OpenRaiser/Intern-Atlas huggingface dataset:
Discriminative Transfer Learning for General Image Restoration
Venue: arXiv cs.CV 2017
Year: 2017

Recently, several discriminative learning approaches have been proposed for effective image restoration, achieving convincing trade-off between image quality and computational efficiency. However, these methods require separate training for each restoration task (e.g., denoising, deblurring, demosaicing) and problem condition (e.g., noise level of input images). This makes it time-consuming and difficult to encompass all tasks and conditions during training. In this paper, we propose a discriminative transfer learning method that incorporates formal proximal optimization and discriminative learning for general image restoration. The method requires a single-pass training and allows for reuse across various problems and conditions while achieving an efficiency comparable to previous discriminative approaches. Furthermore, after being trained, our model can be easily transferred to new likelihood terms to solve untrained tasks, or be combined with existing priors to further improve image restoration quality.
Redesigning OP2 Compiler to Use HPX Runtime Asynchronous Techniques
Venue: arXiv cs.DC 2017
Year: 2017

Maximizing parallelism level in applications can be achieved by minimizing overheads due to load imbalances and waiting time due to memory latencies. Compiler optimization is one of the most effective solutions to tackle this problem. The compiler is able to detect the data dependencies in an application and is able to analyze the specific sections of code for parallelization potential. However, all of these techniques provided with a compiler are usually applied at compile time, so they rely on static analysis, which is insufficient for achieving maximum parallelism and producing desired application scalability. One solution to address this challenge is the use of runtime methods. This strategy can be implemented by delaying certain amount of code analysis to be done at runtime. In this research, we improve the parallel application performance generated by the OP2 compiler by leveraging HPX, a C++ runtime system, to provide runtime optimizations. These optimizations include asynchronous tasking, loop interleaving, dynamic chunk sizing, and data prefetching. The results of the research were evaluated using an Airfoil application which showed a 40-50% improvement in parallel performance.
Immersed boundary model of aortic heart valve dynamics with physiological driving and loading conditions
Venue: arXiv cs.CE 2017
Year: 2017

The immersed boundary (IB) method is a mathematical and numerical framework for problems of fluid-structure interaction, treating the particular case in which an elastic structure is immersed in a viscous incompressible fluid. The IB approach to such problems is to describe the elasticity of the immersed structure in Lagrangian form, and to describe the momentum, viscosity, and incompressibility of the coupled fluid-structure system in Eulerian form. Interaction between Lagrangian and Eulerian variables is mediated by integral equations with Dirac delta function kernels. The IB method provides a unified formulation for fluid-structure interaction models involving both thin elastic boundaries and also thick viscoelastic bodies. In this work, we describe the application of an adaptive, staggered-grid version of the IB method to the three-dimensional simulation of the fluid dynamics of the aortic heart valve. Our model describes the thin leaflets of the aortic valve as immersed elastic boundaries, and describes the wall of the aortic root as a thick, semi-rigid elastic structure. A physiological left-ventricular pressure waveform is used to drive flow through the model valve, and dynamic pressure loading conditions are provided by a reduced (zero-dimensional) circulation model that has been fit to clinical data. We use this model and method to simulate aortic valve dynamics over multiple cardiac cycles. The model is shown to approach rapidly a periodic steady state in which physiological cardiac output is obtained at physiological pressures. These realistic flow rates are not specified in the model, however. Instead, they emerge from the fluid-structure interaction simulation.
Preserving Stabilization while Practically Bounding State Space
Venue: arXiv cs.DC 2017
Year: 2017

Stabilization is a key dependability property for dealing with unanticipated transient faults, as it guarantees that even in the presence of such faults, the system will recover to states where it satisfies its specification. One of the desirable attributes of stabilization is the use of bounded space for each variable. In this paper, we present an algorithm that transforms a stabilizing program that uses variables with unbounded domain into a stabilizing program that uses bounded variables and (practically bounded) physical time. While non-stabilizing programs (that do not handle transient faults) can deal with unbounded variables by assigning large enough but bounded space, stabilizing programs that need to deal with arbitrary transient faults cannot do the same since a transient fault may corrupt the variable to its maximum value. We show that our transformation algorithm is applicable to several problems including logical clocks, vector clocks, mutual exclusion, leader election, diffusing computations, Paxos based consensus, and so on. Moreover, our approach can also be used to bound counters used in an earlier work by Katz and Perry for adding stabilization to a non-stabilizing program. By combining our algorithm with that earlier work by Katz and Perry, it would be possible to provide stabilization for a rich class of problems, by assigning large enough but bounded space for variables.
A Fair Power Allocation Approach to NOMA in Multi-user SISO Systems
Venue: arXiv cs.IT 2017
Year: 2017

A non-orthogonal multiple access (NOMA) approach that always outperforms orthogonal multiple access (OMA) called Fair-NOMA is introduced. In Fair-NOMA, each mobile user is allocated its share of the transmit power such that its capacity is always greater than or equal to the capacity that can be achieved using OMA. For any slow-fading channel gains of the two users, the set of possible power allocation coefficients are derived. For the infimum and supremum of this set, the individual capacity gains and the sum-rate capacity gain are derived. It is shown that the ergodic sum-rate capacity gain approaches 1 b/s/Hz when the transmit power increases for the case when pairing two random users with i.i.d. channel gains. The outage probability of this approach is derived and shown to be better than OMA. The Fair-NOMA approach is applied to the case of pairing a near base-station user and a cell-edge user and the ergodic capacity gap is derived as a function of total number of users in the cell at high SNR. This is then compared to the conventional case of fixed-power NOMA with user-pairing. Finally, Fair-NOMA is extended to $K$ users and prove that the capacity can always be improved for each user, while using less than the total transmit power required to achieve OMA capacities per user.
Edge-matching Problems with Rotations
Venue: arXiv cs.DS 2017
Year: 2017

Edge-matching problems, also called edge matching puzzles, are abstractions of placement problems with neighborhood conditions. Pieces with colored edges have to be placed on a board such that adjacent edges have the same color. The problem has gained interest recently with the (now terminated) Eternity~II puzzle, and new complexity results. In this paper we consider a number of settings which differ in size of the puzzles and the manipulations allowed on the pieces. We investigate the effect of allowing rotations of the pieces on the complexity of the problem, an aspect that is only marginally treated so far. We show that some problems have polynomial time algorithms while others are NP-complete. Especially we show that allowing rotations in one-row puzzles makes the problem NP-hard. The proofs of the hardness result uses a large number of colors. This is essential because we also show that this problem (and another related one) is fixed-parameter tractable, where the relevant parameter is the number of colors.
On the Outage Probability of Localization in Randomly Deployed Wireless Networks
Venue: arXiv cs.NI 2017
Year: 2017

This paper analyzes the localization outage probability (LOP), the probability that the position error exceeds a given threshold, in randomly deployed wireless networks. Two typical cases are considered: a mobile agent uses all the neighboring anchors or select the best pair of anchors for self-localization. We derive the exact LOP for the former case and tight bounds for the LOP for the latter case. The comparison between the two cases reveals the advantage of anchor selection in terms of LOP versus complexity tradeoff, providing insights into the design of efficient localization systems.
Locality preserving projection on SPD matrix Lie group: algorithm and analysis
Venue: arXiv cs.CV 2017
Year: 2017

Symmetric positive definite (SPD) matrices used as feature descriptors in image recognition are usually high dimensional. Traditional manifold learning is only applicable for reducing the dimension of high-dimensional vector-form data. For high-dimensional SPD matrices, directly using manifold learning algorithms to reduce the dimension of matrix-form data is impossible. The SPD matrix must first be transformed into a long vector, and then the dimension of this vector must be reduced. However, this approach breaks the spatial structure of the SPD matrix space. To overcome this limitation, we propose a new dimension reduction algorithm on SPD matrix space to transform high-dimensional SPD matrices into low-dimensional SPD matrices. Our work is based on the fact that the set of all SPD matrices with the same size has a Lie group structure, and we aim to transform the manifold learning to the SPD matrix Lie group. We use the basic idea of the manifold learning algorithm called locality preserving projection (LPP) to construct the corresponding Laplacian matrix on the SPD matrix Lie group. Thus, we call our approach Lie-LPP to emphasize its Lie group character. We present a detailed algorithm analysis and show through experiments that Lie-LPP achieves effective results on human action recognition and human face recognition.
IoT-enabled Channel Selection Approach for WBANs
Venue: arXiv cs.NI 2017
Year: 2017

-Recent advances in microelectronics have enabled the realization of Wireless Body Area Networks (WBANs). However , the massive growth in wireless devices and the push for interconnecting these devices to form an Internet of Things (IoT) can be challenging for WBANs; hence robust communication is necessary through careful medium access arbitration. In this paper, we propose a new protocol to enable WBAN operation within an IoT. Basically, we leverage the emerging Bluetooth Low Energy technology (BLE) and promote the integration of a BLE transceiver and a Cognitive Radio module (CR) within the WBAN coordinator. Accordingly, a BLE informs WBANs through announcements about the frequency channels that are being used in their vicinity. To mitigate interference, the superframe's active period is extended to involve not only a Time Division Multiple Access (TDMA) frame, but also a Flexible Channel Selection (FCS) and a Flexible Backup TDMA (FBTDMA) frames. The WBAN sensors that experience interference on the default channel within the TDMA frame will eventually switch to another Interference Mitigation Channel (IMC). With the help of CR, an IMC is selected for a WBAN and each interfering sensor will be allocated a time-slot within the (FBTDMA) frame to retransmit using such IMC.
Existence and Continuity of Differential Entropy for a Class of Distributions
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we identify a class of absolutely continuous probability distributions, and show that the differential entropy is uniformly convergent over this space under the metric of total variation distance. One of the advantages of this class is that the requirements could be readily verified for a given distribution.
Model Predictive Control for Signal Temporal Logic Specification
Venue: arXiv cs.SY 2017
Year: 2017

We present a mathematical programming-based method for model predictive control of cyber-physical systems subject to signal temporal logic (STL) specifications. We describe the use of STL to specify a wide range of properties of these systems, including safety, response and bounded liveness. For synthesis, we encode STL specifications as mixed integer-linear constraints on the system variables in the optimization problem at each step of a receding horizon control framework. We prove correctness of our algorithms, and present experimental results for controller synthesis for building energy and climate control.
On the Interplay between Non-Functional Requirements and Builds on Continuous Integration
Venue: arXiv cs.SE 2017
Year: 2017

Continuous Integration (CI) implies that a whole developer team works together on the mainline of a software project. CI systems automate the builds of a software. Sometimes a developer checks in code, which breaks the build. A broken build might not be a problem by itself, but it has the potential to disrupt co-workers, hence it affects the performance of the team. In this study, we investigate the interplay between nonfunctional requirements (NFRs) and builds statuses from 1,283 software projects. We found significant differences among NFRs related-builds statuses. Thus, tools can be proposed to improve CI with focus on new ways to prevent failures into CI, specially for efficiency and usability related builds. Also, the time required to put a broken build back on track indicates a bimodal distribution along all NFRs, with higher peaks within a day and lower peaks in six weeks. Our results suggest that more planned schedule for maintainability for Ruby, and for functionality and reliability for Java would decrease delays related to broken builds.
Implications of the Fourth Industrial Age on Higher Education
Venue: arXiv cs.CY 2017
Year: 2017

Higher education in the fourth industrial revolution, HE 4.0, is a complex, dialectical and exciting opportunity which can potentially transform society for the better. The fourth industrial revolution is powered by artificial intelligence and it will transform the workplace from tasks based characteristics to the human centred characteristics. Because of the convergence of man and machine, it will reduce the subject distance between humanities and social science as well as science and technology. This will necessarily require much more interdisciplinary teaching, research and innovation. This paper explores the impact of HE 4.0 on the mission of a university which is teaching, research (including innovation) and service.
Efficient Two-Dimensional Sparse Coding Using Tensor-Linear Combination
Venue: arXiv cs.CV 2017
Year: 2017

Sparse coding (SC) is an automatic feature extraction and selection technique that is widely used in unsupervised learning. However, conventional SC vectorizes the input images, which breaks apart the local proximity of pixels and destructs the elementary object structures of images. In this paper, we propose a novel two-dimensional sparse coding (2DSC) scheme that represents the input images as the tensor-linear combinations under a novel algebraic framework. 2DSC learns much more concise dictionaries because it uses the circular convolution operator, since the shifted versions of atoms learned by conventional SC are treated as the same ones. We apply 2DSC to natural images and demonstrate that 2DSC returns meaningful dictionaries for large patches. Moreover, for mutli-spectral images denoising, the proposed 2DSC reduces computational costs with competitive performance in comparison with the state-of-the-art algorithms.
Fast link prediction for large networks using spectral embedding
Venue: arXiv cs.SI 2017
Year: 2017

Many link prediction algorithms require the computation of a similarity metric on each vertex pair, which is quadratic in the number of vertices and infeasible for large networks. We develop a class of link prediction algorithms based on a spectral embedding and the k closest pairs algorithm that are scalable to very large networks. We compare the prediction accuracy and runtime of these methods to existing algorithms on several large link prediction tasks. Our methods achieve comparable accuracy to standard algorithms but are significantly faster.
Perception Driven Texture Generation
Venue: arXiv cs.CV 2017
Year: 2017

This paper investigates a novel task of generating texture images from perceptual descriptions. Previous work on texture generation focused on either synthesis from examples or generation from procedural models. Generating textures from perceptual attributes have not been well studied yet. Meanwhile, perceptual attributes, such as directionality, regularity and roughness are important factors for human observers to describe a texture. In this paper, we propose a joint deep network model that combines adversarial training and perceptual feature regression for texture generation, while only random noise and user-defined perceptual attributes are required as input. In this model, a preliminary trained convolutional neural network is essentially integrated with the adversarial framework, which can drive the generated textures to possess given perceptual attributes. An important aspect of the proposed model is that, if we change one of the input perceptual features, the corresponding appearance of the generated textures will also be changed. We design several experiments to validate the effectiveness of the proposed method. The results show that the proposed method can produce high quality texture images with desired perceptual properties.
Understanding IoT Security Through the Data Crystal Ball: Where We Are Now and Where We Are Going to Be
Venue: arXiv cs.CR 2017
Year: 2017

Inspired by the boom of the consumer IoT market, many device manufacturers, start-up companies and technology giants have jumped into the space. Unfortunately, the exciting utility and rapid marketization of IoT, come at the expense of privacy and security. Industry reports and academic work have revealed many attacks on IoT systems, resulting in privacy leakage, property loss and large-scale availability problems. To mitigate such threats, a few solutions have been proposed. However, it is still less clear what are the impacts they can have on the IoT ecosystem. In this work, we aim to perform a comprehensive study on reported attacks and defenses in the realm of IoT aiming to find out what we know, where the current studies fall short and how to move forward. To this end, we first build a toolkit that searches through massive amount of online data using semantic analysis to identify over 3000 IoT-related articles. Further, by clustering such collected data using machine learning technologies, we are able to compare academic views with the findings from industry and other sources, in an attempt to understand the gaps between them, the trend of the IoT security risks and new problems that need further attention. We systemize this process, by proposing a taxonomy for the IoT ecosystem and organizing IoT security into five problem areas. We use this taxonomy as a beacon to assess each IoT work across a number of properties we define. Our assessment reveals that relevant security and privacy problems are far from solved. We discuss how each proposed solution can be applied to a problem area and highlight their strengths, assumptions and constraints. We stress the need for a security framework for IoT vendors and discuss the trend of shifting security liability to external or centralized entities. We also identify open research problems and provide suggestions towards a secure IoT ecosystem.
Handover Analysis on High Speed Train with Doppler Frequency Spread
Venue: arXiv cs.NI 2017
Year: 2017

This paper investigates the handover performance on high speed train (HST) via different speed under three scenarios, which are viaduct environment, cutting environment and urban area. To provide stable wireless service, we adopt Long Term Evolution (LTE) for high speed railway (HST) system. Moreover, we consider the effect of speed on handover procedure. Interchannel interference (ICI) caused by the Doppler frequency spread is related to the speed of HST, which is considered into the proposed system to evaluate the system handover performance including handover starting time and handover delay. Numerical results show the parameters in the system including SNR, throughput, handover starting time and handover delay. The proposed system provides some insights for practical railway system.
Iterative Object and Part Transfer for Fine-Grained Recognition
Venue: arXiv cs.CV 2017
Year: 2017

The aim of fine-grained recognition is to identify sub-ordinate categories in images like different species of birds. Existing works have confirmed that, in order to capture the subtle differences across the categories, automatic localization of objects and parts is critical. Most approaches for object and part localization relied on the bottom-up pipeline, where thousands of region proposals are generated and then filtered by pre-trained object/part models. This is computationally expensive and not scalable once the number of objects/parts becomes large. In this paper, we propose a nonparametric data-driven method for object and part localization. Given an unlabeled test image, our approach transfers annotations from a few similar images retrieved in the training set. In particular, we propose an iterative transfer strategy that gradually refine the predicted bounding boxes. Based on the located objects and parts, deep convolutional features are extracted for recognition. We evaluate our approach on the widely-used CUB200-2011 dataset and a new and large dataset called Birdsnap. On both datasets, we achieve better results than many state-of-the-art approaches, including a few using oracle (manually annotated) bounding boxes in the test images.
Parikh-reducing Church-Rosser representations for some classes of regular languages
Venue: arXiv cs.FL 2017
Year: 2017

In this paper the concept of Parikh-reducing Church-Rosser systems is studied. It is shown that for two classes of regular languages there exist such systems which describe the languages using finitely many equivalence classes of the rewriting system. The two classes are: 1.) the class of all regular languages such that the syntactic monoid contains only abelian groups and 2.) the class of all group languages over a two-letter alphabet. The construction of the systems yield a monoid representation such that all subgroups are abelian. Additionally, the complexity of those representations is studied.
Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games
Venue: arXiv cs.AI 2017
Year: 2017

Many artificial intelligence (AI) applications often require multiple intelligent agents to work in a collaborative effort. Efficient learning for intra-agent communication and coordination is an indispensable step towards general AI. In this paper, we take StarCraft combat game as a case study, where the task is to coordinate multiple agents as a team to defeat their enemies. To maintain a scalable yet effective communication protocol, we introduce a Multiagent Bidirectionally-Coordinated Network (BiCNet ['bIknet]) with a vectorised extension of actor-critic formulation. We show that BiCNet can handle different types of combats with arbitrary numbers of AI agents for both sides. Our analysis demonstrates that without any supervisions such as human demonstrations or labelled data, BiCNet could learn various types of advanced coordination strategies that have been commonly used by experienced game players. In our experiments, we evaluate our approach against multiple baselines under different scenarios; it shows state-of-the-art performance, and possesses potential values for large-scale real-world applications.