Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of sentinel gamma probes for 99mTc breast cancer surgery based on NEMA NU3-2004 standard

PurposeHand-held &ggr;-probes are used for the identification of the sentinel node location during intra-operative radio-guided surgeries. Various &ggr;-probes, which use different detectors, collimation and electronics, are available on the market. Spatial resolution, sensitivity and angular resolution of the probes are believed to be determinant for the success of the identification of the sentinel node during radiosurgery. Materials and MethodsWe compared the above-mentioned performances of sentinel probes from six manufacturers available in the European market by means of the NEMA NU3-2004 standard, which allows the users to evaluate the probes during a situation which mimics a intra-operative radio-guided surgery. Results and conclusionThis study presents a summary of characteristics to be expected when using the tested &ggr;-probes during intra-operative radio-guided surgeries, with particular emphasis on breast cancer sentinel node surgery. The results from this study can be used as the guidance for the selection of a sentinel lymph node probe.
An ETF Trading Decision Support System by Using Neural Network and Technical Indicators

This paper proposed a neural network based decision support system (DSS) to provide investors with suggestions on transaction timing and transaction strategies of Taiwan 50 index exchange traded funds (ETF). Various indicators were employed with corresponding application principles as the neural network underwent assigned training and learning procedures. It was revealed in the study results that the simulated transaction behavior following recommendations by the proposed system yielded better profits than solely applying Taiwan's weighted stock index.
PRIVATIZING TELECOMS AND RESIDUAL STATE INFLUENCE ON FINANCIAL PERFORMANCE

We test competing theoretical perspectives explaining likely shareholder returns from material investment decisions announced by privatizing telecommunications firms (“telecoms”) with varying levels of residual state ownership. A “principal-agent” perspective suggests that decrease in residual state ownership in privatizing telecoms leads to more positive shareholder returns. Over time, this effect increases. An alternative “credible” privatization perspective suggests that retention of substantial (though not controlling) residual state ownership leads to more positive shareholder returns, but only in the short run. Over time this ownership effect fades quickly. We examine empirical support for these competing perspectives with an event study analyzing cumulative abnormal shareholder returns (“CARs”) associated with 199 major investments announced from 19862001 by 15 privatizing telecoms domiciled in industrialized and emerging-market countries. In line with the principle-agent perspective, we find that residual state ownership is negatively related to CARs for telecoms from industrialized countries. Emerging-market telecoms may, however, may exhibit a negative curvilinear trend between CARs and residual state ownership more in line with the credible perspective. Also in line with the credible perspective, we find a negative relationship between CARs and time since initial privatization for telecoms from emerging markets. The influence of residual state factors on privatizing enterprise performance is significant but sometimes contrasting in industrialized versus emerging-market settings. The credible perspective may merit closer attention among researchers and policy-makers interested in the privatizing enterprise management and investment, particularly in emerging markets.
3D compressive sensing for nodes localization in WNs based on RSS

Compressive sensing (CS) intends to recover signals at a sampling rate significantly (much) lower than that classically used according to the Nyquist theorem. This allows avoiding unnecessary sampling and complexity. In this paper, a Three-Dimensional Compressive Sensing (3D-CS) approach is proposed for nodes localization in wireless networks. In 3D-CS-R2S2 approach, which is based on the ratio of received signal strength (RSS), a 3D sparsity basis and a 3D measurement matrix are used as radio map and noisy measurements respectively in order to recover the target position. A specific multi-linear algebra procedure was developed using N-way array products, together with an adequate decomposition. Both allow formulating the localization problem in a way that is solvable by an ℓ1-minimization algorithm based on CS theory. 3D-CS-R2S2 improves localization accuracy even if propagation conditions change significantly and/or the effective isotropic radiated power (EIRP) is unknown. Additionally, it enables practical Real Time Localization Systems (RTLS) development since 3D-CS-R2S2 can be functional with a reduced number of base stations without compromising position recovery accuracy. The simulation results show the efficiency of the method that not only succeeds to recover a target position but also improves localization accuracy in presence of noise.
AUGMENTING E-LEARNING STANDARDS WITH ADAPTATION

Collaborative learning, in the context of Learning Management Systems (LMSs), is becoming an important factor in the e-Learning process. LMSs such as Blackboard, Sakai, allow various forms of collaboration between students. Moreover, LMSs allow a high degree of reuse by relying on e-Learning standards. Nevertheless, LMSs lack adaptation and personalization facilities. Adaptive Hypermedia environments allow for adaptation, but do not usually allow for collaboration, and only seldom use standards. In this paper, we present our work towards combining adaptation, standards, and typical LMSs, to achieve personalization in collaborative environments. In particular, we use MOT (My Online Teacher), an authoring system for adaptive hypermedia, IMS Question and Test Interoperability (IMS QTI) and IMS Content Packaging (IMS CP), and the Sakai LMS. In We demonstrate how e-Learning materials based on standards can be augmented; in order to achieve personalization, and adaptive collaborative support, using collaborative adaptive strategies. Thus, this work describes a significant step towards the little explored avenue of adaptive collaborative systems, based on extant learning standards and popular LMS.
COMPREHENSIVE ANALYSIS OF ALTERNATIVE METHODOLOGIES FOR TRUE ORTHOPHOTO GENERATION FROM HIGH RESOLUTION SATELLITE AND AERIAL IMAGERY

Orthophotos provide valuable spatial and spectral information for various GIS and mapping applications. The absence of relief displacement and the uniform scale in orthophotos enable interested users to measure distances, compute areas, derive geographic locations, and quantify changes. Differential rectification has traditionally been used for orthophoto generation. However, differential rectification produces serious problems (in the form of ghost images) when dealing with large scale imagery over urban areas. To avoid these artifacts, true orthophoto generation techniques have been devised to remove ghost images through visibility analysis and occlusion detection. So far, the z-buffer method has been one of the most popular methods for true orthophoto generation. However, it has strict requirements regarding the cell size of the Digital Surface Model (DSM), as the cell size is related to the Ground Sampling Distance (GSD) of the imaging sensor. This paper introduces alternative methodologies for true orthophoto generation that circumvent the drawbacks of the z-buffer technique. Moreover, the proposed techniques are suited for manipulating scenes acquired by both high resolution imaging satellites and aerial imaging system. Specifically, the proposed methodologies focus on object to image projection and occlusion detection. Using real data, the experimental results section describes a comprehensive analysis of the performance of the proposed methodologies when compared to existing techniques. The analysis focuses on the quality of detected occlusions and the computational efficiency. * Corresponding author.
Toward a methodical framework for comprehensively assessing forest multifunctionality

Abstract Biodiversity–ecosystem functioning (BEF) research has extended its scope from communities that are short‐lived or reshape their structure annually to structurally complex forest ecosystems. The establishment of tree diversity experiments poses specific methodological challenges for assessing the multiple functions provided by forest ecosystems. In particular, methodological inconsistencies and nonstandardized protocols impede the analysis of multifunctionality within, and comparability across the increasing number of tree diversity experiments. By providing an overview on key methods currently applied in one of the largest forest biodiversity experiments, we show how methods differing in scale and simplicity can be combined to retrieve consistent data allowing novel insights into forest ecosystem functioning. Furthermore, we discuss and develop recommendations for the integration and transferability of diverse methodical approaches to present and future forest biodiversity experiments. We identified four principles that should guide basic decisions concerning method selection for tree diversity experiments and forest BEF research: (1) method selection should be directed toward maximizing data density to increase the number of measured variables in each plot. (2) Methods should cover all relevant scales of the experiment to consider scale dependencies of biodiversity effects. (3) The same variable should be evaluated with the same method across space and time for adequate larger‐scale and longer‐time data analysis and to reduce errors due to changing measurement protocols. (4) Standardized, practical and rapid methods for assessing biodiversity and ecosystem functions should be promoted to increase comparability among forest BEF experiments. We demonstrate that currently available methods provide us with a sophisticated toolbox to improve a synergistic understanding of forest multifunctionality. However, these methods require further adjustment to the specific requirements of structurally complex and long‐lived forest ecosystems. By applying methods connecting relevant scales, trophic levels, and above‐ and belowground ecosystem compartments, knowledge gain from large tree diversity experiments can be optimized.
Path Visualization for Adjacency Matrices

For displaying a dense graph, an adjacency matrix is superior than a node-link diagram because it is more compact and free of visual clutter. A node-link diagram, however, is far better for the task of path finding because a path can be easily traced by following the corresponding links, provided that the links are not heavily crossed or tangled. We augment adjacency matrices with path visualization and associated interaction techniques to facilitate path finding. Our design is visually pleasing, and also effectively displays multiple paths based on the design commonly found in metro maps. We illustrate and assess the key aspects of our design with the results obtained from two case studies and an informal user study.
Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction

Real-world data are often acquired as a collection of matrices rather than as a single matrix. Such multiblock data are naturally linked and typically share some common features while at the same time exhibiting their own individual features, reflecting the underlying data generation mechanisms. To exploit the linked nature of data, we propose a new framework for common and individual feature extraction (CIFE) which identifies and separates the common and individual features from the multiblock data. Two efficient algorithms termed common orthogonal basis extraction (COBE) are proposed to extract common basis is shared by all data, independent on whether the number of common components is known beforehand. Feature extraction is then performed on the common and individual subspaces separately, by incorporating dimensionality reduction and blind source separation techniques. Comprehensive experimental results on both the synthetic and real-world data demonstrate significant advantages of the proposed CIFE method in comparison with the state-of-the-art.
The g-mode spectrum of reactive neutron star cores

We discuss the impact of nuclear reactions on the spectrum of gravity g-modes of a mature neutron star, demonstrating the anticipated disappearance of these modes when the timescale associated with the oscillations is longer than that of nuclear reactions. This is the expected result, but different aspects of the demonstration may be relevant for related problems in neutron star astrophysics. In particular, we develop the framework required for an explicit implementation of finite-time nuclear reactions in neutron star oscillation problems and demonstrate how this formulation connects with the usual bulk viscosity prescription. We also discuss implications of the absence of very high order g-modes for problems of astrophysical relevance.
Selecting an End User Programming Language for DSS Development

Today managers and policy makers are confronted with an overwhelming range of choices of computer software to develop decision support systems (DSS). The authors argue that DSS language evaluation and selection should be a multi-step process involving most, if not all of the following: 1. End User Needs Assessment and Problem Diagnosis 2. Critical Success Factor Identification 3. Feature Analysis and Capability Review 4. Demonstration Prototype Development 5. External User Surveys 6. Benchmark and Simulation Tests 7. Programmer Productivity and End User Orientation Analysis The objectives of each of these activities are described, as well as specific procedures for accomplishing them. In addition, the authors discuss the usefulness of a multi-disciplinary task force to accomplish the DSS language evaluation and selection process.
Investigating LSTM for punctuation prediction

We present a neural network based punctuation prediction method using Long Short-Term Memory (LSTM) network. The proposed method uses bidirectional LSTM to encode both the past and future observation as its inputs. It models the dependency between input features and output labels through multiple layers. We also empirically study the impacts of modeling the dependency between output labels. Our results show that using a deep bi-directional LSTM is able to achieve state-of-the-art performance in punctuation prediction.
Factors That Predict Negative Versus Positive Addictions in Offspring: Are Dependent Exercisers Different Than Victims of Other Addictions?.

The study reported here assessed individuals' selection of excessive exercise dependent behaviors as they relate to self-esteem, family environment, and perceptions of family members exhibiting dysfunctional behaviors or addictions. A survey was taken of exercisers from a stratified random sample of competitive triathletes selected from the national membership of TRI FED USA (N=112), from voluntary participants (N=185) in the 1991 Gatorade Ironman World championship, and from a national random sample (N=520) of health club members. Participants from TRI FED and the health clubs completed demographic information sheets (assessing addictive/obsessive behaviors in athletes and family members), the Exercise Enjoyment Questionnaire (EEQ), and the Perceived Family Environment Questionnaire (PFEQ). Participants from the Gatorade champiouship were given the same forms (excluding the PFEQ and the family portion of the demographic questionuaire). Exercisers self-selected the exercise category that best fit their exercise behavior from the following: low level, moderate, and heavy exerciser. Exercise dependence was found to occur in 9 percent of exercisers and in larger numbers as exercise intensity increased; the dependent exercisers had significantly higher levels of psychological and physical dependence than non-dependent exercisers as well as family histories of dysfunctional behavior. Eight tables display the survey results. (Contains 23 references.) (CK) *********************************************************************** * Reproductions supplied by EDRS are the best that can be made from the original document. ***********************************************************************
Indoor, Rural and Suburban Channel Models and Parameters for B3G Link and System Level Simulations

Based on the wideband measurements for indoor, rural and suburban environments at 5.25 GHz with 100 MHz bandwidth, the basic required channel parameters and models such as the rms delay spread, number of clusters, path delays, and tapped delay line models were derived for beyond 3G (B3G) wireless communications. The results derived in the paper could be the necessary models and parameters for B3G system level and link level simulations, and also for the planning B3G radio systems
Regret-minimizing representative databases

We propose the k-representative regret minimization query (k-regret) as an operation to support multi-criteria decision making. Like top-k, the k-regret query assumes that users have some utility or scoring functions; however, it never asks the users to provide such functions. Like skyline, it filters out a set of interesting points from a potentially large database based on the users' criteria; however, it never overwhelms the users by outputting too many tuples. In particular, for any number k and any class of utility functions, the k-regret query outputs k tuples from the database and tries to minimize the maximum regret ratio. This captures how disappointed a user could be had she seen k representative tuples instead of the whole database. We focus on the class of linear utility functions, which is widely applicable. The first challenge of this approach is that it is not clear if the maximum regret ratio would be small, or even bounded. We answer this question affirmatively. Theoretically, we prove that the maximum regret ratio can be bounded and this bound is independent of the database size. Moreover, our extensive experiments on real and synthetic datasets suggest that in practice the maximum regret ratio is reasonably small. Additionally, algorithms developed in this paper are practical as they run in linear time in the size of the database and the experiments show that their running time is small when they run on top of the skyline operation which means that these algorithm could be integrated into current database systems.
Arabic-to-English Example Based Machine Translation Using Context-Insensitive Morphological Analysis

We describe and discuss the results of ongoing experiments that use morphological analysis in the context of Example-Based Machine Translation. The goal is to increase the coverage of our training examples so as to capture things that are not directly seen in the training text. This is done through a two stage process of generalization and filtering.
The Boltzmann machine

Boltzmann machines are discrete networks with input, output and hidden units, in which the units update their state with a stochastic function. The output of a given node is calculated using probabilities, rather than threshold or sigmoid function. The paper describes their basic architecture, their processing mechanism, the principles of their operation, and their learning mechanism. The main characteristics of the Boltzmann machine is the fact that, when subjected to reducing noise, it has a final probability of resting in given states which is in direct proportion to Hopfield's calculation of the energy of those states. The key problem is to control the values of such energies by changing the weights.
Effective Online Reordering with Arc-Eager Transitions

We present a new transition system with word reordering for unrestricted non-projective dependency parsing. Our system is based on decomposed arc-eager rather than arc-standard, which allows more flexible ambiguity resolution between a local projective and non-local crossing attachment. In our experiment on Universal Dependencies 2.0, we find our parser outperforms the ordinary swap-based parser particularly on languages with a large amount of non-projectivity.
The Diagnostic Apathia Scale predicts the ability to return to work following depression or anxiety

Objective The aim of this study was to evaluate the validity of a new apathy rating scale in predicting the ability to return to work (RTW) in patients with depression or anxiety a year after discharge from a psychiatric hospital. Methods We evaluated 56 patients with depression or anxiety, who participated in an on-going randomised clinical trial using RTW as primary outcome. The degree of apathy was measured by the Diagnostic Apathia Scale, which contains six items covering the following neuropsychological symptoms: concentration/memory problems, difficulties in decision making, lassitude, tiredness/fatigue, insomnia, and reduced ability to work and engage in personal interests. The scale was analysed for psychometric validity (scalability) and for its ability to predict RTW. Finally, the predictive validity of the Diagnostic Apathia Scale regarding RTW was compared with scales measuring severity of depression/anxiety symptoms, disability, and psychological well-being. Results The Diagnostic Apathia Scale displayed sufficient scalability, that is, the total score was a psychometrically valid measure of apathy. Only the Diagnostic Apathia Scale, and not the scales measuring severity of symptoms, disability, or psychological well-being, had predictive validity regarding RTW. Thus, 76% with ‘clinically significant apathy’ at baseline were unable to RTW versus 50% of the patients without apathy (p<0.05). Conclusion The Diagnostic Apathia Scale was found to have an acceptable predictive validity in terms of patients’ ability to RTW 1 year after discharge from hospitalisation for depression or anxiety.
A META-MODELING BASED SUPPORT FOR ADAPTIVE INTEGRATING IN AGENT-BASED SYSTEMS

Open agent architectures offer support for creating agent-based distributed software systems. These systems functionality results from the logical composition of a collaborating agents collection. This paper proposes a model for adaptive integration of agents in an open agent architecture. This model is based on meta-modeling at the support level and on negotiation at the agent level. Adaptive agent integration means that available functionality of an agent intended to join a distributed agent-based system is added to the global functionality after a negotiation process. The negotiation is realized by agents from the system and is based on the offered functionality features and on system global needs. The main advantage of adaptive integration is represented by the increased performance both in the agent space and at the entire system level, due to the active presence of, and only of, the required functionality subset of any agent.
A distributed coupled atmosphere–wave–ocean model for typhoon wave numerical simulation

A distributed coupled framework system was developed. The framework consists of atmosphere model (MM5), ocean model (Princeton Ocean Model), wave model (WW3) and a coupled programme (coupled model). The coupled programme controls atmosphere–wave–ocean model calculations and data exchanges. The impact of air–sea–wave interactions was considered to examine the validity and performance of this coupled model. Two typical typhoon cases Vongfong (No. 0214) and Rananim (No. 0414) were modelled to examine the wave simulation validity in the China Seas. The coupled model can obviously improve the precision of the significant wave height when compared with the uncoupled run.