arXiv subjects

Bing Li

Publications and source records attributed to Bing Li.

At least 217 records · Page 12Linked to original sources

arXiv2024

Shrinking targets versus recurrence: the quantitative theory

Let $X = [0,1]$, and let $T:X\to X$ be an expanding piecewise linear map sending each interval of linearity to $[0,1]$. For $ψ:\mathbb N\to\mathbb R_{\geq 0}$, $x\in X$, and $N\in\mathbb N$ we consider the recurrence counting function \[ R(x,N;T,ψ) := \#\{1\leq n\leq N: d(T^n x, x) < ψ(n)\}. \] We show that for any $\varepsilon > 0$ we have \[ R(x,N;T,ψ) = Ψ(N)+O\left(Ψ^{1/2}(N) \ (\logΨ(N))^{3/2+\varepsilon}\right) \] for $μ$-almost all $x\in X$ and for all $N\in\mathbb N$, where $Ψ(N):= 2 \sum_{n=1}^N ψ(n)$. We also prove a generalization of this result to higher dimensions.

math.DS↗

arXiv2024

Diophantine approximation and the Mass Transference Principle: incorporating the unbounded setup

We develop the Mass Transference Principle for rectangles of Wang \& Wu (Math. Ann. 2021) to incorporate the `unbounded' setup; that is, when along some direction the lower order (at infinity) of the side lengths of the rectangles under consideration is infinity. As applications, we obtain the Hausdorff dimension of naturally occurring $\limsup$ sets within the classical framework of simultaneous Diophantine approximation and the dynamical framework of shrinking target problems. For instance, concerning the former, for $τ>0$, let $S(τ)$ denote the set of $(x_1,x_2)\in \mathbb{R}^2$ simultaneously satisfying the inequalities $\|q x_1 \| \, < \, q^{-τ} $ and $ \|q x_2 \| \, < \, e^{-q}$ for infinitely many $q \in \mathbb{N}$. Then, the `unbounded' Mass Transference Principle enables us to show that $\dim_{\rm H} S(τ) \, = \, \min \big\{ 1, 3/(1+τ) \big\} \, $.

math.NT↗

arXiv2024

LLM-Aided Efficient Hardware Design Automation

With the rapidly increasing complexity of modern chips, hardware engineers are required to invest more effort in tasks such as circuit design, verification, and physical implementation. These workflows often involve continuous modifications, which are labor-intensive and prone to errors. Therefore, there is an increasing need for more efficient and cost-effective Electronic Design Automation (EDA) solutions to accelerate new hardware development. Recently, large language models (LLMs) have made significant advancements in contextual understanding, logical reasoning, and response generation. Since hardware designs and intermediate scripts can be expressed in text format, it is reasonable to explore whether integrating LLMs into EDA could simplify and fully automate the entire workflow. Accordingly, this paper discusses such possibilities in several aspects, covering hardware description language (HDL) generation, code debugging, design verification, and physical implementation. Two case studies, along with their future outlook, are introduced to highlight the capabilities of LLMs in code repair and testbench generation. Finally, future directions and challenges are highlighted to further explore the potential of LLMs in shaping the next-generation EDA

eess.SY↗

arXiv2024

A Comprehensive Analysis of Insight-HXMT Gamma-Ray Burst Data. I. Power Density Spectrum

Power Density Spectrum (PDS) is one of the powerful tools to study light curves of gamma-ray bursts (GRBs). We show the average PDS and individual PDS analysis with {\it Hard X-ray Modulation Telescope} (also named \insighthxmt) GRBs data. The values of power-law index of average PDS ($α_{\bar{P}}$) for long GRBs (LGRBs) vary from 1.58-1.29 (for 100-245, 245-600, and 600-2000 keV). The \insighthxmt\ data allow us to extend the energy of the LGRBs up to 2000 keV, and a relation between $α_{\bar{P}}$ and energy $E$, $α_{\bar{P}}\propto E^{-0.09}$ (8-2000 keV) is obtained. We first systematically investigate the average PDS and individual PDS for short GRBs (SGRBs), and obtain $α_{\bar{P}}\propto E^{-0.07}$ (8-1000 keV), where the values of $α_{\bar{P}}$ vary from 1.86 to 1.34. The distribution of power-law index of individual PDS ($α$) of SGRB, is consistent with that of LGRB, and the $α$ value for the dominant timescale group (the bent power-law, BPL) is higher than that for the no-dominant timescale group (the single power-law, PL). Both LGRBs and SGRBs show similar $α$ and $α_{\bar{P}}$, which indicates that they may be the result of similar stochastic processes. The typical value of dominant timescale $τ$ for LGRBs and SGRBs is 1.58 s and 0.02 s, respectively. It seems that the $τ$ in proportion to the duration of GRBs $T_{90}$, with a relation $τ\propto T_{90}^{0.86}$. The GRB light curve may result from superposing a number of pulses with different timescales. No periodic and quasi-periodical signal above the 3$σ$ significance threshold is found in our sample.

astro-ph.HE↗

arXiv2024

SAM-Guided Masked Token Prediction for 3D Scene Understanding

Foundation models have significantly enhanced 2D task performance, and recent works like Bridge3D have successfully applied these models to improve 3D scene understanding through knowledge distillation, marking considerable advancements. Nonetheless, challenges such as the misalignment between 2D and 3D representations and the persistent long-tail distribution in 3D datasets still restrict the effectiveness of knowledge distillation from 2D to 3D using foundation models. To tackle these issues, we introduce a novel SAM-guided tokenization method that seamlessly aligns 3D transformer structures with region-level knowledge distillation, replacing the traditional KNN-based tokenization techniques. Additionally, we implement a group-balanced re-weighting strategy to effectively address the long-tail problem in knowledge distillation. Furthermore, inspired by the recent success of masked feature prediction, our framework incorporates a two-stage masked token prediction process in which the student model predicts both the global embeddings and the token-wise local embeddings derived from the teacher models trained in the first stage. Our methodology has been validated across multiple datasets, including SUN RGB-D, ScanNet, and S3DIS, for tasks like 3D object detection and semantic segmentation. The results demonstrate significant improvements over current State-of-the-art self-supervised methods, establishing new benchmarks in this field.

cs.CV↗

arXiv2024

NFT1000: A Cross-Modal Dataset for Non-Fungible Token Retrieval

With the rise of "Metaverse" and "Web 3.0", Non-Fungible Token (NFT) has emerged as a kind of pivotal digital asset, garnering significant attention. By the end of March 2024, more than 1.7 billion NFTs have been minted across various blockchain platforms. To effectively locate a desired NFT, conducting searches within a vast array of NFTs is essential. The challenge in NFT retrieval is heightened due to the high degree of similarity among different NFTs, regarding regional and semantic aspects. In this paper, we will introduce a benchmark dataset named "NFT Top1000 Visual-Text Dataset" (NFT1000), containing 7.56 million image-text pairs, and being collected from 1000 most famous PFP1 NFT collections2 by sales volume on the Ethereum blockchain. Based on this dataset and leveraging the CLIP series of pre-trained models as our foundation, we propose the dynamic masking fine-tuning scheme. This innovative approach results in a 7.4\% improvement in the top1 accuracy rate, while utilizing merely 13\% of the total training data (0.79 million vs. 6.1 million). We also propose a robust metric Comprehensive Variance Index (CVI) to assess the similarity and retrieval difficulty of visual-text pairs data. The dataset will be released as an open-source resource. For more details, please refer to: https://github.com/ShuxunoO/NFT-Net.git.

cs.IR↗

arXiv2024

MIBench: Evaluating Multimodal Large Language Models over Multiple Images

Built on the power of LLMs, numerous multimodal large language models (MLLMs) have recently achieved remarkable performance on various vision-language tasks. However, most existing MLLMs and benchmarks primarily focus on single-image input scenarios, leaving the performance of MLLMs when handling realistic multiple images underexplored. Although a few benchmarks consider multiple images, their evaluation dimensions and samples are very limited. In this paper, we propose a new benchmark MIBench, to comprehensively evaluate fine-grained abilities of MLLMs in multi-image scenarios. Specifically, MIBench categorizes the multi-image abilities into three scenarios: multi-image instruction (MII), multimodal knowledge-seeking (MKS) and multimodal in-context learning (MIC), and constructs 13 tasks with a total of 13K annotated samples. During data construction, for MII and MKS, we extract correct options from manual annotations and create challenging distractors to obtain multiple-choice questions. For MIC, to enable an in-depth evaluation, we set four sub-tasks and transform the original datasets into in-context learning formats. We evaluate several open-source and closed-source MLLMs on the proposed MIBench. The results reveal that although current models excel in single-image tasks, they exhibit significant shortcomings when faced with multi-image inputs, such as limited fine-grained perception, multi-image reasoning and in-context learning abilities. The annotated data of MIBench is available at https://huggingface.co/datasets/StarBottle/MIBench.

cs.CV↗

arXiv2024

Basis Sharing: Cross-Layer Parameter Sharing for Large Language Model Compression

Large Language Models (LLMs) have achieved remarkable breakthroughs. However, the huge number of parameters in LLMs require significant amount of memory storage in inference, which prevents their practical deployment in many applications. To reduce memory storage of LLMs, singular value decomposition (SVD) provides a promising solution to approximate weight matrices for compressing LLMs. In this paper, we take a step further to explore parameter sharing across different layers with SVD to achieve more effective compression for LLMs. Specifically, weight matrices in different layers are decomposed and represented as a linear combination of a set of shared basis vectors and unique coefficients. The types of weight matrices and the layer selection for basis sharing are examined when compressing LLMs to maintain the performance. Comprehensive experiments demonstrate that Basis Sharing outperforms state-of-the-art SVD-based compression approaches and parameter sharing techniques, especially under large compression ratios. Code is available at: https://github.com/TUDa-HWAI/Basis_Sharing

cs.CL↗

arXiv2024

Multiplicative Diophantine approximation with restricted denominators

Let $\{a_n\}_{n\in\mathbb{N}}$, $\{b_n\}_{n\in \mathbb{N}}$ be two infinite subsets of positive integers and $ψ:\mathbb{N}\to \mathbb{R}_{>0}$ be a positive function. We completely determine the Hausdorff dimensions of the set of all points $(x,y)\in [0,1]^2$ which satisfy $\|a_nx\|\|b_ny\|<ψ(n)$ infinitely often, and the set of all $x\in [0,1]$ satisfying $\|a_nx\|\|b_nx\|<ψ(n)$ infinitely often. This is based on establishing general convergence results for Hausdorff measures of these two sets. We also obtain some results on the set of all $x\in [0,1]$ such that $\max\{\|a_nx\|, \|b_nx\|\}<ψ(n)$ infinitely often.

math.NT↗

arXiv2024

Evaluating the Linguistic Coverage of OpenAlex: An Assessment of Metadata Accuracy and Completeness

Clarivate's Web of Science (WoS) and Elsevier's Scopus have been for decades the main sources of bibliometric information. Although highly curated, these closed, proprietary databases are largely biased towards English-language publications, underestimating the use of other languages in research dissemination. Launched in 2022, OpenAlex promised comprehensive, inclusive, and open-source research information. While already in use by scholars and research institutions, the quality of its metadata is currently being assessed. This paper contributes to this literature by assessing the completeness and accuracy of OpenAlex's metadata related to language, through a comparison with WoS, as well as an in-depth manual validation of a sample of 6,836 articles. Results show that OpenAlex exhibits a far more balanced linguistic coverage than WoS. However, language metadata is not always accurate, which leads OpenAlex to overestimate the place of English while underestimating that of other languages. If used critically, OpenAlex can provide comprehensive and representative analyses of languages used for scholarly publishing. However, more work is needed at infrastructural level to ensure the quality of metadata on language.

cs.DL↗

arXiv2024

AirFogSim: A Light-Weight and Modular Simulator for UAV-Integrated Vehicular Fog Computing

Vehicular Fog Computing (VFC) is significantly enhancing the efficiency, safety, and computational capabilities of Intelligent Transportation Systems (ITS), and the integration of Unmanned Aerial Vehicles (UAVs) further elevates these advantages by incorporating flexible and auxiliary services. This evolving UAV-integrated VFC paradigm opens new doors while presenting unique complexities within the cooperative computation framework. Foremost among the challenges, modeling the intricate dynamics of aerial-ground interactive computing networks is a significant endeavor, and the absence of a comprehensive and flexible simulation platform may impede the exploration of this field. Inspired by the pressing need for a versatile tool, this paper provides a lightweight and modular aerial-ground collaborative simulation platform, termed AirFogSim. We present the design and implementation of AirFogSim, and demonstrate its versatility with five key missions in the domain of UAV-integrated VFC. A multifaceted use case is carried out to validate AirFogSim's effectiveness, encompassing several integral aspects of the proposed AirFogSim, including UAV trajectory, task offloading, resource allocation, and blockchain. In general, AirFogSim is envisioned to set a new precedent in the UAV-integrated VFC simulation, bridge the gap between theoretical design and practical validation, and pave the way for future intelligent transportation domains. Our code will be available at https://github.com/ZhiweiWei-NAMI/AirFogSim.

cs.NI↗

arXiv2024

MobileIQA: Exploiting Mobile-level Diverse Opinion Network For No-Reference Image Quality Assessment Using Knowledge Distillation

With the rising demand for high-resolution (HR) images, No-Reference Image Quality Assessment (NR-IQA) gains more attention, as it can ecaluate image quality in real-time on mobile devices and enhance user experience. However, existing NR-IQA methods often resize or crop the HR images into small resolution, which leads to a loss of important details. And most of them are of high computational complexity, which hinders their application on mobile devices due to limited computational resources. To address these challenges, we propose MobileIQA, a novel approach that utilizes lightweight backbones to efficiently assess image quality while preserving image details through high-resolution input. MobileIQA employs the proposed multi-view attention learning (MAL) module to capture diverse opinions, simulating subjective opinions provided by different annotators during the dataset annotation process. The model uses a teacher model to guide the learning of a student model through knowledge distillation. This method significantly reduces computational complexity while maintaining high performance. Experiments demonstrate that MobileIQA outperforms novel IQA methods on evaluation metrics and computational efficiency. The code is available at https://github.com/chencn2020/MobileIQA.

cs.CV↗

arXiv2024

An Efficient General-Purpose Optical Accelerator for Neural Networks

General-purpose optical accelerators (GOAs) have emerged as a promising platform to accelerate deep neural networks (DNNs) due to their low latency and energy consumption. Such an accelerator is usually composed of a given number of interleaving Mach-Zehnder- Interferometers (MZIs). This interleaving architecture, however, has a low efficiency when accelerating neural networks of various sizes due to the mismatch between weight matrices and the GOA architecture. In this work, a hybrid GOA architecture is proposed to enhance the mapping efficiency of neural networks onto the GOA. In this architecture, independent MZI modules are connected with microring resonators (MRRs), so that they can be combined to process large neural networks efficiently. Each of these modules implements a unitary matrix with inputs adjusted by tunable coefficients. The parameters of the proposed architecture are searched using genetic algorithm. To enhance the accuracy of neural networks, selected weight matrices are expanded to multiple unitary matrices applying singular value decomposition (SVD). The kernels in neural networks are also adjusted to use up the on-chip computational resources. Experimental results show that with a given number of MZIs, the mapping efficiency of neural networks on the proposed architecture can be enhanced by 21.87%, 21.20%, 24.69%, and 25.52% for VGG16 and Resnet18 on datasets Cifar10 and Cifar100, respectively. The energy consumption and computation latency can also be reduced by over 67% and 21%, respectively.

cs.NE↗

arXiv2024

Advancing Gamma-Ray Burst Identification through Transfer Learning with Convolutional Neural Networks

The Rapid and accurate identification of Gamma-Ray Bursts (GRBs) is crucial for unraveling their origins. However, current burst search algorithms frequently miss low-threshold signals or lack universality for observations. In this study, we propose a novel approach utilizing transfer learning experiment based on convolutional neural network (CNN) to establish a universal GRB identification method, which validated successfully using GECAM-B data. By employing data augmentation techniques, we enhance the diversity and quantity of the GRB sample. We develop a 1D CNN model with a multi-scale feature cross fusion module (MSCFM) to extract features from samples and perform classification. The comparative results demonstrated significant performance improvements following pre-training and transferring on a large-scale dataset. Our optimal model achieved an impressive accuracy of 96.41% on the source dataset of GECAM-B, and identified three previously undiscovered GRBs by contrast with manual analysis of GECAM-B observations. These innovative transfer learning and data augmentation methods presented in this work hold promise for applications in multi-satellite exploration scenarios characterized by limited data sets and a scarcity of labeled samples in high-energy astronomy.

astro-ph.HE↗

arXiv2024

AutoBench: Automatic Testbench Generation and Evaluation Using LLMs for HDL Design

In digital circuit design, testbenches constitute the cornerstone of simulation-based hardware verification. Traditional methodologies for testbench generation during simulation-based hardware verification still remain partially manual, resulting in inefficiencies in testing various scenarios and requiring expensive time from designers. Large Language Models (LLMs) have demonstrated their potential in automating the circuit design flow. However, directly applying LLMs to generate testbenches suffers from a low pass rate. To address this challenge, we introduce AutoBench, the first LLM-based testbench generator for digital circuit design, which requires only the description of the design under test (DUT) to automatically generate comprehensive testbenches. In AutoBench, a hybrid testbench structure and a self-checking system are realized using LLMs. To validate the generated testbenches, we also introduce an automated testbench evaluation framework to evaluate the quality of generated testbenches from multiple perspectives. Experimental results demonstrate that AutoBench achieves a 57% improvement in the testbench pass@1 ratio compared with the baseline that directly generates testbenches using LLMs. For 75 sequential circuits, AutoBench successfully has a 3.36 times testbench pass@1 ratio compared with the baseline. The source codes and experimental results are open-sourced at this link: https://github.com/AutoBench/AutoBench

cs.SE↗

arXiv2024

TrackNeRF: Bundle Adjusting NeRF from Sparse and Noisy Views via Feature Tracks

Neural radiance fields (NeRFs) generally require many images with accurate poses for accurate novel view synthesis, which does not reflect realistic setups where views can be sparse and poses can be noisy. Previous solutions for learning NeRFs with sparse views and noisy poses only consider local geometry consistency with pairs of views. Closely following \textit{bundle adjustment} in Structure-from-Motion (SfM), we introduce TrackNeRF for more globally consistent geometry reconstruction and more accurate pose optimization. TrackNeRF introduces \textit{feature tracks}, \ie connected pixel trajectories across \textit{all} visible views that correspond to the \textit{same} 3D points. By enforcing reprojection consistency among feature tracks, TrackNeRF encourages holistic 3D consistency explicitly. Through extensive experiments, TrackNeRF sets a new benchmark in noisy and sparse view reconstruction. In particular, TrackNeRF shows significant improvements over the state-of-the-art BARF and SPARF by $\sim8$ and $\sim1$ in terms of PSNR on DTU under various sparse and noisy view setups. The code is available at \href{https://tracknerf.github.io/}.

cs.CV↗

arXiv2024

SuperEncoder: Towards Universal Neural Approximate Quantum State Preparation

Numerous quantum algorithms operate under the assumption that classical data has already been converted into quantum states, a process termed Quantum State Preparation (QSP). However, achieving precise QSP requires a circuit depth that scales exponentially with the number of qubits, making it a substantial obstacle in harnessing quantum advantage. Recent research suggests using a Parameterized Quantum Circuit (PQC) to approximate a target state, offering a more scalable solution with reduced circuit depth compared to precise QSP. Despite this, the need for iterative updates of circuit parameters results in a lengthy runtime, limiting its practical application. In this work, we demonstrate that it is possible to leverage a pre-trained neural network to directly generate the QSP circuit for arbitrary quantum state, thereby eliminating the significant overhead of online iterations. Our study makes a steady step towards a universal neural designer for approximate QSP.

quant-ph↗

arXiv2024

On orbit complexity of dynamical systems: intermediate value property and level set related to a Furstenberg problem

For symbolic dynamics with some mild conditions, we solve the lowering topological entropy problem for subsystems and determine the Hausdorff dimension of the level set with given complexity, where the complexity is represented by Hausdorff dimension of orbit closure. These results can be applied to some dynamical systems such as $β$-transformations, conformal expanding repeller, etc. We also determine the dimension of the Furstenberg level set, which is related to a problem of Furstenberg on the orbits under two multiplicatively independent maps.

math.DS↗