arXiv subjects

Zhi Li

Publications and source records attributed to Zhi Li.

At least 145 records · Page 8Linked to original sources

arXiv2024

ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis

Designing effective models for learning time series representations is foundational for time series analysis. Many previous works have explored time series representation modeling approaches and have made progress in this area. Despite their effectiveness, they lack adaptive perception of local patterns in temporally dependent basic units and fail to capture the multi-scale dependency among these units. Instead of relying on prevalent methods centered around self-attention mechanisms, we propose ConvTimeNet, a hierarchical pure convolutional model designed for time series analysis. ConvTimeNet introduces a deformable patch layer that adaptively perceives local patterns of temporally dependent basic units in a data-driven manner. Based on the extracted local patterns, hierarchical pure convolutional blocks are designed to capture dependency relationships among the representations of basic units at different scales. Moreover, a large kernel mechanism is employed to ensure that convolutional blocks can be deeply stacked, thereby achieving a larger receptive field. In this way, local patterns and their multi-scale dependencies can be effectively modeled within a single model. Extensive experiments comparing a wide range of different types of models demonstrate that pure convolutional models still exhibit strong viability, effectively addressing the aforementioned two challenges and showing superior performance across multiple tasks. The code is available for reproducibility.

cs.LG↗

arXiv2024

A Real-time Degeneracy Sensing and Compensation Method for Enhanced LiDAR SLAM

LiDAR is widely used in Simultaneous Localization and Mapping (SLAM) and autonomous driving. The LiDAR odometry is of great importance in multi-sensor fusion. However, in some unstructured environments, the point cloud registration cannot constrain the poses of the LiDAR due to its sparse geometric features, which leads to the degeneracy of multi-sensor fusion accuracy. To address this problem, we propose a novel real-time approach to sense and compensate for the degeneracy of LiDAR. Firstly, this paper introduces the degeneracy factor with clear meaning, which can measure the degeneracy of LiDAR. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering method adaptively perceives the degeneracy with better environmental generalization. Finally, the degeneracy perception results are utilized to fuse LiDAR and IMU, thus effectively resisting degeneracy effects. Experiments on our dataset show the method's high accuracy and robustness and validate our algorithm's adaptability to different environments and LiDAR scanning modalities.

cs.RO↗

arXiv2024

TeamCraft: A Benchmark for Multi-Modal Multi-Agent Systems in Minecraft

Collaboration is a cornerstone of society. In the real world, human teammates make use of multi-sensory data to tackle challenging tasks in ever-changing environments. It is essential for embodied agents collaborating in visually-rich environments replete with dynamic interactions to understand multi-modal observations and task specifications. To evaluate the performance of generalizable multi-modal collaborative agents, we present TeamCraft, a multi-modal multi-agent benchmark built on top of the open-world video game Minecraft. The benchmark features 55,000 task variants specified by multi-modal prompts, procedurally-generated expert demonstrations for imitation learning, and carefully designed protocols to evaluate model generalization capabilities. We also perform extensive analyses to better understand the limitations and strengths of existing approaches. Our results indicate that existing models continue to face significant challenges in generalizing to novel goals, scenes, and unseen numbers of agents. These findings underscore the need for further research in this area. The TeamCraft platform and dataset are publicly available at https://github.com/teamcraft-bench/teamcraft.

cs.AI↗

arXiv2024

Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity

Deep Reinforcement Learning (DRL) has exhibited efficacy in resolving the Local Path Planning (LPP) problem. However, such application in the real world is immensely limited due to the deficient training efficiency and generalization capability of DRL. To alleviate these two issues, a solution named Color is proposed, which consists of an Actor-Sharer-Learner (ASL) training framework and a mobile robot-oriented simulator Sparrow. Specifically, the ASL intends to improve the training efficiency of DRL algorithms. It employs a Vectorized Data Collection (VDC) mode to expedite data acquisition, decouples the data collection from model optimization by multithreading, and partially connects the two procedures by harnessing a Time Feedback Mechanism (TFM) to evade data underuse or overuse. Meanwhile, the Sparrow simulator utilizes a 2D grid-based world, simplified kinematics, and conversion-free data flow to achieve a lightweight design. The lightness facilitates vectorized diversity, allowing diversified simulation setups across extensive copies of the vectorized environments, resulting in a notable enhancement in the generalization capability of the DRL algorithm being trained. Comprehensive experiments, comprising 57 DRL benchmark environments, 32 simulated and 36 real-world LPP scenarios, have been conducted to corroborate the superiority of our method in terms of efficiency and generalization. The code and the video of this paper are accessible at https://github.com/XinJingHao/Color.

cs.AI↗

arXiv2024

The rigidity theorem for complete Lagrangian self-shrinkers

In this paper, we obtain a rigidity result of $2$-dimensional complete lagrangian self-shrinkers with constant squared norm $|\vec{H}|^{2}$ of the mean curvature vector in the Euclidean space $\mathbb{R}^{4}$. The same idea is also used to give a similar result of Lagrangian $ξ$-submanifolds in $\mathbb{R}^{4}$.

math.DG↗

arXiv2024

Achromatic single-layer hologram

Phase retrieval is a fundamental technique of advanced optical technologies, enabling precise control over wavefront properties. A persistent challenge in diffractive optical element (DOE) design is that a single hologram typically operates within a single wavelength or color channel, limiting it to monochromatic image generation. This limitation in channel capacity significantly restricts the applicability of DOE in optical applications. In this study, we propose a design strategy for full-color, single-layer hologram based on a variable-scale diffraction model. By imposing strict constraints in Fourier domain and reducing depth of focus (DOF), we achieve the simultaneous encryption and storage of red, green, and blue channel information within a single achromatic hologram. This strategy facilitates color separation in large-depth 3D holography and enables achromatic full-color image displays. We demonstrated full-color holographic video playback at a full refresh rate of 60 Hz, achieving a temporal resolution three times greater than that of existing methods. Furthermore, we successfully fabricated achromatic, twin-image-free, full-color binary pure-phase DOEs at low cost. This achromatic strategy addresses the demands across various fields in optics, including high-refresh-rate full-color displays, high-density optical information storage, advanced optical security, high-reusability holographic metasurface optical element, and high-performance achromatic metalenses.

physics.optics↗

arXiv2024

Adaptive Interactive Segmentation for Multimodal Medical Imaging via Selection Engine

In medical image analysis, achieving fast, efficient, and accurate segmentation is essential for automated diagnosis and treatment. Although recent advancements in deep learning have significantly improved segmentation accuracy, current models often face challenges in adaptability and generalization, particularly when processing multi-modal medical imaging data. These limitations stem from the substantial variations between imaging modalities and the inherent complexity of medical data. To address these challenges, we propose the Strategy-driven Interactive Segmentation Model (SISeg), built on SAM2, which enhances segmentation performance across various medical imaging modalities by integrating a selection engine. To mitigate memory bottlenecks and optimize prompt frame selection during the inference of 2D image sequences, we developed an automated system, the Adaptive Frame Selection Engine (AFSE). This system dynamically selects the optimal prompt frames without requiring extensive prior medical knowledge and enhances the interpretability of the model's inference process through an interactive feedback mechanism. We conducted extensive experiments on 10 datasets covering 7 representative medical imaging modalities, demonstrating the SISeg model's robust adaptability and generalization in multi-modal tasks. The project page and code will be available at: [URL].

cs.CV↗

arXiv2024

Emergent dynamical quantum phase transition in a $Z_3$ symmetric chiral clock model

We study the quench dynamics in a $Z_3$ symmetric chiral clock model (CCM). The results reveal that chiral phases can lead to the emergence of dynamical quantum phase transition (DQPT). By analyzing Lee-Yang-Fisher zeros' distribution in the complex plane, we uncover the relation between the chiral phase and the emergence of DQPT. In concrete terms, only by taking some special angles can DQPT be induced. We confirm the above relation by computing the non-analytic points in Loschmidt echo return rate function. Furthermore, through the analysis of the corresponding dynamical partition function, we reveal the mechanism of the emergent DQPT and deduce the analytical expression of dynamical partition function's zero points' coordinates. Based on the analytic expression, one can obtain all the angles that induce DQPT's emergence and predict more possible DQPT in the system.

cond-mat.stat-mech↗

arXiv2024

DisenTS: Disentangled Channel Evolving Pattern Modeling for Multivariate Time Series Forecasting

Multivariate time series forecasting plays a crucial role in various real-world applications. Significant efforts have been made to integrate advanced network architectures and training strategies that enhance the capture of temporal dependencies, thereby improving forecasting accuracy. On the other hand, mainstream approaches typically utilize a single unified model with simplistic channel-mixing embedding or cross-channel attention operations to account for the critical intricate inter-channel dependencies. Moreover, some methods even trade capacity for robust prediction based on the channel-independent assumption. Nonetheless, as time series data may display distinct evolving patterns due to the unique characteristics of each channel (including multiple strong seasonalities and trend changes), the unified modeling methods could yield suboptimal results. To this end, we propose DisenTS, a tailored framework for modeling disentangled channel evolving patterns in general multivariate time series forecasting. The central idea of DisenTS is to model the potential diverse patterns within the multivariate time series data in a decoupled manner. Technically, the framework employs multiple distinct forecasting models, each tasked with uncovering a unique evolving pattern. To guide the learning process without supervision of pattern partition, we introduce a novel Forecaster Aware Gate (FAG) module that generates the routing signals adaptively according to both the forecasters' states and input series' characteristics. The forecasters' states are derived from the Linear Weight Approximation (LWA) strategy, which quantizes the complex deep neural networks into compact matrices. Additionally, the Similarity Constraint (SC) is further proposed to guide each model to specialize in an underlying pattern by minimizing the mutual information between the representations.

cs.LG↗

arXiv2024

Antichiral and trap-skin dynamics in a nonreciprocal bosonic two-leg ladder with artificial magnetic flux

Non-Hermiticity and synthetic gauge fields play two fundamental roles in engineering exotic phases and dynamics in artificial quantum systems. Here we explore the mean-field dynamics of interacting bosons in a two-leg ladder with synthetic magnetic flux and nonreciprocal hopping under the open boundary condition. In the Hermitian limit, we showcase the breakdown of the flux-driven chiral dynamics due to the nonlinear self-trapping effect. We further find that the nonreciprocity can drive the transition between chiral dynamics and antichiral dynamics. The antichiral motion is manifested as the non-Hermitian skin dynamics along the same direction on two legs that are not suppressed by the magnetic flux, while the chiral-antichiral transition is flux-tunable. We also reveal the trap-skin dynamics with the coexistence of the self-tapping and skin dynamics in the ladder. Dynamical phase diagrams with respect to the chiral-antichiral dynamics, skin dynamics, self-trapping dynamics, and trap-skin dynamics are presented. Our results shed light on intriguing dynamical phenomena under the interplay among non-Hermiticity, nonlinearity, and artificial gauge fields.

cond-mat.quant-gas↗

arXiv2024

Asymmetric transfer matrix analysis of Lyapunov exponents in one-dimensional non-reciprocal quasicrystals

The Lyapunov exponent, serving as an indicator of the localized state, is commonly utilized to identify localization transitions in disordered systems. In non-Hermitian quasicrystals, the non-Hermitian effect induced by non-reciprocal hopping can lead to the manifestation of two distinct Lyapunov exponents on opposite sides of the localization center. Building on this observation, we here introduce a comprehensive approach for examining the localization characteristics and mobility edges of non-reciprocal quasicrystals, referred to as asymmetric transfer matrix analysis. We demonstrate the application of this method to three specific scenarios: the non-reciprocal Aubry-André model, the non-reciprocal off-diagonal Aubry-André model, and the non-reciprocal mosaic quasicrystals. This work may contribute valuable insights to the investigation of non-Hermitian quasicrystal and disordered systems.

cond-mat.dis-nn↗

arXiv2024

Optimizing Instruction Synthesis: Effective Exploration of Evolutionary Space with Tree Search

Instruction tuning is a crucial technique for aligning language models with humans' actual goals in the real world. Extensive research has highlighted the quality of instruction data is essential for the success of this alignment. However, creating high-quality data manually is labor-intensive and time-consuming, which leads researchers to explore using LLMs to synthesize data. Recent studies have focused on using a stronger LLM to iteratively enhance existing instruction data, showing promising results. Nevertheless, previous work often lacks control over the evolution direction, resulting in high uncertainty in the data synthesis process and low-quality instructions. In this paper, we introduce a general and scalable framework, IDEA-MCTS (Instruction Data Enhancement using Monte Carlo Tree Search), a scalable framework for efficiently synthesizing instructions. With tree search and evaluation models, it can efficiently guide each instruction to evolve into a high-quality form, aiding in instruction fine-tuning. Experimental results show that IDEA-MCTS significantly enhances the seed instruction data, raising the average evaluation scores of quality, diversity, and complexity from 2.19 to 3.81. Furthermore, in open-domain benchmarks, experimental results show that IDEA-MCTS improves the accuracy of real-world instruction-following skills in LLMs by an average of 5\% in low-resource settings.

cs.AI↗

arXiv2024

The global phase diagram of the cluster-XY spin chain with dissipation

We study the ground-state phase diagram of a non-Hermitian cluster-XY spin chain in the language of free fermions. By calculating the second derivative of ground-state energy density and various types of order parameters, we establish the global ground-state phase diagram of the model, exhibiting rich quantum phases and corresponding phase transitions. Specially, the results reveal that the non-Hermitian cluster-XY model contains five different phases and two critical regions, i.e., ferromagnetic (FM), antiferromagnetic (AFM), symmetry-protected topological (SPT), paramagnetic (PM), Luttinger liquid-like phase, as well as critical region I and II. The order parameters and critical behaviors are investigated and the correctness of the theory is confirmed.

cond-mat.stat-mech↗

arXiv2024

The Milky Way bar potential constrained by the kinematics of SiO maser stars in BAaDE Survey

We introduce a novel method that utilizes the longitude-velocity (l-v) envelope to constrain the Milky Way (MW) bar potential. Previous work (Habing 2016) used the l-v diagram to explain the distribution of the observed high-velocity stars. We successfully reproduce their results, but find that their method is limited to only one single type of periodic orbits. In contrast, we propose that the l-v envelope provides much more comprehensive constraints. We compare the properties of test particles in the Portai et al. (2017) MW potential model (P17) with the observed SiO maser stars from the Bulge Asymmetries and Dynamical Evolution (BAaDE) survey. We find that the l-v envelope generated by the bar potential demonstrates reasonable agreement with the observational data, albeit with slight discrepancies near the Galactic center. The inconsistencies suggest that the P17 potential yields a lower central rotation curve, a slightly larger quadrupole strength, or a possibly underestimated pattern speed. We also adopt an updated version of the P17 potential with a modified central mass component (CMC) proposed by Hunter et al. (2024) (H24). The fitting of the l-v envelope suggests that the H24 potential does not completely address the existing challenges and may hint at a possible underestimation of the central bar mass. Our study demonstrates that the l-v envelope can be used as a valuable tool for constraining the Galactic potential and provides insights into the Milky Way bar potential.

astro-ph.GA↗

arXiv2024

Chaitin Phase Transition

We construct a family of Hamiltonians whose phase diagram is guaranteed to have a single phase transition, yet the location of this phase transition is uncomputable. The Hamiltonians $H(ϕ)$ describe qudits on a two-dimensional square lattice with translationally invariant, nearest-neighbour interactions tuned by a continuous parameter $ϕ\in(0,1]$. For all $ϕ\in(0,1]$, $H(ϕ)$ is in one of two phases, one a gapless phase, the other a gapped phase. The phase transition occurs when $ϕ$ equals the Chaitin's constant $Ω$, a well-defined real number that encodes the Halting problem, and hence is uncomputable for Turing machines and undecidable for any consistent recursive axiomatization of mathematics. Our result implies that no general algorithm exists to determine the phase diagrams even under the promise that the phase diagram is exceedingly simple, and illustrates how uncomputable numbers may manifest in physical systems.

quant-ph↗

arXiv2024

Empowering Sequential Recommendation from Collaborative Signals and Semantic Relatedness

Sequential recommender systems (SRS) could capture dynamic user preferences by modeling historical behaviors ordered in time. Despite effectiveness, focusing only on the \textit{collaborative signals} from behaviors does not fully grasp user interests. It is also significant to model the \textit{semantic relatedness} reflected in content features, e.g., images and text. Towards that end, in this paper, we aim to enhance the SRS tasks by effectively unifying collaborative signals and semantic relatedness together. Notably, we empirically point out that it is nontrivial to achieve such a goal due to semantic gap issues. Thus, we propose an end-to-end two-stream architecture for sequential recommendation, named TSSR, to learn user preferences from ID-based and content-based sequence. Specifically, we first present novel hierarchical contrasting module, including coarse user-grained and fine item-grained terms, to align the representations of inter-modality. Furthermore, we also design a two-stream architecture to learn the dependence of intra-modality sequence and the complex interactions of inter-modality sequence, which can yield more expressive capacity in understanding user interests. We conduct extensive experiments on five public datasets. The experimental results show that the TSSR could yield superior performance than competitive baselines. We also make our experimental codes publicly available at https://github.com/Mingyue-Cheng/TSSR.

cs.IR↗

arXiv2024

Short-range order stabilizes a cubic Fe alloy in Earth's inner core

The phase diagram and sound velocities of the Fe-Si binary alloy, crucial for understanding the Earth's core, are determined at inner core boundary pressure with \textit{ab-initio} accuracy through deep-learning-aided hybrid Monte Carlo simulations. A complex phase diagram emerges close to the melting temperature, where a re-entrance of the body-centered cubic (bcc) phase is observed. The bcc structure is stabilized by a pronounced short-range ordering of the Si atoms. The miscibility gap between the short-range ordered bcc structure and the long-range ordered cubic B2 structure shrinks with increasing temperature and the transition becomes continuous above 6000 K. We find that a bcc Fe-Si solid solution reproduces crucial geophysical data such as the low shear sound velocity and the seismic anisotropy of the inner core much better than other structures.

physics.geo-ph↗

arXiv2024

Expansion-GRR: Efficient Generation of Smooth Global Redundancy Resolution Roadmaps

Global redundancy resolution (GRR) roadmaps is a novel concept in robotics that facilitates the mapping from task space paths to configuration space paths in a legible, predictable, and repeatable way. Such roadmaps could find widespread utility in applications such as safe teleoperation, consistent path planning, and motion primitives generation. However, previous methods to compute GRR roadmaps often necessitate a lengthy computation time and produce non-smooth paths, limiting their practical efficacy. To address this challenge, we introduce a novel method Expansion-GRR that leverages efficient configuration space projections and enables rapid generation of smooth roadmaps that satisfy the task constraints. Additionally, we propose a simple multi-seed strategy that further enhances the final quality. We conducted experiments in simulation with a 5-link planar manipulator and a Kinova arm. We were able to generate the Expansion-GRR roadmaps up to 2 orders of magnitude faster while achieving higher smoothness. We also demonstrate the utility of the GRR roadmaps in teleoperation tasks where our method outperformed prior methods and reactive IK solvers in terms of success rate and solution quality.

cs.RO↗