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Exact Multi-objective Path Finding with Negative Weights 34

Exact Multi-objective Path Finding with Negative Weights

引用

34th International Conference on Automated Planning and Scheduling, ICAPS 2024

作者： Ahmadi, Saman Sturtevant, Nathan R. Harabor, Daniel Jalili, Mahdi School of Engineering RMIT University Australia Department of Computing Science University of Alberta Canada Department of Data Science and AI Monash University Australia

ISBN: (纸本)9781577358893

The point-to-point Multi-objective Shortest Path (MOSP) problem is a classic yet challenging task that involves finding all Pareto-optimal paths between two points in a graph with multiple edge costs. Recent studies have shown that employing A* search can lead to state-of-the-art performance in solving MOSP instances with non-negative costs. This paper proposes a novel A*-based multi-objective search framework that not only handles graphs with negative costs and even negative cycles but also incorporates multiple speed-up techniques to enhance the efficiency of exhaustive search with A*. Through extensive experiments, our algorithm demonstrates remarkable success in solving difficult MOSP instances, outperforming leading solutions by several factors. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Pareto principle

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Variational Information Bottleneck with Gaussian Processes for Time-Series Classification 23

Variational Information Bottleneck with Gaussian Processes f...

引用

23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Efrati, Itamar Fine, Shai School of Computer science Reichman University Herzliya Israel The Data Science Institute Reichman University Herzliya Israel

ISBN: (纸本)9798350374889

Time series classification problems are prevalent across various domains, often characterized by intra-series relationships within features, and inter-series relationships between the same features over time. Developing a generalized model capable of capturing these intricate properties poses a considerable challenge. In this paper, we introduce an innovative approach termed Gaussian Process Variational Information Bottleneck (GP-VIB). This model is designed as an end-to-end system, with a primary focus on acquiring a concise representation of the initial sequence. It aims to retain essential information vital for accurate classification. Through our experiments, we illustrate that the proposed GP-VIB model outperforms existing methods on renowned benchmark datasets. © 2024 IEEE.

关键词： Time series

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Nested Event Extraction upon Pivot Element Recognition 30

Nested Event Extraction upon Pivot Element Recognition

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ren, Weicheng Li, Zixuan Jin, Xiaolong Bai, Long Su, Miao Liu, Yantao Guan, Saiping Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9782493814104

Nested Event Extraction (NEE) aims to extract complex event structures where an event contains other events as its arguments recursively. Nested events involve a kind of Pivot Elements (PEs) that simultaneously act as arguments of outer-nest events and as triggers of inner-nest events, and thus connect them into nested structures. This special characteristic of PEs brings challenges to existing NEE methods, as they cannot well cope with the dual identities of PEs. Therefore, this paper proposes a new model, called PerNee, which extracts nested events mainly based on recognizing PEs. Specifically, PerNee first recognizes the triggers of both inner-nest and outer-nest events and further recognizes the PEs via classifying the relation type between trigger pairs. The model uses prompt learning to incorporate information from both event types and argument roles for better trigger and argument representations to improve NEE performance. Since existing NEE datasets (e.g., Genia11) are limited to specific domains and contain a narrow range of event types with nested structures, we systematically categorize nested events in the generic domain and construct a new NEE dataset, called ACE2005-Nest. Experimental results demonstrate that PerNee consistently achieves state-of-the-art performance on ACE2005-Nest, Genia11, and Genia13. The ACE2005-Nest dataset and the code of the PerNee model are available at https://***/waysonren/PerNee. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Character recognition

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Selective Temporal Knowledge Graph Reasoning 30

Selective Temporal Knowledge Graph Reasoning

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Hou, Zhongni Jin, Xiaolong Li, Zixuan Bai, Long Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9782493814104

Temporal Knowledge Graph (TKG), which characterizes temporally evolving facts in the form of (subject, relation, object, timestamp), has attracted much attention recently. TKG reasoning aims to predict future facts based on given historical ones. However, existing TKG reasoning models are unable to abstain from predictions they are uncertain, which will inevitably bring risks in real-world applications. Thus, in this paper, we propose an abstention mechanism for TKG reasoning, which helps the existing models make selective, instead of indiscriminate, predictions. Specifically, we develop a confidence estimator, called Confidence Estimator with History (CEHis), to enable the existing TKG reasoning models to first estimate their confidence in making predictions, and then abstain from those with low confidence. To do so, CEHis takes two kinds of information into consideration, namely, the certainty of the current prediction and the accuracy of historical predictions. Experiments with representative TKG reasoning models on two benchmark datasets demonstrate the effectiveness of the proposed CEHis. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Forecasting

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Few-shot Link Prediction on Hyper-relational Facts 30

Few-shot Link Prediction on Hyper-relational Facts

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Wei, Jiyao Guan, Saiping Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9782493814104

Hyper-relational facts, which consist of a primary triple (head entity, relation, tail entity) and auxiliary attribute-value pairs, are widely present in real-world Knowledge Graphs (KGs). Link Prediction on Hyper-relational Facts (LPHFs) is to predict a missing element in a hyper-relational fact, which helps populate and enrich KGs. However, existing LPHFs studies usually require an amount of high-quality data. They overlook few-shot relations, which have limited instances, yet are common in real-world scenarios. Thus, we introduce a new task, Few-Shot Link Prediction on Hyper-relational Facts (FSLPHFs). It aims to predict a missing entity in a hyper-relational fact with limited support instances. To tackle FSLPHFs, we propose MetaRH, a model that learns Meta Relational information in Hyper-relational facts. MetaRH comprises three modules: relation learning, support-specific adjustment, and query inference. By capturing meta relational information from limited support instances, MetaRH can accurately predict the missing entity in a query. As there is no existing dataset available for this new task, we construct three datasets to validate the effectiveness of MetaRH. Experimental results on these datasets demonstrate that MetaRH significantly outperforms existing representative models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Knowledge graph

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Low-Dimensional Latent State Space Identification with Application to the Shell Control Process

Low-Dimensional Latent State Space Identification with Appli...

引用

2023 IEEE Conference on Control Technology and Applications, CCTA 2023

作者： Yu, Jiaxin Qin, S. Joe City University of Hong Kong School of Data Science Hong Kong Lingnan University Institute of Data Science Hong Kong

ISBN: (纸本)9798350335446

Multivariable system identifications with exogenous inputs are widely encountered in real-time industrial process control and system identification experiments. This paper presents a low-dimensional latent state space identification algorithm that handles collinear variables and builds latent dynamic state space models with input-output data. The proposed method aims to project the original variables to a lower-dimensional space for optimal predictability while preserving the causal dynamics of process systems. The optimization of this algorithm is achieved through iterative procedures, where the dimension reduction and latent state space identification are performed in a unified manner. The effectiveness of the proposed algorithm is illustrated in the oil fractionator in a shell control process, compared to the traditional subspace identification method. © 2023 IEEE.

关键词： Iterative methods

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Design of Pseudorandom Sequence Generator Based on Cross-Coupled Chebyshev Lattice 5

Design of Pseudorandom Sequence Generator Based on Cross-Cou...

引用

5th Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2024

作者： Su, Wenyi Zhang, Lu Wang, Yao Zhu, Liping Li, Guodong Guilin University of Electronic Technology School of Mathematics and Computing Science Guilin541004 China Changji University School of Mathematics and Data Science Changji831100 China

ISBN: (纸本)9798350374407

With the widespread adoption of the internet, humanity has entered an irreversible era of information. Highquality pseudorandom numbers play a crucial role in ensuring secure communication, necessitating pseudorandom number generators with excellent performance. Therefore, research on pseudorandom number generators based on chaos theory is of vital importance. In this paper, a pseudorandom sequence generator based on the Cross-Coupled Chebyshev Lattice (CCML) model is proposed. CCML is a typical model in spatiotemporal chaos and exhibits better chaotic characteristics compared to low-dimensional chaotic systems. The generator first utilizes the classical Logistic map to generate the initial state values for each lattice cell. It then employs the Tent map to dynamically perturb the coupling coefficients between the lattice cells, enhancing the chaotic properties of the system. The local function in the model is a transformed Chebyshev map. The generator combines several one-dimensional chaotic mappings to demonstrate the excellent performance of a composite system. The pseudo-random sequences generated by the generator obtain high scores in the $0-1$ test, and the spectral entropy is significantly higher than that of other classical chaotic mappings, reaching a level close to that of white noise. It shows that the generator is capable of generating chaotic sequences with high complexity. © 2024 IEEE.

关键词： White noise

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Reducing a 3-SAT Instance to a Set of 2-SAT Instances Using the Idea of Set Covering Problem 5

Reducing a 3-SAT Instance to a Set of 2-SAT Instances Using ...

引用

5th International Conference on Computer and Applications, ICCA 2023

作者： Haque, Sardar Anisul Jaoua, Ali Al Ja'am, Jihad Mohamed Ur Rehman, Hafeez Oryx Universal College in partnership with LJMU School of Computing and Data Science Doha Qatar

ISBN: (纸本)9798350303254

This paper describes a novel algorithm that enumerates a set of Boolean variables from a 3-SAT instance such that for any truth assignments, it will be reduced to a 2-SAT instance or an empty formula or a formula with one or more empty clauses. We showed that the set of Boolean variables of interest can be found by formulating the given 3-SAT instance into an instance of set covering problem. © 2023 IEEE.

关键词： NP-Hard SAT set cover problem

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Disentangling Linear Quadratic Control with Untrusted ML Predictions 38

Disentangling Linear Quadratic Control with Untrusted ML Pre...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Li, Tongxin Liu, Hao Yue, Yisong School of Data Science The Chinese University of Hong Kong Shenzhen China Computing + Mathematical Sciences California Institute of Technology Pasadena United States

Uncertain perturbations in dynamical systems often arise from diverse resources, represented by latent components. The predictions for these components, typically generated by "black-box" machine learning tools, are prone to inaccuracies. To tackle this challenge, we introduce DISC, a novel policy that learns a confidence parameter online to harness the potential of accurate predictions while also mitigating the impact of erroneous forecasts. When predictions are precise, DISC leverages this information to achieve near-optimal performance. Conversely, in the case of significant prediction errors, it still has a worst-case competitive ratio guarantee. We provide competitive ratio bounds for DISC under both linear mixing of latent variables as well as a broader class of mixing functions. Our results highlight a first-of-its-kind "best-of-both-worlds" integration of machine-learned predictions, thus lead to a near-optimal consistency and robustness tradeoff, which provably improves what can be obtained without learning the confidence parameter. We validate the applicability of DISC across a spectrum of practical scenarios. © 2024 Neural information processing systems foundation. All rights reserved.

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Class-Incremental Few-Shot Event Detection 30

Class-Incremental Few-Shot Event Detection

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhao, Kailin Jin, Xiaolong Bai, Long Guo, Jiafeng Cheng, Xueqi Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China

ISBN: (纸本)9782493814104

Event detection is one of the fundamental tasks in information extraction and knowledge graph. However, a realistic event detection system often needs to deal with new event classes constantly. These new classes usually have only a few labeled instances as it is time-consuming and labor-intensive to annotate a large number of unlabeled instances. Therefore, this paper proposes a new task, called class-incremental few-shot event detection. Nevertheless, this task faces two problems, i.e., old knowledge forgetting and new class overfitting. To solve these problems, this paper further presents a novel knowledge distillation and prompt learning based method, called Prompt-KD. Specifically, to handle the forgetting problem about old knowledge, Prompt-KD develops an attention based multi-teacher knowledge distillation framework, where the ancestor teacher model pre-trained on base classes is reused in all learning sessions, and the father teacher model derives the current student model via adaptation. On the other hand, in order to cope with the few-shot learning scenario and alleviate the corresponding new class overfitting problem, Prompt-KD is also equipped with a prompt learning mechanism. Extensive experiments on two benchmark datasets, i.e., FewEvent and MAVEN, demonstrate the superior performance of Prompt-KD. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Distillation

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