Asymptotic efficiency properties of least squares in an ultrastructural model

This paper considers the estimation of both the intercept term and the slope parameter in a linear ultrastructural model using direct regression and reverse regression methods. Without assuming the errors to be normally distributed, asymptotic expressions for the bias and the variance of the estimators are derived and analyzed. The effect of departure of the errors from normality is also studied.     

The importance of information localization in scene gist recognition.

People can recognize the meaning or gist of a scene from a single glance, and a few recent studies have begun to examine the sorts of information that contribute to scene gist recognition. The authors of the present study used visual masking coupled with image manipulations (randomizing phase while maintaining the Fourier amplitude spectrum; random image structure evolution [RISE]; J. Sadr & P. Sinha, 2004) to explore whether and when unlocalized Fourier amplitude information contributes to gist perception. In 4 experiments, the authors found that differences between scene categories in the Fourier amplitude spectrum are insufficient for gist recognition or gist masking. Whereas the global 1/f spatial frequency amplitude spectra of scenes plays a role in gist masking, local phase information is necessary for gist recognition and for the strongest gist masking. Moreover, the ability to recognize the gist of a target image was influenced by mask recognizability, suggesting that conceptual masking occurs even at the earliest stages of scene processing. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Adeno-associated virus 2-mediated high efficiency gene transfer into immature and mature subsets of hematopoietic progenitor cells in human umbilical cord blood

Recombinant adeno-associated virus 2 (AAV) virions were constructed containing a gene for resistance to neomycin (neoR), under the control of either the herpesvirus thymidine kinase (TK) gene promoter (vTK-Neo), or the human parvovirus B19 p6 promoter (vB19-Neo), as well as those containing an upstream erythroid cell-specific enhancer (HS-2) from the locus control region of the human beta-globin gene cluster (vHS2-TK-Neo; vHS2-B19-Neo). These recombinant virions were used to infect either low density or highly enriched populations of CD34+ cells isolated from human umbilical cord blood. In clonogenic assays initiated with cells infected with the different recombinant AAV-Neo virions, equivalent high frequency transduction of the neoR gene into slow-cycling multipotential, erythroid, and granulocyte/macrophage (GM) progenitor cells, including those with high proliferative potential, was obtained without prestimulation with growth factors, indicating that these immature and mature hematopoietic progenitor cells were susceptible to infection by the recombinant AAV virions. Successful transduction did not require and was not enhanced by prestimulation of these cell populations with cytokines. The functional activity of the transduced neo gene was evident by the development of resistance to the drug G418, a neomycin analogue. Individual high and low proliferative colony-forming unit (CFU)-GM, burst-forming unit-erythroid, and CFU-granulocyte erythroid macrophage megakaryocyte colonies from mock-infected, or the recombinant virus-infected cultures were subjected to polymerase chain reaction analysis using a neo-specific synthetic oligonucleotide primer pair. A 276-bp DNA fragment that hybridized with a neo-specific DNA probe on Southern blots was only detected in those colonies cloned from the recombinant virus-infected cells, indicating stable integration of the transduced neo gene. These studies suggest that parvovirus-based vectors may prove to be a useful alternative to the more commonly used retroviral vectors for high efficiency gene transfer into slow or noncycling primitive hematopoietic progenitor cells, without the need for growth factor stimulation, which could potentially lead to differentiation of these cells before transplantation.     

Integrating statistical and nonstatistical audit evidence using belief functions: A case of variable sampling

The main purpose of this article is to show how one can integrate statistical and nonstatistical items of evidence in the belief function framework. First, we use the properties of consonant belief functions to define the belief that the true mean of a variable lies in a given interval when a statistical test is performed for the variable. Second, we use the above definition to determine the sample size for a statistical test when a desired level of belief is needed from the sample. Third, we determine the level of belief that the true mean lies in a given interval when a statistical test yields certain values for the sample mean and the standard deviation of the mean for the variable. Finally, we use the auditing situation to illustrate the process of integrating statistical and nonstatistical items evidence. © 1994 John Wiley & Sons, Inc.     

Dielectric behaviour of hot pressed AlN ceramic exposed to inorganic acid vapours

Dielectric behaviour of hot pressed AIN ceramic is studied before and after exposing the samples to inorganic acid (HCl and HNO₃) vapours with a specific aim to study the effect of these vapours on the dielectric constant () and dissipation factor (tan ). Four samples having different volume percentage of porosity (0.2 to 15%) are selected for this study. Dielectric dispersion increases after exposing the samples to the above acid vapours. Tan also increases quite appreciably; the increase being more at higher porosity. Recovery studies show that the exposure effect is reversible. The exposure time dependence of and tan indicates that these parameters show a maxima at a particular exposure time. However, no such maxima is observed in the gravimetric measurements. The increase in dielectric parameters after exposure to acid vapours is explained in terms of the ionic conduction due to the dissociation of these vapours in the presence of moisture. The porosity dependence of this effect is discussed in terms of closed and open porosity reported by other workers.     

Multiperson interaction recognition in images: A body keypoint based feature image analysis

Most interaction recognition approaches have been limited to single‐person action classification in videos. However, for still images where motion information is not available, the task becomes more complex. Aiming to this point, we propose an approach for multiperson human interaction recognition in images with keypoint‐based feature image analysis. Proposed method is a three‐stage framework. In the first stage, we propose feature‐based neural network (FCNN) for action recognition trained with feature images. Feature images are body features, that is, effective distances between a set of body part pairs and angular relation between body part triplets, rearranged in 2D gray‐scale image to learn effective representation of complex actions. In the later stage, we propose a voting‐based method for direction encoding to anticipate probable motion in steady images. Finally, our multiperson interaction recognition algorithm identifies which human pairs are interacting with each other using an interaction parameter. We evaluate our approach on two real‐world data sets, that is, UT‐interaction and SBU kinect interaction. The empirical experiments show that results are better than the state‐of‐the‐art methods with recognition accuracy of 95.83% on UT‐I set 1, 92.5% on UT‐I set 2, and 94.28% on SBU clean data set.     

High-utility and diverse itemset mining

High-utility Itemset Mining (HUIM) finds patterns from a transaction database with their utility no less than a user-defined threshold. The utility of an itemset is defined as the sum of the utilities of its items. The utility notion enables a data analyst to associate a profit score with each item and thereof to a pattern. We extend the notion of high-utility with diversity to define a new pattern type called High-utility and Diverse pattern (HUD). The notion of diversity of a pattern captures the extent of the different categories covered by the selected items in the pattern. An application of diverse-pattern lies in the recommendation task where a system can recommend to a customer a set of items from a new class based on her previously bought items. Our notion of diversity is easy to compute and also captures the basic essence of a previously proposed diversity notion. The existing algorithm to compute frequent-diverse patterns is 2-phase, i.e., in the first phase, frequent patterns are computed, out of which diverse patterns are filtered out in the second phase. We, in this paper, give an integrated algorithm that efficiently computes high-utility and diverse patterns in a single phase. Our experimental study shows that our proposed algorithm is very efficient as compared to a 2-phase algorithm that extracts high-utility itemsets in the first phase and filters out the diverse itemsets in the second phase.

     

Investigation of multiple heterogeneous relationships using a q-rung orthopair fuzzy multi-criteria decision algorithm

Neural Computing and Applications - Q-rung orthopair fuzzy (q-ROF) set is one of the powerful tools for handling the uncertain multi-criteria decision-making (MCDM) problems, various MCDM methods...     

A novel sine augmented scaled sine cosine algorithm for frequency control issues of a hybrid distributed two-area power system

Neural Computing and Applications - In this research article, a novel approach is proposed by considering the sine augmented scaled sine cosine (SAS-SCA) Algorithm for the load frequency control of...     

Efficient compression and network adaptive video coding for distributed video surveillance

The availability of cheap network based video cameras and the prevalence of wireless networks has lead to a major thrust towards deployment of large scale Distributed Video Surveillance (DVS) systems. This has opened up an important area of research to deal with the issues involved in DVS system for efficient collection and transmission of large scale video streams from the cameras at the guarded sites, to the end users in possibly constrained network conditions. In this paper, we propose a framework based on content-based video classification and scalable compression scheme to provide a robust bandwidth efficient video transmission for DVS. The scheme builds on a Discrete Wavelet Transform (DWT) based Color-Set Partitioning for Hierarchical Trees (CSPIHT) coding to obtain a scalable bitstream. Wavelet domain segmentation and compression assists in development of a DVS architecture. The architecture includes a novel module for dynamic allocation of Network bandwidth based on the current available resources and constraints. Different frame constituents are optimally coded based on their relative significance, perceptual quality, and available estimate of network bandwidth. Experimental result over different video sequences and simulations for Network conditions demonstrate the efficient performance of the approach.