arXiv subjects
Publications and source records attributed to Li Yang.
This demo showcases a simple tool that utilizes AIGC technology, enabling both professional designers and regular users to easily customize clothing for their digital avatars. Customization options include changing clothing colors, textures, logos, and patterns. Compared with traditional 3D modeling processes, our approach significantly enhances efficiency and interactivity and reduces production costs.
Product attribute value extraction involves identifying the specific values associated with various attributes from a product profile. While existing methods often prioritize the development of effective models to improve extraction performance, there has been limited emphasis on extraction efficiency. However, in real-world scenarios, products are typically associated with multiple attributes, necessitating multiple extractions to obtain all corresponding values. In this work, we propose an Efficient product Attribute Value Extraction (EAVE) approach via lightweight sparse-layer interaction. Specifically, we employ a heavy encoder to separately encode the product context and attribute. The resulting non-interacting heavy representations of the context can be cached and reused for all attributes. Additionally, we introduce a light encoder to jointly encode the context and the attribute, facilitating lightweight interactions between them. To enrich the interaction within the lightweight encoder, we design a sparse-layer interaction module to fuse the non-interacting heavy representation into the lightweight encoder. Comprehensive evaluation on two benchmarks demonstrate that our method achieves significant efficiency gains with neutral or marginal loss in performance when the context is long and number of attributes is large. Our code is available \href{https://anonymous.4open.science/r/EAVE-EA18}{here}.
Recently, 3D Gaussian Splatting (3DGS) has become one of the mainstream methodologies for novel view synthesis (NVS) due to its high quality and fast rendering speed. However, as a point-based scene representation, 3DGS potentially generates a large number of Gaussians to fit the scene, leading to high memory usage. Improvements that have been proposed require either an empirical and preset pruning ratio or importance score threshold to prune the point cloud. Such hyperparamter requires multiple rounds of training to optimize and achieve the maximum pruning ratio, while maintaining the rendering quality for each scene. In this work, we propose learning-to-prune 3DGS (LP-3DGS), where a trainable binary mask is applied to the importance score that can find optimal pruning ratio automatically. Instead of using the traditional straight-through estimator (STE) method to approximate the binary mask gradient, we redesign the masking function to leverage the Gumbel-Sigmoid method, making it differentiable and compatible with the existing training process of 3DGS. Extensive experiments have shown that LP-3DGS consistently produces a good balance that is both efficient and high quality.
Many quantum algorithms for attacking symmetric cryptography involve the rank problem of quantum linear equations. In this paper, we first propose two quantum algorithms for solving quantum linear systems of equations with coherent superposition and construct their specific quantum circuits. Unlike previous related works, our quantum algorithms are universal. Specifically, the two quantum algorithms can both compute the rank and general solution by one measurement. The difference between them is whether the data register containing the quantum coefficient matrix can be disentangled with other registers and keep the data qubits unchanged. On this basis, we apply the two quantum algorithms as a subroutine to parallel Simon's algorithm (with multiple periods), Grover Meets Simon algorithm, and Alg-PolyQ2 algorithm, respectively. Afterwards, we construct a quantum classifier within Grover Meets Simon algorithm and the test oracle within Alg-PolyQ2 algorithm in detail, including their respective quantum circuits. To our knowledge, no such specific analysis has been done before. We rigorously analyze the success probability of those algorithms to ensure that the success probability based on the proposed quantum algorithms will not be lower than that of those original algorithms. Finally, we discuss the lower bound of the number of CNOT gates for solving quantum linear systems of equations with coherent superposition, and our quantum algorithms reach the optimum in terms of minimizing the number of CNOT gates. Furthermore, our analysis indicates that the proposed algorithms are mainly suitable for conducting attacks against lightweight symmetric ciphers, within the effective working time of an ion trap quantum computer.
Nowadays, with advanced information technologies deployed citywide, large data volumes and powerful computational resources are intelligentizing modern city development. As an important part of intelligent transportation, route recommendation and its applications are widely used, directly influencing citizens` travel habits. Developing smart and efficient travel routes based on big data (possibly multi-modal) has become a central challenge in route recommendation research. Our survey offers a comprehensive review of route recommendation work based on urban computing. It is organized by the following three parts: 1) Methodology-wise. We categorize a large volume of traditional machine learning and modern deep learning methods. Also, we discuss their historical relations and reveal the edge-cutting progress. 2) Application\-wise. We present numerous novel applications related to route commendation within urban computing scenarios. 3) We discuss current problems and challenges and envision several promising research directions. We believe that this survey can help relevant researchers quickly familiarize themselves with the current state of route recommendation research and then direct them to future research trends.
With the digitization of modern cities, large data volumes and powerful computational resources facilitate the rapid update of intelligent models deployed in smart cities. Continual learning (CL) is a novel machine learning paradigm that constantly updates models to adapt to changing environments, where the learning tasks, data, and distributions can vary over time. Our survey provides a comprehensive review of continual learning methods that are widely used in smart city development. The content consists of three parts: 1) Methodology-wise. We categorize a large number of basic CL methods and advanced CL frameworks in combination with other learning paradigms including graph learning, spatial-temporal learning, multi-modal learning, and federated learning. 2) Application-wise. We present numerous CL applications covering transportation, environment, public health, safety, networks, and associated datasets related to urban computing. 3) Challenges. We discuss current problems and challenges and envision several promising research directions. We believe this survey can help relevant researchers quickly familiarize themselves with the current state of continual learning research used in smart city development and direct them to future research trends.
Growth parameters play a significant role in the crystal quality and physical properties of layered materials. Here we present a case study on a van der Waals magnetic NbFeTe2 material. Two different types of polymorphic NbFeTe2 phases, synthesized at different temperatures, display significantly different behaviors in crystal symmetry, electronic structure, electrical transport, and magnetism. While the phase synthesized at low temperature showing behavior consistent with previous reports, the new phase synthesized at high temperature, has completely different physical properties, such as metallic resistivity, long-range ferromagnetic order, anomalous Hall effect, negative magnetoresistance, and distinct electronic structures. Neutron diffraction reveals out-of-plane ferromagnetism below 70K, consistent with the electrical transport and magnetic susceptibility studies. Our work suggests that simply tuning synthetic parameters in a controlled manner could be an effective route to alter the physical properties of existing materials potentially unlocking new states of matter, or even discovering new materials.
First-principle calculations were employed to analyze the effects induced by vacancies of molybdenum (Mo) and sulfur (S) on the dielectric properties of few-layered MoS2. We explored the combined effects of vacancies and dipole interactions on the dielectric properties of few-layered MoS2. In the presence of dielectric screening, we investigated uniformly distributed Mo and S vacancies, and then considered the case of concentrated vacancies. Our results show that the dielectric screening remarkably depends on the distribution of vacancies owing to the polarization induced by the vacancies and on the interlayer distances. This conclusion was validated for a wide range of wide-gap semiconductors with different positions and distributions of vacancies, providing an effective and reliable method for calculating and predicting electrostatic screening of dimensionally reduced materials. We further provided a method for engineering the dielectric constant by changing the interlayer distance, tuning the number of vacancies and the distribution of vacancies in few-layered van der Waals materials for their application in nanodevices and supercapacitors.
While HDMaps are a crucial component of autonomous driving, they are expensive to acquire and maintain. Estimating these maps from sensors therefore promises to significantly lighten costs. These estimations however overlook existing HDMaps, with current methods at most geolocalizing low quality maps or considering a general database of known maps. In this paper, we propose to account for existing maps of the precise situation studied when estimating HDMaps. We identify 3 reasonable types of useful existing maps (minimalist, noisy, and outdated). We also introduce MapEX, a novel online HDMap estimation framework that accounts for existing maps. MapEX achieves this by encoding map elements into query tokens and by refining the matching algorithm used to train classic query based map estimation models. We demonstrate that MapEX brings significant improvements on the nuScenes dataset. For instance, MapEX - given noisy maps - improves by 38% over the MapTRv2 detector it is based on and by 8% over the current SOTA.
Steane code is one of the most widely studied quantum error-correction codes, which is a natural choice for fault-tolerant quantum computation (FTQC). However, the original Steane code is not fault-tolerant because the CNOT gates in an encoded block may cause error propagation. In this paper, we first propose a fault-tolerant encoding and decoding scheme, which analyzes all possible errors caused by each quantum gate in an error-correction period. In this scheme, we combine the results of measuring redundant qubits with those of syndrome measurements to identify specific errors for different types of errors. But due to the error propagation, there may be cases where different errors produce the same measurement results. Therefore, we introduce the "flag qubits" scheme (providing its usage conditions) to reduce error interference as much as possible, and we consider the errors caused by the introduced quantum gates, realizing the truly fault-tolerant Steane code. Afterwards, we provide the fault-tolerant scheme of the universal quantum gate set, including fault-tolerant preparation and verification of ancillary states. This is the first time that fault tolerance has been considered for every process of FTQC. Finally, We propose an algorithm for a more accurate estimation of thresholds and optimal error-correction period selection. Our simulation results based on this entire scheme demonstrate the effectiveness of this algorithm, satisfying the threshold theorem and the currently widely recognized threshold. We analyze the relationship among the maximum threshold, concatenated levels, and quantum logical depth, showing that quantum operations play a crucial role in increasing the threshold. Furthermore, we analyze the computational theoretical limits of quantum computers from the perspectives of attack and active defense based on our FTQC scheme, thereby assessing the security of a system.
Accurate segmentation of COVID-19 CT images is crucial for reducing the severity and mortality rates associated with COVID-19 infections. In response to blurred boundaries and high variability characteristic of lesion areas in COVID-19 CT images, we introduce CDSE-UNet: a novel UNet-based segmentation model that integrates Canny operator edge detection and a dual-path SENet feature fusion mechanism. This model enhances the standard UNet architecture by employing the Canny operator for edge detection in sample images, paralleling this with a similar network structure for semantic feature extraction. A key innovation is the Double SENet Feature Fusion Block, applied across corresponding network layers to effectively combine features from both image paths. Moreover, we have developed a Multiscale Convolution approach, replacing the standard Convolution in UNet, to adapt to the varied lesion sizes and shapes. This addition not only aids in accurately classifying lesion edge pixels but also significantly improves channel differentiation and expands the capacity of the model. Our evaluations on public datasets demonstrate CDSE-UNet's superior performance over other leading models, particularly in segmenting large and small lesion areas, accurately delineating lesion edges, and effectively suppressing noise
Magnetic tunnel junctions (MTJs) have been widely applied in spintronic devices for efficient spin detection through the imbalance of spin polarization at the Fermi level. The van der Waals (vdW) nature of two-dimensional (2D) magnets with atomic-scale flat surfaces and negligible surface roughness greatly facilitates the development of MTJs, yet is only restricted to ferromagnets. Here, we report A-type antiferromagnetism in 2D vdW single-crystal (Fe0.8Co0.2)3GaTe2 with TN~203 K in bulk and ~185 K in 9-nm nanosheets. The metallic nature and out-of-plane magnetic anisotropy make it a suitable candidate for MTJ electrodes. By constructing heterostructures based on (Fe0.8Co0.2)3GaTe2/WSe2/Fe3GaTe2, we obtain a large tunneling magnetoresistance (TMR) ratio of 180% at low temperature and the TMR retains at near-room temperature 280 K. Moreover, the TMR is tunable by the electric field down to 1 mV, implying the potential in energy-efficient spintronic devices. Our work provides new opportunities for 2D antiferromagnetic spintronics and quantum devices.
Acquisition and processing of point clouds (PCs) is a crucial enabler for many emerging applications reliant on 3D spatial data, such as robot navigation, autonomous vehicles, and augmented reality. In most scenarios, PCs acquired by remote sensors must be transmitted to an edge server for fusion, segmentation, or inference. Wireless transmission of PCs not only puts on increased burden on the already congested wireless spectrum, but also confronts a unique set of challenges arising from the irregular and unstructured nature of PCs. In this paper, we meticulously delineate these challenges and offer a comprehensive examination of existing solutions while candidly acknowledging their inherent limitations. In response to these intricacies, we proffer four pragmatic solution frameworks, spanning advanced techniques, hybrid schemes, and distributed data aggregation approaches. In doing so, our goal is to chart a path toward efficient, reliable, and low-latency wireless PC transmission.
Realizing direct-bandgap quantum dots working within the deep-ultraviolet frequency is highly desired for electro-optical and biomedical applications while remaining challenging. In this work, we combine the first-principles many-body perturbation theory and effective Hamiltonian approximation to propose the realization of arrays of deep-ultraviolet excitonic quantum dots in twisted bilayer hexagonal boron nitride. The effective quantum confinement of excitons can reach ~400 meV within small twisting angles, which is about four times larger than those observed in twisted semiconducting transitional metal dichalcogenides. Especially because of enhanced electron-hole attraction, those excitons will accumulate via the so-call exciton funnel effect to the direct-bandgap regime, giving the possibility to better luminescence performance and manipulating coherent arrays of deep-ultraviolet quantum dots.
The Zero-touch network and Service Management (ZSM) framework represents an emerging paradigm in the management of the fifth-generation (5G) and Beyond (5G+) networks, offering automated self-management and self-healing capabilities to address the escalating complexity and the growing data volume of modern networks. ZSM frameworks leverage advanced technologies such as Machine Learning (ML) to enable intelligent decision-making and reduce human intervention. This paper presents a comprehensive survey of Zero-Touch Networks (ZTNs) within the ZSM framework, covering network optimization, traffic monitoring, energy efficiency, and security aspects of next-generational networks. The paper explores the challenges associated with ZSM, particularly those related to ML, which necessitate the need to explore diverse network automation solutions. In this context, the study investigates the application of Automated ML (AutoML) in ZTNs, to reduce network management costs and enhance performance. AutoML automates the selection and tuning process of a ML model for a given task. Specifically, the focus is on AutoML's ability to predict application throughput and autonomously adapt to data drift. Experimental results demonstrate the superiority of the proposed AutoML pipeline over traditional ML in terms of prediction accuracy. Integrating AutoML and ZSM concepts significantly reduces network configuration and management efforts, allowing operators to allocate more time and resources to other important tasks. The paper also provides a high-level 5G system architecture incorporating AutoML and ZSM concepts. This research highlights the potential of ZTNs and AutoML to revolutionize the management of 5G+ networks, enabling automated decision-making and empowering network operators to achieve higher efficiency, improved performance, and enhanced user experience.
With the rapid development and application of foundation models (FMs), it is foreseeable that FMs will play an important role in future wireless communications. As current Artificial Intelligence (AI) algorithms applied in wireless networks are dedicated models that aim for different neural network architectures and objectives, drawbacks in aspects of generality, performance gain, management, collaboration, etc. need to be conquered. In this paper, we define NetGPT (Network Generative Pre-trained Transformer) -- the foundation models for wireless communications, and summarize ten issues regarding design and application of NetGPT.
Developing novel high-temperature van der Waals ferromagnetic semiconductor materials and investigating their interface coupling effects with two-dimensional topological semimetals are pivotal for advancing next-generation spintronic and quantum devices. However, most van der Waals ferromagnetic semiconductors exhibit ferromagnetism only at low temperatures, limiting the proximity research on their interfaces with topological semimetals. Here, we report an intrinsic, van der Waals layered room-temperature ferromagnetic semiconductor crystal, FeCr0.5Ga1.5Se4 (FCGS), with a Curie temperature as high as 370 K, setting a new record for van der Waals ferromagnetic semiconductors. The saturation magnetization at low temperature (2 K) and room temperature (300 K) reaches 8.2 emu/g and 2.7 emu/g, respectively. Furthermore, FCGS possesses a bandgap of approximately 1.2 eV, which is comparable to the widely used commercial silicon. The FCGS/graphene heterostructure exhibits an impeccably smooth and gapless interface, thereby inducing a robust magnetic proximity coupling effect between FCGS and graphene. After the proximity coupling, graphene undergoes a charge carrier transition from electrons to holes, accompanied by a transition from non-magnetic to ferromagnetic transport behavior with robust anomalous Hall effect. Notably, the anomalous Hall effect remains robust even temperatures up to 400 K.
Knowledge Base Question Answering (KBQA) aims to answer factoid questions based on knowledge bases. However, generating the most appropriate knowledge base query code based on Natural Language Questions (NLQ) poses a significant challenge in KBQA. In this work, we focus on the CCKS2023 Competition of Question Answering with Knowledge Graph Inference for Unmanned Systems. Inspired by the recent success of large language models (LLMs) like ChatGPT and GPT-3 in many QA tasks, we propose a ChatGPT-based Cypher Query Language (CQL) generation framework to generate the most appropriate CQL based on the given NLQ. Our generative framework contains six parts: an auxiliary model predicting the syntax-related information of CQL based on the given NLQ, a proper noun matcher extracting proper nouns from the given NLQ, a demonstration example selector retrieving similar examples of the input sample, a prompt constructor designing the input template of ChatGPT, a ChatGPT-based generation model generating the CQL, and an ensemble model to obtain the final answers from diversified outputs. With our ChatGPT-based CQL generation framework, we achieved the second place in the CCKS 2023 Question Answering with Knowledge Graph Inference for Unmanned Systems competition, achieving an F1-score of 0.92676.