In this paper, a novel data-driven approximation of the Koopman theory algorithm is proposed to simulate the coherent and spatial-temporal correlated sea clutter. The evolution of sea clutter's in-phase and quadra...
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ISBN:
(纸本)9781665460231
In this paper, a novel data-driven approximation of the Koopman theory algorithm is proposed to simulate the coherent and spatial-temporal correlated sea clutter. The evolution of sea clutter's in-phase and quadrature (I/Q) components are well simulated on the state space. Meanwhile, the amplitude distribution and the phase retrieval of sea clutter can be extracted and modeled. The experimental sea clutter data measured by the IPIX radar is used to demonstrate that this newly proposed data-driven approximation of the Koopman theory algorithm can simulate the sea clutter with the phase retrieval, the expected amplitude information, and the spatial-temporal relationship. Our work provides a useful and powerful simulation scheme for the sea clutter, especially when Doppler information is needed.
In this paper, we delve into the problem of using monetary incentives to encourage players to shift from an initial Nash equilibrium to a more favorable one within a game. Our main focus revolves around computing the ...
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ISBN:
(纸本)1577358872
In this paper, we delve into the problem of using monetary incentives to encourage players to shift from an initial Nash equilibrium to a more favorable one within a game. Our main focus revolves around computing the minimum reward required to facilitate this equilibrium transition. The game involves a single row player who possesses m strategies and k column players, each endowed with n strategies. Our findings reveal that determining whether the minimum reward is zero is NP-complete, and computing the minimum reward becomes APX-hard. Nonetheless, we bring some positive news, as this problem can be efficiently handled if either k or n is a fixed constant. Furthermore, we have devised an approximation algorithm with an additive error that runs in polynomial time. Lastly, we explore a specific case wherein the utility functions exhibit single-peaked characteristics, and we successfully demonstrate that the optimal reward can be computed in polynomial time.
Finding densest subgraphs is a fundamental problem in graph mining, with several applications in different fields. In this paper, we consider two variants of the problem of covering a graph with k densest subgraphs, w...
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In many real world networks, there already exists a (not necessarily optimal) k-partitioning of the network. Oftentimes, one aims to find a k-partitioning with a smaller cut value for such networks by moving only a fe...
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In many real world networks, there already exists a (not necessarily optimal) k-partitioning of the network. Oftentimes, one aims to find a k-partitioning with a smaller cut value for such networks by moving only a few nodes across partitions. The number of nodes that can be moved across partitions is often a constraint forced by budgetary limitations. Motivated by such realworld applications, we introduce and study the r-move k-partitioning problem, a natural variant of the Multiway cut problem. Given a graph, a set of k terminals and an initial partitioning of the graph, the r-move k-partitioning problem aims to find a k-partitioning with the minimum-weighted cut among all the k-partitionings that can be obtained by moving at most r non-terminal nodes to partitions different from their initial ones. Our main result is a polynomial time 3(r + 1) approximation algorithm for this problem. We further show that this problem isW[1]-hard, and give an FPTAS for when r is a small constant.
In this paper, we establish a novel connection between total variation (TV) distance estimation and probabilistic inference. In particular, we present an efficient, structure-preserving reduction from relative approxi...
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In this paper, we establish a novel connection between total variation (TV) distance estimation and probabilistic inference. In particular, we present an efficient, structure-preserving reduction from relative approximation of TV distance to probabilistic inference over directed graphical models. This reduction leads to a fully polynomial randomized approximation scheme (FPRAS) for estimating TV distances between same-structure distributions over any class of Bayes nets for which there is an efficient probabilistic inference algorithm. In particular, it leads to an FPRAS for estimating TV distances between distributions that are defined over a common Bayes net of small treewidth. Prior to this work, such approximation schemes only existed for estimating TV distances between product distributions. Our approach employs a new notion of partial couplings of highdimensional distributions, which might be of independent interest.
Counterfactual explanations shed light on the decisions of black-box models by explaining how an input can be altered to obtain a favourable decision from the model (e.g., when a loan application has been rejected). H...
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ISBN:
(纸本)1577358872
Counterfactual explanations shed light on the decisions of black-box models by explaining how an input can be altered to obtain a favourable decision from the model (e.g., when a loan application has been rejected). However, as noted recently, counterfactual explainers may lack robustness in the sense that a minor change in the input can cause a major change in the explanation. This can cause confusion on the user side and open the door for adversarial attacks. In this paper, we study some sources of non-robustness. While there are fundamental reasons for why an explainer that returns a single counterfactual cannot be robust in all instances, we show that some interesting robustness guarantees can be given by reporting multiple rather than a single counterfactual. Unfortunately, the number of counterfactuals that need to be reported for the theoretical guarantees to hold can be prohibitively large. We therefore propose an approximation algorithm that uses a diversity criterion to select a feasible number of most relevant explanations and study its robustness empirically. Our experiments indicate that our method improves the state-of-the-art in generating robust explanations, while maintaining other desirable properties and providing competitive computational performance.
We study recovery of amplitudes and nodes of a finite impulse train from noisy frequency samples. This problem is known as super-resolution under sparsity constraints and has numerous applications. An especially chall...
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We present an approximation algorithm for Weighted Tree Augmentation with approximation factor 1 + ln 2+ epsilon < 1.7. This is the first algorithm beating the longstanding factor of 2, which can be achieved throug...
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ISBN:
(纸本)9781665420556
We present an approximation algorithm for Weighted Tree Augmentation with approximation factor 1 + ln 2+ epsilon < 1.7. This is the first algorithm beating the longstanding factor of 2, which can be achieved through many standard techniques.
A novel semantic communication (SC)-assisted secrecy transmission framework is proposed. In particular, the legitimate transmitter (Tx) sends the superimposed semantic and bit stream to the legitimate receiver (Rx), w...
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ISBN:
(纸本)9781728190549
A novel semantic communication (SC)-assisted secrecy transmission framework is proposed. In particular, the legitimate transmitter (Tx) sends the superimposed semantic and bit stream to the legitimate receiver (Rx), where the information may be eavesdropped by the malicious node (EVE). As the EVE merely has the conventional bit-oriented communication structure, the semantic signal acts as the type of beneficial information-bearing artificial noise (AN), which not only keeps strictly confidential to the EVE but also interferes with the EVE. The ergodic (equivalent) secrecy rate over fading wiretap channels is maximized by jointly optimizing the transmit power, semantic-bit power splitting ratio, and the successive interference cancellation decoding order at the Tx, subject to both the instantaneous peak and long-term average power constraints. To address this non-convex problem, both the optimal and suboptimal algorithms are developed by employing the Lagrangian dual method and the successive convex approximation method, respectively. Numerical results show that the proposed SC-assisted secrecy transmission scheme can significantly enhance the physical layer security compared to the baselines using the conventional bit-oriented communication and no-information-bearing AN. It also shows that the proposed suboptimal algorithm can achieve a near-optimal performance.
In total domination, given a graph G = (V, E), we seek a minimum-size set of nodes S subset of V, such that every node in V \ S has at least one neighbor in S and every node in S also has at least one neighbor in S. W...
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ISBN:
(纸本)9783031203497;9783031203503
In total domination, given a graph G = (V, E), we seek a minimum-size set of nodes S subset of V, such that every node in V \ S has at least one neighbor in S and every node in S also has at least one neighbor in S. We define the fault-tolerant version of total domination, where we extend the requirement for nodes in V \ S. Any node in V \ S must have at least m neighbors in S. Let Delta denote the maximum degree in G. We prove a first 1+ ln(Delta + m - 1) approximation for fault-tolerant total domination. To prove our result, we develop a general submodular function approximation framework we believe is of independent interest.
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