arXiv subjects

Jiang Zhu

Publications and source records attributed to Jiang Zhu.

At least 37 records · Page 2Linked to original sources

arXiv2024

Gaze Label Alignment: Alleviating Domain Shift for Gaze Estimation

Gaze estimation methods encounter significant performance deterioration when being evaluated across different domains, because of the domain gap between the testing and training data. Existing methods try to solve this issue by reducing the deviation of data distribution, however, they ignore the existence of label deviation in the data due to the acquisition mechanism of the gaze label and the individual physiological differences. In this paper, we first point out that the influence brought by the label deviation cannot be ignored, and propose a gaze label alignment algorithm (GLA) to eliminate the label distribution deviation. Specifically, we first train the feature extractor on all domains to get domain invariant features, and then select an anchor domain to train the gaze regressor. We predict the gaze label on remaining domains and use a mapping function to align the labels. Finally, these aligned labels can be used to train gaze estimation models. Therefore, our method can be combined with any existing method. Experimental results show that our GLA method can effectively alleviate the label distribution shift, and SOTA gaze estimation methods can be further improved obviously.

cs.CV↗

arXiv2024

DM-SBL: Channel Estimation under Structured Interference

Channel estimation is a fundamental task in communication systems and is critical for effective demodulation. While most works deal with a simple scenario where the measurements are corrupted by the additive white Gaussian noise (AWGN), this work addresses the more challenging scenario where both AWGN and structured interference coexist. Such conditions arise, for example, when a sonar/radar transmitter and a communication receiver operate simultaneously within the same bandwidth. To ensure accurate channel estimation in these scenarios, the sparsity of the channel in the delay domain and the complicate structure of the interference are jointly exploited. Firstly, the score of the structured interference is learned via a neural network based on the diffusion model (DM), while the channel prior is modeled as a Gaussian distribution, with its variance controlling channel sparsity, similar to the setup of the sparse Bayesian learning (SBL). Then, two efficient posterior sampling methods are proposed to jointly estimate the sparse channel and the interference. Nuisance parameters, such as the variance of the prior are estimated via the expectation maximization (EM) algorithm. The proposed method is termed as DM based SBL (DM-SBL). Numerical simulations demonstrate that DM-SBL significantly outperforms conventional approaches that deal with the AWGN scenario, particularly under low signal-to-interference ratio (SIR) conditions. Beyond channel estimation, DM-SBL also shows promise for addressing other linear inverse problems involving structured interference.

eess.SP↗

arXiv2024

LG-Gaze: Learning Geometry-aware Continuous Prompts for Language-Guided Gaze Estimation

The ability of gaze estimation models to generalize is often significantly hindered by various factors unrelated to gaze, especially when the training dataset is limited. Current strategies aim to address this challenge through different domain generalization techniques, yet they have had limited success due to the risk of overfitting when solely relying on value labels for regression. Recent progress in pre-trained vision-language models has motivated us to capitalize on the abundant semantic information available. We propose a novel approach in this paper, reframing the gaze estimation task as a vision-language alignment issue. Our proposed framework, named Language-Guided Gaze Estimation (LG-Gaze), learns continuous and geometry-sensitive features for gaze estimation benefit from the rich prior knowledges of vision-language models. Specifically, LG-Gaze aligns gaze features with continuous linguistic features through our proposed multimodal contrastive regression loss, which customizes adaptive weights for different negative samples. Furthermore, to better adapt to the labels for gaze estimation task, we propose a geometry-aware interpolation method to obtain more precise gaze embeddings. Through extensive experiments, we validate the efficacy of our framework in four different cross-domain evaluation tasks.

cs.CV↗

arXiv2024

A Modulo Sampling Hardware Prototype and Reconstruction Algorithm Evaluation

Analog-to-digital converters (ADCs) play a vital important role in any devices via manipulating analog signals in a digital manner. Given that the amplitude of the signal exceeds the dynamic range of the ADCs, clipping occurs and the quality of the digitized signal degrades significantly. In this paper, we design a joint modulo sampling hardware and processing prototype which improves the ADCs' dynamic range by folding the signal before sampling. Both the detailed design of the hardware and the recovery results of various state-of-the-art processing algorithms including our proposed unlimited sampling line spectral estimation (USLSE) algorithm are presented. Additionally, key issues that arise during implementation are also addressed. It is demonstrated that the USLSE algorithm successfully recovers the original signal with a frequency of 2.5 kHz and an amplitude 10 times the ADC's dynamic range, and the linear prediction (LP) algorithm successfully recovers the original signal with a frequency of 3.5 kHz and an amplitude 10 times the ADC's dynamic range.

eess.SP↗

arXiv2024

On the Identifiability from Modulo Measurements under DFT Sensing Matrix

Modulo sampling (MS) has been recently introduced to enhance the dynamic range of conventional ADCs by applying a modulo operator before sampling. This paper examines the identifiability of a measurement model where measurements are taken using a discrete Fourier transform (DFT) sensing matrix, followed by a modulo operator (modulo-DFT). Firstly, we derive a necessary and sufficient condition for the unique identification of the modulo-DFT sensing model based on the number of measurements and the indices of zero elements in the original signal. Then, we conduct a deeper analysis of three specific cases: when the number of measurements is a power of $2$, a prime number, and twice a prime number. Additionally, we investigate the identifiability of periodic bandlimited (PBL) signals under MS, which can be considered as the modulo-DFT sensing model with additional symmetric and conjugate constraints on the original signal. We also provide a necessary and sufficient condition based solely on the number of samples in one period for the unique identification of the PBL signal under MS, though with an ambiguity in the direct current (DC) component. Furthermore, we show that when the oversampling factor exceeds $3(1+1/P)$, the PBL signal can be uniquely identified with an ambiguity in the DC component, where $P$ is the number of harmonics, including the fundamental component, in the positive frequency part. Finally, we also present a recovery algorithm that estimates the original signal by solving integer linear equations, and we conduct simulations to validate our conclusions.

cs.IT↗

arXiv2024

Line Spectrum Estimation and Detection with Few-bit ADCs: Theoretical Analysis and Generalized NOMP Algorithm

As radar systems will be equipped with thousands of antenna elements and wide bandwidth, the associated costs and power consumption become exceedingly high, and a potential solution is to adopt low-resolution quantization technology, which not only reduces data storage needs but also lowers power and hardware costs. This paper focuses on line spectral estimation and detection (LSE\&D) with few-bit ADCs (typically 1-4 bits) by investigating the signal-to-noise ratio (SNR) loss, establishing a framework to understand the impact of intersinusoidal interference, the bit-depth of the quantizer, and the noise variance on weak signal detection in scenarios involving multiple sinusoids under low-resolution quantization. Additionally, a low-complexity, super-resolution, and constant false alarm rate (CFAR) algorithm, named generalized Newtonized orthogonal matching pursuit (GNOMP), is proposed. Extensive numerical simulations are conducted to validate the theoretical findings, particularly in terms of the detection probability bound. The effectiveness of GNOMP is demonstrated through comparisons with state-of-the-art algorithms, the Cramér Rao bound, and the detection probability bound. Real data acquired by mmWave radar further substantiates the effectiveness of GNOMP in practical applications.

eess.SP↗

arXiv2024

MOSE: Boosting Vision-based Roadside 3D Object Detection with Scene Cues

3D object detection based on roadside cameras is an additional way for autonomous driving to alleviate the challenges of occlusion and short perception range from vehicle cameras. Previous methods for roadside 3D object detection mainly focus on modeling the depth or height of objects, neglecting the stationary of cameras and the characteristic of inter-frame consistency. In this work, we propose a novel framework, namely MOSE, for MOnocular 3D object detection with Scene cuEs. The scene cues are the frame-invariant scene-specific features, which are crucial for object localization and can be intuitively regarded as the height between the surface of the real road and the virtual ground plane. In the proposed framework, a scene cue bank is designed to aggregate scene cues from multiple frames of the same scene with a carefully designed extrinsic augmentation strategy. Then, a transformer-based decoder lifts the aggregated scene cues as well as the 3D position embeddings for 3D object location, which boosts generalization ability in heterologous scenes. The extensive experiment results on two public benchmarks demonstrate the state-of-the-art performance of the proposed method, which surpasses the existing methods by a large margin.

cs.CV↗

arXiv2024

LoRAMoE: Alleviate World Knowledge Forgetting in Large Language Models via MoE-Style Plugin

Supervised fine-tuning (SFT) is a crucial step for large language models (LLMs), enabling them to align with human instructions and enhance their capabilities in downstream tasks. Increasing instruction data substantially is a direct solution to align the model with a broader range of downstream tasks or notably improve its performance on a specific task. However, we find that large-scale increases in instruction data can damage the world knowledge previously stored in LLMs. To address this challenge, we propose LoRAMoE, a novelty framework that introduces several low-rank adapters (LoRA) and integrates them by using a router network, like a plugin version of Mixture of Experts (MoE). It freezes the backbone model and forces a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks, to alleviate world knowledge-edge forgetting. Experimental results show that, as the instruction data increases, LoRAMoE can significantly improve the ability to process downstream tasks, while maintaining the world knowledge stored in the LLM.

cs.CL↗

arXiv2024

Dark Confinement-Deconfinement Phase Transition: A Roadmap from Polyakov Loop Models to Gravitational Waves

We explore the confinement-deconfinement phase transition (PT) of the first order (FO) arising in $SU(N)$ pure Yang-Mills theory, based on Polyakov loop models (PLMs), in light of the induced gravitational wave (GW) spectra. We demonstrate that the PLMs with the Haar measure term, involving models successful in QCD with $N=3$, are potentially incompatible with the large $N$ scaling for the thermodynamical quantities and the latent heat at around the criticality of the FOPT reported from the lattice simulations. We then propose a couple of models of polynomial form, which we call the 4-6 PLM (with four- and six-point interactions among the basic PL fields which have center charge 1) and 4-8 PLM (with four- and eight-point interactions), and discuss how such models can naturally arise in the presence of a heavy PL with charge 2. We show that those models give the consistent thermodynamical and large $N$ properties at around the criticality. The predicted GW spectra are shown to have high enough sensitivity to be probed in the future prospected interferometers such as LISA, BBO, DICIGO, and TianQin.

hep-ph↗

arXiv2024

Massive Gauge Theory with Quasigluon for Hot $SU(N)$: Phase Transition and Thermodynamics

It is challenging to build a model that can correctly and unifiedly account for the deconfinement phase transition and thermodynamics of the hot $SU(N)$ pure Yang-Mills (PYM) system, for any $N$. In this article, we slightly generalize the massive PYM model to the situation with a quasigluon mass $M_g(T)$ varying with temperature, inspired by the quasigluon model. In such a framework, we can acquire an effective potential for the temporal gauge field background by perturbative calculation, rather than adding by hand. The resulting potential works well to describe the behavior of the hot PYM system for all $N$, via the single parameter $M_g(T)$. Moreover, under the assumption of unified eigenvalue distribution, the $M_g(T)$ fitted by machine learning is found to follow $N$-universality.

hep-ph↗

arXiv2023

One-Bit-Aided Modulo Sampling for DOA Estimation

Modulo sampling has recently drawn a great deal of attention for cutting-edge applications, due to overcoming the barrier of information loss through sensor saturation and clipping. This is a significant problem, especially when the range of signal amplitudes is unknown or in the near-far case. To overcome this fundamental bottleneck, we propose a one-bit-aided (1bit-aided) modulo sampling scheme for direction-of-arrival (DOA) estimation. On the one hand, one-bit quantization involving a simple comparator offers the advantages of low-cost and low-complexity implementation. On the other hand, one-bit quantization provides an estimate of the normalized covariance matrix of the unquantized measurements via the arcsin law. The estimate of the normalized covariance matrix is used to implement blind integer-forcing (BIF) decoder to unwrap the modulo samples to construct the covariance matrix, and subspace methods can be used to perform the DOA estimation. Our approach named as 1bit-aided-BIF addresses the near-far problem well and overcomes the intrinsic low dynamic range of one-bit quantization. Numerical experiments validate the excellent performance of the proposed algorithm.

eess.SP↗

arXiv2023

Line Spectral Estimation via Unlimited Sampling

Frequency Modulated Continuous Wave (FMCW) radar has been widely applied in automotive anti-collision systems, automatic cruise control, and indoor monitoring. However, conventional analog-to-digital converters (ADCs) can suffer from significant information loss when strong and weak targets coexist in ranging applications. To address this issue, the Unlimited Sampling (US) strategy was proposed, which applies a modulo operator prior to sampling. In this paper, we investigate the range estimation problem using FMCW radar in the context of US, which can be formulated as a one-dimensional line spectral estimation (LSE) via US. By exploiting the oversampling property and proving that the leakage onto a certain frequency band can be controlled, we establish an integer optimization problem in the Fourier and first-order difference domain. We then propose a dynamic programming (DP) based algorithm followed by the orthogonal matching pursuit (OMP) method to solve it. In addition, a two-stage US LSE (USLSE) is proposed, where the line spectral signal is first recovered by iteratively executing DP and OMP, and then the parameters are estimated by applying a state-of-the-art LSE algorithm. Substantial numerical simulations and real experiments demonstrate that the proposed algorithm, USLSE, outperforms existing algorithms.

eess.SP↗

arXiv2023

Image Classifier Based Generative Method for Planar Antenna Design

To extend the antenna design on printed circuit boards (PCBs) for more engineers of interest, we propose a simple method that models PCB antennas with a few basic components. By taking two separate steps to decide their geometric dimensions and positions, antenna prototypes can be facilitated with no experience required. Random sampling statistics relate to the quality of dimensions are used in selecting among dimension candidates. A novel image-based classifier using a convolutional neural network (CNN) is introduced to further determine the positions of these fixed-dimension components. Two examples from wearable products have been chosen to examine the entire workflow. Their final designs are realistic and their performance metrics are not inferior to the ones designed by experienced engineers.

cs.CV↗

arXiv2023

Simulation Software of the JUNO Experiment

The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose experiment, under construction in southeast China, that is designed to determine the neutrino mass ordering and precisely measure neutrino oscillation parameters. Monte Carlo simulation plays an important role for JUNO detector design, detector commissioning, offline data processing, and physics processing. The JUNO experiment has the world's largest liquid scintillator detector instrumented with many thousands of PMTs. The broad energy range of interest, long lifetime, and the large scale present data processing challenges across all areas. This paper describes the JUNO simulation software, highlighting the challenges of JUNO simulation and solutions to meet these challenges, including such issues as support for time-correlated analysis, event mixing, event correlation and handling the simulation of many millions of optical photons.

hep-ex↗

arXiv2023

Sample-efficient Surrogate Model for Frequency Response of Linear PDEs using Self-Attentive Complex Polynomials

Linear Partial Differential Equations (PDEs) govern the spatial-temporal dynamics of physical systems that are essential to building modern technology. When working with linear PDEs, designing a physical system for a specific outcome is difficult and costly due to slow and expensive explicit simulation of PDEs and the highly nonlinear relationship between a system's configuration and its behavior. In this work, we prove a parametric form that certain physical quantities in the Fourier domain must obey in linear PDEs, named the CZP (Constant-Zeros-Poles) framework. Applying CZP to antenna design, an industrial application using linear PDEs (i.e., Maxwell's equations), we derive a sample-efficient parametric surrogate model that directly predicts its scattering coefficients without explicit numerical PDE simulation. Combined with a novel image-based antenna representation and an attention-based neural network architecture, CZP outperforms baselines by 10% to 25% in terms of test loss and also is able to find 2D antenna designs verifiable by commercial software with $33\%$ greater success than baselines, when coupled with sequential search techniques like reinforcement learning.

cs.LG↗

arXiv2022

Method for detector description transformation to Unity and application in BESIII

Detector and event visualization are essential parts of the software used in high-energy physics (HEP) experiments. Modern visualization techniques and multimedia production platforms such as Unity provide impressive display effects and professional extensions for visualization in HEP experiments. In this study, a method for automatic detector description transformation is presented, which can convert the complicated HEP detector geometry from GDML in offline software to 3D modeling in Unity. The method was successfully applied in the BESIII experiment and can be further developed into applications such as event displays, data monitoring, or virtual reality. It has great potential in detector design, offline software development, physics analysis, and outreach for next-generation HEP experiments as well as applications in nuclear techniques for the industry.

physics.ins-det↗

arXiv2022

CFAR based NOMP for Line Spectral Estimation and Detection

The line spectrum estimation problem is considered in this paper. We propose a CFAR-based Newtonized OMP (NOMP-CFAR) method which can maintain a desired false alarm rate without the knowledge of the noise variance. The NOMP-CFAR consists of two steps, namely, an initialization step and a detection step. In the initialization step, NOMP is employed to obtain candidate sinusoidal components. In the detection step, CFAR detector is applied to detect each candidate frequency, and remove the most unlikely frequency component. Then, the Newton refinements are used to refine the remaining parameters. The relationship between the false alarm rate and the required threshold is established. By comparing with the NOMP, NOMP-CFAR has only $1$ dB performance loss in additive white Gaussian noise scenario with false alarm probability $10^{-2}$ and detection probability $0.8$ without knowledge of noise variance. For varied noise variance scenario, NOMP-CFAR still preserves its CFAR property, while NOMP violates the CFAR. Besides, real experiments are also conducted to demonstrate the detection performance of NOMP-CFAR, compared to CFAR and NOMP.

cs.IT↗

arXiv2022

A Unitary Transform Based Generalized Approximate Message Passing

We consider the problem of recovering an unknown signal ${\mathbf x}\in {\mathbb R}^n$ from general nonlinear measurements obtained through a generalized linear model (GLM), i.e., ${\mathbf y}= f\left({\mathbf A}{\mathbf x}+{\mathbf w}\right)$, where $f(\cdot)$ is a componentwise nonlinear function. Based on the unitary transform approximate message passing (UAMP) and expectation propagation, a unitary transform based generalized approximate message passing (GUAMP) algorithm is proposed for general measurement matrices $\bf{A}$, in particular highly correlated matrices. Experimental results on quantized compressed sensing demonstrate that the proposed GUAMP significantly outperforms state-of-the-art GAMP and GVAMP under correlated matrices $\bf{A}$.

cs.IT↗