OpenRaiser/Intern-Atlas huggingface dataset:
A Novel Reconfigurable Computing Architecture for Image Signal Processing Using Circuit-Switched NoC and Synchronous Dataflow Model
Venue: arXiv cs.AR 2013
Year: 2013

In this paper, a novel reconfigurable architecture is proposed for multifunctional image signal processing systems. A circuit-switched NoC is used to provide interconnection because the non-TMD links ensure fixed throughput, which is a desirable behavior for computational intensive image processing algorithms compared with packet-switched NoC. Image processing algorithms are modeled as synchronous dataflow graphs which provide a unified model for general computing procedure. An image processing system is considered as several temporally mutually exclusive algorithms. Thus, their dataflow graph representations could be considered as a group and a merging algorithm could be applied to generate a union graph while eliminating spatial redundancy for area consumption optimization. After the union graph have been mapped and routed on the NoC, the reconfigurable system could be configured to any of its target image processing algorithms by properly setting the NoC topology. Experiments show the demo reconfigurable system with two image processing applications cost 26.4% less hardware resource, compared with the non-reconfigurable implementations.
Using Information Theory to Study the Efficiency and Capacity of Caching in the Computer Networks
Venue: arXiv cs.IT 2013
Year: 2013

Nowadays computer networks use different kind of memory whose speeds and capacities vary widely. There exist methods of a so-called caching which are intended to use the different kinds of memory in such a way that the frequently used data are stored in the faster memory, wheres the infrequent ones are stored in the slower memory. We address the problems of estimating the caching efficiency and its capacity. We define the efficiency and capacity of the caching and suggest a method for their estimation based on the analysis of kinds of the accessible memory.
Tracking Serendipitous Interactions: How Individual Cultures Shape the Office
Venue: arXiv cs.CY 2013
Year: 2013

In many work environments, serendipitous interactions between members of different groups may lead to enhanced productivity, collaboration and knowledge dissemination. Two factors that may have an influence on such interactions are cultural differences between individuals in highly multicultural workplaces, and the layout and physical spaces of the workplace itself. In this work, we investigate how these two factors may facilitate or hinder inter-group interactions in the workplace. We analyze traces collected using wearable electronic badges to capture face-to-face interactions and mobility patterns of employees in a research laboratory in the UK. We observe that those who interact with people of different roles tend to come from collectivist cultures that value relationships and where people tend to be comfortable with social hierarchies, and that some locations in particular are more likely to host serendipitous interactions, knowledge that could be used by organizations to enhance communication and productivity.
Evaluating parametric holonomic sequences using rectangular splitting
Venue: arXiv cs.SC 2013
Year: 2013

We adapt the rectangular splitting technique of Paterson and Stockmeyer to the problem of evaluating terms in holonomic sequences that depend on a parameter. This approach allows computing the $n$-th term in a recurrent sequence of suitable type using $O(n^{1/2})$ "expensive" operations at the cost of an increased number of "cheap" operations. Rectangular splitting has little overhead and can perform better than either naive evaluation or asymptotically faster algorithms for ranges of $n$ encountered in applications. As an example, fast numerical evaluation of the gamma function is investigated. Our work generalizes two previous algorithms of Smith.
Green Heron Swarm Optimization Algorithm - State-of-the-Art of a New Nature Inspired Discrete Meta-Heuristics
Venue: arXiv cs.NE 2013
Year: 2013

Many real world problems are NP-Hard problems are a very large part of them can be represented as graph based problems. This makes graph theory a very important and prevalent field of study. In this work a new bio-inspired meta-heuristics called Green Heron Swarm Optimization (GHOSA) Algorithm is being introduced which is inspired by the fishing skills of the bird. The algorithm basically suited for graph based problems like combinatorial optimization etc. However introduction of an adaptive mathematical variation operator called Location Based Neighbour Influenced Variation (LBNIV) makes it suitable for high dimensional continuous domain problems. The new algorithm is being operated on the traditional benchmark equations and the results are compared with Genetic Algorithm and Particle Swarm Optimization. The algorithm is also operated on Travelling Salesman Problem, Quadratic Assignment Problem, Knapsack Problem dataset. The procedure to operate the algorithm on the Resource Constraint Shortest Path and road network optimization is also discussed. The results clearly demarcates the GHOSA algorithm as an efficient algorithm specially considering that the number of algorithms for the discrete optimization is very low and robust and more explorative algorithm is required in this age of social networking and mostly graph based problem scenarios.
A decidable class of (nominal) omega-regular languages over an infinite alphabet
Venue: arXiv cs.FL 2013
Year: 2013

We define a class of languages of infinite words over infinite alphabets, and the corresponding automata. The automata used for recognition are a generalisation of deterministic Muller automata to the setting of nominal sets. Remarkably, the obtained languages are determined by their ultimately periodic fragments, as in the classical case. Closure under complement, union and intersection, and decidability of emptiness and equivalence are preserved by the generalisation. This is shown by using finite representations of the (otherwise infinite-state) defined class of automata.
On Noisy ARQ in Block-Fading Channels
Venue: arXiv cs.IT 2013
Year: 2013

Assuming noisy feedback channels, this paper investigates the data transmission efficiency and robustness of different automatic repeat request (ARQ) schemes using adaptive power allocation. Considering different block-fading channel assumptions, the long-term throughput, the delay-limited throughput, the outage probability and the feedback load of different ARQ protocols are studied. A closed-form expression for the power-limited throughput optimization problem is obtained which is valid for different ARQ protocols and feedback channel conditions. Furthermore, the paper presents numerical investigations on the robustness of different ARQ protocols to feedback errors. It is shown that many analytical assertions about the ARQ protocols are valid both when the channel remains fixed during all retransmission rounds and when it changes in each round (in)dependently. As demonstrated, optimal power allocation is crucial for the performance of noisy ARQ schemes when the goal is to minimize the outage probability.
User Satisfaction in Competitive Sponsored Search
Venue: arXiv cs.GT 2013
Year: 2013

We present a model of competition between web search algorithms, and study the impact of such competition on user welfare. In our model, search providers compete for customers by strategically selecting which search results to display in response to user queries. Customers, in turn, have private preferences over search results and will tend to use search engines that are more likely to display pages satisfying their demands. Our main question is whether competition between search engines increases the overall welfare of the users (i.e., the likelihood that a user finds a page of interest). When search engines derive utility only from customers to whom they show relevant results, we show that they differentiate their results, and every equilibrium of the resulting game achieves at least half of the welfare that could be obtained by a social planner. This bound also applies whenever the likelihood of selecting a given engine is a convex function of the probability that a user's demand will be satisfied, which includes natural Markovian models of user behavior. On the other hand, when search engines derive utility from all customers (independent of search result relevance) and the customer demand functions are not convex, there are instances in which the (unique) equilibrium involves no differentiation between engines and a high degree of randomness in search results. This can degrade social welfare by a factor of the square root of N relative to the social optimum, where N is the number of webpages. These bad equilibria persist even when search engines can extract only small (but non-zero) expected revenue from dissatisfied users, and much higher revenue from satisfied ones.
Weak bisimulations for labelled transition systems weighted over semirings
Venue: arXiv cs.LO 2013
Year: 2013

Weighted labelled transition systems are LTSs whose transitions are given weights drawn from a commutative monoid. WLTSs subsume a wide range of LTSs, providing a general notion of strong (weighted) bisimulation. In this paper we extend this framework towards other behavioural equivalences, by considering semirings of weights. Taking advantage of this extra structure, we introduce a general notion of weak weighted bisimulation. We show that weak weighted bisimulation coincides with the usual weak bisimulations in the cases of non-deterministic and fully-probabilistic systems; moreover, it naturally provides a definition of weak bisimulation also for kinds of LTSs where this notion is currently missing (such as, stochastic systems). Finally, we provide a categorical account of the coalgebraic construction of weak weighted bisimulation; this construction points out how to port our approach to other equivalences based on different notion of observability.
Achievable Rates for Four-Dimensional Coded Modulation with a Bit-Wise Receiver
Venue: arXiv cs.IT 2013
Year: 2013

We study achievable rates for four-dimensional (4D) constellations for spectrally efficient optical systems based on a (suboptimal) bit-wise receiver. We show that PM-QPSK outperforms the best 4D constellation designed for uncoded transmission by approximately 1 dB. Numerical results using LDPC codes validate the analysis.
Optimal Sensor Placement and Enhanced Sparsity for Classification
Venue: arXiv cs.CV 2013
Year: 2013

The goal of compressive sensing is efficient reconstruction of data from few measurements, sometimes leading to a categorical decision. If only classification is required, reconstruction can be circumvented and the measurements needed are orders-of-magnitude sparser still. We define enhanced sparsity as the reduction in number of measurements required for classification over reconstruction. In this work, we exploit enhanced sparsity and learn spatial sensor locations that optimally inform a categorical decision. The algorithm solves an l1-minimization to find the fewest entries of the full measurement vector that exactly reconstruct the discriminant vector in feature space. Once the sensor locations have been identified from the training data, subsequent test samples are classified with remarkable efficiency, achieving performance comparable to that obtained by discrimination using the full image. Sensor locations may be learned from full images, or from a random subsample of pixels. For classification between more than two categories, we introduce a coupling parameter whose value tunes the number of sensors selected, trading accuracy for economy. We demonstrate the algorithm on example datasets from image recognition using PCA for feature extraction and LDA for discrimination; however, the method can be broadly applied to non-image data and adapted to work with other methods for feature extraction and discrimination.
Adaptive Mode Selection in Multiuser MISO Cognitive Networks with Limited Cooperation and Feedback
Venue: arXiv cs.IT 2013
Year: 2013

In this paper, we consider a multiuser MISO downlink cognitive network coexisting with a primary network. With the purpose of exploiting the spatial degree of freedom to counteract the inter-network interference and intra-network (inter-user) interference simultaneously, we propose to perform zero-forcing beamforming (ZFBF) at the multi-antenna cognitive base station (BS) based on the instantaneous channel state information (CSI). The challenge of designing ZFBF in cognitive networks lies in how to obtain the interference CSI. To solve it, we introduce a limited inter-network cooperation protocol, namely the quantized CSI conveyance from the primary receiver to the cognitive BS via purchase. Clearly, the more the feedback amount, the better the performance, but the higher the feedback cost. In order to achieve a balance between the performance and feedback cost, we take the maximization of feedback utility function, defined as the difference of average sum rate and feedback cost while satisfying the interference constraint, as the optimization objective, and derive the transmission mode and feedback amount joint optimization scheme. Moreover, we quantitatively investigate the impact of CSI feedback delay and obtain the corresponding optimization scheme. Furthermore, through asymptotic analysis, we present some simple schemes. Finally, numerical results confirm the effectiveness of our theoretical claims.
Analyzing User Behavior across Social Sharing Environments
Venue: arXiv cs.SI 2013
Year: 2013

In this work we present an in-depth analysis of the user behaviors on different Social Sharing systems. We consider three popular platforms, Flickr, Delicious and StumbleUpon, and, by combining techniques from social network analysis with techniques from semantic analysis, we characterize the tagging behavior as well as the tendency to create friendship relationships of the users of these platforms. The aim of our investigation is to see if (and how) the features and goals of a given Social Sharing system reflect on the behavior of its users and, moreover, if there exists a correlation between the social and tagging behavior of the users. We report our findings in terms of the characteristics of user profiles according to three different dimensions: (i) intensity of user activities, (ii) tag-based characteristics of user profiles, and (iii) semantic characteristics of user profiles.
Discriminative Link Prediction using Local Links, Node Features and Community Structure
Venue: arXiv cs.LG 2013
Year: 2013

A link prediction (LP) algorithm is given a graph, and has to rank, for each node, other nodes that are candidates for new linkage. LP is strongly motivated by social search and recommendation applications. LP techniques often focus on global properties (graph conductance, hitting or commute times, Katz score) or local properties (Adamic-Adar and many variations, or node feature vectors), but rarely combine these signals. Furthermore, neither of these extremes exploit link densities at the intermediate level of communities. In this paper we describe a discriminative LP algorithm that exploits two new signals. First, a co-clustering algorithm provides community level link density estimates, which are used to qualify observed links with a surprise value. Second, links in the immediate neighborhood of the link to be predicted are not interpreted at face value, but through a local model of node feature similarities. These signals are combined into a discriminative link predictor. We evaluate the new predictor using five diverse data sets that are standard in the literature. We report on significant accuracy boosts compared to standard LP methods (including Adamic-Adar and random walk). Apart from the new predictor, another contribution is a rigorous protocol for benchmarking and reporting LP algorithms, which reveals the regions of strengths and weaknesses of all the predictors studied here, and establishes the new proposal as the most robust.
Arbitrary Sequence RAMs
Venue: arXiv cs.CC 2013
Year: 2013

It is known that in some cases a Random Access Machine (RAM) benefits from having an additional input that is an arbitrary number, satisfying only the criterion of being sufficiently large. This is known as the ARAM model. We introduce a new type of RAM, which we refer to as the Arbitrary Sequence RAM (ASRAM), that generalises the ARAM by allowing the generation of additional arbitrary large numbers at will during execution time. We characterise the power contribution of this ability under several RAM variants. In particular, we demonstrate that an arithmetic ASRAM is more powerful than an arithmetic ARAM, that a sufficiently equipped ASRAM can recognise any language in the arithmetic hierarchy in constant time (and more, if it is given more time), and that, on the other hand, in some cases the ASRAM is no more powerful than its underlying RAM.
Secret sharing on the $d$-dimensional cube
Venue: arXiv cs.CR 2013
Year: 2013

We prove that for $d>1$ the best information ratio of the perfect secret sharing scheme based on the edge set of the $d$-dimensional cube is exactly $d/2$. Using the technique developed, we also prove that the information ratio of the infinite $d$-dimensional lattice is $d$.
A Greedy Algorithm for Optimally Pipelining a Reduction
Venue: arXiv cs.DC 2013
Year: 2013

Collective communications are ubiquitous in parallel applications. We present two new algorithms for performing a reduction. The operation associated with our reduction needs to be associative and commutative. The two algorithms are developed under two different communication models (unidirectional and bidirectional). Both algorithms use a greedy scheduling scheme. For a unidirectional, fully connected network, we prove that our greedy algorithm is optimal when some realistic assumptions are respected. Previous algorithms fit the same assumptions and are only appropriate for some given configurations. Our algorithm is optimal for all configurations. We note that there are some configuration where our greedy algorithm significantly outperform any existing algorithms. This result represents a contribution to the state-of-the art. For a bidirectional, fully connected network, we present a different greedy algorithm. We verify by experimental simulations that our algorithm matches the time complexity of an optimal broadcast (with addition of the computation). Beside reversing an optimal broadcast algorithm, the greedy algorithm is the first known reduction algorithm to experimentally attain this time complexity. Simulations show that this greedy algorithm performs well in practice, outperforming any state-of-the-art reduction algorithms. Positive experiments on a parallel distributed machine are also presented.
Distributed Queuing in Dynamic Networks
Venue: arXiv cs.DC 2013
Year: 2013

We consider the problem of forming a distributed queue in the adversarial dynamic network model of Kuhn, Lynch, and Oshman (STOC 2010) in which the network topology changes from round to round but the network stays connected. This is a synchronous model in which network nodes are assumed to be fixed, the communication links for each round are chosen by an adversary, and nodes do not know who their neighbors are for the current round before they broadcast their messages. Queue requests may arrive over rounds at arbitrary nodes and the goal is to eventually enqueue them in a distributed queue. We present two algorithms that give a total distributed ordering of queue requests in this model. We measure the performance of our algorithms through round complexity, which is the total number of rounds needed to solve the distributed queuing problem. We show that in 1-interval connected graphs, where the communication links change arbitrarily between every round, it is possible to solve the distributed queueing problem in O(nk) rounds using O(log n) size messages, where n is the number of nodes in the network and k <= n is the number of queue requests. Further, we show that for more stable graphs, e.g. T-interval connected graphs where the communication links change in every T rounds, the distributed queuing problem can be solved in O(n+ (nk/min(alpha,T))) rounds using the same O(log n) size messages, where alpha > 0 is the concurrency level parameter that captures the minimum number of active queue requests in the system in any round. These results hold in any arbitrary (sequential, one-shot concurrent, or dynamic) arrival of k queue requests in the system. Moreover, our algorithms ensure correctness in the sense that each queue request is eventually enqueued in the distributed queue after it is issued and each queue request is enqueued exactly once. We also provide an impossibility result for this distributed queuing problem in this model. To the best of our knowledge, these are the first solutions to the distributed queuing problem in adversarial dynamic networks.
Robust Leader Election in a Fast-Changing World
Venue: arXiv cs.DC 2013
Year: 2013

We consider the problem of electing a leader among nodes in a highly dynamic network where the adversary has unbounded capacity to insert and remove nodes (including the leader) from the network and change connectivity at will. We present a randomized Las Vegas algorithm that (re)elects a leader in O(D\log n) rounds with high probability, where D is a bound on the dynamic diameter of the network and n is the maximum number of nodes in the network at any point in time. We assume a model of broadcast-based communication where a node can send only 1 message of O(\log n) bits per round and is not aware of the receivers in advance. Thus, our results also apply to mobile wireless ad-hoc networks, improving over the optimal (for deterministic algorithms) O(Dn) solution presented at FOMC 2011. We show that our algorithm is optimal by proving that any randomized Las Vegas algorithm takes at least omega(D\log n) rounds to elect a leader with high probability, which shows that our algorithm yields the best possible (up to constants) termination time.
Asymptotically optimal decision rules for joint detection and source coding
Venue: arXiv cs.IT 2013
Year: 2013

The problem of joint detection and lossless source coding is considered. We derive asymptotically optimal decision rules for deciding whether or not a sequence of observations has emerged from a desired information source, and to compress it if has. In particular, our decision rules asymptotically minimize the cost of compression in the case that the data has been classified as `desirable', subject to given constraints on the two kinds of the probability of error. In another version of this performance criterion, the constraint on the false alarm probability is replaced by the a constraint on the cost of compression in the false alarm event. We then analyze the asymptotic performance of these decision rules and demonstrate that they may exhibit certain phase transitions. We also derive universal decision rules for the case where the underlying sources (under either hypothesis or both) are unknown, and training sequences from each source may or may not be available. Finally, we discuss how our framework can be extended in several directions.