OpenRaiser/Intern-Atlas huggingface dataset:
Multi-Modal Mean-Fields via Cardinality-Based Clamping
Venue: arXiv cs.CV 2016
Year: 2016

Mean Field inference is central to statistical physics. It has attracted much interest in the Computer Vision community to efficiently solve problems expressible in terms of large Conditional Random Fields. However, since it models the posterior probability distribution as a product of marginal probabilities, it may fail to properly account for important dependencies between variables. We therefore replace the fully factorized distribution of Mean Field by a weighted mixture of such distributions, that similarly minimizes the KL-Divergence to the true posterior. By introducing two new ideas, namely, conditioning on groups of variables instead of single ones and using a parameter of the conditional random field potentials, that we identify to the temperature in the sense of statistical physics to select such groups, we can perform this minimization efficiently. Our extension of the clamping method proposed in previous works allows us to both produce a more descriptive approximation of the true posterior and, inspired by the diverse MAP paradigms, fit a mixture of Mean Field approximations. We demonstrate that this positively impacts real-world algorithms that initially relied on mean fields.
Laplacian integrality in P4-sparse and P4-extendible graphs
Venue: arXiv cs.DM 2016
Year: 2016

Let G be a simple graph and L = L(G) the Laplacian matrix of G. G is called L-integral if all its Laplacian eigenvalues are integer numbers. It is known that every cograph, a graph free of P4, is L-integral. The class of P4-sparse graphs and the class of P4-extendible graphs contain the cographs. It seems natural to investigate if the graphs in these classes are still L-integral. In this paper we characterized the L-integral graphs for both cases, P4-sparse graphs and P4-extendible graphs.
Massive MIMO Pilot Retransmission Strategies for Robustification against Jamming
Venue: arXiv cs.IT 2016
Year: 2016

This letter proposes anti-jamming strategies based on pilot retransmission for a single user uplink massive MIMO under jamming attack. A jammer is assumed to attack the system both in the training and data transmission phases. We first derive an achievable rate which enables us to analyze the effect of jamming attacks on the system performance. Counter-attack strategies are then proposed to mitigate this effect under two different scenarios: random and deterministic jamming attacks. Numerical results illustrate our analysis and benefit of the proposed schemes.
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
Venue: arXiv cs.CV 2016
Year: 2016

Semantic image segmentation is an essential component of modern autonomous driving systems, as an accurate understanding of the surrounding scene is crucial to navigation and action planning. Current state-of-the-art approaches in semantic image segmentation rely on pre-trained networks that were initially developed for classifying images as a whole. While these networks exhibit outstanding recognition performance (i.e., what is visible?), they lack localization accuracy (i.e., where precisely is something located?). Therefore, additional processing steps have to be performed in order to obtain pixel-accurate segmentation masks at the full image resolution. To alleviate this problem we propose a novel ResNet-like architecture that exhibits strong localization and recognition performance. We combine multi-scale context with pixel-level accuracy by using two processing streams within our network: One stream carries information at the full image resolution, enabling precise adherence to segment boundaries. The other stream undergoes a sequence of pooling operations to obtain robust features for recognition. The two streams are coupled at the full image resolution using residuals. Without additional processing steps and without pre-training, our approach achieves an intersection-over-union score of 71.8% on the Cityscapes dataset.
Incremental stability of Lur'e systems through piecewise-affine approximations
Venue: arXiv cs.SY 2016
Year: 2016

Lur'e-type nonlinear systems are virtually ubiquitous in applied control theory, which explains the great interest they have attracted throughout the years. The purpose of this paper is to propose conditions to assess incremental asymptotic stability of Lur'e systems that are less conservative than those obtained with the incremental circle criterion. The method is based on the approximation of the nonlinearity by a piecewise-affine function. The Lur'e system can then be rewritten as a so-called piecewise-affine Lur'e system, for which sufficient conditions for asymptotic incremental stability are provided. These conditions are expressed as linear matrix inequalities (LMIs) allowing the construction of a continuous piecewise-quadratic incremental Lyapunov function, which can be efficiently solved numerically. The results are illustrated with numerical examples.
An Overview on Data Representation Learning: From Traditional Feature Learning to Recent Deep Learning
Venue: arXiv cs.LG 2016
Year: 2016

Since about 100 years ago, to learn the intrinsic structure of data, many representation learning approaches have been proposed, including both linear ones and nonlinear ones, supervised ones and unsupervised ones. Particularly, deep architectures are widely applied for representation learning in recent years, and have delivered top results in many tasks, such as image classification, object detection and speech recognition. In this paper, we review the development of data representation learning methods. Specifically, we investigate both traditional feature learning algorithms and state-of-the-art deep learning models. The history of data representation learning is introduced, while available resources (e.g. online course, tutorial and book information) and toolboxes are provided. Finally, we conclude this paper with remarks and some interesting research directions on data representation learning.
CAn't Touch This: Practical and Generic Software-only Defenses Against Rowhammer Attacks
Venue: arXiv cs.CR 2016
Year: 2016

Rowhammer is a hardware bug that can be exploited to implement privilege escalation and remote code execution attacks. Previous proposals on rowhammer mitigation either require hardware changes or follow heuristic-based approaches (based on CPU performance counters). To date, there exists no instant protection against rowhammer attacks on legacy systems. In this paper, we present the design and implementation of two practical and efficient software-only defenses against rowhammer attacks. Our defenses prevent the attacker from leveraging rowhammer to corrupt physically co-located data in memory that is owned by a different system entity. Our first defense, B-CATT, extends the system bootloader to disable vulnerable physical memory. B-CATT is highly practical, does not require changes to the operating system, and can be deployed on virtually all x86-based systems. While B-CATT is able to stop all known rowhammer attacks, it does not yet tackle the fundamental problem of missing memory isolation in physical memory. To address this problem, we introduce our second defense G-CATT, a generic solution that extends the physical memory allocator of the OS to physically isolate the memory of different system entities (e.g., kernel and user space). As proof of concept, we implemented B-CATT on x86, and our generic defense, G-CATT, on x86 and ARM to mitigate rowhammer-based kernel exploits. Our extensive evaluation shows that both mitigation schemes (i) can stop available real- world rowhammer attacks, (ii) impose virtually no run-time overhead for common user and kernel benchmarks as well as commonly used applications, and (iii) do not affect the stability of the overall system.
Demonstration of Fully Nonlinear Spectrum Modulated System in the Highly Nonlinear Optical Transmission Regime
Venue: arXiv cs.IT 2016
Year: 2016

We report a 3 dB increase in the nonlinear threshold of a 64*0.5Gbaud 16-QAM continuous-nonlinear-spectrum modulated signal by nonlinear multiplexing with QPSK modulated multi-solitons, showing the first ever fully nonlinear-spectrum modulated system in the highly nonlinear regime.
On the Graph of the Pedigree Polytope
Venue: arXiv cs.DM 2016
Year: 2016

Pedigree polytopes are extensions of the classical Symmetric Traveling Salesman Problem polytopes whose graphs (1-skeletons) contain the TSP polytope graphs as spanning subgraphs. While deciding adjacency of vertices in TSP polytopes is coNP-complete, Arthanari has given a combinatorial (polynomially decidable) characterization of adjacency in Pedigree polytopes. Based on this characterization, we study the graphs of Pedigree polytopes asymptotically, for large numbers of cities. Unlike TSP polytope graphs, which are vertex transitive, Pedigree graphs are not even regular. Using an "adjacency game" to handle Arthanari's intricate inductive characterization of adjacency, we prove that the minimum degree is asymptotically equal to the number of vertices, i.e., the graph is "asymptotically almost complete".
Pure and Stationary Optimal Strategies in Perfect-Information Stochastic Games with Global Preferences
Venue: arXiv cs.GT 2016
Year: 2016

We examine the problem of the existence of optimal deterministic stationary strategiesintwo-players antagonistic (zero-sum) perfect information stochastic games with finitely many states and actions.We show that the existenceof such strategies follows from the existence of optimal deterministic stationarystrategies for some derived one-player games.Thus we reducethe problem from two-player to one-player games (Markov decisionproblems), where usually it is much easier to tackle.The reduction is very general, it holds not only for all possible payoff mappings but alsoin more a general situations whereplayers' preferences are not expressed by payoffs.
Dissecting the End-to-end Latency of Interactive Mobile Video Applications
Venue: arXiv cs.HC 2016
Year: 2016

In this paper we measure the step-wise latency in the pipeline of three kinds of interactive mobile video applications that are rapidly gaining popularity, namely Remote Graphics Rendering (RGR) of which we focus on mobile cloud gaming, Mobile Augmented Reality (MAR), and Mobile Virtual Reality (MVR). The applications differ from each other by the way in which the user interacts with the application, i.e., video I/O and user controls, but they all share in common the fact that their user experience is highly sensitive to end-to-end latency. Long latency between a user control event and display update renders the application unusable. Hence, understanding the nature and origins of latency of these applications is of paramount importance. We show through extensive measurements that control input and display buffering have a substantial effect on the overall delay. Our results shed light on the latency bottlenecks and the maturity of technology for seamless user experience with these applications.
BliStrTune: Hierarchical Invention of Theorem Proving Strategies
Venue: arXiv cs.LO 2016
Year: 2016

Inventing targeted proof search strategies for specific problem sets is a difficult task. State-of-the-art automated theorem provers (ATPs) such as E allow a large number of user-specified proof search strategies described in a rich domain specific language. Several machine learning methods that invent strategies automatically for ATPs were proposed previously. One of them is the Blind Strategymaker (BliStr), a system for automated invention of ATP strategies. In this paper we introduce BliStrTune -- a hierarchical extension of BliStr. BliStrTune allows exploring much larger space of E strategies by interleaving search for high-level parameters with their fine-tuning. We use BliStrTune to invent new strategies based also on new clause weight functions targeted at problems from large ITP libraries. We show that the new strategies significantly improve E's performance in solving problems from the Mizar Mathematical Library.
Real-Time Video Highlights for Yahoo Esports
Venue: arXiv cs.CV 2016
Year: 2016

Esports has gained global popularity in recent years and several companies have started offering live streaming videos of esports games and events. This creates opportunities to develop large scale video understanding systems for new product features and services. We present a technique for detecting highlights from live streaming videos of esports game matches. Most video games use pronounced visual effects to emphasize highlight moments; we use CNNs to learn convolution filters of those visual effects for detecting highlights. We propose a cascaded prediction approach that allows us to deal with several challenges arise in a production environment. We demonstrate our technique on our new dataset of three popular game titles, Heroes of the Storm, League of Legends, and Dota 2. Our technique achieves 18 FPS on a single CPU with an average precision of up to 83.18%. Part of our technique is currently deployed in production on Yahoo Esports.
SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial Networks
Venue: arXiv cs.CV 2016
Year: 2016

Autonomous driving is one of the most recent topics of interest which is aimed at replicating human driving behavior keeping in mind the safety issues. We approach the problem of learning synthetic driving using generative neural networks. The main idea is to make a controller trainer network using images plus key press data to mimic human learning. We used the architecture of a stable GAN to make predictions between driving scenes using key presses. We train our model on one video game (Road Rash) and tested the accuracy and compared it by running the model on other maps in Road Rash to determine the extent of learning.
Deep Deformable Registration: Enhancing Accuracy by Fully Convolutional Neural Net
Venue: arXiv cs.CV 2016
Year: 2016

Deformable registration is ubiquitous in medical image analysis. Many deformable registration methods minimize sum of squared difference (SSD) as the registration cost with respect to deformable model parameters. In this work, we construct a tight upper bound of the SSD registration cost by using a fully convolutional neural network (FCNN) in the registration pipeline. The upper bound SSD (UB-SSD) enhances the original deformable model parameter space by adding a heatmap output from FCNN. Next, we minimize this UB-SSD by adjusting both the parameters of the FCNN and the parameters of the deformable model in coordinate descent. Our coordinate descent framework is end-to-end and can work with any deformable registration method that uses SSD. We demonstrate experimentally that our method enhances the accuracy of deformable registration algorithms significantly on two publicly available 3D brain MRI data sets.
On the Distinction of Functional and Quality Requirements in Practice
Venue: arXiv cs.SE 2016
Year: 2016

Requirements are often divided into functional requirements (FRs) and quality requirements (QRs). However, we still have little knowledge about to which extent this distinction makes sense from a practical perspective. In this paper, we report on a survey we conducted with 103 practitioners to explore whether and, if so, why they handle requirements labeled as FRs differently from those labeled as QRs. We additionally asked for consequences of this distinction w.r.t. the development process. Our results indicate that the development process for requirements of the two classes strongly differs (e.g., in testing). We identified a number of reasons why practitioners do (or do not) distinguish between QRs and FRs in their documentation and we analyzed both problems and benefits that arise from that. We found, for instance, that many reasons are based on expectations rather than on evidence. Those expectations are, in fact, not reflected in specific negative or positive consequences per se. It therefore seems more important that the decision whether to make an explicit distinction or not should be made consciously such that people are also aware of the risks that this distinction bears so that they may take appropriate countermeasures.
Towards Guidelines for Preventing Critical Requirements Engineering Problems
Venue: arXiv cs.SE 2016
Year: 2016

Context] Problems in Requirements Engineering (RE) can lead to serious consequences during the software development lifecycle. [Goal] The goal of this paper is to propose empirically-based guidelines that can be used by different types of organisations according to their size (small, medium or large) and process model (agile or plan-driven) to help them in preventing such problems. [Method] We analysed data from a survey on RE problems answered by 228 organisations in 10 different countries. [Results] We identified the most critical RE problems, their causes and mitigation actions, organizing this information by clusters of size and process model. Finally, we analysed the causes and mitigation actions of the critical problems of each cluster to get further insights into how to prevent them. [Conclusions] Based on our results, we suggest preliminary guidelines for preventing critical RE problems in response to context characteristics of the companies.
Ranking Research Institutions Based On Related Academic Conferences
Venue: arXiv cs.SI 2016
Year: 2016

The detection of influential nodes in a social network is an active research area with many valuable applications including marketing and advertisement. As a new application in academia, KDD Cup 2016 shed light on the lack of an existing objective ranking for institutions within their respective research areas and proposed a solution for it. In this problem, the academic fields are defined as social networks whose nodes are the active institutions within the field, with the most influential nodes representing the highest contributors. The solution is able to provide a ranking of active institutions within their specific domains. The problem statement provided an annual scoring mechanism for institutions based on their publications and encouraged the use of any publicly available dataset such as the Microsoft Academic Graph (MAG). The contest was focused on research publications in selected conferences and asked for a prediction of the ranking for active institutions within those conferences in 2016. It should be noted that the results of the paper submissions and therefore the ground truths for KDD Cup were unknown at the time of the contest. Each team's final ranking list was evaluated by a metric called NDCG@20 after the results were released. This metric was used to indicate the distance between each team's proposed ranking and the actual one once it was known. After computing the scores of institutions for each year starting from 2011, we aggregated the rankings by summing the normalized scores across the years and using the final score set to provide the final ranking. Since the 2016 ground truths were unknown, we utilized the scores from 2011-2014 and used the 2015 publications as a test bed for evaluating our aggregation method. Based on the testing, summing the normalized scores got us closest to the actual 2015 rankings and using same heuristic for predicting the 2016 results.
An Efficient Max-Min Resource Allocator and Task Scheduling Algorithm in Cloud Computing Environment
Venue: arXiv cs.DC 2016
Year: 2016

Cloud computing is a new archetype that provides dynamic computing services to cloud users through the support of datacenters that employs the services of datacenter brokers which discover resources and assign them Virtually. The focus of this research is to efficiently optimize resource allocation in the cloud by exploiting the Max-Min scheduling algorithm and enhancing it to increase efficiency in terms of completion time (makespan). This is key to enhancing the performance of cloud scheduling and narrowing the performance gap between cloud service providers and cloud resources consumers/users. The current Max-Min algorithm selects tasks with maximum execution time on a faster available machine or resource that is capable of giving minimum completion time. The concern of this algorithm is to give priority to tasks with maximum execution time first before assigning those with the minimum execution time for the purpose of minimizing makespan. The drawback of this algorithm is that, the execution of tasks with maximum execution time first may increase the makespan, and leads to a delay in executing tasks with minimum execution time if the number of tasks with maximum execution time exceeds that of tasks with minimum execution time, hence the need to improve it to mitigate the delay in executing tasks with minimum execution time. CloudSim is used to compare the effectiveness of the improved Max-Min algorithm with the traditional one. The experimented results show that the improved algorithm is efficient and can produce better makespan than Max-Min and DataAware.
The BIN_COUNTS Constraint: Filtering and Applications
Venue: arXiv cs.AI 2016
Year: 2016

We introduce the BIN_COUNTS constraint, which deals with the problem of counting the number of decision variables in a set which are assigned values that lie in given bins. We illustrate a decomposition and a filtering algorithm that achieves generalised arc consistency. We contrast the filtering power of these two approaches and we discuss a number of applications. We show that BIN_COUNTS can be employed to develop a decomposition for the $χ^2$ test constraint, a new statistical constraint that we introduce in this work. We also show how this new constraint can be employed in the context of the Balanced Academic Curriculum Problem and of the Balanced Nursing Workload Problem. For both these problems we carry out numerical studies involving our reformulations. Finally, we present a further application of the $χ^2$ test constraint in the context of confidence interval analysis.
Navigable videos for presenting scientific data on head-mounted displays
Venue: arXiv cs.GR 2016
Year: 2016

Immersive, stereoscopic viewing enables scientists to better analyze the spatial structures of visualized physical phenomena. However, their findings cannot be properly presented in traditional media, which lack these core attributes. Creating a presentation tool that captures this environment poses unique challenges, namely related to poor viewing accessibility. Immersive scientific renderings often require high-end equipment, which can be impractical to obtain. We address these challenges with our authoring tool and navigational interface, which is designed for affordable head-mounted displays. With the authoring tool, scientists can show salient data features as connected 360° video paths, resulting in a "choose-your-own-adventure" experience. Our navigational interface features bidirectional video playback for added viewing control when users traverse the tailor-made content. We evaluate our system's benefits by authoring case studies on several data sets and conducting a usability study on the navigational interface's design. In summary, our approach provides scientists an immersive medium to visually present their research to the intended audience--spanning from students to colleagues--on affordable virtual reality headsets.
A note on the Singleton bounds for codes over finite rings
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we give a notation on the Singleton bounds for linear codes over a finite commutative quasi-Frobenius ring in the work of Shiromoto [5]. We show that there exists a class of finite commutative quasi-Frobenius rings. The Singleton bounds for linear codes over such rings satisfy \[ \frac{d(C)-1}{A}\leq n-\log_{|R|}|C|. \]