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检索条件"任意字段=Conference on Space-Efficient Data Structures, Streams, and Algorithms"

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efficient Learning of Sparse and Decomposable PDEs using Random Projection 38

Efficient Learning of Sparse and Decomposable PDEs using Ran...

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38th conference on Uncertainty in Artificial Intelligence (UAI)

作者： Nasim, Md Zhang, Xinghang El-Azab, Anter Xue, Yexiang Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA Purdue Univ Sch Mat Engn W Lafayette IN 47907 USA

ISBN: (纸本)9781713863298

Learning physics models in the form of Partial Differential Equations (PDEs) is carried out through back-propagation to match the simulations of the physics model with experimental observations. Nevertheless, such matching involves computation over billions of elements, presenting a significant computational overhead. We notice many PDEs in real world problems are sparse and decomposable, where the temporal updates and the spatial features are sparsely concentrated on small interface regions. We propose RAPID-PDE, an algorithm to expedite the learning of sparse and decomposable PDEs. Our RAPID-PDE first uses random projection to compress the high dimensional sparse updates and features into low dimensional representations and then use these compressed signals during learning. Crucially, such a conversion is only carried out once prior to learning and the entire learning process is conducted in the compressed space. Theoretically, we derive a constant factor approximation between the projected loss function and the original one with poly-logarithmic number of projected dimensions. Empirically, we demonstrate RAPID- PDE with data compressed to 0.05% of its original size learns similar models, compared with uncompressed algorithms in learning a set of phase-field models, which govern the spatial-temporal dynamics of nano-scale structures in metallic materials.

关键词： Nanotechnology

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efficient Approximate Conformance Checking Using Trie data structures 3

Efficient Approximate Conformance Checking Using Trie Data S...

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3rd International conference on Process Mining (ICPM)

作者： Awad, Ahmed Raun, Kristo Weidlich, Matthias Univ Tartu Tartu Estonia Cairo Univ Giza Egypt Humboldt Univ Berlin Germany

ISBN: (纸本)9781665435147

Conformance checking compares a process model and recorded executions of a process, i.e., a log of traces. To this end, state-of-the-art approaches compute an alignment between a trace and an execution sequence of the model. Since the construction of alignments is computationally expensive, approximation schemes have been developed to strike a balance between the efficiency and the accuracy of conformance checking. Specifically, conformance checking may rely only on so-called proxy behavior, a subset of the behavior of the model. However, the question how such proxy behavior shall be represented for efficient alignment computation has been largely neglected. In this paper, we contribute a new formulation of the proxy behavior derived from a model for approximate conformance checking. By encoding the proxy behavior using a trie data structure, we obtain a logarithmically reduced search space for alignment computation compared to a set-based representation. We show how our algorithm supports the definition of a budget for alignment computation and also augment it with strategies for meta-heuristic optimization and pruning of the search space. Evaluation experiments with five real-world event logs show that our approach reduces the runtime of alignment construction by two orders of magnitude with a modest estimation error.

关键词： Estimation error Runtime Computational modeling data structures Approximation algorithms Encoding Computational efficiency

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The Role of IoT and Machine Learning in Automating space Docking: Challenges, Advancements, and Future Prospects 2

The Role of IoT and Machine Learning in Automating Space Doc...

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2nd International conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Jikar, Nayan Tale, Yash Tale, Abhay Barhate, Aditya Verma, Prateek Jikar, Aman Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

space docking is an important aspect of modern-day space exploration, enabling crewed missions, shipment transport, and spacecraft refuelling. Traditionally, manual docking has trusted astronaut precision and floor manipulation coordination, which has grown to be inefficient and blunders-susceptible in deep-space missions because of conversation delays. Integrating the Internet of Things (IoT) and Machine Learning (ML) is reworking area docking by introducing self-reliant, actual-time selection-making structures. IoT-enabled sensors continuously monitor spacecraft role, pace, and orientation, while ML algorithms optimize docking manoeuvres, beautify predictive upkeep, and make certain collision avoidance. Reinforcement learning similarly allows adaptive management strategies, making self-sufficient docking more efficient and dependable. This paper explores the improvements in IoT and ML for automating space docking, reading real-global programs along with spaceX Dragon, NASA's AR&D systems, and China's Tianzhou cargo deliver. By addressing key demanding situations-precision, pace, and safety-those technologies pave the way for sustainable space exploration, including future Mars missions and autonomous area stations. The observation highlights how AI-driven docking may be integral for deep-space operations, making sure safer and more green missions are made. © 2025 IEEE.

关键词： Reinforcement learning

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47th International conference on Very Large data Bases, VLDB 2021

47th International Conference on Very Large Data Bases, VLDB...

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47th International conference on Very Large data Bases, VLDB 2021

The proceedings contain 305 papers. The special focus in this conference is on Very Large data Bases. The topics include: Scaling attributed network embedding to massive graphs;Jointly optimizing preprocessing and inference for DNN-based visual analytics;Real-time distance-based outlier detection in data streams;Improving execution efficiency of just-in-time compilation based query processing on GPUs;Aggregated deletion propagation for counting conjunctive query answers;Multi-modal transportation recommendation with unified route representation learning;Toward a better understanding and evaluation of tree structures on flash SSDs;Answering multi-dimensional range queries under local differential privacy;Capturing and querying fine-grained provenance of preprocessing pipelines in data science;Towards an efficient weighted random walk domination;Maximizing social welfare in a competitive diffusion model;Quality of sentiment analysis tools;efficient streaming subgraph Isomorphism with graph neural networks;Hierarchical core maintenance on large dynamic graphs;Optimizing an in-memory database system for AI-powered on-line decision augmentation using persistent memory;Improving information extraction from visually rich documents using visual span representations;Optimization of threshold functions over streams;Building enclave-native storage engines for practical encrypted databases;Randomized error removal for online spread estimation in data streaming;Collective influence maximization for multiple competing products with an awareness-to-influence model;Shortest paths and centrality in uncertain networks;Adaptive data augmentation for supervised learning over missing data;Distributed numerical and machine learning computations via two-phase execution of aggregated join trees;Symmetric continuous subgraph matching with bidirectional dynamic programming;Fast augmentation algorithms for network kernel density visualization;Frequency estimation under local differential priv

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47th International conference on Very Large data Bases, VLDB 2021

47th International Conference on Very Large Data Bases, VLDB...

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47th International conference on Very Large data Bases, VLDB 2021

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efficient Maximal Biclique Enumeration for Large Sparse Bipartite Graphs 48th

Efficient Maximal Biclique Enumeration for Large Sparse Bipa...

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48th International conference on Very Large databases (VLDB)

作者： Chen, Lu Liu, Chengfei Zhou, Rui Xu, Jiajie Li, Jianxin Swinburne Univ Technol Melbourne Australia Soochow Univ Suzhou Peoples R China Deakin Univ Melbourne Australia

Maximal bicliques are effective to reveal meaningful information hidden in bipartite graphs. Maximal biclique enumeration (MBE) is challenging since the number of the maximal bicliques grows exponentially w.r.t. the number of vertices in a bipartite graph in the worst case. However, a large bipartite graph is usually very sparse, which is against the worst case and may lead to fast MBE algorithms. The uncharted opportunity is taking advantage of the sparsity to substantially improve the MBE efficiency for large sparse bipartite graphs. We observe that for a large sparse bipartite graph, a vertex upsilon may converge to a few vertices in the same vertex set as upsilon via its neighbours, which reveals that the enumeration scope for a vertex could be very small. Based on this observation, we propose novel concepts: unilateral coreness for individual vertices, unilateral order for each vertex set and unilateral convergence (sigma) for a large sparse bipartite graph. sigma could be a few thousand for a large sparse bipartite graph with hundreds of million edges. Using the unilateral order, every vertex with tau unilateral coreness only needs to check at most 2(tau) combinations so that all maximal bicliques can be enumerated and tau is bounded by sigma, which leads to a novel MBE algorithm running in O*(2(sigma)). We then propose a batch-pivots technique to eliminate all enumerations resulting in non-maximal bicliques, which guarantees that every maximal biclique is reported in O-(sigma e)-delay, where.. is the number of edges. We devise novel data structures that allow storing subgraphs at omissible space for further speeding up MBE. Extensive experiments are conducted on synthetic and real large datasets to justify that our proposed algorithm is faster and more scalable than the existing algorithms.

关键词： Large dataset

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47th International conference on Very Large data Bases, VLDB 2021

47th International Conference on Very Large Data Bases, VLDB...

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47th International conference on Very Large data Bases, VLDB 2021

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Morbius: Platform-Adaptive Hardware Accelerator for Scalable Sequence Motif Discovery

Morbius: Platform-Adaptive Hardware Accelerator for Scalable...

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IEEE International conference on e-Science and Grid Computing

作者： Se-Min Lim Esmerald Aliaj Sang-Woo Jun Department of Computer Science University of California Irvine Irvine CA USA

ISBN: (数字)9798350365610

ISBN: (纸本)9798350365627

Motif finding is one of the fundamental tools of computational biology. Unfortunately, the benefits of high-performance, low-power acceleration have not been available to this application due to the limited parallelism available within efficient classes of algorithms, such as probabilistic Gibbs sampling. In this work, we present Morbius, which demonstrates the benefits of reconfigurable application-specific hardware acceleration using FPGAs as a solution to the execution time and cost overhead of motif finding. Morbius employs a novel, hardware-optimized data structure called base pair matrix to minimize off-chip data movement, and implements a small number of deep hardware pipelines to achieve high sequential performance. Furthermore, we develop performance and chip space prediction models based on microarchitectural parameters of the accelerator, to facilitate optimal performance on a wide range of accelerators potential users may already have. We compare Morbius on a wide range of FPGA platforms spanning low-profile $250 M.2 FPGA cards to Amazon F1, and demonstrate up to orders of magnitude performance improvements compared to costly server machines, and even higher cost and power efficiency.

关键词： Costs Microarchitecture Pipelines Predictive models Parallel processing Probabilistic logic data structures

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HST+: An efficient index for embedding arbitrary metric spaces 37

HST+: An efficient index for embedding arbitrary metric spac...

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37th IEEE International conference on data Engineering, ICDE 2021

作者： Zeng, Yuxiang Tong, Yongxin Chen, Lei The Hong Kong University of Science and Technology Hong Kong Beihang University SKLSDE Lab BDBC and IRI China

ISBN: (纸本)9781728191843

Metric embeddings have been widely used in approximate algorithms to guarantee the effectiveness of geometric problems. Among the metric embedding techniques, Hierarchically Separated Tree (HST) is one of the most prevalent data structures, which maps the points of the original metric space into a tree-based metric space. A few selected applications of the HST include clustering, task assignment, trip planning, privacy preservation, information routing in wireless sensor networks, etc. Despite the popularity in ensuring the effectiveness, the HST-based solutions can be inefficient in large-scale datasets, since the state-of-the-art construction method has high time and space complexity (O(n3) and O(n2) in the worst-case). Moreover, existing studies overlook the insertion of new points in real applications (deletions can be trivially supported), which can cause the reconstruction of the whole HST. To address these limitations, we focus on designing an efficient index for embedding arbitrary metric spaces by tree metric spaces. Specifically, for construction, we design a dynamic programming-based method, which significantly reduces the time and space complexity to O(n2) and O(n) respectively. For insertion of new points, we propose a new data structure, called Hierarchically Separated Forest (HSF), i.e., a collection of HSTs. An HSF can efficiently support insertion of new points with a tight theoretical guarantee (O(log n)). Finally, extensive experiments demonstrate the superior performance of our proposed algorithms with respect to the effectiveness and the running time. For instance, compared with the state-of-the-art algorithms, our construction method is up to 29.8× faster and our insertion method is up to 491× faster. © 2021 IEEE.

关键词： data structures

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PTHash: Revisiting FCH Minimal Perfect Hashing 21

PTHash: Revisiting FCH Minimal Perfect Hashing

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44th International ACM SIGIR conference on Research and Development in Information Retrieval

作者： Pibiri, Giulio Ermanno Trani, Roberto ISTI CNR Pisa Italy

ISBN: (纸本)9781450380379

Given a set S of n distinct keys, a function f that bijectively maps the keys of S into the range {0, ..., n - 1} is called a minimal perfect hash function for S. algorithms that find such functions when n is large and retain constant evaluation time are of practical interest;for instance, search engines and databases typically use minimal perfect hash functions to quickly assign identifiers to static sets of variable-length keys such as strings. The challenge is to design an algorithm which is efficient in three different aspects: time to find f (construction time), time to evaluate f on a key of S (lookup time), and space of representation for f. Several algorithms have been proposed to trade-off between these aspects. In 1992, Fox, Chen, and Heath (FCH) presented an algorithm at SIGIR providing very fast lookup evaluation. However, the approach received little attention because of its large construction time and higher space consumption compared to other subsequent techniques. Almost thirty years later we revisit their framework and present an improved algorithm that scales well to large sets and reduces space consumption altogether, without compromising the lookup time. We conduct an extensive experimental assessment and show that the algorithm finds functions that are competitive in space with state-of-the art techniques and provide 2 - 4x better lookup time.

关键词： Minimal Perfect Hashing FCH XOR Compressed data structures

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