OpenRaiser/Intern-Atlas huggingface dataset:
An Interactive Medical Image Segmentation Framework Using Iterative Refinement
Venue: arXiv cs.CV 2016
Year: 2016

Image segmentation is often performed on medical images for identifying diseases in clinical evaluation. Hence it has become one of the major research areas. Conventional image segmentation techniques are unable to provide satisfactory segmentation results for medical images as they contain irregularities. They need to be pre-processed before segmentation. In order to obtain the most suitable method for medical image segmentation, we propose a two stage algorithm. The first stage automatically generates a binary marker image of the region of interest using mathematical morphology. This marker serves as the mask image for the second stage which uses GrabCut on the input image thus resulting in an efficient segmented result. The obtained result can be further refined by user interaction which can be done using the Graphical User Interface (GUI). Experimental results show that the proposed method is accurate and provides satisfactory segmentation results with minimum user interaction on medical as well as natural images.
Nighttime Haze Removal with Illumination Correction
Venue: arXiv cs.CV 2016
Year: 2016

Haze removal is important for computational photography and computer vision applications. However, most of the existing methods for dehazing are designed for daytime images, and cannot always work well in the nighttime. Different from the imaging conditions in the daytime, images captured in nighttime haze condition may suffer from non-uniform illumination due to artificial light sources, which exhibit low brightness/contrast and color distortion. In this paper, we present a new nighttime hazy imaging model that takes into account both the non-uniform illumination from artificial light sources and the scattering and attenuation effects of haze. Accordingly, we propose an efficient dehazing algorithm for nighttime hazy images. The proposed algorithm includes three sequential steps. i) It enhances the overall brightness by performing a gamma correction step after estimating the illumination from the original image. ii) Then it achieves a color-balance result by performing a color correction step after estimating the color characteristics of the incident light. iii) Finally, it remove the haze effect by applying the dark channel prior and estimating the point-wise environmental light based on the previous illumination-balance result. Experimental results show that the proposed algorithm can achieve illumination-balance and haze-free results with good color rendition ability.
Better Image Segmentation by Exploiting Dense Semantic Predictions
Venue: arXiv cs.CV 2016
Year: 2016

It is well accepted that image segmentation can benefit from utilizing multilevel cues. The paper focuses on utilizing the FCNN-based dense semantic predictions in the bottom-up image segmentation, arguing to take semantic cues into account from the very beginning. By this we can avoid merging regions of similar appearance but distinct semantic categories as possible. The semantic inefficiency problem is handled. We also propose a straightforward way to use the contour cues to suppress the noise in multilevel cues, thus to improve the segmentation robustness. The evaluation on the BSDS500 shows that we obtain the competitive region and boundary performance. Furthermore, since all individual regions can be assigned with appropriate semantic labels during the computation, we are capable of extracting the adjusted semantic segmentations. The experiment on Pascal VOC 2012 shows our improvement to the original semantic segmentations which derives directly from the dense predictions.
A Deep Learning Approach to Block-based Compressed Sensing of Images
Venue: arXiv cs.CV 2016
Year: 2016

Compressed sensing (CS) is a signal processing framework for efficiently reconstructing a signal from a small number of measurements, obtained by linear projections of the signal. Block-based CS is a lightweight CS approach that is mostly suitable for processing very high-dimensional images and videos: it operates on local patches, employs a low-complexity reconstruction operator and requires significantly less memory to store the sensing matrix. In this paper we present a deep learning approach for block-based CS, in which a fully-connected network performs both the block-based linear sensing and non-linear reconstruction stages. During the training phase, the sensing matrix and the non-linear reconstruction operator are \emph{jointly} optimized, and the proposed approach outperforms state-of-the-art both in terms of reconstruction quality and computation time. For example, at a 25% sensing rate the average PSNR advantage is 0.77dB and computation time is over 200-times faster.
Leveraging energy storage to optimize data center electricity cost in emerging power markets
Venue: arXiv cs.DC 2016
Year: 2016

Energy storage in data centers has mainly been used as devices to backup generators during power outages. Recently, there has been a growing interest in using energy storage devices to actively shape power consumption in data centers to reduce their skyrocketing electricity bills. In this paper, we consider using energy storage in data centers for two applications in a joint fashion: reducing peak demand charges and enabling data centers to participate in regulation markets. We develop an optimization framework that captures the cost of electricity, degradation of energy storage devices, as well as the benefit from regulation markets. Under this frame- work, using real data Microsoft data center traces and PJM regulation signals, we show the electricity bill of a data center can be reduced by up to 20%. Furthermore, we demonstrate that the saving from joint optimization can be even larger than the sum of individually optimizing each component. We quantify the particular aspects of data center load profiles that lead to this superlinear gain. Compared to prior works that consider using energy storage devices for each single application alone, our results suggest that energy storage in data centers can have much larger impacts than previously thought possible.
Active Regression with Adaptive Huber Loss
Venue: arXiv cs.LG 2016
Year: 2016

This paper addresses the scalar regression problem through a novel solution to exactly optimize the Huber loss in a general semi-supervised setting, which combines multi-view learning and manifold regularization. We propose a principled algorithm to 1) avoid computationally expensive iterative schemes while 2) adapting the Huber loss threshold in a data-driven fashion and 3) actively balancing the use of labelled data to remove noisy or inconsistent annotations at the training stage. In a wide experimental evaluation, dealing with diverse applications, we assess the superiority of our paradigm which is able to combine robustness towards noise with both strong performance and low computational cost.
Big Data Caching for Networking: Moving from Cloud to Edge
Venue: arXiv cs.NI 2016
Year: 2016

In order to cope with the relentless data tsunami in $5G$ wireless networks, current approaches such as acquiring new spectrum, deploying more base stations (BSs) and increasing nodes in mobile packet core networks are becoming ineffective in terms of scalability, cost and flexibility. In this regard, context-aware $5$G networks with edge/cloud computing and exploitation of \emph{big data} analytics can yield significant gains to mobile operators. In this article, proactive content caching in $5$G wireless networks is investigated in which a big data-enabled architecture is proposed. In this practical architecture, vast amount of data is harnessed for content popularity estimation and strategic contents are cached at the BSs to achieve higher users' satisfaction and backhaul offloading. To validate the proposed solution, we consider a real-world case study where several hours of mobile data traffic is collected from a major telecom operator in Turkey and a big data-enabled analysis is carried out leveraging tools from machine learning. Based on the available information and storage capacity, numerical studies show that several gains are achieved both in terms of users' satisfaction and backhaul offloading. For example, in the case of $16$ BSs with $30\%$ of content ratings and $13$ Gbyte of storage size ($78\%$ of total library size), proactive caching yields $100\%$ of users' satisfaction and offloads $98\%$ of the backhaul.
Computing the generator polynomials of $\mathbb{Z}_2\mathbb{Z}_4$-additive cyclic codes
Venue: arXiv cs.IT 2016
Year: 2016

A ${\mathbb{Z}}_2{\mathbb{Z}}_4$-additive code ${\cal C}\subseteq{\mathbb{Z}}_2^α\times{\mathbb{Z}}_4^β$ is called cyclic if the set of coordinates can be partitioned into two subsets, the set of ${\mathbb{Z}}_2$ and the set of ${\mathbb{Z}}_4$ coordinates, such that any simultaneous cyclic shift of the coordinates of both subsets leaves invariant the code. These codes can be identified as submodules of the $\mathbb{Z}_4[x]$-module $\mathbb{Z}_2[x]/(x^α-1)\times\mathbb{Z}_4[x]/(x^β-1)$. Any $\mathbb{Z}_2\mathbb{Z}_4$-additive cyclic code ${\cal C}$ is of the form $\langle (b(x)\mid{ 0}), (\ell(x) \mid f(x)h(x) +2f(x)) \rangle$ for some $b(x), \ell(x)\in\mathbb{Z}_2[x]/(x^α-1)$ and $f(x),h(x)\in {\mathbb{Z}}_4[x]/(x^β-1)$. A new algorithm is presented to compute the generator polynomials for ${\mathbb{Z}}_2{\mathbb{Z}}_4$-additive cyclic codes.
On the Degrees of Freedom of MIMO X Networks with Non-Cooperation Transmitters
Venue: arXiv cs.IT 2016
Year: 2016

Due to limited backhaul/feedback link capacity and channel state information (CSI) feedback delay, obtaining global and instantaneous channel state information at the transmitter (CSIT) is a main obstacle in practice. In this paper, novel transmission schemes are proposed for a class of interference networks that can achieve new trade-off regions between the sum of degrees of freedom (sum-DoF) and CSI feedback delay with distributed and temperately-delayed CSIT. More specifically, a distributed space-time interference alignment (STIA) scheme is proposed for the two-user multiple-input multiple-output (MIMO) X channel via a novel precoding method called Cyclic Zero-padding. The achieved sum-DoFs herein for certain antenna configurations are greater than the best known sum-DoFs in literature with delayed CSIT. Furthermore, we propose a distributed retrospective interference alignment (RIA) scheme that achieves more than 1 sum-DoF for the K-user single-input single-output (SISO) X network. Finally, we extend the distributed STIA to the MxN user multiple-input single-output (MISO) X network where each transmitter has N-1 antennas and each receiver has a single antenna, yielding the same sum-DoF as that in the global and instantaneous CSIT case. The discussion and the result of the MISO X network can be extended to the MIMO case due to spatial scale invariance property.
On-board monitoring of 2-D spatially-resolved temperatures in cylindrical lithium-ion batteries: Part II. State estimation via impedance-based temperature sensing
Venue: arXiv cs.SY 2016
Year: 2016

Impedance-based temperature detection (ITD) is a promising approach for rapid estimation of internal cell temperature based on the correlation between temperature and electrochemical impedance. Previously, ITD was used as part of an Extended Kalman Filter (EKF) state-estimator in conjunction with a thermal model to enable estimation of the 1-D temperature distribution of a cylindrical lithium-ion battery. Here, we extend this method to enable estimation of the 2-D temperature field of a battery with temperature gradients in both the radial and axial directions. An EKF using a parameterised 2-D spectral-Galerkin model with ITD measurement input (the imaginary part of the impedance at 215 Hz) is shown to accurately predict the core temperature and multiple surface temperatures of a 32113 LiFePO$_4$ cell, using current excitation profiles based on an Artemis HEV drive cycle. The method is validated experimentally on a cell fitted with a heat sink and asymmetrically cooled via forced air convection. A novel approach to impedance-temperature calibration is also presented, which uses data from a single drive cycle, rather than measurements at multiple uniform cell temperatures as in previous studies. This greatly reduces the time required for calibration, since it overcomes the need for repeated cell thermal equalization.
Neural Machine Translation with External Phrase Memory
Venue: arXiv cs.CL 2016
Year: 2016

In this paper, we propose phraseNet, a neural machine translator with a phrase memory which stores phrase pairs in symbolic form, mined from corpus or specified by human experts. For any given source sentence, phraseNet scans the phrase memory to determine the candidate phrase pairs and integrates tagging information in the representation of source sentence accordingly. The decoder utilizes a mixture of word-generating component and phrase-generating component, with a specifically designed strategy to generate a sequence of multiple words all at once. The phraseNet not only approaches one step towards incorporating external knowledge into neural machine translation, but also makes an effort to extend the word-by-word generation mechanism of recurrent neural network. Our empirical study on Chinese-to-English translation shows that, with carefully-chosen phrase table in memory, phraseNet yields 3.45 BLEU improvement over the generic neural machine translator.
Recurrent Neural Networks for Multivariate Time Series with Missing Values
Venue: arXiv cs.LG 2016
Year: 2016

Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a., informative missingness. There is very limited work on exploiting the missing patterns for effective imputation and improving prediction performance. In this paper, we develop novel deep learning models, namely GRU-D, as one of the early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a state-of-the-art recurrent neural network. It takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results. Experiments of time series classification tasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic datasets demonstrate that our models achieve state-of-the-art performance and provides useful insights for better understanding and utilization of missing values in time series analysis.
Content-Based Interference Management for Video Transmission in D2D Communications Underlaying LTE
Venue: arXiv cs.NI 2016
Year: 2016

A novel interference management approach is proposed for modern communication scenarios, where multiple applications and networks coexist on the same channel resource. The leading principle behind the proposed approach is that the interference level should be adapted to the content being transmitted by the data links to maximize the amount of delivered information. A network setting is considered where Device-to-Device (D2D) communications underlay a Long Term Evolution (LTE) link uploading video content to the network infrastructure. For this scenario, an optimization problem is formulated aiming at the maximization of the D2D throughput under a constraint on the Peak Signal-to-Noise-Ratio of the video data stream. The resulting optimal policy focuses interference on specific packets within the video stream, and significantly increases the throughput achieved by the D2D link compared to an undifferentiated interference strategy. The optimal strategy is applied to a real-world video streaming application to further demonstrate the performance gain.
Open-source Hardware: Opportunities and Challenges
Venue: arXiv cs.AR 2016
Year: 2016

Innovation in hardware is slowing due to rising costs of chip design and diminishing benefits from Moore's law and Dennard scaling. Software innovation, on the other hand, is flourishing, helped in good measure by a thriving open-source ecosystem. We believe that open source can similarly help hardware innovation, but has not yet due to several reasons. We identify these reasons and how the industry, academia, and the hardware community at large can come together to address them.
Human vs. Computer Go: Review and Prospect
Venue: arXiv cs.AI 2016
Year: 2016

The Google DeepMind challenge match in March 2016 was a historic achievement for computer Go development. This article discusses the development of computational intelligence (CI) and its relative strength in comparison with human intelligence for the game of Go. We first summarize the milestones achieved for computer Go from 1998 to 2016. Then, the computer Go programs that have participated in previous IEEE CIS competitions as well as methods and techniques used in AlphaGo are briefly introduced. Commentaries from three high-level professional Go players on the five AlphaGo versus Lee Sedol games are also included. We conclude that AlphaGo beating Lee Sedol is a huge achievement in artificial intelligence (AI) based largely on CI methods. In the future, powerful computer Go programs such as AlphaGo are expected to be instrumental in promoting Go education and AI real-world applications.
User Cooperation for Enhanced Throughput Fairness in Wireless Powered Communication Networks
Venue: arXiv cs.IT 2016
Year: 2016

This paper studies a novel user cooperation method in a wireless powered communication network (WPCN), where a pair of distributed terminal users first harvest wireless energy broadcasted by one energy node (EN) and then use the harvested energy to transmit information cooperatively to a destination node (DN). In particular, the two cooperating users exchange their independent information with each other to form a virtual antenna array and transmit jointly to the DN. By allowing each user to allocate part of its harvested energy to transmit the other's information, the proposed cooperation can effectively mitigate the user unfairness problem in WPCNs, where a user may suffer from very low data rate due to the poor energy harvesting performance and high data transmission consumptions. We derive the maximum common throughput achieved by the cooperation scheme through optimizing the time allocation on wireless energy transfer, user message exchange, and joint information transmissions. Through comparing with some representative benchmark schemes, our results demonstrate the effectiveness of the proposed user cooperation in enhancing the throughput performance under different setups.
Joint Recursive Monocular Filtering of Camera Motion and Disparity Map
Venue: arXiv cs.CV 2016
Year: 2016

Monocular scene reconstruction is essential for modern applications such as robotics or autonomous driving. Although stereo methods usually result in better accuracy than monocular methods, they are more expensive and more difficult to calibrate. In this work, we present a novel second order optimal minimum energy filter that jointly estimates the camera motion, the disparity map and also higher order kinematics recursively on a product Lie group containing a novel disparity group. This mathematical framework enables to cope with non-Euclidean state spaces, non-linear observations and high dimensions which is infeasible for most classical filters. To be robust against outliers, we use a generalized Charbonnier energy function in this framework rather than a quadratic energy function as proposed in related work. Experiments confirm that our method enables accurate reconstructions on-par with state-of-the-art.
Goal Set Inverse Optimal Control and Iterative Re-planning for Predicting Human Reaching Motions in Shared Workspaces
Venue: arXiv cs.RO 2016
Year: 2016

To enable safe and efficient human-robot collaboration in shared workspaces it is important for the robot to predict how a human will move when performing a task. While predicting human motion for tasks not known a priori is very challenging, we argue that single-arm reaching motions for known tasks in collaborative settings (which are especially relevant for manufacturing) are indeed predictable. Two hypotheses underlie our approach for predicting such motions: First, that the trajectory the human performs is optimal with respect to an unknown cost function, and second, that human adaptation to their partner's motion can be captured well through iterative re-planning with the above cost function. The key to our approach is thus to learn a cost function which "explains" the motion of the human. To do this, we gather example trajectories from pairs of participants performing a collaborative assembly task using motion capture. We then use Inverse Optimal Control to learn a cost function from these trajectories. Finally, we predict reaching motions from the human's current configuration to a task-space goal region by iteratively re-planning a trajectory using the learned cost function. Our planning algorithm is based on the trajectory optimizer STOMP, it plans for a 23 DoF human kinematic model and accounts for the presence of a moving collaborator and obstacles in the environment. Our results suggest that in most cases, our method outperforms baseline methods when predicting motions. We also show that our method outperforms baselines for predicting human motion when a human and a robot share the workspace.
Evaluating a Development Framework for Engineering Internet of Things Applications
Venue: arXiv cs.SE 2016
Year: 2016

A critical challenge is to enable IoT application development with minimal effort from various stakeholders involved in the development process. Several approaches to tacking this challenge have been proposed in the fields of wireless sensor networks and ubiquitous and pervasive computing, regarded as precursors to the modern day of IoT. However, although existing approaches provide a wide range of features, stakeholders have specific application development requirements and choosing an appropriate approach requires thorough evaluations on different aspects. To date, this aspect has been investigated to a limited extend. In view of this, this paper provides an extensive set of evaluations based on our previous work on IoT application development framework. Specifically, we evaluate our approach in terms of (1) development effort: the effort required to create a new application, (2) reusability: the extend to which software artifacts can be reused during application development, (3) expressiveness: the characteristics of IoT applications that can be modeled using our approach, (4) memory metrics: the amount of memory and storage a device needs to consume in order to run an application under our framework, and (5) comparison of our approach with state of the art in IoT application development on various dimensions, which does not only provide a comprehensive view of state of the art, but also guides developers in selecting an approach given application requirements in hand. We believe that the above different aspects provide the research community with insight into evaluating, selecting, and developing useful IoT frameworks and applications.