OpenRaiser/Intern-Atlas huggingface dataset:
New affine-invariant codes from lifting
Venue: arXiv cs.IT 2012
Year: 2012

In this work we explore error-correcting codes derived from the "lifting" of "affine-invariant" codes. Affine-invariant codes are simply linear codes whose coordinates are a vector space over a field and which are invariant under affine-transformations of the coordinate space. Lifting takes codes defined over a vector space of small dimension and lifts them to higher dimensions by requiring their restriction to every subspace of the original dimension to be a codeword of the code being lifted. While the operation is of interest on its own, this work focusses on new ranges of parameters that can be obtained by such codes, in the context of local correction and testing. In particular we present four interesting ranges of parameters that can be achieved by such lifts, all of which are new in the context of affine-invariance and some may be new even in general. The main highlight is a construction of high-rate codes with sublinear time decoding. The only prior construction of such codes is due to Kopparty, Saraf and Yekhanin \cite{KSY}. All our codes are extremely simple, being just lifts of various parity check codes (codes with one symbol of redundancy), and in the final case, the lift of a Reed-Solomon code. We also present a simple connection between certain lifted codes and lower bounds on the size of "Nikodym sets". Roughly, a Nikodym set in $\mathbb{F}_q^m$ is a set $S$ with the property that every point has a line passing through it which is almost entirely contained in $S$. While previous lower bounds on Nikodym sets were roughly growing as $q^m/2^m$, we use our lifted codes to prove a lower bound of $(1 - o(1))q^m$ for fields of constant characteristic.
Cooperative Cognitive Relaying with Ordered Cognitive Multiple Access
Venue: arXiv cs.NI 2012
Year: 2012

We investigate a cognitive radio system with two secondary users who can cooperate with the primary user in relaying its packets to the primary receiver. In addition to its own queue, each secondary user has a queue to keep the primary packets that are not received correctly by the primary receiver. The secondary users accept the unreceived primary packets with a certain probability and transmit randomly from either of their queues if both are nonempty. These probabilities are optimized to expand the maximum stable throughput region of the system. Moreover, we suggest a secondary multiple access scheme in which one secondary user senses the channel for $τ$ seconds from the beginning of the time slot and transmits if the channel is found to be free. The other secondary user senses the channel over the period $[0,2τ]$ to detect the possible activity of the primary user and the first-ranked secondary user. It transmits, if possible, starting after $2τ$ seconds from the beginning of the time slot. It compensates for the delayed transmission by increasing its transmission rate so that it still transmits one packet during the time slot. We show the potential advantage of this ordered system over the conventional random access system. We also show the benefit of cooperation in enhancing the network's throughput.
Vector Field k-Means: Clustering Trajectories by Fitting Multiple Vector Fields
Venue: arXiv cs.LG 2012
Year: 2012

Scientists study trajectory data to understand trends in movement patterns, such as human mobility for traffic analysis and urban planning. There is a pressing need for scalable and efficient techniques for analyzing this data and discovering the underlying patterns. In this paper, we introduce a novel technique which we call vector-field $k$-means. The central idea of our approach is to use vector fields to induce a similarity notion between trajectories. Other clustering algorithms seek a representative trajectory that best describes each cluster, much like $k$-means identifies a representative "center" for each cluster. Vector-field $k$-means, on the other hand, recognizes that in all but the simplest examples, no single trajectory adequately describes a cluster. Our approach is based on the premise that movement trends in trajectory data can be modeled as flows within multiple vector fields, and the vector field itself is what defines each of the clusters. We also show how vector-field $k$-means connects techniques for scalar field design on meshes and $k$-means clustering. We present an algorithm that finds a locally optimal clustering of trajectories into vector fields, and demonstrate how vector-field $k$-means can be used to mine patterns from trajectory data. We present experimental evidence of its effectiveness and efficiency using several datasets, including historical hurricane data, GPS tracks of people and vehicles, and anonymous call records from a large phone company. We compare our results to previous trajectory clustering techniques, and find that our algorithm performs faster in practice than the current state-of-the-art in trajectory clustering, in some examples by a large margin.
When Channel Bonding is Beneficial for Opportunistic Spectrum Access Networks
Venue: arXiv cs.NI 2012
Year: 2012

Transmission over multiple frequency bands combined into one logical channel speeds up data transfer for wireless networks. On the other hand, the allocation of multiple channels to a single user decreases the probability of finding a free logical channel for new connections, which may result in a network-wide throughput loss. While this relationship has been studied experimentally, especially in the WLAN configuration, little is known on how to analytically model such phenomena. With the advent of Opportunistic Spectrum Access (OSA) networks, it is even more important to understand the circumstances in which it is beneficial to bond channels occupied by primary users with dynamic duty cycle patterns. In this paper we propose an analytical framework which allows the investigation of the average channel throughput at the medium access control layer for OSA networks with channel bonding enabled. We show that channel bonding is generally beneficial, though the extent of the benefits depend on the features of the OSA network, including OSA network size and the total number of channels available for bonding. In addition, we show that performance benefits can be realized by adaptively changing the number of bonded channels depending on network conditions. Finally, we evaluate channel bonding considering physical layer constraints, i.e. throughput reduction compared to the theoretical throughput of a single virtual channel due to a transmission power limit for any bonding size.
Phases vs. Levels using Decision Trees for Intrusion Detection Systems
Venue: arXiv cs.CR 2012
Year: 2012

Security of computers and the networks that connect them is increasingly becoming of great significance. Intrusion detection system is one of the security defense tools for computer networks. This paper compares two different model Approaches for representing intrusion detection system by using decision tree techniques. These approaches are Phase-model approach and Level-model approach. Each model is implemented by using two techniques, New Attacks and Data partitioning techniques. The experimental results showed that Phase approach has higher classification rate in both New Attacks and Data Partitioning techniques than Level approach.
Solving Quadratic Equations via PhaseLift when There Are About As Many Equations As Unknowns
Venue: arXiv cs.IT 2012
Year: 2012

This note shows that we can recover a complex vector x in C^n exactly from on the order of n quadratic equations of the form |<a_i, x>|^2 = b_i, i = 1, ..., m, by using a semidefinite program known as PhaseLift. This improves upon earlier bounds in [3], which required the number of equations to be at least on the order of n log n. We also demonstrate optimal recovery results from noisy quadratic measurements; these results are much sharper than previously known results.
Pisces: Anonymous Communication Using Social Networks
Venue: arXiv cs.CR 2012
Year: 2012

The architectures of deployed anonymity systems such as Tor suffer from two key problems that limit user's trust in these systems. First, paths for anonymous communication are built without considering trust relationships between users and relays in the system. Second, the network architecture relies on a set of centralized servers. In this paper, we propose Pisces, a decentralized protocol for anonymous communications that leverages users' social links to build circuits for onion routing. We argue that such an approach greatly improves the system's resilience to attackers. A fundamental challenge in this setting is the design of a secure process to discover peers for use in a user's circuit. All existing solutions for secure peer discovery leverage structured topologies and cannot be applied to unstructured social network topologies. In Pisces, we discover peers by using random walks in the social network graph with a bias away from highly connected nodes to prevent a few nodes from dominating the circuit creation process. To secure the random walks, we leverage the reciprocal neighbor policy: if malicious nodes try to exclude honest nodes during peer discovery so as to improve the chance of being selected, then honest nodes can use a tit-for-tat approach and reciprocally exclude the malicious nodes from their routing tables. We describe a fully decentralized protocol for enforcing this policy, and use it to build the Pisces anonymity system. Using theoretical modeling and experiments on real-world social network topologies, we show that (a) the reciprocal neighbor policy mitigates active attacks that an adversary can perform, (b) our decentralized protocol to enforce this policy is secure and has low overhead, and (c) the overall anonymity provided by our system significantly outperforms existing approaches.
Transmission Mode Selection for Downlink Coordinated Multi-Point Systems
Venue: arXiv cs.NI 2012
Year: 2012

Coordinated multi-point (CoMP) transmission has been widely recognized as a spectrally efficient technique in future cellular systems. To exploit the abundant patial resources provided by the cooperating base stations, however, considerable training overhead is required to acquire the channel information. To avoid the extra overhead outweighing the cooperative gain, we propose a method that allows each user to select transmission mode between coherent CoMP and Non-CoMP. We first analyze the average throughput of each user under CoMP and Non-CoMP transmission after taking into account the downlink training overhead. A closed-form mode selection rule is then developed, which depends on the user location and system settings, i.e, the number of cooperating base stations and transmit antennas, training overhead and cell-edge signal to noise ratio. Simulation results show that the proposed downlink transmission mode selection method achieves higher throughput than CoMP for cell-center users and than Non-CoMP for cell-edge users after accounting for the overhead. As a by-product, the backhaul load is also reduced significantly.
Statistically adaptive learning for a general class of cost functions (SA L-BFGS)
Venue: arXiv cs.LG 2012
Year: 2012

We present a system that enables rapid model experimentation for tera-scale machine learning with trillions of non-zero features, billions of training examples, and millions of parameters. Our contribution to the literature is a new method (SA L-BFGS) for changing batch L-BFGS to perform in near real-time by using statistical tools to balance the contributions of previous weights, old training examples, and new training examples to achieve fast convergence with few iterations. The result is, to our knowledge, the most scalable and flexible linear learning system reported in the literature, beating standard practice with the current best system (Vowpal Wabbit and AllReduce). Using the KDD Cup 2012 data set from Tencent, Inc. we provide experimental results to verify the performance of this method.
Learning implicitly in reasoning in PAC-Semantics
Venue: arXiv cs.AI 2012
Year: 2012

We consider the problem of answering queries about formulas of propositional logic based on background knowledge partially represented explicitly as other formulas, and partially represented as partially obscured examples independently drawn from a fixed probability distribution, where the queries are answered with respect to a weaker semantics than usual -- PAC-Semantics, introduced by Valiant (2000) -- that is defined using the distribution of examples. We describe a fairly general, efficient reduction to limited versions of the decision problem for a proof system (e.g., bounded space treelike resolution, bounded degree polynomial calculus, etc.) from corresponding versions of the reasoning problem where some of the background knowledge is not explicitly given as formulas, only learnable from the examples. Crucially, we do not generate an explicit representation of the knowledge extracted from the examples, and so the "learning" of the background knowledge is only done implicitly. As a consequence, this approach can utilize formulas as background knowledge that are not perfectly valid over the distribution---essentially the analogue of agnostic learning here.
Robopinion: Opinion Mining Framework Inspired by Autonomous Robot Navigation
Venue: arXiv cs.CL 2012
Year: 2012

Data association methods are used by autonomous robots to find matches between the current landmarks and the new set of observed features. We seek a framework for opinion mining to benefit from advancements in autonomous robot navigation in both research and development
The Synthesis and Analysis of Stochastic Switching Circuits
Venue: arXiv cs.IT 2012
Year: 2012

Stochastic switching circuits are relay circuits that consist of stochastic switches called pswitches. The study of stochastic switching circuits has widespread applications in many fields of computer science, neuroscience, and biochemistry. In this paper, we discuss several properties of stochastic switching circuits, including robustness, expressibility, and probability approximation. First, we study the robustness, namely, the effect caused by introducing an error of size εto each pswitch in a stochastic circuit. We analyze two constructions and prove that simple series-parallel circuits are robust to small error perturbations, while general series-parallel circuits are not. Specifically, the total error introduced by perturbations of size less than εis bounded by a constant multiple of εin a simple series-parallel circuit, independent of the size of the circuit. Next, we study the expressibility of stochastic switching circuits: Given an integer q and a pswitch set S=\{\frac{1}{q},\frac{2}{q},...,\frac{q-1}{q}\}, can we synthesize any rational probability with denominator q^n (for arbitrary n) with a simple series-parallel stochastic switching circuit? We generalize previous results and prove that when q is a multiple of 2 or 3, the answer is yes. We also show that when q is a prime number larger than 3, the answer is no. Probability approximation is studied for a general case of an arbitrary pswitch set S=\{s_1,s_2,...,s_{|S|}\}. In this case, we propose an algorithm based on local optimization to approximate any desired probability. The analysis reveals that the approximation error of a switching circuit decreases exponentially with an increasing circuit size.
Streaming Algorithms for Optimal Generation of Random Bits
Venue: arXiv cs.IT 2012
Year: 2012

Generating random bits from a source of biased coins (the biased is unknown) is a classical question that was originally studied by von Neumann. There are a number of known algorithms that have asymptotically optimal information efficiency, namely, the expected number of generated random bits per input bit is asymptotically close to the entropy of the source. However, only the original von Neumann algorithm has a `streaming property' - it operates on a single input bit at a time and it generates random bits when possible, alas, it does not have an optimal information efficiency. The main contribution of this paper is an algorithm that generates random bit streams from biased coins, uses bounded space and runs in expected linear time. As the size of the allotted space increases, the algorithm approaches the information-theoretic upper bound on efficiency. In addition, we discuss how to extend this algorithm to generate random bit streams from m-sided dice or correlated sources such as Markov chains.
Efficiently Extracting Randomness from Imperfect Stochastic Processes
Venue: arXiv cs.IT 2012
Year: 2012

We study the problem of extracting a prescribed number of random bits by reading the smallest possible number of symbols from non-ideal stochastic processes. The related interval algorithm proposed by Han and Hoshi has asymptotically optimal performance; however, it assumes that the distribution of the input stochastic process is known. The motivation for our work is the fact that, in practice, sources of randomness have inherent correlations and are affected by measurement's noise. Namely, it is hard to obtain an accurate estimation of the distribution. This challenge was addressed by the concepts of seeded and seedless extractors that can handle general random sources with unknown distributions. However, known seeded and seedless extractors provide extraction efficiencies that are substantially smaller than Shannon's entropy limit. Our main contribution is the design of extractors that have a variable input-length and a fixed output length, are efficient in the consumption of symbols from the source, are capable of generating random bits from general stochastic processes and approach the information theoretic upper bound on efficiency.
Nonuniform Codes for Correcting Asymmetric Errors in Data Storage
Venue: arXiv cs.IT 2012
Year: 2012

The construction of asymmetric error correcting codes is a topic that was studied extensively, however, the existing approach for code construction assumes that every codeword should tolerate $t$ asymmetric errors. Our main observation is that in contrast to symmetric errors, asymmetric errors are content dependent. For example, in Z-channels, the all-1 codeword is prone to have more errors than the all-0 codeword. This motivates us to develop nonuniform codes whose codewords can tolerate different numbers of asymmetric errors depending on their Hamming weights. The idea in a nonuniform codes' construction is to augment the redundancy in a content-dependent way and guarantee the worst case reliability while maximizing the code size. In this paper, we first study nonuniform codes for Z-channels, namely, they only suffer one type of errors, say 1 to 0. Specifically, we derive their upper bounds, analyze their asymptotic performances, and introduce two general constructions. Then we extend the concept and results of nonuniform codes to general binary asymmetric channels, where the error probability for each bit from 0 to 1 is smaller than that from 1 to 0.
A sufficient condition for first order non-definability of arrowing problems
Venue: arXiv cs.CC 2012
Year: 2012

We here present a sufficient condition for general arrowing problems to be non definable in first order logic, based in well known tools of finite model theory e.g. Hanf's Theorem and known concepts in finite combinatorics, like senders and determiners.
Direct computation of diagnoses for ontology debugging
Venue: arXiv cs.AI 2012
Year: 2012

Modern ontology debugging methods allow efficient identification and localization of faulty axioms defined by a user while developing an ontology. The ontology development process in this case is characterized by rather frequent and regular calls to a reasoner resulting in an early user awareness of modeling errors. In such a scenario an ontology usually includes only a small number of conflict sets, i.e. sets of axioms preserving the faults. This property allows efficient use of standard model-based diagnosis techniques based on the application of hitting set algorithms to a number of given conflict sets. However, in many use cases such as ontology alignment the ontologies might include many more conflict sets than in usual ontology development settings, thus making precomputation of conflict sets and consequently ontology diagnosis infeasible. In this paper we suggest a debugging approach based on a direct computation of diagnoses that omits calculation of conflict sets. Embedded in an ontology debugger, the proposed algorithm is able to identify diagnoses for an ontology which includes a large number of faults and for which application of standard diagnosis methods fails. The evaluation results show that the approach is practicable and is able to identify a fault in adequate time.
Stabilizability and Norm-Optimal Control Design subject to Sparsity Constraints
Venue: arXiv cs.SY 2012
Year: 2012

Consider that a linear time-invariant (LTI) plant is given and that we wish to design a stabilizing controller for it. Admissible controllers are LTI and must comply with a pre-selected sparsity pattern. The sparsity pattern is assumed to be quadratically invariant (QI) with respect to the plant, which, from prior results, guarantees that there is a convex parametrization of all admissible stabilizing controllers provided that an initial admissible stable stabilizing controller is provided. This paper addresses the previously unsolved problem of determining necessary and sufficient conditions for the existence of an admissible stabilizing controller. The main idea is to cast the existence of such a controller as the feasibility of an exact model-matching problem with stability restrictions, which can be tackled using existing methods. Furthermore, we show that, when it exists, the solution of the model-matching problem can be used to compute an admissible stabilizing controller. This method also leads to a convex parametrization that may be viewed as an extension of Youla's classical approach so as to incorporate sparsity constraints. Applications of this parametrization on the design of norm-optimal controllers via convex methods are also explored. An illustrative example is provided, and a special case is discussed for which the exact model matching problem has a unique and easily computable solution.
Differentially Private Grids for Geospatial Data
Venue: arXiv cs.CR 2012
Year: 2012

In this paper, we tackle the problem of constructing a differentially private synopsis for two-dimensional datasets such as geospatial datasets. The current state-of-the-art methods work by performing recursive binary partitioning of the data domains, and constructing a hierarchy of partitions. We show that the key challenge in partition-based synopsis methods lies in choosing the right partition granularity to balance the noise error and the non-uniformity error. We study the uniform-grid approach, which applies an equi-width grid of a certain size over the data domain and then issues independent count queries on the grid cells. This method has received no attention in the literature, probably due to the fact that no good method for choosing a grid size was known. Based on an analysis of the two kinds of errors, we propose a method for choosing the grid size. Experimental results validate our method, and show that this approach performs as well as, and often times better than, the state-of-the-art methods. We further introduce a novel adaptive-grid method. The adaptive grid method lays a coarse-grained grid over the dataset, and then further partitions each cell according to its noisy count. Both levels of partitions are then used in answering queries over the dataset. This method exploits the need to have finer granularity partitioning over dense regions and, at the same time, coarse partitioning over sparse regions. Through extensive experiments on real-world datasets, we show that this approach consistently and significantly outperforms the uniform-grid method and other state-of-the-art methods.