Search

With the growing demand for massive internet of things (IoT), new IoT technology, namely ambient IoT (A-IoT), has been studied in the 3rd Generation Partnership Project (3GPP). A-IoT devices are batteryless and consume ultra-low power, relying on energy harvesting and energy storage to capture a small amount of energy for communication. A promising usecase of A-IoT is inventory, where a reader communicates with hundreds of A-IoT devices to identify them. However, energy harvesting required before communication can significantly delay or even fail inventory completion. In this work, solutions including duty cycled monitoring (DCM), device grouping and low-power receiving chain are proposed. Evaluation results show that the time required for a reader to complete an inventory procedure for hundreds of A-IoT devices can be reduced by 50% to 83% with the proposed methods.

This paper introduces DeepFlow, a scalable and serverless AI platform designed to efficiently serve large language models (LLMs) at scale in cloud environments. DeepFlow addresses key challenges such as resource allocation, serving efficiency, and cold start latencies through four main design components. First, it uses a simple serverless abstraction called the request-job-task model, which helps manage AI workloads across post-training and model serving tasks. Second, it builds an in-house serving engine FlowServe using a microkernel-inspired design, NPU-centric execution, and SPMD-based parallelism to optimize LLM serving. The system also includes novel scheduling policies tailored for both PD-disaggregated and PD-colocated configurations. With optimizations like pre-warmed pods, DRAM pre-loading, and NPU-fork, DeepFlow can scale up to 64 instances in seconds. DeepFlow has been in production for over a year, operating on a large Ascend NPU cluster and providing industrystandard APIs for fine-tuning, agent serving, and model serving to our customers.

Large Language Models (LLMs) exhibit strong general-purpose language capabilities. However, fine-tuning these models on domain-specific tasks often leads to catastrophic forgetting, where the model overwrites or loses essential knowledge acquired during pretraining. This phenomenon significantly limits the broader applicability of LLMs. To address this challenge, we propose a novel approach to compute the element-wise importance of model parameters crucial for preserving general knowledge during fine-tuning. Our method utilizes a dual-objective optimization strategy: (1) regularization loss to retain the parameter crucial for general knowledge; (2) cross-entropy loss to adapt to domain-specific tasks. Additionally, we introduce layer-wise coefficients to account for the varying contributions of different layers, dynamically balancing the dual-objective optimization. Extensive experiments on scientific, medical, and physical tasks using GPT-J and LLaMA-3 demonstrate that our approach mitigates catastrophic forgetting while enhancing model adaptability. Compared to previous methods, our solution is approximately 20 times faster and requires only 10%-15% of the storage, highlighting the practical efficiency. The code will be released., Comment: Work in progress

The polar magnets A$_2$Mo$_3$O$_8$ (A=Fe, Mn, Co, and Ni) feature a bipartite structure, where the magnetic A$^{2+}$ ions occupy two different sites with octahedral and tetrahedral oxygen coordinations. This bipartite structure provides a platform for the emergence of nontrivial magnetoelectric (ME) effects and intriguing excitation behaviors, and thus creates significant research interest. In this study, we conduct inelastic neutron scattering measurements on single crystals of Mn$_2$Mo$_3$O$_8$, an L-type ferrimagnet in the A$_2$Mo$_3$O$_8$ family, to investigate its spin dynamics. The obtained magnetic excitation spectra reveal two distinct magnon dispersions corresponding to the octahedral and tetrahedral spins in Mn$_2$Mo$_3$O$_8$. These magnon bands can be well described by a spin Hamiltonian including Heisenberg and single-ion anisotropy terms. Employing our effective spin model, we successfully reproduce the unusual temperature dependence of the L-type ferrimagnetic susceptibility through self-consistent mean-field theory. This research reveals the significance of the bipartite structure in determining the excitation properties of the polar magnets $\rm{A_{2}Mo_{3}O_{8}}$ and provides valuable insights into the spin dynamics of L-type ferrimagnets., Comment: 8 pages, 5 figues, published in PRB

This paper presents a comprehensive study on low-complexity waveform, modulation and coding (WMC) designs for the 3rd Generation Partnership Project (3GPP) Ambient Internet of Things (A-IoT). A-IoT is a low-cost, low-power IoT system inspired by Ultra High Frequency (UHF) Radio Frequency Identification (RFID) and aims to leverage existing cellular network infrastructure for efficient RF tag management. The paper compares the physical layer (PHY) design challenges and requirements of RFID and A-IoT, particularly focusing on backscatter communications. An overview of the standardization for PHY designs in Release 19 A-IoT is provided, along with detailed schemes of the proposed low-complex WMC designs. The performance of device-to-reader link designs is validated through simulations, demonstrating 6 dB improvements of the proposed baseband waveform with coherent receivers compared to RFID line coding-based solutions with non-coherent receivers when channel coding is adopted., Comment: This work has been submitted to the IEEE (IEEE Communications Standards Magazine, Special Issue for Ambient IoT) for possible publication

Fluid antenna multiple access (FAMA), enabled by the fluid antenna system (FAS), offers a new and straightforward solution to massive connectivity. Previous results on FAMA were primarily based on narrowband channels. This paper studies the adoption of FAMA within the fifth-generation (5G) orthogonal frequency division multiplexing (OFDM) framework, referred to as OFDM-FAMA, and evaluate its performance in broadband multipath channels. We first design the OFDM-FAMA system, taking into account 5G channel coding and OFDM modulation. Then the system's achievable rate is analyzed, and an algorithm to approximate the FAS configuration at each user is proposed based on the rate. Extensive link-level simulation results reveal that OFDM-FAMA can significantly improve the multiplexing gain over the OFDM system with fixed-position antenna (FPA) users, especially when robust channel coding is applied and the number of radio-frequency (RF) chains at each user is small., Comment: Submitted, under review

Inspired by the theory of JT supergravity, Stanford-Witten derived a remarkable recursion formula of Weil-Petersson volumes of moduli space of super Riemann surfaces. It is the super version of the celebrated Mirzakhani's recursion formula. In this paper, we generalize Stanford-Witten's formula to include high degree kappa classes.

In this paper we study a nonlinear free boundary problem on the radial growth of a two-layer solid tumor with a quiescent core. The tumor surface and its inner interface separating the proliferating cells and the quiescent cells are both free boundaries. By deeply analyzing their relationship and employing the maximum principle, we show this problem is globally well-posed and prove the existence of a unique positive threshold $\sigma^*$ such that the problem admits a unique stationary solution with a quiescent core if and only if the externally supplied nutrient $\bar\sigma> \sigma^*$. The stationary solution is globally asymptotically stable. The formation of the quiescent core and its interesting connection with the necrotic core are also given.

Using a three-flavor Nambu--Jona-Lasinio model to describe the charge-parity violating effects through axion field, we investigate the axion effects on quark matter and quark-matter cores in massive hybrid stars. The properties of quark matter vary with the scaled axion field $a/f_a$ in a periodic manner, with a period of $2\pi$. Within the range from 0 to $\pi $, axion field decrease the baryon chemical potential of the first-order phase transition, leading to an increase in normalized pressure and stiffening of the quark matter equation of state. The effect of axions on hybrid star matter that includes the hadron-quark phase transition is contrary to expectations. The axion field shifts the onset of the hadron-quark mixed phase to lower densities but slightly softens the equation of state of the mixed phase matter, which also results in a slight decrease in the maximum mass and corresponding radius of the hybrid stars. However, we also find that the lowering of the onset of the mixed phase significantly increases the radius and mass of the quark-matter core in the hybrid star. Therefore, our results indicate with axion effects, a sizable quark-matter core can appear in $2M_{\odot}$ massive neutron stars., Comment: 7 pages, 3 figures

Unknown Object Detection (UOD) aims to identify objects of unseen categories, differing from the traditional detection paradigm limited by the closed-world assumption. A key component of UOD is learning a generalized representation, i.e. objectness for both known and unknown categories to distinguish and localize objects from the background in a class-agnostic manner. However, previous methods obtain supervision signals for learning objectness in isolation from either localization or classification information, leading to poor performance for UOD. To address this issue, we propose a transformer-based UOD framework, UN-DETR. Based on this, we craft Instance Presence Score (IPS) to represent the probability of an object's presence. For the purpose of information complementarity, IPS employs a strategy of joint supervised learning, integrating attributes representing general objectness from the positional and the categorical latent space as supervision signals. To enhance IPS learning, we introduce a one-to-many assignment strategy to incorporate more supervision. Then, we propose Unbiased Query Selection to provide premium initial query vectors for the decoder. Additionally, we propose an IPS-guided post-process strategy to filter redundant boxes and correct classification predictions for known and unknown objects. Finally, we pretrain the entire UN-DETR in an unsupervised manner, in order to obtain objectness prior. Our UN-DETR is comprehensively evaluated on multiple UOD and known detection benchmarks, demonstrating its effectiveness and achieving state-of-the-art performance., Comment: Accepted by AAAI-2025;15 pages, 11figures

In complex physical systems, conventional differential equations often fall short in capturing non-local and memory effects, as they are limited to local dynamics and integer-order interactions. This study introduces a stepwise data-driven framework for discovering fractional differential equations (FDEs) directly from data. FDEs, known for their capacity to model non-local dynamics with fewer parameters than integer-order derivatives, can represent complex systems with long-range interactions. Our framework applies deep neural networks as surrogate models for denoising and reconstructing sparse and noisy observations while using Gaussian-Jacobi quadrature to handle the challenges posed by singularities in fractional derivatives. To optimize both the sparse coefficients and fractional order, we employ an alternating optimization approach that combines sparse regression with global optimization techniques. We validate the framework across various datasets, including synthetic anomalous diffusion data, experimental data on the creep behavior of frozen soils, and single-particle trajectories modeled by L\'{e}vy motion. Results demonstrate the framework's robustness in identifying the structure of FDEs across diverse noise levels and its capacity to capture integer-order dynamics, offering a flexible approach for modeling memory effects in complex systems.

A high-sensitivity wireless pressure sensor with active processing structure designed on the dielectric substrate has been present and evaluated in this paper. The sensor configuration has been optimized by computer-aided design to achieve highest sensitivity and maximum working range for a given dimension. With the average sensitivity of 187kHz/kPa, the proposed pressure sensor is equipped with the ability to measure pressure loaded up to 1.5MPa under room temperature. Additionally, a novel simulation method applied on pressure related design is proposed in this article, with the accuracy reaching threefold enhancement, filling the blank of electromagnetic simulation of pressure deformation. Other characteristics of the devices have been investigated and are presented.

We classify all connected and Lagrangian \'etale algebras in the Drinfeld center $\mathscr{Z}_1(\mathbf{2Vect}^\pi_G)$, where $G$ is a finite group and $\pi$ is a 4-cocycle on $G$. By D\'ecoppet's result every bosonic fusion 2-category $\mathfrak{C}$ has its Drinfeld center equivalent to $\mathscr{Z}_1(\mathbf{2Vect}^\pi_G)$ for some $G$ and $\pi$. Combining this fact with classification of Lagrangian algebras in $\mathscr{Z}_1(\mathbf{2Vect}^\pi_G)$, we obtain a classification of bosonic fusion 2-categories., Comment: Comments welcome!

We investigate the effect of top Al2O3 interlayer thickness on the memory window (MW) of Si channel ferroelectric field-effect transistors (Si-FeFETs) with TiN/Al$_2$O$_3$/Hf$_{0.5}$Zr$_{0.5}$O$_2$/SiO$_x$/Si (MIFIS) gate structure. We find that the MW first increases and then remains almost constant with the increasing thickness of the top Al2O3. The phenomenon is attributed to the lower electric field of the ferroelectric Hf$_{0.5}$Zr$_{0.5}$O$_2$ in the MIFIS structure with a thicker top Al2O3 after a program operation. The lower electric field makes the charges trapped at the top Al2O3/Hf0.5Zr0.5O$_2$ interface, which are injected from the metal gate, cannot be retained. Furthermore, we study the effect of the top Al$_2$O$_3$ interlayer thickness on the reliability (endurance characteristics and retention characteristics). We find that the MIFIS structure with a thicker top Al$_2$O$_3$ interlayer has poorer retention and endurance characteristics. Our work is helpful in deeply understanding the effect of top interlayer thickness on the MW and reliability of Si-FeFETs with MIFIS gate stacks., Comment: 7 pages, 12 figures

Shortcut learning refers to the phenomenon where models employ simple, non-robust decision rules in practical tasks, which hinders their generalization and robustness. With the rapid development of large language models (LLMs) in recent years, an increasing number of studies have shown the impact of shortcut learning on LLMs. This paper provides a novel perspective to review relevant research on shortcut learning in In-Context Learning (ICL). It conducts a detailed exploration of the types of shortcuts in ICL tasks, their causes, available benchmarks, and strategies for mitigating shortcuts. Based on corresponding observations, it summarizes the unresolved issues in existing research and attempts to outline the future research landscape of shortcut learning., Comment: 20 pages, 7 figures

Graph based molecular representation learning is essential for accurately predicting molecular properties in drug discovery and materials science; however, it faces significant challenges due to the intricate relationships among molecules and the limited chemical knowledge utilized during training. While contrastive learning is often employed to handle molecular relationships, its reliance on binary metrics is insufficient for capturing the complexity of these interactions. Multimodal fusion has gained attention for property reasoning, but previous work has explored only a limited range of modalities, and the optimal stages for fusing different modalities in molecular property tasks remain underexplored. In this paper, we introduce MMFRL (Multimodal Fusion with Relational Learning for Molecular Property Prediction), a novel framework designed to overcome these limitations. Our method enhances embedding initialization through multimodal pretraining using relational learning. We also conduct a systematic investigation into the impact of modality fusion at different stages such as early, intermediate, and late, highlighting their advantages and shortcomings. Extensive experiments on MoleculeNet benchmarks demonstrate that MMFRL significantly outperforms existing methods. Furthermore, MMFRL enables task-specific optimizations. Additionally, the explainability of MMFRL provides valuable chemical insights, emphasizing its potential to enhance real-world drug discovery applications.

Using dispersion relations of the scattering amplitudes and semi-definite programming, we calculate causality bounds on the Wilson coefficients in scalar-tensor effective field theories that include parity-violating operators. Particular attention has been paid to the dynamical-Chern-Simons (dCS) and scalar-Gauss-Bonnet (sGB) couplings, along with higher order coefficients, and the interplay between them. For the leading terms, the bounds on the parity-conserving and -violating coefficients are simply projections of the complex coefficients. Some parity-violating coefficients are found to be upper bounded by the the parity-conserving counterparts, or the higher order parity-conserving coefficients. While the observational constraints on parity-violating coefficients are weaker than the parity-conserving counterparts, the causality bounds are of comparable strength and thus may play a more prominent role in constraining strong gravity effects in upcoming observations., Comment: 49 pages, 19 figures, 1 table

Blockchain technology holds promise for Web 3.0, but scalability remains a critical challenge. Here, we present a mathematical theory for a novel blockchain network topology based on fractal N-dimensional simplexes. This Hyper-simplex fractal network folds one-dimensional data blocks into geometric shapes, reflecting both underlying and overlaying network connectivities. Our approach offers near-infinite scalability, accommodating trillions of nodes while maintaining efficiency. We derive the mathematical foundations for generating and describing these network topologies, proving key properties such as node count, connectivity patterns, and fractal dimension. The resulting structure facilitates a hierarchical consensus mechanism and enables deterministic address mapping for rapid routing. This theoretical framework lays the groundwork for next-generation blockchain architectures, potentially revolutionizing large-scale decentralized systems. The Part I work was conducted between March and September 2024.

Recently, there has been significant interest in replacing the reward model in Reinforcement Learning with Human Feedback (RLHF) methods for Large Language Models (LLMs), such as Direct Preference Optimization (DPO) and its variants. These approaches commonly use a binary cross-entropy mechanism on pairwise samples, i.e., minimizing and maximizing the loss based on preferred or dis-preferred responses, respectively. However, while this training strategy omits the reward model, it also overlooks the varying preference degrees within different responses. We hypothesize that this is a key factor hindering LLMs from sufficiently understanding human preferences. To address this problem, we propose a novel Self-supervised Preference Optimization (SPO) framework, which constructs a self-supervised preference degree loss combined with the alignment loss, thereby helping LLMs improve their ability to understand the degree of preference. Extensive experiments are conducted on two widely used datasets of different tasks. The results demonstrate that SPO can be seamlessly integrated with existing preference optimization methods and significantly boost their performance to achieve state-of-the-art performance. We also conduct detailed analyses to offer comprehensive insights into SPO, which verifies its effectiveness. The code is available at https://github.com/lijian16/SPO., Comment: Accepted at EMNLP 2024 Findings

The Internet of Vehicles (IoV), which enables interactions between vehicles, infrastructure, and the environment, faces challenges in maintaining communication security and reliable automated decisions. This paper introduces a decentralized framework comprising a primary layer for managing inter-vehicle communication and a sub-layer for securing intra-vehicle interactions. By implementing blockchain-based protocols like Blockchain-integrated Secure Authentication (BiSA) and Decentralized Blockchain Name Resolution (DBNR), the framework ensures secure, decentralized identity management and reliable data exchanges, thereby supporting safe and efficient autonomous vehicle operations., Comment: The 2024 7th IEEE Conference on Dependable and Secure Computing

We propose a novel approach to assess the public's coping behavior during the COVID-19 outbreak by examining the emotions. Specifically, we explore (1) changes in the public's emotions with the COVID-19 crisis progression and (2) the impacts of the public's emotions on their information-seeking, information-sharing behaviors, and compliance with stay-at-home policies. We base the study on the appraisal tendency framework, detect the public's emotions by fine-tuning a pre-trained RoBERTa model, and cross-analyze third-party behavioral data. We demonstrate the feasibility and reliability of our proposed approach in providing a large-scale examination of the publi's emotions and coping behaviors in a real-world crisis: COVID-19. The approach complements prior crisis communication research, mainly based on self-reported, small-scale experiments and survey data. Our results show that anger and fear are more prominent than other emotions experienced by the public at the pandemic's outbreak stage. Results also show that the extent of low certainty and passive emotions (e.g., sadness, fear) was related to increased information-seeking and information-sharing behaviors. Additionally, high-certainty (e.g., anger) and low-certainty (e.g., sadness, fear) emotions during the outbreak correlated to the public's compliance with stay-at-home orders., Comment: Presented at 10th International Conference on Computational Social Science (IC2S2) 2024

Controllable character animation is an emerging task that generates character videos controlled by pose sequences from given character images. Although character consistency has made significant progress via reference UNet, another crucial factor, pose control, has not been well studied by existing methods yet, resulting in several issues: 1) The generation may fail when the input pose sequence is corrupted. 2) The hands generated using the DWPose sequence are blurry and unrealistic. 3) The generated video will be shaky if the pose sequence is not smooth enough. In this paper, we present RealisDance to handle all the above issues. RealisDance adaptively leverages three types of poses, avoiding failed generation caused by corrupted pose sequences. Among these pose types, HaMeR provides accurate 3D and depth information of hands, enabling RealisDance to generate realistic hands even for complex gestures. Besides using temporal attention in the main UNet, RealisDance also inserts temporal attention into the pose guidance network, smoothing the video from the pose condition aspect. Moreover, we introduce pose shuffle augmentation during training to further improve generation robustness and video smoothness. Qualitative experiments demonstrate the superiority of RealisDance over other existing methods, especially in hand quality., Comment: Technical Report

Blockchain is widely applied in logistics, finance, and agriculture. As single blockchain users grow, scalability becomes crucial. However, existing works lack a comprehensive summary of blockchain scalability. They focus on single chains or cross-chain technologies. This survey summarizes scalability across the physical and logical layers, as well as inner-chain, inter-chain, and technology dimensions. The physical layer covers data and protocols, while the logical layer represents blockchain architecture. Each component is analyzed from inner-chain and inter-chain perspectives, considering technological factors. The aim is to enhance researchers' understanding of blockchain's architecture, data, and protocols to advance scalability research.

Considering the high-performance and low-power requirements of edge AI, this study designs a specialized instruction set processor for edge AI based on the RISC-V instruction set architecture, addressing practical issues in digital signal processing for edge devices. This design enhances the execution efficiency of edge AI and reduces its energy consumption with limited hardware overhead, meeting the demands for efficient large language model (LLM) inference computation in edge AI applications. The main contributions of this paper are as follows: For the characteristics of large language models, custom instructions were extended based on the RISC-V instruction set to perform vector dot product calculations, accelerating the computation of large language models on dedicated vector dot product acceleration hardware. Based on the open-source high-performance RISC-V processor core XiangShan Nanhu architecture, the vector dot product specialized instruction set processor Nanhu-vdot was implemented, which adds vector dot product calculation units and pipeline processing logic on top of the XiangShan Nanhu.The Nanhu-vdot underwent FPGA hardware testing, achieving over four times the speed of scalar methods in vector dot product computation. Using a hardware-software co-design approach for second-generation Generative Pre-Trained Transformer (GPT-2) model inference, the speed improved by approximately 30% compared to pure software implementation with almost no additional consumption of hardware resources and power consumption., Comment: 10 pages, in Chinese language, 6 figures

We study the large N-dimensional limit of the Hessian spectrum at the global minimum of a subclass of spherical mixed p-spin models, extending those results in the regime of topology trivialization., Comment: 16 pages

This paper examines the impact of emerging fluid antenna systems (FAS) on reconfigurable intelligent surface (RIS)-aided secure communications. Specifically, we consider a classic wiretap channel, where a fixed-antenna transmitter sends confidential information to an FAS-equipped legitimate user with the help of an RIS, while an FAS-equipped eavesdropper attempts to decode the message. To evaluate the proposed wireless scenario, we first introduce the cumulative distribution function (CDF) and probability density function (PDF) of the signal-to-noise ratio (SNR) at each node, using the central limit theorem and the Gaussian copula function. We then derive a compact analytical expression for the secrecy outage probability (SOP). Our numerical results reveal how the incorporation of FAS and RIS can significantly enhance the performance of secure communications.

Explainable Recommendation task is designed to receive a pair of user and item and output explanations to justify why an item is recommended to a user. Many models treat review-generation as a proxy of explainable recommendation. Although they are able to generate fluent and grammatical sentences, they suffer from generality and hallucination issues. We propose a personalized, aspect-controlled model called Multi-Aspect Prompt LEarner (MAPLE), in which it integrates aspect category as another input dimension to facilitate the memorization of fine-grained aspect terms. Experiments on two real-world review datasets in restaurant domain show that MAPLE outperforms the baseline review-generation models in terms of text and feature diversity while maintaining excellent coherence and factual relevance. We further treat MAPLE as a retriever component in the retriever-reader framework and employ a Large-Language Model (LLM) as the reader, showing that MAPLE's explanation along with the LLM's comprehension ability leads to enriched and personalized explanation as a result. We will release the code and data in this http upon acceptance., Comment: 8 main pages, 10 pages for appendix. Under review

Cardiac magnetic resonance (CMR) imaging and computed tomography (CT) are two common non-invasive imaging methods for assessing patients with cardiovascular disease. CMR typically acquires multiple sparse 2D slices, with unavoidable respiratory motion artefacts between slices, whereas CT acquires isotropic dense data but uses ionising radiation. In this study, we explore the combination of Slice Shifting Algorithm (SSA), Spatial Transformer Network (STN), and Label Transformer Network (LTN) to: 1) correct respiratory motion between segmented slices, and 2) transform sparse segmentation data into dense segmentation. All combinations were validated using synthetic motion-corrupted CMR slice segmentation generated from CT in 1699 cases, where the dense CT serves as the ground truth. In 199 testing cases, SSA-LTN achieved the best results for Dice score and Huasdorff distance (94.0% and 4.7 mm respectively, average over 5 labels) but gave topological errors in 8 cases. STN was effective as a plug-in tool for correcting all topological errors with minimal impact on overall performance (93.5% and 5.0 mm respectively). SSA also proves to be a valuable plug-in tool, enhancing performance over both STN-based and LTN-based models. The code for these different combinations is available at https://github.com/XESchong/STACOM2024., Comment: 13 pages, STACOM2024

We give a general description of the system evolution under the interaction between qubit and quantum field theory up to the second order perturbation, which is also referred to as the simplified model of light-matter interaction. The results are classified into rotating and counter-rotating wave terms, the former corresponding to stimulated absorption and emission, and the latter to Unruh and anti-Unruh effects. We obtain not only the reduced density matrix of the qubit, but also the backreaction obtained by quantum field theory as the environment. The result shows that the energy variation of the quantum field theory is related to trajectory and the initial state of the qubit, the expectation values of the linear and quadratic field operators, and the temporal order product operator. When the qubit is in accelerated motion, the conventional Unruh effect causes the vacuum state to possess a "temperature", which raises some doubts about the validity of Landauer's principle. We prove that Landauer's principle still holds for any state of motion., Comment: 15 pages; v2: updated references, accepted by EPJC

Compressing a set of unordered points is far more challenging than compressing images/videos of regular sample grids, because of the difficulties in characterizing neighboring relations in an irregular layout of points. Many researchers resort to voxelization to introduce regularity, but this approach suffers from quantization loss. In this research, we use the KNN method to determine the neighborhoods of raw surface points. This gives us a means to determine the spatial context in which the latent features of 3D points are compressed by arithmetic coding. As such, the conditional probability model is adaptive to local geometry, leading to significant rate reduction. Additionally, we propose a dual-layer architecture where a non-learning base layer reconstructs the main structures of the point cloud at low complexity, while a learned refinement layer focuses on preserving fine details. This design leads to reductions in model complexity and coding latency by two orders of magnitude compared to SOTA methods. Moreover, we incorporate an implicit neural representation (INR) into the refinement layer, allowing the decoder to sample points on the underlying surface at arbitrary densities. This work is the first to effectively exploit content-aware local contexts for compressing irregular raw point clouds, achieving high rate-distortion performance, low complexity, and the ability to function as an arbitrary-scale upsampling network simultaneously., Comment: Accepted by ECCV 2024

Data plays a crucial role in training learning-based methods for 3D point cloud registration. However, the real-world dataset is expensive to build, while rendering-based synthetic data suffers from domain gaps. In this work, we present PointRegGPT, boosting 3D point cloud registration using generative point-cloud pairs for training. Given a single depth map, we first apply a random camera motion to re-project it into a target depth map. Converting them to point clouds gives a training pair. To enhance the data realism, we formulate a generative model as a depth inpainting diffusion to process the target depth map with the re-projected source depth map as the condition. Also, we design a depth correction module to alleviate artifacts caused by point penetration during the re-projection. To our knowledge, this is the first generative approach that explores realistic data generation for indoor point cloud registration. When equipped with our approach, several recent algorithms can improve their performance significantly and achieve SOTA consistently on two common benchmarks. The code and dataset will be released on https://github.com/Chen-Suyi/PointRegGPT., Comment: To appear at the European Conference on Computer Vision (ECCV) 2024

In this paper, we investigate the use of diffusion models which are pre-trained on large-scale image-caption pairs for open-vocabulary 3D semantic understanding. We propose a novel method, namely Diff2Scene, which leverages frozen representations from text-image generative models, along with salient-aware and geometric-aware masks, for open-vocabulary 3D semantic segmentation and visual grounding tasks. Diff2Scene gets rid of any labeled 3D data and effectively identifies objects, appearances, materials, locations and their compositions in 3D scenes. We show that it outperforms competitive baselines and achieves significant improvements over state-of-the-art methods. In particular, Diff2Scene improves the state-of-the-art method on ScanNet200 by 12%., Comment: ECCV 2024

In this letter, we study the energy efficiency maximization problem for a fluid antenna system (FAS) in near field communications. Specifically, we consider a point-to-point near-field system where the base station (BS) transmitter has multiple fixed-position antennas and the user receives the signals with multiple fluid antennas. Our objective is to jointly optimize the transmit beamforming of the BS and the fluid antenna positions at the user for maximizing the energy efficiency. Our scheme is based on an alternating optimization algorithm that iteratively solves the beamforming and antenna position subproblems. Our simulation results validate the performance improvement of the proposed algorithm and confirm the effectiveness of FAS.

Image-based inspection systems have been widely deployed in manufacturing production lines. Due to the scarcity of defective samples, unsupervised anomaly detection that only leverages normal samples during training to detect various defects is popular. Existing feature-based methods, utilizing deep features from pretrained neural networks, show their impressive performance in anomaly localization and the low demand for the sample size for training. However, the detected anomalous regions of these methods always exhibit inaccurate boundaries, which impedes the downstream tasks. This deficiency is caused: (i) The decreased resolution of high-level features compared with the original image, and (ii) The mixture of adjacent normal and anomalous pixels during feature extraction. To address them, we propose a novel unified optimization framework (F2PAD) that leverages the Feature-level information to guide the optimization process for Pixel-level Anomaly Detection in the inference stage. The proposed framework is universal and plug-and-play, which can enhance various feature-based methods with limited assumptions. Case studies are provided to demonstrate the effectiveness of our strategy, particularly when applied to three popular backbone methods: PaDiM, CFLOW-AD, and PatchCore.

The advent of the sixth-generation (6G) networks presents another round of revolution for the mobile communication landscape, promising an immersive experience, robust reliability, minimal latency, extreme connectivity, ubiquitous coverage, and capabilities beyond communication, including intelligence and sensing. To achieve these ambitious goals, it is apparent that 6G networks need to incorporate the state-of-the-art technologies. One of the technologies that has garnered rising interest is fluid antenna system (FAS) which represents any software-controllable fluidic, conductive, or dielectric structure capable of dynamically changing its shape and position to reconfigure essential radio-frequency (RF) characteristics. Compared to traditional antenna systems (TASs) with fixed-position radiating elements, the core idea of FAS revolves around the unique flexibility of reconfiguring the radiating elements within a given space. One recent driver of FAS is the recognition of its position-flexibility as a new degree of freedom (dof) to harness diversity and multiplexing gains. In this paper, we provide a comprehensive tutorial, covering channel modeling, signal processing and estimation methods, information-theoretic insights, new multiple access techniques, and hardware designs. Moreover, we delineate the challenges of FAS and explore the potential of using FAS to improve the performance of other contemporary technologies. By providing insights and guidance, this tutorial paper serves to inspire researchers to explore new horizons and fully unleash the potential of FAS., Comment: 53 pages, 45 figures, 5 tables. Accepted by IEEE Communications Surveys and Tutorials

This letter investigates the secret communication problem for a fluid antenna system (FAS)-assisted wiretap channel, where the legitimate transmitter transmits an information-bearing signal to the legitimate receiver, and at the same time, transmits a jamming signal to interfere with the eavesdropper (Eve). Unlike the conventional jamming scheme, which usually transmits Gaussian noise that interferes not only with Eve but also with the legitimate receiver, in this letter, we consider that encoded codewords are transmitted to jam Eve. Then, by employing appropriate coding schemes, the legitimate receiver can successfully decode the jamming signal and then cancel the interference, while Eve cannot, even if it knows the codebooks. We aim to maximize the secrecy rate through port selection and power control. Although the problem is non-convex, we show that the optimal solution can be found. Simulation results show that by using the FAS technique and the proposed jamming scheme, the secrecy rate of the system can be significantly increased., Comment: 6 pages, 3 figures, this paper has been accepted by IEEE Communications Letters

This paper investigates the achievable region of a $K$-user discrete memoryless (DM) multiple access wiretap (MAC-WT) channel, where each user transmits both secret and open messages. All these messages are intended for Bob, while Eve is only interested in the secret messages. In the achievable coding strategy, the confidential information is protected by open messages and also by the introduction of auxiliary messages. When introducing an auxiliary message, one has to ensure that, on one hand, its rate is large enough for protecting the secret message from Eve and, on the other hand, the resulting sum rate (together with the secret and open message rate) does not exceed Bob's decoding capability. This yields an inequality structure involving the rates of all users' secret, open, and auxiliary messages. To obtain the rate region, the auxiliary message rates must be eliminated from the system of inequalities. A direct application of the Fourier-Motzkin elimination procedure is elusive since a) it requires that the number of users $K$ is explicitly given, and b) even for small $K = 3, 4, \ldots$, the number of inequalities becomes extremely large. We prove the result for general $K$ through the combined use of Fourier-Motzkin elimination procedure and mathematical induction. This paper adopts the strong secrecy metric, characterized by information leakage. To prove the achievability under this criterion, we analyze the resolvability region of a $K$-user DM-MAC channel. In addition, we show that users with zero secrecy rate can play different roles and use different strategies in encoding their messages. These strategies yield non-redundant rate inequalities. By considering all possible coding strategies, we provide a new achievable region for the considered channel, and show that it strictly improves those already known in the existing literature by considering a specific example., Comment: 61 pages, 15 figures. arXiv admin note: text overlap with arXiv:2209.05403

In this study, we present a novel platform based on scanning microwave microscopy for manipulating and detecting tiny vibrations of nanoelectromechanical resonators using a single metallic tip. The tip is placed on the top of a grounded silicon nitride membrane, acting as a movable top gate of the coupled resonator. We demonstrate its ability to map mechanical modes and investigate mechanical damping effects in a capacitive coupling scheme, based on its spatial resolution. We also manipulate the energy transfer coherently between the mode of the scanning tip and the underlying silicon nitride membrane, via parametric coupling. Typical features of optomechanics, such as anti-damping and electromechanically induced transparency, have been observed. Since the microwave optomechanical technology is fully compatible with quantum electronics and very low temperature conditions, it should provide a powerful tool for studying phonon tunnelling between two spatially separated vibrating elements, which could potentially be applied to quantum sensing.

This paper aims to balance performance and cost in a two-hop wireless cooperative communication network where the source and relays have contradictory optimization goals and make decisions in a distributed manner. This differs from most existing works that have typically assumed that source and relay nodes follow a schedule created implicitly by a central controller. We propose that the relays form an alliance in an attempt to maximize the benefit of relaying while the source aims to increase the channel capacity cost-effectively. To this end, we establish the trade problem as a Stackelberg game, and prove the existence of its equilibrium. Another important aspect is that we use multi-agent reinforcement learning (MARL) to approach the equilibrium in a situation where the instantaneous channel state information (CSI) is unavailable, and the source and relays do not have knowledge of each other's goal. A multi-agent deep deterministic policy gradient-based framework is designed, where the relay alliance and the source act as agents. Experiments demonstrate that the proposed method can obtain an acceptable performance that is close to the game-theoretic equilibrium for all players under time-invariant environments, which considerably outperforms its potential alternatives and is only about 2.9% away from the optimal solution.

We study the impact of top SiO2 interlayer thickness on the memory window (MW) of Si channel ferroelectric field-effect transistor (FeFET) with TiN/SiO2/Hf0.5Zr0.5O2/SiOx/Si (MIFIS) gate structure. We find that the MW increases with the increasing thickness of the top SiO2 interlayer, and such an increase exhibits a two-stage linear dependence. The physical origin is the presence of the different interfacial charges trapped at the top SiO2/Hf0.5Zr0.5O2 interface. Moreover, we investigate the dependence of endurance characteristics on initial MW. We find that the endurance characteristic degrades with increasing the initial MW. By inserting a 3.4 nm SiO2 dielectric interlayer between the gate metal TiN and the ferroelectric Hf0.5Zr0.5O2, we achieve a MW of 6.3 V and retention over 10 years. Our work is helpful in the device design of FeFET., Comment: 6 pages, 12 figures. arXiv admin note: substantial text overlap with arXiv:2404.15825

Market equilibrium is one of the most fundamental solution concepts in economics and social optimization analysis. Existing works on market equilibrium computation primarily focus on settings with a relatively small number of buyers. Motivated by this, our paper investigates the computation of market equilibrium in scenarios with a large-scale buyer population, where buyers and goods are represented by their contexts. Building on this realistic and generalized contextual market model, we introduce MarketFCNet, a deep learning-based method for approximating market equilibrium. We start by parameterizing the allocation of each good to each buyer using a neural network, which depends solely on the context of the buyer and the good. Next, we propose an efficient method to estimate the loss function of the training algorithm unbiasedly, enabling us to optimize the network parameters through gradient descent. To evaluate the approximated solution, we introduce a metric called Nash Gap, which quantifies the deviation of the given allocation and price pair from the market equilibrium. Experimental results indicate that MarketFCNet delivers competitive performance and significantly lower running times compared to existing methods as the market scale expands, demonstrating the potential of deep learning-based methods to accelerate the approximation of large-scale contextual market equilibrium., Comment: 22 pages

Automated segmentation of Cardiac Magnetic Resonance (CMR) plays a pivotal role in efficiently assessing cardiac function, offering rapid clinical evaluations that benefit both healthcare practitioners and patients. While recent research has primarily focused on delineating structures in the short-axis orientation, less attention has been given to long-axis representations, mainly due to the complex nature of structures in this orientation. Performing pixel-wise segmentation of the left ventricular (LV) myocardium and the four cardiac chambers in 2-D steady-state free precession (SSFP) cine sequences is a crucial preprocessing stage for various analyses. However, the challenge lies in the significant variability in contrast, appearance, orientation, and positioning of the heart across different patients, clinical views, scanners, and imaging protocols. Consequently, achieving fully automatic semantic segmentation in this context is notoriously challenging. In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in magnetic resonance images (MRI). Hence, there is a need for new methods to handle such structures' geometrical and textural complexities. We proposed 2D and 3D two-stage self-supervised deep learning segmentation hybrid transformer and CNN-based architectures for 4CH whole heart segmentation. Accurate segmentation of the ventricles and atria in 4CH views is crucial for analyzing heart health and reconstructing four-chamber meshes, which are essential for estimating various parameters to assess overall heart condition. Our proposed method outperformed state-of-the-art techniques, demonstrating superior performance in this domain., Comment: arXiv admin note: text overlap with arXiv:2206.07349 by other authors

Antiferromagnetic van der Waals family $\rm \textit{M}P\textit{X}_{3}\ (M=Fe,\ Mn,\ Co,\text{ and}\ Ni; X=S\text{ and}\ Se)$ have attracted significant research attention due to the possibility of realizing long-range magnetic order down to the monolayer limit. Here, we perform inelastic neutron scattering measurements on single crystal samples of MnPSe$_3$, a member of the $\rm \textit{M}P\textit{X}_{3}$ family, to study the spin dynamics and determine the effective spin model. The excited magnon bands are well characterized by a spin model, which includes a Heisenberg term with three intraplane exchange parameters ($J_{1}=-0.73$~meV, $J_{2}=-0.014$~meV, $J_{3}=-0.43$~meV) and one interplane parameter ($J_{c}=-0.054$~meV), and an easy-plane single-ion anisotropy term ($D=-0.035$~meV). Additionally, we observe the intersection of the magnon and phonon bands but no anomalous spectral features induced by the formation of magnon-phonon hybrid excitations at the intersecting region. We discuss possible reasons for the absence of such hybrid excitations in MnPSe$_3$., Comment: 11 pages, 5 figures