arXiv subjects

Yihe Wang

Publications and source records attributed to Yihe Wang.

At least 19 recordsLinked to original sources

arXiv2026

Photoelectrons resolve the phase-dependent variance of bright squeezed vacuum

The variance of bright squeezed vacuum (BSV) is not only strong but also phase dependent, yet recent strong-field experiments with BSV have recorded only its magnitude. A coherent field of the same color provides the phase reference that resolves this phase-dependent noise. Strong-field ionization of xenon by this light shows that the photoelectron yield follows the BSV quadrature along the coherent field, and that the nonlinearity of ionization amplifies the quadrature noise many times over in the electron yield. The noise is moreover distributed unevenly over the photoelectron momentum distribution, differing by an order of magnitude between ionization channels. Strong-field ionization thus offers a phase-sensitive, channel-resolved detector of quantum light, while the quadrature noise of quantum light offers a probe of the intensity dependence of individual ionization pathways.

physics.atom-ph↗

arXiv2026

UNWRAP: Single-shot phase analysis of bright squeezed vacuum by Newton-ring interferometry

The mean field of pulsed bright squeezed vacuum (BSV) vanishes, but the variance can be large, and is strongly phase-dependent. We employ ultrafast Newton-ring wavefront retrieval and analysis of phase (UNWRAP) to extract the field amplitude and phase profile of each BSV pulse, by Fourier analysis of the interference rings between the BSV and a coherent reference with different phase-front curvature. The concentric ring interference fringes are robust against pointing mismatch and drift, thereby simplifying the analysis. The measured distribution has two phase peaks separated by π radians. The rings sweep the delay between the two pulses, so a single frame can capture the temporal field profile of the BSV, even when it is composed of several temporal modes.

physics.optics↗

arXiv2026

Large-scale Environmental Dependence of AGN Activity Revealed by JWST Pa$α$ Emitters

The role of the large-scale environment in regulating AGN activity remains an open question. We investigate the Mpc-scale environments of 70 AGNs at $z=1.1$-$1.5$ using a highly complete sample of 3,419 Pa$α$ emitters with accurate spectroscopic redshifts from JWST slitless spectroscopic survey. This dataset allows us to robustly trace the environments and quantify the environmental dependence of AGN properties. We measure the eighth-nearest-neighbor surface density of Pa$α$ emitters around each AGN, probing $\sim$1.5 pMpc scale environments. We find that this environmental density exhibits a weak positive correlation with radio luminosity. Radio-bright AGNs tend to reside in statistically denser environments than their radio-faint counterparts. The environmental density also has a positive correlation with the Eddington ratio and a weak negative correlation with the central black hole mass, implying more efficient black hole accretion in dense environments. We construct Pa$α$ luminosity functions (LFs) in AGN environments and find that they are systematically elevated relative to the field Pa$α$ LF. In addition, their faint-end slopes are shallower, suggesting accelerated galaxy growth and enhanced quenching of star formation. As Pa$α$ emitters trace gas-rich, star-forming galaxies, our results support a scenario in which the large-scale cold gas promotes AGN activity at cosmic noon.

astro-ph.GA↗

arXiv2026

A Comprehensive JWST Slitless Spectroscopic Census of $\sim$12,000 Emission-Line Galaxies at Redshift $0\sim9$ in COSMOS

Emission-line galaxies provide key diagnostics of star formation, chemical enrichment, and AGN activity across cosmic time. We present a systematic census of emission-line sources using JWST/NIRCam wide-field slitless spectroscopy (WFSS) from the COSMOS-3D program, which covers a large contiguous area of 0.33 deg$^2$ and provides F444W grism spectroscopy over $3.9$--$5.1\ μ$m. By combining slitless spectroscopy with deep multi-band imaging data, we construct a homogeneous spectroscopic sample of 12,275 objects at redshift 0--9. The typical $3σ$ line-flux limit reaches $3.2\times10^{-18}\ {\rm erg\ s^{-1}\ cm^{-2}}$. To address source confusion inherent to slitless spectroscopy, we develop a multi-step identification framework that combines information from spectra, images, spectral energy distributions, and spatial distributions. We introduce convolutional neural network and large language model to emulate the human identification process and improve efficiency. The resulting catalog provides spectroscopic redshifts, emission-line measurements, and derived physical properties for all sources. The F444W WFSS enables the detection of multiple emission lines, including primarily [O III], H$α$, He I $\lambda10830$, [S III] $λ\lambda9531,9069$, Pa$β$, Pa$α$, Br$β$, PAH, Br$α$, allowing a continuous census of galaxies across cosmic time. We construct composite spectra in several redshift bins and detect a variety of faint spectral features that are difficult to identify in individual spectra, including higher-order hydrogen recombination lines, CO absorption bands, and [Fe II] lines. This catalog provides a key reference dataset for studies of galaxy evolution and AGN activity, and demonstrates the power of WFSS for constructing large, unbiased spectroscopic samples over wide areas. The full catalog will be publicly available online upon publication.

astro-ph.GA↗

arXiv2026

"I didn't know how to read a map, but now I can": TouchingSpace, an Audio-Haptic Map for Blind and Low-Vision Readers

Accessible map systems either make a layout explorable by hand or convey information through speech; few combine both to support pre-travel spatial understanding for blind and low-vision (BLV) people. We present TouchingSpace: a system that retrieves map data for an outdoor place and renders its surroundings as bounded regions at fixed trackpad positions. During exploration, users receive audio and haptic feedback and can ask a conversational agent open-ended questions. We conducted a user study with fourteen BLV participants who explored a place using TouchingSpace and reflected on the experience. We found participants used sound and vibration to locate places and speech to identify and describe them; the bounded surface supported discovery, revision, and spatial checks by hand; they expected this awareness to support future travel. TouchingSpace demonstrates how a laptop trackpad can support self-directed spatial exploration. These findings suggest accessible AI maps should ground conversation on bounded, user-controlled spatial surfaces.

cs.HC↗

arXiv2026

ERP-FM: A Foundation Model for Universal ERP Analysis

Foundation models have recently shown strong potential for learning generalizable EEG representations, yet their effectiveness for event-related potential (ERP) analysis remains unclear. In this work, we investigate two fundamental questions: 1) can foundation-model learning benefit ERP analysis, and what limits the transfer of existing EEG foundation models to ERP tasks? 2) can the complementary advantages of single-trial and averaged-trial ERP be integrated into a unified training pipeline? To study these questions, we curate a large-scale ERP corpus comprising 1,517,157 single-trial ERPs from 3,696 subjects across 38 datasets and 18 paradigms. Leveraging this corpus, we introduce ERP-FM, to the best of our knowledge, the first foundation model specifically developed for ERP representation learning. ERP-FM uses single-channel tokenization, temporal and spatial positional embeddings, and mixed masked autoencoding for large-scale single-trial pretraining. We compare our model against 17 existing methods on 12 downstream datasets covering ERP event/condition classification and neurological disease classification. Our model achieves the best overall average rank across all evaluated methods. Furthermore, our analyses reveal that both ERP and non-ERP EEG pretraining can provide transferable representations for ERP tasks, while fine-grained temporal tokenization is critical for effectively modeling transient ERP dynamics. We further find that single-trial and averaged-trial ERP play complementary rather than competing roles. Combining single-trial pretraining with averaged-trial downstream adaptation substantially improves neurological disease analysis. Overall, these findings establish an effective foundation-model training pipeline for ERP analysis and represent significant progress toward generalizable ERP representation learning. Source code: https://github.com/DL4mHealth/ERP-FM

cs.CE↗

arXiv2026

LEAD: An EEG Foundation Model for Alzheimer's Disease Detection

Electroencephalography (EEG) provides a non-invasive, highly accessible, and cost-effective approach for detecting Alzheimer's disease (AD). However, existing methods, whether based on handcrafted feature engineering or standard deep learning, face three major challenges: 1) the lack of large-scale EEG-based AD datasets for robust representation learning and evaluation; 2) limited cross-subject generalizability; and 3) difficulty in adapting to highly heterogeneous data. To address these challenges, we curate the world's largest EEG-AD corpus to date, comprising 2,238 subjects. Leveraging this unique resource, we propose LEAD, the first foundation model for EEG-based AD detection. Specifically, we design a gated temporal-spatial Transformer that can adapt to EEG recordings with diverse lengths, channel configurations, and sampling rates. In addition, we introduce a subject-regularized training strategy to enhance end-to-end subject-level detection. We further employ medical contrastive learning to pre-train on 13 datasets, including 4 AD datasets and 9 non-AD neurological disorder datasets, and fine-tune/test the model on the other 5 AD datasets. LEAD achieves the best average ranking across all 20 evaluations on 5 downstream datasets, substantially outperforming existing approaches, including state-of-the-art (SOTA) EEG foundation models. These results strongly demonstrate the effectiveness of our proposed method and significant progress for EEG-based AD detection. Source code: https://github.com/DL4mHealth/LEAD

cs.LG↗

arXiv2026

Does Reasoning Improve Psychological Depth in Large Language Models? It Depends on Who's Judging

LLM-as-a-Judge evaluators are increasingly used to score open-ended generation, yet a judge's correlation with human ratings on its development set may not guarantee valid measurement when outputs are closely matched and human preferences are subjective. We study this failure mode through psychological depth in short stories. Seven human readers and an LLM-judge ensemble selected on the original scalar Psychological Depth Scale dataset ($ρ= 0.646$) evaluated 60 blinded, prompt-matched story pairs from GPT-5 vs.\ GPT-4o and DeepSeek-R1 vs.\ DeepSeek-V3. Human preferences showed no universal reasoning advantage: GPT-5 was modestly preferred over GPT-4o (60.0--62.9\%), whereas DeepSeek-R1 trailed V3 (42.9\%), and inter-reader agreement was near chance (Krippendorff's $α= 0.070$), with within-reader consistency and recurring weighting patterns suggesting structured heterogeneity rather than random responding. The judge, by contrast, favored reasoning outputs in 89.0\% of dimension-level comparisons and 59 of 60 pairs on aggregate PDS, uniformly across all five evaluator configurations, and its scores were associated with surface features such as sentence length and lexical diversity. These results suggest that development-set performance is insufficient evidence for deployment validity on a shifted distribution, and that point-estimate judges can obscure the heterogeneity in subjective human evaluation.

cs.LG↗

arXiv2026

What Causes Performance Degradation in Cross-Subject EEG Classification?

Cross-subject Electroencephalography (EEG) classification typically achieves significantly lower performance than subject-dependent settings. Although this phenomenon has been widely observed in the literature, the underlying causes have not been systematically studied. In this paper, we design a series of controlled experiments to investigate the mechanisms behind the performance drop in cross-subject EEG classification across different EEG tasks. We show that the performance degradation can generally be attributed to two factors: inter-subject variability and shortcut learning. Specifically, multi-class-per-subject EEG classification tasks, such as motor imagery, emotion recognition, and ERP stimulus classification, are mainly affected by inter-subject variability, whereas single-class-per-subject EEG classification tasks, such as brain disease detection, are primarily influenced by shortcut learning based on subject-specific features. These findings provide new insights into the challenges of cross-subject EEG classification and emphasize the importance of appropriate evaluation protocols in EEG research. The code is available at https://github.com/DL4mHealth/EEG-Cross-Subject.

cs.CE↗

arXiv2026

CPPL: A Circuit Prompt Programming Language

Large language models (LLMs) have shown promise in register-transfer level (RTL) design automation, but direct RTL generation remains difficult to validate, optimize, and integrate with compiler-based hardware design flows. Hardware compiler infrastructures such as CIRCT provide typed intermediate representations, legality checks, and optimization passes, yet current LLMs struggle to emit raw compiler IR because of MLIR syntax, SSA discipline, dialect-specific operations, and strict width constraints. This paper presents CPPL, a compiler-mediated design framework that turns LLM-assisted hardware generation into a statically checkable frontend problem rather than an unconstrained RTL text-generation task. CPPL combines a Python frontend DSL for declaring module interfaces and hierarchy with CPPL IR, a JSON-based circuit IR designed to expose compiler-visible structure while remaining accessible to LLMs. The compiler infers operation widths from declared module ports, validates generated IR, checks hierarchy and port bindings, and deterministically lowers the result to CIRCT for synthesizable Verilog generation. On the RTLLM benchmark, CPPL improves functional correctness over direct Verilog and direct CIRCT IR generation, while CIRCT optimization reduces post-synthesis AIG node counts. These results show that a compiler-mediated interface can make LLM-assisted hardware design more reliable, analyzable, and amenable to backend optimization. CPPL is available at https://github.com/SawyDust1228/CPPL.

cs.AR↗

arXiv2026

LLM hallucinations in the wild: Large-scale evidence from non-existent citations

Large language models (LLMs) are known to generate plausible but false information across a wide range of contexts, yet the real-world magnitude and consequences of this hallucination problem remain poorly understood. Here we leverage a uniquely verifiable object - scientific citations - to audit 111 million references across 2.5 million papers in arXiv, bioRxiv, SSRN, and PubMed Central. We find a sharp rise in non-existent references following widespread LLM adoption, with a conservative estimate of 146,932 hallucinated citations in 2025 alone. These errors are diffusely embedded across many papers but especially pronounced in fields with rapid AI uptake, in manuscripts with linguistic signatures of AI-assisted writing, and among small and early-career author teams. At the same time, hallucinated references disproportionately assign credit to already prominent and male scholars, suggesting that LLM-generated errors may reinforce existing inequities in scientific recognition. Preprint moderation and journal publication processes capture only a fraction of these errors, suggesting that the spread of hallucinated content has outpaced existing safeguards. Together, these findings demonstrate that LLM hallucinations are infiltrating knowledge production at scale, threatening both the reliability and equity of future scientific discovery as human and AI systems draw on the existing literature.

cs.DL↗

arXiv2026

Benchmarking ERP Analysis: Manual Features, Deep Learning, and Foundation Models

Event-related potential (ERP), a specialized paradigm of electroencephalographic (EEG), reflects neurological responses to external stimuli or events, generally associated with the brain's processing of specific cognitive tasks. ERP plays a critical role in cognitive analysis, the detection of neurological diseases, and the assessment of psychological states. Recent years have seen substantial advances in deep learning-based methods for spontaneous EEG and other non-time-locked task-related EEG signals. However, their effectiveness on ERP data remains underexplored, and many existing ERP studies still rely heavily on manually extracted features. In this paper, we conduct a comprehensive benchmark study that systematically compares traditional manual features (followed by a linear classifier), deep learning models, and pre-trained EEG foundation models for ERP analysis. We establish a unified data preprocessing and training pipeline and evaluate these approaches on two representative tasks, ERP stimulus classification and ERP-based brain disease detection, across 12 publicly available datasets. Furthermore, we investigate various token-embedding strategies within advanced Transformer architectures to identify embedding designs that better suit ERP data. Our study provides a landmark framework to guide method selection and tailored model design for future ERP analysis. The code is available at https://github.com/DL4mHealth/ERP-Benchmark

cs.NE↗

arXiv2026

Advancing weak lensing mass mapping with a mask-aware HEALPix transformer

We present HEALFormer, a transformer-based neural network architecture for weak gravitational lensing mass mapping that reconstructs convergence maps from incomplete and noisy shear observations on the celestial sphere. The model operates directly on the Hierarchical Equal Area isoLatitude Pixelization and employs learnable mask tokens to handle arbitrary survey geometries without requiring preprocessing. Through a progressive training strategy, HEALFormer efficiently processes high-resolution maps up to Nside = 1024 and demonstrates excellent performance across diverse survey footprints including KiDS, DES, DECaLS, and Planck. The model generalizes robustly to cosmological parameters beyond its training set, producing nearly unbiased reconstructions with superior noise suppression compared to traditional Kaiser-Squires and Wiener filter methods. Remarkably, HEALFormer exceeds the theoretical phase recovery limits of linear reconstruction methods at small scales, achieving a fundamental breakthrough in weak lensing analysis. The combination of computational efficiency, reconstruction accuracy, and adaptability to varying survey configurations makes HEALFormer well-suited for current and next-generation cosmological surveys. Code is available at GitHub.

astro-ph.IM↗

arXiv2025

ADformer: A Multi-Granularity Spatial-Temporal Transformer for EEG-Based Alzheimer Detection

Electroencephalography (EEG) has emerged as a cost-effective and efficient tool to support neurologists in the detection of Alzheimer's Disease (AD). However, most existing approaches rely heavily on manual feature engineering or data transformation. While such techniques may provide benefits when working with small-scale datasets, they often lead to information loss and distortion when applied to large-scale data, ultimately limiting model performance. Moreover, the limited subject scale and demographic diversity of datasets used in prior studies hinder comprehensive evaluation of model robustness and generalizability, thus restricting their applicability in real-world clinical settings. To address these challenges, we propose ADformer, a novel multi-granularity spatial-temporal transformer designed to capture both temporal and spatial features from raw EEG signals, enabling effective end-to-end representation learning. Our model introduces multi-granularity embedding strategies across both spatial and temporal dimensions, leveraging a two-stage intra-inter granularity self-attention mechanism to learn both local patterns within each granularity and global dependencies across granularities. We evaluate ADformer on 4 large-scale datasets comprising a total of 1,713 subjects, representing one of the largest corpora for EEG-based AD detection to date, under a cross-validated, subject-independent setting. Experimental results demonstrate that ADformer consistently outperforms existing methods, achieving subject-level F1 scores of 92.82%, 89.83%, 67.99%, and 83.98% on the 4 datasets, respectively, in distinguishing AD from healthy control (HC) subjects.

eess.SP↗

arXiv2024

1D flat bands in phosphorene nanoribbons with pentagonal nature

Materials with flat bands can serve as a promising platform to investigate strongly interacting phenomena. However, experimental realization of ideal flat bands is mostly limited to artificial lattices or moiré systems. Here we report a general way to construct one-dimensional (1D) flat bands in phosphorene nanoribbons (PNRs) with pentagonal nature: penta-hexa-PNRs and penta-dodeca-PNRs, wherein the corresponding 1D flat bands are directly verified by using angle-resolved photoemission spectroscopy. We confirm that the observed 1D flat bands originate from the electronic 1D zigzag and Lieb lattices, respectively, as revealed by the combination of bond-resolved scanning tunneling microscopy, scanning tunneling spectroscopy, tight-binding models, and first-principles calculations. Our study demonstrates a general way to construct 1D flat bands in 1D solid materials system, which provides a robust platform to explore strongly interacting phases of matter.

cond-mat.mtrl-sci↗

arXiv2024

Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification

Medical time series (MedTS) data, such as Electroencephalography (EEG) and Electrocardiography (ECG), play a crucial role in healthcare, such as diagnosing brain and heart diseases. Existing methods for MedTS classification primarily rely on handcrafted biomarkers extraction and CNN-based models, with limited exploration of transformer-based models. In this paper, we introduce Medformer, a multi-granularity patching transformer tailored specifically for MedTS classification. Our method incorporates three novel mechanisms to leverage the unique characteristics of MedTS: cross-channel patching to leverage inter-channel correlations, multi-granularity embedding for capturing features at different scales, and two-stage (intra- and inter-granularity) multi-granularity self-attention for learning features and correlations within and among granularities. We conduct extensive experiments on five public datasets under both subject-dependent and challenging subject-independent setups. Results demonstrate Medformer's superiority over 10 baselines, achieving top averaged ranking across five datasets on all six evaluation metrics. These findings underscore the significant impact of our method on healthcare applications, such as diagnosing Myocardial Infarction, Alzheimer's, and Parkinson's disease. We release the source code at https://github.com/DL4mHealth/Medformer.

eess.SP↗

arXiv2024

Accurate Kappa Reconstruction Algorithm for masked shear catalog (AKRA)

Weak gravitational lensing is an invaluable tool for understanding fundamental cosmological physics. An unresolved issue in weak lensing cosmology is to accurately reconstruct the lensing convergence $κ$ maps from discrete shear catalog with survey masks, which the seminal Kaiser-Squire (KS) method is not designed to address. We present the Accurate Kappa Reconstruction Algorithm for masked shear catalog (AKRA) to address the issue of mask. AKRA is built upon the prior-free maximum likelihood mapmaking method (or the unbiased minimum variance linear estimator). It is mathematically robust in dealing with mask, numerically stable to implement, and practically effective in improving the reconstruction accuracy. Using simulated maps with mask fractions ranging from 10\% to 50\% and various mask shapes, we demonstrate that AKRA outperforms KS at both the map level and summary statistics such as the auto power spectrum $C_κ$ of the reconstructed map, its cross-correlation coefficient $r_\ell$ with the true $κ$ map, the scatter plot and the localization measure. Unlike the Wiener filter method, it adopts no priors on the signal power spectrum, and therefore avoids the Wiener filter related biases at both the map level and cross-correlation statistics. If we only use the reconstructed map in the unmasked regions, the reconstructed $C_κ$ is accurate to $1\%$ or better and $1-r_\ell \lesssim 1\%$ (excluding $\ell$ at the smallest scales investigated), even for extreme cases of mask fraction and shape. As the first step, the current version of AKRA only addresses the mask issue and therefore ignores complexities such as curved sky and inhomogeneous shape measurement noise. AKRA is capable of dealing with these issues straightfowrardly, and will be addressed in the next version.

astro-ph.CO↗

arXiv2024

Design and Visual Servoing Control of a Hybrid Dual-Segment Flexible Neurosurgical Robot for Intraventricular Biopsy

Traditional rigid endoscopes have challenges in flexibly treating tumors located deep in the brain, and low operability and fixed viewing angles limit its development. This study introduces a novel dual-segment flexible robotic endoscope MicroNeuro, designed to perform biopsies with dexterous surgical manipulation deep in the brain. Taking into account the uncertainty of the control model, an image-based visual servoing with online robot Jacobian estimation has been implemented to enhance motion accuracy. Furthermore, the application of model predictive control with constraints significantly bolsters the flexible robot's ability to adaptively track mobile objects and resist external interference. Experimental results underscore that the proposed control system enhances motion stability and precision. Phantom testing substantiates its considerable potential for deployment in neurosurgery.

cs.RO↗