arXiv subjects

Jian Liu

Publications and source records attributed to Jian Liu.

At least 271 records · Page 15Linked to original sources

arXiv2024

Characterisation of analogue Monolithic Active Pixel Sensor test structures implemented in a 65 nm CMOS imaging process

Analogue test structures were fabricated using the Tower Partners Semiconductor Co. CMOS 65 nm ISC process. The purpose was to characterise and qualify this process and to optimise the sensor for the next generation of Monolithic Active Pixels Sensors for high-energy physics. The technology was explored in several variants which differed by: doping levels, pixel geometries and pixel pitches (10-25 $μ$m). These variants have been tested following exposure to varying levels of irradiation up to 3 MGy and $10^{16}$ 1 MeV n$_\text{eq}$ cm$^{-2}$. Here the results from prototypes that feature direct analogue output of a 4$\times$4 pixel matrix are reported, allowing the systematic and detailed study of charge collection properties. Measurements were taken both using $^{55}$Fe X-ray sources and in beam tests using minimum ionizing particles. The results not only demonstrate the feasibility of using this technology for particle detection but also serve as a reference for future applications and optimisations.

physics.ins-det↗

arXiv2024

REPS: Reconstruction-based Point Cloud Sampling

Sampling is widely used in various point cloud tasks as it can effectively reduce resource consumption. Recently, some methods have proposed utilizing neural networks to optimize the sampling process for various task requirements. Currently, deep downsampling methods can be categorized into two main types: generative-based and score-based. Generative-based methods directly generate sampled point clouds using networks, whereas score-based methods assess the importance of points according to specific rules and then select sampled point clouds based on their scores. However, these methods often result in noticeable clustering effects in high-intensity feature areas, compromising their ability to preserve small-scale features and leading to the loss of some structures, thereby affecting the performance of subsequent tasks. In this paper, we propose REPS, a reconstruction-based scoring strategy that evaluates the importance of each vertex by removing and reconstructing them using surrounding vertices. Our reconstruction process comprises point reconstruction and shape reconstruction. The two aforementioned reconstruction methods effectively evaluate the importance of vertices by removing them at different scales for reconstruction. These reconstructions ensure that our method maintains the overall geometric features of the point cloud and avoids disturbing small-scale structures during sampling. Additionally, we propose the Global-Local Fusion Attention (GLFA) module, which aggregates local and global attention features of point clouds, ensuring high-quality reconstruction and sampling effects. Our method outperforms previous approaches in preserving the structural features of the sampled point clouds. Furthermore, abundant experimental results demonstrate the superior performance of our method across various common tasks.

cs.CV↗

arXiv2024

Stealing Stable Diffusion Prior for Robust Monocular Depth Estimation

Monocular depth estimation is a crucial task in computer vision. While existing methods have shown impressive results under standard conditions, they often face challenges in reliably performing in scenarios such as low-light or rainy conditions due to the absence of diverse training data. This paper introduces a novel approach named Stealing Stable Diffusion (SSD) prior for robust monocular depth estimation. The approach addresses this limitation by utilizing stable diffusion to generate synthetic images that mimic challenging conditions. Additionally, a self-training mechanism is introduced to enhance the model's depth estimation capability in such challenging environments. To enhance the utilization of the stable diffusion prior further, the DINOv2 encoder is integrated into the depth model architecture, enabling the model to leverage rich semantic priors and improve its scene understanding. Furthermore, a teacher loss is introduced to guide the student models in acquiring meaningful knowledge independently, thus reducing their dependency on the teacher models. The effectiveness of the approach is evaluated on nuScenes and Oxford RobotCar, two challenging public datasets, with the results showing the efficacy of the method. Source code and weights are available at: https://github.com/hitcslj/SSD.

cs.CV↗

arXiv2024

Canonical Hamiltonian Guiding Center Dynamics and Its Intrinsic Magnetic Moment

The concept of guiding center is potent in astrophysics, space plasmas, fusion researches, and arc plasmas to solve the multi-scale dynamics of magnetized plasmas. In this letter, we rigorously prove that the guiding center dynamics can generally be described as a constrained canonical Hamiltonian system with two constraints in six dimensional phase space, and that the solution flow of the guiding center lies on a canonical symplectic sub-manifold. The guiding center can thus be modeled as a pseudo-particle with an intrinsic magnetic moment, which properly replaces the charged particle dynamics on time scales larger than the gyro-period. The complete dynamical behaviors, such as the velocity and force, of the guiding center pseudo-particle can be clearly deduced from the model. Furthermore, a series of related theories, such as symplectic numerical methods, the canonical gyro-kinetic theory, and canonical particle-in-cell algorithms can be systematically developed based on the canonical guiding center system. The canonical guiding center theory also provides an enlightenment for the origin of the intrinsic magnetic moment.

physics.plasm-ph↗

arXiv2024

OccTransformer: Improving BEVFormer for 3D camera-only occupancy prediction

This technical report presents our solution, "occTransformer" for the 3D occupancy prediction track in the autonomous driving challenge at CVPR 2023. Our method builds upon the strong baseline BEVFormer and improves its performance through several simple yet effective techniques. Firstly, we employed data augmentation to increase the diversity of the training data and improve the model's generalization ability. Secondly, we used a strong image backbone to extract more informative features from the input data. Thirdly, we incorporated a 3D unet head to better capture the spatial information of the scene. Fourthly, we added more loss functions to better optimize the model. Additionally, we used an ensemble approach with the occ model BevDet and SurroundOcc to further improve the performance. Most importantly, we integrated 3D detection model StreamPETR to enhance the model's ability to detect objects in the scene. Using these methods, our solution achieved 49.23 miou on the 3D occupancy prediction track in the autonomous driving challenge.

cs.CV↗

arXiv2024

Photonic Neural Network Fabricated on Thin Film Lithium Niobate for High-Fidelity and Power-Efficient Matrix Computation

Photonic neural networks (PNNs) have emerged as a promising platform to address the energy consumption issue that comes with the advancement of artificial intelligence technology, and thin film lithium niobate (TFLN) offers an attractive solution as a material platform mainly for its combined characteristics of low optical loss and large electro-optic (EO) coefficients. Here, we present the first implementation of an EO tunable PNN based on the TFLN platform. Our device features ultra-high fidelity, high computation speed, and exceptional power efficiency. We benchmark the performance of our device with several deep learning missions including in-situ training of Circle and Moons nonlinear datasets classification, Iris flower species recognition, and handwriting digits recognition. Our work paves the way for sustainable up-scaling of high-speed, energy-efficient PNNs.

physics.optics↗

arXiv2024

Ubiquitous short-range order in multi-principal element alloys

Recent research in multi-principal element alloys (MPEAs) has increasingly focused on the exploration and exploitation of short-range order (SRO) to enhance material performance. However, the understanding of SRO formation and the precise tuning of it within MPEAs remains poorly understood, limiting the comprehension of its impact on material properties and impeding the advancement of SRO engineering. Here, leveraging advanced additive manufacturing techniques that produce samples with a wide range of cooling rates (up to 10^7 K/s) and an improved quantitative electron microscopy method, we characterize SRO in three CoCrNi-based MPEAs to unravel the role of processing route and thermal history on SRO. Surprisingly, irrespective of the processing and thermal treatment applied, all samples exhibit similar levels of SRO, suggesting that prevalent SRO may form during the solidification process. Atomistic simulations of solidification verify that local chemical ordering arises in the liquid-solid interface (solidification front) even under the extreme cooling rate of 10^11 K/s. This phenomenon stems from the swift atomic diffusion in the supercooled liquid, which matches or even surpasses the rate of solidification. Therefore, SRO is an inherent characteristic of most MPEAs, insensitive to variations in cooling rates and annealing treatments typically available in experiments. Integrating thermal treatment with other strategies, such as mechanical deformation and irradiation, might be more effective approaches for harnessing SRO to achieve controlled material properties.

cond-mat.mtrl-sci↗

arXiv2024

Bohm-like Neoclassical Transport in Highly Collisional Toroidal Plasmas with High Density Gradients

Conventional neoclassical theory in the Pfirsch-Schlüter regime fails to accurately model collision-induced transport in toroidal plasmas with high density gradients. In this scenario, we find that collision suppresses the return flow, leading to the dominance of the transport flux by the vacuum toroidal field drift with a reduced Bohm-like scaling. The new regime is also confirmed by full-orbit particle simulations, and can be employed to improve the accurate modeling of impurity transport in toroidal magnetized plasmas.

physics.plasm-ph↗

arXiv2024

SyCoCa: Symmetrizing Contrastive Captioners with Attentive Masking for Multimodal Alignment

Multimodal alignment between language and vision is the fundamental topic in current vision-language model research. Contrastive Captioners (CoCa), as a representative method, integrates Contrastive Language-Image Pretraining (CLIP) and Image Caption (IC) into a unified framework, resulting in impressive results. CLIP imposes a bidirectional constraints on global representation of entire images and sentences. Although IC conducts an unidirectional image-to-text generation on local representation, it lacks any constraint on local text-to-image reconstruction, which limits the ability to understand images at a fine-grained level when aligned with texts. To achieve multimodal alignment from both global and local perspectives, this paper proposes Symmetrizing Contrastive Captioners (SyCoCa), which introduces bidirectional interactions on images and texts across the global and local representation levels. Specifically, we expand a Text-Guided Masked Image Modeling (TG-MIM) head based on ITC and IC heads. The improved SyCoCa can further leverage textual cues to reconstruct contextual images and visual cues to predict textual contents. When implementing bidirectional local interactions, the local contents of images tend to be cluttered or unrelated to their textual descriptions. Thus, we employ an attentive masking strategy to select effective image patches for interaction. Extensive experiments on five vision-language tasks, including image-text retrieval, image-captioning, visual question answering, and zero-shot/finetuned image classification, validate the effectiveness of our proposed method.

cs.CV↗

arXiv2024

Query-Based Knowledge Sharing for Open-Vocabulary Multi-Label Classification

Identifying labels that did not appear during training, known as multi-label zero-shot learning, is a non-trivial task in computer vision. To this end, recent studies have attempted to explore the multi-modal knowledge of vision-language pre-training (VLP) models by knowledge distillation, allowing to recognize unseen labels in an open-vocabulary manner. However, experimental evidence shows that knowledge distillation is suboptimal and provides limited performance gain in unseen label prediction. In this paper, a novel query-based knowledge sharing paradigm is proposed to explore the multi-modal knowledge from the pretrained VLP model for open-vocabulary multi-label classification. Specifically, a set of learnable label-agnostic query tokens is trained to extract critical vision knowledge from the input image, and further shared across all labels, allowing them to select tokens of interest as visual clues for recognition. Besides, we propose an effective prompt pool for robust label embedding, and reformulate the standard ranking learning into a form of classification to allow the magnitude of feature vectors for matching, which both significantly benefit label recognition. Experimental results show that our framework significantly outperforms state-of-the-art methods on zero-shot task by 5.9% and 4.5% in mAP on the NUS-WIDE and Open Images, respectively.

cs.CV↗

arXiv2023

Correlated Quantum Phenomena of Spin-Orbit Coupled Perovskite Oxide Heterostructures: Cases of SrRuO3 and SrIrO3-Based Artificial Superlattices

Unexpected, yet useful functionalities emerge when two or more materials merge coherently. Artificial oxide superlattices realize atomic and crystal structures that are not available in nature, thus providing controllable correlated quantum phenomena. This review focuses on 4d and 5d oxide superlattices, in which the spin-orbit coupling plays a significant role compared with conventional 3d oxide superlattices. Modulations in crystal structures with octahedral distortion, phonon engineering, electronic structures, spin orderings, and dimensionality control are discussed for 4d oxide superlattices. Atomic and magnetic structures, Jeff = 1/2 pseudospin and charge fluctuations, and the integration of topology and correlation are discussed for 5d oxide superlattices. This review provides insights into how correlated quantum phenomena arise from the deliberate design of superlattice structures that give birth to novel functionalities.

cond-mat.str-el↗

arXiv2023

Persistent Mayer homology and persistent Mayer Laplacian

In algebraic topology, the differential (i.e., boundary operator) typically satisfies $d^{2}=0$. However, the generalized differential $d^{N}=0$ for an integer $N\geq 2$ has been studied in terms of Mayer homology on $N$-chain complexes for more than eighty years. We introduce Mayer Laplacians on $N$-chain complexes. We show that both Mayer homology and Mayer Laplacians offer considerable application potential, providing topological and geometric insights to spaces. We also introduce persistent Mayer homology and persistent Mayer Laplacians at various $N$. The Wasserstein distance and stability of persistence diagrams associated with Mayer homology are investigated. Our computational experiments indicate that the topological features offered by persistent Mayer homology and spectrum given by persistent Mayer Laplacians hold substantial promise for large, complex, and diverse data. We envision that the present work serves as an inaugural step towards integrating Mayer homology and Mayer Laplacians into the realm of topological data analysis.

math.AT↗

arXiv2023

Planning Reliability Assurance Tests for Autonomous Vehicles

Artificial intelligence (AI) technology has become increasingly prevalent and transforms our everyday life. One important application of AI technology is the development of autonomous vehicles (AV). However, the reliability of an AV needs to be carefully demonstrated via an assurance test so that the product can be used with confidence in the field. To plan for an assurance test, one needs to determine how many AVs need to be tested for how many miles and the standard for passing the test. Existing research has made great efforts in developing reliability demonstration tests in the other fields of applications for product development and assessment. However, statistical methods have not been utilized in AV test planning. This paper aims to fill in this gap by developing statistical methods for planning AV reliability assurance tests based on recurrent events data. We explore the relationship between multiple criteria of interest in the context of planning AV reliability assurance tests. Specifically, we develop two test planning strategies based on homogeneous and non-homogeneous Poisson processes while balancing multiple objectives with the Pareto front approach. We also offer recommendations for practical use. The disengagement events data from the California Department of Motor Vehicles AV testing program is used to illustrate the proposed assurance test planning methods.

stat.AP↗

arXiv2023

Extraordinary magnetic response of an anisotropic 2D antiferromagnet via site-dilution

A prominent character of two-dimensional magnetic systems is the enhanced spin fluctuations, which however reduce the ordering temperature. Here we report that a magnetic field of only one-thousandth of the Heisenberg superexchange interaction can induce a crossover, which for practical purposes is the effective ordering transition, at temperatures about 6 times of the Neel transition in a site-diluted two-dimensional anisotropic quantum antiferromagnet. Such a strong magnetic response is enabled because the system directly enters the antiferromagnetically ordered state from the isotropic disordered state skipping the intermediate anisotropic stage. The underlying mechanism is achieved on a pseudospin-half square lattice realized in the [(SrIrO3)1/(SrTiO3)2] superlattice thin film that is designed to linearly couple the staggered magnetization to external magnetic fields by virtue of the rotational symmetry-preserving Dzyaloshinskii Moriya interaction. Our model analysis shows that the skipping of the anisotropic regime despite the finite anisotropy is due to the enhanced isotropic fluctuations under moderate dilution.

cond-mat.str-el↗

arXiv2023

Interaction homotopy and interaction homology

Interactions in complex systems are widely observed across various fields, drawing increased attention from researchers. In mathematics, efforts are made to develop various theories and methods for studying the interactions between spaces. In this work, we present an algebraic topology framework to explore interactions between spaces. We introduce the concept of interaction spaces and investigate their homotopy, singular homology, and simplicial homology. Furthermore, we demonstrate that interaction singular homology serves as an invariant under interaction homotopy. We believe that the proposed framework holds potential for practical applications.

math.AT↗

arXiv2023

Integrated electro-optically tunable narrow-linewidth III-V laser

We demonstrate an integrated electro-optically tunable narrow-linewidth III-V laser with an output power of 738.8 μW and an intrinsic linewidth of 45.55 kHz at the C band. The laser cavity is constructed using a fiber Bragg grating (FBG) and a tunable Sagnac loop reflector (TSLR) fabricated on thin film lithium niobate (TFLN). The combination of the FBG and the electro-optically tunable TSLR offers the advantages of single spatial mode, single-frequency, narrow-linewidth, and wide wavelength tunability for the electrically pumped hybrid integrated laser, which features a frequency tuning range of 20 GHz and a tuning efficiency of 0.8 GHz/V.

physics.optics↗

arXiv2023

Thin Film Lithium Niobate Electro-optic Isolator Fabricated by photolithography assisted chemo-mechanical etching (PLACE)

We report a thin-film lithium niobate electro-optic isolator fabricated by photolithography-assisted chemo-mechanical etching in this work. The device demonstrates 39.50 dB isolation when subjected to a 24 GHz microwave of 25.5 dBm on its electrodes. The measured isolation remains consistently above 30 dB within the 1510 nm to 1600 nm wavelength range. The overall device insertion loss, specifically the fiber-to-fiber insert loss, has been measured to be 2.6 dB, which is attributed to our highly efficient spot size converter and the low propagation loss observed in the fabricated waveguides.

physics.optics↗

arXiv2023

The subdivision of hypergraphs

Hypergraphs, as a generalization of simplicial complexes, have long been a subject of interest in their geometric interpretation. The subdivision of simplicial complexes can, to some extent, provide insights into the geometry of simplicial complexes. In this paper, we introduce the concept of the subdivision of hypergraphs. Notably, the subdivision of hypergraphs can be reduced to the subdivision of simplicial complexes. Moreover, we prove that the subdivision of hypergraphs has the topological invariance with respect to the embedded homology.

math.AT↗