Robust Covariance Adaptation in Adaptive Importance Sampling

Importance sampling (IS) is a Monte Carlo methodology that allows for approximation of a target distribution using weighted samples generated from another proposal distribution. Adaptive importance sampling (AIS) implements an iterative version of IS which adapts the parameters of the proposal distribution in order to improve estimation of the target. While the adaptation of the location (mean) of the proposals has been largely studied, an important challenge of AIS relates to the difficulty of adapting the scale parameter (covariance matrix). In the case of weight degeneracy, adapting the covariance matrix using the empirical covariance results in a singular matrix, which leads to poor performance in subsequent iterations of the algorithm. In this paper, we propose a novel scheme which exploits recent advances in the IS literature to prevent the so-called weight degeneracy. The method efficiently adapts the covariance matrix of a population of proposal distributions and achieves a significant performance improvement in high-dimensional scenarios. We validate the new method through computer simulations

Enhancing image captioning with depth information using a Transformer-based framework

Captioning images is a challenging scene-understanding task that connects computer vision and natural language processing. While image captioning models have been successful in producing excellent descriptions, the field has primarily focused on generating a single sentence for 2D images. This paper investigates whether integrating depth information with RGB images can enhance the captioning task and generate better descriptions. For this purpose, we propose a Transformer-based encoder-decoder framework for generating a multi-sentence description of a 3D scene. The RGB image and its corresponding depth map are provided as inputs to our framework, which combines them to produce a better understanding of the input scene. Depth maps could be ground truth or estimated, which makes our framework widely applicable to any RGB captioning dataset. We explored different fusion approaches to fuse RGB and depth images. The experiments are performed on the NYU-v2 dataset and the Stanford image paragraph captioning dataset. During our work with the NYU-v2 dataset, we found inconsistent labeling that prevents the benefit of using depth information to enhance the captioning task. The results were even worse than using RGB images only. As a result, we propose a cleaned version of the NYU-v2 dataset that is more consistent and informative. Our results on both datasets demonstrate that the proposed framework effectively benefits from depth information, whether it is ground truth or estimated, and generates better captions. Code, pre-trained models, and the cleaned version of the NYU-v2 dataset will be made publically available.Comment: 19 pages, 5 figures, 13 table

Job satisfaction of mathematics teachers: an empirical investigation to quantify the contributions of teacher self‑efficacy and teacher motivation to teach

The shortage of mathematics teachers necessitates deliberate efforts to retain high-quality ones in many parts of the world. This puts teacher job satisfaction in the spotlight since satisfied teachers are likely to retain their jobs. Thus, the purpose of this study is twofold. The first is to quantify the influence of teacher self-efficacy and teacher motivation on teacher job satisfaction. The second is to investigate the patterns of changes in teacher job satisfaction across gender, age, and work experience. Using both descriptive and inferential statistics, we analysed the dataset of 1304 Norwegian mathematics teachers to address two research questions. The results showed that both teacher self-efficacy and social utility motivation have a significant influence on teacher job satisfaction with an additional mediating role of teacher self-efficacy. Contrary to our expectations, personal utility motivation has a negative influence on teacher job satisfaction. We found that women had significantly higher teacher job satisfaction than men. Also, we found a high-low–high pattern of changes in teacher job satisfaction in ascending order of teachers’ age and work experience. One practical implication of these findings is exposing an appropriate time (i.e. at a low stage of job satisfaction) for interventions targeted at teacher job satisfaction to be effective. We discussed other implications of these findings concerning which constructs, gender, and age groups of teachers to prioritise for interventions that would reinforce the job satisfaction of mathematics teachers.publishedVersio

Effects of Buoyancy on Laminar, Transitional, and Turbulent Gas Jet Diffusion Flames

Gas jet diffusion flames have been a subject of research for many years. However, a better understanding of the physical and chemical phenomena occurring in these flames is still needed, and, while the effects of gravity on the burning process have been observed, the basic mechanisms responsible for these changes have yet to be determined. The fundamental mechanisms that control the combustion process are in general coupled and quite complicated. These include mixing, radiation, kinetics, soot formation and disposition, inertia, diffusion, and viscous effects. In order to understand the mechanisms controlling a fire, laboratory-scale laminar and turbulent gas-jet diffusion flames have been extensively studied, which have provided important information in relation to the physico-chemical processes occurring in flames. However, turbulent flames are not fully understood and their understanding requires more fundamental studies of laminar diffusion flames in which the interplay of transport phenomena and chemical kinetics is more tractable. But even this basic, relatively simple flame is not completely characterized in relation to soot formation, radiation, diffusion, and kinetics. Therefore, gaining an understanding of laminar flames is essential to the understanding of turbulent flames, and particularly fires, in which the same basic phenomena occur. In order to improve and verify the theoretical models essential to the interpretation of data, the complexity and degree of coupling of the controlling mechanisms must be reduced. If gravity is isolated, the complication of buoyancy-induced convection would be removed from the problem. In addition, buoyant convection in normal gravity masks the effects of other controlling parameters on the flame. Therefore, the combination of normal-gravity and microgravity data would provide the information, both theoretical and experimental, to improve our understanding of diffusion flames in general, and the effects of gravity on the burning process in particular

Requirements for E1A dependent transcription in the yeast Saccharomyces cerevisiae

<p>Abstract</p> <p>Background</p> <p>The human adenovirus type 5 early region 1A (E1A) gene encodes proteins that are potent regulators of transcription. E1A does not bind DNA directly, but is recruited to target promoters by the interaction with sequence specific DNA binding proteins. In mammalian systems, E1A has been shown to contain two regions that can independently induce transcription when fused to a heterologous DNA binding domain. When expressed in <it>Saccharomyces cerevisiae</it>, each of these regions of E1A also acts as a strong transcriptional activator. This allows yeast to be used as a model system to study mechanisms by which E1A stimulates transcription.</p> <p>Results</p> <p>Using 81 mutant yeast strains, we have evaluated the effect of deleting components of the ADA, COMPASS, CSR, INO80, ISW1, NuA3, NuA4, Mediator, PAF, RSC, SAGA, SAS, SLIK, SWI/SNF and SWR1 transcriptional regulatory complexes on E1A dependent transcription. In addition, we examined the role of histone H2B ubiquitylation by Rad6/Bre1 on transcriptional activation.</p> <p>Conclusion</p> <p>Our analysis indicates that the two activation domains of E1A function via distinct mechanisms, identify new factors regulating E1A dependent transcription and suggest that yeast can serve as a valid model system for at least some aspects of E1A function.</p

Health literacy research in the Eastern Mediterranean Region: an integrative review

© 2019, Swiss School of Public Health (SSPH+). Objectives: This integrative review examines health literacy research in the Eastern Mediterranean Region (EMR) and describes: (1) assessments and screening tools used to measure levels of health literacy, and (2) the focus, methods, and findings of health literacy research in the region. Methods: A total of 246 records were identified through a systematic search of online databases from 1950 to 2017, to include: ProQuest Middle East and Africa, MEDLINE, PubMed, PsycINFO, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Academic OneFile, Web of Science, Scopus, and Google Scholar. The final sample included 49 full-text articles. Results: This research described 7 studies which used existing or new health literacy measures. Levels of health literacy in the EMR were similar to those for Europe and the United States. Low health literacy in EMR countries was more prevalent among females than males. The relationships between health literacy and knowledge, behavior and health outcomes varied across countries. Conclusions: To our knowledge, this study is the first in the EMR. Appropriately designed studies should better define health literacy needs due to variations in socioeconomic status within subregions. Future health literacy measures must consider stronger psychometric properties to guide development and validation

Particle Filtering Under General Regime Switching

In this paper, we consider a new framework for particle filtering under model uncertainty that operates beyond the scope of Markovian switching systems. Specifically, we develop a novel particle filtering algorithm that applies to general regime switching systems, where the model index is augmented as an unknown time-varying parameter in the system. The proposed approach does not require the use of multiple filters and can maintain a diverse set of particles for each considered model through appropriate choice of the particle filtering proposal distribution. The flexibility of the proposed approach allows for long-term dependencies between the models, which enables its use to a wider variety of real-world applications. We validate the method on a synthetic data experiment and show that it outperforms state-of-the-art multiple model particle filtering approaches that require the use of multiple filters.Comment: Accepted to EUSIPCO 202