arXiv subjects

Chao Cao

Publications and source records attributed to Chao Cao.

At least 37 records · Page 2Linked to original sources

arXiv2026

Community-Level Modeling of Gyral Folding Patterns for Robust and Anatomically Informed Individualized Brain Mapping

Cortical folding exhibits substantial inter-individual variability while preserving stable anatomical landmarks that enable fine-scale characterization of cortical organization. Among these, the three-hinge gyrus (3HG) serves as a key folding primitive, showing consistent topology yet meaningful variations in morphology, connectivity, and function. Existing landmark-based methods typically model each 3HG independently, ignoring that 3HGs form higher-order folding communities that capture mesoscale structure. This simplification weakens anatomical representation and makes one-to-one matching sensitive to positional variability and noise. We propose a spectral graph representation learning framework that models community-level folding units rather than isolated landmarks. Each 3HG is encoded using a dual-profile representation combining surface topology and structural connectivity. Subject-specific spectral clustering identifies coherent folding communities, followed by topological refinement to preserve anatomical continuity. For cross-subject correspondence, we introduce Joint Morphological-Geometric Matching, jointly optimizing geometric and morphometric similarity. Across over 1000 Human Connectome Project subjects, the resulting communities show reduced morphometric variance, stronger modular organization, improved hemispheric consistency, and superior alignment compared with atlas-based and landmark-based or embedding-based baselines. These findings demonstrate that community-level modeling provides a robust and anatomically grounded framework for individualized cortical characterization and reliable cross-subject correspondence.

q-bio.NC↗

arXiv2026

Large Language Models for Assisting American College Applications

American college applications require students to navigate fragmented admissions policies, repetitive and conditional forms, and ambiguous questions that often demand cross-referencing multiple sources. We present EZCollegeApp, a large language model (LLM)-powered system that assists high-school students by structuring application forms, grounding suggested answers in authoritative admissions documents, and maintaining full human control over final responses. The system introduces a mapping-first paradigm that separates form understanding from answer generation, enabling consistent reasoning across heterogeneous application portals. EZCollegeApp integrates document ingestion from official admissions websites, retrieval-augmented question answering, and a human-in-the-loop chatbot interface that presents suggestions alongside application fields without automated submission. We describe the system architecture, data pipeline, internal representations, security and privacy measures, and evaluation through automated testing and human quality assessment. Our source code is released on GitHub (https://github.com/ezcollegeapp-public/ezcollegeapp-public) to facilitate the broader impact of this work.

cs.CL↗

arXiv2026

RPT*: Global Planning with Probabilistic Terminals for Target Search in Complex Environments

Routing problems such as Hamiltonian Path Problem (HPP), seeks a path to visit all the vertices in a graph while minimizing the path cost. This paper studies a variant, HPP with Probabilistic Terminals (HPP-PT), where each vertex has a probability representing the likelihood that the robot's path terminates there, and the objective is to minimize the expected path cost. HPP-PT arises in target object search, where a mobile robot must visit all candidate locations to find an object, and prior knowledge of the object's location is expressed as vertex probabilities. While routing problems have been studied for decades, few of them consider uncertainty as required in this work. The challenge lies not only in optimally ordering the vertices, as in standard HPP, but also in handling history dependency: the expected path cost depends on the order in which vertices were previously visited. This makes many existing methods inefficient or inapplicable. To address the challenge, we propose a search-based approach RPT* with solution optimality guarantees, which leverages dynamic programming in a new state space to bypass the history dependency and novel heuristics to speed up the computation. Building on RPT*, we design a Hierarchical Autonomous Target Search (HATS) system that combines RPT* with either Bayesian filtering for lifelong target search with noisy sensors, or autonomous exploration to find targets in unknown environments. Experiments in both simulation and real robot show that our approach can naturally balance between exploitation and exploration, thereby finding targets more quickly on average than baseline methods.

cs.RO↗

arXiv2026

Gyral-Sulcal-Net: An Integrated Network Representation of Brain Folding Patterns

Our brain functions as a complex communication network, and studying it from a network perspective offers valuable insights into its organizational principles and links to cognitive functions and brain disorders. However, most current network studies typically use brain regions as nodes, often overlooking the intricate folding patterns of finer-scale anatomical landmarks within these regions. In this study, we introduce a novel approach to integrate the brain's two primary folding patterns - gyri and sulci - into a unified network termed the Gyral-Sulcal-Net (GS-Net), in which three different types of finer-scale landmarks have been successfully identified. We evaluated the proposed GS-Net across multiple datasets, comprising over 1,600 brains, spanning different age groups (from 34 gestational weeks to elderly adults) and cohorts (healthy brains and those with pathological conditions). The experimental results demonstrate that the GS-Net can effectively integrate and represent diverse cortical folding patterns from a network perspective. More importantly, this approach offers a promising way for integrating different folding patterns into a unified anatomical brain network, alongside structural and functional networks, providing a comprehensive framework for studying brain networks.

q-bio.NC↗

arXiv2026

Multigap nodeless superconductivity in Dirac semimetal PdTe

PdTe has recently been reported to be a type-II Dirac semimetal while a bulk nodal and surface nodeless superconductivity (SC) has been claimed to coexist. In this work, we applied point-contact spectroscopy (PCS) method to systematically study the superconducting gap in PdTe single crystals with a SC transition temperature $T_{c}=4.3$ K. The obtained differential conductance curves show a common deviation from a single-gap superconducting behavior and can be better fitted by a two-gap Blonder-Tinkham-Klapwijk model, suggesting the larger gap $Δ_{L}$ with $2Δ_{L}$=3.7 $k_{B}T_{c}$ and the smaller gap $Δ_S$ yielding $2Δ_{S}$=1.1-2.2 $k_{B}T_{c}$ with a weak interband scattering. The variations of conductance spectra among different contacts are proposed to be caused by the anisotropy of Fermi surface topology associated with different gaps.

cond-mat.supr-con↗

arXiv2025

DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4

The digitization of healthcare has facilitated the sharing and re-using of medical data but has also raised concerns about confidentiality and privacy. HIPAA (Health Insurance Portability and Accountability Act) mandates removing re-identifying information before the dissemination of medical records. Thus, effective and efficient solutions for de-identifying medical data, especially those in free-text forms, are highly needed. While various computer-assisted de-identification methods, including both rule-based and learning-based, have been developed and used in prior practice, such solutions still lack generalizability or need to be fine-tuned according to different scenarios, significantly imposing restrictions in wider use. The advancement of large language models (LLM), such as ChatGPT and GPT-4, have shown great potential in processing text data in the medical domain with zero-shot in-context learning, especially in the task of privacy protection, as these models can identify confidential information by their powerful named entity recognition (NER) capability. In this work, we developed a novel GPT4-enabled de-identification framework (``DeID-GPT") to automatically identify and remove the identifying information. Compared to existing commonly used medical text data de-identification methods, our developed DeID-GPT showed the highest accuracy and remarkable reliability in masking private information from the unstructured medical text while preserving the original structure and meaning of the text. This study is one of the earliest to utilize ChatGPT and GPT-4 for medical text data processing and de-identification, which provides insights for further research and solution development on the use of LLMs such as ChatGPT/GPT-4 in healthcare. Codes and benchmarking data information are available at https://github.com/yhydhx/ChatGPT-API.

cs.CL↗

arXiv2025

Evaluating Large Language Models for Radiology Natural Language Processing

The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP). LLMs have revolutionized a multitude of domains, and they have made a significant impact in the medical field. Large language models are now more abundant than ever, and many of these models exhibit bilingual capabilities, proficient in both English and Chinese. However, a comprehensive evaluation of these models remains to be conducted. This lack of assessment is especially apparent within the context of radiology NLP. This study seeks to bridge this gap by critically evaluating thirty two LLMs in interpreting radiology reports, a crucial component of radiology NLP. Specifically, the ability to derive impressions from radiologic findings is assessed. The outcomes of this evaluation provide key insights into the performance, strengths, and weaknesses of these LLMs, informing their practical applications within the medical domain.

cs.CL↗

arXiv2025

DCMM-Transformer: Degree-Corrected Mixed-Membership Attention for Medical Imaging

Medical images exhibit latent anatomical groupings, such as organs, tissues, and pathological regions, that standard Vision Transformers (ViTs) fail to exploit. While recent work like SBM-Transformer attempts to incorporate such structures through stochastic binary masking, they suffer from non-differentiability, training instability, and the inability to model complex community structure. We present DCMM-Transformer, a novel ViT architecture for medical image analysis that incorporates a Degree-Corrected Mixed-Membership (DCMM) model as an additive bias in self-attention. Unlike prior approaches that rely on multiplicative masking and binary sampling, our method introduces community structure and degree heterogeneity in a fully differentiable and interpretable manner. Comprehensive experiments across diverse medical imaging datasets, including brain, chest, breast, and ocular modalities, demonstrate the superior performance and generalizability of the proposed approach. Furthermore, the learned group structure and structured attention modulation substantially enhance interpretability by yielding attention maps that are anatomically meaningful and semantically coherent.

cs.CV↗

arXiv2025

Inter-orbital spin-triplet superconductivity from altermagnetic fluctuations

Altermagnetic (AM) fluctuations are a new class of collinear spin fluctuations whose role in mediating superconductivity faces a fundamental tension: their $Γ$-point peak favors intra-orbital spin-triplet pairing, while their spin compensation favors inter-orbital singlets. Here, we demonstrate that inversion-symmetry-broken AM fluctuations generically resolve this competition in favor of spin-triplet pairing. As a proof of concept, we study a minimal two-orbital model with two van Hove singularities. The broken inversion symmetry induces momentum-orbital locking: the same orbital dominates at opposite momenta, enhancing the triplet channel. Crucially, a subdominant fluctuation channel arising from inter-van-Hove nesting provides an internal Josephson coupling that locks the phase difference between triplet pairs on different orbitals. We find this coupling changes sign ($+$ to $-$) upon a crossover from AM-dominant to ferromagnetic-dominant fluctuations. The resulting $π$-phase difference manifests as a $τ_z$-type order parameter, $c_{k,1\uparrow}c_{-k,1\uparrow} - c_{k,2\uparrow}c_{-k,2\uparrow}$. Although intra-orbital in the original basis, its orbital-nontrivial character, as manifested by its equivalence to inter-orbital pairing under rotation, defines a general \textit{inter-orbital spin-triplet superconductivity}. This state is distinct from the $τ_0$-triplet pairing mediated by ferromagnetic fluctuations, as evidenced by the canceled intra-orbital supercurrent in a Josephson junction between them.

cond-mat.str-el↗

arXiv2025

Controlling an altermagnetic spin density wave in the kagome magnet CsCr3Sb5

The interplay of charge and spin orders lies at the heart of correlated electron physics and plays a critical role in the emergence of unconventional quantum phases. Kagome magnets provide a particularly promising platform to investigate these phenomena, owing to their geometrically frustrated lattice structure. However, resolving spin and charge orders microscopically and establishing ways to control them remain fundamental challenges. Here, we demonstrate magnetic field control of an altermagnetic spin density wave order intertwined with charge density wave order in kagome magnet CsCr3Sb5. Scanning tunneling microscopy down to deep cryogenic temperature of 50 mK reveals two previously unreported charge density wave orders in the Sb surface. Density functional theory confirms that one of them is coupled to a spin density wave with an altermagnetic ground state. The charge density waves can be tuned in both amplitude and phase by an external magnetic field, reflected in domain switching and stripe sliding of the charge density wave. Our findings deepen the understanding of symmetry-breaking in kagome systems, providing a tunable platform to explore the interplay of electronic correlation with emergent quantum magnetism.

cond-mat.str-el↗

arXiv2025

HEHA: Hierarchical Planning for Heterogeneous Multi-Robot Exploration of Unknown Environments

This paper considers the path planning problem for autonomous exploration of an unknown environment using multiple heterogeneous robots such as drones, wheeled, and legged robots, which have different capabilities to traverse complex terrains. A key challenge there is to intelligently allocate the robots to the unknown areas to be explored and determine the visiting order of those spaces subject to traversablity constraints, which leads to a large scale constrained optimization problem that needs to be quickly and iteratively solved every time when new space are explored. To address the challenge, we propose HEHA (Hierarchical Exploration with Heterogeneous Agents) by leveraging a recent hierarchical method that decompose the exploration into global planning and local planning. The major contribution in HEHA is its global planning, where we propose a new routing algorithm PEAF (Partial Anytime Focal search) that can quickly find bounded sub-optimal solutions to minimize the maximum path length among the agents subject to traversability constraints. Additionally, the local planner in HEHA also considers heterogeneity to avoid repeated and duplicated exploration among the robots. The experimental results show that, our HEHA can reduce up to 30% of the exploration time than the baselines.

cs.RO↗

arXiv2025

Breakdown of Stoner Ferromagnetism by Intrinsic Altermagnetism

The Stoner criterion for ferromagnetism arises from interaction-driven asymmetric filling of spin bands, requiring that the spin susceptibility: (i) peaks dominantly at $\mathbf{Q}=\bm{0}$; and (ii) diverges at a critical interaction strength. Here, we demonstrate that this Stoner mechanism breaks down due to competition with altermagnetic orders, even when both conditions are met. Altermagnetism in solids is characterized by collinear antiparallel spin alignment that preserves translational symmetry, and inherently fulfills these requirements. As a proof of concept, we study a two-orbital Hubbard model with electron filling near Van Hove singularities at high-symmetry momenta. Our results reveal that orbital-resolved spin fluctuations, amplified by strong inter-orbital hopping, stabilize intrinsic altermagnetic order. A quantum phase transition from altermagnetism to ferromagnetism occurs at critical Hund's coupling $J_H$. We further propose directional spin conductivity anisotropy as a detectable signature of this transition via non-local spin transport. This work establishes the pivotal role of altermagnetism in correlated systems.

cond-mat.str-el↗

arXiv2025

Anomalous Nernst Effect and Its Implications for Time-Reversal Symmetry Breaking in Kagome Metal ScV6Sn6

The nonmagnetic kagome metal ScV6Sn6 displays an unconventional charge order (CO) accompanied by signatures of an anomalous Hall effect, hidden magnetism, and multiple lattice instabilities. In this study, we report the observation of unconventional anomalous thermoelectric properties. Notably, unexpected anomalous transverse Nernst signals reach a peak value of ~4 μV/K near the TCDW ~92 K in ScV6Sn6, and these signals persist in the charge-ordered state as the temperature decreases to 10 K. Furthermore, both thermopower and thermal conductivity exhibit significant changes under magnetic fields, even in the nonmagnetic ground state. These observations strongly suggest the emergence of time-reversal symmetry breaking in ScV6Sn6, as supported by muon spin relaxation (μSR) measurements. While hidden magnetism represents the most plausible origin, alternative mechanisms involving orbital currents and chiral charge order remain possible.

cond-mat.str-el↗

arXiv2025

Bridging Brain Connectomes and Clinical Reports for Early Alzheimer's Disease Diagnosis

Integrating brain imaging data with clinical reports offers a valuable opportunity to leverage complementary multimodal information for more effective and timely diagnosis in practical clinical settings. This approach has gained significant attention in brain disorder research, yet a key challenge remains: how to effectively link objective imaging data with subjective text-based reports, such as doctors' notes. In this work, we propose a novel framework that aligns brain connectomes with clinical reports in a shared cross-modal latent space at both the subject and connectome levels, thereby enhancing representation learning. The key innovation of our approach is that we treat brain subnetworks as tokens of imaging data, rather than raw image patches, to align with word tokens in clinical reports. This enables a more efficient identification of system-level associations between neuroimaging findings and clinical observations, which is critical since brain disorders often manifest as network-level abnormalities rather than isolated regional alterations. We applied our method to mild cognitive impairment (MCI) using the ADNI dataset. Our approach not only achieves state-of-the-art predictive performance but also identifies clinically meaningful connectome-text pairs, offering new insights into the early mechanisms of Alzheimer's disease and supporting the development of clinically useful multimodal biomarkers.

cs.CV↗

arXiv2025

Large ferromagnetic-like band splitting in ultrathin ${\mathrm{SmC}}_{6}$ films

Two-dimensional (2D) magnetic materials provide a unique platform for exploring quantum phases from magnetic order in reduced dimensions. While there have been extensive studies on 2D magnetic materials based on 3$d$ electrons, experimental studies on 4$f$-electron counterparts are far fewer, particularly on their electronic structure. In this study, we report the successful synthesis of ultrathin ${\mathrm{SmC}}_{6}$ films using molecular beam epitaxy. Utilizing in situ angle-resolved photoemission spectroscopy (ARPES), we uncover a large band splitting in the valence bands, which we attribute to the ferromagnetic order driven by exchange couplings between Sm 4$f$ moments and conduction electrons. Despite the small magnetic moment of Sm, the observed splitting is comparable to those of Eu- and Gd-based systems with much larger local moments. Interestingly, the surface state also exhibits splitting with similar magnitude and can be eliminated by overannealing, while the valence bands with ferromagnetic-like splittings remain robust. Our work provides spectroscopic insight to understand the electronic origin of magnetic order in Sm-based compounds. Our study also offers a platform to study 2D magnetic materials based on 4$f$ electrons.

cond-mat.str-el↗

arXiv2025

Pressure Induced Altermagnetism in Layered Ternary Iron-Selenides

Employing first-principles based calculations, we reexamined the high-pressure phases of the vacancy-ordered iron-selenides, i.e. A2Fe4Se5 phase. A magnetic transition from the block-spin antiferromagnetic phase to Neel-AM phase is observed under high pressure when the iron-vacancy order is preserved. The transition is first-order, driven by the collapse of c-axis and accompanied by an insulator-metal transition. In addition, the Neel-AM phase is a rare example of intrinsic altermagnetism that does not require ligand atoms nor supercell construction to break the spatial inversion or translational symmetry between the two spin sublattices, with a spin-splitting of the band structure as large as 300 meV. If the re-entrant superconducting phase of KyFe2-xSe2 under high pressure emerges from the Neel-AM normal state, an equal-spin triplet pairing would be naturally favored.

cond-mat.mtrl-sci↗

arXiv2025

Domain-Adaptive Diagnosis of Lewy Body Disease with Transferability Aware Transformer

Lewy Body Disease (LBD) is a common yet understudied form of dementia that imposes a significant burden on public health. It shares clinical similarities with Alzheimer's disease (AD), as both progress through stages of normal cognition, mild cognitive impairment, and dementia. A major obstacle in LBD diagnosis is data scarcity, which limits the effectiveness of deep learning. In contrast, AD datasets are more abundant, offering potential for knowledge transfer. However, LBD and AD data are typically collected from different sites using different machines and protocols, resulting in a distinct domain shift. To effectively leverage AD data while mitigating domain shift, we propose a Transferability Aware Transformer (TAT) that adapts knowledge from AD to enhance LBD diagnosis. Our method utilizes structural connectivity (SC) derived from structural MRI as training data. Built on the attention mechanism, TAT adaptively assigns greater weights to disease-transferable features while suppressing domain-specific ones, thereby reducing domain shift and improving diagnostic accuracy with limited LBD data. The experimental results demonstrate the effectiveness of TAT. To the best of our knowledge, this is the first study to explore domain adaptation from AD to LBD under conditions of data scarcity and domain shift, providing a promising framework for domain-adaptive diagnosis of rare diseases.

cs.LG↗

arXiv2025

$3d$ flat bands and coupled $4f$ moments in the kagome-honeycomb permanent magnet Sm$_{2}$Co$_{17}$

Rare earth permanent magnets (REPMs) with both localized moments and itinerant conduction bands are not only important for fundamental research but also have significant technological applications. In particular, Sm$_{\rm 2}$Co$_{\rm 17}$ is a prototypical high-temperture REPM, where the Co atoms form a kagome-honeycomb stacked lattice. Here we report synthesis of epitaxial Sm$_{\rm 2}$Co$_{\rm 17}$ films using molecular beam epitaxy and measurements of their momentum-resolved electronic structure from \textit{in-situ} angle-resolved photoemission spectroscopy. Our results unveil two flat bands from Co $3d$ orbitals near the Fermi level ($E_F$), one at $\sim$\,--300\,meV and another right at $E_F$, which arise from orbital-selective destructive interference and strong electron correlations, respectively. In addition, our results reveal that Sm $4f$ states are far away from $E_F$ (hence mostly localized) and exhibit an anomalous temperature dependence, caused by the $3d$-$4f$ magnetic coupling. Our findings provide direct spectroscopic insights to understand the strong uniaxial ferromagnetism in Sm$_{\rm 2}$Co$_{\rm 17}$ (and REPMs alike). Our work also opens avenues to explore flat-band physics near $E_F$ and emergent phenomena in correlated kagome-honeycomb lattices.

cond-mat.str-el↗