arXiv subjects

Jun Lu

Publications and source records attributed to Jun Lu.

At least 37 records · Page 2Linked to original sources

arXiv2025

REBCO delamination by transverse electromagnetic stress due to screening current in magnetic field

REBCO coated conductor has great potential to be used in ultra-high field magnets. Commercial REBCO tapes are strong in the longitudinal direction but prone to delamination by tensile stress in the thickness direction. For high field magnet applications, it is crucial to characterize delamination strength of REBCO conductor and better manage the transverse electromagnetic stress. In this work, the electromagnetic stress in high magnetic fields by screen current is used to study the delamination behavior of commercial REBCO tapes. Screening currents are induced in REBCO by either ramping field or rotating sample in magnetic fields up to 35 T. The experimental results are presented. The prospect of using this method for quality assurance in large magnet projects is discussed.

cond-mat.supr-con↗

arXiv2025

Characterization of NbTi wires for the electron-ion collider project

The Electron-Ion Collider (EIC) is a proposed machine to explore the behaviour of the fundamental particles and forces that bind atomic nuclei together. The design and construction of the EIC are underway at Brookhaven National Laboratory in collaboration with Thomas Jefferson National Accelerator Facility. EIC will use several different types of superconducting strands for magnets near the interaction region (IR). At beam injection, the magnetic field is usually very low compared with its maximum operating field. This usually creates considerable field errors mainly generated from magnetization current in superconducting strands even using very fine filament. The accurate magnetization measurement results from those superconducting strands will be critical for the calculation and future correction of magnetic field for EIC. In this work, we characterized three billets of superconductor NbTi strands. The magnetization was measured at 4.2 K and 1.9 K in magnetic fields below 1.5 T. The critical current at 4.2 K and in magnetic field down to 5 T were also measured. Other properties that are important for the safety margin of superconducting magnet fabrication, operation, and quench protection such as residual-resistance-ratio (RRR), filament diameter, Cu to non-Cu ratio, twist pitch, and mechanical properties at 77 K will also be presented.

physics.acc-ph↗

arXiv2025

A rigorous introduction to linear models

This book is meant to provide an introduction to linear models and the theories behind them. Our goal is to give a rigorous introduction to the readers with prior exposure to ordinary least squares. In machine learning, the output is usually a nonlinear function of the input. Deep learning even aims to find a nonlinear dependence with many layers, which require a large amount of computation. However, most of these algorithms build upon simple linear models. We then describe linear models from different perspectives and find the properties and theories behind the models. The linear model is the main technique in regression problems, and the primary tool for it is the least squares approximation, which minimizes a sum of squared errors. This is a natural choice when we're interested in finding the regression function which minimizes the corresponding expected squared error. This book is primarily a summary of purpose, significance of important theories behind linear models, e.g., distribution theory and the minimum variance estimator. We first describe ordinary least squares from three different points of view, upon which we disturb the model with random noise and Gaussian noise. Through Gaussian noise, the model gives rise to the likelihood so that we introduce a maximum likelihood estimator. It also develops some distribution theories via this Gaussian disturbance. The distribution theory of least squares will help us answer various questions and introduce related applications. We then prove least squares is the best unbiased linear model in the sense of mean squared error, and most importantly, it actually approaches the theoretical limit. We end up with linear models with the Bayesian approach and beyond.

cs.LG↗

arXiv2025

Insect-Computer Hybrid Speaker: Speaker using Chirp of the Cicada Controlled by Electrical Muscle Stimulation

We propose "Insect-Computer Hybrid Speaker", which enables us to make musics made from combinations of computer and insects. Lots of studies have proposed methods and interfaces for controlling insects and obtaining feedback. However, there have been less research on the use of insects for interaction with third parties. In this paper, we propose a method in which cicadas are used as speakers triggered by using Electrical Muscle Stimulation (EMS). We explored and investigated the suitable waveform of chirp to be controlled, the appropriate voltage range, and the maximum pitch at which cicadas can chirp.

cs.HC↗

arXiv2025

Addressing Positivity Violations in Extending Inference to a Target Population

Enhancing the external validity of trial results is essential for their applicability to real-world populations. However, violations of the positivity assumption can limit both the generalizability and transportability of findings. To address positivity violations in estimating the average treatment effect for a target population, we propose a framework that integrates characterizing the underrepresented group and performing sensitivity analysis for inference in the original target population. Our approach helps identify limitations in trial sampling and improves the robustness of trial findings for real-world populations. We apply this approach to extend findings from phase IV trials of treatments for opioid use disorder to a real-world population based on the 2021 Treatment Episode Data Set.

stat.ME↗

arXiv2025

Large Language Model Compression via the Nested Activation-Aware Decomposition

In this paper, we tackle the critical challenge of compressing large language models (LLMs) to facilitate their practical deployment and broader adoption. We introduce a novel post-training compression paradigm that focuses on low-rank decomposition of LLM weights. Our analysis identifies two main challenges in this task: the variability in LLM activation distributions and handling unseen activations from different datasets and models. To address these challenges, we propose a nested activation-aware framework (NSVD) for LLMs, a training-free approach designed to enhance the accuracy of low-rank decompositions by managing activation outliers through transforming the weight matrix based on activation distribution and the original weight matrix. This method allows for the absorption of outliers into the transformed weight matrix, improving decomposition accuracy. Our comprehensive evaluation across eight datasets and six models from three distinct LLM families demonstrates the superiority of NSVD over current state-of-the-art methods, especially at medium to large compression ratios or in multilingual and multitask settings.

cs.LG↗

arXiv2025

Generalizable Machine Learning Models for Predicting Data Center Server Power, Efficiency, and Throughput

In the rapidly evolving digital era, comprehending the intricate dynamics influencing server power consumption, efficiency, and performance is crucial for sustainable data center operations. However, existing models lack the ability to provide a detailed and reliable understanding of these intricate relationships. This study employs a machine learning-based approach, using the SPECPower_ssj2008 database, to facilitate user-friendly and generalizable server modeling. The resulting models demonstrate high accuracy, with errors falling within approximately 10% on the testing dataset, showcasing their practical utility and generalizability. Through meticulous analysis, predictive features related to hardware availability date, server workload level, and specifications are identified, providing insights into optimizing energy conservation, efficiency, and performance in server deployment and operation. By systematically measuring biases and uncertainties, the study underscores the need for caution when employing historical data for prospective server modeling, considering the dynamic nature of technology landscapes. Collectively, this work offers valuable insights into the sustainable deployment and operation of servers in data centers, paving the way for enhanced resource use efficiency and more environmentally conscious practices.

cs.LG↗

arXiv2025

S2C: Learning Noise-Resistant Differences for Unsupervised Change Detection in Multimodal Remote Sensing Images

Unsupervised Change Detection (UCD) in multimodal Remote Sensing (RS) images remains a difficult challenge due to the inherent spatio-temporal complexity within data, and the heterogeneity arising from different imaging sensors. Inspired by recent advancements in Visual Foundation Models (VFMs) and Contrastive Learning (CL) methodologies, this research aims to develop CL methodologies to translate implicit knowledge in VFM into change representations, thus eliminating the need for explicit supervision. To this end, we introduce a Semantic-to-Change (S2C) learning framework for UCD in both homogeneous and multimodal RS images. Differently from existing CL methodologies that typically focus on learning multi-temporal similarities, we introduce a novel triplet learning strategy that explicitly models temporal differences, which are crucial to the CD task. Furthermore, random spatial and spectral perturbations are introduced during the training to enhance robustness to temporal noise. In addition, a grid sparsity regularization is defined to suppress insignificant changes, and an IoU-matching algorithm is developed to refine the CD results. Experiments on four benchmark CD datasets demonstrate that the proposed S2C learning framework achieves significant improvements in accuracy, surpassing current state-of-the-art by over 31\%, 9\%, 23\%, and 15\%, respectively. It also demonstrates robustness and sample efficiency, suitable for training and adaptation of various Visual Foundation Models (VFMs) or backbone neural networks. The relevant code will be available at: github.com/DingLei14/S2C.

cs.CV↗

arXiv2025

Practical Topics in Optimization

In an era where data-driven decision-making and computational efficiency are paramount, optimization plays a foundational role in advancing fields such as mathematics, computer science, operations research, machine learning, and beyond. From refining machine learning models to improving resource allocation and designing efficient algorithms, optimization techniques serve as essential tools for tackling complex problems. This book aims to provide both an introductory guide and a comprehensive reference, equipping readers with the necessary knowledge to understand and apply optimization methods within their respective fields. Our primary goal is to demystify the inner workings of optimization algorithms, including black-box and stochastic optimizers, by offering both formal and intuitive explanations. Starting from fundamental mathematical principles, we derive key results to ensure that readers not only learn how these techniques work but also understand when and why to apply them effectively. By striking a careful balance between theoretical depth and practical application, this book serves a broad audience, from students and researchers to practitioners seeking robust optimization strategies.

math.NA↗

arXiv2025

Adaptive weighted approach for high-dimensional statistical learning and inference

We propose a new weighted average estimator for the high dimensional parameters under the distributed learning system, in which the weight assigned to each coordinate is precisely proportional to the inverse of the variance of the local estimates for that coordinate. This strategy empowers the new estimator to achieve a minimal mean squared error, comparable to the current state-of-the-art one-shot distributed learning methods. While at the same time, the new weighting approach maintains remarkably low communication costs, as each agent is required to transmit only two vectors to the central server. As a result, the newly proposed method achieves optimal statistical efficiency while significantly reducing communication overhead. We further demonstrate the effectiveness of the new estimator by investigating the error bound and the asymptotic properties of the estimation, as well as the numerical performance on some simulated examples and a real data analysis.

stat.ME↗

arXiv2024

Numerical Matrix Decomposition

In 1954, Alston S. Householder published \textit{Principles of Numerical Analysis}, one of the first modern treatments on matrix decomposition that favored a (block) LU decomposition-the factorization of a matrix into the product of lower and upper triangular matrices. And now, matrix decomposition has become a core technology in machine learning, largely due to the development of the back propagation algorithm in fitting a neural network. The sole aim of this survey is to give a self-contained introduction to concepts and mathematical tools in numerical linear algebra and matrix analysis in order to seamlessly introduce matrix decomposition techniques and their applications in subsequent sections. However, we clearly realize our inability to cover all the useful and interesting results concerning matrix decomposition and given the paucity of scope to present this discussion, e.g., the separated analysis of the Euclidean space, Hermitian space, Hilbert space, and things in the complex domain. We refer the reader to literature in the field of linear algebra for a more detailed introduction to the related fields. Keywords: Existence and computing of matrix decompositions, Floating point operations (flops), Low-rank approximation, Pivot, LU/PLU decomposition, CR/CUR/Skeleton decomposition, Coordinate transformation, ULV/URV decomposition, Rank decomposition, Rank revealing decomposition, Update/downdate, Tensor decomposition.

math.HO↗

arXiv2024

Study of delamination in REBCO coated conductor by transmission electron microscopy

Delamination strength of REBCO is very important for its applications in large magnet projects. This work presented the transmission electron microscopy (TEM) investigation of the microstructures of the REBCO coated conductor to understand its delamination property. We found that the low delamination strength is associated with nano-voids formed at the IBAD MgO/Y2O3 interface.

cond-mat.supr-con↗

arXiv2024

Thermal conductivity of REBCO tapes with different stabilizers from 4.2 to 200 K

REBCO coated conductor is a high temperature superconductor that has a wide range of applications, one of which is the current leads of magnet systems. In the design of current leads, it is crucial to minimize their thermal conduction while maintain stable electrical conduction. Therefore, thermal conductivity of various REBCO tapes need to be characterized and analyzed. In this research, we measured thermal conductivity of REBCO tapes in the longitudinal direction from 4.2 to 200 K. Samples with Cu, Ag and Ag-3at%Au stabilizers of various thicknesses were measured. The electrical conductivity of these stabilizers was also characterized by residual resistance ratio (RRR) measurements and correlated with the thermal conductivity results. We showed that in samples with 10 micron or less Cu stabilizer, thermal conduction is dominated by that of the Cu which has much higher thermal conductivity than the Hastelloy substrate and the superconductor layer. In addition, the sample with 3 micron Ag-3at%Au stabilizer has significantly lower thermal conductivity than that with 3 micron silver stabilizer. It is concluded that REBCO with Ag-3at%Au stabilizer is promising for current lead applications.

cond-mat.supr-con↗

arXiv2024

Residual resistance ratio of Cu stabilizer in commercial REBCO tapes

Residual resistance ratio (RRR) of Cu stabilizer in REBCO coated conductor is an important design parameter for REBCO magnets. In this work, we measured RRR of electroplated Cu stabilizer in commercial REBCO tapes. Over 130 samples were measured for the quality assurance programs of REBCO magnet projects at the National High Magnetic Field Laboratory, USA (NHMFL). The average RRR value was above 50. In order to investigate the factors that influence RRR, several samples were analyzed by using scanning electron microscopy, secondary ion mass spectroscopy, and inductively coupled plasma mass spectroscopy. We found that, in our samples, RRR was strongly correlated with the grain size. We demonstrated that RRR was primarily determined by grain boundary resistivity. Lower RRR was also strongly correlated with higher concentration of chlorine impurity. This is explained by that higher chlorine impurity hindered the grain growth in the room temperature self annealing process resulting smaller grain. Smaller grain resulted in lower RRR. In addition, thermal annealing significantly enhanced RRR. An activation energy of 0.4 eV was obtained from the annealing experiment which corresponds to the activation of Cu grain growth.

cond-mat.supr-con↗

arXiv2024

REBCO delamination characterization by 90 degree peel test

REBCO tape has successfully used in ultra-high field magnets. Mechanically, it is very strong in its length direction but is prone to delamination in the thickness direction. In an epoxy impregnated REBCO magnet, thermal strain alone could delaminate the conductor. Even for dry wound REBCO coil, a conductor with very low delamination strength is still a concern. Therefore, it is important to characterize the delamination strength of the conductor. In the past decade, significant progresses have been made in the characterization of REBCO delamination strength. Among several developed characterization methods, the 90 degree peel test is simple to setup and seems to offer reproducible results. Therefore, this test could be used as a reliable method to characterize the relative delamination strength of REBCO tapes for quality control purposes. This paper presents our development of 90 degree peel test method for quality assurance test of the 40 T all-superconducting magnet project at the National High Magnetic Field Laboratory. We investigated the factors that influence the test results, such as thickness and RRR of the copper layer. We found that peel strength increases with decreasing Cu thickness. We also found that peel strength is positively correlated with RRR of the copper. Despite these effects, 90 degree peel test is still a valuable quality assurance tool to evaluate delamination strength for large volume of tapes with the same copper thickness.

cond-mat.supr-con↗

arXiv2024

Physico-thermal and geochemical behavior and alteration of the Au indicator gangue hydrothermal quartz at the Kubi Gold Ore Deposits

Altered and gangue quartz in hydrothermal veins from the Kubi Gold deposit in Dunkwa on Offin in the central region of Ghana are investigated for possible Au-associated indicator minerals and to provide the understanding and increase the knowledge of the mineral hosting and alteration processes in quartz. X-ray diffraction, air annealing furnace, differential scanning calorimetry, energy dispersive X-ray spectroscopy, and transmission electron microscopy have been applied on different quartz types outcropping from surface and bedrocks at the Kubi Gold Mining to reveal the material properties at different temperatures. From the diffraction results of the fresh and annealed quartz samples, we find that the samples contain indicator and the impurity minerals iron disulfide, biotite, titanium oxide, and magnetite. These minerals, under oxidation process between 574-1400 °C temperatures experienced hematite alterations and a transformation from α-quartz to \b{eta}-quartz and further to cristobalite as observed from the calorimetry scans for hydrothermally exposed materials. The energy dispersive spectroscopy revealed elemental components of Fe, S, Mg, K, Al, Ti, Na, Si, O, and Ca contained in the samples, and these are attributed to the impurity phase minerals observed in the diffraction. The findings also suggest that during the hydrothermal flow regime, impurity minerals and metals can be trapped by voids and faults. Under favorable temperature conditions, the trapped minerals can be altered to change color at different depositional stages by oxidation and reduction processes leading to hematite alteration which is a useful indicator mineral in mineral exploration.

physics.geo-ph↗

arXiv2024

Improving embedding with contrastive fine-tuning on small datasets with expert-augmented scores

This paper presents an approach to improve text embedding models through contrastive fine-tuning on small datasets augmented with expert scores. It focuses on enhancing semantic textual similarity tasks and addressing text retrieval problems. The proposed method uses soft labels derived from expert-augmented scores to fine-tune embedding models, preserving their versatility and ensuring retrieval capability is improved. The paper evaluates the method using a Q\&A dataset from an online shopping website and eight expert models. Results show improved performance over a benchmark model across multiple metrics on various retrieval tasks from the massive text embedding benchmark (MTEB). The method is cost-effective and practical for real-world applications, especially when labeled data is scarce.

cs.CL↗

arXiv2024

Low-Rank Approximation, Adaptation, and Other Tales

Low-rank approximation is a fundamental technique in modern data analysis, widely utilized across various fields such as signal processing, machine learning, and natural language processing. Despite its ubiquity, the mechanics of low-rank approximation and its application in adaptation can sometimes be obscure, leaving practitioners and researchers with questions about its true capabilities and limitations. This paper seeks to clarify low-rank approximation and adaptation by offering a comprehensive guide that reveals their inner workings and explains their utility in a clear and accessible way. Our focus here is to develop a solid intuition for how low-rank approximation and adaptation operate, and why they are so effective. We begin with basic concepts and gradually build up to the mathematical underpinnings, ensuring that readers of all backgrounds can gain a deeper understanding of low-rank approximation and adaptation. We strive to strike a balance between informal explanations and rigorous mathematics, ensuring that both newcomers and experienced experts can benefit from this survey. Additionally, we introduce new low-rank decomposition and adaptation algorithms that have not yet been explored in the field, hoping that future researchers will investigate their potential applicability.

cs.LG↗