Inhibitory interneurons in the anterior cingulate and medial prefrontal cortex in prenatally malnourished rats

Prenatal protein malnutrition continues to be a significant problem in the world today. Exposure to prenatal protein malnutrition increases the risk of a number of neuropsychiatric disorders that are associated with inhibitory interneurons, including depression, schizophrenia and attention deficit hyperactivity disorder. Previous studies have found that neurons in anterior cingulate and medial prefrontal regions respond excessively to restraint stress in prenatally malnourished rats. In this study, we investigate if prenatal protein malnutrition affects inhibitory the subpopulation of interneurons in the prefrontal cortex in relationship to the higher initial stress response. This was done using double-labeling immunohistochemistry with c-Fos to mark activated neurons and parvalbumin to mark inhibitory interneurons. Numbers of single and double-labeled neurons were quantified with unbiased stereology. Statistical analysis demonstrated that there was no effect of prenatal malnutrition on the total number of neurons or on the number of parvalbumin neurons. However, prenatal malnutrition was associated with a significant increase in the number of inhibitory parvalbumin positive neurons activated by restraint stress. This suggests that prenatal malnutrition altered the excitability of these inhibitory interneurons either directly or by altering their connectivity

Green Biased Technical Change in Terms of Industrial Water Resources in China’s Yangtze River Economic Belt

As a significant ecological corridor from west to east across China, the Yangtze River Economical Belt (YREB) is in great need of green development and transformation. Rather than only focusing on the overall growth of green productivity, it is important to identify whether the technical change is biased towards economic performance or green performance in promoting green productivity. By employing the biased technical change theory and Malmquist index decomposition method, we analyze the green biased technical change in terms of industrial water resources in YREB at the output side and the input side respectively. We find that the green biased technical change varies during 2006–2015 at both the input side and output side in YREB. At the input side, water-saving biased technical change is generally dominant compared to water-using biased technical change during 2006–2015, presenting the substitution effects of non-water production factors. At the output side, the economy-growth biased technical change is the main force to promote green productivity, whereas the role of water-conservation biased technical change is insufficient. The green performance at the output side needs to be strengthened compared to the economic performance in YREB. A series of water-related environmental policies introduced in China since 2008 have promoted the green biased technical change both at the input side and the output side in YREB, but the policy effects at the output side is still inadequate compared to that at the input side. The technological innovation in sewage treatment and control need to catch up with the economic growth in YREB. Our research gives insights to enable a deeper understanding of the green biased technical change in YREB and will benefit more focused policy-making of green innovation

Provable Preimage Under-Approximation for Neural Networks (Full Version)

Neural network verification mainly focuses on local robustness properties, which can be checked by bounding the image (set of outputs) of a given input set. However, often it is important to know whether a given property holds globally for the input domain, and if not then for what proportion of the input the property is true. To analyze such properties requires computing preimage abstractions of neural networks. In this work, we propose an efficient anytime algorithm for generating symbolic under-approximations of the preimage of any polyhedron output set for neural networks. Our algorithm combines a novel technique for cheaply computing polytope preimage under-approximations using linear relaxation, with a carefully-designed refinement procedure that iteratively partitions the input region into subregions using input and ReLU splitting in order to improve the approximation. Empirically, we validate the efficacy of our method across a range of domains, including a high-dimensional MNIST classification task beyond the reach of existing preimage computation methods. Finally, as use cases, we showcase the application to quantitative verification and robustness analysis. We present a sound and complete algorithm for the former, which exploits our disjoint union of polytopes representation to provide formal guarantees. For the latter, we find that our method can provide useful quantitative information even when standard verifiers cannot verify a robustness property

Provable preimage under-approximation for neural networks

Neural network verification mainly focuses on local robustness properties, which can be checked by bounding the image (set of outputs) of a given input set. However, often it is important to know whether a given property holds globally for the input domain, and if not then for what proportion of the input the property is true. To analyze such properties requires computing preimage abstractions of neural networks. In this work, we propose an efficient anytime algorithm for generating symbolic under-approximations of the preimage of any polyhedron output set for neural networks. Our algorithm combines a novel technique for cheaply computing polytope preimage under-approximations using linear relaxation, with a carefully-designed refinement procedure that iteratively partitions the input region into subregions using input and ReLU splitting in order to improve the approximation. Empirically, we validate the efficacy of our method across a range of domains, including a high-dimensional MNIST classification task beyond the reach of existing preimage computation methods. Finally, as use cases, we showcase the application to quantitative verification and robustness analysis. We present a sound and complete algorithm for the former, which exploits our disjoint union of polytopes representation to provide formal guarantees. For the latter, we find that our method can provide useful quantitative information even when standard verifiers cannot verify a robustness property

積み木遊びデータ解析による子供の行動と心理状態推定

Tohoku University北村喜文課

MapPrior: Bird's-Eye View Map Layout Estimation with Generative Models

Despite tremendous advancements in bird's-eye view (BEV) perception, existing models fall short in generating realistic and coherent semantic map layouts, and they fail to account for uncertainties arising from partial sensor information (such as occlusion or limited coverage). In this work, we introduce MapPrior, a novel BEV perception framework that combines a traditional discriminative BEV perception model with a learned generative model for semantic map layouts. Our MapPrior delivers predictions with better accuracy, realism, and uncertainty awareness. We evaluate our model on the large-scale nuScenes benchmark. At the time of submission, MapPrior outperforms the strongest competing method, with significantly improved MMD and ECE scores in camera- and LiDAR-based BEV perception

Parametric analysis of CO2 hydrogenation via fischer-tropsch synthesis: A review based on machine learning for quantitative assessment

This review focuses on the parametric impacts upon conversion and selectivity during CO2 hydrogenation via Fischer-Tropsch (FT) synthesis using iron-based catalyst to provide quantitative evaluation. Using all collected data from reported literatures as training dataset via artificial neural networks (ANNs) in TensorFlow, three categorized parameters (namely: operational, catalyst informatic and mass transfer) were deployed to assess their impacts upon conversions (CO2) and selectivity. The lump kinetic power expressions among literature reports were compared, and the best fit model is the one that was proposed by this work without arbitrarily assuming power values of individual partial pressure (CO and H2). More than five sets of binary parameters were systematically investigated to find out corresponding evolving patterns in conversion and selectivity. Aided by machine learning, tailoring product distributions based on specific selectivity or conversion for optimization purpose is practically achievable by deploying the predictions generated from ANNs in this work