OpenRaiser/Intern-Atlas huggingface dataset:
An improved computer vision method for detecting white blood cells
Venue: arXiv cs.CV 2014
Year: 2014

The automatic detection of White Blood Cells (WBC) still remains as an unsolved issue in medical imaging. The analysis of WBC images has engaged researchers from fields of medicine and computer vision alike. Since WBC can be approximated by an ellipsoid form, an ellipse detector algorithm may be successfully applied in order to recognize them. This paper presents an algorithm for the automatic detection of WBC embedded into complicated and cluttered smear images that considers the complete process as a multi-ellipse detection problem. The approach, based on the Differential Evolution (DE) algorithm, transforms the detection task into an optimization problem where individuals emulate candidate ellipses. An objective function evaluates if such candidate ellipses are really present in the edge image of the smear. Guided by the values of such function, the set of encoded candidate ellipses (individuals) are evolved using the DE algorithm so that they can fit into the WBC enclosed within the edge-only map of the image. Experimental results from white blood cell images with a varying range of complexity are included to validate the efficiency of the proposed technique in terms of accuracy and robustness.
Structure-Preserving Reduction of Finite-Difference Time-Domain Equations with Controllable Stability Beyond the CFL Limit
Venue: arXiv cs.CE 2014
Year: 2014

The timestep of the Finite-Difference Time-Domain method (FDTD) is constrained by the stability limit known as the Courant-Friedrichs-Lewy (CFL) condition. This limit can make FDTD simulations quite time consuming for structures containing small geometrical details. Several methods have been proposed in the literature to extend the CFL limit, including implicit FDTD methods and filtering techniques. In this paper, we propose a novel approach which combines model order reduction and a perturbation algorithm to accelerate FDTD simulations beyond the CFL barrier. We compare the proposed algorithm against existing implicit and explicit CFL extension techniques, demonstrating increased accuracy and performance on a large number of test cases, including resonant cavities, a waveguide structure, a focusing metascreen and a microstrip filter.
Adaptive Mesh Representation and Restoration of Biomedical Images
Venue: arXiv cs.CV 2014
Year: 2014

The triangulation of images has become an active research area in recent years for its compressive representation and ease of image processing and visualization. However, little work has been done on how to faithfully recover image intensities from a triangulated mesh of an image, a process also known as image restoration or decoding from meshes. The existing methods such as linear interpolation, least-square interpolation, or interpolation based on radial basis functions (RBFs) work to some extent, but often yield blurred features (edges, corners, etc.). The main reason for this problem is due to the isotropically-defined Euclidean distance that is taken into consideration in these methods, without considering the anisotropicity of feature intensities in an image. Moreover, most existing methods use intensities defined at mesh nodes whose intensities are often ambiguously defined on or near image edges (or feature boundaries). In the current paper, a new method of restoring an image from its triangulation representation is proposed, by utilizing anisotropic radial basis functions (ARBFs). This method considers not only the geometrical (Euclidean) distances but also the local feature orientations (anisotropic intensities). Additionally, this method is based on the intensities of mesh faces instead of mesh nodes and thus provides a more robust restoration. The two strategies together guarantee excellent feature-preserving restoration of an image with arbitrary super-resolutions from its triangulation representation, as demonstrated by various experiments provided in the paper.
Do altmetrics point to the broader impact of research? An overview of benefits and disadvantages of altmetrics
Venue: arXiv cs.DL 2014
Year: 2014

Today, it is not clear how the impact of research on other areas of society than science should be measured. While peer review and bibliometrics have become standard methods for measuring the impact of research in science, there is not yet an accepted framework within which to measure societal impact. Alternative metrics (called altmetrics to distinguish them from bibliometrics) are considered an interesting option for assessing the societal impact of research, as they offer new ways to measure (public) engagement with research output. Altmetrics is a term to describe web-based metrics for the impact of publications and other scholarly material by using data from social media platforms (e.g. Twitter or Mendeley). This overview of studies explores the potential of altmetrics for measuring societal impact. It deals with the definition and classification of altmetrics. Furthermore, their benefits and disadvantages for measuring impact are discussed.
On zero-rate error exponents of finite-state channels with input-dependent states
Venue: arXiv cs.IT 2014
Year: 2014

We derive a single-letter formula for the zero-rate reliability (error exponent) of a finite-state channel whose state variable depends deterministically (and recursively) on past channel inputs, where the code complies with a given channel input constraint. Special attention is then devoted to the important special case of the Gaussian channel with inter-symbol interference (ISI), where more explicit results are obtained.
Using multi-categorization semantic analysis and personalization for semantic search
Venue: arXiv cs.IR 2014
Year: 2014

Semantic search technology has received more attention in the last years. Compared with the keyword based search, semantic search is used to excavate the latent semantics information and help users find the information items that they want indeed. In this paper, we present a novel approach for semantic search which combines Multi-Categorization Semantic Analysis with personalization technology. The MCSA approach can classify documents into multiple categories, which is distinct from the existing approaches of classifying documents into a single category. Then, the search history and personal information for users are significantly considered in analysing and matching the original search result by Term Vector DataBase. A series of personalization algorithms are proposed to match personal information and search history. At last, the related experiments are made to validate the effectiveness and efficiency of our method. The experimental results show that our method based on MCSA and personalization outperforms some existing methods with the higher search accuracy and the lower extra time cost.
A branch and price procedure for the container premarshalling problem
Venue: arXiv cs.DS 2014
Year: 2014

During the loading phase of a vessel, only the containers that are on top of their stack are directly accessible. If the container that needs to be loaded next is not the top container, extra moves have to be performed, resulting in an increased loading time. One way to resolve this issue is via a procedure called premarshalling. The goal of premarshalling is to reshuffle the containers into a desired lay-out prior to the arrival of the vessel, in the minimum number of moves possible. This paper presents an exact algorithm based on branch and bound, that is evaluated on a large set of instances. The complexity of the premarshalling problem is also considered, and this paper shows that the problem at hand is NP-hard, even in the natural case of stacks with fixed height.
Guruswami-Sinop Rounding without Higher Level Lasserre
Venue: arXiv cs.DS 2014
Year: 2014

Guruswami and Sinop give a $O(1/δ)$ approximation guarantee for the non-uniform Sparsest Cut problem by solving $O(r)$-level Lasserre semidefinite constraints, provided that the generalized eigenvalues of the Laplacians of the cost and demand graphs satisfy a certain spectral condition, namely, $λ_{r+1} \geq Φ^{*}/(1-δ)$. Their key idea is a rounding technique that first maps a vector-valued solution to $[0, 1]$ using appropriately scaled projections onto Lasserre vectors. In this paper, we show that similar projections and analysis can be obtained using only $\ell_{2}^{2}$ triangle inequality constraints. This results in a $O(r/δ^{2})$ approximation guarantee for the non-uniform Sparsest Cut problem by adding only $\ell_{2}^{2}$ triangle inequality constraints to the usual semidefinite program, provided that the same spectral condition, $λ_{r+1} \geq Φ^{*}/(1-δ)$, holds.
Stock Market Prediction from WSJ: Text Mining via Sparse Matrix Factorization
Venue: arXiv cs.LG 2014
Year: 2014

We revisit the problem of predicting directional movements of stock prices based on news articles: here our algorithm uses daily articles from The Wall Street Journal to predict the closing stock prices on the same day. We propose a unified latent space model to characterize the "co-movements" between stock prices and news articles. Unlike many existing approaches, our new model is able to simultaneously leverage the correlations: (a) among stock prices, (b) among news articles, and (c) between stock prices and news articles. Thus, our model is able to make daily predictions on more than 500 stocks (most of which are not even mentioned in any news article) while having low complexity. We carry out extensive backtesting on trading strategies based on our algorithm. The result shows that our model has substantially better accuracy rate (55.7%) compared to many widely used algorithms. The return (56%) and Sharpe ratio due to a trading strategy based on our model are also much higher than baseline indices.
A New Approach for Finding Cloned Profiles in Online Social Networks
Venue: arXiv cs.CR 2014
Year: 2014

Today, Online Social Networks such as Facebook, LinkedIn and Twitter are the most popular platforms on the Internet, on which millions of users register to share personal information with their friends. A large amount of data, social links and statistics about users are collected by Online Social Networks services and they create big digital mines of various statistical data. Leakage of personal information is a significant concern for social network users. Besides information propagation, some new attacks on Online Social Networks such as Identity Clone attack (ICA) have been identified. ICA attempts to create a fake online identity of a victim to fool their friends into believing the authenticity of the fake identity to establish social links in order to reap the private information of the victims friends which is not shared in their public profiles. There are some identity validation services that perform users identity validation, but they are passive services and they only protect users who are informed on privacy concerns and online identity issues. This paper starts with an explanation of two types of profile cloning attacks are explained and a new approach for detecting clone identities is proposed by defining profile similarity and strength of relationship measures. According to similar attributes and strength of relationship among users which are computed in detection steps, it will be decided which profile is clone and which one is genuine by a predetermined threshold. Finally, the experimental results are presented to demonstrate the effectiveness of the proposed approach.
A framework for good SAT translations, with applications to CNF representations of XOR constraints
Venue: arXiv cs.CC 2014
Year: 2014

We present a general framework for good CNF-representations of boolean constraints, to be used for translating decision problems into SAT problems (i.e., deciding satisfiability for conjunctive normal forms). We apply it to the representation of systems of XOR-constraints, also known as systems of linear equations over the two-element field, or systems of parity constraints. The general framework defines the notion of "representation", and provides several methods to measure the quality of the representation by the complexity ("hardness") needed for making implicit "knowledge" of the representation explicit (to a SAT-solving mechanism). We obtain general upper and lower bounds. Applied to systems of XOR-constraints, we show a super-polynomial lower bound on "good" representations under very general circumstances. A corresponding upper bound shows fixed-parameter tractability in the number of constraints. The measurement underlying this upper bound ignores the auxiliary variables needed for shorter representations of XOR-constraints. Improved upper bounds (for special cases) take them into account, and a rich picture begins to emerge, under the various hardness measurements.
Intelligent Emergency Message Broadcasting in VANET Using PSO
Venue: arXiv cs.NI 2014
Year: 2014

The new type of Mobile Ad hoc Network which is called Vehicular Ad hoc Networks (VANET) created a fertile environment for research. In this research, a protocol Particle Swarm Optimization Contention Based Broadcast (PCBB) is proposed, for fast andeffective dissemination of emergency messages within a geographical area to distribute the emergency message and achieve the safety system, this research will help the VANET system to achieve its safety goals in intelligent and efficient way.
An Efficient Read Dominant Data Replication Protocol under Serial Isolation using Quorum Consensus Approach
Venue: arXiv cs.DC 2014
Year: 2014

In distributed systems, data replication provides better availability, higher read capacity, improved access efficiency and lower bandwidth requirements in the system. In this paper, we propose a significantly efficient approach of the data replication for serial isolation by using newly proposed Circular quorum systems. This paper has three major contributions. First, we have proposed the Circular quorum systems that generalize the various existing quorum systems, such as Read-one-write-all (ROWA) quorum systems, Majority quorum systems, Grid quorum systems, Diamond quorum systems, D-Space quorum systems, Multi-dimensional-grid quorum systems and Generalized-grid quorum systems. Second, Circular quorum systems not only generalizes but also improves the performance over existing quorum systems of their category. Third, we proposed a highly available Circular quorum consensus protocol for data replication under serial isolation level that uses a suitable Circular quorum system for read dominant scenario.
Learning to Deblur
Venue: arXiv cs.CV 2014
Year: 2014

We describe a learning-based approach to blind image deconvolution. It uses a deep layered architecture, parts of which are borrowed from recent work on neural network learning, and parts of which incorporate computations that are specific to image deconvolution. The system is trained end-to-end on a set of artificially generated training examples, enabling competitive performance in blind deconvolution, both with respect to quality and runtime.
Achievable Rates of FDD Massive MIMO Systems with Spatial Channel Correlation
Venue: arXiv cs.IT 2014
Year: 2014

It is well known that the performance of frequency-division-duplex (FDD) massive MIMO systems with i.i.d. channels is disappointing compared with that of time-division-duplex (TDD) systems, due to the prohibitively large overhead for acquiring channel state information at the transmitter (CSIT). In this paper, we investigate the achievable rates of FDD massive MIMO systems with spatially correlated channels, considering the CSIT acquisition dimensionality loss, the imperfection of CSIT and the regularized-zero-forcing linear precoder. The achievable rates are optimized by judiciously designing the downlink channel training sequences and user CSIT feedback codebooks, exploiting the multiuser spatial channel correlation. We compare our achievable rates with TDD massive MIMO systems, i.i.d. FDD systems, and the joint spatial division and multiplexing (JSDM) scheme, by deriving the deterministic equivalents of the achievable rates, based on popular channel models. It is shown that, based on the proposed eigenspace channel estimation schemes, the rate-gap between FDD systems and TDD systems is significantly narrowed, even approached under moderate number of base station antennas. Compared to the JSDM scheme, our proposal achieves dimensionality-reduction channel estimation without channel pre-projection, and higher throughput for moderate number of antennas and moderate to large channel coherence time, though at higher computational complexity.
Towards a Generic Application Partitioning and Retraction Framework for Pervasive Environments
Venue: arXiv cs.DC 2014
Year: 2014

Current mobile context-aware applications for pervasive environments have been designed to consume information from computational nodes or devices in their surroundings or environments. As the hardware industry continues making much smaller, compact and cheap hardware, the vision of having plenty of very small powerful digital networking nodes in, for e.g., the living room or bedroom, is not so far. Designing software that can make optimal use of all these computational nodes when needed is still challenging; since software will not only consume information from these nodes but parts of the software can be hosted on these different nodes. In this paper we propose the BubbleCodes Framework which is a generic application partitioning and retraction framework for next generation context-aware applications that will have the capabilities to partition and retract themselves on multiple computational nodes in a pervasive environment.
Dealing with Zero Density Using Piecewise Phase-type Approximation
Venue: arXiv cs.PF 2014
Year: 2014

Every probability distribution can be approximated up to a given precision by a phase-type distribution, i.e. a distribution encoded by a continuous time Markov chain (CTMC). However, an excessive number of states in the corresponding CTMC is needed for some standard distributions, in particular most distributions with regions of zero density such as uniform or shifted distributions. Addressing this class of distributions, we suggest an alternative representation by CTMC extended with discrete-time transitions. Using discrete-time transitions we split the density function into multiple intervals. Within each interval, we then approximate the density with standard phase-type fitting. We provide an experimental evidence that our method requires only a moderate number of states to approximate such distributions with regions of zero density. Furthermore, the usage of CTMC with discrete-time transitions is supported by a number of techniques for their analysis. Thus, our results promise an efficient approach to the transient analysis of a class of non-Markovian models.
When none of us perform better than all of us together: the role of analogical decision rules in groups
Venue: arXiv cs.SI 2014
Year: 2014

During social interactions, groups develop collective competencies that (ideally) should assist groups to outperform average standalone individual members (weak cognitive synergy) or the best performing member in the group (strong cognitive synergy). In two experimental studies we manipulate the type of decision rule used in group decision-making (identify the best vs. collaborative), and the way in which the decision rules are induced (direct vs. analogical) and we test the effect of these two manipulations on the emergence of strong and weak cognitive synergy. Our most important results indicate that an analogically induced decision rule (imitate-the-successful heuristic) in which groups have to identify the best member and build on his/her performance (take-the-best heuristic) is the most conducive for strong cognitive synergy. Our studies bring evidence for the role of analogy-making in groups as well as the role of fast-and-frugal heuristics for group decision-making.
Streaming Graph Partitioning in the Planted Partition Model
Venue: arXiv cs.DS 2014
Year: 2014

The sheer increase in the size of graph data has created a lot of interest into developing efficient distributed graph processing frameworks. Popular existing frameworks such as Graphlab and Pregel rely on balanced graph partitioning in order to minimize communication and achieve work balance. In this work we contribute to the recent research line of streaming graph partitioning \cite{stantonstreaming,stanton,fennel} which computes an approximately balanced $k$-partitioning of the vertex set of a graph using a single pass over the graph stream using degree-based criteria. This graph partitioning framework is well tailored to processing large-scale and dynamic graphs. In this work we introduce the use of higher length walks for streaming graph partitioning and show that their use incurs a minor computational cost which can significantly improve the quality of the graph partition. We perform an average case analysis of our algorithm using the planted partition model \cite{condon2001algorithms,mcsherry2001spectral}. We complement the recent results of Stanton \cite{stantonstreaming} by showing that our proposed method recovers the true partition with high probability even when the gap of the model tends to zero as the size of the graph grows. Furthermore, among the wide number of choices for the length of the walks we show that the proposed length is optimal. Finally, we conduct experiments which verify the value of the proposed method.
Wisdom of the Confident: Using Social Interactions to Eliminate the Bias in Wisdom of the Crowds
Venue: arXiv cs.SI 2014
Year: 2014

Human groups can perform extraordinary accurate estimations compared to individuals by simply using the mean, median or geometric mean of the individual estimations [Galton 1907, Surowiecki 2005, Page 2008]. However, this is true only for some tasks and in general these collective estimations show strong biases. The method fails also when allowing for social interactions, which makes the collective estimation worse as individuals tend to converge to the biased result [Lorenz et al. 2011]. Here we show that there is a bright side of this apparently negative impact of social interactions into collective intelligence. We found that some individuals resist the social influence and, when using the median of this subgroup, we can eliminate the bias of the wisdom of the full crowd. To find this subgroup of individuals more confident in their private estimations than in the social influence, we model individuals as estimators that combine private and social information with different relative weights [Perez-Escudero & de Polavieja 2011, Arganda et al. 2012]. We then computed the geometric mean for increasingly smaller groups by eliminating those using in their estimations higher values of the social influence weight. The trend obtained in this procedure gives unbiased results, in contrast to the simpler method of computing the median of the complete group. Our results show that, while a simple operation like the mean, median or geometric mean of a group may not allow groups to make good estimations, a more complex operation taking into account individuality in the social dynamics can lead to a better collective intelligence.