OpenRaiser/Intern-Atlas huggingface dataset:
A Kernel Classification Framework for Metric Learning
Venue: arXiv cs.LG 2013
Year: 2013

Learning a distance metric from the given training samples plays a crucial role in many machine learning tasks, and various models and optimization algorithms have been proposed in the past decade. In this paper, we generalize several state-of-the-art metric learning methods, such as large margin nearest neighbor (LMNN) and information theoretic metric learning (ITML), into a kernel classification framework. First, doublets and triplets are constructed from the training samples, and a family of degree-2 polynomial kernel functions are proposed for pairs of doublets or triplets. Then, a kernel classification framework is established, which can not only generalize many popular metric learning methods such as LMNN and ITML, but also suggest new metric learning methods, which can be efficiently implemented, interestingly, by using the standard support vector machine (SVM) solvers. Two novel metric learning methods, namely doublet-SVM and triplet-SVM, are then developed under the proposed framework. Experimental results show that doublet-SVM and triplet-SVM achieve competitive classification accuracies with state-of-the-art metric learning methods such as ITML and LMNN but with significantly less training time.
Simple Destination-Swap Strategies for Adaptive Intra- and Inter-Tenant VM Migration
Venue: arXiv cs.DC 2013
Year: 2013

This paper investigates the opportunities and limitations of adaptive virtual machine (VM) migration to reduce communication costs in a virtualized environment. We introduce a new formal model for the problem of online VM migration in two scenarios: (1) VMs can be migrated arbitrarily in the substrate network; e.g., a private cloud provider may have an incentive to reduce the overall communication cost in the network. (2) VMs can only be migrated within a given tenant; e.g., a user that was assigned a set of physical machines may exchange the functionality of the VMs on these machines. We propose a simple class of Destination-Swap algorithms which are based on an aggressive collocation strategy (inspired by splay datastructures) and which maintain a minimal and local amount of per-node (amortized cost) information to decide where to migrate a VM and how; thus, the algorithms react quickly to changes in the load. The algorithms come in two main flavors, an indirect and distributed variant which keeps existing VM placements local, and a direct variant which keeps the number of affected VMs small. We show that naturally, inter-tenant optimizations yield a larger potential for optimization, but generally also a tenant itself can improve its embedding. Moreover, there exists an interesting tradeoff between direct and indirect strategies: indirect variants are preferable under skewed and sparse communication patterns due to their locality properties.
A Probabilistic Analysis of Kademlia Networks
Venue: arXiv cs.NI 2013
Year: 2013

Kademlia is currently the most widely used searching algorithm in P2P (peer-to-peer) networks. This work studies an essential question about Kademlia from a mathematical perspective: how long does it take to locate a node in the network? To answer it, we introduce a random graph K and study how many steps are needed to locate a given vertex in K using Kademlia's algorithm, which we call the routing time. Two slightly different versions of K are studied. In the first one, vertices of K are labelled with fixed IDs. In the second one, vertices are assumed to have randomly selected IDs. In both cases, we show that the routing time is about c*log(n), where n is the number of nodes in the network and c is an explicitly described constant.
Smooth minimization of nonsmooth functions with parallel coordinate descent methods
Venue: arXiv cs.DC 2013
Year: 2013

We study the performance of a family of randomized parallel coordinate descent methods for minimizing the sum of a nonsmooth and separable convex functions. The problem class includes as a special case L1-regularized L1 regression and the minimization of the exponential loss ("AdaBoost problem"). We assume the input data defining the loss function is contained in a sparse $m\times n$ matrix $A$ with at most $ω$ nonzeros in each row. Our methods need $O(n β/τ)$ iterations to find an approximate solution with high probability, where $τ$ is the number of processors and $β= 1 + (ω-1)(τ-1)/(n-1)$ for the fastest variant. The notation hides dependence on quantities such as the required accuracy and confidence levels and the distance of the starting iterate from an optimal point. Since $β/τ$ is a decreasing function of $τ$, the method needs fewer iterations when more processors are used. Certain variants of our algorithms perform on average only $O(\nnz(A)/n)$ arithmetic operations during a single iteration per processor and, because $β$ decreases when $ω$ does, fewer iterations are needed for sparser problems.
On the Success Rate of Crossover Operators for Genetic Programming with Offspring Selection
Venue: arXiv cs.NE 2013
Year: 2013

Genetic programming is a powerful heuristic search technique that is used for a number of real world applications to solve among others regression, classification, and time-series forecasting problems. A lot of progress towards a theoretic description of genetic programming in form of schema theorems has been made, but the internal dynamics and success factors of genetic programming are still not fully understood. In particular, the effects of different crossover operators in combination with offspring selection are largely unknown. This contribution sheds light on the ability of well-known GP crossover operators to create better offspring when applied to benchmark problems. We conclude that standard (sub-tree swapping) crossover is a good default choice in combination with offspring selection, and that GP with offspring selection and random selection of crossover operators can improve the performance of the algorithm in terms of best solution quality when no solution size constraints are applied.
Fenchel Duals for Drifting Adversaries
Venue: arXiv cs.LG 2013
Year: 2013

We describe a primal-dual framework for the design and analysis of online convex optimization algorithms for {\em drifting regret}. Existing literature shows (nearly) optimal drifting regret bounds only for the $\ell_2$ and the $\ell_1$-norms. Our work provides a connection between these algorithms and the Online Mirror Descent ($\omd$) updates; one key insight that results from our work is that in order for these algorithms to succeed, it suffices to have the gradient of the regularizer to be bounded (in an appropriate norm). For situations (like for the $\ell_1$ norm) where the vanilla regularizer does not have this property, we have to {\em shift} the regularizer to ensure this. Thus, this helps explain the various updates presented in \cite{bansal10, buchbinder12}. We also consider the online variant of the problem with 1-lookahead, and with movement costs in the $\ell_2$-norm. Our primal dual approach yields nearly optimal competitive ratios for this problem.
XML Compression via DAGs
Venue: arXiv cs.DS 2013
Year: 2013

Unranked trees can be represented using their minimal dag (directed acyclic graph). For XML this achieves high compression ratios due to their repetitive mark up. Unranked trees are often represented through first child/next sibling (fcns) encoded binary trees. We study the difference in size (= number of edges) of minimal dag versus minimal dag of the fcns encoded binary tree. One main finding is that the size of the dag of the binary tree can never be smaller than the square root of the size of the minimal dag, and that there are examples that match this bound. We introduce a new combined structure, the hybrid dag, which is guaranteed to be smaller than (or equal in size to) both dags. Interestingly, we find through experiments that last child/previous sibling encodings are much better for XML compression via dags, than fcns encodings. We determine the average sizes of unranked and binary dags over a given set of labels (under uniform distribution) in terms of their exact generating functions, and in terms of their asymptotical behavior.
A Complexity of double dummy bridge
Venue: arXiv cs.GT 2013
Year: 2013

This paper presents an analysis of complexity of double dummy bridge. Values of both, a state-space (search-space) complexity and a game tree complexity have been estimated. ----- Oszacowanie złożoności problemu rozgrywki w otwarte karty w brydżu Artykuł zawiera analiz{\ke} złożoności problemu rozgrywki w otwarte karty w brydżu, przy użyciu miar zaproponowanych przez Louisa Victora Allisa. Oszacowane s{\ka} w nim złożoności przestrzeni stanów i drzewa wspomnianej gry.
Applications of Continuous Amortization to Bisection-based Root Isolation
Venue: arXiv cs.DS 2013
Year: 2013

Continuous amortization is a technique for computing the complexity of algorithms, and it was first presented by the author in Burr, Krahmer, & Yap (2009). Continuous amortization can result in simpler and more straight-forward complexity analyses, and it was used in Burr, Krahmer, & Yap (2009), Burr & Krahmer (2012), and Sharma & Yap (2012) to provide complexity bounds for simple root isolation algorithms. This paper greatly extends the reach of continuous amortization to serve as an overarching technique which can be used to compute complexity of many root isolation techniques in a straight-forward manner. Additionally, the technique of continuous amortization is extended to higher dimensions and to the computation of the bit-complexity of algorithms. In this paper, six continuous amortization calculations are performed to compute complexity bounds (on either the size of the subdivision tree or the bit complexity) for several algorithms (including algorithms based on Sturm sequences, Descartes' rule of signs, and polynomial evaluation); in each case, continuous amortization achieves an optimal complexity bound.
Extended Security Risks in IP Networks
Venue: arXiv cs.NI 2013
Year: 2013

Exploitation techniques targeting intermediate (transit) network nodes in public and private networks have been theoretically known and empirically proven to work for quite some time. However, very little effort has been made to look into the network-specific risks of compromising the Internet infrastructure to this date. In this publication, we describe several methods of hijacking live network traffic following a successful attack on a router or switch. We demonstrate that modern network platforms are capable of targeted traffic replication and redirection for online and offline analysis and modification, which can be a threat far greater than loss of service or other risks frequently associated with such exploits.
Discordant Compact Logic-Arithmetic Structures in Discrete Optimization Problems
Venue: arXiv cs.DS 2013
Year: 2013

In sphere of research of discrete optimization algorithms efficiency the important place occupies a method of polynomial reducibility of some problems to others with use of special purpose components. In this paper a novel method of compact representation for sets of binary sequences in the form of "compact triplets structures" (CTS) and "compact couples structures" (CCS) is stated, supposing both logic and arithmetic interpretation of data. It is shown that any non-empty CTS in dual interpretation represents some unique Boolean formula in 3-CNF and the tabular CTS contains all satisfyig sets of the formula as concatenations of the triplets chosen from the neighbouring tiers. In general, any 3-CNF formula is transformed by decomposition to a system of discordant CTS's, each being associated with an individual permutation of variables constructed by a polynomial algorithm. As a result the problem of the formula satisfiability is reduced to the following one: ascertain the fact of existence (or absence) of a "joint satisfying set" (JSS) for all discordant structures, based on the different permutations. Further transformation of each CTS to CCS is used; correctness of preservation of the allowed sets is reached by simple algorithmic restrictions on triplets concatenation. Then the procedure of "inverting of the same name columns" in the various structures is entered for the purpose of reducing the problem of JSS revealing to elementary detection of n-tuples of zeros in the CCS system. The formula is synthesized, being on the structure a variation of 2-CNF, associated with the calculation procedure realizing adaptation of the polynomial algorithm of constraints distribution (well-known in the optimization theory) to the efficient resolving Boolean formula coded by means of discordant compact structures.
A Simple FPTAS for Counting Edge Covers
Venue: arXiv cs.DS 2013
Year: 2013

An edge cover of a graph is a set of edges such that every vertex has at least an adjacent edge in it. Previously, approximation algorithm for counting edge covers is only known for 3 regular graphs and it is randomized. We design a very simple deterministic fully polynomial-time approximation scheme (FPTAS) for counting the number of edge covers for any graph. Our main technique is correlation decay, which is a powerful tool to design FPTAS for counting problems. In order to get FPTAS for general graphs without degree bound, we make use of a stronger notion called computationally efficient correlation decay, which is introduced in [Li, Lu, Yin SODA 2012].
Transducers with origin information
Venue: arXiv cs.FL 2013
Year: 2013

Call a string-to-string transducer regular if it can be realised by one of the following equivalent models: mso transductions, two-way deterministic automata with output, and streaming transducers with registers. This paper proposes to treat origin information as part of the semantics of a regular string-to-string transducer. With such semantics, the model admits a machine-independent characterisation, Angluin-style learning in polynomial time, as well as effective characterisations of natural subclasses such as one-way transducers or first-order definable transducers.
Scan-based Compressed Terahertz Imaging and Real-Time Reconstruction via the Complex-valued Fast Block Sparse Bayesian Learning Algorithm
Venue: arXiv cs.CV 2013
Year: 2013

Compressed Sensing based Terahertz imaging (CS-THz) is a computational imaging technique. It uses only one THz receiver to accumulate the random modulated image measurements where the original THz image is reconstruct from these measurements using compressed sensing solvers. The advantage of the CS-THz is its reduced acquisition time compared with the raster scan mode. However, when it applied to large-scale two-dimensional (2D) imaging, the increased dimension resulted in both high computational complexity and excessive memory usage. In this paper, we introduced a novel CS-based THz imaging system that progressively compressed the THz image column by column. Therefore, the CS-THz system could be simplified with a much smaller sized modulator and reduced dimension. In order to utilize the block structure and the correlation of adjacent columns of the THz image, a complex-valued block sparse Bayesian learning algorithm was proposed. We conducted systematic evaluation of state-of-the-art CS algorithms under the scan based CS-THz architecture. The compression ratios and the choices of the sensing matrices were analyzed in detail using both synthetic and real-life THz images. Simulation results showed that both the scan based architecture and the proposed recovery algorithm were superior and efficient for large scale CS-THz applications.
Predicting Adoption Probabilities in Social Networks
Venue: arXiv cs.SI 2013
Year: 2013

In a social network, adoption probability refers to the probability that a social entity will adopt a product, service, or opinion in the foreseeable future. Such probabilities are central to fundamental issues in social network analysis, including the influence maximization problem. In practice, adoption probabilities have significant implications for applications ranging from social network-based target marketing to political campaigns; yet, predicting adoption probabilities has not received sufficient research attention. Building on relevant social network theories, we identify and operationalize key factors that affect adoption decisions: social influence, structural equivalence, entity similarity, and confounding factors. We then develop the locally-weighted expectation-maximization method for Naïve Bayesian learning to predict adoption probabilities on the basis of these factors. The principal challenge addressed in this study is how to predict adoption probabilities in the presence of confounding factors that are generally unobserved. Using data from two large-scale social networks, we demonstrate the effectiveness of the proposed method. The empirical results also suggest that cascade methods primarily using social influence to predict adoption probabilities offer limited predictive power, and that confounding factors are critical to adoption probability predictions.
Multiple-object tracking in cluttered and crowded public spaces
Venue: arXiv cs.CV 2013
Year: 2013

This paper addresses the problem of tracking moving objects of variable appearance in challenging scenes rich with features and texture. Reliable tracking is of pivotal importance in surveillance applications. It is made particularly difficult by the nature of objects encountered in such scenes: these too change in appearance and scale, and are often articulated (e.g. humans). We propose a method which uses fast motion detection and segmentation as a constraint for both building appearance models and their robust propagation (matching) in time. The appearance model is based on sets of local appearances automatically clustered using spatio-kinetic similarity, and is updated with each new appearance seen. This integration of all seen appearances of a tracked object makes it extremely resilient to errors caused by occlusion and the lack of permanence of due to low data quality, appearance change or background clutter. These theoretical strengths of our algorithm are empirically demonstrated on two hour long video footage of a busy city marketplace.
The Dragon and the Computer: Why Intellectual Property Theft is Compatible with Chinese Cyber-Warfare Doctrine
Venue: arXiv cs.CR 2013
Year: 2013

Along with the USA and Russia, China is often considered one of the leading cyber-powers in the world. In this excerpt, we explore how Chinese military thought, developed in the 1990s, influenced their cyber-operations in the early 2000s. In particular, we examine the ideas of "Unrestricted Warfare" and "Active Offense" and discuss how they can permit for the theft of intellectual property. We then specifically look at how the case study of Operation Aurora, a cyber-operation directed against many major U.S. technology and defense firms, reflects some of these ideas.
Assessment of OpenStreetMap Data - A Review
Venue: arXiv cs.CY 2013
Year: 2013

The meaning and purposes of web has been changing and evolving day by day. Web 2. 0 encouraged more contribution by the end users. This movement provided revolutionary methods of sharing and computing data by crowdsourcing such as OpenStreetmap, also called "the wikification of maps" by some researchers. When crowdsourcing collects huge data with help of general public with varying level of mapping experience, the focus of researcher should be on analysing the data rather than collecting it. Researchers have assessed the quality of OpenStreetMap data by comparing it with proprietary data or data of governmental map agencies. This study reviews the research work for assessment of Open- StreetMap Data and also discusses about the future directions.
Characterness: An Indicator of Text in the Wild
Venue: arXiv cs.CV 2013
Year: 2013

Text in an image provides vital information for interpreting its contents, and text in a scene can aide with a variety of tasks from navigation, to obstacle avoidance, and odometry. Despite its value, however, identifying general text in images remains a challenging research problem. Motivated by the need to consider the widely varying forms of natural text, we propose a bottom-up approach to the problem which reflects the `characterness' of an image region. In this sense our approach mirrors the move from saliency detection methods to measures of `objectness'. In order to measure the characterness we develop three novel cues that are tailored for character detection, and a Bayesian method for their integration. Because text is made up of sets of characters, we then design a Markov random field (MRF) model so as to exploit the inherent dependencies between characters. We experimentally demonstrate the effectiveness of our characterness cues as well as the advantage of Bayesian multi-cue integration. The proposed text detector outperforms state-of-the-art methods on a few benchmark scene text detection datasets. We also show that our measurement of `characterness' is superior than state-of-the-art saliency detection models when applied to the same task.
Generalization of Rashmi-Shah-Kumar Minimum-Storage-Regenerating Codes
Venue: arXiv cs.IT 2013
Year: 2013

In this paper, we propose a generalized version of the Rashmi-Shah-Kumar Minimum-Storage-Regenerating(RSK-MSR) codes based on the product-matrix framework. For any $(n,k,d)$ such that $d \geq 2k-2$ and $d \leq n-1$, we can directly construct an $(n,k,d)$ MSR code without constructing a larger MSR code and shortening of the larger MSR code. As a result, the size of a finite field over which the proposed code is defined is smaller than or equal to the size of a finite field over which the RSK-MSR code is defined. In addition, the $\{\ell,\ell'\}$ secure codes based on the generalized RSK-MSR codes can be obtained by applying the construction method of $\{\ell,\ell'\}$ secure codes proposed by Shah, Rashmi and Kumar. Furthermore, the message matrix of the $(n,k,d)$ generalized RSK-MSR code is derived from that of the RSK-MSR code by using the construction method of the $\{\ell=k,\ell'=0\}$ secure code.
The Bregman Variational Dual-Tree Framework
Venue: arXiv cs.LG 2013
Year: 2013

Graph-based methods provide a powerful tool set for many non-parametric frameworks in Machine Learning. In general, the memory and computational complexity of these methods is quadratic in the number of examples in the data which makes them quickly infeasible for moderate to large scale datasets. A significant effort to find more efficient solutions to the problem has been made in the literature. One of the state-of-the-art methods that has been recently introduced is the Variational Dual-Tree (VDT) framework. Despite some of its unique features, VDT is currently restricted only to Euclidean spaces where the Euclidean distance quantifies the similarity. In this paper, we extend the VDT framework beyond the Euclidean distance to more general Bregman divergences that include the Euclidean distance as a special case. By exploiting the properties of the general Bregman divergence, we show how the new framework can maintain all the pivotal features of the VDT framework and yet significantly improve its performance in non-Euclidean domains. We apply the proposed framework to different text categorization problems and demonstrate its benefits over the original VDT.
Learning Sparse Causal Models is not NP-hard
Venue: arXiv cs.AI 2013
Year: 2013

This paper shows that causal model discovery is not an NP-hard problem, in the sense that for sparse graphs bounded by node degree k the sound and complete causal model can be obtained in worst case order N^{2(k+2)} independence tests, even when latent variables and selection bias may be present. We present a modification of the well-known FCI algorithm that implements the method for an independence oracle, and suggest improvements for sample/real-world data versions. It does not contradict any known hardness results, and does not solve an NP-hard problem: it just proves that sparse causal discovery is perhaps more complicated, but not as hard as learning minimal Bayesian networks.