المؤلفون: Wang, Hao, Chen, Zhichao, Shen, Yuan, Fan, Jiajun, Liu, Zhaoran, Yang, Degui, Liu, Xinggao, Li, Haoxuan

مصطلحات موضوعية: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning

الوصف: Heterogeneous treatment effect (HTE) estimation from observational data poses significant challenges due to treatment selection bias. Existing methods address this bias by minimizing distribution discrepancies between treatment groups in latent space, focusing on global alignment. However, the fruitful aspect of local proximity, where similar units exhibit similar outcomes, is often overlooked. In this study, we propose Proximity-aware Counterfactual Regression (PCR) to exploit proximity for representation balancing within the HTE estimation context. Specifically, we introduce a local proximity preservation regularizer based on optimal transport to depict the local proximity in discrepancy calculation. Furthermore, to overcome the curse of dimensionality that renders the estimation of discrepancy ineffective, exacerbated by limited data availability for HTE estimation, we develop an informative subspace projector, which trades off minimal distance precision for improved sample complexity. Extensive experiments demonstrate that PCR accurately matches units across different treatment groups, effectively mitigates treatment selection bias, and significantly outperforms competitors. Code is available at https://anonymous.4open.science/status/ncr-B697Test.
Comment: Code is available at https://anonymous.4open.science/status/ncr-B697Test

الوصول الحر: http://arxiv.org/abs/2407.01111Test

المؤلفون: Chen, Zhichao, Li, Haoxuan, Wang, Fangyikang, Zhang, Odin, Xu, Hu, Jiang, Xiaoyu, Song, Zhihuan, Wang, Eric H.

مصطلحات موضوعية: Computer Science - Machine Learning, Statistics - Machine Learning

الوصف: Diffusion models (DMs) have gained attention in Missing Data Imputation (MDI), but there remain two long-neglected issues to be addressed: (1). Inaccurate Imputation, which arises from inherently sample-diversification-pursuing generative process of DMs. (2). Difficult Training, which stems from intricate design required for the mask matrix in model training stage. To address these concerns within the realm of numerical tabular datasets, we introduce a novel principled approach termed Kernelized Negative Entropy-regularized Wasserstein gradient flow Imputation (KnewImp). Specifically, based on Wasserstein gradient flow (WGF) framework, we first prove that issue (1) stems from the cost functionals implicitly maximized in DM-based MDI are equivalent to the MDI's objective plus diversification-promoting non-negative terms. Based on this, we then design a novel cost functional with diversification-discouraging negative entropy and derive our KnewImp approach within WGF framework and reproducing kernel Hilbert space. After that, we prove that the imputation procedure of KnewImp can be derived from another cost functional related to the joint distribution, eliminating the need for the mask matrix and hence naturally addressing issue (2). Extensive experiments demonstrate that our proposed KnewImp approach significantly outperforms existing state-of-the-art methods.

الوصول الحر: http://arxiv.org/abs/2406.15762Test

المؤلفون: Wang, Hao, Pan, Licheng, Chen, Zhichao, Yang, Degui, Zhang, Sen, Yang, Yifei, Liu, Xinggao, Li, Haoxuan, Tao, Dacheng

مصطلحات موضوعية: Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Applications, Statistics - Machine Learning

الوصف: Time series modeling is uniquely challenged by the presence of autocorrelation in both historical and label sequences. Current research predominantly focuses on handling autocorrelation within the historical sequence but often neglects its presence in the label sequence. Specifically, emerging forecast models mainly conform to the direct forecast (DF) paradigm, generating multi-step forecasts under the assumption of conditional independence within the label sequence. This assumption disregards the inherent autocorrelation in the label sequence, thereby limiting the performance of DF-based models. In response to this gap, we introduce the Frequency-enhanced Direct Forecast (FreDF), which bypasses the complexity of label autocorrelation by learning to forecast in the frequency domain. Our experiments demonstrate that FreDF substantially outperforms existing state-of-the-art methods including iTransformer and is compatible with a variety of forecast models.

الوصول الحر: http://arxiv.org/abs/2402.02399Test

المؤلفون: Monka-Ewe, Carsten, Berkelmann, Lukas, Wolf, Richard, Oelerich, Jan Oliver, Schönfelder, Thorsten, Kühnel, Marco, Chen, Zhichao, Stanescu, Alin, Kushova, Oliver, Siekmann, Daniel, Jannsen, Bert

مصطلحات موضوعية: Electrical Engineering and Systems Science - Signal Processing

الوصف: The ever-increasing need for spectrum for mobile broadband systems has led to the recent allocation of spectral resources for International Mobile Telecommunication (IMT) services in the upper mid band (6.425 - 7.125 GHz) at the World Radio Conference (WRC-23) as well as to the creation of an agenda item on identifying future IMT bands in the frequency region 7.125 - 10.5 GHz at WRC-27. The severity of the impact of these frequency allocations on existing UWB systems, which have been using this part of the spectrum as a sub-secondary user for many years, is still subject to controversial discussions. This paper contributes a study on the impact of IMT on a real-world vehicular UWB keyless entry system to this discussion. It is shown that both the car's wireless on-board unit and a nearby basestation may drastically affect the system's performance.

الوصول الحر: http://arxiv.org/abs/2401.17889Test

المؤلفون: Chen, Zhichao, Li, Zixu

مصطلحات موضوعية: Mathematics - Representation Theory, Mathematics - Combinatorics, Mathematics - Rings and Algebras, 13F60, 11D09, 11D25

الوصف: We consider the mutation invariants of cluster algebras of rank 2. We characterize the mutation invariants of finite type. Two examples are provided for the affine type and we prove the non-existence of Laurent mutation invariants of non-affine type. As an application, a class of Diophantine equations encoded with cluster algebras are studied.
Comment: v2: 34 pages, some typos are corrected, some references are added. Accepted for publication in Journal of Algebra

الوصول الحر: http://arxiv.org/abs/2401.09704Test

المؤلفون: Wang, Hao, Chen, Zhichao, Fan, Jiajun, Li, Haoxuan, Liu, Tianqiao, Liu, Weiming, Dai, Quanyu, Wang, Yichao, Dong, Zhenhua, Tang, Ruiming

مصطلحات موضوعية: Computer Science - Machine Learning, Statistics - Applications, Statistics - Machine Learning

الوصف: Estimating conditional average treatment effect from observational data is highly challenging due to the existence of treatment selection bias. Prevalent methods mitigate this issue by aligning distributions of different treatment groups in the latent space. However, there are two critical problems that these methods fail to address: (1) mini-batch sampling effects (MSE), which causes misalignment in non-ideal mini-batches with outcome imbalance and outliers; (2) unobserved confounder effects (UCE), which results in inaccurate discrepancy calculation due to the neglect of unobserved confounders. To tackle these problems, we propose a principled approach named Entire Space CounterFactual Regression (ESCFR), which is a new take on optimal transport in the context of causality. Specifically, based on the framework of stochastic optimal transport, we propose a relaxed mass-preserving regularizer to address the MSE issue and design a proximal factual outcome regularizer to handle the UCE issue. Extensive experiments demonstrate that our proposed ESCFR can successfully tackle the treatment selection bias and achieve significantly better performance than state-of-the-art methods.
Comment: Accepted as NeurIPS 2023 Poster

الوصول الحر: http://arxiv.org/abs/2310.18286Test

المؤلفون: Chen, Zhichao, Ding, Leilei, Chu, Zhixuan, Qi, Yucheng, Huang, Jianmin, Wang, Hao

مصطلحات موضوعية: Computer Science - Machine Learning

الوصف: Time-Series Forecasting based on Cumulative Data (TSFCD) is a crucial problem in decision-making across various industrial scenarios. However, existing time-series forecasting methods often overlook two important characteristics of cumulative data, namely monotonicity and irregularity, which limit their practical applicability. To address this limitation, we propose a principled approach called Monotonic neural Ordinary Differential Equation (MODE) within the framework of neural ordinary differential equations. By leveraging MODE, we are able to effectively capture and represent the monotonicity and irregularity in practical cumulative data. Through extensive experiments conducted in a bonus allocation scenario, we demonstrate that MODE outperforms state-of-the-art methods, showcasing its ability to handle both monotonicity and irregularity in cumulative data and delivering superior forecasting performance.
Comment: Accepted as CIKM'23 Applied Research Track

الوصول الحر: http://arxiv.org/abs/2309.13452Test

المؤلفون: Liu, Qihui, Chen, Hao, Xie, Fei, Hu, Yuqiang, Zhang, Jin, Wang, Nan, Wang, Lihao, Liu, Yichen, Wang, Yang, Chen, Zhichao, Li, Lingyun, Cheng, Jiangong, Wu, Zhenyu

مصطلحات موضوعية: Physics - Instrumentation and Detectors

الوصف: Negatively charged nitrogen-vacancy (NV) centers in diamond have been extensively studied as a promising high sensitivity solid-state magnetic field sensor at room temperature. However, their use for current sensing applications is limited due to the challenge of integration and miniaturization of the diamond NV sensor. Here, we demonstrate an integrated NV sensor fabricated with standard microfabrication process. The sensor device incorporated with a toroidal magnetic yoke enables a high-precision wide range direct current sensing with galvanic isolation. The performance of the diamond NV current sensor in an open loop configuration has been investigated. A current measuring range of 0 A ~ 1000 A with an uncertainty of 46 ppm are achieved. Taking advantage of dual spin resonance modulation, temperature drift is suppressed to 10 ppm/K. This configuration opens new possibilities as a robust and scalable platform for current quantum sensing technologies.
Comment: 16pages, 6figures

الوصول الحر: http://arxiv.org/abs/2304.07998Test

المؤلفون: Pan, Licheng, Wang, Hao, Chen, Zhichao, Huang, Yuxing, Liu, Xinggao

مصطلحات موضوعية: Computer Science - Artificial Intelligence, Statistics - Applications

الوصف: Multi-variate soft sensor seeks accurate estimation of multiple quality variables using measurable process variables, which have emerged as a key factor in improving the quality of industrial manufacturing. The current progress stays in some direct applications of multitask network architectures; however, there are two fundamental issues remain yet to be investigated with these approaches: (1) negative transfer, where sharing representations despite the difference of discriminate representations for different objectives degrades performance; (2) seesaw phenomenon, where the optimizer focuses on one dominant yet simple objective at the expense of others. In this study, we reformulate the multi-variate soft sensor to a multi-objective problem, to address both issues and advance state-of-the-art performance. To handle the negative transfer issue, we first propose an Objective-aware Mixture-of-Experts (OMoE) module, utilizing objective-specific and objective-shared experts for parameter sharing while maintaining the distinction between objectives. To address the seesaw phenomenon, we then propose a Pareto Objective Routing (POR) module, adjusting the weights of learning objectives dynamically to achieve the Pareto optimum, with solid theoretical supports. We further present a Task-aware Mixture-of-Experts framework for achieving the Pareto optimum (TMoE-P) in multi-variate soft sensor, which consists of a stacked OMoE module and a POR module. We illustrate the efficacy of TMoE-P with an open soft sensor benchmark, where TMoE-P effectively alleviates the negative transfer and seesaw issues and outperforms the baseline models.
Comment: 13 pages,14 figures

الوصول الحر: http://arxiv.org/abs/2302.10477Test

المؤلفون: Chen, Zhichao, Ge, Zhiqiang

مصطلحات موضوعية: Computer Science - Artificial Intelligence, Computer Science - Machine Learning

الوصف: Bayesian network is a frequently-used method for fault detection and diagnosis in industrial processes. The basis of Bayesian network is structure learning which learns a directed acyclic graph (DAG) from data. However, the search space will scale super-exponentially with the increase of process variables, which makes the data-driven structure learning a challenging problem. To this end, the DAGs with NOTEARs methods are being well studied not only for their conversion of the discrete optimization into continuous optimization problem but also their compatibility with deep learning framework. Nevertheless, there still remain challenges for NOTEAR-based methods: 1) the infeasible solution results from the gradient descent-based optimization paradigm; 2) the truncation operation to promise the learned graph acyclic. In this work, the reason for challenge 1) is analyzed theoretically, and a novel method named DAGs with Tears method is proposed based on mix-integer programming to alleviate challenge 2). In addition, prior knowledge is able to incorporate into the new proposed method, making structure learning more practical and useful in industrial processes. Finally, a numerical example and an industrial example are adopted as case studies to demonstrate the superiority of the developed method.

الوصول الحر: http://arxiv.org/abs/2302.02160Test