Symmetric Mass Generation of K\"ahler-Dirac Fermions from the Perspective of Symmetry-Protected Topological Phases

The K\"ahler-Dirac fermion, recognized as an elegant geometric approach, offers an alternative to traditional representations of relativistic fermions. Recent studies have demonstrated that symmetric mass generation (SMG) can precisely occur with two copies of K\"ahler-Dirac fermions across any spacetime dimensions. This conclusion stems from the study of anomaly cancellation within the fermion system. Our research provides an alternative understanding of this phenomenon from a condensed matter perspective, by associating the interacting K\"ahler-Dirac fermion with the boundary of bosonic symmetry-protected topological (SPT) phases. We show that the low-energy bosonic fluctuations in a single copy of the K\"ahler-Dirac fermion can be mapped to the boundary modes of a $\mathbb{Z}_2$-classified bosonic SPT state, protected by an inversion symmetry universally across all dimensions. This implies that two copies of K\"ahler-Dirac fermions can always undergo SMG through interactions mediated by these bosonic modes. This picture aids in systematically designing SMG interactions for K\"ahler-Dirac fermions in any dimension. We present the exact lattice Hamiltonian of these interactions and validate their efficacy in driving SMG.Comment: 12 pages, 2 figures, 4 table

concentration-dependent lamin assembly and its role in mitotic spindle assembly

Lamins are type-V intermediate filament proteins that polymerize into the nuclear lamina. Mutations in lamin genes cause severe developmental defects and human genetic diseases. Although lamins are known to perform many biological functions, the underlying molecular mechanisms remain poorly understood. The expression of multiple lamins including lamin-A/C, lamin-B1, and lamin-B2 in mammals has made it difficult to study the assembly and function of lamins. Consequently, whether different lamins depend on one another for proper nuclear lamina assembly and which lamin functions are shared by all lamins or are specific to one lamin are unclear. Here I examined the assembly and function of lamins in mouse cells deleted of all or different combinations of lamins. I found the assembly of lamins into the nuclear lamina depended primarily on the total lamin concentration in the nucleus. Each lamin alone can assemble into a smooth nuclear lamina when expressed at a sufficiently high level, which ensures the even distribution of the nuclear pore complexes (NPCs). However, only lamin-A is required for the nuclear retention of emerin. I also found lamins ensure proper NPC distribution and prophase centrosome separation by resisting dynein forces on NPCs. Thus, when studying the roles of lamins in development and diseases, it is critical to establish the amount of each lamin in cells of interest and distinguish the shared and unique functions of lamins. Lamin-regulated centrosome separation also implies a potential mechanism by which lamins regulate spindle assembly and spindle orientation

Joint Device-Edge Digital Semantic Communication with Adaptive Network Split and Learned Non-Linear Quantization

Semantic communication, an intelligent communication paradigm that aims to transmit useful information in the semantic domain, is facilitated by deep learning techniques. Although robust semantic features can be learned and transmitted in an analog fashion, it poses new challenges to hardware, protocol, and encryption. In this paper, we propose a digital semantic communication system, which consists of an encoding network deployed on a resource-limited device and a decoding network deployed at the edge. To acquire better semantic representation for digital transmission, a novel non-linear quantization module is proposed with the trainable quantization levels that efficiently quantifies semantic features. Additionally, structured pruning by a sparse scaling vector is incorporated to reduce the dimension of the transmitted features. We also introduce a semantic learning loss (SLL) function to reduce semantic error. To adapt to various channel conditions and inputs under constraints of communication and computing resources, a policy network is designed to adaptively choose the split point and the dimension of the transmitted semantic features. Experiments using the CIFAR-10 dataset for image classification are employed to evaluate the proposed digital semantic communication network, and ablation studies are conducted to assess the proposed modules including the quantization module, structured pruning and SLL

Towards the Transferable Audio Adversarial Attack via Ensemble Methods

In recent years, deep learning (DL) models have achieved significant progress in many domains, such as autonomous driving, facial recognition, and speech recognition. However, the vulnerability of deep learning models to adversarial attacks has raised serious concerns in the community because of their insufficient robustness and generalization. Also, transferable attacks have become a prominent method for black-box attacks. In this work, we explore the potential factors that impact adversarial examples (AEs) transferability in DL-based speech recognition. We also discuss the vulnerability of different DL systems and the irregular nature of decision boundaries. Our results show a remarkable difference in the transferability of AEs between speech and images, with the data relevance being low in images but opposite in speech recognition. Motivated by dropout-based ensemble approaches, we propose random gradient ensembles and dynamic gradient-weighted ensembles, and we evaluate the impact of ensembles on the transferability of AEs. The results show that the AEs created by both approaches are valid for transfer to the black box API.Comment: Submitted to Cybersecurity journal 202

Pharmacological Study of Phenolic Components in Parkinson's Disease

In this study, cell experiments were conducted to investigate the effects of extracts on cell viability and apoptosis of Parkinson model in vitro, as well as the expression of cysteine protease-3 (Caspase-3) and B lymphocytoma-2-associated X protein (BAX). The results showed that extract of phenols could improve the loss of cell viability and apoptosis induced by MPP+, and inhibit the enhanced expression of Bax and Caspase-3 by MPP+. The potential targets and signaling pathways of phenols in the treatment of Parkinson's disease were predicted by network pharmacology