OpenRaiser/Intern-Atlas huggingface dataset:
A Gabor Filter Texture Analysis Approach for Histopathological Brain Tumor Subtype Discrimination
Venue: arXiv cs.CV 2017
Year: 2017

Meningioma brain tumour discrimination is challenging as many histological patterns are mixed between the different subtypes. In clinical practice, dominant patterns are investigated for signs of specific meningioma pathology; however the simple observation could result in inter- and intra-observer variation due to the complexity of the histopathological patterns. Also employing a computerised feature extraction approach applied at a single resolution scale might not suffice in accurately delineating the mixture of histopathological patterns. In this work we propose a novel multiresolution feature extraction approach for characterising the textural properties of the different pathological patterns (i.e. mainly cell nuclei shape, orientation and spatial arrangement within the cytoplasm). The pattern textural properties are characterised at various scales and orientations for an improved separability between the different extracted features. The Gabor filter energy output of each magnitude response was combined with four other fixed-resolution texture signatures (2 model-based and 2 statistical-based) with and without cell nuclei segmentation. The highest classification accuracy of 95% was reported when combining the Gabor filters energy and the meningioma subimage fractal signature as a feature vector without performing any prior cell nuceli segmentation. This indicates that characterising the cell-nuclei self-similarity properties via Gabor filters can assists in achieving an improved meningioma subtype classification, which can assist in overcoming variations in reported diagnosis.
Performance Impact of Base Station Antenna Heights in Dense Cellular Networks
Venue: arXiv cs.NI 2017
Year: 2017

In this paper, we present a new and significant theoretical discovery. If the absolute height difference between base station (BS) antenna and user equipment (UE) antenna is larger than zero, then the network performance in terms of both the coverage probability and the area spectral efficiency (ASE) will continuously decrease toward zero as the BS density increases for ultra-dense (UD) small cell networks (SCNs). Such findings are completely different from the conclusions in existing works, both quantitatively and qualitatively. In particular, this performance behavior has a tremendous impact on the deployment of UD SCNs in the 5th-generation (5G) era. Network operators may invest large amounts of money in deploying more network infrastructure to only obtain an even less network capacity. Our study results reveal that one way to address this issue is to lower the SCN BS antenna height to the UE antenna height. However, this requires a revolutionized approach of BS architecture and deployment, which is explored in this paper too.
Exploiting Data Longevity for Enhancing the Lifetime of Flash-based Storage Class Memory
Venue: arXiv cs.AR 2017
Year: 2017

Storage-class memory (SCM) combines the benefits of a solid-state memory, such as high-performance and robustness, with the archival capabilities and low cost of conventional hard-disk magnetic storage. Among candidate solid-state nonvolatile memory technologies that could potentially be used to construct SCM, flash memory is a well-established technology and have been widely used in commercially available SCM incarnations. Flash-based SCM enables much better tradeoffs between performance, space and power than disk-based systems. However, write endurance is a significant challenge for a flash-based SCM (each act of writing a bit may slightly damage a cell, so one flash cell can be written 10^4--10^5 times, depending on the flash technology, before it becomes unusable). This is a well-documented problem and has received a lot of attention by manufactures that are using some combination of write reduction and wear-leveling techniques for achieving longer lifetime. In an effort to improve flash lifetime, first, by quantifying data longevity in an SCM, we show that a majority of the data stored in a solid-state SCM do not require long retention times provided by flash memory (i.e., up to 10 years in modern devices); second, by exploiting retention time relaxation, we propose a novel mechanism, called Dense-SLC (D-SLC), which enables us perform multiple writes into a cell during each erase cycle for lifetime extension; and finally, we discuss the required changes in the flash management software (FTL) in order to use this characteristic for extending the lifetime of the solid-state part of an SCM. Using an extensive simulation-based analysis of a flash-based SCM, we demonstrate that D-SLC is able to significantly improve device lifetime (between 5.1X and 8.6X) with no performance overhead and also very small changes at the FTL software.
Algorithms for Pattern Containment in 0-1 Matrices
Venue: arXiv cs.DM 2017
Year: 2017

We say a zero-one matrix $A$ avoids another zero-one matrix $P$ if no submatrix of $A$ can be transformed to $P$ by changing some ones to zeros. A fundamental problem is to study the extremal function $ex(n,P)$, the maximum number of nonzero entries in an $n \times n$ zero-one matrix $A$ which avoids $P$. To calculate exact values of $ex(n,P)$ for specific values of $n$, we need containment algorithms which tell us whether a given $n \times n$ matrix $A$ contains a given pattern matrix $P$. In this paper, we present optimal algorithms to determine when an $n \times n$ matrix $A$ contains a given pattern $P$ when $P$ is a column of all ones, an identity matrix, a tuple identity matrix, an $L$-shaped pattern, or a cross pattern. These algorithms run in $Θ(n^2)$ time, which is the lowest possible order a containment algorithm can achieve. When $P$ is a rectangular all-ones matrix, we also obtain an improved running time algorithm, albeit with a higher order.
Fast 2-D Complex Gabor Filter with Kernel Decomposition
Venue: arXiv cs.CV 2017
Year: 2017

2-D complex Gabor filtering has found numerous applications in the fields of computer vision and image processing. Especially, in some applications, it is often needed to compute 2-D complex Gabor filter bank consisting of the 2-D complex Gabor filtering outputs at multiple orientations and frequencies. Although several approaches for fast 2-D complex Gabor filtering have been proposed, they primarily focus on reducing the runtime of performing the 2-D complex Gabor filtering once at specific orientation and frequency. To obtain the 2-D complex Gabor filter bank output, existing methods are repeatedly applied with respect to multiple orientations and frequencies. In this paper, we propose a novel approach that efficiently computes the 2-D complex Gabor filter bank by reducing the computational redundancy that arises when performing the Gabor filtering at multiple orientations and frequencies. The proposed method first decomposes the Gabor basis kernels to allow a fast convolution with the Gaussian kernel in a separable manner. This enables reducing the runtime of the 2-D complex Gabor filter bank by reusing intermediate results of the 2-D complex Gabor filtering computed at a specific orientation. Furthermore, we extend this idea into 2-D localized sliding discrete Fourier transform (SDFT) using the Gaussian kernel in the DFT computation, which lends a spatial localization ability as in the 2-D complex Gabor filter. Experimental results demonstrate that our method runs faster than state-of-the-arts methods for fast 2-D complex Gabor filtering, while maintaining similar filtering quality.
Robust Optical Flow Estimation in Rainy Scenes
Venue: arXiv cs.CV 2017
Year: 2017

Optical flow estimation in the rainy scenes is challenging due to background degradation introduced by rain streaks and rain accumulation effects in the scene. Rain accumulation effect refers to poor visibility of remote objects due to the intense rainfall. Most existing optical flow methods are erroneous when applied to rain sequences because the conventional brightness constancy constraint (BCC) and gradient constancy constraint (GCC) generally break down in this situation. Based on the observation that the RGB color channels receive raindrop radiance equally, we introduce a residue channel as a new data constraint to reduce the effect of rain streaks. To handle rain accumulation, our method decomposes the image into a piecewise-smooth background layer and a high-frequency detail layer. It also enforces the BCC on the background layer only. Results on both synthetic dataset and real images show that our algorithm outperforms existing methods on different types of rain sequences. To our knowledge, this is the first optical flow method specifically dealing with rain.
A Tractable Approach to Dynamic Network Dimensioning Based on the Best-cell Configuration
Venue: arXiv cs.NI 2017
Year: 2017

Spatial distributions of other cell interference (OCIF) and interference to own-cell power ratio (IOPR) with reference to the distance between a mobile and its serving base station (BS) are modeled for the down-link reception of cellular systems based on the best-cell configuration instead of the nearest-cell configuration. This enables a more realistic evaluation of two competing objectives in network dimensioning: coverage and rate capacity. More outcomes useful for dynamic network dimensioning are also derived, including maximum BS transmission power per cell size and the cell density required for an adequate coverage of a given traffic density.
The Application of Distributed Control Algorithms using VOLTTRON-based Software Platform
Venue: arXiv cs.SY 2017
Year: 2017

This paper gives an insight into the applications of an open-source control system platform named VOLTTRON. This platform was developed by the Pacific Northwest National Laboratory. A brief introduction is given on the functionality and key features of the platform. Potential applications in the areas of building control and electric vehicle charging are stated, along with an overview of existing projects. A comparison is also made between VOLTTRON and other related software. An actual implementation case of VOLTTRON is then presented in the case study. The demonstration uses the VOLTTRON platform as a message bus. Decentralized generators and consumers are simulated by 16 single-board computers.
A Study of Deep Learning Robustness Against Computation Failures
Venue: arXiv cs.NE 2017
Year: 2017

For many types of integrated circuits, accepting larger failure rates in computations can be used to improve energy efficiency. We study the performance of faulty implementations of certain deep neural networks based on pessimistic and optimistic models of the effect of hardware faults. After identifying the impact of hyperparameters such as the number of layers on robustness, we study the ability of the network to compensate for computational failures through an increase of the network size. We show that some networks can achieve equivalent performance under faulty implementations, and quantify the required increase in computational complexity.
Waveform Design for Wireless Power Transfer with Limited Feedback
Venue: arXiv cs.IT 2017
Year: 2017

Waveform design is a key technique to jointly exploit a beamforming gain, the channel frequency-selectivity and the rectifier nonlinearity, so as to enhance the end-to-end power transfer efficiency of Wireless Power Transfer (WPT). Those waveforms have been designed assuming perfect channel state information at the transmitter. This paper proposes two waveform strategies relying on limited feedback for multi-antenna multi-sine WPT over frequency-selective channels. In the waveform selection strategy, the Energy Transmitter (ET) transmits over multiple timeslots with every time a different waveform precoder within a codebook, and the Energy Receiver (ER) reports the index of the precoder in the codebook that leads to the largest harvested energy. In the waveform refinement strategy, the ET sequentially transmits two waveforms in each stage, and the ER reports one feedback bit indicating an increase/decrease in the harvested energy during this stage. Based on multiple one-bit feedback, the ET successively refines waveform precoders in a tree-structured codebook over multiple stages. By employing the framework of the generalized Lloyd's algorithm, novel algorithms are proposed for both strategies to optimize the codebooks in both space and frequency domains. The proposed limited feedback-based waveform strategies are shown to outperform a set of baselines, achieving higher harvested energy.
A Fast Implementation of Singular Value Thresholding Algorithm using Recycling Rank Revealing Randomized Singular Value Decomposition
Venue: arXiv cs.NA 2017
Year: 2017

In this paper, we present a fast implementation of the Singular Value Thresholding (SVT) algorithm for matrix completion. A rank-revealing randomized singular value decomposition (R3SVD) algorithm is used to adaptively carry out partial singular value decomposition (SVD) to fast approximate the SVT operator given a desired, fixed precision. We extend the R3SVD algorithm to a recycling rank revealing randomized singular value decomposition (R4SVD) algorithm by reusing the left singular vectors obtained from the previous iteration as the approximate basis in the current iteration, where the computational cost for partial SVD at each SVT iteration is significantly reduced. A simulated annealing style cooling mechanism is employed to adaptively adjust the low-rank approximation precision threshold as SVT progresses. Our fast SVT implementation is effective in both large and small matrices, which is demonstrated in matrix completion applications including image recovery and movie recommendation system.
Simultaneous Policy Learning and Latent State Inference for Imitating Driver Behavior
Venue: arXiv cs.LG 2017
Year: 2017

In this work, we propose a method for learning driver models that account for variables that cannot be observed directly. When trained on a synthetic dataset, our models are able to learn encodings for vehicle trajectories that distinguish between four distinct classes of driver behavior. Such encodings are learned without any knowledge of the number of driver classes or any objective that directly requires the models to learn encodings for each class. We show that driving policies trained with knowledge of latent variables are more effective than baseline methods at imitating the driver behavior that they are trained to replicate. Furthermore, we demonstrate that the actions chosen by our policy are heavily influenced by the latent variable settings that are provided to them.
Proposal of Vital Data Analysis Platform using Wearable Sensor
Venue: arXiv cs.DC 2017
Year: 2017

In this paper, we propose a vital data analysis platform which resolves existing problems to utilize vital data for real-time actions. Recently, IoT technologies have been progressed but in the healthcare area, real-time actions based on analyzed vital data are not considered sufficiently yet. The causes are proper use of analyzing methods of stream / micro batch processing and network cost. To resolve existing problems, we propose our vital data analysis platform. Our platform collects vital data of Electrocardiograph and acceleration using an example of wearable vital sensor and analyzes them to extract posture, fatigue and relaxation in smart phones or cloud. Our platform can show analyzed dangerous posture or fatigue level change. We implemented the platform. And we are now preparing a field test.
Testing Docker Performance for HPC Applications
Venue: arXiv cs.PF 2017
Year: 2017

The main goal for this article is to compare performance penalties when using KVM virtualization and Docker containers for creating isolated environments for HPC applications. The article provides both data obtained using commonly accepted synthetic tests (High Performance Linpack) and real life applications (OpenFOAM). The article highlights the influence on resulting application performance of major infrastructure configuration options: CPU type presented to VM, networking connection type used.
A Technical Report on PLS-Completeness of Single-Swap for Unweighted Metric Facility Location and $K$-Means
Venue: arXiv cs.CC 2017
Year: 2017

Recently, [Bra17] showed that the single-swap heuristic for weighted metric uncapacitated facility location and $K$-Means is tightly PLS-complete. We build upon this work and present a stronger reduction, which proves tight PLS-completeness for the unweighted version of both problems.
Skeleton Boxes: Solving skeleton based action detection with a single deep convolutional neural network
Venue: arXiv cs.CV 2017
Year: 2017

Action recognition from well-segmented 3D skeleton video has been intensively studied. However, due to the difficulty in representing the 3D skeleton video and the lack of training data, action detection from streaming 3D skeleton video still lags far behind its recognition counterpart and image based object detection. In this paper, we propose a novel approach for this problem, which leverages both effective skeleton video encoding and deep regression based object detection from images. Our framework consists of two parts: skeleton-based video image mapping, which encodes a skeleton video to a color image in a temporal preserving way, and an end-to-end trainable fast skeleton action detector (Skeleton Boxes) based on image detection. Experimental results on the latest and largest PKU-MMD benchmark dataset demonstrate that our method outperforms the state-of-the-art methods with a large margin. We believe our idea would inspire and benefit future research in this important area.
Analytical study of the "master-worker" framework scalability on multiprocessors with distributed memory
Venue: arXiv cs.DC 2017
Year: 2017

The paper is devoted to an analytical study of the "master-worker" framework scalability on multiprocessors with distributed memory. A new model of parallel computations called BSF is proposed. The BSF model is based on BSP and SPMD models. The scope of BSF model is the compute-intensive applications. The architecture of BSF-computer is defined. The structure of BSF-program is described. The Using this metric, the upper scalability bounds of BSF programs on distributed memory multiprocessors are evaluated. The formulas for estimating the parallel efficiency of BSF programs also proposed.
Learn to Model Motion from Blurry Footages
Venue: arXiv cs.CV 2017
Year: 2017

It is difficult to recover the motion field from a real-world footage given a mixture of camera shake and other photometric effects. In this paper we propose a hybrid framework by interleaving a Convolutional Neural Network (CNN) and a traditional optical flow energy. We first conduct a CNN architecture using a novel learnable directional filtering layer. Such layer encodes the angle and distance similarity matrix between blur and camera motion, which is able to enhance the blur features of the camera-shake footages. The proposed CNNs are then integrated into an iterative optical flow framework, which enable the capability of modelling and solving both the blind deconvolution and the optical flow estimation problems simultaneously. Our framework is trained end-to-end on a synthetic dataset and yields competitive precision and performance against the state-of-the-art approaches.
End-to-End Multi-View Networks for Text Classification
Venue: arXiv cs.CL 2017
Year: 2017

We propose a multi-view network for text classification. Our method automatically creates various views of its input text, each taking the form of soft attention weights that distribute the classifier's focus among a set of base features. For a bag-of-words representation, each view focuses on a different subset of the text's words. Aggregating many such views results in a more discriminative and robust representation. Through a novel architecture that both stacks and concatenates views, we produce a network that emphasizes both depth and width, allowing training to converge quickly. Using our multi-view architecture, we establish new state-of-the-art accuracies on two benchmark tasks.
Equitable Colorings of $l$-Corona Products of Cubic Graphs
Venue: arXiv cs.DM 2017
Year: 2017

A graph $G$ is equitably $k$-colorable if its vertices can be partitioned into $k$ independent sets in such a way that the number of vertices in any two sets differ by at most one. The smallest integer $k$ for which such a coloring exists is known as the \emph{equitable chromatic number} of $G$ and it is denoted by $χ_{=}(G)$. In this paper the problem of determinig the value of equitable chromatic number for multicoronas of cubic graphs $G \circ^l H$ is studied. The problem of ordinary coloring of multicoronas of cubic graphs is solvable in polynomial time. The complexity of equitable coloring problem is an open question for these graphs. We provide some polynomially solvable cases of cubical multicoronas and give simple linear time algorithms for equitable coloring of such graphs which use at most $χ_=(G \circ ^l H) + 1$ colors in the remaining cases.
Unassisted Quantitative Evaluation Of Despeckling Filters
Venue: arXiv cs.CV 2017
Year: 2017

SAR (Synthetic Aperture Radar) imaging plays a central role in Remote Sensing due to, among other important features, its ability to provide high-resolution, day-and-night and almost weather-independent images. SAR images are affected from a granular contamination, speckle, that can be described by a multiplicative model. Many despeckling techniques have been proposed in the literature, as well as measures of the quality of the results they provide. Assuming the multiplicative model, the observed image $Z$ is the product of two independent fields: the backscatter $X$ and the speckle $Y$. The result of any speckle filter is $\widehat X$, an estimator of the backscatter $X$, based solely on the observed data $Z$. An ideal estimator would be the one for which the ratio of the observed image to the filtered one $I=Z/\widehat X$ is only speckle: a collection of independent identically distributed samples from Gamma variates. We, then, assess the quality of a filter by the closeness of $I$ to the hypothesis that it is adherent to the statistical properties of pure speckle. We analyze filters through the ratio image they produce with regards to first- and second-order statistics: the former check marginal properties, while the latter verifies lack of structure. A new quantitative image-quality index is then defined, and applied to state-of-the-art despeckling filters. This new measure provides consistent results with commonly used quality measures (equivalent number of looks, PSNR, MSSIM, $β$ edge correlation, and preservation of the mean), and ranks the filters results also in agreement with their visual analysis. We conclude our study showing that the proposed measure can be successfully used to optimize the (often many) parameters that define a speckle filter.
Temporal Clustering
Venue: arXiv cs.DS 2017
Year: 2017

We study the problem of clustering sequences of unlabeled point sets taken from a common metric space. Such scenarios arise naturally in applications where a system or process is observed in distinct time intervals, such as biological surveys and contagious disease surveillance. In this more general setting existing algorithms for classical (i.e.~static) clustering problems are not applicable anymore. We propose a set of optimization problems which we collectively refer to as 'temporal clustering'. The quality of a solution to a temporal clustering instance can be quantified using three parameters: the number of clusters $k$, the spatial clustering cost $r$, and the maximum cluster displacement $δ$ between consecutive time steps. We consider spatial clustering costs which generalize the well-studied $k$-center, discrete $k$-median, and discrete $k$-means objectives of classical clustering problems. We develop new algorithms that achieve trade-offs between the three objectives $k$, $r$, and $δ$. Our upper bounds are complemented by inapproximability results.