Excited States of Crystalline Point Defects with Multireference Density Matrix Embedding Theory

Accurate and affordable methods to characterize the electronic structure of solids are important for targeted materials design. Embedding-based methods provide an appealing balance in the trade-off between cost and accuracy - particularly when studying localized phenomena. Here, we use the density matrix embedding theory (DMET) algorithm to study the electronic excitations in solid-state defects with a restricted open-shell Hartree--Fock (ROHF) bath and multireference impurity solvers, specifically, complete active space self-consistent field (CASSCF) and n-electron valence state second-order perturbation theory (NEVPT2). We apply the method to investigate an oxygen vacancy (OV) on a MgO(100) surface and find absolute deviations within 0.05 eV between DMET using the CASSCF/NEVPT2 solver, denoted as CAS-DMET/NEVPT2-DMET, and the non-embedded CASSCF/NEVPT2 approach. Next, we establish the practicality of DMET by extending it to larger supercells for the OV defect and a neutral silicon-vacancy in diamond where the use of non-embedded CASSCF/NEVPT2 is extremely expensive.

Development of a model for fibroblast-led collective migration from breast cancer cell spheroids to study radiation effects on invasiveness

Background Invasiveness is a major factor contributing to metastasis of tumour cells. Given the broad variety and plasticity of invasion mechanisms, assessing potential metastasis-promoting effects of irradiation for specific mechanisms is important for further understanding of potential adverse effects of radiotherapy. In fibroblast-led invasion mechanisms, fibroblasts produce tracks in the extracellular matrix in which cancer cells with epithelial traits can follow. So far, the influence of irradiation on this type of invasion mechanisms has not been assessed. Methods By matrix-embedding coculture spheroids consisting of breast cancer cells (MCF-7, BT474) and normal fibroblasts, we established a model for fibroblast-led invasion. To demonstrate applicability of this model, spheroid growth and invasion behaviour after irradiation with 5 Gy were investigated by microscopy and image analysis. Results When not embedded, irradiation caused a significant growth delay in the spheroids. When irradiating the spheroids with 5 Gy before embedding, we find comparable maximum migration distance in fibroblast monoculture and in coculture samples as seen in unirradiated samples. Depending on the fibroblast strain, the number of invading cells remained constant or was reduced. Conclusion In this spheroid model and with the cell lines and fibroblast strains used, irradiation does not have a major invasion-promoting effect. 3D analysis of invasiveness allows to uncouple effects on invading cell number and maximum invasion distance when assessing radiation effects.

An Embedding Strategy Using Q-Ary Convolutional Codes for Large and Small Payloads

Matrix embedding (ME) code is a commonly used steganography technique, which uses linear block codes to improve embedding efficiency. However, its main disadvantage is the inability to perform maximum likelihood decoding due to the high complexity of decoding large ME codes. As such, it is difficult to improve the embedding efficiency. The proposed q-ary embedding code can provide excellent embedding efficiency and is suitable for various embedding rates (large and small payloads). This article discusses that by using perforation technology, a convolutional code with a high embedding rate can be easily converted into a convolutional code with a low embedding rate. By keeping the embedding rate of the (2, 1) convolutional code unchanged, convolutional codes with different embedding rates can be designed through puncturing.

A merged molecular representation learning for molecular properties prediction with a web-based service

Deep learning has brought a dramatic development in molecular property prediction that is crucial in the field of drug discovery using various representations such as fingerprints, SMILES, and graphs. In particular, SMILES is used in various deep learning models via character-based approaches. However, SMILES has a limitation in that it is hard to reflect chemical properties. In this paper, we propose a new self-supervised method to learn SMILES and chemical contexts of molecules simultaneously in pre-training the Transformer. The key of our model is learning structures with adjacency matrix embedding and learning logics that can infer descriptors via Quantitative Estimation of Drug-likeness prediction in pre-training. As a result, our method improves the generalization of the data and achieves the best average performance by benchmarking downstream tasks. Moreover, we develop a web-based fine-tuning service to utilize our model on various tasks.

Reversible Data Hiding for AMBTC Compressed Images Based on Matrix and Hamming Coding

In this paper, we propose a two-layer data hiding method by using the Hamming code to enhance the hiding capacity without causing significantly increasing computation complexity for AMBTC-compressed images. To achieve our objective, for the first layer, four disjoint sets using different combinations of the mean value (AVG) and the standard deviation (VAR) are derived according to the combination of secret bits and the corresponding bitmap, following Lin et al.’s method. For the second layer, these four disjoint sets are extended to eight by adding or subtracting 1, according to a matrix embedding with (7, 4) Hamming code. To maintain reversibility, we must return the irreversible block to its previous state, which is the state after the first layer of data is embedded. Then, to losslessly recover the AMBTC-compressed images after extracting the secret bits, we use continuity feature, the parity of pixels value, and the unique number of changed pixels in the same row to restore AVG and VAR. Finally, in comparison with state-of-the-art AMBTC-based schemes, it is confirmed that our proposed method provided two times the hiding capacity comparing with other six representative AMBTC-based schemes while maintaining acceptable file size of steog-images.