arXiv subjects

Minxue Tang

Publications and source records attributed to Minxue Tang.

17 recordsLinked to original sources

arXiv2026

Direct High-Magnetic-Field Coupling to Stripe Order in a Cuprate Superconductor

Superconductivity in cuprates emerges out of a complex normal state that hosts density waves, pseudogap physics, and strange metal properties. Here, we access this normal state by synchronizing free-electron laser x-rays with high-magnetic-field pulses up to 44 T. We observe a linear increase in charge order amplitude and correlation length that persists far above the vortex melting transition. This behavior is incompatible with standard phase competition between charge order and superconductivity. By means of conventional hard x-ray diffraction and magnetostriction, we show that applied fields also enhance monoclinic lattice distortions. However, this magnetoelastic response is weaker and an epiphenomenon of the stripe order enhancement. Combined with recent observations of field-linear spin freezing, our results point to a direct coupling between magnetic field and the spin component of stripe order in the high-field normal state -- a mechanism independent of superconductivity suppression that has so far remained hidden from scattering probes.

cond-mat.str-el↗

arXiv2026

FinTrace: Holistic Trajectory-Level Evaluation of LLM Tool Calling for Long-Horizon Financial Tasks

Recent studies demonstrate that tool-calling capability enables large language models (LLMs) to interact with external environments for long-horizon financial tasks. While existing benchmarks have begun evaluating financial tool calling, they focus on limited scenarios and rely on call-level metrics that fail to capture trajectory-level reasoning quality. To address this gap, we introduce FinTrace, a benchmark comprising 800 expert-annotated trajectories spanning 34 real-world financial task categories across multiple difficulty levels. FinTrace employs a rubric-based evaluation protocol with nine metrics organized along four axes -- action correctness, execution efficiency, process quality, and output quality -- enabling fine-grained assessment of LLM tool-calling behavior. Our evaluation of 13 LLMs reveals that while frontier models achieve strong tool selection, all models struggle with information utilization and final answer quality, exposing a critical gap between invoking the right tools and reasoning effectively over their outputs. To move beyond diagnosis, we construct FinTrace-Training, the first trajectory-level preference dataset for financial tool-calling, containing 8,196 curated trajectories with tool-augmented contexts and preference pairs. We fine-tune Qwen-3-8B/32B using supervised fine-tuning followed by direct preference optimization (DPO) and show that training on FinTrace-Training consistently improves intermediate reasoning metrics, with DPO more effectively suppressing failure modes. However, end-to-end answer quality remains a bottleneck, indicating that trajectory-level improvements do not yet fully propagate to final output quality.

cs.AI↗

arXiv2026

Iron spin crossover in ferropericlase and its effect on lower-mantle thermal conductivity

Thermal conductivity of Earths lower mantle controls heat transfer across the core-mantle boundary (CMB) and strongly influences mantle convection. We report direct measurements of the thermal conductivity of single-crystal ferropericlase (Mg$_{1-x}$Fe$_x$O, $x = 0.09$-0.13), the second most abundant lower-mantle mineral, using optical laser flash and X-ray free-electron laser heating in diamond-anvil cells up to $\sim2200$~K and 130~GPa. These experiments provide the first conductivity data for ferropericlase at simultaneous lower-mantle pressures and temperatures. A marked reduction in conductivity between 60 and 100~GPa at $\sim1700$~K is consistent with the iron spin crossover. Combined with our previous results for Fe- and Fe,Al-bearing bridgmanite, the data define a lower-mantle conductivity profile that increases with pressure to $\sim10$~W\,m$^{-1}$\,K$^{-1}$ near the CMB, constraining mantle heat flux, plume buoyancy, and long-term geodynamic evolution.

physics.geo-ph↗

arXiv2026

Adaptive Data Augmentation with Multi-armed Bandit: Sample-Efficient Embedding Calibration for Implicit Pattern Recognition

Recognizing implicit visual and textual patterns is essential in many real-world applications of modern AI. However, tackling long-tail pattern recognition tasks remains challenging for current pre-trained foundation models such as LLMs and VLMs. While finetuning pre-trained models can improve accuracy in recognizing implicit patterns, it is usually infeasible due to a lack of training data and high computational overhead. In this paper, we propose ADAMAB, an efficient embedding calibration framework for few-shot pattern recognition. To maximally reduce the computational costs, ADAMAB trains embedder-agnostic light-weight calibrators on top of fixed embedding models without accessing their parameters. To mitigate the need for large-scale training data, we introduce an adaptive data augmentation strategy based on the Multi-Armed Bandit (MAB) mechanism. With a modified upper confidence bound algorithm, ADAMAB diminishes the gradient shifting and offers theoretically guaranteed convergence in few-shot training. Our multi-modal experiments justify the superior performance of ADAMAB, with up to 40% accuracy improvement when training with less than 5 initial data samples of each class.

cs.CV↗

arXiv2026

XFEL Imaging Techniques for High Energy Density and Inertial Fusion Energy Research at HED-HiBEF

The imaging platform developed at the High Energy Density - Helmholtz International Beamline for Extreme Fields (HED-HiBEF) instrument at the European XFEL and its applications to high energy density and fusion related research are presented. The platform combines the XFEL beam with the high-intensity short-pulse laser ReLaX and the high-energy nanosecond-pulse laser DiPOLE-100X. The spatial resolution is better than 500 nm and the temporal resolution of the order of 50 fs. We show examples of blast waves and converging cylindrical shocks in aluminium, resonant absorption measurements of specific charged states in copper with ReLaX and planar shocks in polystyrene material generated by DiPOLE-100X. We also discuss the possibilities introduced by combining this imaging platform with a kJ-class laser.

physics.plasm-ph↗

arXiv2025

Structural evolution of iron oxides melts at Earth's outer-core pressures

Oxygen and other light elements comprise up to 5 wt% of the Earth's outer-core, and may significantly influence its physical properties and the operation of the geodynamo. Here we report in situ x-ray diffraction measurements of Fe, Fe + 4.5 FeO (atomic proportion), and Fe2O3 melts at 177-438 GPa, achieved using laser-driven shock compression at an x-ray free-electron laser. The melts exhibit Fe-O coordination numbers between 4.0(0.4) and 4.5(0.4), indicating predominantly four-fold coordination environments. These coordination states are significantly smaller than those of Fe-bearing lower-mantle phases such as bridgmanite and ferropericlase. Shorter Fe-Fe interatomic distances in compressed iron oxide melts drive the denser packing relative to ambient melts, while the structural differences between Fe + 4.5 FeO and Fe2O3 melts under shock indicate that the oxidation state modulates oxygen solubility in liquid Fe. At around 177 GPa (380 km below the core-mantle boundary), Fe2O3 melts exhibit higher Fe-O coordination, suggesting that local variations in oxygen content could contribute to the stratification in the uppermost outer-core inferred from seismological and geomagnetic observations.

cond-mat.mtrl-sci↗

arXiv2025

A Momentum-Resolved X-ray Thomson Scattering Benchmark of Electronic-Response Models in Warm Dense Aluminium

The robust diagnosis of conditions generated in warm dense matter (WDM) experiments remains a persistent challenge. Here we describe the measurement of shock-compressed aluminium at 50 GPa with angle-resolved femtosecond x-ray Thomson scattering (XRTS) over a wide range of scattering wavevectors at the European XFEL. The measured plasmon dispersion and line shape show that the de facto standard approach for analysing XRTS spectra, based on uniform-electron-gas models, systematically overestimates the resonance energy by up to 8 eV. We present an ab initio approach that agrees within the experimental uncertainty and demonstrates that accounting for shock-induced disorder is critical for interpreting shock-compressed systems, providing evidence that ab initio treatments are required for reliable XRTS inference in warm dense aluminium.

physics.plasm-ph↗

arXiv2025

Observation of Body-Centered Cubic Iron above 200 Gigapascals

The crystallographic structure of iron under extreme conditions is a key benchmark for cutting-edge experimental and numerical methods. Moreover, it plays a crucial role in understanding planetary cores, as it significantly influences the interpretation of observational data and, consequently, insights into their internal structure and dynamics. However, even the structure of pure solid iron under the Earth's core conditions remains uncertain, with the commonly expected hexagonal close-packed structure energetically competitive with various cubic lattices. In this study, iron was compressed in a diamond anvil cell to above 200 GPa, and dynamically probed near the melting point using MHz frequency X-ray pulses from the European X-ray Free Electron Laser. The emergence of an additional diffraction line at high temperatures suggests the formation of an entropically stabilized bcc structure. Rapid heating and cooling cycles captured intermediate phases, offering new insights into iron's phase transformation paths. The appearance of the bcc phase near melting at extreme pressures challenges current understanding of the iron phase diagram under Earth's core conditions.

cond-mat.mtrl-sci↗

arXiv2025

High-Quality Ultra-Fast Total Scattering and Pair Distribution Function Data using an X-ray Free Electron Laser

High-quality total scattering data, a key tool for understanding atomic-scale structure in disordered materials, require stable instrumentation and access to high momentum transfers. This is now routine at dedicated synchrotron instrumentation using high-energy X-ray beams, but it is very challenging to measure a total scattering dataset in less than a few microseconds. This limits their effectiveness for capturing structural changes that occur at the much faster timescales of atomic motion. Current X-ray free-electron lasers (XFELs) provide femtosecond-pulsed X-ray beams with maximum energies of approximately 24 keV, giving the potential to measure total scattering and the attendant pair distribution functions (PDFs) on femtosecond timescales. Here, we show that this potential has been realised using the HED scientific instrument at the European XFEL and present normalised total scattering data for 0.35 \r{A}-1 < Q < 16.6 \r{A}-1 and their PDFs from a broad spectrum of materials, including crystalline, nanocrystalline and amorphous solids, liquids, and clusters in solution. We analyse the data using a variety of methods, including Rietveld refinement, small-box PDF refinement, joint reciprocal-real space refinement, cluster refinement, and Debye scattering analysis. The resolution function of the setup is also characterised. We conclusively show that high-quality data can be obtained from a single approximately 30 fs XFEL pulse. Our efforts not only significantly increase the existing maximum reported Q-range for an S(Q) measured at an XFEL but also mean that XFELs are now a viable X-ray source for the broad community of people using reciprocal space total scattering and PDF methods in their research.

cond-mat.mtrl-sci↗

arXiv2025

Proactive Privacy Amnesia for Large Language Models: Safeguarding PII with Negligible Impact on Model Utility

With the rise of large language models (LLMs), increasing research has recognized their risk of leaking personally identifiable information (PII) under malicious attacks. Although efforts have been made to protect PII in LLMs, existing methods struggle to balance privacy protection with maintaining model utility. In this paper, inspired by studies of amnesia in cognitive science, we propose a novel approach, Proactive Privacy Amnesia (PPA), to safeguard PII in LLMs while preserving their utility. This mechanism works by actively identifying and forgetting key memories most closely associated with PII in sequences, followed by a memory implanting using suitable substitute memories to maintain the LLM's functionality. We conduct evaluations across multiple models to protect common PII, such as phone numbers and physical addresses, against prevalent PII-targeted attacks, demonstrating the superiority of our method compared with other existing defensive techniques. The results show that our PPA method completely eliminates the risk of phone number exposure by 100% and significantly reduces the risk of physical address exposure by 9.8% - 87.6%, all while maintaining comparable model utility performance.

cs.CL↗

arXiv2024

FedProphet: Memory-Efficient Federated Adversarial Training via Robust and Consistent Cascade Learning

Federated Adversarial Training (FAT) can supplement robustness against adversarial examples to Federated Learning (FL), promoting a meaningful step toward trustworthy AI. However, FAT requires large models to preserve high accuracy while achieving strong robustness, incurring high memory-swapping latency when training on memory-constrained edge devices. Existing memory-efficient FL methods suffer from poor accuracy and weak robustness due to inconsistent local and global models. In this paper, we propose FedProphet, a novel FAT framework that can achieve memory efficiency, robustness, and consistency simultaneously. FedProphget reduces the memory requirement in local training while guaranteeing adversarial robustness by adversarial cascade learning with strong convexity regularization, and we show that the strong robustness also implies low inconsistency in FedProphet. We also develop a training coordinator on the server of FL, with Adaptive Perturbation Adjustment for utility-robustness balance and Differentiated Module Assignment for objective inconsistency mitigation. FedPeophet significantly outperforms other baselines under different experimental settings, maintaining the accuracy and robustness of end-to-end FAT with 80% memory reduction and up to 10.8x speedup in training time.

cs.LG↗

arXiv2023

Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction

Due to limited communication capacities of edge devices, most existing federated learning (FL) methods randomly select only a subset of devices to participate in training for each communication round. Compared with engaging all the available clients, the random-selection mechanism can lead to significant performance degradation on non-IID (independent and identically distributed) data. In this paper, we show our key observation that the essential reason resulting in such performance degradation is the class-imbalance of the grouped data from randomly selected clients. Based on our key observation, we design an efficient heterogeneity-aware client sampling mechanism, i.e., Federated Class-balanced Sampling (Fed-CBS), which can effectively reduce class-imbalance of the group dataset from the intentionally selected clients. In particular, we propose a measure of class-imbalance and then employ homomorphic encryption to derive this measure in a privacy-preserving way. Based on this measure, we also design a computation-efficient client sampling strategy, such that the actively selected clients will generate a more class-balanced grouped dataset with theoretical guarantees. Extensive experimental results demonstrate Fed-CBS outperforms the status quo approaches. Furthermore, it achieves comparable or even better performance than the ideal setting where all the available clients participate in the FL training.

cs.LG↗

arXiv2023

FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devices

Federated adversarial training can effectively complement adversarial robustness into the privacy-preserving federated learning systems. However, the high demand for memory capacity and computing power makes large-scale federated adversarial training infeasible on resource-constrained edge devices. Few previous studies in federated adversarial training have tried to tackle both memory and computational constraints simultaneously. In this paper, we propose a new framework named Federated Adversarial Decoupled Learning (FADE) to enable AT on heterogeneous resource-constrained edge devices. FADE differentially decouples the entire model into small modules to fit into the resource budget of each device, and each device only needs to perform AT on a single module in each communication round. We also propose an auxiliary weight decay to alleviate objective inconsistency and achieve better accuracy-robustness balance in FADE. FADE offers theoretical guarantees for convergence and adversarial robustness, and our experimental results show that FADE can significantly reduce the consumption of memory and computing power while maintaining accuracy and robustness.

cs.LG↗

arXiv2022

Towards Collaborative Intelligence: Routability Estimation based on Decentralized Private Data

Applying machine learning (ML) in design flow is a popular trend in EDA with various applications from design quality predictions to optimizations. Despite its promise, which has been demonstrated in both academic researches and industrial tools, its effectiveness largely hinges on the availability of a large amount of high-quality training data. In reality, EDA developers have very limited access to the latest design data, which is owned by design companies and mostly confidential. Although one can commission ML model training to a design company, the data of a single company might be still inadequate or biased, especially for small companies. Such data availability problem is becoming the limiting constraint on future growth of ML for chip design. In this work, we propose an Federated-Learning based approach for well-studied ML applications in EDA. Our approach allows an ML model to be collaboratively trained with data from multiple clients but without explicit access to the data for respecting their data privacy. To further strengthen the results, we co-design a customized ML model FLNet and its personalization under the decentralized training scenario. Experiments on a comprehensive dataset show that collaborative training improves accuracy by 11% compared with individual local models, and our customized model FLNet significantly outperforms the best of previous routability estimators in this collaborative training flow.

cs.LG↗

arXiv2022

FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning

Client-wise data heterogeneity is one of the major issues that hinder effective training in federated learning (FL). Since the data distribution on each client may vary dramatically, the client selection strategy can significantly influence the convergence rate of the FL process. Active client selection strategies are popularly proposed in recent studies. However, they neglect the loss correlations between the clients and achieve only marginal improvement compared to the uniform selection strategy. In this work, we propose FedCor -- an FL framework built on a correlation-based client selection strategy, to boost the convergence rate of FL. Specifically, we first model the loss correlations between the clients with a Gaussian Process (GP). Based on the GP model, we derive a client selection strategy with a significant reduction of expected global loss in each round. Besides, we develop an efficient GP training method with a low communication overhead in the FL scenario by utilizing the covariance stationarity. Our experimental results show that compared to the state-of-the-art method, FedCorr can improve the convergence rates by $34\%\sim 99\%$ and $26\%\sim 51\%$ on FMNIST and CIFAR-10, respectively.

cs.LG↗

arXiv2020

Learning Low-rank Deep Neural Networks via Singular Vector Orthogonality Regularization and Singular Value Sparsification

Modern deep neural networks (DNNs) often require high memory consumption and large computational loads. In order to deploy DNN algorithms efficiently on edge or mobile devices, a series of DNN compression algorithms have been explored, including factorization methods. Factorization methods approximate the weight matrix of a DNN layer with the multiplication of two or multiple low-rank matrices. However, it is hard to measure the ranks of DNN layers during the training process. Previous works mainly induce low-rank through implicit approximations or via costly singular value decomposition (SVD) process on every training step. The former approach usually induces a high accuracy loss while the latter has a low efficiency. In this work, we propose SVD training, the first method to explicitly achieve low-rank DNNs during training without applying SVD on every step. SVD training first decomposes each layer into the form of its full-rank SVD, then performs training directly on the decomposed weights. We add orthogonality regularization to the singular vectors, which ensure the valid form of SVD and avoid gradient vanishing/exploding. Low-rank is encouraged by applying sparsity-inducing regularizers on the singular values of each layer. Singular value pruning is applied at the end to explicitly reach a low-rank model. We empirically show that SVD training can significantly reduce the rank of DNN layers and achieve higher reduction on computation load under the same accuracy, comparing to not only previous factorization methods but also state-of-the-art filter pruning methods.

cs.LG↗

arXiv2019

Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards

Hierarchical Reinforcement Learning (HRL) is a promising approach to solving long-horizon problems with sparse and delayed rewards. Many existing HRL algorithms either use pre-trained low-level skills that are unadaptable, or require domain-specific information to define low-level rewards. In this paper, we aim to adapt low-level skills to downstream tasks while maintaining the generality of reward design. We propose an HRL framework which sets auxiliary rewards for low-level skill training based on the advantage function of the high-level policy. This auxiliary reward enables efficient, simultaneous learning of the high-level policy and low-level skills without using task-specific knowledge. In addition, we also theoretically prove that optimizing low-level skills with this auxiliary reward will increase the task return for the joint policy. Experimental results show that our algorithm dramatically outperforms other state-of-the-art HRL methods in Mujoco domains. We also find both low-level and high-level policies trained by our algorithm transferable.

cs.LG↗