Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization (ZoMBI)

Needle-in-a-Haystack problems exist across a wide range of applications including rare disease prediction, ecological resource management, fraud detection, and material property optimization. A Needle-in-a-Haystack problem arises when there is an extreme imbalance of optimum conditions relative to the size of the dataset. For example, only $0.82\%$ out of $146$k total materials in the open-access Materials Project database have a negative Poisson's ratio. However, current state-of-the-art optimization algorithms are not designed with the capabilities to find solutions to these challenging multidimensional Needle-in-a-Haystack problems, resulting in slow convergence to a global optimum or pigeonholing into a local minimum. In this paper, we present a Zooming Memory-Based Initialization algorithm, entitled ZoMBI. ZoMBI actively extracts knowledge from the previously best-performing evaluated experiments to iteratively zoom in the sampling search bounds towards the global optimum "needle" and then prunes the memory of low-performing historical experiments to accelerate compute times by reducing the algorithm time complexity from $O(n^3)$ to $O(\phi^3)$ for $\phi$ forward experiments per activation, which trends to a constant $O(1)$ over several activations. Additionally, ZoMBI implements two custom adaptive acquisition functions to further guide the sampling of new experiments toward the global optimum. We validate the algorithm's optimization performance on three real-world datasets exhibiting Needle-in-a-Haystack and further stress-test the algorithm's performance on an additional 174 analytical datasets. The ZoMBI algorithm demonstrates compute time speed-ups of 400x compared to traditional Bayesian optimization as well as efficiently discovering optima in under 100 experiments that are up to 3x more highly optimized than those discovered by similar methods MiP-EGO, TuRBO, and HEBO.Comment: Paper 16 pages; SI 6 page

Microbial traits determine soil C emission in response to fresh carbon inputs in forests across biomes

Soil priming is a microbial-driven process, which determines key soil–climate feedbacks in response to fresh carbon inputs. Despite its importance, the microbial traits behind this process are largely undetermined. Knowledge of the role of these traits is integral to advance our understanding of how soil microbes regulate carbon (C) emissions in forests, which support the largest soil carbon stocks globally. Using metagenomic sequencing and C-glucose, we provide unprecedented evidence that microbial traits explain a unique portion of the variation in soil priming across forest biomes from tropical to cold temperature regions. We show that microbial functional profiles associated with the degradation of labile C, especially rapid simple sugar metabolism, drive soil priming in different forests. Genes involved in the degradation of lignin and aromatic compounds were negatively associated with priming effects in temperate forests, whereas the highest level of soil priming was associated with β-glucosidase genes in tropical/subtropical forests. Moreover, we reconstructed, for the first time, 42 whole bacterial genomes associated with the soil priming effect and found that these organisms support important gene machinery involved in priming effect. Collectively, our work demonstrates the importance of microbial traits to explain soil priming across forest biomes and suggests that rapid carbon metabolism is responsible for priming effects in forests. This knowledge is important because it advances our understanding on the microbial mechanisms mediating soil–climate feedbacks at a continental scale.This work were financially supported by the National Natural Science Foundation of China (41907031), the Chinese Academy of Sciences “Light of West China” Program for Introduced Talent in the West, the National Natural Science Foundation of China (31570440, 31270484), the Key International Scientific and Technological Cooperation and Exchange Project of Shaanxi Province, China (2020KWZ-010), the 2021 First Funds for Central Government to Guide Local Science and Technology Development in Qinghai Province (2021ZY002), the i-LINK +2018 (LINKA20069) from CSIC, and a Ramón y Cajal grant from the Spanish Ministry of Science and Innovation (RYC2018-025483-I

Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks

X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine-learning-enabled approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD patterns. We overcome the scarce-data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data. As a test case, 115 thin-film metal halides spanning 3 dimensionalities and 7 space-groups are synthesized and classified. After testing various algorithms, we develop and implement an all convolutional neural network, with cross validated accuracies for dimensionality and space-group classification of 93% and 89%, respectively. We propose average class activation maps, computed from a global average pooling layer, to allow high model interpretability by human experimentalists, elucidating the root causes of misclassification. Finally, we systematically evaluate the maximum XRD pattern step size (data acquisition rate) before loss of predictive accuracy occurs, and determine it to be 0.16{\deg}, which enables an XRD pattern to be obtained and classified in 5.5 minutes or less.Comment: Accepted with minor revisions in npj Computational Materials, Presented in NIPS 2018 Workshop: Machine Learning for Molecules and Material

SUMOylation Represses Nanog Expression via Modulating Transcription Factors Oct4 and Sox2

Nanog is a pivotal transcription factor in embryonic stem (ES) cells and is essential for maintaining the pluripotency and self-renewal of ES cells. SUMOylation has been proved to regulate several stem cell markers' function, such as Oct4 and Sox2. Nanog is strictly regulated by Oct4/Sox2 heterodimer. However, the direct effects of SUMOylation on Nanog expression remain unclear. In this study, we reported that SUMOylation repressed Nanog expression. Depletion of Sumo1 or its conjugating enzyme Ubc9 increased the expression of Nanog, while high SUMOylation reduced its expression. Interestingly, we found that SUMOylation of Oct4 and Sox2 regulated Nanog in an opposing manner. SUMOylation of Oct4 enhanced Nanog expression, while SUMOylated Sox2 inhibited its expression. Moreover, SUMOylation of Oct4 by Pias2 or Sox2 by Pias3 impaired the interaction between Oct4 and Sox2. Taken together, these results indicate that SUMOylation has a negative effect on Nanog expression and provides new insights into the mechanism of SUMO modification involved in ES cells regulation

Accelerate materials development using Machine learning: from Photovoltaic to other functional materials

Ph.DDOCTOR OF PHILOSOPHY (FOE

Relationship between Soil Organic Carbon Stocks and Clay Content under Different Climatic Conditions in Central China

Understanding the association between soil organic carbon (SOC) and texture under different climatic conditions is important for assessing the effects of future climate changes on SOC stocks. In this study, we conducted a climatic gradient experiment covering three climate types (humid, sub-humid, and semi-arid) with a steep rainfall ranging from 345 to 910 mm, and specifically determined SOC dynamics, clay content, and vegetation and soil characteristics. The results showed that, from semi-arid to humid regions, SOC stocks, SOC, and clay content increased synchronously and were closely related in layers of depths of both 0–10 and 10–20 cm. In contrast, under similar climatic conditions, SOC dynamics were mainly affected by vegetation and soil characteristics, especially total nitrogen and total phosphorus dynamics, but not the soil clay content. Therefore, these results suggest that the relationship between SOC stocks and clay content depended on scale sizes. Specifically, on a larger scale with different climatic gradients, the climate may partly determine the changes in SOC and clay dynamics, whereas, at a smaller scale where climate type does not vary considerably, the changes in SOC stocks and clay content may be related to vegetation diversity and soil nutrient dynamics. These results may contribute to future model development and the projection of changes in soil carbon storage

A Novel Flux-weakening Control Method with Quadrature Voltage Constrain for Electrolytic Capacitorless PMSM Drives

In electrolytic capacitorless permanent magnet synchronous motor (PMSM) drives, the DC-link voltage will fluctuate in a wide range due to the use of slim film capacitor. When the flux-weakening current is lower than −ψf/Ld during the high speed operation, the flux-weakening control loop will transform to a positive feedback mode, which means the reduction of flux-weakening current will lead to the acceleration of the voltage saturation, thus the whole system will be unstable. In order to solve this issue, this paper proposes a novel flux-weakening method for electrolytic capacitorless motor drives to maintain a negative feedback characteristic of the control loop during high speed operation. Based on the analysis of the instability mechanism in flux-weakening region, a quadrature voltage constrain mechanism is constructed to stabilize the system. Meanwhile, the parameters of the controller are theoretically designed for easier industrial application. The proposed algorithm is implemented on a 1.5kW electrolytic capacitorless PMSM drive to verify the effectiveness of the flux-weakening performance