Pointed Trees Of Projective Spaces

We introduce a smooth projective variety T(d,n) which compactifies the space of configurations of it distinct points oil affine d-space modulo translation and homothety. The points in the boundary correspond to n-pointed stable rooted trees of d-dimensional projective spaces, which for d = 1, are (n + 1)-pointed stable rational curves. In particular, T(1,n) is isomorphic to ($) over bar (0,n+1), the moduli space of such curves. The variety T(d,n) shares many properties with (M) over bar (0,n+1). For example, as we prove, the boundary is a smooth normal crossings divisor whose components are products of T(d,i) for i \u3c n and it has an inductive construction analogous to but differing from Keel\u27s for (0,n+1). This call be used to describe its Chow groups and Chow motive generalizing [Trans. Airier. Math. Soc. 330 (1992), 545-574]. It also allows us to compute its Poincare polynomials, giving all alternative to the description implicit in [Progr. Math., vol. 129, Birkhauser, 1995, pp. 401-417]. We give a presentation of the Chow rings of T(d,n), exhibit explicit dual bases for the dimension I and codimension 1 cycles. The variety T(d,n) is embedded in the Fulton-MacPherson spaces X[n] for any smooth variety X, and we use this connection in a number of ways. In particular we give a family of ample divisors on T(d,n) and an inductive presentation of the Chow motive of X[n]. This also gives an inductive presentation of the Chow groups of X[n] analogous to Keel\u27s presentation for (M) over bar (0,n+1), solving a problem posed by Fulton and MacPherson

Early Delirium Assessment for Hospitalized Older People in Indonesia: a Systematic Review

Background: Due to the increasing risk of getting co-morbidity and frailty, older people tend to be prone to hospitalization. Hospitalization in older people brings many adverse effects. Moreover, when these elderly get delirium, the mortality and morbidity will increase. The risk of getting deterioration and worsening condition because of delirium would also increase. In fact, delirium assessment is not a high priority in taking care older people during hospitalization because the focus of care is treating the disease.Delirium screening as an early recognition of delirium in the hospitalized elderly inIndonesia remains unreported and even do not well evaluated. Therefore, delirium as a preventable problem or causing problems remains unrecognized.Purpose: This paper aims to review the current evidence of early assessment of delirium in hospitalized older people.Methods: A systematic review was conducted from four databases yielding to 4 articles which met the inclusion and exclusion criteria.Results: There are four focuses on the result, namely delirium screening tools, patient characteristics, identified early delirium assessment, and outcomes affected by early delirium assessment. Confusion Assessment Method (CAM) was used as the delirium screening tool in the hospital. Establishing the care team involving many disciplines will give a better way to improve the integrated care and collaborative care.Conclusion: Performing CAM integrated into comprehensive geriatric assessment can be the most important thing to be undertaken when looking after the hospitalized elderly

A mass-balance/photochemical assessment of DMS sea-to-air flux as inferred from NASA GTE PEM-West a and B observations

This study reports dimethyl sulfide (DMS) sea-to-air fluxes derived from a mass-balance/photochemical-modeling approach. The region investigated was the western North Pacific covering the latitude range of 0°-30°N. Two NASA airborne databases were used in this study: PEM-West A in September-October 1991 and PEM-West B in February-March 1994. A total of 35 boundary layer (BL) sampling runs were recorded between the two programs. However, after filtering these data for pollution impacts and DMS lifetime considerations, this total was reduced to 13. Input for each analysis consisted of atmospheric DMS measurements, the equivalent mixing depth (EMD) for DMS, and model estimated values for OH and NO3. The evaluation of the EMD took into account both DMS within the BL as well as that transported into the overlying atmospheric buffer layer (BuL). DMS fluxes ranged from 0.6 to 3.0 μmol m-2d-1 for PEM-West A (10 sample runs) and 1.4 to 1.9 μmol m-2d-1 for PEM-West B (3 sample runs). Sensitivity analyses showed that the photochemically evaluated DMS flux was most influenced by the DMS vertical profile and the diel profile for OH. A propagation of error analysis revealed that the uncertainty associated with individual flux determinations ranged from a factor of 1.3 to 1.5. Also assessed were potential systematic errors. The first of these relates to our noninclusion of large-scale mean vertical motion as it might appear in the form of atmospheric subsidence or as a convergence. Our estimates here would place this error in the range of O to 30%. By far the largest systematic error is that associated with stochastic events (e.g., those involving major changes in cloud coverage). In the latter case, sensitivity tests suggested that the error could be as high as a factor of 2. With improvements in such areas as BL sampling time, direct observations of OH, improved DMS vertical profiling, direct assessment of vertical velocity in the field, and preflight (24 hours) detailed meteorological data, it appears that the uncertainty in this approach could be reduced to ±25%. Copyright 1999 by the American Geophysical Union

Machine learning paradigms for modeling spatial and temporal information in multimedia data mining

Multimedia data mining and knowledge discovery is a fast emerging interdisciplinary applied research area. There is tremendous potential for effective use of multimedia data mining (MDM) through intelligent analysis. Diverse application areas are increasingly relying on multimedia under-standing systems. Advances in multimedia understanding are related directly to advances in signal processing, computer vision, machine learning, pattern recognition, multimedia databases, and smart sensors. The main mission of this special issue is to identify state-of-the-art machine learning paradigms that are particularly powerful and effective for modeling and combining temporal and spatial media cues such as audio, visual, and face information and for accomplishing tasks of multimedia data mining and knowledge discovery. These models should be able to bridge the gap between low-level audiovisual features which require signal processing and high-level semantics. A number of papers have been submitted to the special issue in the areas of imaging, artificial intelligence; and pattern recognition and five contributions have been selected covering state-of-the-art algorithms and advanced related topics. The first contribution by D. Xiang et al. “Evaluation of data quality and drought monitoring capability of FY-3A MERSI data” describes some basic parameters and major technical indicators of the FY-3A, and evaluates data quality and drought monitoring capability of the Medium-Resolution Imager (MERSI) onboard the FY-3A. The second contribution by A. Belatreche et al. “Computing with biologically inspired neural oscillators: application to color image segmentation” investigates the computing capabilities and potential applications of neural oscillators, a biologically inspired neural model, to gray scale and color image segmentation, an important task in image understanding and object recognition. The major contribution of this paper is the ability to use neural oscillators as a learning scheme for solving real world engineering problems. The third paper by A. Dargazany et al. entitled “Multibandwidth Kernel-based object tracking” explores new methods for object tracking using the mean shift (MS). A bandwidth-handling MS technique is deployed in which the tracker reach the global mode of the density function not requiring a specific staring point. It has been proven via experiments that the Gradual Multibandwidth Mean Shift tracking algorithm can converge faster than the conventional kernel-based object tracking (known as the mean shift). The fourth contribution by S. Alzu’bi et al. entitled “3D medical volume segmentation using hybrid multi-resolution statistical approaches” studies new 3D volume segmentation using multiresolution statistical approaches based on discrete wavelet transform and hidden Markov models. This system commonly reduced the percentage error achieved using the traditional 2D segmentation techniques by several percent. Furthermore, a contribution by G. Cabanes et al. entitled “Unsupervised topographic learning for spatiotemporal data mining” proposes a new unsupervised algorithm, suitable for the analysis of noisy spatiotemporal Radio Frequency Identification (RFID) data. The new unsupervised algorithm depicted in this article is an efficient data mining tool for behavioral studies based on RFID technology. It has the ability to discover and compare stable patterns in a RFID signal, and is appropriate for continuous learning. Finally, we would like to thank all those who helped to make this special issue possible, especially the authors and the reviewers of the articles. Our thanks go to the Hindawi staff and personnel, the journal Manager in bringing about the issue and giving us the opportunity to edit this special issue

X-alpha calculation of transition energies in multiply ionized atoms

It is shown that the accuracy of calculations can be improved if appropriate (different) values of alpha are used for each configuration. Alternatively, the Slater Transition state can be used, wherein a total energy difference is related to a difference in single electron eigenvalues. By a series expansion, the value of alpha for an excited configuration can be related to its value for the ground state configuration. The terms Delta alpha (delta Epsilon/delta alpha) exhibit a similar dependence on atomic number as the ground state values of alpha. Results of sample calculations are reported and compared with experiment

On Horizontal and Vertical Separation in Hierarchical Text Classification

Hierarchy is a common and effective way of organizing data and representing their relationships at different levels of abstraction. However, hierarchical data dependencies cause difficulties in the estimation of "separable" models that can distinguish between the entities in the hierarchy. Extracting separable models of hierarchical entities requires us to take their relative position into account and to consider the different types of dependencies in the hierarchy. In this paper, we present an investigation of the effect of separability in text-based entity classification and argue that in hierarchical classification, a separation property should be established between entities not only in the same layer, but also in different layers. Our main findings are the followings. First, we analyse the importance of separability on the data representation in the task of classification and based on that, we introduce a "Strong Separation Principle" for optimizing expected effectiveness of classifiers decision based on separation property. Second, we present Hierarchical Significant Words Language Models (HSWLM) which capture all, and only, the essential features of hierarchical entities according to their relative position in the hierarchy resulting in horizontally and vertically separable models. Third, we validate our claims on real-world data and demonstrate that how HSWLM improves the accuracy of classification and how it provides transferable models over time. Although discussions in this paper focus on the classification problem, the models are applicable to any information access tasks on data that has, or can be mapped to, a hierarchical structure.Comment: Full paper (10 pages) accepted for publication in proceedings of ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR'16