OpenRaiser/Intern-Atlas huggingface dataset:
Implementation without commitment in moral hazard environments
Venue: arXiv cs.GT 2013
Year: 2013

Interdependent-choice equilibrium is defined as an extension of correlated equilibrium in which the mediator is able to choose the timing of her signals, and observe the actions taken by the players. The set of interdependent-choice equilibria is a nonempty, closed and convex polytope. It characterizes all the outcomes that can be implemented in single shot interactions without repetition, side payments, binding contracts or any other form of delegation.
GBM Volumetry using the 3D Slicer Medical Image Computing Platform
Venue: arXiv cs.CV 2013
Year: 2013

Volumetric change in glioblastoma multiforme (GBM) over time is a critical factor in treatment decisions. Typically, the tumor volume is computed on a slice-by-slice basis using MRI scans obtained at regular intervals. (3D)Slicer - a free platform for biomedical research - provides an alternative to this manual slice-by-slice segmentation process, which is significantly faster and requires less user interaction. In this study, 4 physicians segmented GBMs in 10 patients, once using the competitive region-growing based GrowCut segmentation module of Slicer, and once purely by drawing boundaries completely manually on a slice-by-slice basis. Furthermore, we provide a variability analysis for three physicians for 12 GBMs. The time required for GrowCut segmentation was on an average 61% of the time required for a pure manual segmentation. A comparison of Slicer-based segmentation with manual slice-by-slice segmentation resulted in a Dice Similarity Coefficient of 88.43 +/- 5.23% and a Hausdorff Distance of 2.32 +/- 5.23 mm.
Enumerating Abelian Returns to Prefixes of Sturmian Words
Venue: arXiv cs.FL 2013
Year: 2013

We follow the works of Puzynina and Zamboni, and Rigo et al. on abelian returns in Sturmian words. We determine the cardinality of the set $\mathcal{APR}_u$ of abelian returns of all prefixes of a Sturmian word $u$ in terms of the coefficients of the continued fraction of the slope, dependingly on the intercept. We provide a simple algorithm for finding the set $\mathcal{APR}_u$ and we determine it for the characteristic Sturmian words.
Runtime Verification Based on Executable Models: On-the-Fly Matching of Timed Traces
Venue: arXiv cs.SE 2013
Year: 2013

Runtime verification is checking whether a system execution satisfies or violates a given correctness property. A procedure that automatically, and typically on the fly, verifies conformance of the system's behavior to the specified property is called a monitor. Nowadays, a variety of formalisms are used to express properties on observed behavior of computer systems, and a lot of methods have been proposed to construct monitors. However, it is a frequent situation when advanced formalisms and methods are not needed, because an executable model of the system is available. The original purpose and structure of the model are out of importance; rather what is required is that the system and its model have similar sets of interfaces. In this case, monitoring is carried out as follows. Two "black boxes", the system and its reference model, are executed in parallel and stimulated with the same input sequences; the monitor dynamically captures their output traces and tries to match them. The main problem is that a model is usually more abstract than the real system, both in terms of functionality and timing. Therefore, trace-to-trace matching is not straightforward and allows the system to produce events in different order or even miss some of them. The paper studies on-the-fly conformance relations for timed systems (i.e., systems whose inputs and outputs are distributed along the time axis). It also suggests a practice-oriented methodology for creating and configuring monitors for timed systems based on executable models. The methodology has been successfully applied to a number of industrial projects of simulation-based hardware verification.
Top-Down and Bottom-Up Approach for Model-Based Testing of Product Lines
Venue: arXiv cs.SE 2013
Year: 2013

Systems tend to become more and more complex. This has a direct impact on system engineering processes. Two of the most important phases in these processes are requirements engineering and quality assurance. Two significant complexity drivers located in these phases are the growing number of product variants that have to be integrated into the requirements engineering and the ever growing effort for manual test design. There are modeling techniques to deal with both complexity drivers like, e.g., feature modeling and model-based test design. Their combination, however, has been seldom the focus of investigation. In this paper, we present two approaches to combine feature modeling and model-based testing as an efficient quality assurance technique for product lines. We present the corresponding difficulties and approaches to overcome them. All explanations are supported by an example of an online shop product line.
Embedded Online Optimization for Model Predictive Control at Megahertz Rates
Venue: arXiv cs.SY 2013
Year: 2013

Faster, cheaper, and more power efficient optimization solvers than those currently offered by general-purpose solutions are required for extending the use of model predictive control (MPC) to resource-constrained embedded platforms. We propose several custom computational architectures for different first-order optimization methods that can handle linear-quadratic MPC problems with input, input-rate, and soft state constraints. We provide analysis ensuring the reliable operation of the resulting controller under reduced precision fixed-point arithmetic. Implementation of the proposed architectures in FPGAs shows that satisfactory control performance at a sample rate beyond 1 MHz is achievable even on low-end devices, opening up new possibilities for the application of MPC on embedded systems.
Termite-hill: From natural to artificial termites in sensor networks
Venue: arXiv cs.NI 2013
Year: 2013

Termites present a very good natural metaphor to evolutionary computation. While each individuals computational power is small compared to more evolved species, it is the power of their colonies that inspires communication engineers. This paper presents a study of artificial termites in sensor networks for the purpose of solving its routing problem. The behaviors of each of the termites in their colony allow their simulation in a restricted environment. The simulating behavior demonstrates how the termites make use of an auto-catalytic behavior in order to collectively find a solution for a posed problem in reasonable time. The derived algorithm termed Termite-hill demonstrates the principle of termites behavior to routing problem solving in the real applications of sensor networks. The performance of the algorithm was tested on static and dynamic sink scenarios. The results as compared with other routing algorithms and with varying network density show that Termite-hill is scalable and improved on network energy consumption with a control over best-effort-service.
Stabilizing switching signals for switched linear systems
Venue: arXiv cs.SY 2013
Year: 2013

This article deals with stability of continuous-time switched linear systems under constrained switching. Given a family of linear systems, possibly containing unstable dynamics, we characterize a new class of switching signals under which the switched linear system generated by it and the family of systems is globally asymptotically stable. Our characterization of such stabilizing switching signals involves the asymptotic frequency of switching, the asymptotic fraction of activation of the constituent systems, and the asymptotic densities of admissible transitions among them. Our techniques employ multiple Lyapunov-like functions, and extend preceding results both in scope and applicability.
On Considering Uncertainty and Alternatives in Low-Level Vision
Venue: arXiv cs.AI 2013
Year: 2013

In this paper we address the uncertainty issues involved in the low-level vision task of image segmentation. Researchers in computer vision have worked extensively on this problem, in which the goal is to partition (or segment) an image into regions that are homogeneous or uniform in some sense. This segmentation is often utilized by some higher level process, such as an object recognition system. We show that by considering uncertainty in a Bayesian formalism, we can use statistical image models to build an approximate representation of a probability distribution over a space of alternative segmentations. We give detailed descriptions of the various levels of uncertainty associated with this problem, discuss the interaction of prior and posterior distributions, and provide the operations for constructing this representation.
Forecasting Sleep Apnea with Dynamic Network Models
Venue: arXiv cs.AI 2013
Year: 2013

Dynamic network models (DNMs) are belief networks for temporal reasoning. The DNM methodology combines techniques from time series analysis and probabilistic reasoning to provide (1) a knowledge representation that integrates noncontemporaneous and contemporaneous dependencies and (2) methods for iteratively refining these dependencies in response to the effects of exogenous influences. We use belief-network inference algorithms to perform forecasting, control, and discrete event simulation on DNMs. The belief network formulation allows us to move beyond the traditional assumptions of linearity in the relationships among time-dependent variables and of normality in their probability distributions. We demonstrate the DNM methodology on an important forecasting problem in medicine. We conclude with a discussion of how the methodology addresses several limitations found in traditional time series analyses.
Using First-Order Probability Logic for the Construction of Bayesian Networks
Venue: arXiv cs.AI 2013
Year: 2013

We present a mechanism for constructing graphical models, specifically Bayesian networks, from a knowledge base of general probabilistic information. The unique feature of our approach is that it uses a powerful first-order probabilistic logic for expressing the general knowledge base. This logic allows for the representation of a wide range of logical and probabilistic information. The model construction procedure we propose uses notions from direct inference to identify pieces of local statistical information from the knowledge base that are most appropriate to the particular event we want to reason about. These pieces are composed to generate a joint probability distribution specified as a Bayesian network. Although there are fundamental difficulties in dealing with fully general knowledge, our procedure is practical for quite rich knowledge bases and it supports the construction of a far wider range of networks than allowed for by current template technology.
Graph-Grammar Assistance for Automated Generation of Influence Diagrams
Venue: arXiv cs.AI 2013
Year: 2013

One of the most difficult aspects of modeling complex dilemmas in decision-analytic terms is composing a diagram of relevance relations from a set of domain concepts. Decision models in domains such as medicine, however, exhibit certain prototypical patterns that can guide the modeling process. Medical concepts can be classified according to semantic types that have characteristic positions and typical roles in an influence-diagram model. We have developed a graph-grammar production system that uses such inherent interrelationships among medical terms to facilitate the modeling of medical decisions.
An Algorithm for the Construction of Bayesian Network Structures from Data
Venue: arXiv cs.AI 2013
Year: 2013

Previous algorithms for the construction of Bayesian belief network structures from data have been either highly dependent on conditional independence (CI) tests, or have required an ordering on the nodes to be supplied by the user. We present an algorithm that integrates these two approaches - CI tests are used to generate an ordering on the nodes from the database which is then used to recover the underlying Bayesian network structure using a non CI based method. Results of preliminary evaluation of the algorithm on two networks (ALARM and LED) are presented. We also discuss some algorithm performance issues and open problems.
Using Potential Influence Diagrams for Probabilistic Inference and Decision Making
Venue: arXiv cs.AI 2013
Year: 2013

The potential influence diagram is a generalization of the standard "conditional" influence diagram, a directed network representation for probabilistic inference and decision analysis [Ndilikilikesha, 1991]. It allows efficient inference calculations corresponding exactly to those on undirected graphs. In this paper, we explore the relationship between potential and conditional influence diagrams and provide insight into the properties of the potential influence diagram. In particular, we show how to convert a potential influence diagram into a conditional influence diagram, and how to view the potential influence diagram operations in terms of the conditional influence diagram.
Constructing Lower Probabilities
Venue: arXiv cs.AI 2013
Year: 2013

An elaboration of Dempster's method of constructing belief functions suggests a broadly applicable strategy for constructing lower probabilities under a variety of evidentiary constraints.
Forms and Linear Network Codes
Venue: arXiv cs.IT 2013
Year: 2013

We present a general theory to obtain linear network codes utilizing forms and obtain explicit families of equidimensional vector spaces, in which any pair of distinct vector spaces intersect in the same small dimension. The theory is inspired by the methods of the author utilizing the osculating spaces of Veronese varieties. Linear network coding transmits information in terms of a basis of a vector space and the information is received as a basis of a possibly altered vector space. Ralf Koetter and Frank R. Kschischang introduced a metric on the set af vector spaces and showed that a minimal distance decoder for this metric achieves correct decoding if the dimension of the intersection of the transmitted and received vector space is sufficiently large. The vector spaces in our construction are equidistant in the above metric and the distance between any pair of vector spaces is large making them suitable for linear network coding. The parameters of the resulting linear network codes are determined.
Model-guided Performance Analysis of the Sparse Matrix-Matrix Multiplication
Venue: arXiv cs.PF 2013
Year: 2013

Achieving high efficiency with numerical kernels for sparse matrices is of utmost importance, since they are part of many simulation codes and tend to use most of the available compute time and resources. In addition, especially in large scale simulation frameworks the readability and ease of use of mathematical expressions are essential components for the continuous maintenance, modification, and extension of software. In this context, the sparse matrix-matrix multiplication is of special interest. In this paper we thoroughly analyze the single-core performance of sparse matrix-matrix multiplication kernels in the Blaze Smart Expression Template (SET) framework. We develop simple models for estimating the achievable maximum performance, and use them to assess the efficiency of our implementations. Additionally, we compare these kernels with several commonly used SET-based C++ libraries, which, just as Blaze, aim at combining the requirements of high performance with an elegant user interface. For the different sparse matrix structures considered here, we show that our implementations are competitive or faster than those of the other SET libraries for most problem sizes on a current Intel multicore processor.
ALPRS - A New Approach for License Plate Recognition using the Sift Algorithm
Venue: arXiv cs.CV 2013
Year: 2013

This paper presents a new approach for the automatic license plate recognition, which includes the SIFT algorithm in step to locate the plate in the input image. In this new approach, besides the comparison of the features obtained with the SIFT algorithm, the correspondence between the spatial orientations and the positioning associated with the keypoints is also observed. Afterwards, an algorithm is used for the character recognition of the plates, very fast, which makes it possible its application in real time. The results obtained with the proposed approach presented very good success rates, so much for locating the characters in the input image, as for their recognition.
K-Nearest Neighbour algorithm coupled with logistic regression in medical case-based reasoning systems. Application to prediction of access to the renal transplant waiting list in Brittany
Venue: arXiv cs.AI 2013
Year: 2013

Introduction. Case Based Reasoning (CBR) is an emerg- ing decision making paradigm in medical research where new cases are solved relying on previously solved similar cases. Usually, a database of solved cases is provided, and every case is described through a set of attributes (inputs) and a label (output). Extracting useful information from this database can help the CBR system providing more reliable results on the yet to be solved cases. Objective. For that purpose we suggest a general frame- work where a CBR system, viz. K-Nearest Neighbor (K-NN) algorithm, is combined with various information obtained from a Logistic Regression (LR) model. Methods. LR is applied, on the case database, to assign weights to the attributes as well as the solved cases. Thus, five possible decision making systems based on K-NN and/or LR were identified: a standalone K-NN, a standalone LR and three soft K-NN algorithms that rely on the weights based on the results of the LR. The evaluation of the described approaches is performed in the field of renal transplant access waiting list. Results and conclusion. The results show that our suggested approach, where the K-NN algorithm relies on both weighted attributes and cases, can efficiently deal with non relevant attributes, whereas the four other approaches suffer from this kind of noisy setups. The robustness of this approach suggests interesting perspectives for medical problem solving tools using CBR methodology.
Design and Development of an Ultrasonic Motion Detector
Venue: arXiv cs.OH 2013
Year: 2013

The ultrasonic motion detector devices emit ultrasonic sound energy into an area of interest (monitored area), and this further reacts to a change in the reflected energy pattern. The system uses a technique that is based on a frequency shift in reflected energy to detect a movement or change in position (motion). In this system, ultrasonic sound is transmitted from the transmitting device which is normally in the form of energy. The transmitted sound utilizes air as its medium and this travel in a wave type motion. The wave is reflected back from the surroundings in the room/hallway and the device hears a pitch characteristic of the protected environment. In this system, the wave pattern is disturbed and reflected back more quickly, thus increasing the pitch and signaling an alarm whenever motion is detected. The main contribution of this work is the design of a circuit that can sense motion through movement of anything, a low cost and portable motion detector, and the design of a circuit that can be used to trigger another circuit whether to ON or OFF depending on the circuit attached to it. Generally, the design is made to detect movement or moving object in a an enclosed area. In this work, a transmitter transducer generates a signal at a frequency of 40khz, and when the signal is blocked by any moving object, and this in turn, triggers a buzzer via a timing circuit. This system works on the principle of the signal interference by a moving body, and the system is dependent on the presence of an intruder or moving object within a monitored area. The system after design and construction was tested and found to work in accordance with specifications.
An electronic digital combination lock: A precise and reliable security system
Venue: arXiv cs.OH 2013
Year: 2013

The increasing rate of crime, attacks by thieves, intruders and vandals, despite all forms of security gadgets and locks still need the attention of researchers to find a permanent solution to the well being of lives and properties of individuals. To this end, we design a cheap and effective security system for buildings, cars, safes, doors and gates, so as to prevent unauthorized person from having access to ones properties through the use of codes, we therefore experiment the application of electronic devices as locks. However, a modular approach was employed in the design in which the combination lock was divided into units and each unit designed separately before being coupled to form a whole functional system. During the design, we conducted Twenty tests with the first eight combinations being four in number, the next seven tests being five and the last five combinations being six. This was done because of the incorporation of 2 dummy switches in the combinations. From the result obtained, combinations 8, 11, 13 gave the correct output combination. However, 8 being the actual combination gave the required output. The general operation of the system and performance is dependent on the key combinations. The overall system was constructed and tested and it works perfectly.
Enhancing community detection using a network weighting strategy
Venue: arXiv cs.SI 2013
Year: 2013

A community within a network is a group of vertices densely connected to each other but less connected to the vertices outside. The problem of detecting communities in large networks plays a key role in a wide range of research areas, e.g. Computer Science, Biology and Sociology. Most of the existing algorithms to find communities count on the topological features of the network and often do not scale well on large, real-life instances. In this article we propose a strategy to enhance existing community detection algorithms by adding a pre-processing step in which edges are weighted according to their centrality w.r.t. the network topology. In our approach, the centrality of an edge reflects its contribute to making arbitrary graph tranversals, i.e., spreading messages over the network, as short as possible. Our strategy is able to effectively complements information about network topology and it can be used as an additional tool to enhance community detection. The computation of edge centralities is carried out by performing multiple random walks of bounded length on the network. Our method makes the computation of edge centralities feasible also on large-scale networks. It has been tested in conjunction with three state-of-the-art community detection algorithms, namely the Louvain method, COPRA and OSLOM. Experimental results show that our method raises the accuracy of existing algorithms both on synthetic and real-life datasets.
Simplifying Energy Optimization using Partial Enumeration
Venue: arXiv cs.CV 2013
Year: 2013

Energies with high-order non-submodular interactions have been shown to be very useful in vision due to their high modeling power. Optimization of such energies, however, is generally NP-hard. A naive approach that works for small problem instances is exhaustive search, that is, enumeration of all possible labelings of the underlying graph. We propose a general minimization approach for large graphs based on enumeration of labelings of certain small patches. This partial enumeration technique reduces complex high-order energy formulations to pairwise Constraint Satisfaction Problems with unary costs (uCSP), which can be efficiently solved using standard methods like TRW-S. Our approach outperforms a number of existing state-of-the-art algorithms on well known difficult problems (e.g. curvature regularization, stereo, deconvolution); it gives near global minimum and better speed. Our main application of interest is curvature regularization. In the context of segmentation, our partial enumeration technique allows to evaluate curvature directly on small patches using a novel integral geometry approach.