performing active localization of objects in instances of visual situations. A visual situation is an abstract concept---e.g., "a boxing match", "a birthday party", "walking the dog", "waiting for a bus"---whose image instantiations are linked more by their common spatial and semantic structure than by low-level visual similarity. Our system combines given and learned knowledge of the structure of a particular situation, and adapts that knowledge to a new situation instance as it actively searches for objects. More specifically, the system learns a set of probability distributions describing spatial and other relationships among relevant objects. The system uses those distributions to iteratively sample object proposals on a test image, but also continually uses information from those object proposals to adaptively modify the distributions based on what the system has detected. We test our approach's ability to efficiently localize objects, using a situation-specific image dataset created by our group. We compare the results with several baselines and variations on our method, and demonstrate the strong benefit of using situation knowledge and active context-driven localization. Finally, we contrast our method with several other approaches that use context as well as active search for object localization in images.
Integrated Task and Motion Planning for Multiple Robots under Path and Communication Uncertainties
Venue: arXiv cs.RO 2016
Year: 2016

We consider a problem called task ordering with path uncertainty (TOP-U) where multiple robots are provided with a set of task locations to visit in a bounded environment, but the length of the path between a pair of task locations is initially known only coarsely by the robots. The objective of the robots is to find the order of tasks that reduces the path length (or, energy expended) to visit the task locations in such a scenario. To solve this problem, we propose an abstraction called a task reachability graph (TRG) that integrates the task ordering with the path planning by the robots. The TRG is updated dynamically based on inter-task path costs calculated using a sampling-based motion planner, and, a Hidden Markov Model (HMM)-based technique that calculates the belief in the current path costs based on the environment perceived by the robot's sensors and task completion information received from other robots. We then describe a Markov Decision Process (MDP)-based algorithm that can select the paths that reduce the overall path length to visit the task locations and a coordination algorithm that resolves path conflicts between robots. We have shown analytically that our task selection algorithm finds the lowest cost path returned by the motion planner, and, that our proposed coordination algorithm is deadlock free. We have also evaluated our algorithm on simulated Corobot robots within different environments while varying the number of task locations, obstacle geometries and number of robots, as well as on physical Corobot robots. Our results show that the TRG-based approach can perform considerably better in planning and locomotion times, and number of re-plans, while traveling almost-similar distances as compared to a closest first, no uncertainty (CFNU) task selection algorithm.
Networked MIMO with Fractional Joint Transmission in Energy Harvesting Systems
Venue: arXiv cs.IT 2016
Year: 2016

This paper considers two base stations (BSs) powered by renewable energy serving two users cooperatively. With different BS energy arrival rates, a fractional joint transmission (JT) strategy is proposed, which divides each transmission frame into two subframes. In the first subframe, one BS keeps silent to store energy while the other transmits data, and then they perform zero-forcing JT (ZF-JT) in the second subframe. We consider the average sum-rate maximization problem by optimizing the energy allocation and the time fraction of ZF-JT in two steps. Firstly, the sum-rate maximization for given energy budget in each frame is analyzed. We prove that the optimal transmit power can be derived in closed-form, and the optimal time fraction can be found via bi-section search. Secondly, approximate dynamic programming (DP) algorithm is introduced to determine the energy allocation among frames. We adopt a linear approximation with the features associated with system states, and determine the weights of features by simulation. We also operate the approximation several times with random initial policy, named as policy exploration, to broaden the policy search range. Numerical results show that the proposed fractional JT greatly improves the performance. Also, appropriate policy exploration is shown to perform close to the optimal.
3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes
Venue: arXiv cs.CV 2016
Year: 2016

Automatic liver segmentation from CT volumes is a crucial prerequisite yet challenging task for computer-aided hepatic disease diagnosis and treatment. In this paper, we present a novel 3D deeply supervised network (3D DSN) to address this challenging task. The proposed 3D DSN takes advantage of a fully convolutional architecture which performs efficient end-to-end learning and inference. More importantly, we introduce a deep supervision mechanism during the learning process to combat potential optimization difficulties, and thus the model can acquire a much faster convergence rate and more powerful discrimination capability. On top of the high-quality score map produced by the 3D DSN, a conditional random field model is further employed to obtain refined segmentation results. We evaluated our framework on the public MICCAI-SLiver07 dataset. Extensive experiments demonstrated that our method achieves competitive segmentation results to state-of-the-art approaches with a much faster processing speed.
Improvement of the Orthogonal Code Convolution Capabilities Using FPGA Implementation
Venue: arXiv cs.IT 2016
Year: 2016

When data is stored, compressed, or communicated through a media such as cable or air, sources of noise and other parameters such as EMI, crosstalk, and distance can considerably affect the reliability of these data. Error detection and correction techniques are therefore required. Orthogonal Code is one of the codes that can detect errors and correct corrupted data. An n-bit orthogonal code has n/2 1s and n/2 0s. In a previous work these properties have been exploited to detect and correct errors. In this paper we present a new methodology to enhance error detection capabilities of the orthogonal code. The technique was implemented experimentally using Field Programmable Gate Arrays (FPGA). The results show that the proposed technique improves the detection capabilities of the orthogonal code by approximately 50%, resulting in 99.9% error detection, and corrects as predicted up to (n/4-1) bits of error in the received impaired code with bandwidth efficiency.