Measurement Errors in Recall Food Expenditure Data

Household expenditure data is an important input into the study of consumption and savings behaviour and of living standards and inequality. Because it is collected in many surveys, food expenditure data has formed the basis of much work in these areas. Recently, there has been considerable interest in properties of different ways of collecting expenditure information. It has also been suggested that measurement error in expenditure data seriously affects empirical work based on such data. The Canadian Food Expenditure Survey asks respondents to first estimate their household's food expenditures and then record food expenditures in a diary for two weeks. This unique experiment allows us to compare recall and diary based expenditure data collected from the same individuals. Under the assumption that the diary measures are "true" food consumption, this allows us to observe errors in measures of recall food consumption directly, and to study the properties of those errors. Under this assumption, measurement errors in recall food consumption data appear to be substantial, and they do not have many of the properties of classical measurement error. In particular, they are neither uncorrelated with true consumption nor conditionally homoscedastic. In addition, they are not well approximated by either a normal or log normal distribution. We also show evidence that diary measures are themselves imperfect, suffering for example, from "diary exhaustion". This suggests alternative interpretations for the differences between recall and diary consumption measures. Finally, we compare estimates of income and household size elasticities of per capita food consumption based on the two kinds of expenditure data and, in contrast to some previous work, find little difference between the two.expenditure, consumption, surveys

Measurement Errors in Recall Food Expenditure Data

Household expenditure data is an important input into the study of consumption and savings behaviour and of living standards and inequality. Because it is collected in many surveys, food expenditure data has formed the basis of much work in these areas. Recently, there has been considerable interest in properties of different ways of collecting expenditure information. It has also been suggested that measurement error in expenditure data seriously affects empirical work based on such data. The Canadian Food Expenditure Survey asks respondents to first estimate their household's food expenditures and then record food expenditures in a diary for two weeks. This unique experiment allows us to compare recall and diary based expenditure data collected from the same individuals. Under the assumption that the diary measures are "true" food consumption, this allows us to observe errors in measures of recall food consumption directly, and to study the properties of those errors. Under this assumption, measurement errors in recall food consumption data appear to be substantial, and they do not have many of the properties of classical measurement error. In particular, they are neither uncorrelated with true consumption nor conditionally homoscedastic. In addition, they are not well approximated by either a normal or log normal distribution. We also show evidence that diary measures are themselves imperfect, suffering for example, from "diary exhaustion". This suggests alternative interpretations for the differences between recall and diary consumption measures. Finally, we compare estimates of income and household size elasticities of per capita food consumption based on the two kinds of expenditure data and, in contrast to some previous work, find little difference between the two.expenditure, consumption, surveys

CroLSSim: Cross‐language software similarity detector using hybrid approach of LSA‐based AST‐MDrep features and CNN‐LSTM model

Software similarity in different programming codes is a rapidly evolving field because of its numerous applications in software development, software cloning, software plagiarism, and software forensics. Currently, software researchers and developers search cross-language open-source repositories for similar applications for a variety of reasons, such as reusing programming code, analyzing different implementations, and looking for a better application. However, it is a challenging task because each programming language has a unique syntax and semantic structure. In this paper, a novel tool called Cross-Language Software Similarity (CroLSSim) is designed to detect similar software applications written in different programming codes. First, the Abstract Syntax Tree (AST) features are collected from different programming codes. These are high-quality features that can show the abstract view of each program. Then, Methods Description (MDrep) in combination with AST is used to examine the relationship among different method calls. Second, the Term Frequency Inverse Document Frequency approach is used to retrieve the local and global weights from AST-MDrep features. Third, the Latent Semantic Analysis-based features extraction and selection method is proposed to extract the semantic anchors in reduced dimensional space. Fourth, the Convolution Neural Network (CNN)-based features extraction method is proposed to mine the deep features. Finally, a hybrid deep learning model of CNN-Long-Short-Term Memory is designed to detect semantically similar software applications from these latent variables. The data set contains approximately 9.5K Java, 8.8K C#, and 7.4K C++ software applications obtained from GitHub. The proposed approach outperforms as compared with the state-of-the-art methods

Are three flavors special?

It has become clearer recently that the regular pattern of three flavor nonets describing the low spin meson multiplets seems to require some modification for the case of the spin 0 scalar mesons. One picture which has had some success, treats the scalars in a chiral Lagrangian framework and considers them to populate two nonets. These are, in turn, taken to result from the mixing of two "bare" nonets, one of which is of quark- antiquark type and the other of two quark- two antiquark type. Here we show that such a mixing is, before chiral symmetry breaking terms are included, only possible for three flavors. In other cases, the two types of structure can not have the same chiral symmetry transformation property. Specifically, our criterion would lead one to believe that scalar and pseudoscalar states containing charm would not have "four quark" admixtures. This work is of potential interest for constructing chiral Lagrangians based on exact chiral symmetry which is then broken by well known specific terms. It may also be of interest in studying some kinds of technicolor theories

Chiral Nonet Mixing in pi pi Scattering

Pion pion scattering is studied in a generalized linear sigma model which contains two scalar nonets (one of quark-antiquark type and the other of diquark-antidiquark type) and two corresponding pseudoscalar nonets. An interesting feature concerns the mixing of the four isosinglet scalar mesons which yield poles in the scattering amplitude. Some realism is introduced by enforcing exact unitarity via the K-matrix method. It is shown that a reasonable agreement with experimental data is obtained up to about 1 GeV. The poles in the unitarized scattering amplitude are studied in some detail. The lowest pole clearly represents the sigma meson (or f0(600)) with a mass and decay width around 500 MeV. The second pole invites comparison with the f0(980) which has a mass around 1 GeV and decay width around 100 MeV. The third and fourth poles, resemble some of the isosinglet state in the complicated 1-2 GeV region. Some comparison is made to the situation in the usual SU(3) linear sigma model with a single scalar nonet

Electrical transport and optical studies of ferromagnetic Cobalt doped ZnO nanoparticles exhibiting a metal-insulator transition

The observed correlation of oxygen vacancies and room temperature ferromagnetic ordering in Co doped ZnO1-o nanoparticles reported earlier (Naeem et al Nanotechnology 17, 2675-2680) has been further explored by transport and optical measurements. In these particles room temperature ferromagnetic ordering had been observed to occur only after annealing in forming gas. In the current work the optical properties have been studied by diffuse reflection spectroscopy in the UV-Vis region and the band gap of the Co doped compositions has been found to decrease with Co addition. Reflections minima are observed at the energies characteristic of Co+2 d-d (tethrahedral symmetry) crystal field transitions, further establishing the presence of Co in substitutional sites. Electrical transport measurements on palletized samples of the nanoparticles show that the effect of a forming gas is to strongly decrease the resistivity with increasing Co concentration. For the air annealed and non-ferromagnetic samples the variation in the resistivity as a function of Co content are opposite to those observed in the particles prepared in forming gas. The ferromagnetic samples exhibit an apparent change from insulator to metal with increasing temperatures for T>380K and this change becomes more pronounced with increasing Co content. The magnetic and resistive behaviors are correlated by considering the model by Calderon et al [M. J. Calderon and S. D. Sarma, Annals of Physics 2007 (Accepted doi: 10.1016/j.aop.2007.01.010] where the ferromagnetism changes from being mediated by polarons in the low temperature insulating region to being mediated by the carriers released from the weakly bound states in the higher temperature metallic region.Comment: 7 pages, 6 figure

A global mental health fund for serious mental illness in low-income and middle-income countries.

Serious Mental Illnesses (SMI) are psychiatric disorders (excluding developmental and substance use disorders) that result in considerable functional impairment. These conditions receive little or no funding in most Low and Middle Income (LAMI) countries. The huge gap in resources for SMI can only be met by a global fund to provide the treatment of SMI in LAMI countries. The Global Fund to fight AIDS established more than two decades ago, not only provided enormous funding but most importantly, generated the hope that the condition could be treated. We argue that SMI stand today where HIV-AIDS was a couple of decades ago. The cost effective interventions for these disorders are available. For example, it is estimated that that an extra 11 Naira or I$ 0.27 per capita would need to be invested each year to increase the present treatment coverage for schizophrenia of 20% to a level of 70% in Nigeria. The treatment package should include free access to essential medicines to treat psychotic disorders and a component of appropriate evidence based psychosocial intervention, which have been evaluated in number of studies in these countries. It is ethical and public health imperative that a Global Fund to provide the basic treatment for those suffering from SMI is established and the seed money for the proposed fund should be provided by rapidly developing LAMI countries such as India and South Africa

Cyber-threat detection system using a hybrid approach of transfer learning and multi-model image representation

Currently, Android apps are easily targeted by malicious network traffic because of their constant network access. These threats have the potential to steal vital information and disrupt the commerce, social system, and banking markets. In this paper, we present a malware detection system based on word2vec-based transfer learning and multi-model image representation. The proposed method combines the textual and texture features of network traffic to leverage the advantages of both types. Initially, the transfer learning method is used to extract trained vocab from network traffic. Then, the malware-to-image algorithm visualizes network bytes for visual analysis of data traffic. Next, the texture features are extracted from malware images using a combination of scale-invariant feature transforms (SIFTs) and oriented fast and rotated brief transforms (ORBs). Moreover, a convolutional neural network (CNN) is designed to extract deep features from a set of trained vocab and texture features. Finally, an ensemble model is designed to classify and detect malware based on the combination of textual and texture features. The proposed method is tested using two standard datasets, CIC-AAGM2017 and CICMalDroid 2020, which comprise a total of 10.2K malware and 3.2K benign samples. Furthermore, an explainable AI experiment is performed to interpret the proposed approach