Nonlinear beam fields simulation of a mixed wave and definition of nonlinearity parameter with diffraction correction

The acoustic nonlinearity parameter has been frequently measured for early detection of micro damage in various materials. The technique typically employs a toneburst signal of single frequency and measures the second harmonic generation during its propagation in through-transmission mode. In this work, we propose a two wave mixing technique and the use of difference frequency components in determining the nonlinearity parameter. One important advantage of this technique is to use difference frequency components apart from higher harmonics including the second harmonic, therefore effects of source nonlinearity can be minimized and low attenuating nonlinear signal can be acquired. Beam fields radiated from various configurations of radiating transducers are simulated. The fundamental and difference frequency waves are calculated using the multi-Gaussian beam model based on the quasilinear solution for the Westervelt equation. Explicit expressions for diffraction and attenuation corrections are derived, and the nonlinearity parameter is newly defined with these corrections included

Towards a Physiological Computing Infrastructure for Researching Students’ Flow in Remote Learning – Preliminary Results from a Field Study

With the advent of physiological computing systems, new avenues are emerging for the field of learning analytics related to the potential integration of physiological data. To this end, we developed a physiological computing infrastructure to collect physiological data, surveys, and browsing behavior data to capture students’ learning journey in remote learning. Specifically, our solution is based on the Raspberry Pi minicomputer and Polar H10 chest belt. In this work-in-progress paper, we present preliminary results and experiences we collected from a field study with medical students using our developed infrastructure. Our results do not only provide a new direction for more effectively capturing different types of data in remote learning by addressing the underlying challenges of remote setups, but also serve as a foundation for future work on developing a less obtrusive, (near) real-time measurement method based on the classification of cognitive-affective states such as flow or other learning-relevant constructs with the captured data using supervised machine learning

Curriculum-Based Imitation of Versatile Skills

Learning skills by imitation is a promising concept for the intuitive teaching of robots. A common way to learn such skills is to learn a parametric model by maximizing the likelihood given the demonstrations. Yet, human demonstrations are often multi-modal, i.e., the same task is solved in multiple ways which is a major challenge for most imitation learning methods that are based on such a maximum likelihood (ML) objective. The ML objective forces the model to cover all data, it prevents specialization in the context space and can cause mode-averaging in the behavior space, leading to suboptimal or potentially catastrophic behavior. Here, we alleviate those issues by introducing a curriculum using a weight for each data point, allowing the model to specialize on data it can represent while incentivizing it to cover as much data as possible by an entropy bonus. We extend our algorithm to a Mixture of (linear) Experts (MoE) such that the single components can specialize on local context regions, while the MoE covers all data points. We evaluate our approach in complex simulated and real robot control tasks and show it learns from versatile human demonstrations and significantly outperforms current SOTA methods. A reference implementation can be found at https://github.com/intuitive-robots/ml-cu

Curriculum-Based Imitation of Versatile Skills

Learning skills by imitation is a promising concept for the intuitive teaching of robots. A common way to learn such skills is to learn a parametric model by maximizing the likelihood given the demonstrations. Yet, human demonstrations are often multi-modal, i.e., the same task is solved in multiple ways which is a major challenge for most imitation learning methods that are based on such a maximum likelihood (ML) objective. The ML objective forces the model to cover all data, it prevents specialization in the context space and can cause mode-averaging in the behavior space, leading to suboptimal or potentially catastrophic behavior. Here, we alleviate those issues by introducing a curriculum using a weight for each data point, allowing the model to specialize on data it can represent while incentivizing it to cover as much data as possible by an entropy bonus. We extend our algorithm to a Mixture of (linear) Experts (MoE) such that the single components can specialize on local context regions, while the MoE covers all data points. We evaluate our approach in complex simulated and real robot control tasks and show it learns from versatile human demonstrations and significantly outperforms current SOTA methods. A reference implementation can be found at https://github.com/intuitive-robots/ml-cu

Information Maximizing Curriculum: A Curriculum-Based Approach for Training Mixtures of Experts

Mixtures of Experts (MoE) are known for their ability to learn complex conditional distributions with multiple modes. However, despite their potential, these models are challenging to train and often tend to produce poor performance, explaining their limited popularity. Our hypothesis is that this under-performance is a result of the commonly utilized maximum likelihood (ML) optimization, which leads to mode averaging and a higher likelihood of getting stuck in local maxima. We propose a novel curriculum-based approach to learning mixture models in which each component of the MoE is able to select its own subset of the training data for learning. This approach allows for independent optimization of each component, resulting in a more modular architecture that enables the addition and deletion of components on the fly, leading to an optimization less susceptible to local optima. The curricula can ignore data-points from modes not represented by the MoE, reducing the mode-averaging problem. To achieve a good data coverage, we couple the optimization of the curricula with a joint entropy objective and optimize a lower bound of this objective. We evaluate our curriculum-based approach on a variety of multimodal behavior learning tasks and demonstrate its superiority over competing methods for learning MoE models and conditional generative models

Comparison Between Flat and Round Peaches, Genomic Evidences of Heterozygosity Events

Bud sports occur in many plant species, including fruit trees. Although they are correlated with genetic variance in somatic cells, the mechanisms responsible for bud sports are mostly unknown. In this study, a peach bud sport whose fruit shape was transformed to round from flat was identified by next generation sequencing (NGS), and we provide evidence that a long loss of heterozygosity (LOH) event may be responsible for this alteration in fruit shape. Moreover, compared to the reference genome, we identified 237,476 high quality single nucleotide polymorphisms (SNPs) in the wild-type and bud sport genomes. Using this SNP set, a long LOH event was identified at the distal end of scaffold Pp06 of the bud sport genome. Haplotypes from 155 additional peach accessions were phased, suggesting that the homozygous distal end of scaffold Pp06 of the bud sport was likely derived from only one haplotype of the wild-type flat peach. A genome-wide association study (GWAS) of 127 peach accessions was conducted to associate a SNP found at 26,924,482 bp of scaffold Pp06 to differences in fruit shape. All accessions with round-shaped fruit were found to have an A/A genotype, while those with A/T, or T/T genotypes had flat-shaped fruits. Finally, we also found that 236 peach accessions and 141 Prunus species with round-type fruit were found to have an A/A genotype at this SNP, while 22 flat peach accessions had an A/T genotype. Taken together, our results suggest that genes flanking this A/T polymorphism, and haplotyped carrying the T allele may determine flat fruit shape in this population. Furthermore, the LOH event resulting in the loss of the haplotype carrying the T allele may therefore be responsible for fruit shape alteration in wild-type flat peach

Association of ERCC gene polymorphism with osteosarcoma risk

Background: The relationship between ERCC gene polymorphism and osteosarcoma risk / overall survival of osteosarcoma is still conflicting, and this meta-analysis was performed to assess these associations. Material and methods: The association studies were identified from PubMed, and eligible reports were included and calculated using meta-analysis method. Results: Four studies were included for the association of ERCC gene polymorphism with osteosarcoma risk, and nine studies were recruited into this meta-analysis for the relationship between ERCC gene polymorphism and overall survival of osteosarcoma. The meta-analysis indicated that ERCC1 rs3212986 (8092 C>A) gene polymorphism, ERCC1 rs11615 (19007 T>C) gene polymorphism, ERCC2 rs1799793 (A>G) gene polymorphism, ERCC2 rs13181 (Lys751Gln) gene polymorphism were not associated with osteosarcoma risk. ERCC1 rs2298881 (C>A) gene polymorphism, ERCC1 rs3212986 (8092 C>A) gene polymorphism, ERCC1 rs11615 (19007 T>C) gene polymorphism, ERCC2 rs1799793 (Asp312Asn) gene polymorphism were not associated with overall survival of osteosarcoma. Interestingly, ERCC2 rs13181 A allele and GG genotype were associated with overall survival of osteosarcoma, but AA genotype not (A allele: OR = 0.78, 95% CI: 0.65-0.93, P = 0.007; GG genotype: OR = 1.32, 95% CI: 1.05-1.65, P = 0.02; AA genotype: OR = 0.69, 95% CI: 0.45-1.04, P = 0.08). Conclusion: ERCC2 rs13181 A allele and GG genotype were associated with overall survival of osteosarcoma

High-Throughput Sequencing of MicroRNAs in Adenovirus Type 3 Infected Human Laryngeal Epithelial Cells

Adenovirus infection can cause various illnesses depending on the infecting serotype, such as gastroenteritis, conjunctivitis, cystitis, and rash illness, but the infection mechanism is still unknown. MicroRNAs (miRNA) have been reported to play essential roles in cell proliferation, cell differentiation, and pathogenesis of human diseases including viral infections. We analyzed the miRNA expression profiles from adenovirus type 3 (AD3) infected Human laryngeal epithelial (Hep2) cells using a SOLiD deep sequencing. 492 precursor miRNAs were identified in the AD3 infected Hep2 cells, and 540 precursor miRNAs were identified in the control. A total of 44 miRNAs demonstrated high expression and 36 miRNAs showed lower expression in the AD3 infected cells than control. The biogenesis of miRNAs has been analyzed, and some of the SOLiD results were confirmed by Quantitative PCR analysis. The present studies may provide a useful clue for the biological function research into AD3 infection