A mapping strategy for MIMD computers

In this paper, a heuristic mapping approach which maps parallel programs, described by precedence graphs, to MIMD architectures, described by system graphs, is presented. The complete execution time of a parallel program is used as a measure, and the concept of critical edges is utilized as the heuristic to guide the search for a better initial assignment and subsequent refinement. An important feature is the use of a termination condition of the refinement process. This is based on deriving a lower bound on the total execution time of the mapped program. When this has been reached, no further refinement steps are necessary. The algorithms have been implemented and applied to the mapping of random problem graphs to various system topologies, including hypercubes, meshes, and random graphs. The results show reductions in execution times of the mapped programs of up to 77 percent over random mapping

Characterizing the Function of CSLD Proteins During Plant Cell Wall Deposition in Arabidopsis

As one of the most significant features of plant cells, the cell wall not only defines plant cell shape but also provides strength and rigidity to the plant. During plant development, changes in cell shape are primarily driven by cell expansion, which is controlled by cell wall deposition and modification. The two major mechanisms that control these changes are called diffuse growth and tip growth. During diffuse expansion, cell wall materials are synthesized and integrated in a polarized fashion along the entire expanding face of the cells. In contrast, during tip growth new cell wall deposition is restricted to a limited plasma membrane domain, leading to the highly polarized cell expansion associated with this directed cell wall construction. As the major load-bearing component in plant cell walls, cellulose is also the most abundant biopolymer on earth. Unlike other cell wall polysaccharides, cellulose is synthesized in the plasma membranes by large integral membrane protein complexes called cellulose synthase complexes (CSCs). The catalytic subunits of the CSCs are encoded by members of the Cellulose Synthase (CESA) family. Previous research showed that CESA1, CESA3, and CESA6 are required for the formation of active CSCs involved in the synthesis of cellulose in the primary cell wall of cells undergoing diffuse growth in Arabidopsis. Interestingly, our laboratory previously demonstrated that CSCs containing CESA3 and CESA6 did not appear to be required for new cellulose synthesis at the apical plasma membranes of root hair cells undergoing tip growth. Instead, members of a related family of Cellulose Synthase-Like D (CSLD) proteins showed tip-specific localization in these membranes and provided cell wall synthase activity required for maintenance of structural integrity of the cell wall in these tip-growing root hairs. However, while these CSLD cell wall synthases are essential, the nature of the polysaccharides generated by CSLD proteins has remained elusive. Here, I use genetic and biochemical approaches to characterize the catalytic activity of one member of the CSLD family, CSLD3. Genetic complementation of a cesa6 mutant with a chimeric CESA6 protein containing a CSLD3 catalytic domain demonstrated that the CSLD catalytic domains can successfully generate β-1,4-glucan polymers for cellulose synthesis. Time-lapse fluorescence microscopy demonstrated that these CESA6-CSLD3 chimeric proteins assembled into CSC complexes with similar mobility as CESA6-labeled complexes in hypocotyl cells. Proteoliposomes containing purified, detergent-solubilized CSLD3 and CESA6 proteins could specifically utilize UDP-glucose as an enzymatic substrate and synthesize products that are only sensitive to endo-β-1,4-glucanase. Taken together, these data strongly support the conclusion that CSLD3 represents a UDP-glucose-dependent β-1,4-glucan synthase. However, whether CSLD proteins require the formation of higher-order complexes to perform β-1,4 glucan synthase activities remained unclear. Here, I used genetic methods to demonstrate that CSLD2 and CSLD3 proteins are functionally interchangeable with each other during root hair elongation and cell plate formation. CSLD5 could partially rescue the root hair elongation defects in csld3 mutants. However, it plays a unique and essential function during cell plate formation. Proteoliposomes containing CSLD2 and CSLD5 displayed conserved β-1,4 glucan synthases activities similar to those described for CSLD3. Taken together, these results indicate that while all three vegetatively expressed CSLD proteins possess conserved β-1,4 glucan synthases activities, CSLD5 has a more complicated and specialized role during cell plate formation. To sum up, my dissertation research further supports that CSLD proteins represent a distinct family of cellulose synthase in Arabidopsis.PHDMolecular, Cellular, and Developmental BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162885/1/jiyuany_1.pd

Flexible and Creative Chinese Poetry Generation Using Neural Memory

It has been shown that Chinese poems can be successfully generated by sequence-to-sequence neural models, particularly with the attention mechanism. A potential problem of this approach, however, is that neural models can only learn abstract rules, while poem generation is a highly creative process that involves not only rules but also innovations for which pure statistical models are not appropriate in principle. This work proposes a memory-augmented neural model for Chinese poem generation, where the neural model and the augmented memory work together to balance the requirements of linguistic accordance and aesthetic innovation, leading to innovative generations that are still rule-compliant. In addition, it is found that the memory mechanism provides interesting flexibility that can be used to generate poems with different styles

Low-coercive-field ferroelectric hafnia with mobile domain walls

The high coercive field ($\mathcal{E}_c$) of hafnia-based ferroelectrics presents a major obstacle to their applications. The ferroelectric switching mechanisms in hafnia that dictate $\mathcal{E}_c$, especially those related to nucleation-and-growth at the domain wall (DW), have remained elusive. Through deep-learning-assisted multiscale simulations, we determine the finite-temperature thermodynamics and switching mechanisms for diverse types of 180$^\circ$ DWs, revealing a complex, stress-sensitive mobility landscape. The propagation velocities for mobile DW types under various thermal conditions can be characterized with a single creep equation, featuring a creep exponent of 2. This unconventional critical exponent results from the nucleation of a half-unit-cell-thin, elliptically-shaped critical nucleus. Our multiscale approach not only reproduces the experimental thickness ($d$) scaling, $\mathcal{E}_c\propto d^{-\frac{2}{3}}$, but also predicts that $\mathcal{E}_c$ of HfO$_2$ can be engineered to $\approx$0.1 MV/cm, even lower than perovskite ferroelectrics. The theoretical lower bound of $\mathcal{E}_c$ afforded by ferroelectric hafnia offers opportunities to realize power-efficient, high-fidelity ferroelectric nanoelectronics.Comment: 19 pages, 4 figure

AST: Effective Dataset Distillation through Alignment with Smooth and High-Quality Expert Trajectories

Training large AI models typically requires large-scale datasets in the machine learning process, making training and parameter-tuning process both time-consuming and costly. Some researchers address this problem by carefully synthesizing a very small number of highly representative and informative samples from real-world datasets. This approach, known as Dataset Distillation (DD), proposes a perspective for data-efficient learning. Despite recent progress in this field, the performance of existing methods still cannot meet expectations, and distilled datasets cannot effectively replace original datasets. In this paper, unlike previous methods that focus solely on improving the effectiveness of student distillation, we recognize and leverage the important mutual influence between expert and student models. We observed that the smoothness of expert trajectories has a significant impact on subsequent student parameter alignment. Based on this, we propose an effective DD framework named AST, standing for Alignment with Smooth and high-quality expert Trajectories. We devise the integration of clipping loss and gradient penalty to regulate the rate of parameter changes in expert trajectory generation. To further refine the student parameter alignment with expert trajectory, we put forward representative initialization for the synthetic dataset and balanced inner-loop loss in response to the sensitivity exhibited towards randomly initialized variables during distillation. We also propose two enhancement strategies, namely intermediate matching loss and weight perturbation, to mitigate the potential occurrence of cumulative errors. We conduct extensive experiments on datasets of different scales, sizes, and resolutions. The results demonstrate that the proposed method significantly outperforms prior methods

Dry and warm conditions in Australia exacerbated by aerosol reduction in China

A substantial decline in anthropogenic aerosols in China has been observed since the initiation of clean air actions in 2013. Concurrently, Australia experienced anomalously dry and warm conditions in 2010s. This study reveals a linkage between aerosol reductions in China and the drying and warming trends in Australia during 2013–2019 based on aerosol-climate model simulations and multi-source observations. Aerosol decline in China triggered alterations in temperature and pressure gradients between the two hemispheres, leading to intensified outflow from Asia towards the South Indian Ocean, strengthening the Southern Indian Subtropical High and its related Southern Trade Winds. Consequently, this atmospheric pattern resulted in a moisture divergence over Australia. The reduction in surface moisture further resulted in more surface energy being converted into sensible heat instead of evaporating as latent heat, warming the near-surface air. Aerosol reductions in China are found to contribute to 19 % of the observed decreases in precipitation and relative humidity and 8 % of the increase in surface air temperature in Australia during 2013–2019. The intensified dry and warm climate conditions during 2013–2019 further explain 12 %–19 % of the increase in wildfire risks during fire seasons in Australia. Our study illuminates the impact of distant aerosols on precipitation and temperature variations in Australia, offering valuable insights for drought and wildfire risk mitigation in Australia

Proteomic analysis of effluents from perfused human heart for transplantation: identification of potential biomarkers for ischemic heart damage

<p>Abstract</p> <p>Background</p> <p>Biomarkers released from the heart at early stage of ischemia are very important to diagnosis of ischemic heart disease and salvage myocytes from death. Known specific markers for blood tests including CK-MB, cardiac troponin T (cTnT) and cardiac troponin I (cTnI) are released after the onset of significant necrosis instead of early ischemia. Thus, they are not good biomarkers to diagnose myocardial injury before necrosis happens. Therefore, in this study, we performed proteomic analysis on effluents from perfused human hearts of donors at different ischemic time.</p> <p>Results</p> <p>After global ischemia for 0 min, 30 min and 60 min at 4°C, effluents from five perfused hearts were analyzed respectively, by High performance liquid chromatography-Chip-Mass spectrometry (HPLC-Chip-MS) system. Total 196 highly reliable proteins were identified. 107 proteins were identified at the beginning of ischemia, 174 and 175 proteins at ischemic 30 min and ischemic 60 min, respectively. With the exception of cardiac troponin I and T, all known biomarkers for myocardial ischemia were detected in our study. However, there were four glycolytic enzymes and two targets of matrix metalloproteinase released significantly from the heart when ischemic time was increasing. These proteins were L-lactate dehydrogenase B(LDHB), glyceraldehyde-3-phosphate dehydrogenase, glucose-6-phosphate isomerase (GPI), phosphoglycerate mutase 2 (PGAM2), gelsolin and isoform 8 of titin. PGAM2, LDHB and titin were measured with enzyme-linked immunosorbent assays kits. The mean concentrations of LDHB and PGAM2 in samples showed an increasing trend when ischemic time was extending. In addition, 33% identified proteins are involved in metabolism. Protein to protein interaction network analysis showed glycolytic enzymes, such as isoform alpha-enolase of alpha-enolase, isoform 1 of triosephosphate isomerase and glyceraldehyde-3-phosphate dehydrogenase, had more connections than other proteins in myocardial metabolism during ischemia.</p> <p>Conclusion</p> <p>It is the first time to use effluents of human perfused heart to study the proteins released during myocardial ischemia by HPLC-Chip-MS system. There might be many potential biomarkers for mild ischemic injury in myocardium, especially isoform 8 of titin and M-type of PGAM2 that are more specific in the cardiac tissue than in the others. Furthermore, glycolysis is one of the important conversions during early ischemia in myocardium. This finding may provide new insight into pathology and biology of myocardial ischemia, and potential diagnostic and therapeutic biomarkers.</p

Can ChatGPT reduce human financial analysts’ optimistic biases?

This paper examines the potential of ChatGPT, a large language model, as a financial advisor for listed firm performance forecasts. We focus on the constituent stocks of the China Securities Index 300 and compare ChatGPT’s forecasts for major financial performance measures with human analysts’ forecasts and the realised values. Our findings suggest that ChatGPT can correct the optimistic biases of human analysts. This study contributes to the literature by exploring the potential of ChatGPT as a financial advisor and demonstrating its role in reducing human biases in financial decision-making

FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation Learning

Cross-domain Sequential Recommendation (CSR) which leverages user sequence data from multiple domains has received extensive attention in recent years. However, the existing CSR methods require sharing origin user data across domains, which violates the General Data Protection Regulation (GDPR). Thus, it is necessary to combine federated learning (FL) and CSR to fully utilize knowledge from different domains while preserving data privacy. Nonetheless, the sequence feature heterogeneity across different domains significantly impacts the overall performance of FL. In this paper, we propose FedDCSR, a novel federated cross-domain sequential recommendation framework via disentangled representation learning. Specifically, to address the sequence feature heterogeneity across domains, we introduce an approach called inter-intra domain sequence representation disentanglement (SRD) to disentangle the user sequence features into domain-shared and domain-exclusive features. In addition, we design an intra domain contrastive infomax (CIM) strategy to learn richer domain-exclusive features of users by performing data augmentation on user sequences. Extensive experiments on three real-world scenarios demonstrate that FedDCSR achieves significant improvements over existing baselines

Modular development of deep potential for complex solid solutions

The multicomponent oxide solid solution is a versatile platform to tune the delicate balance between competing spin, charge, orbital, and lattice degrees of freedom for materials design and discovery. The development of compositionally complex oxides with superior functional properties has been largely empirical and serendipitous, in part due to the exceedingly complex chemistry and structure of solid solutions that span a range of length scales. The classical molecular dynamics (MD), as a powerful statistical method to investigate materials properties over large spatial and temporal scales, often plays a secondary role in computer-aided materials discovery because of the limited availability and accuracy of classical force fields. Here, we introduce the strategy of ``modular developing deep potential" (ModDP) that enables a systematic development and improvement of deep neural network-based model potential, termed as deep potential, for complex solid solutions with minimum human intervention. The converged training database associated with an end-member material is treated as an independent module and is reused to train the deep potential of solid solutions via a concurrent learning procedure. We apply ModDP to obtain classical force fields of two technologically important solid solutions, Pb$_x$Sr$_{1-x}$TiO$_3$ and Hf$_x$Zr$_{1-x}$O$_2$. For both materials systems, a single model potential is capable of predicting various properties of solid solutions including temperature-driven and composition-driven phase transitions over a wide range of compositions. In particular, the deep potential of Pb$_x$Sr$_{1-x}$TiO$_3$ reproduces a few known topological textures such as polar vortex lattice and electric dipole waves in PbTiO$_3$/SrTiO$_3$ superlattices, paving the way for MD investigations on the dynamics of topological structures in response to external stimuli.Comment: 32 pages, 9 figure