arXiv subjects

Kang Wang

Publications and source records attributed to Kang Wang.

At least 55 records · Page 3Linked to original sources

arXiv2025

Learning Segmentation from Radiology Reports

Tumor segmentation in CT scans is key for diagnosis, surgery, and prognosis, yet segmentation masks are scarce because their creation requires time and expertise. Public abdominal CT datasets have from dozens to a couple thousand tumor masks, but hospitals have hundreds of thousands of tumor CTs with radiology reports. Thus, leveraging reports to improve segmentation is key for scaling. In this paper, we propose a report-supervision loss (R-Super) that converts radiology reports into voxel-wise supervision for tumor segmentation AI. We created a dataset with 6,718 CT-Report pairs (from the UCSF Hospital), and merged it with public CT-Mask datasets (from AbdomenAtlas 2.0). We used our R-Super to train with these masks and reports, and strongly improved tumor segmentation in internal and external validation--F1 Score increased by up to 16% with respect to training with masks only. By leveraging readily available radiology reports to supplement scarce segmentation masks, R-Super strongly improves AI performance both when very few training masks are available (e.g., 50), and when many masks were available (e.g., 1.7K). Project: https://github.com/MrGiovanni/R-Super

eess.IV↗

arXiv2025

PanTS: The Pancreatic Tumor Segmentation Dataset

PanTS is a large-scale, multi-institutional dataset curated to advance research in pancreatic CT analysis. It contains 36,390 CT scans from 145 medical centers, with expert-validated, voxel-wise annotations of over 993,000 anatomical structures, covering pancreatic tumors, pancreas head, body, and tail, and 24 surrounding anatomical structures such as vascular/skeletal structures and abdominal/thoracic organs. Each scan includes metadata such as patient age, sex, diagnosis, contrast phase, in-plane spacing, slice thickness, etc. AI models trained on PanTS achieve significantly better performance in pancreatic tumor detection, localization, and segmentation compared to those trained on existing public datasets. Our analysis indicates that these gains are directly attributable to the 16x larger-scale tumor annotations and indirectly supported by the 24 additional surrounding anatomical structures. As the largest and most comprehensive resource of its kind, PanTS offers a new benchmark for developing and evaluating AI models in pancreatic CT analysis.

eess.IV↗

arXiv2025

Precise Measurement of Short-Range Correlations in Nuclei from Bremsstrahlung Gamma Ray Emission in Low-Energy Heavy-Ion Collisions

Atomic nuclei and dense nucleonic matter in neutron stars exhibit short-range correlations (SRCs), where nucleons form temporally correlated pairs in proximity beyond mean-field approximation. It is essential to make precision measurement of the fraction of SRC since it carries the signature of underlying quark dynamics in nuclear medium. In this letter, we present the first high-precision measurement of neutron-proton bremsstrahlung $γ$-ray emission from the symmetric $\rm ^{124}Sn$+$\rm ^{124}Sn$ reactions at 25 MeV/u. From the observed spectral hardening, the precise SRC fraction in the $\rm ^{124}Sn$ nucleus is extracted to be $(20 \pm 3)\%$. This result provides a novel, direct and unambiguous evidence of SRCs, and demonstrates that low-energy heavy-ion collisions offers a new approach to studying nuclear structure in connection with quark-level dynamics.

nucl-ex↗

arXiv2025

Medical World Model: Generative Simulation of Tumor Evolution for Treatment Planning

Providing effective treatment and making informed clinical decisions are essential goals of modern medicine and clinical care. We are interested in simulating disease dynamics for clinical decision-making, leveraging recent advances in large generative models. To this end, we introduce the Medical World Model (MeWM), the first world model in medicine that visually predicts future disease states based on clinical decisions. MeWM comprises (i) vision-language models to serve as policy models, and (ii) tumor generative models as dynamics models. The policy model generates action plans, such as clinical treatments, while the dynamics model simulates tumor progression or regression under given treatment conditions. Building on this, we propose the inverse dynamics model that applies survival analysis to the simulated post-treatment tumor, enabling the evaluation of treatment efficacy and the selection of the optimal clinical action plan. As a result, the proposed MeWM simulates disease dynamics by synthesizing post-treatment tumors, with state-of-the-art specificity in Turing tests evaluated by radiologists. Simultaneously, its inverse dynamics model outperforms medical-specialized GPTs in optimizing individualized treatment protocols across all metrics. Notably, MeWM improves clinical decision-making for interventional physicians, boosting F1-score in selecting the optimal TACE protocol by 13%, paving the way for future integration of medical world models as the second readers.

cs.CV↗

arXiv2025

Towards Unified Neural Decoding with Brain Functional Network Modeling

Recent achievements in implantable brain-computer interfaces (iBCIs) have demonstrated the potential to decode cognitive and motor behaviors with intracranial brain recordings; however, individual physiological and electrode implantation heterogeneities have constrained current approaches to neural decoding within single individuals, rendering interindividual neural decoding elusive. Here, we present Multi-individual Brain Region-Aggregated Network (MIBRAIN), a neural decoding framework that constructs a whole functional brain network model by integrating intracranial neurophysiological recordings across multiple individuals. MIBRAIN leverages self-supervised learning to derive generalized neural prototypes and supports group-level analysis of brain-region interactions and inter-subject neural synchrony. To validate our framework, we recorded stereoelectroencephalography (sEEG) signals from a cohort of individuals performing Mandarin syllable articulation. Both real-time online and offline decoding experiments demonstrated significant improvements in both audible and silent articulation decoding, enhanced decoding accuracy with increased multi-subject data integration, and effective generalization to unseen subjects. Furthermore, neural predictions for regions without direct electrode coverage were validated against authentic neural data. Overall, this framework paves the way for robust neural decoding across individuals and offers insights for practical clinical applications.

q-bio.NC↗

arXiv2025

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges

Time series analysis is pivotal in domains like financial forecasting and biomedical monitoring, yet traditional methods are constrained by limited nonlinear feature representation and long-term dependency capture. The emergence of Large Language Models (LLMs) offers transformative potential by leveraging their cross-modal knowledge integration and inherent attention mechanisms for time series analysis. However, the development of general-purpose LLMs for time series from scratch is still hindered by data diversity, annotation scarcity, and computational requirements. This paper presents a systematic review of pre-trained LLM-driven time series analysis, focusing on enabling techniques, potential applications, and open challenges. First, it establishes an evolutionary roadmap of AI-driven time series analysis, from the early machine learning era, through the emerging LLM-driven paradigm, to the development of native temporal foundation models. Second, it organizes and systematizes the technical landscape of LLM-driven time series analysis from a workflow perspective, covering LLMs' input, optimization, and lightweight stages. Finally, it critically examines novel real-world applications and highlights key open challenges that can guide future research and innovation. The work not only provides valuable insights into current advances but also outlines promising directions for future development. It serves as a foundational reference for both academic and industrial researchers, paving the way for the development of more efficient, generalizable, and interpretable systems of LLM-driven time series analysis.

cs.LG↗

arXiv2025

Heterogeneous Data Game: Characterizing the Model Competition Across Multiple Data Sources

Data heterogeneity across multiple sources is common in real-world machine learning (ML) settings. Although many methods focus on enabling a single model to handle diverse data, real-world markets often comprise multiple competing ML providers. In this paper, we propose a game-theoretic framework -- the Heterogeneous Data Game -- to analyze how such providers compete across heterogeneous data sources. We investigate the resulting pure Nash equilibria (PNE), showing that they can be non-existent, homogeneous (all providers converge on the same model), or heterogeneous (providers specialize in distinct data sources). Our analysis spans monopolistic, duopolistic, and more general markets, illustrating how factors such as the "temperature" of data-source choice models and the dominance of certain data sources shape equilibrium outcomes. We offer theoretical insights into both homogeneous and heterogeneous PNEs, guiding regulatory policies and practical strategies for competitive ML marketplaces.

cs.GT↗

arXiv2025

Towards Multi-agent Reinforcement Learning based Traffic Signal Control through Spatio-temporal Hypergraphs

Traffic signal control systems (TSCSs) are integral to intelligent traffic management, fostering efficient vehicle flow. Traditional approaches often simplify road networks into standard graphs, which results in a failure to consider the dynamic nature of traffic data at neighboring intersections, thereby neglecting higher-order interconnections necessary for real-time control. To address this, we propose a novel TSCS framework to realize intelligent traffic control. This framework collaborates with multiple neighboring edge computing servers to collect traffic information across the road network. To elevate the efficiency of traffic signal control, we have crafted a multi-agent soft actor-critic (MA-SAC) reinforcement learning algorithm. Within this algorithm, individual agents are deployed at each intersection with a mandate to optimize traffic flow across the road network collectively. Furthermore, we introduce hypergraph learning into the critic network of MA-SAC to enable the spatio-temporal interactions from multiple intersections in the road network. This method fuses hypergraph and spatio-temporal graph structures to encode traffic data and capture the complex spatio-temporal correlations between multiple intersections. Our empirical evaluation, tested on varied datasets, demonstrates the superiority of our framework in minimizing average vehicle travel times and sustaining high-throughput performance. This work facilitates the development of more intelligent urban traffic management solutions. We release the code to support the reproducibility of this work at https://github.com/Edun-Eyes/TSC

cs.MA↗

arXiv2025

Achieving >97% on GSM8K: Deeply Understanding the Problems Makes LLMs Better Solvers for Math Word Problems

Chain-of-Thought (CoT) prompting has enhanced the performance of Large Language Models (LLMs) across various reasoning tasks. However, CoT still falls short in dealing with complex math word problems, as it usually suffers from three pitfalls: semantic misunderstanding errors, calculation errors, and step-missing errors. Prior studies involve addressing the calculation errors and step-missing errors, but neglect the semantic misunderstanding errors, which is the major factor limiting the reasoning performance of LLMs. To this end, we propose a simple-yet-effective method, namely Deeply Understanding the Problems (DUP), to improve the LLMs' math problem-solving ability by addressing semantic misunderstanding errors. The core of our method is to encourage the LLMs to deeply understand the problems and extract the key problem-solving information used for better reasoning. Extensive experiments on 10 diverse reasoning benchmarks show that our DUP method consistently outperforms the other counterparts by a large margin. More encouragingly, DUP achieves a new SOTA result on the GSM8K benchmark, with an accuracy of 97.1% under the zero-shot setting.

cs.CL↗

arXiv2025

Thermodynamics of the Hubbard Model on the Bethe Lattice

We investigate the thermodynamic properties of the Hubbard model on the Bethe lattice with a coordination number of 3 using the thermal canonical tree tensor network method. Our findings reveal two distinct thermodynamic phases: a low-temperature antiferromagnetic phase, where spin SU(2) symmetry is broken, and a high-temperature paramagnetic phase. A key feature of the system is the separation of energy scales for charge and spin excitations, which is reflected in the temperature dependence of thermodynamic quantities and the disparity between spin and charge gaps extracted from their respective susceptibilities. At the critical point, both spin and charge susceptibilities exhibit singularities, suggesting that charge excitations are not fully decoupled from their spin counterparts. Additionally, the double occupancy number exhibits a non-monotonic temperature dependence, indicative of an entropy-driven Pomeranchuk effect. These results demonstrate that the loopless Bethe lattice effectively captures the essential physics of the Hubbard model while providing a computationally efficient framework for studying strongly correlated electronic systems.

cond-mat.str-el↗

arXiv2025

Emergent quantum Majorana metal from a chiral spin liquid

We propose a mechanism to explain the emergence of an intermediate gapless spin liquid phase in the antiferromagnetic Kitaev model in an externally applied magnetic field, sandwiched between the well-known gapped chiral spin liquid and the gapped partially polarized phase. We propose that, in moderate fields, $π$-fluxes nucleate in the ground state and trap Majorana zero modes. As these fluxes proliferate with increasing field, the Majorana zero modes overlap creating an emergent $\mathbb{Z}_2$ quantum Majorana metallic state with a `Fermi surface' at zero energy. We further show that the Majorana spectral function captures the dynamical spin and dimer correlations obtained by the infinite Projected Entangled Pair States method, thereby validating our variational approach.

cond-mat.str-el↗

arXiv2025

Fractionalization Signatures in the Dynamics of Quantum Spin Liquids

We investigate the signatures of fractionalization in quantum spin liquids by studying different phases of the Kitaev honeycomb model in the presence of an out-of-plane magnetic field through which the model becomes non-integrable. Using the infinite projected entangled pair states (iPEPS) ansatz, along with analytical calculations and exact diagonalization, we calculate dynamical signatures of fractionalized particles through spin-spin and dimer-dimer correlations. Our analysis demonstrates the ability of these correlations to discern distinct fractionalized quantum sectors, namely Majorana fermions and the emergent $Z_2$ fluxes, in both the chiral spin liquid (CSL) phase under weak field and the emergent intermediate gapless phase (IGP) under moderate field. Importantly, our calculation reveals the nature of IGP observed at moderate fields, a region of ongoing debate, indicating that this phase is a Majorana metal induced by strong flux fluctuations.

cond-mat.str-el↗

arXiv2025

Extract neutron-neutron interaction strength and spatial-temporal dynamics of neutron emission from two-particle correlation function

The neutron-neutron ($nn$) correlation function has been measured in 25 MeV/u $^{124}$Sn+$^{124}$Sn reactions. Using the Lednický-Lyuboshitz approach, the $nn$ scattering length and effective range ($f_{0}^{nn}$, $d_{0}^{nn}$), as well as the reduced space-time size $R^{(0)}$ of the neutron emission source are simultaneously extracted as ($18.9^{+1.3}_{-1.2}$ fm, $1.9^{+1.3}_{-1.0}$ fm) and $4.12 \pm 0.12$ fm, respectively. The measured $nn$ scattering length is consistent with the results obtained in the low-energy scattering $^{2}{\rm H}(π^{-},γ)2n$, indicating heavy-ion collisions can serve as an effective approach for measuring $nn$ interactions and further investigating the charge symmetry breaking of nuclear force. The space-time size extracted from momentum-gated correlation functions exhibits clear dependence on the pair momentum, with $R^{(0)}=2.8 \pm 0.1 $ fm and $4.9 \pm 0.2$ fm being determined for the high and low momentum neutrons, respectively.

nucl-ex↗

arXiv2024

Text-Driven Tumor Synthesis

Tumor synthesis can generate examples that AI often misses or over-detects, improving AI performance by training on these challenging cases. However, existing synthesis methods, which are typically unconditional -- generating images from random variables -- or conditioned only by tumor shapes, lack controllability over specific tumor characteristics such as texture, heterogeneity, boundaries, and pathology type. As a result, the generated tumors may be overly similar or duplicates of existing training data, failing to effectively address AI's weaknesses. We propose a new text-driven tumor synthesis approach, termed TextoMorph, that provides textual control over tumor characteristics. This is particularly beneficial for examples that confuse the AI the most, such as early tumor detection (increasing Sensitivity by +8.5%), tumor segmentation for precise radiotherapy (increasing DSC by +6.3%), and classification between benign and malignant tumors (improving Sensitivity by +8.2%). By incorporating text mined from radiology reports into the synthesis process, we increase the variability and controllability of the synthetic tumors to target AI's failure cases more precisely. Moreover, TextoMorph uses contrastive learning across different texts and CT scans, significantly reducing dependence on scarce image-report pairs (only 141 pairs used in this study) by leveraging a large corpus of 34,035 radiology reports. Finally, we have developed rigorous tests to evaluate synthetic tumors, including Text-Driven Visual Turing Test and Radiomics Pattern Analysis, showing that our synthetic tumors is realistic and diverse in texture, heterogeneity, boundaries, and pathology.

eess.IV↗

arXiv2024

Origin of competing charge density waves in kagome metal ScV$_6$Sn$_6$

Understanding competing charge density wave (CDW) orders in the bilayer kagome metal ScV$_6$Sn$_6$ remains challenging. Experimentally, upon cooling, short-range order with wave vector $\mathbf{q}_{2}=(\frac{1}{3},\frac{1}{3},\frac{1}{2})$ forms, which is subsequently suppressed by the condensation of long-range $\mathbf{q}_{3}=(\frac{1}{3},\frac{1}{3},\frac{1}{3})$ CDW order at lower temperature. Theoretically, however, the $\mathbf{q}_{2}$ CDW is predicted as the ground state, leaving the CDW mechanism elusive. Here, using anharmonic phonon-phonon calculations combined with density functional theory, we predict a temperature-driven structural phase transitions from the high-temperature pristine phase to the $\mathbf{q}_{2}$ CDW, followed by the low-temperature $\mathbf{q}_{3}$ CDW, explaining experimental observations. We demonstrate that semi-core electron states stabilize the $\mathbf{q}_{3}$ CDW over the $\mathbf{q}_{2}$ CDW. Furthermore, we find that the out-of-plane lattice parameter controls the competing CDWs, motivating us to propose compressive bi-axial strain as an experimental protocol to stabilize the $\mathbf{q}_{2}$ CDW. Finally, we suggest Ge or Pb doping at the Sn site as another potential avenue to control CDW instabilities. Our work provides a full theory of CDWs in ScV$_6$Sn$_6$, rationalizing experimental observations and resolving earlier discrepancies between theory and experiment.

cond-mat.str-el↗

arXiv2024

Comprehensive Performance Evaluation of YOLOv11, YOLOv10, YOLOv9, YOLOv8 and YOLOv5 on Object Detection of Power Equipment

With the rapid development of global industrial production, the demand for reliability in power equipment has been continuously increasing. Ensuring the stability of power system operations requires accurate methods to detect potential faults in power equipment, thereby guaranteeing the normal supply of electrical energy. In this article, the performance of YOLOv5, YOLOv8, YOLOv9, YOLOv10, and the state-of-the-art YOLOv11 methods was comprehensively evaluated for power equipment object detection. Experimental results demonstrate that the mean average precision (mAP) on a public dataset for power equipment was 54.4%, 55.5%, 43.8%, 48.0%, and 57.2%, respectively, with the YOLOv11 achieving the highest detection performance. Moreover, the YOLOv11 outperformed other methods in terms of recall rate and exhibited superior performance in reducing false detections. In conclusion, the findings indicate that the YOLOv11 model provides a reliable and effective solution for power equipment object detection, representing a promising approach to enhancing the operational reliability of power systems.

cs.CV↗

arXiv2024

A wireless passive pressure sensor with high sensitivity

A high-sensitivity wireless pressure sensor with active processing structure designed on the dielectric substrate has been present and evaluated in this paper. The sensor configuration has been optimized by computer-aided design to achieve highest sensitivity and maximum working range for a given dimension. With the average sensitivity of 187kHz/kPa, the proposed pressure sensor is equipped with the ability to measure pressure loaded up to 1.5MPa under room temperature. Additionally, a novel simulation method applied on pressure related design is proposed in this article, with the accuracy reaching threefold enhancement, filling the blank of electromagnetic simulation of pressure deformation. Other characteristics of the devices have been investigated and are presented.

physics.ins-det↗

arXiv2024

Magnetism measurements of two-dimensional van der Waals antiferromagnet CrPS4 using dynamic cantilever magnetometry

Recent experimental and theoretical work has focused on two-dimensional van der Waals (2D vdW) magnets due to their potential applications in sensing and spintronics devises. In measurements of these emerging materials, conventional magnetometry often encounters challenges in characterizing the magnetic properties of small-sized vdW materials, especially for antiferromagnets with nearly compensated magnetic moments. Here, we investigate the magnetism of 2D antiferromagnet CrPS4 with a thickness of 8nm by using dynamic cantilever magnetometry (DCM). Through a combination of DCM experiment and the calculation based on a Stoner--Wohlfarth-type model, we unravel the magnetization states in 2D CrPS4 antiferromagnet. In the case of H parallel with c, a two-stage phase transition is observed. For H perpendicular to c, a hump in the effective magnetic restoring force is noted, which implies the presence of spin reorientation as temperature increases. These results demonstrate the benefits of DCM for studying magnetism of 2D magnets.

cond-mat.mes-hall↗