Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast nonlinear filter for continuous-discrete time multiple models

A fast algorithm is proposed for computing online the optimal nonlinear filter in the continuous-discrete time, multiple model setting. Using the finite element approximation on a spatial grid with N points and performing part of the computations off line, the online complexity of the algorithm is shown to be O(N) for all dimensions of the state process. The error of the approximation is also studied.
Physics Tutorial for Residents MR Imaging : Brief Overview and Emerging Applications 1

Magnetic resonance (MR) imaging has become established as a diagnostic and research tool in many areas of medicine because of its ability to provide excellent soft-tissue delineation in different areas of interest. In addition to T1and T2-weighted imaging, many specialized MR techniques have been designed to extract metabolic or biophysical information. Diffusion-weighted imaging gives insight into the movement of water molecules in tissue, and diffusion-tensor imaging can reveal fiber orientation in the white matter tracts. Metabolic information about the object of interest can be obtained with spectroscopy of protons, in addition to imaging of other nuclei, such as sodium. Dynamic contrast material–enhanced imaging and recently proton spectroscopy play an important role in oncologic imaging. When these techniques are combined, they can assist the physician in making a diagnosis or monitoring a treatment regimen. One of the major advantages of the different types of MR imaging is the ability of the operator to manipulate image contrast with a variety of selectable parameters that affect the kind and quality of the information provided. The elements used to obtain MR images and the factors that affect formation of an MR image include MR instrumentation, localization of the MR signal, gradients, k-space, and pulse sequences. RSNA, 2007
Artificial Neural Network Approach for Classification of Heart Disease Dataset

Artificial Neural Networks (ANNs) play an important role in the field of medical science in solving health problems and diagnosing various diseases . To accurately diagnose the persons' disease condition it is important to use appropriate methods that minimize the errors in diagnosis. So in this paper, for the classification of heart disease dataset, multilayer feed forward network with backpropogation algorithm is proposed. Artificial Neural Network (ANN) is widely used data mining method to extract patterns. Data mining is the process of automating information discovery. Main aim of data mining is to find relationships in data data and to predict outcomes. Classification is one of the important data mining techniques for classifying given set of input data. Many real world problems in various fields can be solved by using classification approach such as business, science, industry and medicine. For classification, medical data related to Heart disease is considered and analyzed using artificial neural network (ANN) [14]. To perform classification task of heart disease data, the neural network is trained using back propagation algorithm with momentum. For the classification, four databases such as Cleveland, Hungarian, Switzerland and long-Beach V.A are considered.
A Meta-learning-based Approach for Detecting Profile Injection Attacks in Collaborative Recommender Systems

Recent research has shown the significant vulnerabilities of collaborative recommender systems in the face of profile injection attacks, in which malicious users insert fake profiles into the rating database in order to bias the system’s output. A single Support Vector Machine (SVM) approach for the detection of profile injection attacks, however, suffers from low precision. With this problem in mind, in this paper we propose a meta-learning-based approach to detect such attacks. In particular, we propose an algorithm to create the diverse base-level training sets through flexible combination of various attack types. Combining the created training sets with SVM, we construct the base-level and meta-level classifiers. Based on these classifiers, we present a meta-learning-based detection algorithm which uses the meta-classifier to integrate the outputs of the base-classifiers and generates the final results of detection. The diversities among the base-classifiers effectively reduce the correlation of the misclassifications and improve the predictive capability of the meta-level. We conduct comparative experiments with a single SVM and the voting-based ensemble method on different-scale MovieLens datasets. The experimental results show that the proposed approach can effectively improve the precision under the condition of holding a high recall.
Lifting wavelet packet transform based damage detection of composite plate structures

A general approach for the online damage detection of laminated composite plates is proposed in this paper. In order to detect the damage of plate structure, a damage feature vector extraction method based on the lifting wavelet packet transform is proposed for detecting damage from original signals. Structural damage feature vectors are calculated based on energy variation of the structural vibration response signals decomposed by lifting wavelet packet transform between the intact and damaged structures. The results demonstrate that the energy feature vector is a good damage index that is sensitive to structural local damage.
Using Dominant Sets for Object Tracking with Freely Moving Camera

Object tracking with freely moving cameras is an open issue, since background information cannot be exploited for foreground segmentation, and plain feature tracking is not robust enough for target tracking, due to occlusions, distractors and object deformations. In order to deal with such challenging conditions a traditional approach, based on Camshift-like color-based features, is augmented by introducing a structural model of the object to be tracked incorporating previous knowledge about the spatial relations between the parts. Hence, an attributed graph is built on top of the features extracted from each frame and a graph matching technique based on Dominant Set clustering is used to find the optimal match with the model. Pixel-wise and object-wise comparison with other tracking techniques with respect to manually-obtained ground truth are presented.
Mumford-Shah Meets Stereo: Integration of Weak Depth Hypotheses

Recent results on stereo indicate that an accurate segmentation is crucial for obtaining faithful depth maps. Variational methods have successfully been applied to both image segmentation and computational stereo. In this paper we propose a combination in a unified framework. In particular, we use a Mumford-Shah-like functional to compute a piecewise smooth depth map of a stereo pair. Our approach has two novel features: First, the regularization term of the functional combines edge information obtained from the color segmentation with flow-driven depth discontinuities emerging during the optimization procedure. Second, we propose a robust data term which adoptively selects the best matches obtained from different weak stereo algorithms. We integrate these features in a theoretically consistent framework. The final depth map is the minimizer of the energy functional, which can be solved by the associated functional derivatives. The underlying numerical scheme allows an efficient implementation on modern graphics hardware. We illustrate the performance of our algorithm using the Middlebury database as well as on real imagery.
Scalable point-to-multipoint communication for cloud networking using information-centric networking

This paper describes a scalable mechanism to set up a point-to-multipoint tree which supports ICN-oriented services such as publish-subscribe and request aggregation. The mechanism is based on autoconfiguration and legacy unicast IP routing. The scalability issues with several current ICN approaches that apply routing based on data object names can thereby be avoided. An application-independent network service can be built on this point-to-multipoint mechanism to enable a variety of use cases, e.g. content distribution, live video streaming, and machine-to-machine communication. We believe that this network service can be an important component in a cloud infrastructure. For evaluation purposes we have made two implementations of the mechanism. One implementation, which focuses on ease of migration, uses CoAP with small modifications. The other implementation uses the new NetInf protocol combined with legacy HTTP. The evaluation results show a performance improvement compared to traditional HTTP caching, and that the aggregation hierarchy inherent in the point-to-multipoint mechanism allows for scaling to a large number of clients.
Interbank Contagion : Evidence from Real Transactions †

Suppose there is a failure of a bank: Would balance sheet connections among banks arising from interbank commitments propagate a crisis? Would a bank’s position in the interbank market affect its level of depositor runs? What role would the interbank market play during a crisis? How would the release of public information affect the financial crisis? Though these questions are at the heart of many debates, answering them has proven difficult because of lack of data on interbank exposures. We use a unique dataset from India that allows us to identify the interbank commitments in order to test contagion in the banking system of an idiosyncratic shock --caused due to a fraud in one of the banks. Interestingly, we find that the level of exposure with the failed bank is an important determinant of depositor runs. What makes this result even more intriguing is the fact that information on the exposure with the failed bank was not available to the depositors. We find that despite other controls, exposure level with the failed bank retains its explanatory power. We also find that release of information by media has a destabilizing effect on banks’ deposits. Furthermore, we find that banks that have lower level of exposure with the failed bank voluntarily release information on their exposure level. Finally, we find that outstanding interbank claims among other banks intensify the negative impact of the initial shock. These results provide strong evidence in favour of financial linkages as an important mechanism for contagion and hold important implications for policy formulation.
Stochastic Global Optimization Method for Solving Constrained Engineering Design Optimization Problems

This work presents a stochastic global optimization (SGO) approach, which integrates an artificial immune algorithm and a particle swarm optimization (AIA-PSO) approach. to solve constrained engineering design optimization problems (e.g., tension/compression string design and pressure vessel design problems), the proposed AIA-PSO algorithm uses a penalty function method to transform a constrained engineering design optimization problem to an unconstrained optimization problem. Based on an external AIA approach, the constriction coefficient, cognitive parameter, social parameter, penalty parameter and mutation probability of an internal PSO algorithm are optimized. Constrained engineering design optimization problems are then solved using the internal PSO algorithm. Moreover, numerical results obtained using the proposed AIA-PSO algorithm is compared with those of published individual genetic algorithm (GA) with AIA methods and hybrid algorithms. Experimental results indicate that the optimum parameter settings of the internal PSO algorithm can be obtained using the external AIA approach. Also, the proposed AIA-PSO algorithm performs significantly better than those of some published individual GA with AIA approaches and hybrid algorithms for solving the pressure vessel design problem. Therefore, the proposed AIA-PSO algorithm can be considered as a promising SGO approach for solving constrained engineering design optimization problems.
Data/knowledge Bases for Biological Papers and Techniques

The vast majority of these papers report experimental work and do so in a highly structured manner. There are now long-term AI-based e orts in progress to produce full knowledge bases from these papers[Fut89]. In the shorter term, there is important information which can be captured and stored in databases. Moreover, local laboratory versions of the database software can be developed. When integrated with other tools this software can provide working scientists with a laboratory notebook software to assist them in their everyday
A Tamper-Resistant and Portable Healthcare Folder

Electronic health record (EHR) projects have been launched in most developed countries to increase the quality of healthcare while decreasing its cost. The benefits provided by centralizing the healthcare information in database systems are unquestionable in terms of information quality, availability, and protection against failure. Yet, patients are reluctant to give to a distant server the control over highly sensitive data (e.g., data revealing a severe or shameful disease). This paper capitalizes on a new hardware portable device, associating the security of a smart card to the storage capacity of a USB key, to give back to the patient the control over his medical data. This paper shows how this device can complement a traditional EHR server to (1) protect and share highly sensitive data among trusted parties and (2) provide a seamless access to the data even in disconnected mode. The proposed architecture is experimented in the context of a medicosocial network providing medical care and social services at home for elderly people.
It Takes a Village: A Social Network Approach to NGOs’ International Public Engagement

Nongovernmental organizations (NGOs), like most strategic communicators, are challenged with breaking through the saturated international media environment as they attempt to influence public opinion. Yet, before strategic communicators can build an agenda, they must get attention from the public. The current study examined how NGOs’ network positions in the public Twitter discussion of the global refugee crisis influenced the public engagement NGOs received during two news events. Drawing from network theory and research, we posed two parallel sets of hypotheses. First, we predicted that NGOs with characteristics of “star” network structures will be well-situated in the network to garner attention. Second, we hypothesized that the “village” around an NGO also contributes to capturing attention. The results from our regressions offer evidence to support both hypotheses.
Beyond Chart Parsing: An Analytic Comparison of Dependency Chart Parsing Algorithms

In this paper, we give a summary of various dependency chart parsing algorithms in terms of the use of parsing histories for a new dependency arc decision. Some parsing histories are closely related to the target dependency arc, and it is necessary for the parsing algorithm to take them into consideration. Each dependency treebank may have some unique characteristics, and it requires for the parser to model them by certain parsing histories. We show in experiments that proper selection of the parsing algorithm which reflect the dependency annotation of the coordinate structures improves the overall performance.
Error-Free Bypass of 7,8-dihydro-8-oxo-2′-deoxyguanosineby DNA Polymerase of Pseudomonas aeruginosa Phage PaP1

As one of the most common forms of oxidative DNA damage, 7,8-dihydro-8-oxo-2′-deoxyguanosine (8-oxoG) generally leads to G:C to T:A mutagenesis. To study DNA replication encountering 8-oxoG by the sole DNA polymerase (Gp90) of Pseudomonas aeruginosa phage PaP1, we performed steady-state and pre-steady-state kinetic analyses of nucleotide incorporation opposite 8-oxoG by Gp90 D234A that lacks exonuclease activities on ssDNA and dsDNA substrates. Gp90 D234A could bypass 8-oxoG in an error-free manner, preferentially incorporate dCTP opposite 8-oxoG, and yield similar misincorporation frequency to unmodified G. Gp90 D234A could extend beyond C:8-oxoG or A:8-oxoG base pairs with the same efficiency. dCTP incorporation opposite G and dCTP or dATP incorporation opposite 8-oxoG showed fast burst phases. The burst of incorporation efficiency (kpol/Kd,dNTP) is decreased as dCTP:G > dCTP:8-oxoG > dATP:8-oxoG. The presence of 8-oxoG in DNA does not affect its binding to Gp90 D234A in a binary complex but it does affect it in a ternary complex with dNTP and Mg2+, and dATP misincorporation opposite 8-oxoG further weakens the binding of Gp90 D234A to DNA. This study reveals Gp90 D234A can bypass 8-oxoG in an error-free manner, providing further understanding in DNA replication encountering oxidation lesion for P.aeruginosa phage PaP1.
Self-adaptive Topology Management For Mobile Ad-hoc Network

This paper proposes an algorithm for coordinated adaptive movement of mobile nodes in a wireless ad-hoc network to maintain the neighbourhood topology by message communication. A wireless ad-hoc network operates without a fixed infrastructure and is mobile because of the proposed applications (battlefield, mountain climbing). A coordinator is elected within a network to control movement of the other nodes in the network by directing them to stop, start or rush. The coordinator is not a newly introduced node rather one of the member nodes. The concept is simulated in a few example networks. The sample simulation results, where snaps of movement for continuous two hours are collected, shows that the networks always remain connected during movement, for each network.
Recursive Prediction Error Methods for Adaptive Estimation

Convenient recursive prediction error algorithms for identification and adaptive state estimation are proposed, and the convergence of these algorithms to achieve off-line prediction error minimization solutions is studied. To set the recursive prediction error algorithms in another perspective, specializations are derived from significant simplifications to a class of extended Kalman filters. The latter are designed for linear state space models with the unknown parameters augmenting the state vector and in such a way as to yield good convergence properties. Also, specializations to approximate maximum likelihood recursions, Kalman filters with adaptive gains, and connections to the extended least squares algorithms are noted.
2 . 1 Lossless Coding and Compression of Radar Reflectivity Data

The coding and compression of weather imagery can be improved by following a domain-specific approach in which the characteristics of the data are taken into account in devising the encoding scheme. We show that by predicting that the next valid data element is identical to the current one, a dramatic reduction in source entropy (information content) is achieved. We also show that encoding runs of missing values in combination with such a prediction scheme makes further compression unnecessary. Finally, the method introduced in this paper is compared with existing methods and shown to outperform all of them.
TOWARDS ENERGY EFFICIENT FOLLOW BEHAVIORS FOR UNMANNED GROUND VEHICLES OVER RUGGED TERRAINS

This paper focuses on the development of a follow behavior for an unmanned ground vehicle (UGV) in collaborative scenarios. The scenario being studied involves a human traveling over a rugged terrain on foot. The UGV follows the human. We present an approach for automatically generating a reactive energy-efficient follow behavior that maps the vehicle’s states into motion goals. We start by partitioning the state space that encodes the relationship between the state of the vehicle and the human’s state, and the environment. For each cell in the partitioned state space, we either directly generate the motion goal for the vehicle to execute or a function that produces the motion goal. The motion goal defines not only the location towards which the vehicle should move but also specifies a zero activity zone around the human within which the vehicle is supposed to slow down and remain stationary to save its energy until it gets outside the margin caused by the movement of the human. Our approach utilizes off-line simulations to assess the performance of the generated behavior. Our simulation results show that the automatically generated follow behavior significantly outperforms a simple conservative tracking rule in terms of distance traveled and violation of proximity constraints. We anticipate that the approach presented in this paper will ultimately enable us to implement energy efficient follow behaviors on physical UGVs.
eggNOG v2.0: extending the evolutionary genealogy of genes with enhanced non-supervised orthologous groups, species and functional annotations

The identification of orthologous relationships forms the basis for most comparative genomics studies. Here, we present the second version of the eggNOG database, which contains orthologous groups (OGs) constructed through identification of reciprocal best BLAST matches and triangular linkage clustering. We applied this procedure to 630 complete genomes (529 bacteria, 46 archaea and 55 eukaryotes), which is a 2-fold increase relative to the previous version. The pipeline yielded 224 847 OGs, including 9724 extended versions of the original COG and KOG. We computed OGs for different levels of the tree of life; in addition to the species groups included in our first release (i.e. fungi, metazoa, insects, vertebrates and mammals), we have now constructed OGs for archaea, fishes, rodents and primates. We automatically annotate the non-supervised orthologous groups (NOGs) with functional descriptions, protein domains, and functional categories as defined initially for the COG/KOG database. In-depth analysis is facilitated by precomputed high-quality multiple sequence alignments and maximum-likelihood trees for each of the available OGs. Altogether, eggNOG covers 2 242 035 proteins (built from 2 590 259 proteins) and provides a broad functional description for at least 1 966 709 (88%) of them. Users can access the complete set of orthologous groups via a web interface at: http://eggnog.embl. de.
PARALLEL PREFERENCE STRUCTURES IN LABOR SUPPLY AND COMMODITY DEMAND: AN ADAPTATION OF THE GORMAN POLAR FORM

This paper discusses the properties of parallel preference structures and their potential usefulness in empirical research. The parallel structure can accommodate arbitrarily flexible substitution properties and linear or nonlinear income-consumption curves, with the linear form representing a special case of the Gorman polar form. The global properties are distinctive in that indifference loci are parallel surfaces, identical in shape and scale at all utility levels. These properties are potentially appropriate for models of family labor supply but are less suitable for applications to production over a wide range of output. Alternative parameterizations and estimation forms are discussed and the model is shown to provide a basis for interpretation of models of labor supply estimated by Professors Ashenfelter and Heckman. PARALLEL PREFERENCE STRUCTURES are characterized by indifference surfaces that are identical in shape and scale, each being a translation of a basic surface along parallel income-consumption curves. The purpose of this paper is to discuss the properties of parallel structures and their potential usefulness in models of labor supply and commodity demand. Limited applications in production analysis are also discussed but are not the primary focus of the paper. In their most tractable form, with linear income-consumption curves, parallel 2 structures are a special case of the Gorman polar form. A suitably parameterized cost or expenditure function for a linear parallel structure provides a second order point approximation to an arbitrary general cost or expenditure function. By that criterion, a variety of simple versions of the parallel structure are on roughly equal footing with other flexible functional forms employed in recent demand, produc