OpenRaiser/Intern-Atlas huggingface dataset:
GraphVista: Interactive Exploration Of Large Graphs
Venue: arXiv cs.DB 2015
Year: 2015

The potential to gain business insights from graph-structured data through graph analytics is increasingly attracting companies from a variety of industries, ranging from web companies to traditional enterprise businesses. To analyze a graph, a user often executes isolated graph queries using a dedicated interface---a procedural graph programming interface or a declarative graph query language. The results are then returned and displayed using a specific visualization technique. This follows the classical ad-hoc Query$\rightarrow$Result interaction paradigm and often requires multiple query iterations until an interesting aspect in the graph data is identified. This is caused on the one hand by the schema flexibility of graph data and on the other hand by the intricacies of declarative graph query languages. To lower the burden for the user to explore an unknown graph without prior knowledge of a graph query language, visual graph exploration provides an effective and intuitive query interface to navigate through the graph interactively. We demonstrate GRAPHVISTA, a graph visualization and exploration tool that can seamlessly combine ad-hoc querying and interactive graph exploration within the same query session. In our demonstration, conference attendees will see GRAPHVISTA running against a large real-world graph data set. They will start by identifying entry points of interest with the help of ad-hoc queries and will then discover the graph interactively through visual graph exploration.
Cloud Services Brokerage: A Survey and Research Roadmap
Venue: arXiv cs.DC 2015
Year: 2015

A Cloud Services Brokerage (CSB) acts as an intermediary between cloud service providers (e.g., Amazon and Google) and cloud service end users, providing a number of value adding services. CSBs as a research topic are in there infancy. The goal of this paper is to provide a concise survey of existing CSB technologies in a variety of areas and highlight a roadmap, which details five future opportunities for research.
Sensitivity Analysis of Resonant Circuits
Venue: arXiv cs.OH 2015
Year: 2015

We use first-order perturbation theory to provide a local linear relation between the circuit parameters and the poles of an RLC network. The sensitivity matrix, which defines this relationship, is obtained from the systems eigenvectors and the derivative of its eigenvalues. In general, the sensitivity matrix is related to the equilibrium fluctuations of the system. In particular, it may be used as the basis for a statistical model to efficiently predict the sensitivity of the circuit response to small component variations. The method is illustrated with a calculation of conditional probabilities by Monte Carlo Simulation.
Assessing Efficiency-Effectiveness Tradeoffs in Multi-Stage Retrieval Systems Without Using Relevance Judgments
Venue: arXiv cs.IR 2015
Year: 2015

Large-scale retrieval systems are often implemented as a cascading sequence of phases -- a first filtering step, in which a large set of candidate documents are extracted using a simple technique such as Boolean matching and/or static document scores; and then one or more ranking steps, in which the pool of documents retrieved by the filter is scored more precisely using dozens or perhaps hundreds of different features. The documents returned to the user are then taken from the head of the final ranked list. Here we examine methods for measuring the quality of filtering and preliminary ranking stages, and show how to use these measurements to tune the overall performance of the system. Standard top-weighted metrics used for overall system evaluation are not appropriate for assessing filtering stages, since the output is a set of documents, rather than an ordered sequence of documents. Instead, we use an approach in which a quality score is computed based on the discrepancy between filtered and full evaluation. Unlike previous approaches, our methods do not require relevance judgments, and thus can be used with virtually any query set. We show that this quality score directly correlates with actual differences in measured effectiveness when relevance judgments are available. Since the quality score does not require relevance judgments, it can be used to identify queries that perform particularly poorly for a given filter. Using these methods, we explore a wide range of filtering options using thousands of queries, categorize the relative merits of the different approaches, and identify useful parameter combinations.
Peeling Algorithm on Random Hypergraphs with Superlinear Number of Hyperedges
Venue: arXiv cs.DM 2015
Year: 2015

When we try to solve a system of linear equations, we can consider a simple iterative algorithm in which an equation including only one variable is chosen at each step, and the variable is fixed to the value satisfying the equation. The dynamics of this algorithm is captured by the peeling algorithm. Analyses of the peeling algorithm on random hypergraphs are required for many problems, e.g., the decoding threshold of low-density parity check codes, the inverting threshold of Goldreich's pseudorandom generator, the load threshold of cuckoo hashing, etc. In this work, we deal with random hypergraphs including superlinear number of hyperedges, and derive the tight threshold for the succeeding of the peeling algorithm. For the analysis, Wormald's method of differential equations, which is commonly used for analyses of the peeling algorithm on random hypergraph with linear number of hyperedges, cannot be used due to the superlinear number of hyperedges. A new method called the evolution of the moment generating function is proposed in this work.
Phase Transitions for the Uniform Distribution in the PML Problem and its Bethe Approximation
Venue: arXiv cs.DM 2015
Year: 2015

The pattern maximum likelihood (PML) estimate, introduced by Orlitsky et al., is an estimate of the multiset of probabilities in an unknown probability distribution $\mathbf{p}$, the estimate being obtained from $n$ i.i.d. samples drawn from $\mathbf{p}$. The PML estimate involves solving a difficult optimization problem over the set of all probability mass functions (pmfs) of finite support. In this paper, we describe an interesting phase transition phenomenon in the PML estimate: at a certain sharp threshold, the uniform distribution goes from being a local maximum to being a local minimum for the optimization problem in the estimate. We go on to consider the question of whether a similar phase transition phenomenon also exists in the Bethe approximation of the PML estimate, the latter being an approximation method with origins in statistical physics. We show that the answer to this question is a qualified "Yes". Our analysis involves the computation of the mean and variance of the $(i,j)$th entry, $a_{i,j}$, in a random $k \times k$ non-negative integer matrix $A$ with row and column sums all equal to $M$, drawn according to a distribution that assigns to $A$ a probability proportional to $\prod_{i,j} \frac{(M-a_{i,j})!}{a_{i,j}!}$.
Parameterized mixed cluster editing via modular decomposition
Venue: arXiv cs.DS 2015
Year: 2015

In this paper we introduce a natural generalization of the well-known problems Cluster Editing and Bicluster Editing, whose parameterized versions have been intensively investigated in the recent literature. The generalized problem, called Mixed Cluster Editing or ${\cal M}$-Cluster Editing, is formulated as follows. Let ${\cal M}$ be a family of graphs. Given a graph $G$ and a nonnegative integer $k$, transform $G$, through a sequence of at most $k$ edge editions, into a target graph $G'$ with the following property: $G'$ is a vertex-disjoint union of graphs $G_1, G_2, \ldots$ such that every $G_i$ is a member of ${\cal M}$. The graph $G'$ is called a mixed cluster graph or ${\cal M}$-cluster graph. Let ${\cal K}$ denote the family of complete graphs, ${\cal KL}$ the family of complete $l$-partite graphs ($l \geq 2$), and $Ł={\cal K} \cup {\cal KL}$. In this work we focus on the case ${\cal M} = {\cal L}$. Using modular decomposition techniques previously applied to Cluster/Bicluster Editing, we present a linear-time algorithm to construct a problem kernel for the parameterized version of ${\cal L}$-Cluster Editing. Keywords: bicluster graphs, cluster graphs, edge edition problems, edge modification problems, fixed-parameter tractability, NP-complete problems.
Combining Two And Three-Way Embeddings Models for Link Prediction in Knowledge Bases
Venue: arXiv cs.AI 2015
Year: 2015

This paper tackles the problem of endogenous link prediction for Knowledge Base completion. Knowledge Bases can be represented as directed graphs whose nodes correspond to entities and edges to relationships. Previous attempts either consist of powerful systems with high capacity to model complex connectivity patterns, which unfortunately usually end up overfitting on rare relationships, or in approaches that trade capacity for simplicity in order to fairly model all relationships, frequent or not. In this paper, we propose Tatec a happy medium obtained by complementing a high-capacity model with a simpler one, both pre-trained separately and then combined. We present several variants of this model with different kinds of regularization and combination strategies and show that this approach outperforms existing methods on different types of relationships by achieving state-of-the-art results on four benchmarks of the literature.
Linear Types Can Change the Blockchain
Venue: arXiv cs.LO 2015
Year: 2015

We give an interpretation of full classical linear logic, and linear proofs in terms of operations on the blockchain.
A CMOS Spiking Neuron for Dense Memristor-Synapse Connectivity for Brain-Inspired Computing
Venue: arXiv cs.NE 2015
Year: 2015

Neuromorphic systems that densely integrate CMOS spiking neurons and nano-scale memristor synapses open a new avenue of brain-inspired computing. Existing silicon neurons have molded neural biophysical dynamics but are incompatible with memristor synapses, or used extra training circuitry thus eliminating much of the density advantages gained by using memristors, or were energy inefficient. Here we describe a novel CMOS spiking leaky integrate-and-fire neuron circuit. Building on a reconfigurable architecture with a single opamp, the described neuron accommodates a large number of memristor synapses, and enables online spike timing dependent plasticity (STDP) learning with optimized power consumption. Simulation results of an 180nm CMOS design showed 97% power efficiency metric when realizing STDP learning in 10,000 memristor synapses with a nominal 1MΩ memristance, and only 13μA current consumption when integrating input spikes. Therefore, the described CMOS neuron contributes a generalized building block for large-scale brain-inspired neuromorphic systems.
Unsupervised domain adaption dictionary learning for visual recognition
Venue: arXiv cs.CV 2015
Year: 2015

Over the last years, dictionary learning method has been extensively applied to deal with various computer vision recognition applications, and produced state-of-the-art results. However, when the data instances of a target domain have a different distribution than that of a source domain, the dictionary learning method may fail to perform well. In this paper, we address the cross-domain visual recognition problem and propose a simple but effective unsupervised domain adaption approach, where labeled data are only from source domain. In order to bring the original data in source and target domain into the same distribution, the proposed method forcing nearest coupled data between source and target domain to have identical sparse representations while jointly learning dictionaries for each domain, where the learned dictionaries can reconstruct original data in source and target domain respectively. So that sparse representations of original data can be used to perform visual recognition tasks. We demonstrate the effectiveness of our approach on standard datasets. Our method performs on par or better than competitive state-of-the-art methods.
Distance estimation with efference copies and optical flow maneuvers: a stability-based strategy
Venue: arXiv cs.RO 2015
Year: 2015

The visual cue of optical flow plays a major role in the navigation of flying insects, and is increasingly studied for use by small flying robots as well. A major problem is that successful optical flow control seems to require distance estimates, while optical flow is known to provide only the ratio of velocity to distance. In this article, a novel, stability-based strategy is proposed to estimate distances with monocular optical flow and knowledge of the control inputs (efference copies). It is shown analytically that given a fixed control gain, the stability of a constant divergence control loop only depends on the distance to the approached surface. At close distances, the control loop first starts to exhibit self-induced oscillations, eventually leading to instability. The proposed stability-based strategy for estimating distances has two major attractive characteristics. First, self-induced oscillations are easy for the robot to detect and are hardly influenced by wind. Second, the distance can be estimated during a zero divergence maneuver, i.e., around hover. The stability-based strategy is implemented and tested both in simulation and with a Parrot AR drone 2.0. It is shown that it can be used to: (1) trigger a final approach response during a constant divergence landing with fixed gain, (2) estimate the distance in hover, and (3) estimate distances during an entire landing if the robot uses adaptive gain control to continuously stay on the 'edge of oscillation'.
Implementation of Training Convolutional Neural Networks
Venue: arXiv cs.CV 2015
Year: 2015

Deep learning refers to the shining branch of machine learning that is based on learning levels of representations. Convolutional Neural Networks (CNN) is one kind of deep neural network. It can study concurrently. In this article, we gave a detailed analysis of the process of CNN algorithm both the forward process and back propagation. Then we applied the particular convolutional neural network to implement the typical face recognition problem by java. Then, a parallel strategy was proposed in section4. In addition, by measuring the actual time of forward and backward computing, we analysed the maximal speed up and parallel efficiency theoretically.
3D Wireless: Modeling Wireless Performance by Combining Spatial and Temporal Behaviors
Venue: arXiv cs.NI 2015
Year: 2015

Performance characterization is a fundamental issue in wireless networks for real time routing, wireless network simulation, and etc. There are four basic wireless operations that are required to be modeled, i.e., unicast, anycast, broadcast, and multicast. As observed in many recent works, the temporal and spatial distribution of packet receptions can have significant impact on wireless performance involving multiple links (anycast/broadcast/multicast). However, existing performance models and simulations overlook these two wireless behaviors, leading to biased performance estimation and simulation results. In this paper, we first explicitly identify the necessary "3-Dimension" information for wireless performance modeling, i.e., packet reception rate (PRR), PRR spatial distribution, and temporal distribution. We then propose a comprehensive modeling approach considering 3-Dimension Wireless information (called 3DW model). Further, we demonstrate the generality and wide applications of 3DW model by two case studies: 3DWbased network simulation and 3DW-based real time routing protocol. Extensive simulation and testbed experiments have been conducted. The results show that 3DW model achieves much more accurate performance estimation for both anycast and broadcast/multicast. 3DW-based simulation can effectively reserve the end-to-end performance metric of the input empirical traces. 3DW-based routing can select more efficient senders, achieving better transmission efficiency.
Circuit Complexity Meets Ontology-Based Data Access
Venue: arXiv cs.LO 2015
Year: 2015

Ontology-based data access is an approach to organizing access to a database augmented with a logical theory. In this approach query answering proceeds through a reformulation of a given query into a new one which can be answered without any use of theory. Thus the problem reduces to the standard database setting. However, the size of the query may increase substantially during the reformulation. In this survey we review a recently developed framework on proving lower and upper bounds on the size of this reformulation by employing methods and results from Boolean circuit complexity.
Adaptive event sensing in networks of autonomous mobile agents
Venue: arXiv cs.MA 2015
Year: 2015

Given a connected region in two-dimensional space where events of a certain kind occur according to a certain time-varying density, we consider the problem of setting up a network of autonomous mobile agents to detect the occurrence of those events and possibly record them in as effective a manner as possible. We assume that agents can communicate with one another wirelessly within a fixed communication radius, and moreover that initially no agent has any information regarding the event density. We introduce a new distributed algorithm for agent control based on the notion of an execution mode, which essentially lets each agent roam the target region either at random or following its local view of a density-dependent gradient. Agents can switch back and forth between the two modes, and the precise manner of such changes depends on the setting of various parameters that can be adjusted as a function of the application at hand. We provide simulation results on some synthetic applications especially designed to highlight the algorithm's behavior relative to the possible execution modes.
Impossibility Theorems and the Universal Algebraic Toolkit
Venue: arXiv cs.CC 2015
Year: 2015

We elucidate a close connection between the Theory of Judgment Aggregation (more generally, Evaluation Aggregation), and a relatively young but rapidly growing field of universal algebra, that was primarily developed to investigate constraint satisfaction problems. Our connection yields a full classification of non-binary evaluations into possibility and impossibility domains both under the idempotent and the supportive conditions. Prior to the current result E. Dokow and R. Holzman nearly classified non-binary evaluations in the supportive case, by combinatorial means. The algebraic approach gives us new insights to the easier binary case as well, which had been fully classified by the above authors. Our algebraic view lets us put forth a suggestion about a strengthening of the Non-dictatorship criterion, that helps us avoid "outliers" like the affine subspace. Finally, we give upper bounds on the complexity of computing if a domain is impossible or not (to our best knowledge no finite time bounds were given earlier).
Comparing the Performance of L*A*B* and HSV Color Spaces with Respect to Color Image Segmentation
Venue: arXiv cs.CV 2015
Year: 2015

Color image segmentation is a very emerging topic for image processing research. Since it has the ability to present the result in a way that is much more close to the human yes perceive, so todays more research is going on this area. Choosing a proper color space is a very important issue for color image segmentation process. Generally LAB and HSV are the two frequently chosen color spaces. In this paper a comparative analysis is performed between these two color spaces with respect to color image segmentation. For measuring their performance, we consider the parameters: mse and psnr . It is found that HSV color space is performing better than LAB.
Gold type codes of higher relative dimension
Venue: arXiv cs.IT 2015
Year: 2015

Some new Gold type codes of higher relative dimension are introduced. Their weight distribution is determined.
Optimum Decoder for an Additive Video Watermarking with Laplacian Noise in H.264
Venue: arXiv cs.MM 2015
Year: 2015

In this paper, we investigate an additive video watermarking method in H.264 standard in presence of the Laplacian noise. In some applications, due to the loss of some pixels or a region of a frame, we resort to Laplacian noise rather than Gaussian one. The embedding is performed in the transform domain; while an optimum and a sub-optimum decoder are derived for the proposed Laplacian model. Simulation results show that the proposed watermarking scheme has suitable performance with enough transparency required for watermarking applications.
Multilayer Structured NMF for Spectral Unmixing of Hyperspectral Images
Venue: arXiv cs.CV 2015
Year: 2015

One of the challenges in hyperspectral data analysis is the presence of mixed pixels. Mixed pixels are the result of low spatial resolution of hyperspectral sensors. Spectral unmixing methods decompose a mixed pixel into a set of endmembers and abundance fractions. Due to nonnegativity constraint on abundance fraction values, NMF based methods are well suited to this problem. In this paper multilayer NMF has been used to improve the results of NMF methods for spectral unmixing of hyperspectral data under the linear mixing framework. Sparseness constraint on both spectral signatures and abundance fractions matrices are used in this paper. Evaluation of the proposed algorithm is done using synthetic and real datasets in terms of spectral angle and abundance angle distances. Results show that the proposed algorithm outperforms other previously proposed methods.
The Meta Distribution of the SIR in Poisson Bipolar and Cellular Networks
Venue: arXiv cs.IT 2015
Year: 2015

The calculation of the SIR distribution at the typical receiver (or, equivalently, the success probability of transmissions over the typical link) in Poisson bipolar and cellular networks with Rayleigh fading is relatively straightforward, but it only provides limited information on the success probabilities of the individual links. This paper introduces the notion of the meta distribution of the SIR, which is the distribution of the conditional success probability $P$ given the point process, and provides bounds, an exact analytical expression, and a simple approximation for it. The meta distribution provides fine-grained information on the SIR and answers questions such as "What fraction of users in a Poisson cellular network achieve 90% link reliability if the required SIR is 5 dB?". Interestingly, in the bipolar model, if the transmit probability $p$ is reduced while increasing the network density $λ$ such that the density of concurrent transmitters $λp$ stays constant as $p\to 0$, $P$ degenerates to a constant, i.e., all links have exactly the same success probability in the limit, which is the one of the typical link. In contrast, in the cellular case, if the interfering base stations are active independently with probability $p$, the variance of $P$ approaches a non-zero constant when $p$ is reduced to $0$ while keeping the mean success probability constant.
Coauthorship and Thematic Networks in AAEP Annual Meetings
Venue: arXiv cs.SI 2015
Year: 2015

We analyze the coauthorship production of the AAEP Annual Meeting since 1964. We use social network analysis for creating coauthorship networks and given that any paper must be tagged with two JEL codes, we use this information for also structuring a thematic network. Then we calculate network metrics and find main actors and clusters for coauthors and topics. We distinguish a gender gap in the sample. Thematic networks show a cluster of codes and the analysis of the cluster shows the preeminence of the tags related to trade, econometric, distribution/poverty and health and education topics.