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On the Schrödingerization method for linear non-unitary dynamics with optimal dependence on matrix queries

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arXiv 2025年

作者： Jin, Shi Liu, Nana Ma, Chuwen Yu, Yue School of Mathematical Sciences Institute of Natural Sciences MOE-LSC Shanghai Jiao Tong University Shanghai200240 China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai200240 China Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education National Center for Applied Mathematics in Hunan School of Mathematics and Computational Science Xiangtan University Hunan Xiangtan411105 China

The Schrödingerization method converts linear partial and ordinary differential equations with non-unitary dynamics into systems of Schrödinger-type equations with unitary evolution. It does so via the so-called warped phase transformation that maps the original equation into a Schrödinger-type equation in one higher dimension [1,2]. We show that by employing a smooth initialization of the warped phase transform [3], Schrödingerization can in fact achieve optimal scaling in matrix queries. This paper presents the detailed implementation of three smooth initializations for the Schrödingerization method: (a) the cut-off function, (b) the higher-order polynomial interpolation, and (c) the Fourier transform methods, that achieve optimality for (a) and near-optimality for (b) and (c). A detailed analysis of key parameters affecting time complexity is conducted. Copyright © 2025, The Authors. All rights reserved.

关键词： Ordinary differential equations

FastClass: A Time-Efficient Approach to Weakly-Supervised Text Classification

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FastClass: A Time-Efficient Approach to Weakly-Supervised Te...

2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Xia, Tingyu Wang, Yue Tian, Yuan Chang, Yi School of Artificial Intelligence Jilin University China School of Information and Library Science University of North Carolina Chapel Hill United States Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University China International Center of Future Science Jilin University China

Weakly-supervised text classification aims to train a classifier using only class descriptions and unlabeled data. Recent research shows that keyword-driven methods can achieve state-of-the-art performance on various tasks. However, these methods not only rely on carefully-crafted class descriptions to obtain class-specific keywords but also require substantial amount of unlabeled data and takes a long time to train. This paper proposes FastClass, an efficient weakly-supervised classification approach. It uses dense text representation to retrieve class-relevant documents from external unlabeled corpus and selects an optimal subset to train a classifier. Compared to keyword-driven methods, our approach is less reliant on initial class descriptions as it no longer needs to expand each class description into a set of class-specific keywords. Experiments on a wide range of classification tasks show that the proposed approach frequently outperforms keyword-driven models in terms of classification accuracy and often enjoys orders-of-magnitude faster training speed. © 2022 Association for computational Linguistics.

关键词： Classification (of information)

Testing Non-Commutativity of Reduce Functions with Multi-Column Inputs

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SSRN

SSRN 2024年

作者： Zhang, Xuan Zhu, Chenlu Li, Ning Zhang, Peng Liu, Lei College of Software Jilin University Jilin China College of Computer Science and Technology Jilin University Jilin China Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Jilin China

With the continuous development of the MapReduce programming model, it is necessary to ensure the reliability of MapReduce programs. In practice, the non-commutativity of Reduce functions seriously affects the reliability of the MapReduce program, which is difficult to debug and even causes errors. Current researches on the non-commutability detection of Reduce function consider the case that the input value is a single attribute. However, such researches ignore the situation where inputs to most reduce functions in practical applications consist of multiple columns (such as a table). To test the commutativity of reduce functions where each input record may contain several input attributes, a new testing method is proposed. This approach uses symbolic execution tools to help generate a few input records, and breaks their data dependencies to generate an original test case t0, with a dynamic program slicing technique to lessen the scale of t0. And the ultimate test suite is consisted of different permutations of records in t0. In the end, experiments demonstrate the effectiveness of our testing method and that the permutation method Gm is helpful to reduce its complexity. © 2024, The Authors. All rights reserved.

关键词： MapReduce

Learning generalizable agents via saliency-guided features decorrelation 23

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Learning generalizable agents via saliency-guided features d...

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Sili Huang Yanchao Sun Jifeng Hu Siyuan Guo Hechang Chen Yi Chang Lichao Sun Bo Yang Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education and School of Artificial Intelligence Jilin University China Department of Computer Science University of Maryland College Park School of Artificial Intelligence Jilin University China Lehigh University Bethlehem Pennsylvania Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education

In visual-based Reinforcement Learning (RL), agents often struggle to generalize well to environmental variations in the state space that were not observed during training. The variations can arise in both task-irrelevant features, such as background noise, and task-relevant features, such as robot configurations, that are related to the optimal decisions. To achieve generalization in both situations, agents are required to accurately understand the impact of changed features on the decisions, i.e., establishing the true associations between changed features and decisions in the policy model. However, due to the inherent correlations among features in the state space, the associations between features and decisions become entangled, making it difficult for the policy to distinguish them. To this end, we propose Saliency-Guided Features Decorrelation (SGFD) to eliminate these correlations through sample reweighting. Concretely, SGFD consists of two core techniques: Random Fourier Functions (RFF) and the saliency map. RFF is utilized to estimate the complex non-linear correlations in high-dimensional images, while the saliency map is designed to identify the changed features. Under the guidance of the saliency map, SGFD employs sample reweighting to minimize the estimated correlations related to changed features, thereby achieving decorrelation in visual RL tasks. Our experimental results demonstrate that SGFD can generalize well on a wide range of test environments and significantly outperforms state-of-the-art methods in handling both task-irrelevant variations and task-relevant variations.

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Schrödingerization based quantum algorithms for the fractional Poisson equation

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arXiv 2025年

作者： Jin, Shi Liu, Nana Yu, Yue School of Mathematical Sciences Institute of Natural Sciences MOE-LSC Shanghai Jiao Tong University Shanghai200240 China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai200240 China School of Mathematics and Computational Science Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education National Center for Applied Mathematics in Hunan Xiangtan University Hunan Xiangtan411105 China

We develop a quantum algorithm for solving high-dimensional fractional Poisson equations. By applying the Caffarelli-Silvestre extension, the d-dimensional fractional equation is reformulated as a local partial differential equation in d + 1 dimensions. We propose a quantum algorithm for the finite element discretization of this local problem, by capturing the steady-state of the corresponding differential equations using the Schrödingerization approach from [29, 39, 40]. The Schrödingerization technique transforms general linear partial and ordinary differential equations into Schrödinger-type systems, making them suitable for quantum simulation. This is achieved through the warped phase transformation, which maps the equation into a higher-dimensional space. We provide detailed implementations of the method and conduct a comprehensive complexity analysis, which can show up to exponential advantage – with respect to the inverse of the mesh size in high dimensions – compared to its classical counterpart. Specifically, while the classical method requires Oe(d1/233d/2h−d−2) operations, the quantum counterpart requires Oe(d33d/2h−2.5) queries to the block-encoding input models, with the quantum complexity being independent of the dimension d in terms of the inverse mesh size h−1. Numerical experiments are conducted to verify the validity of our formulation. © 2025, CC BY-NC-ND.

关键词： Poisson equation

Novel Amorphous Feooh-Modified Co9s8 Nanosheets with Enhanced Oxygen Evolution Reaction Activity

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SSRN

SSRN 2024年

作者： Wang, Chong Tu, Huanlu Hao, Zeyu Li, Yaxin Xu, Jian Hu, Xiaoying Yu, Shansheng Tian, Hongwei Key Laboratory of Automobile Materials of MOE School of Materials Science and Engineering Jilin Provincial International Cooperation Key Laboratory of High-Efficiency Clean Energy Materials Jilin University Jilin Changchun130012 China College of Science Laboratory of Materials Design and Quantum Simulation Changchun University Jilin Changchun130022 China

Efficient oxygen evolution reaction (OER) is important for water electrolysis and advanced hydrogen production energy. However, the sluggish kinetics of this reaction require significant overpotentials, leading to high energy consumption. Therefore, developing OER electrocatalysts with exceptional performance and long-term durability is crucial for enhancing the energy efficiency and cost-effectiveness of the hydrogen production process. In this paper, novel FeOOH/Co9S8 catalysts were prepared through a two-step hydrothermal reaction followed by one-step electrodeposition on nickel foam substrates for an alkaline oxygen evolution reaction (OER). The as-obtained catalysts possessed abundant non-homogeneous interfaces between FeOOH and Co9S8 nanosheets, conducive to optimized coordination environments of Fe and Co sites through the redistribution of interfacial charges. This strengthened the chemisorption of oxygenated intermediates, leading to accelerated reaction kinetics, abundant active sites, and enhanced OER properties. The optimized electrocatalyst FeOOH/Co9S8 achieved a current density of 10 mA cm-2 at an overpotential of 248 mV coupled with good stability for over 140 h. Overall, a novel approach for producing effective and durable alkaline dielectric OER electrocatalysts was proposed, useful for future manufacturing of advanced energy devices. © 2024, The Authors. All rights reserved.

关键词： Energy utilization

A novel dual-stage algorithm for capacitated arc routing problems with time-dependent service costs

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arXiv 2024年

作者： Li, Qingya Liu, Shengcai Zou, Juan Tang, Ke Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Xiangtan411105 China The Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan University Xiangtan411105 China

This paper focuses on solving the capacitated arc routing problem with time-dependent service costs (CARPTDSC), which is motivated by winter gritting applications. In the current literature, exact algorithms designed for CARPTDSC can only handle small-scale instances, while heuristic algorithms fail to obtain high-quality solutions. To overcome these limitations, we propose a novel dual-stage algorithm, called MAENS-GN, that consists of a routing stage and a vehicle departure time optimization stage. The former obtains the routing plan, while the the latter determines the vehicle departure time. Importantly, existing literature often ignores the characteristic information contained in the relationship between the route cost and the vehicle departure time. The most significant innovation in this paper lies in the exploitation of this characteristic information during the vehicle departure time optimization stage. Specifically, we conduct a detailed analysis of this relationship under various scenarios and employ tailored methods to obtain the (approximately) optimal vehicle departure time. Furthermore, we propose an improved initialization strategy that considers time-dependent characteristics to achieve better solution quality. In addition to the modified benchmark test sets, we also experiment on a real-world test set. Experimental results demonstrate that MAENS-GN can obtain high-quality solutions on both small-scale and larger-scale instances of CARPTDSC. Copyright © 2024, The Authors. All rights reserved.

关键词： Combinatorial optimization

Electrically Switchable Longitudinal Nonlinear Conductivity in Magnetic Semiconductors

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Physical Review Letters 2025年第4期134卷 046801-046801页

作者： Hong Jian Zhao Yuhao Fu Yurong Yang Yanchao Wang Laurent Bellaiche Yanming Ma Key Laboratory of Material Simulation Methods and Software of Ministry of Education College of Physics Jilin University Changchun 130012 China Key Laboratory of Physics and Technology for Advanced Batteries (Ministry of Education) College of Physics Jilin University Changchun 130012 China International Center of Future Science Jilin University Changchun 130012 China National Laboratory of Solid State Microstructures and Jiangsu Key Laboratory of Artificial Functional Materials Department of Materials Science and Engineering Nanjing University Nanjing 210093 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun 130012 China Smart Functional Materials Center Physics Department and Institute for Nanoscience and Engineering University of Arkansas Fayetteville Arkansas 72701 USA Department of Materials Science and Engineering Tel Aviv University Ramat Aviv Tel Aviv 6997801 Israel

Writing data by electric field (as opposed to electric current) offers promises for energy efficient memory devices. While this data writing scheme is enabled by the magnetoelectric effect, the narrow spectrum of room-temperature magnetoelectrics hinders the design of practical magnetoelectric memories, and the exploration of other mechanisms toward low-power memories is greatly demanding. Here, we propose a mechanism that allows the electric-field writing of data beyond the framework of magnetoelectric effect. By symmetry analysis, we show that electric field can induce longitudinal nonlinear conductivity (LNC) in a wide spectrum of magnetic materials, including ferromagnets, antiferromagnets, magnetoelectrics, and nonmagnetoelectrics. The LNC is electrically switchable by reversing the electric field, where the switched LNC is detectable by transport measurements. Our first-principles simulations combined with transport calculations further predict YFeO3 and CuFeS2 (room-temperature antiferromagnets) to showcase electrically switchable LNC. Our Letter helps enrich the research avenues in nonlinear charge transport, and offers a pathway for designing energy efficient devices based on LNC.

关键词： Magnetoelectric effects

Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive Learning

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arXiv 2025年

作者： Liu, Yonghao Li, Mengyu Pang, Wei Giunchiglia, Fausto Huang, Lan Feng, Xiaoyue Guan, Renchu Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Jilin University China Mathematical and Computer Sciences Heriot-Watt University United Kingdom University of Trento Italy

Short text classification, as a research subtopic in natural language processing, is more challenging due to its semantic sparsity and insufficient labeled samples in practical scenarios. We propose a novel model named MI-DELIGHT for short text classification in this work. Specifically, it first performs multi-source information (i.e., statistical information, linguistic information, and factual information) exploration to alleviate the sparsity issues. Then, the graph learning approach is adopted to learn the representation of short texts, which are presented in graph forms. Moreover, we introduce a dual-level (i.e., instance-level and cluster-level) contrastive learning auxiliary task to effectively capture different-grained contrastive information within massive unlabeled data. Meanwhile, previous models merely perform the main task and auxiliary tasks in parallel, without considering the relationship among tasks. Therefore, we introduce a hierarchical architecture to explicitly model the correlations between tasks. We conduct extensive experiments across various benchmark datasets, demonstrating that MI-DELIGHT significantly surpasses previous competitive models. It even outperforms popular large language models on several datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning