OpenRaiser/Intern-Atlas huggingface dataset:
Semi-Automatic Segmentation of Autosomal Dominant Polycystic Kidneys using Random Forests
Venue: arXiv cs.CV 2015
Year: 2015

This paper presents a method for 3D segmentation of kidneys from patients with autosomal dominant polycystic kidney disease (ADPKD) and severe renal insufficiency, using computed tomography (CT) data. ADPKD severely alters the shape of the kidneys due to non-uniform formation of cysts. As a consequence, fully automatic segmentation of such kidneys is very challenging. We present a segmentation method with minimal user interaction based on a random forest classifier. One of the major novelties of the proposed approach is the usage of geodesic distance volumes as additional source of information. These volumes contain the intensity weighted distance to a manual outline of the respective kidney in only one slice (for each kidney) of the CT volume. We evaluate our method qualitatively and quantitatively on 55 CT acquisitions using ground truth annotations from clinical experts.
Opacity Proof for CaPR+ Algorithm
Venue: arXiv cs.DC 2015
Year: 2015

In this paper, we describe an enhanced Automatic Check- pointing and Partial Rollback algorithm(CaP R + ) to realize Software Transactional Memory(STM) that is based on con- tinuous conflict detection, lazy versioning with automatic checkpointing, and partial rollback. Further, we provide a proof of correctness of CaP R+ algorithm, in particular, Opacity, a STM correctness criterion, that precisely captures the intuitive correctness guarantees required of transactional memories. The algorithm provides a natural way to realize a hybrid system of pure aborts and partial rollbacks. We have also implemented the algorithm, and shown its effectiveness with reference to the Red-black tree micro-benchmark and STAMP benchmarks. The results obtained demonstrate the effectiveness of the Partial Rollback mechanism over pure abort mechanisms, particularly in applications consisting of large transaction lengths.
Predicting Face Recognition Performance Using Image Quality
Venue: arXiv cs.CV 2015
Year: 2015

This paper proposes a data driven model to predict the performance of a face recognition system based on image quality features. We model the relationship between image quality features (e.g. pose, illumination, etc.) and recognition performance measures using a probability density function. To address the issue of limited nature of practical training data inherent in most data driven models, we have developed a Bayesian approach to model the distribution of recognition performance measures in small regions of the quality space. Since the model is based solely on image quality features, it can predict performance even before the actual recognition has taken place. We evaluate the performance predictive capabilities of the proposed model for six face recognition systems (two commercial and four open source) operating on three independent data sets: MultiPIE, FRGC and CAS-PEAL. Our results show that the proposed model can accurately predict performance using an accurate and unbiased Image Quality Assessor (IQA). Furthermore, our experiments highlight the impact of the unaccounted quality space -- the image quality features not considered by IQA -- in contributing to performance prediction errors.
Evolutionary Landscape and Management of Population Diversity
Venue: arXiv cs.NE 2015
Year: 2015

The search ability of an Evolutionary Algorithm (EA) depends on the variation among the individuals in the population [3, 4, 8]. Maintaining an optimal level of diversity in the EA population is imperative to ensure that progress of the EA search is unhindered by premature convergence to suboptimal solutions. Clearer understanding of the concept of population diversity, in the context of evolutionary search and premature convergence in particular, is the key to designing efficient EAs. To this end, this paper first presents a brief analysis of the EA population diversity issues. Next we present an investigation on a counter-niching EA technique [4] that introduces and maintains constructive diversity in the population. The proposed approach uses informed genetic operations to reach promising, but unexplored or under-explored areas of the search space, while discouraging premature local convergence. Simulation runs on a suite of standard benchmark test functions with Genetic Algorithm (GA) implementation shows promising results.
Statistical Parsing by Machine Learning from a Classical Arabic Treebank
Venue: arXiv cs.CL 2015
Year: 2015

Research into statistical parsing for English has enjoyed over a decade of successful results. However, adapting these models to other languages has met with difficulties. Previous comparative work has shown that Modern Arabic is one of the most difficult languages to parse due to rich morphology and free word order. Classical Arabic is the ancient form of Arabic, and is understudied in computational linguistics, relative to its worldwide reach as the language of the Quran. The thesis is based on seven publications that make significant contributions to knowledge relating to annotating and parsing Classical Arabic. A central argument of this thesis is that using a hybrid representation closely aligned to traditional grammar leads to improved parsing for Arabic. To test this hypothesis, two approaches are compared. As a reference, a pure dependency parser is adapted using graph transformations, resulting in an 87.47% F1-score. This is compared to an integrated parsing model with an F1-score of 89.03%, demonstrating that joint dependency-constituency parsing is better suited to Classical Arabic.
Impact of Dual Slope Path Loss on User Association in HetNets
Venue: arXiv cs.IT 2015
Year: 2015

Intelligent load balancing is essential to fully realize the benefits of dense heterogeneous networks. Current techniques have largely been studied with single slope path loss models, though multi-slope models are known to more closely match real deployments. This paper develops insight into the performance of biasing and uplink/downlink decoupling for user association in HetNets with dual slope path loss models. It is shown that dual slope path loss models change the tradeoffs inherent in biasing and reduce gains from both biasing and uplink/downlink decoupling. The results show that with the dual slope path loss models, the bias maximizing the median rate is not optimal for other users, e.g., edge users. Furthermore, optimal downlink biasing is shown to realize most of the gains from downlink-uplink decoupling. Moreover, the user association gains in dense networks are observed to be quite sensitive to the path loss exponent beyond the critical distance in a dual slope model.
A Hybrid Approach for Speech Enhancement Using MoG Model and Neural Network Phoneme Classifier
Venue: arXiv cs.SD 2015
Year: 2015

In this paper we present a single-microphone speech enhancement algorithm. A hybrid approach is proposed merging the generative mixture of Gaussians (MoG) model and the discriminative neural network (NN). The proposed algorithm is executed in two phases, the training phase, which does not recur, and the test phase. First, the noise-free speech power spectral density (PSD) is modeled as a MoG, representing the phoneme based diversity in the speech signal. An NN is then trained with phoneme labeled database for phoneme classification with mel-frequency cepstral coefficients (MFCC) as the input features. Given the phoneme classification results, a speech presence probability (SPP) is obtained using both the generative and discriminative models. Soft spectral subtraction is then executed while simultaneously, the noise estimation is updated. The discriminative NN maintain the continuity of the speech and the generative phoneme-based MoG preserves the speech spectral structure. Extensive experimental study using real speech and noise signals is provided. We also compare the proposed algorithm with alternative speech enhancement algorithms. We show that we obtain a significant improvement over previous methods in terms of both speech quality measures and speech recognition results.
Finding Temporally Consistent Occlusion Boundaries in Videos using Geometric Context
Venue: arXiv cs.CV 2015
Year: 2015

We present an algorithm for finding temporally consistent occlusion boundaries in videos to support segmentation of dynamic scenes. We learn occlusion boundaries in a pairwise Markov random field (MRF) framework. We first estimate the probability of an spatio-temporal edge being an occlusion boundary by using appearance, flow, and geometric features. Next, we enforce occlusion boundary continuity in a MRF model by learning pairwise occlusion probabilities using a random forest. Then, we temporally smooth boundaries to remove temporal inconsistencies in occlusion boundary estimation. Our proposed framework provides an efficient approach for finding temporally consistent occlusion boundaries in video by utilizing causality, redundancy in videos, and semantic layout of the scene. We have developed a dataset with fully annotated ground-truth occlusion boundaries of over 30 videos ($5000 frames). This dataset is used to evaluate temporal occlusion boundaries and provides a much needed baseline for future studies. We perform experiments to demonstrate the role of scene layout, and temporal information for occlusion reasoning in dynamic scenes.
Enhancing Non-Orthogonal Multiple Access By Forming Relaying Broadcast Channels
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, using relaying broadcast channels (RBCs) as component channels for non-orthogonal multiple access (NOMA) is proposed to enhance the performance of NOMA in single-input single-output (SISO) cellular downlink systems. To analyze the performance of the proposed scheme, an achievable rate region of a RBC with compress-and-forward (CF) relaying is newly derived based on the recent work of noisy network coding (NNC). Based on the analysis of the achievable rate region of a RBC with decode-and-forward (DF) relaying, CF relaying, or CF relaying with dirty-paper coding (DPC) at the transmitter, the overall system performance of NOMA equipped with RBC component channels is investigated. It is shown that NOMA with RBC-DF yields marginal gain and NOMA with RBC-CF/DPC yields drastic gain over the simple NOMA based on broadcast component channels in a practical system setup. By going beyond simple broadcast channel (BC)/successive interference cancellation (SIC) to advanced multi-terminal encoding including DPC and CF/NNC, far larger gains can be obtained for NOMA.
The Human Kernel
Venue: arXiv cs.LG 2015
Year: 2015

Bayesian nonparametric models, such as Gaussian processes, provide a compelling framework for automatic statistical modelling: these models have a high degree of flexibility, and automatically calibrated complexity. However, automating human expertise remains elusive; for example, Gaussian processes with standard kernels struggle on function extrapolation problems that are trivial for human learners. In this paper, we create function extrapolation problems and acquire human responses, and then design a kernel learning framework to reverse engineer the inductive biases of human learners across a set of behavioral experiments. We use the learned kernels to gain psychological insights and to extrapolate in human-like ways that go beyond traditional stationary and polynomial kernels. Finally, we investigate Occam's razor in human and Gaussian process based function learning.
Aggregating Deep Convolutional Features for Image Retrieval
Venue: arXiv cs.CV 2015
Year: 2015

Several recent works have shown that image descriptors produced by deep convolutional neural networks provide state-of-the-art performance for image classification and retrieval problems. It has also been shown that the activations from the convolutional layers can be interpreted as local features describing particular image regions. These local features can be aggregated using aggregation approaches developed for local features (e.g. Fisher vectors), thus providing new powerful global descriptors. In this paper we investigate possible ways to aggregate local deep features to produce compact global descriptors for image retrieval. First, we show that deep features and traditional hand-engineered features have quite different distributions of pairwise similarities, hence existing aggregation methods have to be carefully re-evaluated. Such re-evaluation reveals that in contrast to shallow features, the simple aggregation method based on sum pooling provides arguably the best performance for deep convolutional features. This method is efficient, has few parameters, and bears little risk of overfitting when e.g. learning the PCA matrix. Overall, the new compact global descriptor improves the state-of-the-art on four common benchmarks considerably.
A multi-mode area-efficient SCL polar decoder
Venue: arXiv cs.IT 2015
Year: 2015

Polar codes are of great interest since they are the first provably capacity-achieving forward error correction codes. To improve throughput and to reduce decoding latency of polar decoders, maximum likelihood (ML) decoding units are used by successive cancellation list (SCL) decoders as well as successive cancellation (SC) decoders. This paper proposes an approximate ML (AML) decoding unit for SCL decoders first. In particular, we investigate the distribution of frozen bits of polar codes designed for both the binary erasure and additive white Gaussian noise channels, and take advantage of the distribution to reduce the complexity of the AML decoding unit, improving the area efficiency of SCL decoders. Furthermore, a multi-mode SCL decoder with variable list sizes and parallelism is proposed. If high throughput or small latency is required, the decoder decodes multiple received codewords in parallel with a small list size. However, if error performance is of higher priority, the multi-mode decoder switches to a serial mode with a bigger list size. Therefore, the multi-mode SCL decoder provides a flexible tradeoff between latency, throughput and error performance, and adapts to different throughput and latency requirements at the expense of small overhead. Hardware implementation and synthesis results show that our polar decoders not only have a better area efficiency but also easily adapt to different communication channels and applications.
A dictionary learning and source recovery based approach to classify diverse audio sources
Venue: arXiv cs.SD 2015
Year: 2015

A dictionary learning based audio source classification algorithm is proposed to classify a sample audio signal as one amongst a finite set of different audio sources. Cosine similarity measure is used to select the atoms during dictionary learning. Based on three objective measures proposed, namely, signal to distortion ratio (SDR), the number of non-zero weights and the sum of weights, a frame-wise source classification accuracy of 98.2% is obtained for twelve different sources. Cent percent accuracy has been obtained using moving SDR accumulated over six successive frames for ten of the audio sources tested, while the two other sources require accumulation of 10 and 14 frames.
Redesigning pattern mining algorithms for supercomputers
Venue: arXiv cs.DC 2015
Year: 2015

Upcoming many core processors are expected to employ a distributed memory architecture similar to currently available supercomputers, but parallel pattern mining algorithms amenable to the architecture are not comprehensively studied. We present a novel closed pattern mining algorithm with a well-engineered communication protocol, and generalize it to find statistically significant patterns from personal genome data. For distributing communication evenly, it employs global load balancing with multiple stacks distributed on a set of cores organized as a hypercube with random edges. Our algorithm achieved up to 1175-fold speedup by using 1200 cores for solving a problem with 11,914 items and 697 transactions, while the naive approach of separating the search space failed completely.
Performance evaluation of parallel TCP, and its impact on bandwidth utilization and fairness in high-BDP networks based on test-bed
Venue: arXiv cs.NI 2015
Year: 2015

After the presence of high Bandwidth-Delay Product (high-BDP) networks, many researches have been conducted to prove either the existing TCP variants can achieve an excellent performance without wasting the bandwidth of these networks or not. In this paper, a comparative test-bed experiment on a set of high speed TCP variants has been conducted to show their differences in bandwidth utilization, loss ratio and TCP-Fairness. The involved TCP Variants in this experiment are: NewReno, STCP, HS-TCP, H-TCP and CUBIC. These TCP variants have been examined in both cases of single and parallel schemes. The core of this work is how to evaluate these TCP variants over a single bottleneck network using a new parallel scheme to fully utilize the bandwidth of this network, and to show the impact of accelerating these variants on bandwidth utilization, loss-ratio and fairness. The results of this work reveal that, first: the proposed parallel scheme strongly outperforms the single based TCP in terms of bandwidth utilization and fairness. Second: CUBIC achieved better performance than NewReno, STCP, H-TCP and HS-TCP in both cases of single and parallel schemes. Briefly, parallel TCP scheme increases the utilization of network resources, and it is relatively good in fairness.
Standards for language resources in ISO -- Looking back at 13 fruitful years
Venue: arXiv cs.CL 2015
Year: 2015

This paper provides an overview of the various projects carried out within ISO committee TC 37/SC 4 dealing with the management of language (digital) resources. On the basis of the technical experience gained in the committee and the wider standardization landscape the paper identifies some possible trends for the future.
Probabilistic Formal Analysis of App Usage to Inform Redesign
Venue: arXiv cs.SE 2015
Year: 2015

This paper sets out a process of app analysis intended to support understanding of use but also redesign. From usage logs we infer activity patterns - Markov models - and employ probabilistic formal analysis to ask questions about the use of the app. The core of this paper's contribution is a bridging of stochastic and formal modelling, but we also describe the work to make that analytic core utile within a design team. We illustrate our work via a case study of a mobile app presenting analytic findings and discussing how they are feeding into redesign. We had posited that two activity patterns indicated two separable sets of users, each of which might benefit from a differently tailored app version, but our subsequent analysis detailed users' interleaving of activity patterns over time - evidence speaking more in favour of redesign that supports each pattern in an integrated way. We uncover patterns consisting of brief glances at particular data and recommend them as possible candidates for new design work on widget extensions: small displays available while users use other apps.
Downlink Power Control in Two-Tier Cellular Networks with Energy-Harvesting Small Cells as Stochastic Games
Venue: arXiv cs.NI 2015
Year: 2015

Energy harvesting in cellular networks is an emerging technique to enhance the sustainability of power-constrained wireless devices. This paper considers the co-channel deployment of a macrocell overlaid with small cells. The small cell base stations (SBSs) harvest energy from environmental sources whereas the macrocell base station (MBS) uses conventional power supply. Given a stochastic energy arrival process for the SBSs, we derive a power control policy for the downlink transmission of both MBS and SBSs such that they can achieve their objectives (e.g., maintain the signal-to-interference-plus-noise ratio (SINR) at an acceptable level) on a given transmission channel. We consider a centralized energy harvesting mechanism for SBSs, i.e., there is a central energy storage (CES) where energy is harvested and then distributed to the SBSs. When the number of SBSs is small, the game between the CES and the MBS is modeled as a single-controller stochastic game and the equilibrium policies are obtained as a solution of a quadratic programming problem. However, when the number of SBSs tends to infinity (i.e., a highly dense network), the centralized scheme becomes infeasible, and therefore, we use a mean field stochastic game to obtain a distributed power control policy for each SBS. By solving a system of partial differential equations, we derive the power control policy of SBSs given the knowledge of mean field distribution and the available harvested energy levels in the batteries of the SBSs.
From Low-Level Pointers to High-Level Containers
Venue: arXiv cs.PL 2015
Year: 2015

We propose a method that transforms a C program manipulating containers using low-level pointer statements into an equivalent program where the containers are manipulated via calls of standard high-level container operations like push_back or pop_front. The input of our method is a C program annotated by a special form of shape invariants which can be obtained from current automatic shape analysers after a slight modification. The resulting program where the low-level pointer statements are summarized into high-level container operations is more understandable and (among other possible benefits) better suitable for program analysis. We have implemented our approach and successfully tested it through a number of experiments with list-based containers, including experiments with simplification of program analysis by separating shape analysis from analysing data-related properties.
Hybrid One-Shot 3D Hand Pose Estimation by Exploiting Uncertainties
Venue: arXiv cs.CV 2015
Year: 2015

Model-based approaches to 3D hand tracking have been shown to perform well in a wide range of scenarios. However, they require initialisation and cannot recover easily from tracking failures that occur due to fast hand motions. Data-driven approaches, on the other hand, can quickly deliver a solution, but the results often suffer from lower accuracy or missing anatomical validity compared to those obtained from model-based approaches. In this work we propose a hybrid approach for hand pose estimation from a single depth image. First, a learned regressor is employed to deliver multiple initial hypotheses for the 3D position of each hand joint. Subsequently, the kinematic parameters of a 3D hand model are found by deliberately exploiting the inherent uncertainty of the inferred joint proposals. This way, the method provides anatomically valid and accurate solutions without requiring manual initialisation or suffering from track losses. Quantitative results on several standard datasets demonstrate that the proposed method outperforms state-of-the-art representatives of the model-based, data-driven and hybrid paradigms.
Raptor Codes in the Low SNR Regime
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we revisit the design of Raptor codes for binary input additive white Gaussian noise (BIAWGN) channels, where we are interested in very low signal to noise ratios (SNRs). A linear programming degree distribution optimization problem is defined for Raptor codes in the low SNR regime through several approximations. We also provide an exact expression for the polynomial representation of the degree distribution with infinite maximum degree in the low SNR regime, which enables us to calculate the exact value of the fractions of output nodes of small degrees. A more practical degree distribution design is also proposed for Raptor codes in the low SNR regime, where we include the rate efficiency and the decoding complexity in the optimization problem, and an upper bound on the maximum rate efficiency is derived for given design parameters. Simulation results show that the Raptor code with the designed degree distributions can approach rate efficiencies larger than 0.95 in the low SNR regime.