—This study introduces a novel approach to autonomous motion planning, informing an analytical algorithm with a reinforcement learning (RL) agent within a Frenet coordinate system. The combination directly addresses ...
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This paper presents a unique solution to challenges in medical image processing by incorporating an adaptive curve grey wolf optimization (ACGWO) algorithm into neural network backpropagation. Neural networks show pot...
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The concept of negative prompts, emerging from conditional generation models like Stable Diffusion, allows users to specify what to exclude from the generated images. Despite the widespread use of negative prompts, th...
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Multi-dimensional time series data are generated daily in lots of domains. Compressing these data to reduce storage overhead is an important topic. Existing compression algorithms resort to converting multi-dimensiona...
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ISBN:
(数字)9798331541750
ISBN:
(纸本)9798331541767
Multi-dimensional time series data are generated daily in lots of domains. Compressing these data to reduce storage overhead is an important topic. Existing compression algorithms resort to converting multi-dimensional time series data into multiple separate one-dimensional data to compress. However, these algorithms fail to exploit both intra- and inter-correlation among dimensions and the compression is not efficient. To solve the problem, this paper introduces a novel multi-dimensional time series compression framework called ACD, a lossless compression based on dimensionality reduction. Initially, the n-dimensional time series data undergo dimensionality reduction via space-filling curves(SFCs) encoding. Subsequently, we partition the encoded data heuristically into several segments based on distinctive patterns in different time windows. Finally, suitable compression is conducted according to segment patterns. We implement our algorithm in an out-of-box commercial time series database engine VictoriaMetrics. Our experiments demonstrate that ACD achieves at least 40% improvement over existing methods such as Chimp, Gorilla, and DeltaZstd as evaluated through the Time Series Benchmark Suite(TSBS).
In the Graph Reconstruction (GR) problem, a player initially only knows the vertex set V of an input graph G = (V, E) and is required to learn its set of edges E. To this end, the player submits queries to an oracle a...
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In the Graph Reconstruction (GR) problem, a player initially only knows the vertex set V of an input graph G = (V, E) and is required to learn its set of edges E. To this end, the player submits queries to an oracle and must deduce E from the oracle’s answers. Angluin and Chen [Journal of Computer and System Sciences, 2008] resolved the number of Independent Set (IS) queries necessary and sufficient for GR on m-edge graphs. In this setting, each query consists of a subset of vertices U ⊆ V, and the oracle responds with a boolean, indicating whether U is an independent set in G. They gave algorithms that use O(m · log n) IS queries, which is best possible. In this paper, we initiate the study of GR via Maximal Independent Set (MIS) queries, a more powerful variant of IS queries. Given a query U ⊆ V, the oracle responds with any, potentially adversarially chosen, maximal independent set I ⊆ U in the induced subgraph G[U ]. We show that, for GR, MIS queries are strictly more powerful than IS queries when parametrized by the maximum degree ∆ of the input graph. We give tight (up to poly-logarithmic factors) upper and lower bounds for this problem: 1. We observe that the simple strategy of taking uniform independent random samples of V and submitting those to the oracle yields a non-adaptive randomized algorithm that executes (formula presented) queries and succeeds with high probability. This should be contrasted with the fact that (formula presented) IS queries are required for such graphs, which shows that MIS queries are strictly more powerful than IS queries. Interestingly, combining the strategy of taking uniform random samples of V with the probabilistic method, we show the existence of a deterministic non-adaptive algorithm that executes (formula presented) queries. 2. Regarding lower bounds, we prove that the additional ∆ factor when going from randomized non-adaptive algorithms to deterministic non-adaptive algorithms is necessary. We show that every non-adapti
This paper develops and discusses a residual-based a posteriori error estimate and a space–time adaptive algorithm for solving parabolic surface partial differential equations on closed stationary surfaces. The full ...
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Safety is the cornerstone of L2+ autonomous driving and one of the fundamental tasks is forward collision warning that detects potential rear-end collisions. Potential collisions are also known as conflicts, which hav...
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ISBN:
(数字)9798350348811
ISBN:
(纸本)9798350348828
Safety is the cornerstone of L2+ autonomous driving and one of the fundamental tasks is forward collision warning that detects potential rear-end collisions. Potential collisions are also known as conflicts, which have long been indicated using Time-to-Collision with a critical threshold to distinguish safe and unsafe situations. Such indication, however, focuses on a single scenario and cannot cope with dynamic traffic environments. For example, TTC-based crash warning frequently misses potential collisions in congested traffic, and issues false alarms during lane-changing or parking. Aiming to minimise missed and false alarms in conflict detection, this study proposes a more reliable approach based on vehicle spacing patterns. To test this approach, we use both synthetic and real-world conflict data. Our experiments show that the proposed approach outperforms single-threshold TTC unless conflicts happened in the exact way that TTC is defined, which is rarely true. When conflicts are heterogeneous and when the information of conflict situation is incompletely known, as is the case with real-world conflicts, our approach can achieve less missed and false detection. This study offers a new perspective for conflict detection, and also a general framework allowing for further elaboration to minimise missed and false alarms. Less missed alarms will contribute to fewer accidents, meanwhile, fewer false alarms will promote people’s trust in collision avoidance systems. We thus expect this study to contribute to safer and more trustworthy autonomous driving.
This study focuses on the application of GIS technology in the design of urban renewal planning system, aiming to improve the scientific and practical effectiveness of planning through innovative spatial analysis meth...
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ISBN:
(数字)9798350368208
ISBN:
(纸本)9798350368215
This study focuses on the application of GIS technology in the design of urban renewal planning system, aiming to improve the scientific and practical effectiveness of planning through innovative spatial analysis methods and intelligent algorithms. Firstly, a comprehensive urban renewal planning system is constructed, which integrates GIS platform and uses wavelet transform spatial analysis technology to analyze urban spatial data at multiple scales, to reveal the inherent laws and potential problems of urban development. The introduction of wavelet transform enables the system to capture the subtle changes of urban spatial structure and provide more accurate decision basis for planners. Secondly, this study developed an adaptive algorithm, which can dynamically adjust urban renewal strategies according to real-time data and historical trends, optimize resource allocation, and improve the flexibility and adaptability of planning. The application of adaptive algorithms makes urban renewal planning no longer an immutable blueprint, but an organism that can self-evolve with the pulsation of urban development. Finally, through system simulation, this study verifies the effectiveness of GIS technology combined with wavelet transform spatial analysis and adaptive algorithm in urban renewal planning. The simulation results show that the system can significantly improve the accuracy and efficiency of planning, and provide strong technical support for the implementation of urban renewal projects.
In this study, we delve into the Thresholding Linear Bandit (TLB) problem, a nuanced domain within stochastic Multi-Armed Bandit (MAB) problems, focusing on maximizing decision accuracy against a linearly defined thre...
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In the Generalized Mastermind problem, there is an unknown subset H of the hypercube {0, 1}d containing n points. The goal is to learn H by making a few queries to an oracle, which, given a point q in {0, 1}d, returns...
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