pinecone/core-2020-05-10-deduplication huggingface dataset:
3D Ultrasound-Guided Motion Compensation System for Beating Heart Mitral Valve Repair

Beating heart intracardiac procedures promise significant benefits for patients, however, the fast motion of the heart poses serious challenges to surgeons. We present a new 3D ultrasound-guided motion (3DUS) compensation system that synchronizes instrument motion with the heart. The system utilizes the fact that the motion of some intracardiac structures, including the mitral valve annulus, is largely constrained to translation along one axis. This allows the development of a real-time 3DUS tissue tracker which we integrate with a 1 degree-of-freedom actuated surgical instrument, real-time 3DUS instrument tracker, and predictive filter to devise a system with synchronization accuracy of 1.8 mm RMSE. User studies involving the deployment of surgical anchors in a simulated mitral annuloplasty procedure demonstrate that the system increases success rates by over 100%. Furthermore, it enables more careful anchor deployment by reducing forces to the tissue by 50% while allowing instruments to remain in contact with the tissue for longer periods.Engineering and Applied Science
Rician noise removal by non-Local Means filtering for low signal-to-noise ratio MRI: applications to DT-MRI.

International audienceDiffusion-Weighted MRI (DW-MRI) is subject to random noise yielding measures that are different from their real values, and thus biasing the subsequently estimated tensors. The Non-Local Means (NLMeans) filter has recently been proposed to denoise MRI with high signal-to-noise ratio (SNR). This filter has been shown to allow the best restoration of image intensities for the estimation of diffusion tensors (DT) compared to state-of-the-art methods. However, for DW-MR images with high b-values (and thus low SNR), the noise, which is strictly Rician-distributed, can no longer be approximated as additive white Gaussian, as implicitly assumed in the classical formulation of the NLMeans. High b-values are typically used in high angular resolution diffusion imaging (HARDI) or q-space imaging (QSI), for which an optimal restoration is critical. In this paper, we propose to adapt the NLMeans filter to Rician noise corrupted data. Validation is performed on synthetic data and on real data for both conventional MR images and DT images. Our adaptation outperforms the original NLMeans filter in terms of peak-signal-to-noise ratio (PSNR) for DW-MRI
Fast marker based c-arm pose estimation

To estimate the pose of a C-Arm during interventions therapy we have developed a small sized X-Ray Target including a special set of beads with known locations in 3D space. Since the patient needs to remain in the X-Ray path for all feasible poses of the C-Arm during the intervention, we cannot construct a single marker which is entirely visible in all images. Therefore finding 2D-3D point correspondences is a non-trivial task. The marker pattern has to be chosen in a way such that its projection onto the image plane is unique in a minimal-sized window for all relevant poses of the C-Arm. We use a two dimensional adaption of a linear feedback shift register (LFSR) to generate a two-dimensional pattern with unique sub-patterns in a certain window range. Thereby uniqueness is not achieved by placing unique 2D sub patterns side by side but by the code property itself. The code is designed in a way that any sub window of a minimal size guarantees uniqueness and that even occlusions from medical instruments can be handled. Experiments showed that we were able to estimate the C-Arm’s pose from a single image within one second with a precision below one millimeter and one degree
Constitutive modeling of human liver based on in vivo measurements

In vivo aspiration experiments on human livers are analyzed and material parameters for a non-linear-viscoelastic constitutive model are determined. A novel procedure is applied for the inverse analysis that accounts for the initial tissue deformation in the experiment and for the non-homogeneity of liver tissue. A numerical model is used consisting of a surface layer (capsule) and an underlying non-linear-viscoelastic solid (parenchyma). The capsule is modeled as hyperelastic membrane using data from measurements on bovine and human tissue. In a two step optimization procedure the set of constitutive model parameters for the "average" response of liver parenchyma is obtained. The proposed model is in line with literature values of high strain rate elastic modulus obtained from dynamic elastography. The model can be used to predict the nonlinear, time dependent behavior of human liver in computer simulations related to surgery training and planning
Achieving Fast BGP Reroute with Traffic Engineering Using Multiple Routing Planes

In today’s BGP routing architecture, traffic delivery is in general based on single path selection paradigms. The lack of path diversity hinders the support for resilience, traffic engineering and QoS provisioning across the Internet. Some recently proposed multi-plane extensions to BGP offer a promising mechanism to enable diverse inter-domain routes towards destination prefixes. Based on these enhanced BGP protocols, we propose in this paper a novel technique to enable controlled fast egress router switching for handling network failures. In order to minimize the disruptions to real-time services caused by the failures, backup egress routers can be immediately activated through locally remarking affected traffic towards alternative routing planes without waiting for IGP routing re-convergence. According to our evaluation results, the proposed multi-plane based egress router selection algorithm is able to provide both high path diversity and balanced load distribution across inter-domain links with a small number of planes
Multiconstrained optimization of networks with multicast and unicast traffic

This paper presents an OSPF routing optimization framework taking into account a set of multiconstrained QoS requirements of the networking domain. The proposed optimization approach, based on Evolutionary Computation, is able to handle network scenarios with both unicast and multicast traffic, providing high quality conﬁgurations for single-topology or multi-topology routing approaches. The results clearly show the effectiveness of the devisedoptimization methods, allowing for the development of management tools automatically providing enhanced conﬁgurations to improve the QoS performance of the network.Fundação para a Ciência e a Tecnologia (FCT) - PTDC/EIA/64541/2006
Intra-Domain Delay-Based Quality of Service Using Differentiated Routing

Differentiated routing is an approach to providing service differentiation in networks, a field that is currently receiving significant research attention. In this report we present an algorithm, namely Intra-Domain Differentiated Routing (IDDR), which supports qualitative delay differentiation in IP networks. We review existing differentiated routing approaches and then introduce IDDR and present initial results. We demonstrate that using IDDR we can achieve qualitative delay differentiation for two classes of flows
An Efficient Lagrangian Relaxation for the Contact Map Overlap Problem

International audienceAmong the measures for quantifying the similarity between protein 3-D structures, contact map overlap (CMO) maximization deserved sustained attention during past decade. Despite this large involvement, the known algorithms possess a modest performance and are not applicable for large scale comparison. This paper offers a clear advance in this respect. We present a new integer programming model for CMO and propose an exact B&B algorithm with bounds obtained by a novel Lagrangian relaxation. The efficiency of the approach is demonstrated on a popular small benchmark (Skolnick set, 40 domains). On this set our algorithm significantly outperforms the best existing exact algorithms. Many hard CMO instances have been solved for the first time. To assess furthermore our approach, we constructed a large scale set of 300 protein domains. Computing the similarity measure for any of the 44850 couples, we obtained a classification in excellent agreement with SCOP
Pattern-based model-to-model transformation

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-87405-8_29Proceedings of 4th International Conference, ICGT 2008, Leicester, United Kingdom, September 7-13, 2008We present a new, high-level approach for the specification of model-to-model transformations based on declarative patterns. These are (atomic or composite) constraints on triple graphs declaring the allowed or forbidden relationships between source and target models. In this way, a transformation is defined by specifying a set of triple graph constraints that should be satisfied by the result of the transformation.
The description of the transformation is then compiled into lower-level operational mechanisms to perform forward or backward transformations, as well as to establish mappings between two existent models. In this paper we study one of such mechanisms based on the generation of operational triple graph grammar rules. Moreover, we exploit deduction techniques at the specification level in order to generate more specialized constraints (preserving the specification semantics) reflecting pattern dependencies, from which additional rules can be derived.Work supported by the Spanish Ministry of Education and Science, projects TSI2005-08225-C07-06 and TIN2006-09678
On iterative decoding of high-density parity-check codes using edge-local complementation

The overall topic of this work is a graph operation known as edgelocal
complementation (ELC) and its applications to iterative decoding of
classical codes. Although these legacy codes are arguably not well-suited
for graph-based decoding, they have other desirable properties resulting
in much current research on the general problem of forging this alloy.
From this perspective, these codes are typically referred to as highdensity
parity-check codes. Our approach is to gain diversity by means
of ELC. Based on the known link between ELC and the information
sets of a code, C, we identify a one-to-one relationship between ELC
operations and the automorphism group of a code, Aut(C). With respect
to a specific parity-check matrix, H, we classify these code-preserving
permutations into trivial and nontrivial permutations, based on whether
the matrix is preserved (under ELC) up to row permutations, or not.
The corresponding iso-ELC operations preserve the structure of the
graph, and simulation data are presented on the performance benefit of
using iso-ELC operations as a source of diversity. Generalizing this to
random (noniso) ELC, we explore the benefits of a simplified, entirely
graph-local (i.e., distributive) implementation of ELC-based decoding.
Special codes are chosen, which are structurally well-suited for this
type of random ELC decoding. At an extreme, certain codes are ELCpreserved,
in that any ELC operation is an iso-ELC operation. Although
less useful from a coding perspective, the corresponding graphs are
interesting to determine and classify. However, in the general case, we
observe negative effects of random ELC, which causes the weight of
the graph to increase. Based on this, we explore the specific structural
properties which determine which ELC operations (i.e., which edges
of a graph) are desirable for ELC, from a decoding perspective. This is
dominated by the weight increase (in terms of number of new edges)
resulting from ELC, which also causes detrimental short cycles, so we
identify the graphical conditions (i.e., subgraphs) for which ELC is
weight-bounding ELC (WB-ELC). These operations are used to reduce
the weight of a systematic H (given a code), and also used in a proposed
decoder based on WB-ELC. At a slightly different approach, we also
apply ELC operations in an adaptive decoding scheme. As ELC is a
local operation, we can, to a certain extent, target the (inferred) least
reliable positions specifically. This is a well-known technique to improve
decoding, normally implemented via Gaussian elimination, which we
improve by using beneficial properties of the ELC operatio
Evaluating the impact of information distortion on normalized compression distance

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-87448-5_8Proceedings of Second International Castle Meeting, ICMCTA 2008, Castillo de la Mota, Medina del Campo, Spain, September 15-19, 2008.In this paper we apply different techniques of information distortion on a set of classical books written in English. We study the impact that these distortions have upon the Kolmogorov complexity and the clustering by compression technique (the latter based on Normalized Compression Distance, NCD). We show how to decrease the complexity of the considered books introducing several modifications in them. We measure how the information contained in each book is maintained using a clustering error measure. We find experimentally that the best way to keep the clustering error is by means of modifications in the most frequent words. We explain the details of these information distortions and we compare with other kinds of modifications like random word distortions and unfrequent word distortions. Finally, some phenomenological explanations from the different empirical results that have been carried out are presented.This work was supported by TIN 2004-04363-CO03-03, TIN 2007-65989, CAM
S-SEM-0255-2006, TIN2007-64718 and TSI 2005-08255-C07-06. We would also
like to thank Franscico Sánchez for his useful comments on this draft
Delineation of valleys and valley floors

Methods to automatically derive landforms have typically focused on pixel-based, bottom-up approaches and most commonly on the derivation of topographic eminences. In this paper we describe an object-based, top-down algorithm to identify valley floors. The algorithm is based on a region growing approach, seeded by thalwegs with pixels added to the region according to a threshold gradient value. Since such landforms are fiat we compare the results of our algorithm for a particular valley with a number\ud
of textual sources describing that valley. In a further comparison, we computed a pixel-based six-fold morphometric classification for regions we classified as either being, or not being, valley floor. The regions classified as valley floor are dominated by pla nar slopes and channels,\ud
though the algorithm is robust enough to allow local convexities to be classified as within the valley floor. Future work will explore the delineation of valley sides, and thus complete valleys
A Context-Aware Broadcast Protocol for Mobile Wireless Networks

International audienceDelay Tolerant Networks (DTNs) are a sub-class of mobile ad hoc networks (MANETs). They are mobile wireless networks that feature inherent connection disruption. In particular such networks are generally non-connected. In this paper we focus on defining a broadcast service which operate on DTNs. A number of protocols solving the problem of broadcasting across DTNs have been proposed in the past, but all of them exhibit a static behavior, i.e. they provide no control parameter. However, at the application level, flexible broadcasting schemes are desirable. In particular, it is important that the user (the source of the broadcast message) can control the way the message gets spread across the network. This paper introduces a new broadcasting protocol dedicated to DTNs, called Context-Aware Broadcasting Protocol (CABP), which adapts its greediness according to the “urgency” (priority) of the broadcast message. A formal presentation of its strategy is proposed and through preliminary experiments, the cost-effectiveness of CABP is enlightened
Learning to Predict One or More Ranks in Ordinal Regression Tasks

We present nondeterministic hypotheses learned from an ordinal regression task. They try to predict the true rank for an entry, but when the classification is uncertain the hypotheses predict a set of consecutive ranks (an interval). The aim is to keep the set of ranks as small as possible, while still containing the true rank. The justification for learning such a hypothesis is based on a real world problem arisen in breeding beef cattle. After defining a family of loss functions inspired in Information Retrieval, we derive an algorithm for minimizing them. The algorithm is based on posterior probabilities of ranks given an entry. A couple of implementations are compared: one based on a multiclass SVM and other based on Gaussian processes designed to minimize the linear loss in ordinal regression task
Parameter learning in probabilistic databases:  A least squares approach

acceptance rate = 20%We introduce the problem of learning the parameters of the probabilistic database ProbLog. Given the observed success probabilities of a set of queries, we compute the probabilities attached to facts that have a low approximation error on the training examples as well as on unseen examples. Assuming Gaussian error terms on the observed success probabilities, this naturally leads to a least squares optimization problem. Our approach, called LeProbLog, is able to learn both from queries and from proofs and even from both simultaneously. This makes it flexible and allows faster training in domains where the proofs are available. Experiments on real world data show the usefulness and effectiveness of this least squares calibration of probabilistic databases.status: publishe
Efficient frequent connected subgraph mining in graphs of bounded treewidth

Acceptance rate = 104/521 = 20%The frequent connected subgraph mining problem, i.e., the problem of listing all connected graphs that are subgraph isomorphic to at least a certain specified number of elements of a database of transaction graphs, cannot be solved in output polynomial time in the general case. If, however, the transaction graphs are restricted to trees then the problem becomes tractable. In this paper, we generalize the positive complexity result on trees to graphs of bounded treewidth. In particular, we show that for this class of transaction graphs, frequent connected subgraphs can be listed with incremental polynomial delay. Since subgraph isomorphism remains NP-complete for bounded treewidth graphs, the positive complexity result of this paper implies that efficient frequent pattern mining is possible even for computationally hard pattern matching operators.status: publishe
A simple model for sequences of relational state descriptions

acceptance rate = 16%Artificial intelligence aims at developing agents that learn and act in 
complex environments. Realistic environments typically feature a variable number of objects, relations amongst them, and non-deterministic transition behavior. 
Standard probabilistic sequence models provide efficient inference and learning  techniques, but typically cannot fully capture the relational complexity. On the  other hand, statistical relational learning techniques are often too inefficient. In  this paper, we present a simple model that occupies an intermediate position in this expressiveness/efficiency trade-off. It is based on CP-logic, an expressive probabilistic logic for modeling causality. However, by specializing CP-logic to represent a probability distribution over sequences of relational state descriptions, and employing a Markov assumption, inference and learning become more tractable and effective. We show that the resulting model is able to handle probabilistic relational domains with a substantial number of objects and relations.status: publishe
A Combined Ant Colony and Differential Evolution Feature Selection Algorithm

Feature selection is an important step in many pattern recognition systems that aims to overcome the so-called curse of dimensionality problem. Although Ant Colony Optimization (ACO) proved to be a powerful technique in different optimization problems, but it still needs some improvements when applied to the feature selection problem. This is due to the fact that it builds its solutions sequentially, where in feature selection this behavior will most likely not lead to the optimal solution. In this paper, a novel feature selection algorithm based on a combination of ACO and a simple, yet powerful, Differential Evolution (DE) operator is presented. The proposed combination enhances both the exploration and exploitation capabilities of the search procedure. The new algorithm is tested on two biosignal-driven applications. The performance of the proposed algorithm is compared with other dimensionality reduction techniques to prove its superiority
cAnt-Miner: an ant colony classification algorithm to cope with continuous attributes

This paper presents an extension to Ant-Miner, named cAnt-Miner (Ant-Miner coping with continuous attributes), which incorporates an entropy-based discretization method in order to cope with continuous attributes during the rule construction process. By having the ability to create discrete intervals for continuous attributes "on-the-fly", cAnt-Miner does not requires a discretization method in a preprocessing step, as Ant-Miner requires. cAnt-Miner has been compared against Ant-Miner in eight public domain datasets with respect to predictive accuracy and simplicity of the discovered rules. Empirical results show that creating discrete intervals during the rule construction process facilitates the discovery of more accurate and significantly simpler classification rule
Quantitative Game Semantics for Linear Logic

International audienceWe present a game-based semantic framework into which the time complexity of any MELL proof can be read out of its interpretation. This gives a compositional view of the geometry of interaction framework introduced by the first author. In our model the time measure is given by means of slots, as introduced by Ghica in a recent paper. The cost associated to a strategy is polynomially related to the normalization time of the interpreted proof, in the style of a complexity-theoretical full abstraction result
Recognizing Facial Expressions :
                    A Comparison of Computational Approaches

Original article can be found at http://springerlink.comRecognizing facial expressions are a key part of human social interaction,and processing of facial expression information is largely automatic, but it is a non-trivial task for a computational system. The purpose of this work is to develop computational models capable of differentiating between a range of human facial expressions. Raw face images are examples of high dimensional data, so here we use some dimensionality reduction techniques: Linear Discriminant Analysis, Principal Component Analysis and Curvilinear Component Analysis. We also preprocess the images with a bank of Gabor filters, so that important features in the face images are identified. Subsequently the faces are classified using a Support Vector Machine. We show that it is possible to differentiate faces with a neutral expression from those with a smiling expression with high accuracy. Moreover we can achieve this with data that has been massively reduced in size: in the best case the original images are reduced to just 11 dimensions
Implementing Bayes’ rule with neural fields

Bayesian statistics is has been very successful in describing behavioural data on decision making and cue integration under noisy circumstances. However, it is still an open question how the human brain actually incorporates this functionality. Here we compare three ways in which Bayes rule can be implemented using neural fields. The result is a truly dynamic framework that can easily be extended by non-Bayesian mechanisms such as learning and memory.European Union Joint-Action Science and Technology Project (IST-FP6-003747