Distributed Graph Neural Network Training: A Survey

Graph neural networks (GNNs) are a type of deep learning models that are trained on graphs and have been successfully applied in various domains. Despite the effectiveness of GNNs, it is still challenging for GNNs to efficiently scale to large graphs. As a remedy, distributed computing becomes a promising solution of training large-scale GNNs, since it is able to provide abundant computing resources. However, the dependency of graph structure increases the difficulty of achieving high-efficiency distributed GNN training, which suffers from the massive communication and workload imbalance. In recent years, many efforts have been made on distributed GNN training, and an array of training algorithms and systems have been proposed. Yet, there is a lack of systematic review on the optimization techniques for the distributed execution of GNN training. In this survey, we analyze three major challenges in distributed GNN training that are massive feature communication, the loss of model accuracy and workload imbalance. Then we introduce a new taxonomy for the optimization techniques in distributed GNN training that address the above challenges. The new taxonomy classifies existing techniques into four categories that are GNN data partition, GNN batch generation, GNN execution model, and GNN communication protocol. We carefully discuss the techniques in each category. In the end, we summarize existing distributed GNN systems for multi-GPUs, GPU-clusters and CPU-clusters, respectively, and give a discussion about the future direction on distributed GNN training

Modulate Molecular Interaction between Hole Extraction Polymers and Lead Ions toward Hysteresis-Free and Efficient Perovskite Solar Cells

Herein three polymeric hole extraction materials (HEMs), poly(benzene‐dithiophene) (PB2T)‐O, PB2T‐S, and PB2T‐SO are presented for p–i–n perovskite solar cells (PVSCs). This study reveals that the perovskite device hysteresis and performance heavily rely on the perovskite grain boundary conditions. More specifically, they are predetermined through the molecular interaction between Lewis base atoms of HEMs and perovskites. It is revealed that only changing the side chain terminals (-OCH_3, -SCH_3, and –SOCH_3) of HEMs results in effective modulating PVSC performance and hysteresis, due to the effective tune of interaction strength between HEM and perovskite. With an in situ grown perovskite‐HEM bulk heterojunction structure, PB2T‐O with weak binding group (-OCH_3, −78.9 kcal mol^(−1) bonding energy) to lead ions allows delivering hysteresis‐free and efficient devices, which is sharp contrast to the strong binding PB2T‐SO (−119.3 kcal mol^(−1) bonding energy). Overall, this work provides new insights on PVSC hysteresis and the related curing methods via multifunctional HEM design in PVSCs

Achieving high-performance thick-film perovskite solar cells with electron transporting Bingel fullerenes

Two Bingel fullerenes, PCP and MCM, as electron transporting materials (ETMs) have been developed for achieving thick-film perovskite solar cells (PVSCs) with efficiencies beyond 19% with a planar absorber layer over 1 micrometer. Almost no PVSCs have exhibited PCEs above 18% with a 1 micrometer planar layer before, owing to the excess perovskite defects deteriorating charge extraction and the performance of thick-film based devices. Benefiting from the nearly identical optoelectronic properties of two ETMs stemming from tailored chemical structures, the studies on them allow us to unveil the fact that subtle molecular interaction (anion–π and Lewis acid–base) between ETMs and perovskites strongly affects the charge extraction at the heterointerface, which in turn influences the device hysteresis and performance. Particularly, weak Lewis base–acid O–Pb^(2+) interaction between MCM and the perovskite helps passivate the trap-states at the interface, resulting in a smooth electron extraction and reduced device hysteresis (the average hysteresis index (HI) of 0.03 ± 0.01). However, the strong N–Pb^(2+) coordination induces misalignment of the energy levels at the perovskite/PCP heterojunction, causing electron accumulation at the junction, and hence the large HI (0.17 ± 0.05) in devices. This work provides new insights into the charge extraction at the perovskite/organic interface and the possible molecular interaction from organics to cure perovskite defects

Green growth, economic development, and carbon dioxide emissions: an evaluation based on cointegration and vector error correction models

Economic development is mainly dependent on fossil fuels. The massive use of fossil fuels has led to changes in the climate environment, in which the deterioration of air quality has affected people’s daily lives. This paper introduces the green growth level as a control variable to explore the connection between carbon dioxide emissions and the level of economic growth. It uses the EKC algorithm and VEC model to analyze Nanjing city’s data from 1993 to 2018. Given the data availability, the ARIMA algorithm was used to project carbon emissions for 2019–2025. It is found that the EKC curve of Nanjing City shows an N-shape, and the growth of economic level will cause the enhancement of carbon dioxide emissions. Carbon emissions will reach 7,592,140 tons in 2025. At present, we are in an essential stage of transition from N-shape to inverted U-shape, and this paper makes several recommendations based on the findings

The Oncogenic Roles of Nuclear Receptor Coactivator 1 in Human Esophageal Carcinoma

Nuclear receptor coactivator 1 (NCOA1) plays crucial roles in the regulation of gene expression mediated by a wide spectrum of steroid receptors such as androgen receptor (AR), estrogen receptor α (ER α), and estrogen receptor β (ER β). Therefore, dysregulations of NCOA1 have been found in a variety of cancer types. However, the clinical relevance and the functional roles of NCOA1 in human esophageal squamous cell carcinoma (ESCC) are less known. We found in this study that elevated levels of NCOA1 protein and/or mRNA as well as amplification of the NCOA1 gene occur in human ESCC. Elevated levels of NCOA1 due to these dysregulations were not only associated with more aggressive clinic-pathologic parameters but also poorer survival. Results from multiple cohorts of ESCC patients strongly suggest that the levels of NCOA1 could serve as an independent predictor of overall survival. In addition, silencing NCOA1 in ESCC cells remarkably decreased proliferation, migration, and invasion. These findings not only indicate that NCOA1 plays important roles in human ESCC but the levels of NCOA1 also could serve as a potential prognostic biomarker of ESCC and targeting NCOA1 could be an efficacious strategy in ESCC treatment

Molecular Engineered Hole-Extraction Materials to Enable Dopant-Free, Efficient p-i-n Perovskite Solar Cells

Two hole-extraction materials (HEMs), TPP-OMeTAD and TPP-SMeTAD, have been developed to facilitate the fabrication of efficient p-i-n perovskite solar cells (PVSCs). By replacing the oxygen atom on HEM with sulfur (from TPP-OMeTAD to TPP-SMeTAD), it effectively lowers the highest occupied molecular orbital of the molecule and provides stronger Pb-S interaction with perovskites, leading to efficient charge extraction and surface traps passivation. The TPP-SMeTAD-based PVSCs exhibit both improved photovoltaic performance and reduced hysteresis in p-i-n PVSCs over those based on TPP-OMeTAD. This work not only provides new insights on creating perovskite-HEM heterojunction but also helps in designing new HEM to enable efficient organic–inorganic hybrid PVSCs

Modulate Molecular Interaction between Hole Extraction Polymers and Lead Ions toward Hysteresis-Free and Efficient Perovskite Solar Cells

Herein three polymeric hole extraction materials (HEMs), poly(benzene‐dithiophene) (PB2T)‐O, PB2T‐S, and PB2T‐SO are presented for p–i–n perovskite solar cells (PVSCs). This study reveals that the perovskite device hysteresis and performance heavily rely on the perovskite grain boundary conditions. More specifically, they are predetermined through the molecular interaction between Lewis base atoms of HEMs and perovskites. It is revealed that only changing the side chain terminals (-OCH_3, -SCH_3, and –SOCH_3) of HEMs results in effective modulating PVSC performance and hysteresis, due to the effective tune of interaction strength between HEM and perovskite. With an in situ grown perovskite‐HEM bulk heterojunction structure, PB2T‐O with weak binding group (-OCH_3, −78.9 kcal mol^(−1) bonding energy) to lead ions allows delivering hysteresis‐free and efficient devices, which is sharp contrast to the strong binding PB2T‐SO (−119.3 kcal mol^(−1) bonding energy). Overall, this work provides new insights on PVSC hysteresis and the related curing methods via multifunctional HEM design in PVSCs

A gene expression signature distinguishes innate response and resistance to proteasome inhibitors in multiple myeloma

Extensive interindividual variation in response to chemotherapy is a major stumbling block in achieving desirable efficacy in the treatment of cancers, including multiple myeloma (MM). In this study, our goal was to develop a gene expression signature that predicts response specific to proteasome inhibitor (PI) treatment in MM. Using a well-characterized panel of human myeloma cell lines (HMCLs) representing the biological and genetic heterogeneity of MM, we created an in vitro chemosensitivity profile in response to treatment with the four PIs bortezomib, carfilzomib, ixazomib and oprozomib as single agents. Gene expression profiling was performed using next-generation high-throughput RNA-sequencing. Applying machine learning-based computational approaches including the supervised ensemble learning methods Random forest and Random survival forest, we identified a 42-gene expression signature that could not only distinguish good and poor PI response in the HMCL panel, but could also be successfully applied to four different clinical data sets on MM patients undergoing PI-based chemotherapy to distinguish between extraordinary (good and poor) outcomes. Our results demonstrate the use of in vitro modeling and machine learning-based approaches to establish predictive biomarkers of response and resistance to drugs that may serve to better direct myeloma patient treatment options