Center of mass detection via an active pixel sensor

An imaging system for identifying the location of the center of mass (COM) in an image. In one aspect, an imaging system includes a plurality of photosensitive elements arranged in a matrix. A center of mass circuit coupled to the photosensitive elements includes a resistive network and a normalization circuit including at least one bipolar transistor. The center of mass circuit identifies a center of mass location in the matrix and includes: a row circuit, where the row circuit identifies a center of mass row value in each row of the matrix and identifies a row intensity for each row; a horizontal circuit, where the horizontal circuit identifies a center of mass horizontal value; and a vertical circuit, where the vertical circuit identifies a center of mass vertical value. The horizontal and vertical center of mass values indicate the coordinates of the center of mass location for the image

Center of mass detection via an active pixel sensor

An imaging system for identifying the location of the center of mass (COM) in an image. In one aspect, an imaging system includes a plurality of photosensitive elements arranged in a matrix. A center of mass circuit coupled to the photosensitive elements includes a resistive network and a normalization circuit including at least one bipolar transistor. The center of mass circuit identifies a center of mass location in the matrix and includes: a row circuit, where the row circuit identifies a center of mass row value in each row of the matrix and identifies a row intensity for each row; a horizontal circuit, where the horizontal circuit identifies a center of mass horizontal value; and a vertical circuit, where the vertical circuit identifies a center of mass vertical value. The horizontal and vertical center of mass values indicate the coordinates of the center of mass location for the image

Increasing the Power to Detect Causal Associations by Combining Genotypic and Expression Data in Segregating Populations

To dissect common human diseases such as obesity and diabetes, a systematic approach is needed to study how genes interact with one another, and with genetic and environmental factors, to determine clinical end points or disease phenotypes. Bayesian networks provide a convenient framework for extracting relationships from noisy data and are frequently applied to large-scale data to derive causal relationships among variables of interest. Given the complexity of molecular networks underlying common human disease traits, and the fact that biological networks can change depending on environmental conditions and genetic factors, large datasets, generally involving multiple perturbations (experiments), are required to reconstruct and reliably extract information from these networks. With limited resources, the balance of coverage of multiple perturbations and multiple subjects in a single perturbation needs to be considered in the experimental design. Increasing the number of experiments, or the number of subjects in an experiment, is an expensive and time-consuming way to improve network reconstruction. Integrating multiple types of data from existing subjects might be more efficient. For example, it has recently been demonstrated that combining genotypic and gene expression data in a segregating population leads to improved network reconstruction, which in turn may lead to better predictions of the effects of experimental perturbations on any given gene. Here we simulate data based on networks reconstructed from biological data collected in a segregating mouse population and quantify the improvement in network reconstruction achieved using genotypic and gene expression data, compared with reconstruction using gene expression data alone. We demonstrate that networks reconstructed using the combined genotypic and gene expression data achieve a level of reconstruction accuracy that exceeds networks reconstructed from expression data alone, and that fewer subjects may be required to achieve this superior reconstruction accuracy. We conclude that this integrative genomics approach to reconstructing networks not only leads to more predictive network models, but also may save time and money by decreasing the amount of data that must be generated under any given condition of interest to construct predictive network models

Large Scale Benchmark of Materials Design Methods

Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with both perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC) and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data-points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboar