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arXiv 2024年

作者： Kang, Justin Singh Erginbas, Yigit Efe Butler, Landon Pedarsani, Ramtin Ramchandran, Kannan UC Berkeley United States UC Santa Barbara United States

One of the key challenges in machine learning is to find interpretable representations of learned functions. The Möbius transform is essential for this purpose, as its coefficients correspond to unique importance scores for sets of input variables. This transform is closely related to widely used game-theoretic notions of importance like the Shapley and Bhanzaf value, but it also captures crucial higher-order interactions. Although computing the Möbius Transform of a function with n inputs involves 2n coefficients, it becomes tractable when the function is sparse and of low-degree as we show is the case for many real-world functions. Under these conditions, the complexity of the transform computation is significantly reduced. When there are K non-zero coefficients, our algorithm recovers the Möbius transform in O(Kn) samples and O(Kn2) time asymptotically under certain assumptions, the first non-adaptive algorithm to do so. We also uncover a surprising connection between group testing and the Möbius transform. For functions where all interactions involve at most t inputs, we use group testing results to compute the Möbius transform with O(Ktlog n) sample complexity and O(K poly(n)) time. A robust version of this algorithm withstands noise and maintains this complexity. This marks the first n sub-linear query complexity, noise-tolerant algorithm for the Möbius transform. In several examples, we observe that representations generated via sparse Möbius transform are up to twice as faithful to the original function, as compared to Shaply and Banzhaf values, while using the same number of terms. © 2024, CC BY.

关键词： adaptive algorithms

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Troubleshooting Solution for Traffic Congestion Control

SSRN

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SSRN 2024年

作者： Tong, Van Souihi, Sami Tran, Hai Anh Mellouk, Abdelhamid School of Information and Communications Technology Hanoi University of Science and Technology Hanoi Viet Nam University of Paris-Est Creteil LISSI Vitry-sur-SeineF-94400 France

The Internet has existed since the 1970s as a means of data exchange between network devices in small networks. In the early stage, there was a small number of devices, but today there is an ever-increasing number of devices, leading to congestion in the network. Therefore, congestion control has attracted so much attention in the academic community and the industry for the past 30 years. Recently, Google has developed BBR (Bottleneck Bandwidth and Round-Trip Time), a rate-based congestion control algorithm. BBR controls transmission rates based on delivery rate and round-trip time (RTT). However, such a static congestion control algorithm (e.g., BBR, etc.) cannot achieve high performance in various network conditions (e.g., low bandwidth, etc.). Concretely, these static algorithms cannot adapt to the dynamic changes of the network environment. Therefore, in this paper, we propose an adaptive algorithm (called ABBR) for congestion control in next-generation networks. ABBR takes into account the reinforcement learning algorithm to learn relevant policies to change the transmission rate corresponding to each congestion control algorithm to optimize long-term performance. The experimental results show that our proposal can achieve good performance in terms of throughput, RTT, and fairness compared to the benchmarks. © 2024, The Authors. All rights reserved.

关键词： adaptive algorithms

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FPGA Implementation of 32-Bit Parallel LMS adaptive Beamforming Architecture using Two 8-Bit Stages

FPGA Implementation of 32-Bit Parallel LMS Adaptive Beamform...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Mohini R. Damodar Yogita Vaidya Electronics and Telecommunication VLSI & Embedded system COEP Technological university Pune India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This research project analyses a hardware pipelined reduced complexity parallel least mean square (RC-pLMS) implementation for adaptive beamforming. The composite of every single stage of defects in RC-pLMS, which is composed of two massively parallel, least mean square (LMS) phases, produces the overall error signal (pLMS). By removing the second independent weight set, the pLMS has been further reduced in size, ending in a less complex pLMS architecture (RC-pLMS). Our proposed RCpLMS algorithm (DRC-pLMS) will utilise a pipelined hardware architecture because to this approach alongside the delay and sum relaxation approach. Synthesise and implementation outcomes show that the recommended design delivers a maximum operational frequency and its take maximum resource utilisations. The objective of this study is to improve efficiency by switching the DRC-pLMS design with two 8-bit input levels. At the end, this work was translated into Verilog HDL, produced on a Xilinx Vertex-5 FPGA, and all the parameters related to area, delay, and power were compared.

关键词： Frequency synthesizers Array signal processing adaptive algorithms Mean square error methods Hardware Delays Hardware design languages Field programmable gate arrays Adders Convergence

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Repetitive positioning control for DISO systems by adaptive feed-forward cancellation

Repetitive positioning control for DISO systems by adaptive ...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Shota Yabui Takenori Atsumi Faculty of Science and Engineering Tokyo City University Tokyo Japan Faculty of Engineering Chiba Institute of Technology Chiba Japan

ISBN: (数字)9798350355369

ISBN: (纸本)9798350355376

Repetitive motion is an important operation in manufacturing processes and other applications. It is thus necessary to design control systems that can track repetitive-motion reference signals for dual-input-single-output (DISO) systems. This paper proposes a control scheme to realize precise repetitive motion in DISO systems. The proposed design method utilizes adaptive feed-forward cancellation (AFC) to compensate the position error signal for the reference signal. This approach contributes to improving DISO system control performance. In addition, the control system can eliminate the negative impact due to the waterbed effect. The proposed control system was implemented in an experimental system, and the experimental results indicate the effectiveness of the proposed method.

关键词： adaptive systems Sensitivity Mechatronics Manufacturing processes Tracking Design methodology adaptive algorithms

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adaptive Iterative Learning Optimal Control for Linear Multi-Agent Systems

Adaptive Iterative Learning Optimal Control for Linear Multi...

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Data Driven Control and Learning Systems (DDCLS)

作者： Duhui Chang Yan Geng School of Science Xi'an Polytechnic University Xi'an China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In order to improve tracking performance of a class of linear discrete time-invariant multi-agent systems, an adaptive optimal iterative learning control strategy is designed. To design the control protocol, a parameter adaptive algorithm is constructed to estimate the Markov parameters of the system. Then, virtual leader is used instead of expected trajectory. By optimizing the index function and incorporating estimated parameters into the learning process. An adaptive iterative learning optimal control strategy is constructed. Through rigorous analysis, the parameter estimation error is bounded, the tracking error is convergent, and the proof process is independent of system parameters. Finally, numerical simulations validate the effectiveness of this designed control strategy.

关键词： Protocols Simulation Optimal control adaptive algorithms Iterative algorithms Trajectory Topology

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adaptive Climbing Strategy of Snake Robot Based on Compliance Control

Adaptive Climbing Strategy of Snake Robot Based on Complianc...

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Intelligent Robotics and Systems (ISoIRS), International Symposium on

作者： Huizhuo Yang Jiyuan Dong Meng Wang Shiyong Meng Lei Yang Jiashuo Xue School of Electronic and Information Engineering Tiangong University Tianjin China Department of Computer Science and Technology Tiangong University Tianjin China Department of Software Engineering Tiangong University Tianjin China

ISBN: (数字)9798350364798

ISBN: (纸本)9798350364804

Snake robots, exhibiting a high degree of redundancy, are a class of biomimetic robots capable of forming a helical rolling gait for climbing trees and pipes. The surface structure of naturally grown trees is irregular, which may hinder the snake robots from achieving optimal surface conformation. Additionally, the presence of numerous branching structures in pipes introduces unpredictability in pipe climbing. To address these challenges, we propose an adaptive climbing strategy for snake robots based on compliant control. By adjusting a pre-set elasticity coefficient, the snake robot modulates the output torque of each joint, thereby altering the target angle of the joint. This adjustment enables the snake robot to adapt to unstructured environments. We conducted an experiment on climbing curved pipes to verify the effectiveness of this method. The result demonstrate that under unknown conditions, the robustness of the snake robot’s climbing motion is enhanced by modifying the pre-set elasticity coefficient based on compliant control. This finding underscores the potential of compliant control in improving the adaptability and performance of Snake robots in complex environments.

关键词： Torque Shape Snake robots Pipelines Redundancy adaptive algorithms Elasticity

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Constant-Factor Distortion Mechanisms for k-Committee Election

arXiv

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arXiv 2025年

作者： Pulyassary, Haripriya Swamy, Chaitanya School of ORIE Cornell University IthacaNY14853 United States Dept. of Combinatorics & Optimization Univ. Waterloo WaterlooONN2L 3G1 Canada

In the k-committee election problem, we wish to aggregate the preferences of n agents over a set of alternatives and select a committee of k alternatives that minimizes the cost incurred by the agents. While we typically assume that agent preferences are captured by a cardinal utility function, in many contexts we only have access to ordinal information, namely the agents’ rankings over the outcomes. As preference rankings are not as expressive as cardinal utilities, a loss of efficiency is inevitable, and is quantified by the notion of distortion. We study the problem of electing a k-committee that minimizes the sum of the -largest costs incurred by the agents, when agents and candidates are embedded in a metric space. This problem is called the -centrum problem and captures both the utilitarian and egalitarian objectives. When k ≥ 2, it is not possible to compute a bounded-distortion committee using purely ordinal information. We develop the first algorithms (that we call mechanisms) for the -centrum problem (when k ≥ 2), which achieve O(1)distortion while eliciting only a very limited amount of cardinal information via value queries. We obtain two types of query-complexity guarantees: O(log k log n) queries per agent, and O(k2 log2 n) queries in total (while achieving O(1)-distortion in both cases). En route, we give a simple adaptive-sampling algorithm for the -centrum k-clustering problem. Copyright © 2025, The Authors. All rights reserved.

关键词： adaptive algorithms

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Underwater Frequency Selection Algorithm Based on the Received Signal

Underwater Frequency Selection Algorithm Based on the Receiv...

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International Symposium on Underwater Technology

作者： Raji Alahmad Mohammad Albaroudi Abdullah Alraee Kazuo Ishii Department of Human Intelligence Systems Kyushu Institute of Technology Kitakyushu Fukuoka Japan Department of Life Science and System Engineering Kyushu Institute of Technology Kitakyushu Fukuoka Japan

ISBN: (数字)9798331534189

ISBN: (纸本)9798331534196

Underwater communication is widely regarded as one of the most significant challenges due to the unique physical properties of water. Among the available communication methods, radiofrequency communication offers higher data transmission rates than acoustic communication. However, higher frequencies suffer from substantial attenuation in water, making it difficult to achieve long-distance transmission. Addressing the tradeoff between transmission rate and communication distance is essential for achieving optimal performance. Unlike conventional systems that rely on fixed operating frequencies, this study introduces a dynamic frequency selection algorithm. This algorithm dynamically adjusts the operating frequency in real-time based on channel conditions and transmission rate feedback, ensuring a more adaptive and efficient communication system. To validate this approach, a dipole antenna with multiple impedance matching was designed and tested in a water tank environment. The experimental results revealed that bandwidth performance varied across different frequencies. Transmission rate analysis showed that higher frequencies provided higher data transmission over short distances. Conversely, lower frequencies exhibited better signal stability and reliability for long-distance communication due to their reduced attenuation. These findings highlight the importance of adaptive frequency selection in underwater radiofrequency communication, where a balance between transmission rate and communication distance must be achieved to support various operational requirements.

关键词： Radio frequency Water Underwater communication Dipole antennas Heuristic algorithms adaptive algorithms Bandwidth Attenuation Real-time systems Data communication

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HEX: Hierarchical Emergence Exploitation in Self-Supervised algorithms

HEX: Hierarchical Emergence Exploitation in Self-Supervised ...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Kiran Kokilepersaud Seulgi Kim Mohit Prabhushankar Ghassan AlRegib OLIVES at the Center for Signal and Information Processing CSIP School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

In this paper, we propose an algorithm that can be used on top of a wide variety of self-supervised (SSL) approaches to take advantage of hierarchical structures that emerge during training. SSL approaches typically work through some invariance term to ensure consistency between similar samples and a regularization term to prevent global dimensional collapse. Dimensional collapse refers to data representations spanning a lower-dimensional subspace. Recent work has demonstrated that the representation space of these algorithms gradually reflects a semantic hierarchical structure as training progresses. Data samples of the same hierarchical grouping tend to exhibit greater dimensional collapse locally compared to the dataset as a whole due to sharing features in common with each other. Ideally, SSL algorithms would take advantage of this hierarchical emergence to have an additional regularization term to account for this local dimensional collapse effect. However, the construction of existing SSL algorithms does not account for this property. To address this, we propose an adaptive algorithm that performs a weighted decomposition of the denominator of the InfoNCE loss into two terms: local hierarchical and global collapse regularization respectively. This decomposition is based on an adaptive threshold that gradually lowers to reflect the emerging hierarchical structure of the representation space throughout training. It is based on an analysis of the cosine similarity distribution of samples in a batch. We demonstrate that this hierarchical emergence exploitation (HEX) approach can be integrated across a wide variety of SSL algorithms. Empirically, we show performance improvements of up to 5.6% relative improvement over baseline SSL approaches on classification accuracy on Imagenet with 100 epochs of training.

关键词： Training Measurement Computer vision Accuracy Semantics Data visualization adaptive algorithms Classification algorithms

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Implementing a Pass-Through Mechanism to Mitigate Ransomware-Induced Encryption on NTFS

Research Square

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Research Square 2024年

作者： Lerivi, Vincenzo Vasquez, Eduardo Hoffmann, Lucas Caruso, Alessandro

Ransomware attacks have increasingly exploited the NT File System (NTFS) to encrypt critical data, leading to significant disruptions and financial losses across various sectors. The introduction of a pass-through mechanism offers a novel and effective approach to mitigating such threats through directly interfacing with NTFS to monitor and intercept unauthorized encryption attempts in real time. The proposed mechanism was evaluated in a controlled environment, demonstrating a high detection rate against various ransomware strains and minimal impact on system performance. Its capability to distinguish between legitimate and malicious encryption activities, combined with swift file recovery functionality, reinforced its potential as a robust solution for NTFS security. Despite some challenges related to false positives and system overhead, the mechanism’s integration within NTFS showcases a promising advancement in enhancing file system resilience against ransomware. Future work may focus on adaptive learning algorithms to further improve detection accuracy and extending support to other file systems, broadening the scope of its application in cybersecurity. © 2024, CC BY.

关键词： adaptive algorithms

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