903 resultados para inductive inference


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Detecting anomalies in the online social network is a significant task as it assists in revealing the useful and interesting information about the user behavior on the network. This paper proposes a rule-based hybrid method using graph theory, Fuzzy clustering and Fuzzy rules for modeling user relationships inherent in online-social-network and for identifying anomalies. Fuzzy C-Means clustering is used to cluster the data and Fuzzy inference engine is used to generate rules based on the cluster behavior. The proposed method is able to achieve improved accuracy for identifying anomalies in comparison to existing methods.

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A new strategy has emerged to improve healing of bone defects using exogenous glycosaminoglycans by increasing the effectiveness of bone-anabolic growth factors. Wnt ligands play an important role in bone formation. However, their functional interactions with heparan sulfate/heparin have only been investigated in non-osseous tissues. Our study now shows that the osteogenic activity of Wnt3a is cooperatively stimulated through physical interactions with exogenous heparin. N-Sulfation and to a lesser extent O-sulfation of heparin contribute to the physical binding and optimal co-stimulation of Wnt3a. Wnt3a-heparin signaling synergistically increases osteoblast differentiation with minimal effects on cell proliferation. Thus, heparin selectively reduces the effective dose of Wnt3a needed to elicit osteogenic, but not mitogenic responses. Mechanistically, Wnt3a-heparin signaling strongly activates the phosphoinositide 3-kinase/Akt pathway and requires the bone-related transcription factor RUNX2 to stimulate alkaline phosphatase activity, which parallels canonical beta-catenin signaling. Collectively, our findings establish the osteo-inductive potential of a heparin-mediated Wnt3a-phosphoinositide 3-kinase/Akt-RUNX2 signaling network and suggest that heparan sulfate supplementation may selectively reduce the therapeutic doses of peptide factors required to promote bone formation.

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Disjoint top-view networked cameras are among the most commonly utilized networks in many applications. One of the open questions for these cameras' study is the computation of extrinsic parameters (positions and orientations), named extrinsic calibration or localization of cameras. Current approaches either rely on strict assumptions of the object motion for accurate results or fail to provide results of high accuracy without the requirement of the object motion. To address these shortcomings, we present a location-constrained maximum a posteriori (LMAP) approach by applying known locations in the surveillance area, some of which would be passed by the object opportunistically. The LMAP approach formulates the problem as a joint inference of the extrinsic parameters and object trajectory based on the cameras' observations and the known locations. In addition, a new task-oriented evaluation metric, named MABR (the Maximum value of All image points' Back-projected localization errors' L2 norms Relative to the area of field of view), is presented to assess the quality of the calibration results in an indoor object tracking context. Finally, results herein demonstrate the superior performance of the proposed method over the state-of-the-art algorithm based on the presented MABR and classical evaluation metric in simulations and real experiments.

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This paper presents a robust place recognition algorithm for mobile robots that can be used for planning and navigation tasks. The proposed framework combines nonlinear dimensionality reduction, nonlinear regression under noise, and Bayesian learning to create consistent probabilistic representations of places from images. These generative models are incrementally learnt from very small training sets and used for multi-class place recognition. Recognition can be performed in near real-time and accounts for complexity such as changes in illumination, occlusions, blurring and moving objects. The algorithm was tested with a mobile robot in indoor and outdoor environments with sequences of 1579 and 3820 images, respectively. This framework has several potential applications such as map building, autonomous navigation, search-rescue tasks and context recognition.

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A high-level relationPopper dimension—( Exclusion dimension—( VC dimension—( between Karl Popper’s ideas on “falsifiability of scientific theories” and the notion of “overfitting”Overfitting in statistical learning theory can be easily traced. However, it was pointed out that at the level of technical details the two concepts are significantly different. One possible explanation that we suggest is that the process of falsification is an active process, whereas statistical learning theory is mainly concerned with supervised learningSupervised learning, which is a passive process of learning from examples arriving from a stationary distribution. We show that concepts that are closer (although still distant) to Karl Popper’s definitions of falsifiability can be found in the domain of learning using membership queries, and derive relations between Popper’s dimension, exclusion dimension, and the VC-dimensionVC dimension.

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In this paper we discuss the strengths and weaknesses of a range of artificial intelligence approaches used in legal domains. Symbolic reasoning systems which rely on deductive, inductive and analogical reasoning are described and reviewed. The role of statistical reasoning in law is examined, and the use of neural networks analysed. There is discussion of architectures for, and examples of, systems which combine a number of these reasoning strategies. We conclude that to build intelligent legal decision support systems requires a range of reasoning strategies.

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Research conducted over past decades has investigated selected service encounter behaviors from either a customer or service provider perspective. However, a comprehensive, dual-perspective framework is lacking. Such a framework is needed to organize knowledge of these behaviors, and thereby provide structure, clarity, and parsimony to the field. This paper describes a three-tier framework of service encounter behavior that was developed by applying grounded theory principles to interviews with customers, service employees, and other stakeholders. These informants described many ways in which they behave when executing service exchanges, dealing with service difficulties, and managing themselves in the process. Using an iterative inductive approach, a conceptual framework was developed in which specific (Tier 1) behaviors were placed within broader (Tier 2) categories, and these lower classification levels were, in turn, interpreted within a conceptual space defined by the (Tier 3) dimensions of task, relationship, and self. This framework was then elaborated and refined by reference to the psychology and marketing literature, a set of 157 audio-recorded service interactions, and an expert panel study. The paper includes comparisons between the framework and those previously proposed, propositions regarding service encounter processes and outcomes, and implications for future research and practice.

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A robust visual tracking system requires an object appearance model that is able to handle occlusion, pose, and illumination variations in the video stream. This can be difficult to accomplish when the model is trained using only a single image. In this paper, we first propose a tracking approach based on affine subspaces (constructed from several images) which are able to accommodate the abovementioned variations. We use affine subspaces not only to represent the object, but also the candidate areas that the object may occupy. We furthermore propose a novel approach to measure affine subspace-to-subspace distance via the use of non-Euclidean geometry of Grassmann manifolds. The tracking problem is then considered as an inference task in a Markov Chain Monte Carlo framework via particle filtering. Quantitative evaluation on challenging video sequences indicates that the proposed approach obtains considerably better performance than several recent state-of-the-art methods such as Tracking-Learning-Detection and MILtrack.

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In this paper, we present fully Bayesian experimental designs for nonlinear mixed effects models, in which we develop simulation-based optimal design methods to search over both continuous and discrete design spaces. Although Bayesian inference has commonly been performed on nonlinear mixed effects models, there is a lack of research into performing Bayesian optimal design for nonlinear mixed effects models that require searches to be performed over several design variables. This is likely due to the fact that it is much more computationally intensive to perform optimal experimental design for nonlinear mixed effects models than it is to perform inference in the Bayesian framework. In this paper, the design problem is to determine the optimal number of subjects and samples per subject, as well as the (near) optimal urine sampling times for a population pharmacokinetic study in horses, so that the population pharmacokinetic parameters can be precisely estimated, subject to cost constraints. The optimal sampling strategies, in terms of the number of subjects and the number of samples per subject, were found to be substantially different between the examples considered in this work, which highlights the fact that the designs are rather problem-dependent and require optimisation using the methods presented in this paper.

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This study examines the process by which newly recruited nuclear engineering and technical staff came to understand, define, think, feel and behave within a distinct group that has a direct contribution to the organization's overall emphasis on a culture of reliability and system safety. In the field of organizational behavior the interactive model of social identity formation has been recently proposed to explain the process by which the internalization of shared norms and values occurs, an element critical in identity formation. Using this rich model of organizational behavior we analyzed multiple sources of data from nine new hires over a period of three years. This was done from the time they were employed to investigate the construction of social identity by new entrants entering into a complex organizational setting reflected in the context of a nuclear facility. Informed by our data analyses, we found support for the interactive model of social identity development and report the unexpected finding that a newly appointed member's age and level of experience appears to influence the manner in which they adapt, and assimilate into their surroundings. This study represents an important contribution to the safety and reliability literature as it provides a rich insight into the way newly recruited employees enact the process by which their identities are formed and hence act, particularly under conditions of duress or significant organizational disruption in complex organizational settings.

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We have recently shown that Matrigel-filled chambers containing fibroblast growth factor-2 (FGF2) and placed around an epigastric pedicle in the mouse were highly adipogenic. Contact of this construct with pre-existing tissue or a free adipose graft was required. To further investigate the mechanisms underpinning formation of new adipose tissue, we seeded these chambers with human adipose biopsies and human adipose-derived cell populations in severe combined immunodeficient mice and assessed the origin of the resultant adipose tissue after 6 weeks using species-specific probes. The tissues were negative for human-specific vimentin labeling, suggesting that the fat originates from the murine host rather than the human graft. This was supported by the strong presence of mouse-specific Cot-1 deoxyribonucleic acid labeling, and the absence of human Cot-1 labeling in the new fat. Even chambers seeded with FGF2/Matrigel containing cultured human stromal-vascular fraction (SVF) labeled strongly only for human vimentin in cells that did not have a mature adipocyte phenotype; the newly formed fat tissue was negative for human vimentin. These findings indicate that grafts placed in the chamber have an inductive function for neo-adipogenesis, rather than supplying adipocyte-precursor cells to generate the new fat tissue, and preliminary observations implicate the SVF in producing inductive factors. This surprising finding opens the door for refinement of current adipose tissue-engineering approaches.

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Aim Our aim was to clarify the lineage-level relationships for Melomys cervinipes and its close relatives and investigate whether the patterns of divergence observed for these wet-forest-restricted mammals may be associated with recognized biogeographical barriers. Location Mesic closed forest along the east coast of Australia, from north Queensland to mid-eastern New South Wales. Methods To enable rigorous phylogenetic reconstruction, divergence-date estimation and phylogeographical inference, we analysed DNA sequence and microsatellite data from 307 specimens across the complete distribution of M. cervinipes (45 localities). Results Three divergent genetic lineages were found within M. cervinipes, corresponding to geographically delineated northern, central and southern clades. Additionally, a fourth lineage, comprising M. rubicola and M. capensis, was identified and was most closely related to the northern M. cervinipes lineage. Secondary contact of the northern and central lineages was identified at one locality to the north of the Burdekin Gap. Main conclusions Contemporary processes of repeated habitat fragmentation and contraction, local extinction events and subsequent re-expansion across both small and large areas, coupled with the historical influence of the Brisbane Valley Barrier, the St Lawrence Gap and the Burdekin Gap, have contributed to the present phylogeographical structure within M. cervinipes. Our study highlights the need to sample close to the periphery of putative biogeographical barriers or risk missing vital phylogeographical information that may significantly alter the interpretation of biogeographical hypotheses.

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Knowledge Management (KM) is vital factor to successfully undertake projects. The temporary nature of projects necessitates employing useful KM practices to reduce any issues such as knowledge leakiness and rework. The Project Management Office (PMO) is a unit within organisations to facilitate and oversee organisational projects. Project Management Maturity Models (PMMM) show the development of PMOs from immature to mature levels. The existing PMMMs have focused on discussing Project Management (PM) practices, however, the management of project knowledge is yet to be addressed, at various levels of maturity. A research project was undertaken to investigate the mentioned gap for addressing KM practices at the existing PMMMs. Due to the exploratory and inductive nature of this research, qualitative methods using case studies were chosen as the research methodology to investigate the problem in the real world. In total, three cases selected from different industries: research; mining and government organisations, to provide broad categories for research and research questions were examined using the developed framework. This paper presents the findings from the investigation of the research organisation with the lowest level of maturity. From KM process point of view, knowledge creation and capturing are the most important processes, while knowledge transferring and reusing received less attention. In addition, it was revealed that provision of “knowledge about client” and “project management knowledge” are the most important types of knowledge that are required at this level of maturity. The results also revealed that PMOs with higher maturity level have better knowledge management, however, some improvement is needed. In addition, the importance of KM processes varies at different levels of maturity. In conclusion, the outcomes of this paper could provide powerful guidance to PMOs at lowest level of maturity from KM point of view.

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The development, operation, and applications of two configurations of an integrated plasma-aided nanofabrication facility (IPANF) comprising low-frequency inductively coupled plasma-assisted, low-pressure, multiple-target RF magnetron sputtering plasma source, are reported. The two configurations of the plasma source have different arrangements of the RF inductive coil: a conventional external flat spiral "pancake" coil and an in-house developed internal antenna comprising two orthogonal RF current sheets. The internal antenna configuration generates a "unidirectional" RF current that deeply penetrates into the plasma bulk and results in an excellent uniformity of the plasma over large areas and volumes. The IPANF has been employed for various applications, including low-temperature plasma-enhanced chemical vapor deposition of vertically aligned single-crystalline carbon nanotips, growth of ultra-high aspect ratio semiconductor nanowires, assembly of optoelectronically important Si, SiC, and Al1-xInxN quantum dots, and plasma-based synthesis of bioactive hydroxyapatite for orthopedic implants.

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Operation and mode jumps in low-frequency (500 kHz) radio-frequency inductively coupled plasmas are investigated. The discharge is driven by a flat inductive coil which can excite the electrostatic (E) and electromagnetic (H) discharge modes. The power transfer efficiency and mode transition behavior are studied. It is found that the power reflection coefficient as a function of the input power is minimal in the vicinity of the mode transitions and exhibits hysteresis, which is also observed when the operating gas pressure is varied.