Constructions of Pure Asymmetric Quantum Alternant Codes Based on Subclasses of Alternant Codes

In this paper, we construct asymmetric quantum error-correcting codes(AQCs) based on subclasses of Alternant codes. Firstly, We propose a new subclass of Alternant codes which can attain the classical Gilbert-Varshamov bound to construct AQCs. It is shown that when $d_x=2$, $Z$-parts of the AQCs can attain the classical Gilbert-Varshamov bound. Then we construct AQCs based on a famous subclass of Alternant codes called Goppa codes. As an illustrative example, we get three $[[55,6,19/4]],[[55,10,19/3]],[[55,15,19/2]]$ AQCs from the well known $[55,16,19]$ binary Goppa code. At last, we get asymptotically good binary expansions of asymmetric quantum GRS codes, which are quantum generalizations of Retter's classical results. All the AQCs constructed in this paper are pure

Construction and Performance of Quantum Burst Error Correction Codes for Correlated Errors

© 2018 IEEE. In practical communication and computation systems, errors occur predominantly in adjacent positions rather than in a random manner. In this paper, we develop a stabilizer formalism for quantum burst error correction codes (QBECC) to combat such error patterns in the quantum regime. Our contributions are as follows. Firstly, we derive an upper bound for the correctable burst errors of QBECCs, the quantum Reiger bound (QRB). Secondly, we propose two constructions of QBECCs: one by heuristic computer search and the other by concatenating two quantum tensor product codes (QTPCs). We obtain several new QBECCs with better parameters than existing codes with the same coding length. Moreover, some of the constructed codes can saturate the quantum Reiger bounds. Finally, we perform numerical experiments for our constructed codes over Markovian correlated depolarizing quantum memory channels, and show that QBECCs indeed outperform standard QECCs in this scenario

Tackling MARCKS-PIP3 circuit attenuates fibroblast activation and fibrosis progression.

Targeting activated fibroblasts, including myofibroblast differentiation, has emerged as a key therapeutic strategy in patients with idiopathic pulmonary fibrosis (IPF). However, there is no available therapy capable of selectively eradicating myofibroblasts or limiting their genesis. Through an integrative analysis of the regulator genes that are responsible for the activation of IPF fibroblasts, we noticed the phosphatidylinositol 4,5-bisphosphate (PIP2)-binding protein, myristoylated alanine-rich C-kinase substrate (MARCKS), as a potential target molecule for IPF. Herein, we have employed a 25-mer novel peptide, MARCKS phosphorylation site domain sequence (MPS), to determine if MARCKS inhibition reduces pulmonary fibrosis through the inactivation of PI3K/protein kinase B (AKT) signaling in fibroblast cells. We first observed that higher levels of MARCKS phosphorylation and the myofibroblast marker α-smooth muscle actin (α-SMA) were notably overexpressed in all tested IPF lung tissues and fibroblast cells. Treatment with the MPS peptide suppressed levels of MARCKS phosphorylation in primary IPF fibroblasts. A kinetic assay confirmed that this peptide binds to phospholipids, particularly PIP2, with a dissociation constant of 17.64 nM. As expected, a decrease of phosphatidylinositol (3,4,5)-trisphosphate pools and AKT activity occurred in MPS-treated IPF fibroblast cells. MPS peptide was demonstrated to impair cell proliferation, invasion, and migration in multiple IPF fibroblast cells in vitro as well as to reduce pulmonary fibrosis in bleomycin-treated mice in vivo. Surprisingly, we found that MPS peptide decreases α-SMA expression and synergistically interacts with nintedanib treatment in IPF fibroblasts. Our data suggest MARCKS as a druggable target in pulmonary fibrosis and also provide a promising antifibrotic agent that may lead to effective IPF treatments.-Yang, D. C., Li, J.-M., Xu, J., Oldham, J., Phan, S. H., Last, J. A., Wu, R., Chen, C.-H. Tackling MARCKS-PIP3 circuit attenuates fibroblast activation and fibrosis progression

Hydrogen jet diffusion modeling by using physics-informed graph neural network and sparsely-distributed sensor data

Efficient modeling of jet diffusion during accidental release is critical for operation and maintenance management of hydrogen facilities. Deep learning has proven effective for concentration prediction in gas jet diffusion scenarios. Nonetheless, its reliance on extensive simulations as training data and its potential disregard for physical laws limit its applicability to unseen accidental scenarios. Recently, physics-informed neural networks (PINNs) have emerged to reconstruct spatial information by using data from sparsely-distributed sensors which are easily collected in real-world applications. However, prevailing approaches use the fully-connected neural network as the backbone without considering the spatial dependency of sensor data, which reduces the accuracy of concentration prediction. This study introduces the physics-informed graph deep learning approach (Physic_GNN) for efficient and accurate hydrogen jet diffusion prediction by using sparsely-distributed sensor data. Graph neural network (GNN) is used to model the spatial dependency of such sensor data by using graph nodes at which governing equations describing the physical law of hydrogen jet diffusion are immediately solved. The computed residuals are then applied to constrain the training process. Public experimental data of hydrogen jet is used to compare the accuracy and efficiency between our proposed approach Physic_GNN and state-of-the-art PINN. The results demonstrate our Physic_GNN exhibits higher accuracy and physical consistency of centerline concentration prediction given sparse concentration compared to PINN and more efficient compared to OpenFOAM. The proposed approach enables accurate and robust real-time spatial consequence reconstruction and underlying physical mechanisms analysis by using sparse sensor data

Positional multi-length and mutual-attention network for epileptic seizure classification

The automatic classification of epilepsy electroencephalogram (EEG) signals plays a crucial role in diagnosing neurological diseases. Although promising results have been achieved by deep learning methods in this task, capturing the minute abnormal characteristics, contextual information, and long dependencies of EEG signals remains a challenge. To address this challenge, a positional multi-length and mutual-attention (PMM) network is proposed for the automatic classification of epilepsy EEG signals. The PMM network incorporates a positional feature encoding process that extracts minute abnormal characteristics from the EEG signal and utilizes a multi-length feature learning process with a hierarchy residual dilated LSTM (RDLSTM) to capture long contextual dependencies. Furthermore, a mutual-attention feature reinforcement process is employed to learn the global and relative feature dependencies and enhance the discriminative abilities of the network. To validate the effectiveness PMM network, we conduct extensive experiments on the public dataset and the experimental results demonstrate the superior performance of the PMM network compared to state-of-the-art methods

Broad-Wavevector Spin Pumping of Flat-Band Magnons

We report the experimental observation of large spin pumping signals in YIG/Pt system driven by broad-wavevector spin-wave spin current. 280 nm-wide microwave inductive antennas offer broad-wavevector excitation which, in combination with quasi-flatband of YIG, allows a large number of magnons to participate in spin pumping at a given frequency. Through comparison with ferromagnetic resonance spin pumping, we attribute the enhancement of the spin current to the multichromatic magnons. The high efficiency of spin current generation enables us to uncover nontrivial propagating properties in ultra-low power regions. Additionally, our study achieves the spatially separated detection of magnons, allowing the direct extraction of the decay length. The synergistic combination of the capability of broad-wavevector excitation, enhanced voltage signals, and nonlocal detection provides a new avenue for the electrical exploration of spin waves dynamics