arXiv subjects

Zihan Weng

Publications and source records attributed to Zihan Weng.

4 recordsLinked to original sources

arXiv2025

SPECTRE: Spectral Pre-training Embeddings with Cylindrical Temporal Rotary Position Encoding for Fine-Grained sEMG-Based Movement Decoding

Decoding fine-grained movement from non-invasive surface Electromyography (sEMG) is a challenge for prosthetic control due to signal non-stationarity and low signal-to-noise ratios. Generic self-supervised learning (SSL) frameworks often yield suboptimal results on sEMG as they attempt to reconstruct noisy raw signals and lack the inductive bias to model the cylindrical topology of electrode arrays. To overcome these limitations, we introduce SPECTRE, a domain-specific SSL framework. SPECTRE features two primary contributions: a physiologically-grounded pre-training task and a novel positional encoding. The pre-training involves masked prediction of discrete pseudo-labels from clustered Short-Time Fourier Transform (STFT) representations, compelling the model to learn robust, physiologically relevant frequency patterns. Additionally, our Cylindrical Rotary Position Embedding (CyRoPE) factorizes embeddings along linear temporal and annular spatial dimensions, explicitly modeling the forearm sensor topology to capture muscle synergies. Evaluations on multiple datasets, including challenging data from individuals with amputation, demonstrate that SPECTRE establishes a new state-of-the-art for movement decoding, significantly outperforming both supervised baselines and generic SSL approaches. Ablation studies validate the critical roles of both spectral pre-training and CyRoPE. SPECTRE provides a robust foundation for practical myoelectric interfaces capable of handling real-world sEMG complexities.

cs.HC↗

arXiv2025

Emergent electronic insulating states in a one-dimensional moir\'e superlattice

Two-dimensional (2D) van der Waals (vdW) moir\'e superlattices have provided a powerful knob to engineer a plethora of new quantum states. However, extending such moir\'e engineering to one-dimensional (1D) vdW systems has remained challenging. Here we report the moir\'e-engineered electronic insulating states in a new 1D moir\'e superlattice, by crystallographically aligning an armchair single-walled carbon nanotube (SWNT) to 2D hexagonal boron nitride (hBN) substrate. Remarkably, we observe the emergence of pronounced insulating states at charge neutrality point (CNP), full and half moir\'e fillings in lattice-aligned armchair SWNT/hBN heterostructures by low-temperature electrical transport measurements. In strong contrast, armchair SWNT devices without hBN alignment do not show any of these insulating behaviors, providing compelling evidence for the significant 1D moir\'e effect. Our density functional theory (DFT) and tight-binding calculations reveal that synergetic nanotube partial flattening and in-plane lattice reconstruction at 1D moir\'e interface expand the most stable AB' stacking regions (carbon on top of boron) and open sizable band gaps at both CNP and full moir\'e fillings at the single-particle level. Our one-body theory predicts no band gaps at half moir\'e fillings, suggesting that electron correlation and/or electron-phonon interaction may give rise to these emergent insulating behaviors in our 1D moir\'e systems. Our work establishes a new and definite moir\'e engineering route for 1D vdW materials and opens an exciting avenue for exploring interaction-induced quantum phases in 1D.

cond-mat.mes-hall↗

arXiv2025

Original-energy-dissipation-preserving methods for the incompressible Navier-Stokes equations

This paper introduces a robust reformulation of the incompressible Navier-Stokes equations, establishing a foundational framework for designing efficient, structure-preserving algorithms that strictly conserve the original energy dissipation law. By leveraging Crank-Nicolson schemes and backward differentiation formulas, we develop four first- and second-order time-discrete schemes. These schemes exactly preserve the original energy dissipation law at each time step, requiring only the solutions of three linear Stokes systems and one $2\times 2$ system of linear equations. Furthermore, the finite difference approximation on a staggered grid is employed for these time-discrete systems to derive fully discrete structure-preserving schemes. We rigorously prove that all proposed fully discrete methods both maintain the original energy dissipation law and admit unique solutions. Moreover, we present their efficient implementation. Extensive numerical experiments are carried out to verify the accuracy, efficacy, and advantageous performance of our newly developed methods.

math.NA↗

arXiv2025

Caption This, Reason That: VLMs Caught in the Middle

Vision-Language Models (VLMs) have shown remarkable progress in visual understanding in recent years. Yet, they still lag behind human capabilities in specific visual tasks such as counting or relational reasoning. To understand the underlying limitations, we adopt methodologies from cognitive science, analyzing VLM performance along core cognitive axes: Perception, Attention, and Memory. Using a suite of tasks targeting these abilities, we evaluate state-of-the-art VLMs, including GPT-4o. Our analysis reveals distinct cognitive profiles: while advanced models approach ceiling performance on some tasks (e.g. category identification), a significant gap persists, particularly in tasks requiring spatial understanding or selective attention. Investigating the source of these failures and potential methods for improvement, we employ a vision-text decoupling analysis, finding that models struggling with direct visual reasoning show marked improvement when reasoning over their own generated text captions. These experiments reveal a strong need for improved VLM Chain-of-Thought (CoT) abilities, even in models that consistently exceed human performance. Furthermore, we demonstrate the potential of targeted fine-tuning on composite visual reasoning tasks and show that fine-tuning smaller VLMs substantially improves core cognitive abilities. While this improvement does not translate to large enhancements on challenging, out-of-distribution benchmarks, we show broadly that VLM performance on our datasets strongly correlates with performance on these other benchmarks. Our work provides a detailed analysis of VLM cognitive strengths and weaknesses and identifies key bottlenecks in simultaneous perception and reasoning while also providing an effective and simple solution.

cs.CV↗