OpenRaiser/Intern-Atlas huggingface dataset:
Decoding and File Transfer Delay Balancing in Network Coding Broadcast
Venue: arXiv cs.IT 2016
Year: 2016

Network Coding is a packet encoding technique which has recently been shown to improve network performance (by reducing delays and increasing throughput) in broadcast and multicast communications. The cost for such an improvement comes in the form of increased decoding complexity (and thus delay) at the receivers end. Before delivering the file to higher layers, the receiver should first decode those packets. In our work we consider the broadcast transmission of a large file to N wireless users. The file is segmented into a number of blocks (each containing K packets - the Coding Window Size). The packets of each block are encoded using Random Linear Network Coding (RLNC).We obtain the minimum coding window size so that the completion time of the file transmission is upper bounded by a used defined delay constraint.
A Quaternion Frequency and Phasor Estimator for Three-Phase Power Distribution Networks
Venue: arXiv cs.SY 2016
Year: 2016

For the first time quaternions have been used for real-time frequency estimation, where the multi-dimensional nature of quaternions allows for the full characterization of three-phase power systems. This is achieved through the use of quaternions to provide a unified framework for incorporating voltage measurements from all the phases of a three-phase system and then employing the recently introduced HR-calculus to derive a state space estimator based on the quaternion extended Kalman filter (QEKF). The components of the state space vector are designed such that they can be deployed for adaptive estimation of the system phasors. Finally, the proposed algorithm is validated through simulations using both synthetic and real-world data, which indicate that the developed quaternion frequency estimator can outperform its complex-valued counterparts.
The Sub-band Vectoring Technique for Multi-Operator Environments
Venue: arXiv cs.NI 2016
Year: 2016

Self- and alien-FEXT disturbances on the same cable severely limit performance in the commonly employed VDSL2 17a, i.e. 17.6 MHz, standard profile. Then, in a multi-operator environment, VDSL2 may be unable to provide the 100 Mbit/s speed required by the EC's policy target, unless vectoring is adopted along with a suitable Multi-Operator Vectoring (MOV) technique. Some vendors recently proposed enlarging the bandwidth up to 35.2~MHz (so-called e-VDSL) as one possible solution to increase the 100 Mbit/s coverage. However, as we show in the paper, the bandwidth advantage is illusory as an "ideal MOV" is not implementable with today's technology, and alien-FEXT practically destroys the envisaged data-rate increase. Therefore, we introduce the Sub-band Vectoring (SBV) technique, as one practical solution, which turns out to be near optimal in terms of achievable data-rate, as well as incremental for those Countries where the NRA adopts sub-loop unbundling regulation. SBV adopts a novel frequency division multiplexing approach, which imposes one fairness condition to equalize data-rate values and overall bandwidth per operator at different distances. The paper shows that e-VDSL with SBV is robust, as it can cope with relatively high values of vectoring impairments. It also shows that the SBV allows up to three co-located operators with e-VDSL to coexist, without imposing any of the burdensome coordination limitations of the ideal MOV architecture. Our results show that the SBV allows achieving up to 210 Mbit/s per user for e-VDSL and up to 620 Mbit/s per user for a bandwidth expanded up to 105.6 MHz, with two telecom operators. Finally, the paper shows that a migration strategy towards usage of the G.fast standard thanks to SBV is achieved in a simple way.
Weighted Pushdown Systems with Indexed Weight Domains
Venue: arXiv cs.FL 2016
Year: 2016

The reachability analysis of weighted pushdown systems is a very powerful technique in verification and analysis of recursive programs. Each transition rule of a weighted pushdown system is associated with an element of a bounded semiring representing the weight of the rule. However, we have realized that the restriction of the boundedness is too strict and the formulation of weighted pushdown systems is not general enough for some applications. To generalize weighted pushdown systems, we first introduce the notion of stack signatures that summarize the effect of a computation of a pushdown system and formulate pushdown systems as automata over the monoid of stack signatures. We then generalize weighted pushdown systems by introducing semirings indexed by the monoid and weaken the boundedness to local boundedness.
A multinomial probabilistic model for movie genre predictions
Venue: arXiv cs.IR 2016
Year: 2016

This paper proposes a movie genre-prediction based on multinomial probability model. To the best of our knowledge, this problem has not been addressed yet in the field of recommender system. The prediction of a movie genre has many practical applications including complementing the items categories given by experts and providing a surprise effect in the recommendations given to a user. We employ mulitnomial event model to estimate a likelihood of a movie given genre and the Bayes rule to evaluate the posterior probability of a genre given a movie. Experiments with the MovieLens dataset validate our approach. We achieved 70% prediction rate using only 15% of the whole set for training.
Investigation Into The Effectiveness Of Long Short Term Memory Networks For Stock Price Prediction
Venue: arXiv cs.NE 2016
Year: 2016

The effectiveness of long short term memory networks trained by backpropagation through time for stock price prediction is explored in this paper. A range of different architecture LSTM networks are constructed trained and tested.
Rollerchain, a Blockchain With Safely Pruneable Full Blocks
Venue: arXiv cs.CR 2016
Year: 2016

Bitcoin is the first successful decentralized global digital cash system. Its mining process requires intense computational resources, therefore its usefulness remains a disputable topic. We aim to solve three problems with Bitcoin and other blockchain systems of today by repurposing their work. First, space to store a blockchain is growing linearly with number of transactions. Second, a honest node is forced to be irrational regarding storing full blocks by a way implementations are done. Third, a trustless bootstrapping process for a new node involves downloading and processing all the transactions ever written into a blockchain. In this paper we present a new consensus protocol for Bitcoin-like peer-to-peer systems where a right to generate a block is given to a party providing non-interactive proofs of storing a subset of the past state snapshots. Unlike the blockchain systems in use today, a network using our protocol is safe if the nodes prune full blocks not needed for mining. We extend the GKL model to describe our Proof-of-Work scheme and a transactional model modifications needed for it. We provide a detailed analysis of our protocol and proofs of its security.
Global versus Local Weak-Indication Self-Timed Function Blocks - A Comparative Analysis
Venue: arXiv cs.AR 2016
Year: 2016

This paper analyzes the merits and demerits of global weak-indication self-timed function blocks versus local weak-indication self-timed function blocks, implemented using a delay-insensitive data code and adhering to 4-phase return-to-zero handshaking. A self-timed ripple carry adder is considered as an example function block for the analysis. The analysis shows that while global weak-indication could help in optimizing the power, latency and area parameters, local weak-indication facilitates the optimum performance in terms of realizing the data-dependent cycle time that is characteristic of a weak-indication self-timed design.
Altmetrics of "altmetrics" using Google Scholar, Twitter, Mendeley, Facebook, Google-plus, CiteULike, Blogs and Wiki
Venue: arXiv cs.SI 2016
Year: 2016

We measure the impact of "altmetrics" field by deploying altmetrics indicators using the data from Google Scholar, Twitter, Mendeley, Facebook, Google-plus, CiteULike, Blogs and Wiki during 2010- 2014. To capture the social impact of scientific publications, we propose an index called alt-index, analogues to h-index. Across the deployed indices, our results have shown high correlation among the indicators that capture social impact. While we observe medium Pearson's correlation (\r{ho}= .247) among the alt-index and h-index, a relatively high correlation is observed between social citations and scholarly citations (\r{ho}= .646). Interestingly, we find high turnover of social citations in the field compared with the traditional scholarly citations, i.e. social citations are 42.2 % more than traditional citations. The social mediums such as Twitter and Mendeley appear to be the most effective channels of social impact followed by Facebook and Google-plus. Overall, altmetrics appears to be working well in the field of "altmetrics".
Resnet in Resnet: Generalizing Residual Architectures
Venue: arXiv cs.LG 2016
Year: 2016

Residual networks (ResNets) have recently achieved state-of-the-art on challenging computer vision tasks. We introduce Resnet in Resnet (RiR): a deep dual-stream architecture that generalizes ResNets and standard CNNs and is easily implemented with no computational overhead. RiR consistently improves performance over ResNets, outperforms architectures with similar amounts of augmentation on CIFAR-10, and establishes a new state-of-the-art on CIFAR-100.
Persistent self-supervised learning principle: from stereo to monocular vision for obstacle avoidance
Venue: arXiv cs.RO 2016
Year: 2016

Self-Supervised Learning (SSL) is a reliable learning mechanism in which a robot uses an original, trusted sensor cue for training to recognize an additional, complementary sensor cue. We study for the first time in SSL how a robot's learning behavior should be organized, so that the robot can keep performing its task in the case that the original cue becomes unavailable. We study this persistent form of SSL in the context of a flying robot that has to avoid obstacles based on distance estimates from the visual cue of stereo vision. Over time it will learn to also estimate distances based on monocular appearance cues. A strategy is introduced that has the robot switch from stereo vision based flight to monocular flight, with stereo vision purely used as 'training wheels' to avoid imminent collisions. This strategy is shown to be an effective approach to the 'feedback-induced data bias' problem as also experienced in learning from demonstration. Both simulations and real-world experiments with a stereo vision equipped AR drone 2.0 show the feasibility of this approach, with the robot successfully using monocular vision to avoid obstacles in a 5 x 5 room. The experiments show the potential of persistent SSL as a robust learning approach to enhance the capabilities of robots. Moreover, the abundant training data coming from the own sensors allows to gather large data sets necessary for deep learning approaches.
Classification of Large-Scale Fundus Image Data Sets: A Cloud-Computing Framework
Venue: arXiv cs.CV 2016
Year: 2016

Large medical image data sets with high dimensionality require substantial amount of computation time for data creation and data processing. This paper presents a novel generalized method that finds optimal image-based feature sets that reduce computational time complexity while maximizing overall classification accuracy for detection of diabetic retinopathy (DR). First, region-based and pixel-based features are extracted from fundus images for classification of DR lesions and vessel-like structures. Next, feature ranking strategies are used to distinguish the optimal classification feature sets. DR lesion and vessel classification accuracies are computed using the boosted decision tree and decision forest classifiers in the Microsoft Azure Machine Learning Studio platform, respectively. For images from the DIARETDB1 data set, 40 of its highest-ranked features are used to classify four DR lesion types with an average classification accuracy of 90.1% in 792 seconds. Also, for classification of red lesion regions and hemorrhages from microaneurysms, accuracies of 85% and 72% are observed, respectively. For images from STARE data set, 40 high-ranked features can classify minor blood vessels with an accuracy of 83.5% in 326 seconds. Such cloud-based fundus image analysis systems can significantly enhance the borderline classification performances in automated screening systems.
Learning Hough Regression Models via Bridge Partial Least Squares for Object Detection
Venue: arXiv cs.CV 2016
Year: 2016

Popular Hough Transform-based object detection approaches usually construct an appearance codebook by clustering local image features. However, how to choose appropriate values for the parameters used in the clustering step remains an open problem. Moreover, some popular histogram features extracted from overlapping image blocks may cause a high degree of redundancy and multicollinearity. In this paper, we propose a novel Hough Transform-based object detection approach. First, to address the above issues, we exploit a Bridge Partial Least Squares (BPLS) technique to establish context-encoded Hough Regression Models (HRMs), which are linear regression models that cast probabilistic Hough votes to predict object locations. BPLS is an efficient variant of Partial Least Squares (PLS). PLS-based regression techniques (including BPLS) can reduce the redundancy and eliminate the multicollinearity of a feature set. And the appropriate value of the only parameter used in PLS (i.e., the number of latent components) can be determined by using a cross-validation procedure. Second, to efficiently handle object scale changes, we propose a novel multi-scale voting scheme. In this scheme, multiple Hough images corresponding to multiple object scales can be obtained simultaneously. Third, an object in a test image may correspond to multiple true and false positive hypotheses at different scales. Based on the proposed multi-scale voting scheme, a principled strategy is proposed to fuse hypotheses to reduce false positives by evaluating normalized pointwise mutual information between hypotheses. In the experiments, we also compare the proposed HRM approach with its several variants to evaluate the influences of its components on its performance. Experimental results show that the proposed HRM approach has achieved desirable performances on popular benchmark datasets.
Unsupervised Domain Adaptation in the Wild: Dealing with Asymmetric Label Sets
Venue: arXiv cs.CV 2016
Year: 2016

The goal of domain adaptation is to adapt models learned on a source domain to a particular target domain. Most methods for unsupervised domain adaptation proposed in the literature to date, assume that the set of classes present in the target domain is identical to the set of classes present in the source domain. This is a restrictive assumption that limits the practical applicability of unsupervised domain adaptation techniques in real world settings ("in the wild"). Therefore, we relax this constraint and propose a technique that allows the set of target classes to be a subset of the source classes. This way, large publicly available annotated datasets with a wide variety of classes can be used as source, even if the actual set of classes in target can be more limited and, maybe most importantly, unknown beforehand. To this end, we propose an algorithm that orders a set of source subspaces that are relevant to the target classification problem. Our method then chooses a restricted set from this ordered set of source subspaces. As an extension, even starting from multiple source datasets with varied sets of categories, this method automatically selects an appropriate subset of source categories relevant to a target dataset. Empirical analysis on a number of source and target domain datasets shows that restricting the source subspace to only a subset of categories does indeed substantially improve the eventual target classification accuracy over the baseline that considers all source classes.
Capacity Analysis for Spatially Non-wide Sense Stationary Uplink Massive MIMO Systems
Venue: arXiv cs.IT 2016
Year: 2016

Channel measurements show that significant spatially non-wide-sense-stationary characteristics rise in massive MIMO channels. Notable parameter variations are experienced along the base station array, such as the average received energy at each antenna, and the directions of arrival of signals impinging on different parts of the array. In this paper, a new channel model is proposed to describe this spatial non-stationarity in massive MIMO channels by incorporating the concepts of partially visible clusters and wholly visible clusters. Furthermore, a closed-form expression of an upper bound on the ergodic sum capacity is derived for the new model, and the influence of the spatial non-stationarity on the sum capacity is analyzed. Analysis shows that for non-identically-and-independent-distributed (i.i.d.) Rayleigh fading channels, the non-stationarity benefits the sum capacity by bringing a more even spread of channel eigenvalues. Specifically, more partially visible clusters, smaller cluster visibility regions and a larger antenna array can all help to yield a well-conditioned channel, and benefit the sum capacity. This shows the advantage of using a large antenna array in a non-i.i.d. channel: the sum capacity benefits not only from a higher array gain, but also from a more spatially non-stationary channel. Numerical results demonstrate our analysis and the tightness of the upper bound.
How useful is photo-realistic rendering for visual learning?
Venue: arXiv cs.CV 2016
Year: 2016

Data seems cheap to get, and in many ways it is, but the process of creating a high quality labeled dataset from a mass of data is time-consuming and expensive. With the advent of rich 3D repositories, photo-realistic rendering systems offer the opportunity to provide nearly limitless data. Yet, their primary value for visual learning may be the quality of the data they can provide rather than the quantity. Rendering engines offer the promise of perfect labels in addition to the data: what the precise camera pose is; what the precise lighting location, temperature, and distribution is; what the geometry of the object is. In this work we focus on semi-automating dataset creation through use of synthetic data and apply this method to an important task -- object viewpoint estimation. Using state-of-the-art rendering software we generate a large labeled dataset of cars rendered densely in viewpoint space. We investigate the effect of rendering parameters on estimation performance and show realism is important. We show that generalizing from synthetic data is not harder than the domain adaptation required between two real-image datasets and that combining synthetic images with a small amount of real data improves estimation accuracy.
To Follow or Not to Follow: Analyzing the Growth Patterns of the Trumpists on Twitter
Venue: arXiv cs.SI 2016
Year: 2016

In this paper, we analyze the growth patterns of Donald Trump's followers (Trumpists, henceforth) on Twitter. We first construct a random walk model with a time trend to study the growth trend and the effects of public debates. We then analyze the relationship between Trump's activity on Twitter and the growth of his followers. Thirdly, we analyze the effects of such controversial events as calling for Muslim ban and his 'schlonged' remark.
Planning Problems for Sophisticated Agents with Present Bias
Venue: arXiv cs.GT 2016
Year: 2016

Present bias, the tendency to weigh costs and benefits incurred in the present too heavily, is one of the most widespread human behavioral biases. It has also been the subject of extensive study in the behavioral economics literature. While the simplest models assume that the agents are naive, reasoning about the future without taking their bias into account, there is considerable evidence that people often behave in ways that are sophisticated with respect to present bias, making plans based on the belief that they will be present-biased in the future. For example, committing to a course of action to reduce future opportunities for procrastination or overconsumption are instances of sophisticated behavior in everyday life. Models of sophisticated behavior have lacked an underlying formalism that allows one to reason over the full space of multi-step tasks that a sophisticated agent might face. This has made it correspondingly difficult to make comparative or worst-case statements about the performance of sophisticated agents in arbitrary scenarios. In this paper, we incorporate the notion of sophistication into a graph-theoretic model that we used in recent work for modeling naive agents. This new synthesis of two formalisms - sophistication and graph-theoretic planning - uncovers a rich structure that wasn't apparent in the earlier behavioral economics work on this problem. In particular, our graph-theoretic model makes two kinds of new results possible. First, we give tight worst-case bounds on the performance of sophisticated agents in arbitrary multi-step tasks relative to the optimal plan. Second, the flexibility of our formalism makes it possible to identify new phenomena that had not been seen in prior literature: these include a surprising non-monotonic property in the use of rewards to motivate sophisticated agents and a framework for reasoning about commitment devices.
DeLight-Net: Decomposing Reflectance Maps into Specular Materials and Natural Illumination
Venue: arXiv cs.CV 2016
Year: 2016

In this paper we are extracting surface reflectance and natural environmental illumination from a reflectance map, i.e. from a single 2D image of a sphere of one material under one illumination. This is a notoriously difficult problem, yet key to various re-rendering applications. With the recent advances in estimating reflectance maps from 2D images their further decomposition has become increasingly relevant. To this end, we propose a Convolutional Neural Network (CNN) architecture to reconstruct both material parameters (i.e. Phong) as well as illumination (i.e. high-resolution spherical illumination maps), that is solely trained on synthetic data. We demonstrate that decomposition of synthetic as well as real photographs of reflectance maps, both in High Dynamic Range (HDR), and, for the first time, on Low Dynamic Range (LDR) as well. Results are compared to previous approaches quantitatively as well as qualitatively in terms of re-renderings where illumination, material, view or shape are changed.
Non-Greedy L21-Norm Maximization for Principal Component Analysis
Venue: arXiv cs.LG 2016
Year: 2016

Principal Component Analysis (PCA) is one of the most important unsupervised methods to handle high-dimensional data. However, due to the high computational complexity of its eigen decomposition solution, it hard to apply PCA to the large-scale data with high dimensionality. Meanwhile, the squared L2-norm based objective makes it sensitive to data outliers. In recent research, the L1-norm maximization based PCA method was proposed for efficient computation and being robust to outliers. However, this work used a greedy strategy to solve the eigen vectors. Moreover, the L1-norm maximization based objective may not be the correct robust PCA formulation, because it loses the theoretical connection to the minimization of data reconstruction error, which is one of the most important intuitions and goals of PCA. In this paper, we propose to maximize the L21-norm based robust PCA objective, which is theoretically connected to the minimization of reconstruction error. More importantly, we propose the efficient non-greedy optimization algorithms to solve our objective and the more general L21-norm maximization problem with theoretically guaranteed convergence. Experimental results on real world data sets show the effectiveness of the proposed method for principal component analysis.