arXiv subjects

Ming Zhang

Publications and source records attributed to Ming Zhang.

At least 163 records · Page 9Linked to original sources

arXiv2025

Numerical Study On Temperature Variations Of Superheated Steam Flowing Through A Regulation Valve

Superheated steam is widely employed in various energy systems, particularly in power plants, chemical industries, and other applications where high-temperature and high-pressure steam is essential for efficient energy conversion and process control. In these systems, regulation valves are crucial components that control the flow of steam, adjusting its pressure and temperature to ensure safe and efficient operation. Accurate understanding and prediction of temperature variations within regulation valves are essential for optimizing their performance and improving the overall system efficiency. This study investigates the temperature variations of superheated steam flowing through a regulation valve using computational fluid dynamics (CFD) simulations combined with Proper Orthogonal Decomposition (POD) techniques. The analysis begins with an examination of the internal flow field parameters, including temperature and pressure, to understand the overall fluid dynamics within the valve. POD is applied to reduce the dimensionality of the CFD results. Singular Value Decomposition (SVD) is employed to extract the dominant modes that capture the key flow structures responsible for heat transfer and temperature fluctuations. The POD analysis reveals that the most influential modes are associated with regions of high turbulence intensity and significant temperature gradients, which are critical to the thermal performance of the steam flow through the regulation valve. The application of POD to 3D CFD results represents a novel approach, particularly for complex fluid flow models such as steam flow through regulation valves. The insights gained from this study have practical implications for the design and optimization of temperature and pressure regulation valves in energy systems, providing a theoretical foundation for enhancing the efficiency and reliability of these systems.

physics.flu-dyn↗

arXiv2025

Attention Bootstrapping for Multi-Modal Test-Time Adaptation

Test-time adaptation aims to adapt a well-trained model to potential distribution shifts at test time using only unlabeled test data, without access to the original training data. While previous efforts mainly focus on a single modality, test-time distribution shift in the multi-modal setting is more complex and calls for new solutions. This paper tackles the problem of multi-modal test-time adaptation by proposing a novel method named Attention Bootstrapping with Principal Entropy Minimization (ABPEM). We observe that test-time distribution shift causes misalignment across modalities, leading to a large gap between intra-modality discrepancies (measured by self-attention) and inter-modality discrepancies (measured by cross-attention). We name this the attention gap. This attention gap widens with more severe distribution shifts, hindering effective modality fusion. To mitigate this attention gap and encourage better modality fusion, we propose attention bootstrapping that promotes cross-attention with the guidance of self-attention. Moreover, to reduce the gradient noise in the commonly-used entropy minimization, we adopt principal entropy minimization, a refinement of entropy minimization that reduces gradient noise by focusing on the principal parts of entropy, excluding less reliable gradient information. Extensive experiments on the benchmarks validate the effectiveness of the proposed ABPEM in comparison with competing baselines.

cs.AI↗

arXiv2025

MMEvalPro: Calibrating Multimodal Benchmarks Towards Trustworthy and Efficient Evaluation

Large Multimodal Models (LMMs) exhibit impressive cross-modal understanding and reasoning abilities, often assessed through multiple-choice questions (MCQs) that include an image, a question, and several options. However, many benchmarks used for such evaluations suffer from systematic biases. Remarkably, Large Language Models (LLMs) without any visual perception capabilities achieve non-trivial performance, undermining the credibility of these evaluations. To address this issue while maintaining the efficiency of MCQ evaluations, we propose MMEvalPro, a benchmark designed to avoid Type-I errors through a trilogy evaluation pipeline and more rigorous metrics. For each original question from existing benchmarks, human annotators augment it by creating one perception question and one knowledge anchor question through a meticulous annotation process. MMEvalPro comprises $2,138$ question triplets, totaling $6,414$ distinct questions. Two-thirds of these questions are manually labeled by human experts, while the rest are sourced from existing benchmarks (MMMU, ScienceQA, and MathVista). Compared with the existing benchmarks, our experiments with the latest LLMs and LMMs demonstrate that MMEvalPro is more challenging (the best LMM lags behind human performance by $31.73\%$, compared to an average gap of $8.03\%$ in previous benchmarks) and more trustworthy (the best LLM trails the best LMM by $23.09\%$, whereas the gap for previous benchmarks is just $14.64\%$). Our in-depth analysis explains the reason for the large performance gap and justifies the trustworthiness of evaluation, underscoring its significant potential for advancing future research.

cs.CV↗

arXiv2025

Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention

Long-context modeling is crucial for next-generation language models, yet the high computational cost of standard attention mechanisms poses significant computational challenges. Sparse attention offers a promising direction for improving efficiency while maintaining model capabilities. We present NSA, a Natively trainable Sparse Attention mechanism that integrates algorithmic innovations with hardware-aligned optimizations to achieve efficient long-context modeling. NSA employs a dynamic hierarchical sparse strategy, combining coarse-grained token compression with fine-grained token selection to preserve both global context awareness and local precision. Our approach advances sparse attention design with two key innovations: (1) We achieve substantial speedups through arithmetic intensity-balanced algorithm design, with implementation optimizations for modern hardware. (2) We enable end-to-end training, reducing pretraining computation without sacrificing model performance. As shown in Figure 1, experiments show the model pretrained with NSA maintains or exceeds Full Attention models across general benchmarks, long-context tasks, and instruction-based reasoning. Meanwhile, NSA achieves substantial speedups over Full Attention on 64k-length sequences across decoding, forward propagation, and backward propagation, validating its efficiency throughout the model lifecycle.

cs.CL↗

arXiv2025

Semi-supervised Fine-tuning for Large Language Models

Supervised fine-tuning (SFT) is crucial in adapting large language model (LLMs) to a specific domain or task. However, only a limited amount of labeled data is available in practical applications, which poses a severe challenge for SFT in yielding satisfactory results. Therefore, a data-efficient framework that can fully exploit labeled and unlabeled data for LLM fine-tuning is highly anticipated.Towards this end, we introduce a semi-supervised fine-tuning(SemiFT) task and a framework named SemiEvol for LLM alignment from a propagate-and-select manner. For knowledge propagation, SemiEvol adopts a bi-level approach, propagating knowledge from labeled data to unlabeled data through both in-weight and in-context methods. For knowledge selection, SemiEvol incorporates a collaborative learning mechanism, selecting higher-quality pseudo-response samples. We conducted experiments using GPT-4o-mini and Llama-3.1 on seven general or domain-specific datasets, demonstrating significant improvements in model performance on target data. Furthermore, we compared SemiEvol with SFT and self-evolution methods, highlighting its practicality in hybrid data scenarios.

cs.CL↗

arXiv2025

DisCo: Graph-Based Disentangled Contrastive Learning for Cold-Start Cross-Domain Recommendation

Recommender systems are widely used in various real-world applications, but they often encounter the persistent challenge of the user cold-start problem. Cross-domain recommendation (CDR), which leverages user interactions from one domain to improve prediction performance in another, has emerged as a promising solution. However, users with similar preferences in the source domain may exhibit different interests in the target domain. Therefore, directly transferring embeddings may introduce irrelevant source-domain collaborative information. In this paper, we propose a novel graph-based disentangled contrastive learning framework to capture fine-grained user intent and filter out irrelevant collaborative information, thereby avoiding negative transfer. Specifically, for each domain, we use a multi-channel graph encoder to capture diverse user intents. We then construct the affinity graph in the embedding space and perform multi-step random walks to capture high-order user similarity relationships. Treating one domain as the target, we propose a disentangled intent-wise contrastive learning approach, guided by user similarity, to refine the bridging of user intents across domains. Extensive experiments on four benchmark CDR datasets demonstrate that DisCo consistently outperforms existing state-of-the-art baselines, thereby validating the effectiveness of both DisCo and its components.

cs.IR↗

arXiv2025

Origin of the Diagonal Double-Stripe Spin-Density-Wave and Potential Superconductivity in Bulk La$_3$Ni$_2$O$_{7}$ at Ambient Pressure

The discovery of high-temperature superconductivity (SC) with $T_c\approx 80$ K in the pressurized La$_3$Ni$_2$O$_{7}$ has aroused great interests. Currently, due to technical difficulties, most experiments on La$_3$Ni$_2$O$_{7}$ can only be performed at ambient pressure (AP). Particularly, various experiments have revealed the presence of spin-density wave (SDW) in the unidirectional diagonal double-stripe pattern with wave vector near $(π/2,π/2)$ in La$_3$Ni$_2$O$_{7}$ at AP. In this work, we employ first-principle calculations followed by the random phase approximation (RPA)-based study to clarify the origin of this special SDW pattern and the potential SC in La$_3$Ni$_2$O$_{7}$ at AP. Starting from our density-functional-theory band structure, we construct an eight-band bilayer tight-binding model using the Ni-$3d_{z^2}$ and $3d_{x^2-y^2}$ orbitals, which is equipped with the standard multi-orbital Hubbard interaction. Our RPA calculation reveals an SDW order driven by Fermi-surface nesting with wave vector ${Q}\approx(0,\pm0.84π)$ in the folded Brillouin zone (BZ). From the view of the unfolded BZ, the wave vector turns to ${Q}_0\approx\pm(0.58π,0.58π)$, which is near the one detected by various experiments. Further more, this SDW exhibits an interlayer antiferromagnetic order with a unidirectional diagonal double-stripe pattern, consistent with recent soft X-ray scattering experiment. This result suggests that the origin of the SDW order in La$_3$Ni$_2$O$_{7}$ at AP can be well understood in the itinerant picture as driven by Fermi surfaces nesting. In the aspect of SC, our RPA study yields an approximate $s^\pm$-wave spin-singlet pairing with $T_c$ much lower than that under high pressure. Further more, the $T_c$ can be strongly enhanced through hole doping, leading to possible high-temperature SC at AP.

cond-mat.supr-con↗

arXiv2025

Quantum spectrum and Gamma structures for quasi-homogeneous polynomials of general type

Let $W$ be a quasi-homogeneous polynomial of general type and $ $ be the cyclic symmetry group of $W$ generated by the exponential grading element $J$. We study the quantum spectrum and asymptotic behavior in Fan-Jarvis-Ruan-Witten theory of the Landau-Ginzburg pair $(W, )$. Inspired by Galkin-Golyshev-Iritani's Gamma conjectures for quantum cohomology of Fano manifolds, we propose Gamma conjectures for Fan-Jarvis-Ruan-Witten theory of general type. We prove the quantum spectrum conjecture and the Gamma conjectures for Fermat homogeneous polynomials and the mirror simple singularities. The Gamma structures in Fan-Jarvis-Ruan-Witten theory also provide a bridge from the category of matrix factorizations of the Landau-Ginzburg pair (the algebraic aspect) to its analytic aspect. We will explain the relationship among the Gamma structures, Orlov's semiorthogonal decompositions, and the Stokes phenomenon.

math.AG↗

arXiv2025

First Detection of Radio Emission from the Intermediate Mass Black Hole in POX 52: Deep Multi-Band Observations with ATCA and VLA

We present the first multi-band centimeter detection of POX 52, a nearby dwarf galaxy believed to habor a robust intermediate mass black hole (IMBH). We conducted the deep observations using the Australia Telescope Compact Array (ATCA), spanning frequencies from 4.5 to 10 GHz, as well as the sensitive observations from the Karl G. Jansky Very Large Array (VLA) operating in its most extended A-configuration at S band (2--4 GHz) and C band (4--8 GHz). In the ATCA observations, the source shows a compact morphology, with only one direction marginally resolved. The higher resolution of the VLA allowed us to slightly resolve the source, fitting it well with a two-dimensional Gaussian model. The detected radio emission confirms the presence of Active Galactic Nucleus (AGN) activity, indicating either a low-power jet or AGN-driven winds/outflows. Our dual-epoch observations with ATCA and VLA, together with previous non-detection flux density upper limits, reveal radio emission variability spanning two decades. In addition, we find that POX 52 aligns well with the low-mass extension of the fundamental plane for high-accretion, radio-quiet massive AGNs.

astro-ph.GA↗

arXiv2024

Cluster-guided Contrastive Class-imbalanced Graph Classification

This paper studies the problem of class-imbalanced graph classification, which aims at effectively classifying the graph categories in scenarios with imbalanced class distributions. While graph neural networks (GNNs) have achieved remarkable success, their modeling ability on imbalanced graph-structured data remains suboptimal, which typically leads to predictions biased towards the majority classes. On the other hand, existing class-imbalanced learning methods in vision may overlook the rich graph semantic substructures of the majority classes and excessively emphasize learning from the minority classes. To address these challenges, we propose a simple yet powerful approach called C$^3$GNN that integrates the idea of clustering into contrastive learning to enhance class-imbalanced graph classification. Technically, C$^3$GNN clusters graphs from each majority class into multiple subclasses, with sizes comparable to the minority class, mitigating class imbalance. It also employs the Mixup technique to generate synthetic samples, enriching the semantic diversity of each subclass. Furthermore, supervised contrastive learning is used to hierarchically learn effective graph representations, enabling the model to thoroughly explore semantic substructures in majority classes while avoiding excessive focus on minority classes. Extensive experiments on real-world graph benchmark datasets verify the superior performance of our proposed method against competitive baselines.

cs.LG↗

arXiv2024

RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response

Supervised fine-tuning (SFT) plays a crucial role in adapting large language models (LLMs) to specific domains or tasks. However, as demonstrated by empirical experiments, the collected data inevitably contains noise in practical applications, which poses significant challenges to model performance on downstream tasks. Therefore, there is an urgent need for a noise-robust SFT framework to enhance model capabilities in downstream tasks. To address this challenge, we introduce a robust SFT framework (RobustFT) that performs noise detection and relabeling on downstream task data. For noise identification, our approach employs a multi-expert collaborative system with inference-enhanced models to achieve superior noise detection. In the denoising phase, we utilize a context-enhanced strategy, which incorporates the most relevant and confident knowledge followed by careful assessment to generate reliable annotations. Additionally, we introduce an effective data selection mechanism based on response entropy, ensuring only high-quality samples are retained for fine-tuning. Extensive experiments conducted on multiple LLMs across five datasets demonstrate RobustFT's exceptional performance in noisy scenarios.

cs.CL↗

arXiv2024

Multi-Aircraft Scheduling Optimization in Urban Environments

With the increasing development of intelligent transportation systems and advancements in aviation technology, the concept of Advanced Air Mobility (AAM) is gaining attention. This study aims to improve operational safety and service quality within Urban Air Mobility (UAM) through a trajectory-based operation (TBO). A multi-layer operational risk assessment model is introduced to capture the effects of aircraft failure scenarios on critical urban entities, including ground personnel, vehicles, and in-flight UAVs (unmanned aerial vehicles). Based on this, a single-aircraft track planning model is designed to balance operational risk and transportation cost under the performance constraints of eVTOL (electric Vertical Take-off and Landing) aircraft. A customized track planning algorithm with safety buffer zones is used to identify the most efficient flight paths. Additionally, a multi-aircraft scheduling optimization model is proposed to minimize delays and reduce mid-air collision risks. Experimental results show that the presented approach improves both efficiency and safety, providing practical solutions for UAM operations.

eess.SY↗

arXiv2024

Ultra-low-loss slow-light thin-film lithium-niobate optical modulator

Electro-optic modulators for next-generation optical interconnects require low loss-efficiency products, compact footprints, high modulation efficiency, broad bandwidths, and low losses. Here we propose and demonstrate a low-loss high-efficiency thin-film lithium-niobate Mach Zehnder modulator enabled by a novel ultralow-loss slow-light structure based on apodized gratings in cascade. The present loss-engineered slow-light structure achieves excess losses as low as 0.6 dB/mm experimentally, which is tens of times lower than conventional slow-light structures, and a high modulation bandwidth up to 320GHz in theory is achieved with optimally-designed capacitively-loaded traveling-wave electrodes. Experimentally, the fabricated slow-light modulator with a 2.8-mm-long modulation region has an ultra-low loss-efficiency product of 7.4 VdB and a flat electro-optic response up to 67 GHz, enabling 100-Gbps on-off keying with high ERs of 4.5 dB at a low driving voltage of 2Vpp, while 200-Gbps PAM4 and 150-Gbps PAM8 signals are also generated to show great promise for advanced modulation formats. In particular, it has also achieved the highest figure-of-merit(FOM) of 182 for high-speed optical modulation , including the bit rate, the extinction ratio normalized with respective to Vpp, the modulation efficiency. The outstanding performance of the present apodized-grating-based slow-light modulator shows great potential and paves the way for developing high-speed optical interconnects for both data-centers and high-performance computing systems.

physics.optics↗

arXiv2024

Symmetric Perception and Ordinal Regression for Detecting Scoliosis Natural Image

Scoliosis is one of the most common diseases in adolescents. Traditional screening methods for the scoliosis usually use radiographic examination, which requires certified experts with medical instruments and brings the radiation risk. Considering such requirement and inconvenience, we propose to use natural images of the human back for wide-range scoliosis screening, which is a challenging problem. In this paper, we notice that the human back has a certain degree of symmetry, and asymmetrical human backs are usually caused by spinal lesions. Besides, scoliosis severity levels have ordinal relationships. Taking inspiration from this, we propose a dual-path scoliosis detection network with two main modules: symmetric feature matching module (SFMM) and ordinal regression head (ORH). Specifically, we first adopt a backbone to extract features from both the input image and its horizontally flipped image. Then, we feed the two extracted features into the SFMM to capture symmetric relationships. Finally, we use the ORH to transform the ordinal regression problem into a series of binary classification sub-problems. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods as well as human performance, which provides a promising and economic solution to wide-range scoliosis screening. In particular, our method achieves accuracies of 95.11% and 81.46% in estimation of general severity level and fine-grained severity level of the scoliosis, respectively.

cs.CV↗

arXiv2024

Chip-to-chip quantum photonic controlled-NOT gate teleportation

Quantum networks provide a novel framework for quantum information processing, significantly enhancing system capacity through the interconnection of modular quantum nodes. Beyond the capability to distribute quantum states, the ability to remotely control quantum gates is a pivotal step for quantum networks. In this Letter, we implement high fidelity quantum controlled-NOT (CNOT) gate teleportation with state-of-the-art silicon photonic integrated circuits. Based on on-chip generation of path-entangled quantum state, CNOT gate operation and chip-to-chip quantum photonic interconnect, the CNOT gate is teleported between two remote quantum nodes connected by the single-mode optical fiber. Equip with 5 m (1 km)-long interconnecting fiber, quantum gate teleportation is verified by entangling remote qubits with 95.69% +- 1.19% (94.07% +- 1.54%) average fidelity and gate tomography with 94.81% +- 0.81% (93.04% +- 1.09%) fidelity. These results advance the realization of large-scale and practical quantum networks with photonic integrated circuits.

quant-ph↗

arXiv2024

Exploring Non-Linear Effects of Built Environment on Travel Using an Integrated Machine Learning and Inferential Modeling Approach: A Three-Wave Repeated Cross-Sectional Study

This study investigates the dynamic relationship between the built environment and travel in Austin, Texas, over a 20-year period. Using three waves of household travel surveys from 1997, 2006, and 2017, the research employs a repeated cross-sectional approach to address the limitations of traditional longitudinal and cross-sectional studies. Methodologically, it integrates machine learning and inferential modeling to uncover non-linear relationships and threshold effects of built environment characteristics on travel. Findings reveal that the built environment serves as a sustainable tool for managing travel in the long term, contributing 50% or more to the total feature importance in predicting individual travel-surpassing the combined effects of personal and household characteristics. Increased transit accessibility, local and regional destination accessibility, population and employment density, and diversity significantly reduce travel, particularly within their identified thresholds, though the magnitude of their influence varies across time periods. These findings highlight the potential of smart growth policies-such as expanding transit accessibility, promoting high-density and mixed-use development, and discouraging single-use development and peripheral sprawl-as effective strategies to reduce car dependency and manage travel demand.

cs.CY↗

arXiv2024

Spin-density wave and superconductivity in La$_4$Ni$_3$O$_{10}$ under ambient pressure

We investigate the spin-density wave (SDW) behavior and the potential for superconductivity (SC) in La$_4$Ni$_3$O$_{10}$ under ambient pressure using a multi-orbital random-phase approximation (RPA). Starting with a twelve-orbital tight-binding model derived from density functional theory (DFT) calculations, we explore the influence of Hubbard interactions on SDW formation. Our analysis reveals a stripe-like SDW characterized by an incommensurate wave vector, $Q\approx(\pm 0.7π,0)$, suggesting a possible density wave instability in agreement with recent experiments. This configuration is driven by nesting of outer-layer Ni $d_{z^2}$ orbitals and exhibits interlayer antiferromagnetic ordering between the top and bottom NiO layers, with the middle layer serving as a node. We demonstrate that the Hund's coupling $J_H$ is the primary driver of the observed SDW. While superconductivity is absent in the undoped system under ambient pressure, it becomes attainable with appropriate hole doping ($δ=-0.4$), resulting in a SC gap structure similar to the high-pressure phase. Our study identifies the specific conditions for realizing the ambient pressure stripe density wave: $J_H>0.16U$. Additionally, when doping leads to sufficient nesting at (0,0), the system's magnetic fluctuations transition into a stable Neel-type antiferromagnetic state, analogous to the high-pressure case.

cond-mat.supr-con↗

arXiv2024

Simulation of solar energetic particle events originated from coronal mass ejection shocks with a data-driven physics-based transport model

Solar energetic particle (SEP) events are associated with coronal mass ejections (CMEs) and/or solar flares. SEPs travel through the corona and interplanetary space to reach Earth, posing a radiation hazard to spacecraft and astronauts working in space and the electronics on spacecraft. Due to the distinct magnetic field configuration and solar eruption kinematic properties associated with each event, the utilization of a data-driven model becomes essential for predicting SEP hazards. In this study, we use a developed model that utilizes photospheric magnetic field measurements and CME shock observations as inputs to simulate several historical SEP events associated with fast CME speeds (>700 km/s). The model includes an SEP source term aligned with the theory of diffusive shock acceleration by the CME shock. The performance of the model is accessed by comparing simulations and observations of SEP intensity time profiles at SOHO, ACE, STEREO-A and STEREO-B. The results generally matched observations well, particularly for protons below 40.0 MeV. However, discrepancies arose for higher-energy protons, notably for the events on 2011 March 7 and 2014 February 25, where the simulation tended to overestimate the proton flux . At STEREO-A, the modeled proton intensities for the SEP events on 2013 April 11 and 2011 March 7 display a very different behavior compared to observations because of the efficient transport in longitude caused by the weak magnetic field.

astro-ph.SR↗