arXiv subjects

Junhong Chen

Publications and source records attributed to Junhong Chen.

At least 19 recordsLinked to original sources

arXiv2026

On Weak Set Theories Interpreted in PA

We classify a broad family of weak first-order set theories, under ordinary parameter-free interpretability, by the first-order arithmetical theories with which they are mutually interpretable. This also determines their consistency strength. Set theories that correspond to the same arithmetical theory are often related by deductive extension. It is therefore enough to interpret a stronger set theory in the arithmetical theory and to recover the arithmetical theory in a weaker set theory; all intermediate cases then follow. The set theories under consideration fall roughly into three classes: theories with neither Power Set nor Infinity, theories with Power Set but without Infinity, and theories with Infinity but without Power Set. We conclude with a brief account of the higher levels that remain to be investigated.

math.LO↗

arXiv2026

The elementary theory of full $n$-branching ordinal trees with successor functions

We investigate the first-order theories of full $n$-branching ordinal trees $\mathfrak{T}_\alpha^n$. We obtain a complete classification of the standard trees up to elementary equivalence: every $\mathfrak{T}_\alpha^n$ is elementarily equivalent to one of four canonical types determined by the ordinal $\alpha$. For each canonical type we establish effective quantifier elimination and prove decidability of the theory. Along the way we develop the realizability theory of colored ordinal characters and obtain a sharp bound on the length of minimal witnesses. We also clarify the relationship between these trees and monadic second-order logic over ordinals, and show that the equal-height relation is not first-order definable in any standard tree.

math.LO↗

arXiv2026

Frucht's theorem and other set-theoretic principles below the axiom of choice and the axiom of foundation

We take the first step toward the study of set-theoretic principles below the axiom of choice $\mathsf{AC}$ and the axiom of foundation $\mathsf{AF}$ by studying Frucht's theorem, an ordinary mathematical theorem which is provable with either $\mathsf{AC}$ or $\mathsf{AF}$ but not provable without both, and its variants. Specifically, we propose a number of such principles, study the relations between these principles and the standard axioms, and prove provability and unprovability results using (infinite) graph-theoretic constructions and permutation models, which draw a preliminary map of this new area of set theory.

math.LO↗

arXiv2026

The Coding Conception of Set

We propose the Coding Conception of ordinals and sets, which takes Cantor's three generating principles as its sole foundation. Bounded sets of ordinals are generated synchronously with the ordinals themselves through a bijective encoding function that, at each stage, selects only the finitely many bounded sets actually required by the successor, limit, and restriction principles. This selective coding yields the first-order theory $SC^{reg}$, which we establish is the metamathematically correct theory of the ordinals: it is bi-interpretable with $ZFGC^+$, yet makes no claim about the general concept of set. Extending the conception to full set theory via a monadic second-order ordinal theory with arithmetic and class comprehension produces two mutually inconsistent first-order set theories according to distinct maximality intuitions: a Type-A universe $MC_A$, in which the power set of every ordinal is a set and the universe satisfies $ZFC$; and a Type-B universe $MC_B^+$, in which sets are strictly more than ordinals and a ``largeness cardinal'' exists, beyond which power sets remain unencodable. We prove that this Power Set Dichotomy is unavoidable, even under potentialism, and conclude that $ZFC^-+$``every cardinal has a successor'' is the only philosophically uncontroversial common fragment of any true set theory; the status of the full power-set axiom remains the sole open philosophical choice point.

math.LO↗

arXiv2026

Graph Neural Network Predictions of Carbon 1s Binding Energies with Near-Experimental Accuracy

Graph neural networks are promising architectures for fast, accurate and transferable predictions of core-electron binding energies, which depend on the local bond environment. Here we present a graph neural network model for predicting carbon 1s core-electron binding energies in organic molecules. The model is trained with multiconfiguration pair-density functional theory on 8637 carbon atoms in 2116 molecules with 4-16 atoms and evaluated against 570 experimental values in 113 different molecules containing 3-45 atoms. Previous work benchmarked a mean absolute error of 0.27 eV to experiment for the training data level of theory [J. Phys. Chem. A 2025, 129, 36, 8419-8431] and the present model demonstrates an experimental evaluation error of 0.33 eV with good size transferability to larger organic molecules. An equivariant graph neural network is benchmarked against its rotationally invariant analogue and a model comprised of the smooth overlap of atomic positions descriptors and kernel ridge regression for training data efficiency and stability to non-equilibrium geometries absent from the training data. All models show good training data efficiency and the graph based models have improved transferability to non-equilibrium geometries. The use of chemically informed, graph-normalized node features reduces the graph neural network's dependence on message passing depth. A case study on the 45 atom avobenzone tautomers demonstrates the model's ability for instant and precise analysis of complex molecules. The software and data are provided by the open-source AugerNet package at https://doi.org/10.5281/zenodo.19689244.

physics.chem-ph↗

arXiv2026

Interfacial Potential Transduction for Diagnostics

A major barrier to decentralized, near-patient diagnostics is the lack of a signal transduction modality that is both analytically precise and accessible at the point of care. Optical readouts remain instrument-dependent and difficult to miniaturize, while compact electrochemical readouts are prone to matrix-derived signal distortion, limiting their biomarker coverage in real clinical settings. Here, we define interfacial potential transduction as a standardized electrical modality for portable, clinical-grade diagnostics across diverse assay formats. A mechanistic framework identifying key sample matrix parameters within the interfacial potentials transduction system enables control of biofluid-derived interference, and is demonstrated in a widely accessible lateral flow immunoassay format through quantitative detection of estradiol, progesterone, and luteinizing hormone in human plasma with high correlation (r2 > 0.97) to clinical analyzers. Broader applicability across representative diagnostic sectors is further demonstrated through exceptional performance including glucose quantification for biochemical analysis with limit of detection (LOD) of 0.92 ug/dL, HIV p24 capsid protein under an immunomagnetic separation workflow (LOD = 44.8 fg/mL), and hepatitis B virus detection within 5 min via loop-mediated isothermal amplification for molecular diagnostics. Together, these results establish interfacial potentials transduction as a unified diagnostic paradigm for near-patient deployment beyond optical and electrochemical approaches.

q-bio.BM↗

arXiv2026

Surgi-HDTMR: Closing the Sensorimotor Loop in Bimanual Microsurgery via Haptics, Digital Twin, and Mixed Reality

Robotic microsurgery demands precise bimanual control, intuitive interaction, and informative force feedback. However, most training platforms for robotic microsurgery lack immersive 3D interaction and high-fidelity haptics. Here, we present Surgi-HDTMR, a mixed-reality (MR) and digital-twin (DT) training system that couples bimanual haptic teleoperation with a benchtop microsurgical robotic platform, and 3D-printed phantoms. A metrically co-registered, time-synchronized DT aligns in-situ MR guidance with the physical workspace and drives a depth-adaptive haptic model that renders contact, puncture, and tissue-retraction forces. In a within-subjects study of simulated cortical navigation and tumor resection, Surgi-HDTMR shortened task time, reduced harmful contacts and collisions, and improved perceptual accuracy relative to non-haptic and non-adaptive baselines. These results suggest that tightly coupling MR overlays with a synchronized DT, together with depth-adaptive haptics, can accelerate skill acquisition and improve safety in robot-assisted microsurgery, pointing toward next-generation surgical training.

eess.SY↗

arXiv2026

Beyond Conditional Computation: Retrieval-Augmented Genomic Foundation Models with Gengram

Current genomic foundation models (GFMs) rely on extensive neural computation to implicitly approximate conserved biological motifs from single-nucleotide inputs. We propose Gengram, a conditional memory module that introduces an explicit and highly efficient lookup primitive for multi-base motifs via a genomic-specific hashing scheme, establishing genomic "syntax". Integrated into the backbone of state-of-the-art GFMs, Gengram achieves substantial gains (up to 14%) across several functional genomics tasks. The module demonstrates robust architectural generalization, while further inspection of Gengram's latent space reveals the emergence of meaningful representations that align closely with fundamental biological knowledge. By establishing structured motif memory as a modeling primitive, Gengram simultaneously boosts empirical performance and mechanistic interpretability, providing a scalable and biology-aligned pathway for the next generation of GFMs. The code is available at https://github.com/zhejianglab/Genos, and the model checkpoint is available at https://huggingface.co/ZhejiangLab/Gengram.

q-bio.GN↗

arXiv2026

Atomic Alignment in PbS Nanocrystal Superlattices with Compact Inorganic Ligands via Reversible Oriented Attachment of Nanocrystals

Nanocrystals (NCs) serve as versatile building blocks for the creation of functional materials, with NC self-assembly offering opportunities to enable novel material properties. Here, we demonstrate that PbS NCs functionalized with strongly negatively charged metal chalcogenide complex (MCC) ligands, such as $Sn_2S_6^{4-}$ and $AsS_4^{3-}$, can self-assemble into all-inorganic superlattices with both long-range superlattice translational and atomic-lattice orientational order. Structural characterizations reveal that the NCs adopt unexpected edge-to-edge alignment, and numerical simulation clarifies that orientational order is thermodynamically stabilized by many-body ion correlations originating from the dense electrolyte. Furthermore, we show that the superlattices of $Sn_2S_6^{4-}$-functionalized PbS NCs can be fully disassembled back into the colloidal state, which is highly unusual for orientationally attached superlattices with atomic-lattice alignment. The reversible oriented attachment of NCs, enabling their dynamic assembly and disassembly into effectively single-crystalline superstructures, offers a pathway toward designing reconfigurable materials with adaptive and controllable electronic and optoelectronic properties.

cond-mat.mtrl-sci↗

arXiv2025

DASP: Self-supervised Nighttime Monocular Depth Estimation with Domain Adaptation of Spatiotemporal Priors

Self-supervised monocular depth estimation has achieved notable success under daytime conditions. However, its performance deteriorates markedly at night due to low visibility and varying illumination, e.g., insufficient light causes textureless areas, and moving objects bring blurry regions. To this end, we propose a self-supervised framework named DASP that leverages spatiotemporal priors for nighttime depth estimation. Specifically, DASP consists of an adversarial branch for extracting spatiotemporal priors and a self-supervised branch for learning. In the adversarial branch, we first design an adversarial network where the discriminator is composed of four devised spatiotemporal priors learning blocks (SPLB) to exploit the daytime priors. In particular, the SPLB contains a spatial-based temporal learning module (STLM) that uses orthogonal differencing to extract motion-related variations along the time axis and an axial spatial learning module (ASLM) that adopts local asymmetric convolutions with global axial attention to capture the multiscale structural information. By combining STLM and ASLM, our model can acquire sufficient spatiotemporal features to restore textureless areas and estimate the blurry regions caused by dynamic objects. In the self-supervised branch, we propose a 3D consistency projection loss to bilaterally project the target frame and source frame into a shared 3D space, and calculate the 3D discrepancy between the two projected frames as a loss to optimize the 3D structural consistency and daytime priors. Extensive experiments on the Oxford RobotCar and nuScenes datasets demonstrate that our approach achieves state-of-the-art performance for nighttime depth estimation. Ablation studies further validate the effectiveness of each component.

cs.CV↗

arXiv2025

Hierarchical Deep Research with Local-Web RAG: Toward Automated System-Level Materials Discovery

We present a long-horizon, hierarchical deep research (DR) agent designed for complex materials and device discovery problems that exceed the scope of existing Machine Learning (ML) surrogates and closed-source commercial agents. Our framework instantiates a locally deployable DR instance that integrates local retrieval-augmented generation with large language model reasoners, enhanced by a Deep Tree of Research (DToR) mechanism that adaptively expands and prunes research branches to maximize coverage, depth, and coherence. We systematically evaluate across 27 nanomaterials/device topics using a large language model (LLM)-as-judge rubric with five web-enabled state-of-the-art models as jurors. In addition, we conduct dry-lab validations on five representative tasks, where human experts use domain simulations (e.g., density functional theory, DFT) to verify whether DR-agent proposals are actionable. Results show that our DR agent produces reports with quality comparable to--and often exceeding--those of commercial systems (ChatGPT-5-thinking/o3/o4-mini-high Deep Research) at a substantially lower cost, while enabling on-prem integration with local data and tools.

cs.LG↗

arXiv2025

Multi-Agent Systems for Robotic Autonomy with LLMs

Since the advent of Large Language Models (LLMs), various research based on such models have maintained significant academic attention and impact, especially in AI and robotics. In this paper, we propose a multi-agent framework with LLMs to construct an integrated system for robotic task analysis, mechanical design, and path generation. The framework includes three core agents: Task Analyst, Robot Designer, and Reinforcement Learning Designer. Outputs are formatted as multimodal results, such as code files or technical reports, for stronger understandability and usability. To evaluate generalizability comparatively, we conducted experiments with models from both GPT and DeepSeek. Results demonstrate that the proposed system can design feasible robots with control strategies when appropriate task inputs are provided, exhibiting substantial potential for enhancing the efficiency and accessibility of robotic system development in research and industrial applications.

cs.RO↗

arXiv2025

Characterizing Fragments of Collection in Set Theory by Model-Theoretic Properties

We compare the model theory of the weak set theory $\mathsf{DB}_0$ with that of the algebraic theory $\mathsf{PA}^-$. Every model of $\mathsf{DB}_0$ has a proper taller* end extension with an exact transitive cover, paralleling the corresponding end-extension result for $\mathsf{PA}^-$. More substantially, we prove that $\mathsf{DB}_0$ and $\mathsf{PA}^-$ are mutually interpretable. The new direction interprets $\mathsf{DB}_0$ in $I\Delta_0+\Omega_1$ by finite rooted acyclic graphs; bisimulation supplies equality, and bounded truth on the graphs supplies $\Delta_0$-Separation. We then characterize fragments of set-theoretic Collection by Gaifman-style splitting and cofinal elementarity. Finally, we separate two end-extension mechanisms. Without a resolution, Kaufmann's construction characterizes the relevant Collection fragments for countable and locally for $\aleph_1$-like models. With a strict transitive resolution, Power Set, Infinity, and strong Collection, a finite-strength Keisler--Morley construction gives $\Sigma_N$-elementary taller* end extensions whose output retains strong $\Sigma_{N-2}$-Collection. The proof is choice-free inside the model and also recovers the classical $\mathsf{ZF}$ theorem.

math.LO↗

arXiv2025

Clinician-Friendly Foundation Models for Ophthalmic Image Diagnostics without Fine-Tuning or Technical Barriers

Artificial intelligence (AI) shows remarkable potential in medical imaging diagnostics, yet most current models require retraining when applied across different clinical settings, limiting their scalability. We developed GlobeReady, a deployment-oriented platform powered by the RetiGlobe foun- dation model and local feature augmentation. RetiGlobe was pretrained in two stages: 1) self-supervised learning using DINOv2 on 38 million synthetic ophthalmic images, and 2) contrastive learning using CLIP on 475,845 real image-text pairs spanning diverse ethnicities, imaging devices, and geographic regions worldwide. We evaluate GlobeReady on 488,448 ophthalmic images, including color fundus photographs (CFPs) and optical coherence tomography scans, from multi-centres in China, Singapore, Vietnam and the UK. Prospective testing included usability assessment with 31 ophthalmologists. Exploratory analyses evaluated domain generalisability, Bayesian uncertainty quantification, out-of-distribution (OOD) detection, and feature-based case retrieval.

cs.CV↗

arXiv2025

Measuring Top Yukawa Coupling through $2\rightarrow 3$ VBS at Muon Collider

We study the measurement of top Yukawa coupling through $2\rightarrow 3$ VBS at future muon colliders, focusing on the lepton and semi-lepton channels of $ννtth/z$. First, analyzing the partonic amplitudes of $W_LW_L\rightarrow t\bar t h/Z_L$ and simulating the full processes of $ννtth/z$ without decaying, we find they are highly sensitive to the anomalous top Yukawa $δy_t$. This sensitivity is enhanced by selecting helicities of the final $t\bar t$ and $Z$ to be $t_L\bar t_L+t_R\bar t_R$ and $Z_L$, which serves as the default and core setting of our analysis. We then obtain the limits on $δy_t$ with this setting, giving $[-1.0\%, 1.1\%]$ for $ννtth$ only and $[-0.36\%, 0.92\%]$ for $ννtth$ and $ννttz$ combined at $30$ TeV and $1σ$. Second, we proceed to analyze the processes after decaying and with background processes. To enhance the sensitivity to $δy_t$, our settings include selecting the helicities of the final particles, as well as applying suitable cuts. However, we don't do bin-by-bin analysis. We obtain the limits on $δy_t$ for those channels at $10/30$ TeV and $1σ/2 σ$. The best limit is from the semi-lepton channel of $ννtth$. With spin tagging efficiency at $ε_s=0.9$, it gives $[-1.6\% , 1.8\%]$ at $1σ$ and $ [-2.4\%, 2.7\% ]$ at $2σ$ at $30$ TeV; $[-7.0\%, 6.7\%]$ at $1σ$ and $[-9.8\%, 9.8\%]$ at $2σ$ at $10$ TeV.

hep-ph↗

arXiv2024

Radical-mediated Electrical Enzyme Assay For At-home Clinical Test

To meet the growing demand for accurate, rapid, and cost-effective at-home clinical testing, we developed a radical-mediated enzyme assay (REEA) integrated with a paper fluidic system and electrically read by a handheld field-effect transistor (FET) device. The REEA utilizes horseradish peroxidase (HRP) to catalyze the conversion of aromatic substrates into radical forms, producing protons detected by an ion-sensitive FET for biomarker quantification. Through screening 14 phenolic compounds, halogenated phenols emerged as optimal substrates for the REEA. Encased in an affordable cartridge ($0.55 per test), the system achieved a detection limit of 146 fg/mL for estradiol (E2), with a coefficient of variation (CV) below 9.2% in E2-spiked samples and an r2 of 0.963 across a measuring range of 19 to 4,551 pg/mL in clinical plasma samples, providing results in under 10 minutes. This adaptable system not only promises to offer a fast and reliable platform, but also holds significant potential for expansion to a wide array of biomarkers, paving the way for broader clinical and home-based applications.

q-bio.QM↗

arXiv2024

Multiscale Simulation and Machine Learning Facilitated Design of Two-Dimensional Nanomaterials-Based Tunnel Field-Effect Transistors: A Review

Traditional transistors based on complementary metal-oxide-semiconductor (CMOS) and metal-oxide-semiconductor field-effect transistors (MOSFETs) are facing significant limitations as device scaling reaches the limits of Moore's Law. These limitations include increased leakage currents, pronounced short-channel effects (SCEs), and quantum tunneling through the gate oxide, leading to higher power consumption and deviations from ideal behavior. Tunnel Field-Effect Transistors (TFETs) can overcome these challenges by utilizing quantum tunneling of charge carriers to switch between on and off states and achieve a subthreshold swing (SS) below 60 mV/decade. This allows for lower power consumption, continued scaling, and improved performance in low-power applications. This review focuses on the design and operation of TFETs, emphasizing the optimization of device performance through material selection and advanced simulation techniques. The discussion will specifically address the use of two-dimensional (2D) materials in TFET design and explore simulation methods ranging from multi-scale (MS) approaches to machine learning (ML)-driven optimization.

physics.app-ph↗

arXiv2024

YZS-model: A Predictive Model for Organic Drug Solubility Based on Graph Convolutional Networks and Transformer-Attention

Accurate prediction of drug molecule solubility is crucial for therapeutic effectiveness and safety. Traditional methods often miss complex molecular structures, leading to inaccuracies. We introduce the YZS-Model, a deep learning framework integrating Graph Convolutional Networks (GCN), Transformer architectures, and Long Short-Term Memory (LSTM) networks to enhance prediction precision. GCNs excel at capturing intricate molecular topologies by modeling the relationships between atoms and bonds. Transformers, with their self-attention mechanisms, effectively identify long-range dependencies within molecules, capturing global interactions. LSTMs process sequential data, preserving long-term dependencies and integrating temporal information within molecular sequences. This multifaceted approach leverages the strengths of each component, resulting in a model that comprehensively understands and predicts molecular properties. Trained on 9,943 compounds and tested on an anticancer dataset, the YZS-Model achieved an $R^2$ of 0.59 and an RMSE of 0.57, outperforming benchmark models ($R^2$ of 0.52 and RMSE of 0.61). In an independent test, it demonstrated an RMSE of 1.05, improving accuracy by 45.9%. The integration of these deep learning techniques allows the YZS-Model to learn valuable features from complex data without predefined parameters, handle large datasets efficiently, and adapt to various molecular types. This comprehensive capability significantly improves predictive accuracy and model generalizability. Its precision in solubility predictions can expedite drug development by optimizing candidate selection, reducing costs, and enhancing efficiency. Our research underscores deep learning's transformative potential in pharmaceutical science, particularly for solubility prediction and drug design.

cs.LG↗