OpenRaiser/Intern-Atlas huggingface dataset:
Left Ventricle Segmentation and Volume Estimation on Cardiac MRI using Deep Learning
Venue: arXiv cs.CV 2018
Year: 2018

In the United States, heart disease is the leading cause of death for both men and women, accounting for 610,000 deaths each year [1]. Physicians use Magnetic Resonance Imaging (MRI) scans to take images of the heart in order to non-invasively estimate its structural and functional parameters for cardiovascular diagnosis and disease management. The end-systolic volume (ESV) and end-diastolic volume (EDV) of the left ventricle (LV), and the ejection fraction (EF) are indicators of heart disease. These measures can be derived from the segmented contours of the LV; thus, consistent and accurate segmentation of the LV from MRI images are critical to the accuracy of the ESV, EDV, and EF, and to non-invasive cardiac disease detection. In this work, various image preprocessing techniques, model configurations using the U-Net deep learning architecture, postprocessing methods, and approaches for volume estimation are investigated. An end-to-end analytics pipeline with multiple stages is provided for automated LV segmentation and volume estimation. First, image data are reformatted and processed from DICOM and NIfTI formats to raw images in array format. Secondly, raw images are processed with multiple image preprocessing methods and cropped to include only the Region of Interest (ROI). Thirdly, preprocessed images are segmented using U-Net models. Lastly, post processing of segmented images to remove extra contours along with intelligent slice and frame selection are applied, followed by calculation of the ESV, EDV, and EF. This analytics pipeline is implemented and runs on a distributed computing environment with a GPU cluster at the San Diego Supercomputer Center at UCSD.
Multi-hop assortativities for networks classification
Venue: arXiv cs.LG 2018
Year: 2018

Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when it is available. For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the research field of a scientific collaboration network. Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as the number of triangles, or the assortativity of node metadata. We introduce the concept of multi-hop assortativity, that captures the similarity of the nodes situated at the extremities of a randomly selected path of a given length. We show that multi-hop assortativity unifies various existing concepts and offers a versatile family of 'fingerprints' to characterize networks. These fingerprints allow in turn to recover the functionalities of a network, with the help of the machine learning toolbox. Our method is evaluated empirically on established social and chemoinformatic network benchmarks. Results reveal that our assortativity based features are competitive providing highly accurate results often outperforming state of the art methods for the network classification task.
Persistent Coverage Control for Teams of Heterogeneous Agents - Extended Version
Venue: arXiv cs.SY 2018
Year: 2018

A distributed cooperative control law for persistent coverage tasks is proposed, capable of coordinating a team of heterogeneous agents in a structured environment. Team heterogeneity is considered both at vehicles' dynamics and at coverage capabilities levels. More specifically, the general dynamics of nonholonomic vehicles are considered. Agent heterogeneous sensing capabilities are addressed by means of the descriptor function framework, a set of analytical tools for controlling agents involved in generic coverage tasks. By means of formal arguments, we prove that the team performs the task and no collision occurs between agents nor with obstacles. A numerical simulation validates the proposed strategy.
Effective Predictions of Gaokao Admission Scores for College Applications in Mainland China
Venue: arXiv cs.CY 2018
Year: 2018

Gaokao is the annual academic qualification examination for college admissions in mainland China. Organized by each provincial-level administrative region (PAR), Gaokao takes place at the same time nationwide in early June. To enroll in a university in September, students must take Gaokao and submit common applications for admission to their home PAR Gaokao office in July, listing a small and fixed number of universities and majors they intend to attend and study. About 9.5 million high- school seniors participate in Gaokao every year, and the Gaokao scores are good for just one year. A student has a strong chance to be accepted if their Gaokao score is better than the admission scores of the universities they selected in their applications. However, the admission scores of universities are unknown at the time when filling out applications, which to be determined dynamically during the admission process and may fluctuate from year to year. To increase their chances of acceptance to a best- suited university, students need to predict admission score of each university they are interested in. Early prediction methods are empirical without the backing of in-depth data studies. We fill this void by presenting well-tested mathematical models based on the ranking of Gaokao scores in a PAR. We show that our methods significantly outperform the methods commonly used by teachers and experts, and can predict admission scores with an accuracy of 91% within a 7-point margin in an exam of a 750-point grading scale.
Self Configuration in Machine Learning
Venue: arXiv cs.LG 2018
Year: 2018

In this paper we first present a class of algorithms for training multi-level neural networks with a quadratic cost function one layer at a time starting from the input layer. The algorithm is based on the fact that for any layer to be trained, the effect of a direct connection to an optimized linear output layer can be computed without the connection being made. Thus, starting from the input layer, we can train each layer in succession in isolation from the other layers. Once trained, the weights are kept fixed and the outputs of the trained layer then serve as the inputs to the next layer to be trained. The result is a very fast algorithm. The simplicity of this training arrangement allows the activation function and step size in weight adjustment to be adaptive and self-adjusting. Furthermore, the stability of the training process allows relatively large steps to be taken and thereby achieving in even greater speeds. Finally, in our context configuring the network means determining the number of outputs for each layer. By decomposing the overall cost function into separate components related to approximation and estimation, we obtain an optimization formula for determining the number of outputs for each layer. With the ability to self-configure and set parameters, we now have more than a fast training algorithm, but the ability to build automatically a fully trained deep neural network starting with nothing more than data.
Block Chain based Intelligent Industrial Network (DSDIN)
Venue: arXiv cs.CY 2018
Year: 2018

The manufacturing industry featured centralization in the past due to technical limitations, and factories (especially large manufacturers) gathered almost all of the resources for manufacturing, including: technologies, raw materials, equipment, workers, market information, etc. However, such centralized production is costly, inefficient and inflexible, and difficult to respond to rapidly changing, diverse and personalized user needs. This paper introduces an Intelligent Industrial Network (DSDIN), which provides a fully distributed manufacturing network where everyone can participate in manufacturing due to decentralization and no intermediate links, allowing them to quickly get the products or services they want and also to be authorized, recognized and get returns in a low-cost way due to their efforts (such as providing creative ideas, designs or equipment, raw materials or physical strength). DSDIN is a blockchain based IoT and AI technology platform, and also an IoT based intelligent service standard. Due to the intelligent network formed by DSDIN, the manufacturing center is no longer a factory, and actually there are no manufacturing centers. DSDIN provides a multi-participation peer-to-peer network for people and things (including raw materials, equipment, finished / semi-finished products, etc.). The information transmitted through the network is called Intelligent Service Algorithm (ISA). The user can send a process model, formula or control parameter to a device via an ISA, and every transaction in DSDIN is an intelligent service defined by ISA.
Runtime Monitoring Neuron Activation Patterns
Venue: arXiv cs.LG 2018
Year: 2018

For using neural networks in safety critical domains, it is important to know if a decision made by a neural network is supported by prior similarities in training. We propose runtime neuron activation pattern monitoring - after the standard training process, one creates a monitor by feeding the training data to the network again in order to store the neuron activation patterns in abstract form. In operation, a classification decision over an input is further supplemented by examining if a pattern similar (measured by Hamming distance) to the generated pattern is contained in the monitor. If the monitor does not contain any pattern similar to the generated pattern, it raises a warning that the decision is not based on the training data. Our experiments show that, by adjusting the similarity-threshold for activation patterns, the monitors can report a significant portion of misclassfications to be not supported by training with a small false-positive rate, when evaluated on a test set.
Symbolic Tensor Neural Networks for Digital Media - from Tensor Processing via BNF Graph Rules to CREAMS Applications
Venue: arXiv cs.CV 2018
Year: 2018

This tutorial material on Convolutional Neural Networks (CNN) and its applications in digital media research is based on the concept of Symbolic Tensor Neural Networks. The set of STNN expressions is specified in Backus-Naur Form (BNF) which is annotated by constraints typical for labeled acyclic directed graphs (DAG). The BNF induction begins from a collection of neural unit symbols with extra (up to five) decoration fields (including tensor depth and sharing fields). The inductive rules provide not only the general graph structure but also the specific shortcuts for residual blocks of units. A syntactic mechanism for network fragments modularization is introduced via user defined units and their instances. Moreover, the dual BNF rules are specified in order to generate the Dual Symbolic Tensor Neural Network (DSTNN). The joined interpretation of STNN and DSTNN provides the correct flow of gradient tensors, back propagated at the training stage. The proposed symbolic representation of CNNs is illustrated for six generic digital media applications (CREAMS): Compression, Recognition, Embedding, Annotation, 3D Modeling for human-computer interfacing, and data Security based on digital media objects. In order to make the CNN description and its gradient flow complete, for all presented applications, the symbolic representations of mathematically defined loss/gain functions and gradient flow equations for all used core units, are given. The tutorial is to convince the reader that STNN is not only a convenient symbolic notation for public presentations of CNN based solutions for CREAMS problems but also that it is a design blueprint with a potential for automatic generation of application source code.
RumourEval 2019: Determining Rumour Veracity and Support for Rumours
Venue: arXiv cs.CL 2018
Year: 2018

This is the proposal for RumourEval-2019, which will run in early 2019 as part of that year's SemEval event. Since the first RumourEval shared task in 2017, interest in automated claim validation has greatly increased, as the dangers of "fake news" have become a mainstream concern. Yet automated support for rumour checking remains in its infancy. For this reason, it is important that a shared task in this area continues to provide a focus for effort, which is likely to increase. We therefore propose a continuation in which the veracity of further rumours is determined, and as previously, supportive of this goal, tweets discussing them are classified according to the stance they take regarding the rumour. Scope is extended compared with the first RumourEval, in that the dataset is substantially expanded to include Reddit as well as Twitter data, and additional languages are also included.
A Fog Robotic System for Dynamic Visual Servoing
Venue: arXiv cs.RO 2018
Year: 2018

Cloud Robotics is a paradigm where distributed robots are connected to cloud services via networks to access unlimited computation power, at the cost of network communication. However, due to limitations such as network latency and variability, it is difficult to control dynamic, human compliant service robots directly from the cloud. In this work, by leveraging asynchronous protocol with a heartbeat signal, we combine cloud robotics with a smart edge device to build a Fog Robotic system. We use the system to enable robust teleoperation of a dynamic self-balancing robot from the cloud. We first use the system to pick up boxes from static locations, a task commonly performed in warehouse logistics. To make cloud teleoperation more efficient, we deploy image based visual servoing (IBVS) to perform box pickups automatically. Visual feedbacks, including apriltag recognition and tracking, are performed in the cloud to emulate a Fog Robotic object recognition system for IBVS. We demonstrate the feasibility of real-time dynamic automation system using this cloud-edge hybrid, which opens up possibilities of deploying dynamic robotic control with deep-learning recognition systems in Fog Robotics. Finally, we show that Fog Robotics enables the self-balancing service robot to pick up a box automatically from a person under unstructured environments.
A generalized financial time series forecasting model based on automatic feature engineering using genetic algorithms and support vector machine
Venue: arXiv cs.AI 2018
Year: 2018

We propose the genetic algorithm for time window optimization, which is an embedded genetic algorithm (GA), to optimize the time window (TW) of the attributes using feature selection and support vector machine. This GA is evolved using the results of a trading simulation, and it determines the best TW for each technical indicator. An appropriate evaluation was conducted using a walk-forward trading simulation, and the trained model was verified to be generalizable for forecasting other stock data. The results show that using the GA to determine the TW can improve the rate of return, leading to better prediction models than those resulting from using the default TW.
Improving Moderation of Online Discussions via Interpretable Neural Models
Venue: arXiv cs.CL 2018
Year: 2018

Growing amount of comments make online discussions difficult to moderate by human moderators only. Antisocial behavior is a common occurrence that often discourages other users from participating in discussion. We propose a neural network based method that partially automates the moderation process. It consists of two steps. First, we detect inappropriate comments for moderators to see. Second, we highlight inappropriate parts within these comments to make the moderation faster. We evaluated our method on data from a major Slovak news discussion platform.
Identification of FIR Systems with Binary Input and Output Observations
Venue: arXiv cs.SY 2018
Year: 2018

This paper considers the identification of FIR systems, where information about the inputs and outputs of the system undergoes quantization into binary values before transmission to the estimator. In the case where the thresholds of the input and output quantizers can be adapted, but the quantizers have no computation and storage capabilities, we propose identification schemes which are strongly consistent for Gaussian distributed inputs and noises. This is based on exploiting the correlations between the quantized input and output observations to derive nonlinear equations that the true system parameters must satisfy, and then estimating the parameters by solving these equations using stochastic approximation techniques. If, in addition, the input and output quantizers have computational and storage capabilities, strongly consistent identification schemes are proposed which can handle arbitrary input and noise distributions. In this case, some conditional expectation terms are computed at the quantizers, which can then be estimated based on binary data transmitted by the quantizers, subsequently allowing the parameters to be identified by solving a set of linear equations. The algorithms and their properties are illustrated in simulation examples.
Testing SensoGraph, a geometric approach for fast sensory evaluation
Venue: arXiv cs.CG 2018
Year: 2018

This paper introduces SensoGraph, a novel approach for fast sensory evaluation using two-dimensional geometric techniques. In the tasting sessions, the assessors follow their own criteria to place samples on a tablecloth, according to the similarity between samples. In order to analyse the data collected, first a geometric clustering is performed to each tablecloth, extracting connections between the samples. Then, these connections are used to construct a global similarity matrix. Finally, a graph drawing algorithm is used to obtain a 2D consensus graphic, which reflects the global opinion of the panel by (1) positioning closer those samples that have been globally perceived as similar and (2) showing the strength of the connections between samples. The proposal is validated by performing four tasting sessions, with three types of panels tasting different wines, and by developing a new software to implement the proposed techniques. The results obtained show that the graphics provide similar positionings of the samples as the consensus maps obtained by multiple factor analysis (MFA), further providing extra information about connections between samples, not present in any previous method. The main conclusion is that the use of geometric techniques provides information complementary to MFA, and of a different type. Finally, the method proposed is computationally able to manage a significantly larger number of assessors than MFA, which can be useful for the comparison of pictures by a huge number of consumers, via the Internet.
Mapless Online Detection of Dynamic Objects in 3D Lidar
Venue: arXiv cs.RO 2018
Year: 2018

This paper presents a model-free, setting-independent method for online detection of dynamic objects in 3D lidar data. We explicitly compensate for the moving-while-scanning operation (motion distortion) of present-day 3D spinning lidar sensors. Our detection method uses a motion-compensated freespace querying algorithm and classifies between dynamic (currently moving) and static (currently stationary) labels at the point level. For a quantitative analysis, we establish a benchmark with motion-distorted lidar data using CARLA, an open-source simulator for autonomous driving research. We also provide a qualitative analysis with real data using a Velodyne HDL-64E in driving scenarios. Compared to existing 3D lidar methods that are model-free, our method is unique because of its setting independence and compensation for pointcloud motion distortion.
A Study of Energy Trading in a Low-Voltage Network: Centralised and Distributed Approaches
Venue: arXiv cs.SY 2018
Year: 2018

Over the past years, distributed energy resources (DER) have been the object of many studies, which recognise and establish their emerging role in the future of power systems. However, the implementation of many scenarios and mechanism are still challenging. This paper provides an overview of a local energy market and explores the approaches in which consumers and prosumers take part in this market. Therefore, the purpose of this paper is to review the benefits of local markets for users. This study assesses the performance of distributed and centralised trading mechanisms, comparing scenarios where the objective of the exchange may be based on individual or social welfare. Simulation results show the advantages of local markets and demonstrate the importance of advancing the understanding of local markets.
What Role Can NOMA Play in Massive MIMO?
Venue: arXiv cs.IT 2018
Year: 2018

This paper seeks to answer a simple but fundamental question: What role can NOMA play in massive MIMO? It is well-established that power-domain non-orthogonal multiple access (NOMA) schemes can outperform conventional orthogonal multiple access (OMA) schemes in cellular networks. However, this fact does not imply that NOMA is the most efficient way to communicate in massive MIMO setups, where the base stations have many more antennas than there are users in the cell. These setups are becoming the norm in future networks and are usually studied by assuming spatial multiplexing of the users using linear multi-user beamforming. To answer the above question, we analyze and compare the performance achieved by NOMA and multi-user beamforming in both NLOS and LOS scenarios. We reveal that the latter scheme gives the highest average sum rate in massive MIMO setups. We also identify specific cases where NOMA is the better choice in massive MIMO and explain how NOMA plays an essential role in creating a hybrid of NOMA and multi-user beamforming that is shown to perform better than two standalone schemes do.
Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss
Venue: arXiv cs.CV 2018
Year: 2018

Deep neural networks have exhibited promising performance in image super-resolution (SR) due to the power in learning the non-linear mapping from low-resolution (LR) images to high-resolution (HR) images. However, most deep learning methods employ feed-forward architectures, and thus the dependencies between LR and HR images are not fully exploited, leading to limited learning performance. Moreover, most deep learning based SR methods apply the pixel-wise reconstruction error as the loss, which, however, may fail to capture high-frequency information and produce perceptually unsatisfying results, whilst the recent perceptual loss relies on some pre-trained deep model and they may not generalize well. In this paper, we introduce a mask to separate the image into low- and high-frequency parts based on image gradient magnitude, and then devise a gradient sensitive loss to well capture the structures in the image without sacrificing the recovery of low-frequency content. Moreover, by investigating the duality in SR, we develop a dual reconstruction network (DRN) to improve the SR performance. We provide theoretical analysis on the generalization performance of our method and demonstrate its effectiveness and superiority with thorough experiments.