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A base for a finite permutation group $G \le \mathrm{Sym}(\Omega)$ is a subset of $\Omega$ with trivial pointwise stabiliser in $G$, and the base size of $G$ is the smallest size of a base for $G$. Motivated by the interest in groups of base size two, Burness and Giudici introduced the notion of the Saxl graph. This graph has vertex set $\Omega$, with edges between elements if they form a base for $G$. We define a generalisation of this graph that encodes useful information about $G$ whenever $b(G) \ge 2$: here, the edges are the pairs of elements of $\Omega$ that can be extended to bases of size $b(G)$. In particular, for primitive groups, we investigate the completeness and arc-transitivity of the generalised graph, and the generalisation of Burness and Giudici's Common Neighbour Conjecture on the original Saxl graph., Comment: 35 pages; minor formatting changes

Two-dimensional (2D) van der Waals (vdW) magnets with layer-dependent magnetic states and/or diverse magnetic interactions and anisotropies have attracted extensive research interest. Despite the advances, a notable challenge persists in effectively manipulating the tunneling anisotropic magnetoresistance (TAMR) of 2D vdW magnet-based magnetic tunnel junctions (MTJs). Here, we report the novel and anomalous tunneling magnetoresistance (TMR) oscillations and pioneering demonstration of bias and gate voltage controllable TAMR in 2D vdw MTJs, utilizing few-layer CrPS4. This material, inherently an antiferromagnet, transitions to a canted magnetic order upon application of external magnetic fields. Through TMR measurements, we unveil the novel, layer-dependent oscillations in the tunneling resistance for few-layer CrPS4 devices under both out-of-plane and in-plane magnetic fields, with a pronounced controllability via gate voltage. Intriguingly, we demonstrate that both the polarity and magnitude of TAMR in CrPS4 can be effectively tuned through either a bias or gate voltage. We further elucidate the mechanism behind this electrically tunable TAMR through first-principles calculations. The implications of our findings are far-reaching, providing new insights into 2D magnetism and opening avenues for the development of innovative spintronic devices based on 2D vdW magnets., Comment: 22 pages, 4 figures

The efficiency of checking certificate status is one of the key indicators in the public key infrastructure (PKI). This prompted researchers to design the Online Certificate Status Protocol (OCSP) standard, defined in RFC 6960, to guide developers in implementing OCSP components. However, as the environment increasingly relies on PKI for identity authentication, it is essential to protect the communication between clients and servers from rogue elements. This can be achieved by using SSL/TLS techniques to establish a secure channel, allowing Certificate Authorities (CAs) to safely transfer certificate status information. In this work, we introduce the OCSP Stapling approach to optimize OCSP query costs in our smart grid environment. This approach reduces the number of queries from the Device Language Message Specification (DLMS) server to the OCSP server. Our experimental results show that OCSP stapling increases both efficiency and security, creating a more robust architecture for the smart grid., Comment: 12 pages, 13 figures, Cryptology and Information Security Conference 2024

Graph Neural Networks (GNNs) have been widely employed for feature representation learning in molecular graphs. Therefore, it is crucial to enhance the expressiveness of feature representation to ensure the effectiveness of GNNs. However, a significant portion of current research primarily focuses on the structural features within individual molecules, often overlooking the structural similarity between molecules, which is a crucial aspect encapsulating rich information on the relationship between molecular properties and structural characteristics. Thus, these approaches fail to capture the rich semantic information at the molecular structure level. To bridge this gap, we introduce the \textbf{Molecular Structural Similarity Motif GNN (MSSM-GNN)}, a novel molecular graph representation learning method that can capture structural similarity information among molecules from a global perspective. In particular, we propose a specially designed graph that leverages graph kernel algorithms to represent the similarity between molecules quantitatively. Subsequently, we employ GNNs to learn feature representations from molecular graphs, aiming to enhance the accuracy of property prediction by incorporating additional molecular representation information. Finally, through a series of experiments conducted on both small-scale and large-scale molecular datasets, we demonstrate that our model consistently outperforms eleven state-of-the-art baselines. The codes are available at https://github.com/yaoyao-yaoyao-cell/MSSM-GNN.

Large language models (LLMs) have excelled in various natural language processing tasks, but challenges in interpretability and trustworthiness persist, limiting their use in high-stakes fields. Causal discovery offers a promising approach to improve transparency and reliability. However, current evaluations are often one-sided and lack assessments focused on interpretability performance. Additionally, these evaluations rely on synthetic data and lack comprehensive assessments of real-world datasets. These lead to promising methods potentially being overlooked. To address these issues, we propose a flexible evaluation framework with metrics for evaluating differences in causal structures and causal effects, which are crucial attributes that help improve the interpretability of LLMs. We introduce the Open Causal Discovery Benchmark (OCDB), based on real data, to promote fair comparisons and drive optimization of algorithms. Additionally, our new metrics account for undirected edges, enabling fair comparisons between Directed Acyclic Graphs (DAGs) and Completed Partially Directed Acyclic Graphs (CPDAGs). Experimental results show significant shortcomings in existing algorithms' generalization capabilities on real data, highlighting the potential for performance improvement and the importance of our framework in advancing causal discovery techniques.

Despite the remarkable capabilities demonstrated by Graph Neural Networks (GNNs) in graph-related tasks, recent research has revealed the fairness vulnerabilities in GNNs when facing malicious adversarial attacks. However, all existing fairness attacks require manipulating the connectivity between existing nodes, which may be prohibited in reality. To this end, we introduce a Node Injection-based Fairness Attack (NIFA), exploring the vulnerabilities of GNN fairness in such a more realistic setting. In detail, NIFA first designs two insightful principles for node injection operations, namely the uncertainty-maximization principle and homophily-increase principle, and then optimizes injected nodes' feature matrix to further ensure the effectiveness of fairness attacks. Comprehensive experiments on three real-world datasets consistently demonstrate that NIFA can significantly undermine the fairness of mainstream GNNs, even including fairness-aware GNNs, by injecting merely 1% of nodes. We sincerely hope that our work can stimulate increasing attention from researchers on the vulnerability of GNN fairness, and encourage the development of corresponding defense mechanisms. Our code and data are released at: https://github.com/CGCL-codes/NIFA., Comment: Accepted by NeurIPS 2024

In this study, the impact of triangle singularity is investigated in the isospin-breaking process $J/\psi \to \Lambda \bar{\Lambda} \pi$. The triangle singularity is found to play a significant role in the process, resulting in the creation of a resonance-like structure around 1.4 GeV in the $\Lambda\pi(\bar{\Lambda}\pi)$ invariant mass spectrum. To amplify the impact of this triangle singularity, the presence of two $\Sigma^*(\frac{1}{2}^-)$ states around 1.4 GeV and 1.6 GeV is essential, yet these states have not been definitively identified in the current baryon spectrum. We recommend that experiments, particularly the Beijing Spectrometer (BESIII) and the future Super Tau-Charm Factory (STCF), to investigate the process $J/\psi \to \Lambda \bar{\Lambda} \pi$ to offer direct evidences for our predicted triangle singularity and additional evidence regarding the $\Sigma(\frac{1}{2}^-)$ states.

Absolute measurements were conducted to study the bremsstrahlung emission from ~5-25 keV electrons incident on three thick solid conductive compounds of MoSi$_2$, TiB$_2$ and ZrB$_2$. The additivity approximation was applied in the Monte Carlo PENELOPE simulations for compounds and mixtures. The results showed that in general the experimental bremsstrahlung spectra were in good agreement with the Monte Carlo simulation results, suggesting the feasibility of the additivity approximation in Monte Carlo simulations for the studied cases even in the absolute measurements and that the significant differences between experiments and Monte Carlo simulations near the Duane-Hunt limit for insulating targets in previous studies do not appear in the present studies.

Let $G\leqslant\mathrm{Sym}(\Omega)$ be a finite transitive permutation group with point stabiliser $H$. We say that a subgroup $K$ of $G$ is a fixer if every element of $K$ has fixed points, and we say that $K$ is large if $|K| \geqslant |H|$. There is a special interest in studying large fixers due to connections with Erd\H{o}s-Ko-Rado type problems. In this paper, we classify up to conjugacy the large fixers of the almost simple primitive groups with socle $\mathrm{PSL}_2(q)$, and we use this result to verify a special case of a conjecture of Spiga on permutation characters. We also present some results on large fixers of almost simple primitive groups with socle an alternating or sporadic group., Comment: 33 pages

Previous studies on music style transfer have mainly focused on one-to-one style conversion, which is relatively limited. When considering the conversion between multiple styles, previous methods required designing multiple modes to disentangle the complex style of the music, resulting in large computational costs and slow audio generation. The existing music style transfer methods generate spectrograms with artifacts, leading to significant noise in the generated audio. To address these issues, this study proposes a music style transfer framework based on diffusion models (DM) and uses spectrogram-based methods to achieve multi-to-multi music style transfer. The GuideDiff method is used to restore spectrograms to high-fidelity audio, accelerating audio generation speed and reducing noise in the generated audio. Experimental results show that our model has good performance in multi-mode music style transfer compared to the baseline and can generate high-quality audio in real-time on consumer-grade GPUs., Comment: 8 pages, 6 figures, ICMC 2023

Federated learning (FL) promotes decentralized training while prioritizing data confidentiality. However, its application on resource-constrained devices is challenging due to the high demand for computation and memory resources to train deep learning models. Neural network pruning techniques, such as dynamic pruning, could enhance model efficiency, but directly adopting them in FL still poses substantial challenges, including post-pruning performance degradation, high activation memory usage, etc. To address these challenges, we propose FedMef, a novel and memory-efficient federated dynamic pruning framework. FedMef comprises two key components. First, we introduce the budget-aware extrusion that maintains pruning efficiency while preserving post-pruning performance by salvaging crucial information from parameters marked for pruning within a given budget. Second, we propose scaled activation pruning to effectively reduce activation memory footprints, which is particularly beneficial for deploying FL to memory-limited devices. Extensive experiments demonstrate the effectiveness of our proposed FedMef. In particular, it achieves a significant reduction of 28.5% in memory footprint compared to state-of-the-art methods while obtaining superior accuracy., Comment: Accepted by CVPR2024

In this work, we introduce a strong Designated Verifier Signature (DVS) scheme that incorporates a message recovery mechanism inspired by the concept of the Universal Designated Verifier Signature (UDVS) scheme. It is worth noting that Saeednia's strong designated verifier signature scheme fails to guarantee the privacy of the signature, making it unsuitable for certain applications such as medical record certificates or voting systems. To overcome this limitation, we extend Lee's strong designated verifier signature with a message recovery scheme to develop a universal designated verifier signature scheme. This universal designated verifier scheme is crafted to safeguard the privacy of signature holders, ensuring that only designated verifiers can authenticate the true signer and recover the messages., Comment: 11 pages

Evaluating and enhancing the general capabilities of large language models (LLMs) has been an important research topic. Graph is a common data structure in the real world, and understanding graph data is a crucial part for advancing general intelligence. To evaluate and enhance the graph understanding abilities of LLMs, in this paper, we propose a benchmark named GraphInstruct, which comprehensively includes 21 classical graph reasoning tasks, providing diverse graph generation pipelines and detailed reasoning steps. Based on GraphInstruct, we further construct GraphLM through efficient instruction-tuning, which shows prominent graph understanding capability. In order to enhance the LLM with graph reasoning capability as well, we propose a step mask training strategy, and construct a model named GraphLM+. As one of the pioneering efforts to enhance the graph understanding and reasoning abilities of LLMs, extensive experiments have demonstrated the superiority of GraphLM and GraphLM+ over other LLMs. We look forward to more researchers exploring the potential of LLMs in the graph data mining domain through GraphInstruct. Our code for generating GraphInstruct is released publicly at: https://github.com/CGCL-codes/GraphInstruct., Comment: 9 pages

Shan et al. (2023) recently reported significant seasonal advances of intense tropical cyclones (TCs) in both the Northern Hemisphere (NH) and Southern Hemisphere (SH) since the 1980s and emphasized the data insensitivity of this conclusion, based on the advanced Dvorak Technique-Hurricane Satellite (ADT-HURSAT) and the International Best Track Archive for Climate Stewardship (IBTrACS) datasets. However, upon rigorously repeating the analysis procedure in Shan et al. (2023), we are unable to replicate their conclusion. Our analyses reveal that insignificant trends in median of intense TC occurrence dates (hereafter referred to as $\tilde{date}_{intenseTC}$) are observed in the SH based on the ADT-HURSAT, and in both hemispheres based on the IBTrACS. In addition, we discover inconsistencies in $\tilde{date}_{intenseTC}$ trends between the two datasets, which primarily arise from the differences in identification of intense TC cases. The above discrepancies raise doubts about the reproducibility and validity of Shan et al.'s conclusions regarding the seasonal advance of intense TCs. Based on these findings, as well as the limitations of both datasets, we argue that the reported seasonal advance of intense TCs since the 1980s is inconclusive and further investigations with alternative datasets and approaches are needed., Comment: This preprint is a commentary article regarding a recent publication on Nature (Shan, K., Lin, Y., Chu, PS. et al. Seasonal advance of intense tropical cyclones in a warming climate. Nature 623, 83-89 (2023) https://doi.org/10.1038/s41586-023-06544-0)

Large Language Models (LLMs) have greatly advanced the natural language processing paradigm. However, the high computational load and huge model sizes pose a grand challenge for deployment on edge devices. To this end, we propose APTQ (Attention-aware Post-Training Mixed-Precision Quantization) for LLMs, which considers not only the second-order information of each layer's weights, but also, for the first time, the nonlinear effect of attention outputs on the entire model. We leverage the Hessian trace as a sensitivity metric for mixed-precision quantization, ensuring an informed precision reduction that retains model performance. Experiments show APTQ surpasses previous quantization methods, achieving an average of 4 bit width a 5.22 perplexity nearly equivalent to full precision in the C4 dataset. In addition, APTQ attains state-of-the-art zero-shot accuracy of 68.24\% and 70.48\% at an average bitwidth of 3.8 in LLaMa-7B and LLaMa-13B, respectively, demonstrating its effectiveness to produce high-quality quantized LLMs., Comment: 6 pages, 2 figures, published to DAC 2024: 61st IEEE/ACM Design Automation Conference. (DAC'24)

Controllable 3D indoor scene synthesis stands at the forefront of technological progress, offering various applications like gaming, film, and augmented/virtual reality. The capability to stylize and de-couple objects within these scenarios is a crucial factor, providing an advanced level of control throughout the editing process. This control extends not just to manipulating geometric attributes like translation and scaling but also includes managing appearances, such as stylization. Current methods for scene stylization are limited to applying styles to the entire scene, without the ability to separate and customize individual objects. Addressing the intricacies of this challenge, we introduce a unique pipeline designed for synthesis 3D indoor scenes. Our approach involves strategically placing objects within the scene, utilizing information from professionally designed bounding boxes. Significantly, our pipeline prioritizes maintaining style consistency across multiple objects within the scene, ensuring a cohesive and visually appealing result aligned with the desired aesthetic. The core strength of our pipeline lies in its ability to generate 3D scenes that are not only visually impressive but also exhibit features like photorealism, multi-view consistency, and diversity. These scenes are crafted in response to various natural language prompts, demonstrating the versatility and adaptability of our model.

Hsu et al. (2022) proposed a cryptographic scheme within the public key infrastructure to bolster the security of smart grid meters. Their proposal involved developing the Certificate Management over CMS mechanism to establish Simple Certificate Enrollment Protocol and Enrollment over Secure Transport protocol. Additionally, they implemented Online Certificate Status Protocol (OCSP) services to independently query the status of certificates. However, their implementation featured a single OCSP server handling all query requests. Considering the typical scenario in smart grid PKI environments with over tens of thousands of end-meters, we introduced a Hybrid Online Certificate Status Protocol mechanism. This approach decreases demand of query resources from the client to OCSP servers collaborating with Certificate Revocation Lists. Our simulations, mimicking meter behavior, demonstrated increased efficiency, creating a more robust architecture tailored to the smart grid meter landscape., Comment: 8 pages, 7 figures

The classification of the finite primitive permutation groups of rank $3$ was completed in the 1980s and this landmark achievement has found a wide range of applications. In the general transitive setting, a classical result of Higman shows that every finite imprimitive rank $3$ permutation group $G$ has a unique non-trivial block system $\mathcal{B}$ and this provides a natural way to partition the analysis of these groups. Indeed, the induced permutation group $G^{\mathcal{B}}$ is $2$-transitive and one can also show that the action induced on each block in $\mathcal{B}$ is also $2$-transitive (and so both induced groups are either affine or almost simple). In this paper, we make progress towards a classification of the rank $3$ imprimitive groups by studying the case where the induced action of $G$ on a block in $\mathcal{B}$ is of affine type. Our main theorem divides these rank $3$ groups into four classes, which are defined in terms of the kernel of the action of $G$ on $\mathcal{B}$. In particular, we completely determine the rank $3$ semiprimitive groups for which $G^{\mathcal{B}}$ is almost simple, extending recent work of Baykalov, Devillers and Praeger. We also prove that if $G$ is rank $3$ semiprimitive and $G^{\mathcal{B}}$ is affine, then $G$ has a regular normal subgroup which is a special $p$-group for some prime $p$.

Landauer's principle shows that the minimum energy cost to reset a classical bit in a bath with temperature $T$ is $k_{B}T\ln2$ in the infinite time. However, the task to reset the bit in finite time has posted a new challenge, especially for quantum bit (qubit) where both the operation time and controllability are limited. We design a shortcut-to-isothermal scheme to reset a qubit in finite time $\tau$ with limited controllability. The energy cost is minimized with the optimal control scheme with and without nonholonomic constraint. This optimal control scheme can provide a reference to realize qubit reset with minimum energy cost for the limited time., Comment: 8 pages, 7 figures

Machine learning makes multimedia data (e.g., images) more attractive, however, multimedia data is usually distributed and privacy sensitive. Multiple distributed multimedia clients can resort to federated learning (FL) to jointly learn a global shared model without requiring to share their private samples with any third-party entities. In this paper, we show that FL suffers from the cross-client generative adversarial networks (GANs)-based (C-GANs) attack, in which a malicious client (i.e., adversary) can reconstruct samples with the same distribution as the training samples from other clients (i.e., victims). Since a benign client's data can be leaked to the adversary, this attack brings the risk of local data leakage for clients in many security-critical FL applications. Thus, we propose Fed-EDKD (i.e., Federated Ensemble Data-free Knowledge Distillation) technique to improve the current popular FL schemes to resist C-GANs attack. In Fed-EDKD, each client submits a local model to the server for obtaining an ensemble global model. Then, to avoid model expansion, Fed-EDKD adopts data-free knowledge distillation techniques to transfer knowledge from the ensemble global model to a compressed model. By this way, Fed-EDKD reduces the adversary's control capability over the global model, so Fed-EDKD can effectively mitigate C-GANs attack. Finally, the experimental results demonstrate that Fed-EDKD significantly mitigates C-GANs attack while only incurring a slight accuracy degradation of FL.

The phenomenon of floating can be fascinating in any field, with its presence seen in art, films, and scientific research. This phenomenon is a captivating and pertinent subject with practical applications, such as Penning traps for antimatter confinement and Ion traps as essential architectures for quantum computing models. In our project, we reproduced the 1893 water bridge experiment using glycerol and first observed that lump-like macroscopic dipole moments can undergo near-periodic oscillations that exhibit floating effects and do not need classical bridge form. By combining experimental analysis, neural networks, investigation of Kelvin force generated by the Finite element method, and exploration of discharging, we gain insights into the mechanisms of motion. Our discovery has overturned the previous impression of a bridge floating in the water, leading to a deeper understanding of the new trap mechanism under strong electric fields with a single pair of electrodes., Comment: 5 pages, 5 figures

The unique ability of fire ants to form a raft to survive flooding rain has enchanted biologists as well as researchers in other disciplines. It has been established during the last decade that an aggregation of fire ants exhibits viscoelasticity with respect to external compression and shearing among numerous unusual mechanical properties. In addition to clarifying that the Cheerios effect is neither sufficient nor essential for the ant raft, we perform the force-displacement and creep experiments on the ant raft and concentrate on unearthing properties that derive from the unique combination of self-healing and activeness of its constituent. Varying pull speed results in distinct mechanical responses and fracture patterns, characteristic of ductile and brittle material. By image processing, we count the number of ants that actively participate in the stress-strain relation and determine their orientation to map out the force chain. The latter information reveals that the pull force expedites the alignment of fire ants, in analogy to the effect of an electric field on liquid crystal polymers. In addition, the raft can be tailored not to transversely deform in response to the axial strain. Without resorting to specific geometry structures, this property of zero Poisson's ratio is enabled by the active recruitment of ants from the top to bottom layer to keep the raft from disintegrating. Furthermore, effective Young's modulus can also be customized and is proportion to either the raft length or its inverse, depending on whether the raft is in the elastic or plastic region., Comment: 7 pages, 7 figures

We show the following result: Let $(M,g_0)$ be a compact manifold of dimension $n\geq 12$ with positive isotropic curvature. Then $M$ is diffeomorphic to a spherical space form, or a quotient manifold of $\mathbb{S}^{n-1}\times \mathbb{R}$ by a cocompact discrete subgroup of the isometry group of the round cylinder $\mathbb{S}^{n-1}\times \mathbb{R}$, or a connected sum of a finite number of such manifolds. This extends previous works of Brendle and Chen-Tang-Zhu, and improves a work of Huang. The proof uses Ricci flow with surgery on compact orbifolds, with the help of the ambient isotopy uniqueness of closed tubular neighborhoods of compact suborbifolds., Comment: 17 pages, added two corollaries

Semantic segmentation of multichannel images is a fundamental task for many applications. Selecting an appropriate channel combination from the original multichannel image can improve the accuracy of semantic segmentation and reduce the cost of data storage, processing and future acquisition. Existing channel selection methods typically use a reasonable selection procedure to determine a desirable channel combination, and then train a semantic segmentation network using that combination. In this study, the concept of pruning from a supernet is used for the first time to integrate the selection of channel combination and the training of a semantic segmentation network. Based on this concept, a One-Shot Task-Adaptive (OSTA) channel selection method is proposed for the semantic segmentation of multichannel images. OSTA has three stages, namely the supernet training stage, the pruning stage and the fine-tuning stage. The outcomes of six groups of experiments (L7Irish3C, L7Irish2C, L8Biome3C, L8Biome2C, RIT-18 and Semantic3D) demonstrated the effectiveness and efficiency of OSTA. OSTA achieved the highest segmentation accuracies in all tests (62.49% (mIoU), 75.40% (mIoU), 68.38% (mIoU), 87.63% (mIoU), 66.53% (mA) and 70.86% (mIoU), respectively). It even exceeded the highest accuracies of exhaustive tests (61.54% (mIoU), 74.91% (mIoU), 67.94% (mIoU), 87.32% (mIoU), 65.32% (mA) and 70.27% (mIoU), respectively), where all possible channel combinations were tested. All of this can be accomplished within a predictable and relatively efficient timeframe, ranging from 101.71% to 298.1% times the time required to train the segmentation network alone. In addition, there were interesting findings that were deemed valuable for several fields.

In this paper, we present CarGameAR: An Integrated AR Car Game Authoring Interface for Custom-Built Car Programed on Arduino Board. The car consists of an Arduino board, an H-bridge, and motors. The objective of the project is to create a system that can move a car in different directions using a computer application. The system uses Unity software to create a virtual environment where the user can control the car using keyboard commands. The car's motion is achieved by sending signals from the computer to the Arduino board, which then drives the motors through the H-bridge. The project provides a cost-effective and efficient way to build a car, which can be used for educational purposes, such as teaching programming. Moreover, this project is not limited to the control of the car through keyboard commands in a virtual environment. The system can be adapted to support augmented reality (AR) technology, providing an even more immersive and engaging user experience. By integrating the car with AR, the user can control the car's motion using physical gestures and movements, adding an extra layer of interactivity to the system. This makes the car an ideal platform for game development in AR, allowing the user to create driving games that blend the physical and virtual worlds seamlessly. Additionally, the car's affordability and ease of construction make it an accessible and valuable tool for teaching programming and principles in a fun and interactive way. Overall, this project demonstrates the versatility and potential of the car system, highlighting the various applications and possibilities it offers for both education and entertainment.

Let $G$ be a permutation group on a finite set $\Omega$. The base size of $G$ is the minimal size of a subset of $\Omega$ with trivial pointwise stabiliser in $G$. In this paper, we extend earlier work of Fawcett by determining the precise base size of every finite primitive permutation group of diagonal type. In particular, this is the first family of primitive groups arising in the O'Nan-Scott theorem for which the exact base size has been computed in all cases. Our methods also allow us to determine all the primitive groups of diagonal type with a unique regular suborbit., Comment: to appear in Forum Math. Sigma

Music, enchanting and poetic, permeates every corner of human civilization. Although music is not unfamiliar to people, our understanding of its essence remains limited, and there is still no universally accepted scientific description. This is primarily due to music being regarded as a product of both reason and emotion, making it difficult to define. In this article, we focus on the fundamental elements of music and construct an evolutionary network from the perspective of music as a natural language, aligning with the statistical characteristics of texts. Through this approach, we aim to comprehend the structural differences in music across different periods, enabling a more scientific exploration of music. Relying on the advantages of structuralism, we can concentrate on the relationships and order between the physical elements of music, rather than getting entangled in the blurred boundaries of science and philosophy. The scientific framework we present not only conforms to past conclusions in music, but also serves as a bridge that connects music to natural language processing and knowledge graphs., Comment: 7 pages, 8 figures

In this paper, we extend well-designed 3D face masks into AR face masks and demonstrate the possibility of transforming this into an NFT-copyrighted AR face mask that helps authenticate the ownership of the AR mask user so as to improve creative control, brand identification, and ID protection. The output of this project will not only potentially validate the value of the NFT technology but also explore how to combine the NFT technology with AR technology so as to be applied to e-commerce and e-business aspects of the multimedia industry.

Neural network pruning is an essential technique for reducing the size and complexity of deep neural networks, enabling large-scale models on devices with limited resources. However, existing pruning approaches heavily rely on training data for guiding the pruning strategies, making them ineffective for federated learning over distributed and confidential datasets. Additionally, the memory- and computation-intensive pruning process becomes infeasible for recourse-constrained devices in federated learning. To address these challenges, we propose FedTiny, a distributed pruning framework for federated learning that generates specialized tiny models for memory- and computing-constrained devices. We introduce two key modules in FedTiny to adaptively search coarse- and finer-pruned specialized models to fit deployment scenarios with sparse and cheap local computation. First, an adaptive batch normalization selection module is designed to mitigate biases in pruning caused by the heterogeneity of local data. Second, a lightweight progressive pruning module aims to finer prune the models under strict memory and computational budgets, allowing the pruning policy for each layer to be gradually determined rather than evaluating the overall model structure. The experimental results demonstrate the effectiveness of FedTiny, which outperforms state-of-the-art approaches, particularly when compressing deep models to extremely sparse tiny models. FedTiny achieves an accuracy improvement of 2.61% while significantly reducing the computational cost by 95.91% and the memory footprint by 94.01% compared to state-of-the-art methods., Comment: This paper has been accepted to ICDCS 2023