School facilities and student achievements: evidence from the Timss

This paper studies the link between school facilities and student achievements in eight countries using data from the TIMSS 2003 database. OLS and propensity score matching is used to control for observable characteristics. Both methods indicate that poor school facilities may be negatively associated with student achievements, but the estimated coefficients are mainly insignificant. Significantly negative estimates are found in only three out of eight countries when using OLS. When using matching on propensity scores I only find significant coefficients in one of the countries.

School building conditions and student achievments: Norwegian evidence

This paper studies the effects from poor school building conditions on student achievements in Norwegian primary schools based on results from national tests in mathematics, English and Norwegian. The benchmark OLS results suggest a negative relationship, but the estimates are mostly insignificant. Further, a municipality fixed effects (MFE) and an instrumental variable approach (IV) is suggested as alternatives to OLS in order to battle potential endogeneity issues due to unobservable characteristics. The results from the OLS and IV procedures are mostly similar to the OLS results.

Temporal coherence length of light in semiclassical field theory models

The following work is motivated by the conceptual problems associated with the wave-particle duality and the notion of the photon. Two simple classical models for radiation from individual emitters are compared, one based on sines with random phasejumps, another based on pulse trains. The sum signal is calculated for a varying number of emitters. The focus lies on the final signal's statistical features quantified by means of the temporal coherence function and the temporal coherence length. We show how these features might be used to experimentally differentiate between the models. We also point to ambiguities in the definition of the temporal coherence length.Comment: 7 pages, 3 figures. The following article has been submitted to AIP Conference Proceedings: Advances in Quantum Theory, Vaxjo 201

When the Advantaged Become Disadvantaged: Men’s and Women’s Actions Against Gender Discrimination

Intergroup theories suggest that different social identities will either discourage or encourage the taking of action against discrimination (Bartky, 1977; Jost & Banaji, 1994). However, research (e.g., Branscombe, 1998) has shown that discrimination is a less negative experience for men than for women. As such, it is possible that men may take greater action than women, regardless of identity. However, men’s responses to their perceived disadvantage has not yet been tested. Among those induced to ascribe to a gendered stereotype identity, men endorsed more action than women did.Among those induced to ascribe to an identity based on a gendered social experienced, women endorsed marginally more action than men did. Differences in responses are proposed to be a function of the different efficacy levels developed by each gender within each social identity

Urban Land Cover Classification with Missing Data Modalities Using Deep Convolutional Neural Networks

Automatic urban land cover classification is a fundamental problem in remote sensing, e.g. for environmental monitoring. The problem is highly challenging, as classes generally have high inter-class and low intra-class variance. Techniques to improve urban land cover classification performance in remote sensing include fusion of data from different sensors with different data modalities. However, such techniques require all modalities to be available to the classifier in the decision-making process, i.e. at test time, as well as in training. If a data modality is missing at test time, current state-of-the-art approaches have in general no procedure available for exploiting information from these modalities. This represents a waste of potentially useful information. We propose as a remedy a convolutional neural network (CNN) architecture for urban land cover classification which is able to embed all available training modalities in a so-called hallucination network. The network will in effect replace missing data modalities in the test phase, enabling fusion capabilities even when data modalities are missing in testing. We demonstrate the method using two datasets consisting of optical and digital surface model (DSM) images. We simulate missing modalities by assuming that DSM images are missing during testing. Our method outperforms both standard CNNs trained only on optical images as well as an ensemble of two standard CNNs. We further evaluate the potential of our method to handle situations where only some DSM images are missing during testing. Overall, we show that we can clearly exploit training time information of the missing modality during testing

Do school building conditions matter for student achievements in Norway?

This paper analyzes the relationship between the condition school buildings and student achievement in primary schools in Norway and highlights the importance of estimation uncertainty when interpreting the empirical results. The findings indicate that the relationship between school building conditions and student achievements is for the most part statistically insignificant. However, this is more due to large estimation standard errors than small coefficients. Hence, even though I for the most part cannot reject a zero effect, I cannot reject a sizable effect either