OpenRaiser/Intern-Atlas huggingface dataset:
Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network
Venue: arXiv cs.CV 2018
Year: 2018

Histology imaging is an essential diagnosis method to finalize the grade and stage of cancer of different tissues, especially for breast cancer diagnosis. Specialists often disagree on the final diagnosis on biopsy tissue due to the complex morphological variety. Although convolutional neural networks (CNN) have advantages in extracting discriminative features in image classification, directly training a CNN on high resolution histology images is computationally infeasible currently. Besides, inconsistent discriminative features often distribute over the whole histology image, which incurs challenges in patch-based CNN classification method. In this paper, we propose a novel architecture for automatic classification of high resolution histology images. First, an adapted residual network is employed to explore hierarchical features without attenuation. Second, we develop a robust deep fusion network to utilize the spatial relationship between patches and learn to correct the prediction bias generated from inconsistent discriminative feature distribution. The proposed method is evaluated using 10-fold cross-validation on 400 high resolution breast histology images with balanced labels and reports 95% accuracy on 4-class classification and 98.5% accuracy, 99.6% AUC on 2-class classification (carcinoma and non-carcinoma), which substantially outperforms previous methods and close to pathologist performance.
Towards a directed homotopy type theory
Venue: arXiv cs.LO 2018
Year: 2018

In this paper, we present a directed homotopy type theory for reasoning synthetically about (higher) categories, directed homotopy theory, and its applications to concurrency. We specify a new `homomorphism' type former for Martin-Löf type theory which is roughly analogous to the identity type former originally introduced by Martin-Löf. The homomorphism type former is meant to capture the notions of morphism (from the theory of categories) and directed path (from directed homotopy theory) just as the identity type former is known to capture the notions of isomorphism (from the theory of groupoids) and path (from homotopy theory). Our main result is an interpretation of these homomorphism types into Cat, the category of small categories. There, the interpretation of each homomorphism type hom(a,b) is indeed the set of morphisms between the objects a and b of a category C. We end the paper with an analysis of the interpretation in Cat with which we argue that our homomorphism types are indeed the directed version of Martin-Löf's identity types.
Sparse Range-constrained Learning and Its Application for Medical Image Grading
Venue: arXiv cs.CV 2018
Year: 2018

Sparse learning has been shown to be effective in solving many real-world problems. Finding sparse representations is a fundamentally important topic in many fields of science including signal processing, computer vision, genome study and medical imaging. One important issue in applying sparse representation is to find the basis to represent the data,especially in computer vision and medical imaging where the data is not necessary incoherent. In medical imaging, clinicians often grade the severity or measure the risk score of a disease based on images. This process is referred to as medical image grading. Manual grading of the disease severity or risk score is often used. However, it is tedious, subjective and expensive. Sparse learning has been used for automatic grading of medical images for different diseases. In the grading, we usually begin with one step to find a sparse representation of the testing image using a set of reference images or atoms from the dictionary. Then in the second step, the selected atoms are used as references to compute the grades of the testing images. Since the two steps are conducted sequentially, the objective function in the first step is not necessarily optimized for the second step. In this paper, we propose a novel sparse range-constrained learning(SRCL)algorithm for medical image grading.Different from most of existing sparse learning algorithms, SRCL integrates the objective of finding a sparse representation and that of grading the image into one function. It aims to find a sparse representation of the testing image based on atoms that are most similar in both the data or feature representation and the medical grading scores. We apply the new proposed SRCL to CDR computation and cataract grading. Experimental results show that the proposed method is able to improve the accuracy in cup-to-disc ratio computation and cataract grading.
Two-Layer Mixture Network Ensemble for Apparel Attributes Classification
Venue: arXiv cs.CV 2018
Year: 2018

Recognizing apparel attributes has recently drawn great interest in the computer vision community. Methods based on various deep neural networks have been proposed for image classification, which could be applied to apparel attributes recognition. An interesting problem raised is how to ensemble these methods to further improve the accuracy. In this paper, we propose a two-layer mixture framework for ensemble different networks. In the first layer of this framework, two types of ensemble learning methods, bagging and boosting, are separately applied. Different from traditional methods, our bagging process makes use of the whole training set, not random subsets, to train each model in the ensemble, where several differentiated deep networks are used to promote model variance. To avoid the bias of small-scale samples, the second layer only adopts bagging to mix the results obtained with bagging and boosting in the first layer. Experimental results demonstrate that the proposed mixture framework outperforms any individual network model or either independent ensemble method in apparel attributes classification.
Visual Attention driven by Convolutional Features
Venue: arXiv cs.CV 2018
Year: 2018

The understanding of where humans look in a scene is a problem of great interest in visual perception and computer vision. When eye-tracking devices are not a viable option, models of human attention can be used to predict fixations. In this paper we give two contribution. First, we show a model of visual attention that is simply based on deep convolutional neural networks trained for object classification tasks. A method for visualizing saliency maps is defined which is evaluated in a saliency prediction task. Second, we integrate the information of these maps with a bottom-up differential model of eye-movements to simulate visual attention scanpaths. Results on saliency prediction and scores of similarity with human scanpaths demonstrate the effectiveness of this model.
Construction of MDS Self-dual Codes over Finite Fields
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we obtain some new results on the existence of MDS self-dual codes utilizing (extended) generalized Reed-Solomon codes over finite fields of odd characteristic. For some fixed $q$, our results can produce more classes of MDS self-dual codes than previous works.
Thinging for Software Engineers
Venue: arXiv cs.SE 2018
Year: 2018

The aim of this paper is to promote the terms thing and thinging (which refers to the act of defining a boundary around some portion of reality and labeling it with a name) as valued notions that play an important role in software engineering modeling. Additionally, we attempt to furnish operational definitions for terms thing, object, process, and thinging. The substantive discussion is based on the conception of an (abstract) machine, named the Thinging Machine (TM), used in several research works. The TM creates, processes, receives, releases, and transfers things. Accordingly, a diagrammatic representation of the TM is used to model reality. In the discussion section, this paper clarifies interesting issues related to conceptual modeling in software engineering. The substance of this paper and its conclusion suggest that thinging should be more meaningfully emphasized as a valuable research and teaching topic, at least in the requirement analysis phase of the software development cycle.
Connected Components at Scale via Local Contractions
Venue: arXiv cs.DC 2018
Year: 2018

As a fundamental tool in hierarchical graph clustering, computing connected components has been a central problem in large-scale data mining. While many known algorithms have been developed for this problem, they are either not scalable in practice or lack strong theoretical guarantees on the parallel running time, that is, the number of communication rounds. So far, the best proven guarantee is $\Oh(\log n)$, which matches the running time in the PRAM model. In this paper, we aim to design a distributed algorithm for this problem that works well in theory and practice. In particular, we present a simple algorithm based on contractions and provide a scalable implementation of it in MapReduce. On the theoretical side, in addition to showing $\Oh(\log n)$ convergence for all graphs, we prove an $\Oh(\log \log n)$ parallel running time with high probability for a certain class of random graphs. We work in the MPC model that captures popular parallel computing frameworks, such as MapReduce, Hadoop or Spark. On the practical side, we show that our algorithm outperforms the state-of-the-art MapReduce algorithms. To confirm its scalability, we report empirical results on graphs with several trillions of edges.
Dynamic Placement of VNF Chains for Proactive Caching in Mobile Edge Networks
Venue: arXiv cs.NI 2018
Year: 2018

Notwithstanding the significant research effort Network Function Virtualization (NFV) architectures received over the last few years little attention has been placed on optimizing proactive caching when considering it as a service chain. Since caching of popular content is envisioned to be one of the key technologies in emerging 5G networks to increase network efficiency and overall end user perceived quality of service we explicitly consider in this paper the interplay and subsequent optimization of caching based VNF service chains. To this end, we detail a novel mathematical programming framework tailored to VNF caching chains and detail also a scale-free heuristic to provide competitive solutions for large network instances since the problem itself can be seen as a variant of the classical NP-hard Uncapacitated Facility Location (UFL) problem. A wide set of numerical investigations are presented for characterizing the attainable system performance of the proposed schemes.
One-Shot Generation of Near-Optimal Topology through Theory-Driven Machine Learning
Venue: arXiv cs.LG 2018
Year: 2018

We introduce a theory-driven mechanism for learning a neural network model that performs generative topology design in one shot given a problem setting, circumventing the conventional iterative process that computational design tasks usually entail. The proposed mechanism can lead to machines that quickly response to new design requirements based on its knowledge accumulated through past experiences of design generation. Achieving such a mechanism through supervised learning would require an impractically large amount of problem-solution pairs for training, due to the known limitation of deep neural networks in knowledge generalization. To this end, we introduce an interaction between a student (the neural network) and a teacher (the optimality conditions underlying topology optimization): The student learns from existing data and is tested on unseen problems. Deviation of the student's solutions from the optimality conditions is quantified, and used for choosing new data points to learn from. We call this learning mechanism "theory-driven", as it explicitly uses domain-specific theories to guide the learning, thus distinguishing itself from purely data-driven supervised learning. We show through a compliance minimization problem that the proposed learning mechanism leads to topology generation with near-optimal structural compliance, much improved from standard supervised learning under the same computational budget.
Sparse Matrix Code Dependence Analysis Simplification at Compile Time
Venue: arXiv cs.PL 2018
Year: 2018

Analyzing array-based computations to determine data dependences is useful for many applications including automatic parallelization, race detection, computation and communication overlap, verification, and shape analysis. For sparse matrix codes, array data dependence analysis is made more difficult by the use of index arrays that make it possible to store only the nonzero entries of the matrix (e.g., in A[B[i]], B is an index array). Here, dependence analysis is often stymied by such indirect array accesses due to the values of the index array not being available at compile time. Consequently, many dependences cannot be proven unsatisfiable or determined until runtime. Nonetheless, index arrays in sparse matrix codes often have properties such as monotonicity of index array elements that can be exploited to reduce the amount of runtime analysis needed. In this paper, we contribute a formulation of array data dependence analysis that includes encoding index array properties as universally quantified constraints. This makes it possible to leverage existing SMT solvers to determine whether such dependences are unsatisfiable and significantly reduces the number of dependences that require runtime analysis in a set of eight sparse matrix kernels. Another contribution is an algorithm for simplifying the remaining satisfiable data dependences by discovering equalities and/or subset relationships. These simplifications are essential to make a runtime-inspection-based approach feasible.
A Survey of Cyber Security Countermeasures Using Hardware Performance Counters
Venue: arXiv cs.CR 2018
Year: 2018

Cyber attacks and malware are now more prevalent than ever and the trend is ever upward. There have been several approaches to attack detection including resident software applications at the root or user level, e.g., virus detection, and modifications to the OS, e.g., encryption, application signing, etc. Some approaches have moved to lower level detection and preven- tion, e.g., Data Execution Prevention. An emerging approach in countermeasure development is the use of hardware performance counters existing in the micro-architecture of modern processors. These are at the lowest level, implemented in processor hardware, and the wealth of data collected by these counters affords some very promising countermeasures with minimal overhead as well as protection from being sabotaged themselves by attackers. Here, we conduct a survey of recent techniques in realizing effective countermeasures for cyber attack detection from these hardware performance counters.
Transportation Modes Classification Using Feature Engineering
Venue: arXiv cs.LG 2018
Year: 2018

Predicting transportation modes from GPS (Global Positioning System) records is a hot topic in the trajectory mining domain. Each GPS record is called a trajectory point and a trajectory is a sequence of these points. Trajectory mining has applications including but not limited to transportation mode detection, tourism, traffic congestion, smart cities management, animal behaviour analysis, environmental preservation, and traffic dynamics are some of the trajectory mining applications. Transportation modes prediction as one of the tasks in human mobility and vehicle mobility applications plays an important role in resource allocation, traffic management systems, tourism planning and accident detection. In this work, the proposed framework in Etemad et al. is extended to consider other aspects in the task of transportation modes prediction. Wrapper search and information retrieval methods were investigated to find the best subset of trajectory features. Finding the best classifier and the best feature subset, the framework is compared against two related papers that applied deep learning methods. The results show that our framework achieved better performance. Moreover, the ground truth noise removal improved accuracy of transportation modes prediction task; however, the assumption of having access to test set labels in pre-processing task is invalid. Furthermore, the cross validation approaches were investigated and the performance results show that the random cross validation method provides optimistic results.
Team Diagonalization
Venue: arXiv cs.CC 2018
Year: 2018

Ten years ago, Glaßer, Pavan, Selman, and Zhang [GPSZ08] proved that if P $\neq$ NP, then all NP-complete sets can be simply split into two NP-complete sets. That advance might naturally make one wonder about a quite different potential consequence of NP-completeness: Can the union of easy NP sets ever be hard? In particular, can the union of two non-NP-complete NP sets ever be NP-complete? Amazingly, Ladner [Lad75] resolved this more than forty years ago: If P $\neq$ NP, then all NP-complete sets can be simply split into two non-NP-complete NP sets. Indeed, this holds even when one requires the two non-NP-complete NP sets to be disjoint. We present this result as a mini-tutorial. We give a relatively detailed proof of this result, using the same technique and idea Ladner [Lad75] invented and used in proving a rich collection of results that include many that are more general than this result: delayed diagonalization. In particular, the proof presented is based on what one can call team diagonalization (or if one is being playful, perhaps even tag-team diagonalization): Multiple sets are formed separately by delayed diagonalization, yet those diagonalizations are mutually aware and delay some of their actions until their partner(s) have also succeeded in some coordinated action. We relatedly note that, as a consequence of Ladner's result, if P $\neq$ NP, there exist OptP functions f and g whose composition is NP-hard yet neither f nor g is NP-hard.
Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model
Venue: arXiv cs.CV 2018
Year: 2018

Omnidirectional video enables spherical stimuli with the $360 \times 180^ \circ$ viewing range. Meanwhile, only the viewport region of omnidirectional video can be seen by the observer through head movement (HM), and an even smaller region within the viewport can be clearly perceived through eye movement (EM). Thus, the subjective quality of omnidirectional video may be correlated with HM and EM of human behavior. To fill in the gap between subjective quality and human behavior, this paper proposes a large-scale visual quality assessment (VQA) dataset of omnidirectional video, called VQA-OV, which collects 60 reference sequences and 540 impaired sequences. Our VQA-OV dataset provides not only the subjective quality scores of sequences but also the HM and EM data of subjects. By mining our dataset, we find that the subjective quality of omnidirectional video is indeed related to HM and EM. Hence, we develop a deep learning model, which embeds HM and EM, for objective VQA on omnidirectional video. Experimental results show that our model significantly improves the state-of-the-art performance of VQA on omnidirectional video.
Optimum Depth of the Bounded Pipeline
Venue: arXiv cs.DC 2018
Year: 2018

The paper is devoted to studying the performance of a computational pipeline, the number of simultaneously executing stages of which at each time is bounded from above by a fixed number. A look at the restriction as a structural hazard makes it possible to construct an analytical model for calculating the processing time of a given input data amount. Using this model, led to a formula for calculating the optimal depth of a bounded pipeline for a given volume of input data. The formula shows that the optimal depth can get large changes for small changes in the amount of data. To eliminate this disadvantage and to obtain a more convenient formula for optimal depth, a pipeline with a single random hazard is constructed, the mathematical expectation of a random value of the processing time of which approximates the analytical model of the bounded pipeline. In addition, a pipeline with two hazards has been built, the analytical model of which allowed obtaining formulas for calculating the optimal depth of a bounded pipeline with restart for a given amount of data. To check whether the proposed analytical models are consistent with the experiments to calculate the processing time, two methods of computer simulation of bounded pipelines are used, the first of which is constructed as a multi-threaded application, and the second is based on the theory of free partially commutative monoids.
Texture Mixing by Interpolating Deep Statistics via Gaussian Models
Venue: arXiv cs.CV 2018
Year: 2018

Recently, enthusiastic studies have devoted to texture synthesis using deep neural networks, because these networks excel at handling complex patterns in images. In these models, second-order statistics, such as Gram matrix, are used to describe textures. Despite the fact that these model have achieved promising results, the structure of their parametric space is still unclear, consequently, it is difficult to use them to mix textures. This paper addresses the texture mixing problem by using a Gaussian scheme to interpolate deep statistics computed from deep neural networks. More precisely, we first reveal that the statistics used in existing deep models can be unified using a stationary Gaussian scheme. We then present a novel algorithm to mix these statistics by interpolating between Gaussian models using optimal transport. We further apply our scheme to Neural Style Transfer, where we can create mixed styles. The experiments demonstrate that our method can achieve state-of-the-art results. Because all the computations are implemented in closed forms, our mixing algorithm adds only negligible time to the original texture synthesis procedure.
MPTCP meets FEC: Supporting Latency-Sensitive Applications over Heterogeneous Networks
Venue: arXiv cs.NI 2018
Year: 2018

Over the past years, TCP has gone through numerous updates to provide performance enhancement under diverse network conditions. However, with respect to losses, little can be achieved with legacy TCP detection and recovery mechanisms. Both fast retransmission and retransmission timeout take at least one extra round trip time to perform, and this might significantly impact performance of latency-sensitive applications, especially in lossy or high delay networks. While forward error correction (FEC) is not a new initiative in this direction, the majority of the approaches consider FEC inside the application. In this paper, we design and implement a framework, where FEC is integrated within TCP. Our main goal with this design choice is to enable latency sensitive applications over TCP in high delay and lossy networks, but remaining application agnostic. We further incorporate this design into multipath TCP (MPTCP), where we focus particularly on heterogeneous settings, considering the fact that TCP recovery mechanisms further escalate head-of-line blocking in multipath. We evaluate the performance of the proposed framework and show that such a framework can bring significant benefits compared to legacy TCP and MPTCP for latency-sensitive real application traffic, such as video streaming and web services.