Theoretical description of time-resolved pump/probe photoemission in TaS\_2: a single-band DFT+DMFT(NRG) study within the quasiequilibrium approximation

In this work, we theoretically examine recent pump/probe photoemission experiments on the strongly correlated charge-density-wave insulator TaS\_2. We describe the general nonequilibrium many-body formulation of time-resolved photoemission in the sudden approximation, and then solve the problem using dynamical mean-field theory with the numerical renormalization group and a bare density of states calculated from density functional theory including the charge-density-wave distortion of the ion cores and spin-orbit coupling We find a number of interesting results: (i) the bare band structure actually has more dispersion in the perpendicular direction than in the two-dimensional planes; (ii) the DMFT approach can produce upper and lower Hubbard bands that resemble those in the experiment, but the upper bands will overlap in energy with other higher energy bands; (iii) the effect of the finite width of the probe pulse is minimal on the shape of the photoemission spectra; and (iv) the quasiequilibrium approximation does not fully describe the behavior in this system.Comment: (7 pages, 5 figures, proceedings for Coherence and correlations in nanosystems conference, September 5-10, Ustron, Poland

Rapid pathway prototyping and engineering using <i>in vitro</i> and <i>in vivo</i> synthetic genome SCRaMbLE-in methods

AbstractExogenous pathway optimization and chassis engineering are two crucial methods for heterologous pathway expression. The two methods are normally carried out step-wise and in a trial-and-error manner. Here we report a recombinase-based combinatorial method (termed “SCRaMbLE-in”) to tackle both challenges simultaneously. SCRaMbLE-in includes an in vitro recombinase toolkit to rapidly prototype and diversify gene expression at the pathway level and an in vivo genome reshuffling system to integrate assembled pathways into the synthetic yeast genome while combinatorially causing massive genome rearrangements in the host chassis. A set of loxP mutant pairs was identified to maximize the efficiency of the in vitro diversification. Exemplar pathways of β-carotene and violacein were successfully assembled, diversified, and integrated using this SCRaMbLE-in method. High-throughput sequencing was performed on selected engineered strains to reveal the resulting genotype-to-phenotype relationships. The SCRaMbLE-in method proves to be a rapid, efficient, and universal method to fast track the cycle of engineering biology.</jats:p

Landscape Evolution of Historic Campuses from the Perspective of Historic Layering: A Case Study of Three University Campuses in Nanjing, China

As China\u27s social development enters a new stage of connotative progression, campus heritage is attracting attention as an essential part of the cultural landscape in historic cities. Historic campuses are the spatial carriers of campus heritage, a superimposed collage of campus landscapes from multiple historical periods with outstanding value. Campus space presents the development history of campus planning and construction concepts, showing the unique cultural connotation. Related research has expanded from studying "points" of historic buildings to the holistic study of "surfaces" such as spatial patterns and landscape environments.With the support of "Historic Layering" and "Anchoring-Layering" in the theory of historic urban landscape (HUL), this article takes the three cases of Southeast University (Sipailou Campus), Nanjing University (Gulou Campus), and Nanjing Normal University (Suiyuan Campus) to interpret landscape evolution of historic campuses in Nanjing. Combining the technical support of campus planning and construction drawings from different decades with historical photos, documents, and on-site surveys, the dynamic process characteristics and layering rules of campus landscape are investigated under the constant collision and compromise between planning ideals and social reality.The study found that the historic campuses show the evolutionary characteristics of the hybridization and collage of multiple landscapes and the spatial and temporal correlation between architecture and environmental elements in landscape shaping from the early architectural dominance to the late architectural and environmental co-action. Moreover, different campuses have unique landscape characters, especially the pre-1949 campuses dominated by Western classicism or the Chinese-Western fusion, which has become an essential cultural gene of the campus.This can serve as a reference for cultural interpretation of the historic campus landscape\u27s dynamic evolution and characterizing the contemporary campus space

Transforming to Yoked Neural Networks to Improve ANN Structure

Most existing classical artificial neural networks (ANN) are designed as a tree structure to imitate neural networks. In this paper, we argue that the connectivity of a tree is not sufficient to characterize a neural network. The nodes of the same level of a tree cannot be connected with each other, i.e., these neural unit cannot share information with each other, which is a major drawback of ANN. Although ANN has been significantly improved in recent years to more complex structures, such as the directed acyclic graph (DAG), these methods also have unidirectional and acyclic bias for ANN. In this paper, we propose a method to build a bidirectional complete graph for the nodes in the same level of an ANN, which yokes the nodes of the same level to formulate a neural module. We call our model as YNN in short. YNN promotes the information transfer significantly which obviously helps in improving the performance of the method. Our YNN can imitate neural networks much better compared with the traditional ANN. In this paper, we analyze the existing structural bias of ANN and propose a model YNN to efficiently eliminate such structural bias. In our model, nodes also carry out aggregation and transformation of features, and edges determine the flow of information. We further impose auxiliary sparsity constraint to the distribution of connectedness, which promotes the learned structure to focus on critical connections. Finally, based on the optimized structure, we also design small neural module structure based on the minimum cut technique to reduce the computational burden of the YNN model. This learning process is compatible with the existing networks and different tasks. The obtained quantitative experimental results reflect that the learned connectivity is superior to the traditional NN structure.Comment: arXiv admin note: text overlap with arXiv:2008.08261 by other authors. arXiv admin note: text overlap with arXiv:2008.08261 by other author

The First Zagreb Index, Vertex-Connectivity, Minimum Degree And Independent Number in Graphs

Let G be a simple, undirected and connected graph. Defined by M1(G) and RMTI(G) the first Zagreb index and the reciprocal Schultz molecular topological index of G, respectively. In this paper, we determined the graphs with maximal M1 among all graphs having prescribed vertex-connectivity and minimum degree, vertex-connectivity and bipartition, vertex-connectivity and vertex-independent number, respectively. As applications, all maximal elements with respect to RMTI are also determined among the above mentioned graph families, respectively

The Impact of Stigmatizing Language in EHR Notes on AI Performance and Fairness

Today, there is significant interest in using electronic health record data to generate new clinical insights for diagnosis and treatment decisions. However, there are concerns that such data may be biased and result in accentuating racial disparities. We study how clinician biases reflected in EHR notes affect the performance and fairness of artificial intelligence models in the context of mortality prediction for intensive care unit patients. We apply a Transformer-based deep learning model and explainable AI techniques to quantify negative impacts on performance and fairness. Our findings demonstrate that stigmatizing language written by clinicians adversely affects AI performance, particularly so for black patients, highlighting SL as a source of racial disparity in AI model development. As an effective mitigation approach, removing SL from EHR notes can significantly improve AI performance and fairness. This study provides actionable insights for responsible AI development and contributes to understanding clinician EHR note writing