Searcharxiv⌕ Search

arXiv subjects

Yichen Liu

Publications and source records attributed to Yichen Liu.

At least 91 records · Page 5Linked to original sources
arXiv2024

Crossed real nodal-line phonons in gold monobromide

Spacetime inversion symmetry can generate intriguing types of spinless excitations in crystalline materials. Here, we propose a topological phase protected by spacetime inversion symmetry - the crossed real nodal line (RNL) in the phonon spectrum of gold monobromide (AuBr). In AuBr, there exist four straight nodal lines, which are linked by a crossed nodal line formed by two lower bands. Remarkably, each adjacent two of the four straight nodal lines is a pair, forming a crossed RNL with nontrivial real Chern number. Such configuration and pairing mode of RNL have never been reported. The crossed RNL exhibits unique surface and hinge states distinguished from that of the conventional RNLs. The symmetry protection and the transformation under the symmetry-preserving strain of the crossed RNL are also investigated. Our results open the door to a new class of topological states, and predict its realization in experimentally synthesized material.

cond-mat.mtrl-sci↗
arXiv2024

DAVOS: Dwarf Active Galactic Nuclei from Variability for the Origins of Seeds: Properties of Variability-Selected Active Galactic Nuclei in the COSMOS Field and Expectations for the Rubin Observatory

We study the black hole mass $-$ host galaxy stellar mass relation, $M_{\rm{BH}}-M_{\ast}$, of a sample of $z<4$ optically-variable AGNs in the COSMOS field. The parent sample of 491 COSMOS AGNs were identified by optical variability from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) program. Using publicly-available catalogs and spectra, we consolidate their spectroscopic redshifts and estimate virial black hole masses using broad line widths and luminosities. We show that variability searches with deep, high precision photometry like the HSC-SSP can identity AGNs in low mass galaxies up to $z\sim1$. However, their black holes are more massive given their host galaxy stellar masses than predicted by the local relation for active galaxies. We report that $z\sim 0.5-4$ variability-selected AGNs are meanwhile more consistent with the $M_{\rm{BH}}-M_{\ast}$ relation for local inactive early-type galaxies. This result is consistent with most previous studies of the $M_{\rm{BH}}-M_{\ast}$ relation at similar redshifts and indicates that AGNs selected from variability are not intrinsically different from the broad-line Type 1 AGN population at similar luminosities. Our results demonstrate the need for robust black hole and stellar mass estimates for intermediate-mass black hole candidates in low-mass galaxies at similar redshifts. Assuming that these results do not reflect a selection bias, they appear to be consistent with self-regulated feedback models wherein the central black hole and stars in galaxies grow in tandem.

astro-ph.GA↗
arXiv2024

Valley polarization in twisted altermagnetism

The combination of altermagnetism, twistronics and valleytronics is of great significance for potential applications in advanced electronic devices. Twisted magnetic van der Waals bilayers have been identified as an ideal platform for altermagnetism of any type, such as $d$-wave, $g$-wave, and $i$-wave, by choosing the constituent monolayer with specific symmetry [arXiv:2404.17146 (2024)]. Here, we propose a way for achieving valley polarization in twisted altermagnetism by applying out-of-plane external electric field. Since the out-of-plane electric field creates a layer-dependent electrostatic potential, the valleys form different layers will stagger, producing valley polarization. We also demonstrate the effectiveness of our proposed way using the twisted tight-binding model. It is found that the applied electric field can also induce valley/spin-gapless semiconductor and half metal besides valley polarization. Based on first-principles calculations, our proposed way to achieve valley polarization can be verified in twisted bilayer VOBr and monolayer $\mathrm{Ca(CoN)_2}$ as a special twisted altermagnet. These findings provide new opportunities for innovative spintronics, twistronics and valleytronics applications.

cond-mat.mtrl-sci↗
arXiv2024

VP-LLM: Text-Driven 3D Volume Completion with Large Language Models through Patchification

Recent conditional 3D completion works have mainly relied on CLIP or BERT to encode textual information, which cannot support complex instruction. Meanwhile, large language models (LLMs) have shown great potential in multi-modal understanding and generation tasks. Inspired by the recent advancements of LLM, we present Volume Patch LLM (VP-LLM), which leverages LLMs to perform conditional 3D completion in a single-forward pass. To integrate a 3D model into the LLM tokenization configuration, the incomplete 3D object is first divided into small patches that can be encoded independently. These encoded patches are then fed into an LLM along with the text prompt, instructing the LLM to capture the relations between these patches as well as injecting semantic meanings into the 3D object. Our results demonstrate a strong ability of LLMs to interpret complex text instructions and understand 3D objects, surpassing state-of-the-art diffusion-based 3D completion models in generation quality.

cs.CV↗
arXiv2024

Creation and Manipulation of Higher-Order Topological States by Altermagnets

We propose to implement tunable higher-order topological states in a heterojunction consisting of a two-dimensional (2D) topological insulator and the recently discovered altermagnets, whose unique spin-polarization in both real and reciprocal space and null magnetization are in contrast to conventional ferromagnets and antiferromagnets. Based on symmetry analysis and effective edge theory, we show that the special spin splitting in altermagnets with different symmetries, such as $d$-wave, can introduce Dirac mass terms with opposite signs on the adjacent boundaries of the topological insulator, resulting in the higher-order topological state with mass-domain bound corner states. Moreover, by adjusting the direction of the Néel vector, we can manipulate such topological corner states by moving their positions. By first-principles calculations, taking a 2D topological insulator bismuthene with a square lattice on an altermagnet MnF$_2$ as an example, we demonstrate the feasibility of creating and manipulating the higher-order topological states through altermagnets. Finally, we discuss the experimental implementation and detection of the tunable topological corner states, as well as the potential non-Abelian braiding of the Dirac corner fermions.

cond-mat.mes-hall↗
arXiv2024

Neural Radiance Field in Autonomous Driving: A Survey

Neural Radiance Field (NeRF) has garnered significant attention from both academia and industry due to its intrinsic advantages, particularly its implicit representation and novel view synthesis capabilities. With the rapid advancements in deep learning, a multitude of methods have emerged to explore the potential applications of NeRF in the domain of Autonomous Driving (AD). However, a conspicuous void is apparent within the current literature. To bridge this gap, this paper conducts a comprehensive survey of NeRF's applications in the context of AD. Our survey is structured to categorize NeRF's applications in Autonomous Driving (AD), specifically encompassing perception, 3D reconstruction, simultaneous localization and mapping (SLAM), and simulation. We delve into in-depth analysis and summarize the findings for each application category, and conclude by providing insights and discussions on future directions in this field. We hope this paper serves as a comprehensive reference for researchers in this domain. To the best of our knowledge, this is the first survey specifically focused on the applications of NeRF in the Autonomous Driving domain.

cs.CV↗
arXiv2024

Twisted Magnetic Van der Waals Bilayers: An Ideal Platform for Altermagnetism

We introduce a universal methodology for generating and manipulating altermagnetism in two-dimensional (2D) magnetic van der Waals (MvdW) materials through twisting. We find that a key in-plane 2-fold rotational operation can be achieved in a twisted bilayer of any 2D MvdW material, which takes one of all five 2D Bravais lattices, thereby inducing altermagnetism. By choosing the constituent MvdW monolayer with specific symmetry, our approach can tailor altermagnetism of any type, such as $d$-wave, $g$-wave, and $i$-wave. Furthermore, the properties of our twisted altermagnetic materials can be easily engineered. Taking a transition-metal oxyhalide VOBr as an example, we find that by tuning the twist angle and Fermi level a giant spin Hall angle can be obtained, much larger than the experimentally reported. This approach establishes a general, robust, and adjustable platform to explore altermagnetism, and provides a new efficient way to generate and manipulate the spin current.

cond-mat.mtrl-sci↗
arXiv2024

DepthMOT: Depth Cues Lead to a Strong Multi-Object Tracker

Accurately distinguishing each object is a fundamental goal of Multi-object tracking (MOT) algorithms. However, achieving this goal still remains challenging, primarily due to: (i) For crowded scenes with occluded objects, the high overlap of object bounding boxes leads to confusion among closely located objects. Nevertheless, humans naturally perceive the depth of elements in a scene when observing 2D videos. Inspired by this, even though the bounding boxes of objects are close on the camera plane, we can differentiate them in the depth dimension, thereby establishing a 3D perception of the objects. (ii) For videos with rapidly irregular camera motion, abrupt changes in object positions can result in ID switches. However, if the camera pose are known, we can compensate for the errors in linear motion models. In this paper, we propose \textit{DepthMOT}, which achieves: (i) detecting and estimating scene depth map \textit{end-to-end}, (ii) compensating the irregular camera motion by camera pose estimation. Extensive experiments demonstrate the superior performance of DepthMOT in VisDrone-MOT and UAVDT datasets. The code will be available at \url{https://github.com/JackWoo0831/DepthMOT}.

cs.CV↗
arXiv2024

SANeRF-HQ: Segment Anything for NeRF in High Quality

Recently, the Segment Anything Model (SAM) has showcased remarkable capabilities of zero-shot segmentation, while NeRF (Neural Radiance Fields) has gained popularity as a method for various 3D problems beyond novel view synthesis. Though there exist initial attempts to incorporate these two methods into 3D segmentation, they face the challenge of accurately and consistently segmenting objects in complex scenarios. In this paper, we introduce the Segment Anything for NeRF in High Quality (SANeRF-HQ) to achieve high-quality 3D segmentation of any target object in a given scene. SANeRF-HQ utilizes SAM for open-world object segmentation guided by user-supplied prompts, while leveraging NeRF to aggregate information from different viewpoints. To overcome the aforementioned challenges, we employ density field and RGB similarity to enhance the accuracy of segmentation boundary during the aggregation. Emphasizing on segmentation accuracy, we evaluate our method on multiple NeRF datasets where high-quality ground-truths are available or manually annotated. SANeRF-HQ shows a significant quality improvement over state-of-the-art methods in NeRF object segmentation, provides higher flexibility for object localization, and enables more consistent object segmentation across multiple views. Results and code are available at the project site: https://lyclyc52.github.io/SANeRF-HQ/.

cs.CV↗
arXiv2024

Isoleucine gate blocks K+ conduction in C-type inactivation

Many voltage-gated potassium (Kv) channels display a time-dependent phenomenon called C-type inactivation, whereby prolonged activation by voltage leads to the inhibition of ionic conduction, a process that involves a conformational change at the selectivity filter toward a non-conductive state. Recently, a high-resolution structure of a strongly inactivating triple-mutant channel kv1.2-kv2.1-3m revealed a novel conformation of the selectivity filter that is dilated at its outer end, distinct from the well-characterized conductive state. While the experimental structure was interpreted as the elusive non-conductive state, molecular dynamics simulations and electrophysiology measurements demonstrate that the dilated filter of kv1.2-kv2.1-3m, however, is conductive and, as such, cannot completely account for the inactivation of the channel observed in functional experiments. An additional conformational change implicating isoleucine residues at position 398 along the pore lining segment S6 is required to effectively block ion conduction. It is shown that the I398 residues from the four subunits act as a state-dependent hydrophobic gate located immediately beneath the selectivity filter. As a critical piece of the C-type inactivation machinery, this structural feature is the potential target of a broad class of QA blockers and negatively charged activators thus opening new research directions towards the development of drugs that specifically modulate gating-states of Kv channels.

physics.bio-ph↗
arXiv2024

DiffYOLO: Object Detection for Anti-Noise via YOLO and Diffusion Models

Object detection models represented by YOLO series have been widely used and have achieved great results on the high quality datasets, but not all the working conditions are ideal. To settle down the problem of locating targets on low quality datasets, the existing methods either train a new object detection network, or need a large collection of low-quality datasets to train. However, we propose a framework in this paper and apply it on the YOLO models called DiffYOLO. Specifically, we extract feature maps from the denoising diffusion probabilistic models to enhance the well-trained models, which allows us fine-tune YOLO on high-quality datasets and test on low-quality datasets. The results proved this framework can not only prove the performance on noisy datasets, but also prove the detection results on high-quality test datasets. We will supplement more experiments later (with various datasets and network architectures).

cs.CV↗
arXiv2023

Packing Densities of Delzant and Semitoric Polygons

Exploiting the relationship between 4-dimensional toric and semitoric integrable systems with Delzant and semitoric polygons, respectively, we develop techniques to compute certain equivariant packing densities and equivariant capacities of these systems by working exclusively with the polygons. This expands on results of Pelayo and Pelayo-Schmidt. We compute the densities of several important examples and we also use our techniques to solve the equivariant semitoric perfect packing problem, i.e., we list all semitoric polygons for which the associated semitoric system admits an equivariant packing which fills all but a set of measure zero of the manifold. This paper also serves as a concise and accessible introduction to Delzant and semitoric polygons in dimension four.

math.SG↗
arXiv2023

Coexistence of multiuser entanglement distribution and classical light in optical fiber network with a semiconductor chip

Building communication links among multiple users in a scalable and robust way is a key objective in achieving large-scale quantum networks. In realistic scenario, noise from the coexisting classical light is inevitable and can ultimately disrupt the entanglement. The previous significant fully connected multiuser entanglement distribution experiments are conducted using dark fiber links and there is no explicit relation between the entanglement degradations induced by classical noise and its error rate. Here we fabricate a semiconductor chip with a high figure-of-merit modal overlap to directly generate broadband polarization entanglement. Our monolithic source maintains polarization entanglement fidelity above 96% for 42 nm bandwidth with a brightness of 1.2*10^7 Hz/mW. We perform a continuously working quantum entanglement distribution among three users coexisting with classical light. Under finite-key analysis, we establish secure keys and enable images encryption as well as quantum secret sharing between users. Our work paves the way for practical multiparty quantum communication with integrated photonic architecture compatible with real-world fiber optical communication network.

quant-ph↗
arXiv2023

Instance Neural Radiance Field

This paper presents one of the first learning-based NeRF 3D instance segmentation pipelines, dubbed as Instance Neural Radiance Field, or Instance NeRF. Taking a NeRF pretrained from multi-view RGB images as input, Instance NeRF can learn 3D instance segmentation of a given scene, represented as an instance field component of the NeRF model. To this end, we adopt a 3D proposal-based mask prediction network on the sampled volumetric features from NeRF, which generates discrete 3D instance masks. The coarse 3D mask prediction is then projected to image space to match 2D segmentation masks from different views generated by existing panoptic segmentation models, which are used to supervise the training of the instance field. Notably, beyond generating consistent 2D segmentation maps from novel views, Instance NeRF can query instance information at any 3D point, which greatly enhances NeRF object segmentation and manipulation. Our method is also one of the first to achieve such results in pure inference. Experimented on synthetic and real-world NeRF datasets with complex indoor scenes, Instance NeRF surpasses previous NeRF segmentation works and competitive 2D segmentation methods in segmentation performance on unseen views. Watch the demo video at https://youtu.be/wW9Bme73coI. Code and data are available at https://github.com/lyclyc52/Instance_NeRF.

cs.CV↗
arXiv2023

centroIDA: Cross-Domain Class Discrepancy Minimization Based on Accumulative Class-Centroids for Imbalanced Domain Adaptation

Unsupervised Domain Adaptation (UDA) approaches address the covariate shift problem by minimizing the distribution discrepancy between the source and target domains, assuming that the label distribution is invariant across domains. However, in the imbalanced domain adaptation (IDA) scenario, covariate and long-tailed label shifts both exist across domains. To tackle the IDA problem, some current research focus on minimizing the distribution discrepancies of each corresponding class between source and target domains. Such methods rely much on the reliable pseudo labels' selection and the feature distributions estimation for target domain, and the minority classes with limited numbers makes the estimations more uncertainty, which influences the model's performance. In this paper, we propose a cross-domain class discrepancy minimization method based on accumulative class-centroids for IDA (centroIDA). Firstly, class-based re-sampling strategy is used to obtain an unbiased classifier on source domain. Secondly, the accumulative class-centroids alignment loss is proposed for iterative class-centroids alignment across domains. Finally, class-wise feature alignment loss is used to optimize the feature representation for a robust classification boundary. A series of experiments have proved that our method outperforms other SOTA methods on IDA problem, especially with the increasing degree of label shift.

cs.LG↗
arXiv2023

Building a digital twin of EDFA: a grey-box modeling approach

To enable intelligent and self-driving optical networks, high-accuracy physical layer models are required. The dynamic wavelength-dependent gain effects of non-constant-pump erbium-doped fiber amplifiers (EDFAs) remain a crucial problem in terms of modeling, as it determines optical-to-signal noise ratio as well as the magnitude of fiber nonlinearities. Black-box data-driven models have been widely studied, but it requires a large size of data for training and suffers from poor generalizability. In this paper, we derive the gain spectra of EDFAs as a simple univariable linear function, and then based on it we propose a grey-box EDFA gain modeling scheme. Experimental results show that for both automatic gain control (AGC) and automatic power control (APC) EDFAs, our model built with 8 data samples can achieve better performance than the neural network (NN) based model built with 900 data samples, which means the required data size for modeling can be reduced by at least two orders of magnitude. Moreover, in the experiment the proposed model demonstrates superior generalizability to unseen scenarios since it is based on the underlying physics of EDFAs. The results indicate that building a customized digital twin of each EDFA in optical networks become feasible, which is essential especially for next generation multi-band network operations.

eess.SP↗
arXiv2023

On Uni-Modal Feature Learning in Supervised Multi-Modal Learning

We abstract the features (i.e. learned representations) of multi-modal data into 1) uni-modal features, which can be learned from uni-modal training, and 2) paired features, which can only be learned from cross-modal interactions. Multi-modal models are expected to benefit from cross-modal interactions on the basis of ensuring uni-modal feature learning. However, recent supervised multi-modal late-fusion training approaches still suffer from insufficient learning of uni-modal features on each modality. We prove that this phenomenon does hurt the model's generalization ability. To this end, we propose to choose a targeted late-fusion learning method for the given supervised multi-modal task from Uni-Modal Ensemble(UME) and the proposed Uni-Modal Teacher(UMT), according to the distribution of uni-modal and paired features. We demonstrate that, under a simple guiding strategy, we can achieve comparable results to other complex late-fusion or intermediate-fusion methods on various multi-modal datasets, including VGG-Sound, Kinetics-400, UCF101, and ModelNet40.

cs.CV↗