Approximate and pseudo-amenability of various classes of Banach algebras

We continue the investigation of notions of approximate amenability that were introduced in work of the second and third authors. It is shown that every boundedly approximately contractible Banach algebra has a bounded approximate identity. Among our other results, it is shown that the Fourier algebra of the free group on two generators is not approximately amenable. Further examples are obtained of ${\ell}^1$-semigroup algebras which are approximately amenable but not amenable; using these, we show that bounded approximate amenability need not imply sequential approximate amenability. Results are also given for Segal subalgebras of $L^1(G)$, where $G$ is a locally compact group, and the algebras $PF_p(\Gamma)$ of $p$-pseudofunctions on a discrete group $\Gamma$ (of which the reduced $C^*$-algebra is a special case).Comment: 35 pages, revision of Jan '08 preprint. Abstract and MSC added; bibliograpy updated; slight tweaks to Section 4; and correction of a few typos. The final version is to appear in J. Funct. Ana

Photonic stop bands in quasi-random nanoporous anodic alumina structures

The existence of photonic stop bands in the self-assembled arrangement of pores in porous anodic alumina structures is investigated by means of rigorous 2D finite- difference time-domain calculations. Self-assembled porous anodic alumina shows a random distribution of domains, each of them with a very definite triangular pattern, constituting a quasi-random structure. The observed stop bands are similar to those of photonic quasicrystals or random structures. As the pores of nanoporous anodic alumina can be infiltrated with noble metals, nonlinear or active media, it makes this material very attractive and cost-effective for applications including inhibition of spontaneous emission, random lasing, LEDs and biosensors

General Latent Feature Modeling for Data Exploration Tasks

This paper introduces a general Bayesian non- parametric latent feature model suitable to per- form automatic exploratory analysis of heterogeneous datasets, where the attributes describing each object can be either discrete, continuous or mixed variables. The proposed model presents several important properties. First, it accounts for heterogeneous data while can be inferred in linear time with respect to the number of objects and attributes. Second, its Bayesian nonparametric nature allows us to automatically infer the model complexity from the data, i.e., the number of features necessary to capture the latent structure in the data. Third, the latent features in the model are binary-valued variables, easing the interpretability of the obtained latent features in data exploration tasks

Linear and Second-order Optical Response of the III-V Mono-layer Superlattices

We report the first fully self-consistent calculations of the nonlinear optical properties of superlattices. The materials investigated are mono-layer superlattices with GaP grown on the the top of InP, AlP and GaAs (110) substrates. We use the full-potential linearized augmented plane wave method within the generalized gradient approximation to obtain the frequency dependent dielectric tensor and the second-harmonic-generation susceptibility. The effect of lattice relaxations on the linear optical properties are studied. Our calculations show that the major anisotropy in the optical properties is the result of strain in GaP. This anisotropy is maximum for the superlattice with maximum lattice mismatch between the constituent materials. In order to differentiate the superlattice features from the bulk-like transitions an improvement over the existing effective medium model is proposed. The superlattice features are found to be more pronounced for the second-order than the linear optical response indicating the need for full supercell calculations in determining the correct second-order response.Comment: 9 pages, 4 figures, submitted to Phy. Rev.

Emergence of Sensory Representations Using Prediction in Partially Observable Environments

n order to explore and act autonomously in an environment,an agent can learn from the sensorimotor information that is capturedwhile acting. By extracting the regularities in this sensorimotor stream,it can build a model of the world, which in turn can be used as a basis foraction and exploration. It requires the acquisition of compact representa-tions from possibly high dimensional raw observations. In this paper, wepropose a model which integrates sensorimotor information over time,and project it in a sensory representation. It is trained by preformingsensorimotor prediction. We emphasize on a simple example the role ofmotor and memory for learning sensory representations

An Algorithmic Approach to Missing Data Problem in Modeling Human Aspects in Software Development

Background: In our previous research, we built defect prediction models by using confirmation bias metrics. Due to confirmation bias developers tend to perform unit tests to make their programs run rather than breaking their code. This, in turn, leads to an increase in defect density. The performance of prediction model that is built using confirmation bias was as good as the models that were built with static code or churn metrics. Aims: Collection of confirmation bias metrics may result in partially "missing data" due to developers' tight schedules, evaluation apprehension and lack of motivation as well as staff turnover. In this paper, we employ Expectation-Maximization (EM) algorithm to impute missing confirmation bias data. Method: We used four datasets from two large-scale companies. For each dataset, we generated all possible missing data configurations and then employed Roweis' EM algorithm to impute missing data. We built defect prediction models using the imputed data. We compared the performances of our proposed models with the ones that used complete data. Results: In all datasets, when missing data percentage is less than or equal to 50% on average, our proposed model that used imputed data yielded performance results that are comparable with the performance results of the models that used complete data. Conclusions: We may encounter the "missing data" problem in building defect prediction models. Our results in this study showed that instead of discarding missing or noisy data, in our case confirmation bias metrics, we can use effective techniques such as EM based imputation to overcome this problem

Growth and development status in the first two years of uninfected children born from HIV positive mothers

Recently prevention of HIV transmission from mother to child by antiretroviral regimens has resulted in growing the numbers of HIV exposed but uninfected children (HIV-EU). The aim of present study was evaluation of growth and neurodevelopment status among less than 2-year-old HIV exposed uninfected children. A cohort study was carried out at Vali-e-Asr Hospital (Tehran-Iran). Thirty-nine HIV-EU neonates were recruited (2014 to 2016). Neonates and infants with concern to growth and neurodevelopment status were evaluated at 6, 12, 18, and 24 months by an expert physician. Neurodevelopment assessment was based on WHO Milestones Chart and Age and Stage Questionnaire. Of all children, 22 were male, and 17 were female. Regarding growth indices, although mean birth weight in half of the neonates was lower than normal population; no postnatal descending trend was observed in their growth chart. No significant differences were found between two groups' height and head circumference. Among the neurodevelopmental parameters measured, in 6th months of life, 2 cases had abnormality in the gross motor while at 12 months, 6 cases had delay in language, social problem, and motor disorders. At 18 and 24 months, 7 infants showed developmental problems of which 71.4 of their mothers were younger than others (age<25 years, P=0.009). Prevalence of neurodevelopmental disorders including delay in language, motor, and social domains was common among HIV-EU children. As several environmental factors may involve the etiology of neurodevelopmental disorders, nearly-full postnatal control and prevention seem necessary. © 2018 Tehran University of Medical Sciences. All rights reserved

Growth and development status in the first two years of uninfected children born from HIV positive mothers

Recently prevention of HIV transmission from mother to child by antiretroviral regimens has resulted in growing the numbers of HIV exposed but uninfected children (HIV-EU). The aim of present study was evaluation of growth and neurodevelopment status among less than 2-year-old HIV exposed uninfected children. A cohort study was carried out at Vali-e-Asr Hospital (Tehran-Iran). Thirty-nine HIV-EU neonates were recruited (2014 to 2016). Neonates and infants with concern to growth and neurodevelopment status were evaluated at 6, 12, 18, and 24 months by an expert physician. Neurodevelopment assessment was based on WHO Milestones Chart and Age and Stage Questionnaire. Of all children, 22 were male, and 17 were female. Regarding growth indices, although mean birth weight in half of the neonates was lower than normal population; no postnatal descending trend was observed in their growth chart. No significant differences were found between two groups' height and head circumference. Among the neurodevelopmental parameters measured, in 6th months of life, 2 cases had abnormality in the gross motor while at 12 months, 6 cases had delay in language, social problem, and motor disorders. At 18 and 24 months, 7 infants showed developmental problems of which 71.4 of their mothers were younger than others (age<25 years, P=0.009). Prevalence of neurodevelopmental disorders including delay in language, motor, and social domains was common among HIV-EU children. As several environmental factors may involve the etiology of neurodevelopmental disorders, nearly-full postnatal control and prevention seem necessary. © 2018 Tehran University of Medical Sciences. All rights reserved