arXiv subjects

Chao Jiang

Publications and source records attributed to Chao Jiang.

At least 37 records · Page 2Linked to original sources

arXiv2025

Unified Error Correction Code Transformer with Low Complexity

Channel coding is vital for reliable sixth-generation (6G) data transmission, employing diverse error correction codes for various application scenarios. Traditional decoders require dedicated hardware for each code, leading to high hardware costs. Recently, artificial intelligence (AI)-driven approaches, such as the error correction code Transformer (ECCT) and its enhanced version, the foundation error correction code Transformer (FECCT), have been proposed to reduce the hardware cost by leveraging the Transformer to decode multiple codes. However, their excessively high computational complexity of $\mathcal{O}(N^2)$ due to the self-attention mechanism in the Transformer limits scalability, where $N$ represents the sequence length. To reduce computational complexity, we propose a unified Transformer-based decoder that handles multiple linear block codes within a single framework. Specifically, a standardized unit is employed to align code length and code rate across different code types, while a redesigned low-rank unified attention module, with computational complexity of $\mathcal{O}(N)$, is shared across various heads in the Transformer. Additionally, a sparse mask, derived from the parity-check matrix's sparsity, is introduced to enhance the decoder's ability to capture inherent constraints between information and parity-check bits, improving decoding accuracy and further reducing computational complexity by $86\%$. Extensive experimental results demonstrate that the proposed unified Transformer-based decoder outperforms existing methods and provides a high-performance, low-complexity solution for next-generation wireless communication systems.

cs.IT↗

arXiv2025

Parameterizing empirical interatomic potentials for predicting thermophysical properties via an irreducible derivative approach: the case of ThO$_2$ and UO$_2$

The accuracy of classical physical property predictions using molecular dynamics simulations is determined by the quality of the interatomic potentials. Here we introduce a training approach for empirical interatomic potentials (EIPs) which is well suited for capturing phonons and phonon-related properties. Our approach is based on direct comparisons of the second- and third-order irreducible derivatives between an EIP and the Born-Oppenheimer potential within density functional theory (DFT) calculations. Irreducible derivatives fully exploit space group symmetry and allow for training without redundant information. We demonstrate the fidelity of our approach in the context of ThO$_2$ and UO$_2$, where we optimize parameters of an embedded-atom method potential in addition to core-shell interactions. Our EIPs provide thermophysical properties in good agreement with DFT and outperform widely utilized EIPs for phonon dispersion and thermal conductivity predictions. Reasonable estimates of thermal expansion and formation energies of Frenkel pairs are also obtained.

cond-mat.mtrl-sci↗

arXiv2025

Nondestructive quantitative estimation of cross-sectional corrosion degree of rebar using self-magnetic flux leakage field variation

To accurately assess the structural performance of corroded reinforced concrete structures, obtaining precise information on the corrosion range and corrosion degree of rebar is crucial. In this study, based on a comprehensive analysis of extensive high-precision magnetic field and three-dimensional structural light scanning data of 21 corroded rebars, it was found that the self-magnetic flux leakage can accurately identify the corrosion range, with an error not exceeding 3%. A proposed quantitative index NHx of the self-magnetic flux leakage amplitude exhibits a linear correlation with the cross-sectional corrosion degree of rebar, whose probability density distribution can be accurately described using the Weibull distribution function. Utilizing the Weibull distribution function of NHx and a Bayesian model, automatically rapid quantification of the rebar's cross-sectional corrosion degree based on the non-destructive testing-derived NHx values can be conveniently realized. This self-magnetic flux leakage-based novel method for quantifying rebar's cross-sectional corrosion degree is accurate, efficient, and well-suited for practical engineering applications, providing robust support for a precise assessment of the structural performance of corroded reinforced concrete structures.

physics.app-ph↗

arXiv2025

Universal relations and bounds for fluctuations in quasistatic small heat engines

The efficiency of any heat engine, defined as the ratio of average work output to heat input, is bounded by Carnot's celebrated result. However, this measure is insufficient to characterize the properties of miniaturized heat engines carrying non-negligible fluctuations, and a study of higher-order statistics of their energy exchanges is required. Here, we generalize Carnot's result for reversible cycles to arbitrary order moment of the work and heat fluctuations. Our results show that, in the quasistatic limit, higher-order statistics of a small engine's energetics depend solely on the ratio between the temperatures of the thermal baths. We further prove that our result for the second moment gives universal bounds for the ratio between the variances of work and heat for quasistatic cycles. We test this theory with our previous experimental results of a Brownian Carnot engine and observe the consistency between them, even beyond the quasistatic regime. Our results can be exploited in the design of thermal nanomachines to reduce their fluctuations of work output without marginalizing its average value and efficiency.

cond-mat.stat-mech↗

arXiv2025

Xenon-metal pair formation in UO2 investigated using DFT+U

A recent experimental study on a spent uranium dioxide (UO2) fuel sample from Belgium Reactor 3 (BR3) identified a unique pair structure formed by the noble metal phase (NMP) and fission gas (xenon [Xe]) precipitate. However, the fundamental mechanism behind this structure remains unclear. The present study aims to provide an understanding of the interaction between five different metal precipitates (molybdenum [Mo], ruthenium [Ru], palladium [Pd], technetium [Tc], and rhodium [Rh]) and the Xe fission gas atoms in UO2, by using density functional theory (DFT) in combination with the Hubbard U correction to compute the formation energies involved. All DFT+U calculations were performed with occupation matrix control to ensure antiferromagnetic ordering of UO2. The calculated formation and binding energies of the Xe and solid fission products in the NMP reveal that these metal precipitates form stable pair structures with Xe. Notably, the formation energy of Xe-metal pairs is lower than that of the isolated single defects in all instances, with Pd and Mo showing the most favourable binding energy, likely accounting for the observed pair structure formation.

cond-mat.mtrl-sci↗

arXiv2025

Dephasing Enabled Fast Charging of Quantum Batteries

We propose and analyze a universal method to obtain fast charging of a quantum battery by a driven charger system using controlled, pure dephasing of the charger. While the battery displays coherent underdamped oscillations of energy for weak charger dephasing, the quantum Zeno freezing of the charger energy at high dephasing suppresses the rate of transfer of energy to the battery. Choosing an optimum dephasing rate between the regimes leads to a fast charging of the battery. We illustrate our results with the charger and battery modeled by either two-level systems or harmonic oscillators. Apart from the fast charging, the dephasing also renders the charging performance more robust to detuning between the charger, drive, and battery frequencies for the two-level systems case.

quant-ph↗

arXiv2025

HgPCN: A Heterogeneous Architecture for E2E Embedded Point Cloud Inference

Point cloud is an important type of geometric data structure for many embedded applications such as autonomous driving and augmented reality. Current Point Cloud Networks (PCNs) have proven to achieve great success in using inference to perform point cloud analysis, including object part segmentation, shape classification, and so on. However, point cloud applications on the computing edge require more than just the inference step. They require an end-to-end (E2E) processing of the point cloud workloads: pre-processing of raw data, input preparation, and inference to perform point cloud analysis. Current PCN approaches to support end-to-end processing of point cloud workload cannot meet the real-time latency requirement on the edge, i.e., the ability of the AI service to keep up with the speed of raw data generation by 3D sensors. Latency for end-to-end processing of the point cloud workloads stems from two reasons: memory-intensive down-sampling in the pre-processing phase and the data structuring step for input preparation in the inference phase. In this paper, we present HgPCN, an end-to-end heterogeneous architecture for real-time embedded point cloud applications. In HgPCN, we introduce two novel methodologies based on spatial indexing to address the two identified bottlenecks. In the Pre-processing Engine of HgPCN, an Octree-Indexed-Sampling method is used to optimize the memory-intensive down-sampling bottleneck of the pre-processing phase. In the Inference Engine, HgPCN extends a commercial DLA with a customized Data Structuring Unit which is based on a Voxel-Expanded Gathering method to fundamentally reduce the workload of the data structuring step in the inference phase.

cs.AR↗

arXiv2024

Frustratingly Easy Label Projection for Cross-lingual Transfer

Translating training data into many languages has emerged as a practical solution for improving cross-lingual transfer. For tasks that involve span-level annotations, such as information extraction or question answering, an additional label projection step is required to map annotated spans onto the translated texts. Recently, a few efforts have utilized a simple mark-then-translate method to jointly perform translation and projection by inserting special markers around the labeled spans in the original sentence. However, as far as we are aware, no empirical analysis has been conducted on how this approach compares to traditional annotation projection based on word alignment. In this paper, we present an extensive empirical study across 57 languages and three tasks (QA, NER, and Event Extraction) to evaluate the effectiveness and limitations of both methods, filling an important gap in the literature. Experimental results show that our optimized version of mark-then-translate, which we call EasyProject, is easily applied to many languages and works surprisingly well, outperforming the more complex word alignment-based methods. We analyze several key factors that affect the end-task performance, and show EasyProject works well because it can accurately preserve label span boundaries after translation. We will publicly release all our code and data.

cs.CL↗

arXiv2024

MedReadMe: A Systematic Study for Fine-grained Sentence Readability in Medical Domain

Medical texts are notoriously challenging to read. Properly measuring their readability is the first step towards making them more accessible. In this paper, we present a systematic study on fine-grained readability measurements in the medical domain at both sentence-level and span-level. We introduce a new dataset MedReadMe, which consists of manually annotated readability ratings and fine-grained complex span annotation for 4,520 sentences, featuring two novel "Google-Easy" and "Google-Hard" categories. It supports our quantitative analysis, which covers 650 linguistic features and automatic complex word and jargon identification. Enabled by our high-quality annotation, we benchmark and improve several state-of-the-art sentence-level readability metrics for the medical domain specifically, which include unsupervised, supervised, and prompting-based methods using recently developed large language models (LLMs). Informed by our fine-grained complex span annotation, we find that adding a single feature, capturing the number of jargon spans, into existing readability formulas can significantly improve their correlation with human judgments. The data is available at tinyurl.com/medreadme-repo

cs.CL↗

arXiv2024

Nonadiabatic evolution and thermodynamics for a boundary-driven system with a weak intrasubsystem interaction

We derive a time-dependent master equation for an externally driven system whose subsystems weakly interact with each other and locally connect to the thermal reservoirs. The nonadiabatic equation obtained here can be viewed as a generalization of the local master equation, which has already been extensively used in describing the dynamics of a boundary-driven system. In addition, we investigate the fundamental reason underlying the thermodynamic inconsistency generated by the local and nonadiabatic master equations. We fnd that these two equations are consistent with the second law of thermodynamics when the system is far away from the steady state, while they give rise to the contradiction at the steady state. Finally, we numerically confrm our results by considering a toy model consisting of two qubits and two local heat baths.

quant-ph↗

arXiv2024

Benchmarking phonon anharmonicity in machine learning interatomic potentials

Machine learning approaches have recently emerged as powerful tools to probe structure-property relationships in crystals and molecules. Specifically, Machine learning interatomic potentials (MLIP) can accurately reproduce first-principles data at a cost similar to that of conventional interatomic potential approaches. While MLIP have been extensively tested across various classes of materials and molecules, a clear characterization of the anharmonic terms encoded in the MLIP is lacking. Here, we benchmark popular MLIP using the anharmonic vibrational Hamiltonian of ThO$_2$ in the fluorite crystal structure. This anharmonic Hamiltonian was constructed from density functional theory (DFT) using our highly accurate and efficient irreducible derivative methods, and then used to generate molecular dynamics trajectories. This data set was used to train three classes of MLIP: Gaussian Approximation Potentials, Artificial Neural Networks (ANN), and Graph Neural Networks (GNN). The results were assessed by directly comparing phonons and their interactions, as well as phonon linewidths, phonon lineshifts, and thermal conductivity. The models were also trained on a DFT molecular dynamics dataset, demonstrating good agreement up to fifth-order for the ANN and GNN. Our analysis demonstrates that MLIP have great potential for accurately characterizing anharmonicity in materials systems at a fraction of the cost of conventional first principles-based approaches.

cond-mat.mtrl-sci↗

arXiv2024

Phonon thermal transport in UO$_2$ via self-consistent perturbation theory

Computing thermal transport from first-principles in UO$_2$ is complicated due to the challenges associated with Mott physics. Here we use irreducible derivative approaches to compute the cubic and quartic phonon interactions in UO$_2$ from first-principles, and we perform enhanced thermal transport computations by evaluating the phonon Green's function via self-consistent diagrammatic perturbation theory. Our predicted phonon lifetimes at $T=600$ K agree well with our inelastic neutron scattering measurements across the entire Brillouin zone, and our thermal conductivity predictions agree well with previous measurements. Both the changes due to thermal expansion and self-consistent contributions are nontrivial at high temperatures, though the effects tend to cancel, and interband transitions yield a substantial contribution.

cond-mat.mtrl-sci↗

arXiv2024

First-principles determination of the phonon-point defect scattering and thermal transport due to fission products in ThO2

This work presents the first principles calculations of the lattice thermal conductivity degradation due to point defects in thorium dioxide using an alternative solution of the Pierels-Boltzmann transport equation. We have used the non-perturbative Green's function methodology to compute the phonon point defect scattering rates that consider the local distortion around the point defect, including the mass difference changes, interatomic force constants and structural relaxation near the point defects. The point defects considered in the work include the vacancy of thorium and oxygen, substitution of helium, krypton, zirconium, iodine, xenon, in the thorium site, and the three different configuration of the Schottky defects. The results of the phonon-defect scattering rate reveals that among the considered intrinsic defects, the thorium vacancy and helium substitution in the thorium site scatter the phonon most due to substantial changes in the force constant and structural distortions. The scattering of phonons due to the substitutional defects unveils that the zirconium atom scatters phonons the least, followed by xenon, iodine, krypton, and helium. This is contrary to the intuition that the scattering strength follows HeTh > KrTh > ZrTh > ITh > XeTh based on the mass difference. This striking difference in the zirconium phonon scattering is due to the local chemical environment changes. Zirconium is an electropositive element with valency similar to thorium and, therefore, can bond with the oxygen atoms, thus creating less force constant variance compared to iodine, an electronegative element, noble gas helium, xenon, and krypton. These results can serve as the benchmark for the analytical models and help the engineering-scale modeling effort for nuclear design.

cond-mat.mtrl-sci↗

arXiv2023

An effective self-supervised learning method for various seismic noise attenuation

Faced with the scarcity of clean label data in real scenarios, seismic denoising methods based on supervised learning (SL) often encounter performance limitations. Specifically, when a model trained on synthetic data is directly applied to field data, its performance would drastically decline due to significant differences in feature distributions between the two. To address this challenge, we develop an effective self-supervised strategy. This strategy, while relying on a single denoising network model, adeptly attenuates various types of seismic noise. The strategy comprises two main phases: 1. The warm-up phase. By using prior knowledge or extracting information from real data, we introduce additional noise to the original noisy data, constructing a noisier data with intensified noise. This data serves as the input, with the original noisy data acting as pseudo-labels. This facilitates rapid pre-training of the network to capture a certain noise characteristics and boosts network stability, setting the stage for the subsequent phase. 2. Iterative data refinement (IDR) phase. During this phase, we use the predictions of the original noisy data from the network trained in the previous epoch as the pseudo-labels. We continue to add noise to the predictions, creating a new noisier-noisy dataset for the current epoch of network training. Through this iterative process, we progressively reduce the discrepancy between the original noisy data and the desired clean data. Ultimately, the network's predictions on the original noisy data become our denoised results. Validations under scenarios with random noise, backscattered noise, and blending noise reveal that our method not only matches the traditional SL techniques on synthetic data but significantly outperforms them on field data.

physics.geo-ph↗

arXiv2023

Successfully Applying Lottery Ticket Hypothesis to Diffusion Model

Despite the success of diffusion models, the training and inference of diffusion models are notoriously expensive due to the long chain of the reverse process. In parallel, the Lottery Ticket Hypothesis (LTH) claims that there exists winning tickets (i.e., aproperly pruned sub-network together with original weight initialization) that can achieve performance competitive to the original dense neural network when trained in isolation. In this work, we for the first time apply LTH to diffusion models. We empirically find subnetworks at sparsity 90%-99% without compromising performance for denoising diffusion probabilistic models on benchmarks (CIFAR-10, CIFAR-100, MNIST). Moreover, existing LTH works identify the subnetworks with a unified sparsity along different layers. We observe that the similarity between two winning tickets of a model varies from block to block. Specifically, the upstream layers from two winning tickets for a model tend to be more similar than the downstream layers. Therefore, we propose to find the winning ticket with varying sparsity along different layers in the model. Experimental results demonstrate that our method can find sparser sub-models that require less memory for storage and reduce the necessary number of FLOPs. Codes are available at https://github.com/osier0524/Lottery-Ticket-to-DDPM.

cs.LG↗

arXiv2022

Relation between fluctuations and efficiency at maximum power for small heat engines

We study the ratio between the variances of work output and heat input, $η^{(2)}$, for a class of four-stroke heat engines which covers various typical cycles. Recent studies on the upper and lower bounds of $η^{(2)}$ are based on the quasistatic limit and the linear response regime, respectively. We extend these relations to the finite-time regime within the endoreversible approximation. We consider the ratio $η_{\text{MP}}^{(2)}$ at maximum power and find that the square of the Curzon-Ahlborn efficiency, $η_{\text{CA}}^2$, gives a good estimate of $η_{\text{MP}}^{(2)}$ for the class of heat engines considered, i.e., $η_{\text{MP}}^{(2)} \simeq η_{\text{CA}}^2$. This resembles the situation where the Curzon-Ahlborn efficiency gives a good estimate of the efficiency at maximum power for various kinds of finite-time heat engines. Taking an overdamped Brownian particle in a harmonic potential as an example, we can realize such endoreversible small heat engines and give an expression of the cumulants of work output and heat input. The approximate relation $η_{\text{MP}}^{(2)} \simeq η_{\text{CA}}^2$ is verified by numerical simulations. This relation also suggests a trade-off between the efficiency and the stability of finite-time heat engines at maximum power.

cond-mat.stat-mech↗

arXiv2022

Improving Large-scale Paraphrase Acquisition and Generation

This paper addresses the quality issues in existing Twitter-based paraphrase datasets, and discusses the necessity of using two separate definitions of paraphrase for identification and generation tasks. We present a new Multi-Topic Paraphrase in Twitter (MultiPIT) corpus that consists of a total of 130k sentence pairs with crowdsoursing (MultiPIT_crowd) and expert (MultiPIT_expert) annotations using two different paraphrase definitions for paraphrase identification, in addition to a multi-reference test set (MultiPIT_NMR) and a large automatically constructed training set (MultiPIT_Auto) for paraphrase generation. With improved data annotation quality and task-specific paraphrase definition, the best pre-trained language model fine-tuned on our dataset achieves the state-of-the-art performance of 84.2 F1 for automatic paraphrase identification. Furthermore, our empirical results also demonstrate that the paraphrase generation models trained on MultiPIT_Auto generate more diverse and high-quality paraphrases compared to their counterparts fine-tuned on other corpora such as Quora, MSCOCO, and ParaNMT.

cs.CL↗

arXiv2022

arXivEdits: Understanding the Human Revision Process in Scientific Writing

Scientific publications are the primary means to communicate research discoveries, where the writing quality is of crucial importance. However, prior work studying the human editing process in this domain mainly focused on the abstract or introduction sections, resulting in an incomplete picture. In this work, we provide a complete computational framework for studying text revision in scientific writing. We first introduce arXivEdits, a new annotated corpus of 751 full papers from arXiv with gold sentence alignment across their multiple versions of revision, as well as fine-grained span-level edits and their underlying intentions for 1,000 sentence pairs. It supports our data-driven analysis to unveil the common strategies practiced by researchers for revising their papers. To scale up the analysis, we also develop automatic methods to extract revision at document-, sentence-, and word-levels. A neural CRF sentence alignment model trained on our corpus achieves 93.8 F1, enabling the reliable matching of sentences between different versions. We formulate the edit extraction task as a span alignment problem, and our proposed method extracts more fine-grained and explainable edits, compared to the commonly used diff algorithm. An intention classifier trained on our dataset achieves 78.9 F1 on the fine-grained intent classification task. Our data and system are released at tiny.one/arxivedits.

cs.CL↗