Phase diagram of In–Co–Sb system and thermoelectric properties of In-containing skutterudites

In-containing skutterudites have long attracted much attention and debate partly due to the solubility limit issue of indium in CoSb_3. The isothermal section of the equilibrium phase diagram for the In–Co–Sb system at 873 K is proposed using knowledge of the related binary phase diagrams and experimental data, which explains the debated indium solubility that depends on Sb content. In this paper, a series of In-containing skutterudite samples (In_xCo_4Sb_(12−x/3) with x varying from 0.075 to 0.6 and In_(0.3)Co_(4−y)Sb_(11.9+y) with y changing from −0.20 to 0.20) are synthesized and characterized. X-ray analysis and scanning electron microscopy images indicate that, up to x = 0.27, single-phase skutterudites are obtained with lattice constant increasing with In fraction x. A fixed-composition skutterudite In_(0.27±0.01)Co_4Sb_(11.9) was determined for the Co-rich side of In–CoSb_3 which is in coexistence with liquid InSb and CoSb_2. Indium, like Ga, is expected, from DFT calculations, to form compound defects in In-containing skutterudites. However, relatively higher carrier concentrations of In-containing skutterudites compared to Ga-containing skutterudites indicate the existence of not fully charge-compensated compound defects, which can also be explained by DFT calculations. The net n-type carrier concentration that naturally forms from the complex defects is close to the optimum for thermoelectric performance, enabling a maximum zT of 1.2 for the fixed skutterudite composition In_(0.27)Co_4Sb_(11.9) at 750 K

Exercise-Induced Changes in Glucose Metabolism Promote Physiological Cardiac Growth

Background: Exercise promotes metabolic remodeling in the heart, which is associated with physiological cardiac growth; however, it is not known whether or how physical activity–induced changes in cardiac metabolism cause myocardial remodeling. In this study, we tested whether exercise-mediated changes in cardiomyocyte glucose metabolism are important for physiological cardiac growth. Methods: We used radiometric, immunologic, metabolomic, and biochemical assays to measure changes in myocardial glucose metabolism in mice subjected to acute and chronic treadmill exercise. To assess the relevance of changes in glycolytic activity, we determined how cardiac-specific expression of mutant forms of 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase affect cardiac structure, function, metabolism, and gene programs relevant to cardiac remodeling. Metabolomic and transcriptomic screenings were used to identify metabolic pathways and gene sets regulated by glycolytic activity in the heart. Results: Exercise acutely decreased glucose utilization via glycolysis by modulating circulating substrates and reducing phosphofructokinase activity; however, in the recovered state following exercise adaptation, there was an increase in myocardial phosphofructokinase activity and glycolysis. In mice, cardiac-specific expression of a kinase-deficient 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase transgene (GlycoLo mice) lowered glycolytic rate and regulated the expression of genes known to promote cardiac growth. Hearts of GlycoLo mice had larger myocytes, enhanced cardiac function, and higher capillary-to-myocyte ratios. Expression of phosphatase-deficient 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase in the heart (GlycoHi mice) increased glucose utilization and promoted a more pathological form of hypertrophy devoid of transcriptional activation of the physiological cardiac growth program. Modulation of phosphofructokinase activity was sufficient to regulate the glucose–fatty acid cycle in the heart; however, metabolic inflexibility caused by invariantly low or high phosphofructokinase activity caused modest mitochondrial damage. Transcriptomic analyses showed that glycolysis regulates the expression of key genes involved in cardiac metabolism and remodeling. Conclusions: Exercise-induced decreases in glycolytic activity stimulate physiological cardiac remodeling, and metabolic flexibility is important for maintaining mitochondrial health in the heart

Hyperparameter optimization in black-box image processing using differentiable proxies

Nearly every commodity imaging system we directly interact with, or indirectly rely on, leverages power efficient, application-adjustable black-box hardware image signal processing (ISPs) units, running either in dedicated hardware blocks, or as proprietary software modules on programmable hardware. The configuration parameters of these black-box ISPs often have complex interactions with the output image, and must be adjusted prior to deployment according to application-specific quality and performance metrics. Today, this search is commonly performed manually by "golden eye" experts or algorithm developers leveraging domain expertise. We present a fully automatic system to optimize the parameters of black-box hardware and software image processing pipelines according to any arbitrary (i.e., application-specific) metric. We leverage a differentiable mapping between the configuration space and evaluation metrics, parameterized by a convolutional neural network that we train in an end-to-end fashion with imaging hardware in-the-loop. Unlike prior art, our differentiable proxies allow for high-dimension parameter search with stochastic first-order optimizers, without explicitly modeling any lower-level image processing transformations. As such, we can efficiently optimize black-box image processing pipelines for a variety of imaging applications, reducing application-specific configuration times from months to hours. Our optimization method is fully automatic, even with black-box hardware in the loop. We validate our method on experimental data for real-time display applications, object detection, and extreme low-light imaging. The proposed approach outperforms manual search qualitatively and quantitatively for all domain-specific applications tested. When applied to traditional denoisers, we demonstrate that---just by changing hyperparameters---traditional algorithms can outperform recent deep learning methods by a substantial margin on recent benchmarks