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2024 Winter Simulation Conference, WSC 2024

作者： Winkels, Jan Özkul, Felix Sutherland, Robin Löhn, Jannik Wenzel, Sigrid Rehof, Jakob Department of Computer Science TU Dortmund University Germany University of Kassel Germany Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany

ISBN: (纸本)9798331534202

Despite relevant research endeavors, modeling efforts related to the building of discrete-event simulation models for planning changeable material flow systems still limit their practical application. This is because simulation experts have to model many possible structural variants and compare them based on key performance indicators such as throughput, workload or investment costs, while also ensuring sufficient system changeability. This article presents a methodology for reducing efforts for structural variation during the experimental phase of a simulation study. Starting from a valid initial simulation model, structural variants of this simulation model are automatically generated by applying component-based software synthesis which uses combinatorial logic;thereby, a range of simulation models is provided for the user. This paper presents the outlined methodology using a case study and places it in the research context of reducing efforts associated with the design and execution of simulation experiments. © 2024 IEEE.

关键词： Discrete event simulation

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The Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networks 38

The Map Equation Goes Neural: Mapping Network Flows with Gra...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Blöcker, Christopher Tan, Chester Scholtes, Ingo Data Analytics Group Department of Informatics University of Zurich Switzerland Machine Learning for Complex Networks Center for Artificial Intelligence and Data Science Julius-Maximilians-Universität Würzburg Germany

Community detection is an essential tool for unsupervised data exploration and revealing the organisational structure of networked systems. With a long history in network science, community detection typically relies on objective functions, optimised with custom-tailored search algorithms, but often without leveraging recent advances in deep learning. Recently, first works have started incorporating such objectives into loss functions for deep graph clustering and pooling. We consider the map equation, a popular information-theoretic objective function for unsupervised community detection, and express it in differentiable tensor form for optimisation through gradient descent. Our formulation turns the map equation compatible with any neural network architecture, enables end-to-end learning, incorporates node features, and chooses the optimal number of clusters automatically, all without requiring explicit regularisation. Applied to unsupervised graph clustering tasks, we achieve competitive performance against state-of-the-art deep graph clustering baselines in synthetic and real-world datasets. © 2024 Neural information processing systems foundation. All rights reserved.

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Organic Farming Automation to Revolutionize the Agricultural Industry Than Traditional Farming Practices Using IOT and Technological Development

Organic Farming Automation to Revolutionize the Agricultural...

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International Conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2023

作者： Geetha Rani, E. Chalasani, Ramadevi Anusha, D. Dhanalakshmi, M. Vadlamudi, Sandyarani Department of Computer Science Engineering Alliance University Karnataka Bengaluru India Department of Computer Science Engineering Sir C R Reddy College of Engineering Andhra Pradesh Eluru India Department of CSE-Artificial Intelligence and Machine Learning SRKIT Andhra Pradesh Vijayawada India Department of Computer Science Engineering NHCE Karnataka Bengaluru India Department of Aritficial Intelligence and Machine Learning New Horizon College of Engineeing Karnataka Bengaluru India

ISBN: (纸本)9789819746491

Agriculture has remained the spine of human sustenance on earth. Since times unknown, humans have learned and developed methods to produce food and other resources for their livelihood. Agriculture has seen large developments, especially in the last century. Scientific methods have accelerated the process of agriculture to a large extent. These developments have improved productivity by leaps and bounds. Though these developments come with a lot of benefits, they also include several demerits that adversely affect the soil, humans, and livestock. To retain and multiply the benefits contributed by this scientific development and to overcome the demerits and challenges faced in today’s agricultural practices, this paper aims at discussing methodologies that can be used to build an agricultural model that is much simpler and more efficient. The model aims at automation using IoT that could lead to minimized labor efforts by almost refraining human intervention in agricultural practices. machine learning algorithms can be used for weather and soil-based crop detection for better productivity. The downside caused due to the use of synthetic chemicals can be overcome using organic methods. A web portal to facilitate direct producer-to-consumer retail can be deployed to curb the exploitation of farmers by the elimination of middlemen. Hence, the paper aims at developing an agricultural model based on IoT, machine learning, organic farming, and a web portal for direct producer-to-consumer retail. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Enhanced Model for Mango Detection and Quality Classification Using Optimized Feature Extraction Techniques

Enhanced Model for Mango Detection and Quality Classificatio...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Aryan, Adla Mohammed, Abdul Aleem Chabra, Manish Rasheed, Syed Saarib Adnan, Mohammed Raoof, Mohammed Abdul Vardhaman College of Engineering Department of Artificial Intelligence & Machine Learning Telangana Hyderabad501218 India Muffakham Jah College of Engineering and Technology Department of Computer Science and Engineering Hyderabad500034 India Methodist College of Engineering and Technology Department of Artificial Intelligence & Data Science Telangana Hyderabad500001 India

ISBN: (纸本)9798331529833

This paper introduces an automated grading system for mangoes, enhancing efficiency and accuracy compared to human-based methods. The system uses the Lion Assisted Firefly Algorithm (LA-FF) to extract the best features from multiple highlights, enhancing grading efficiency and accuracy. The LA-FF algorithm is then used to fine-tune the convolutional layers of a deep CNN based on the specific requirements of mango grading. The system integrates the latest algorithms, automation, and adaptation to create an even more effective and precise grading system suitable for rural agricultural contexts. The LA-FF algorithm is used to extract the best features from multiple highlights, resulting in a more accurate and efficient grading process. © 2025 IEEE.

关键词： Fruits

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On the Origins of Linear Representations in Large Language Models 41

On the Origins of Linear Representations in Large Language M...

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41st International Conference on machine learning, ICML 2024

作者： Jiang, Yibo Rajendran, Goutham Ravikumar, Pradeep Aragam, Bryon Veitch, Victor Department of Computer Science University of Chicago United States Machine Learning Department Carnegie Mellon University United States Booth School of Business University of Chicago United States Department of Statistics University of Chicago United States Data Science Institute University of Chicago United States

Recent works have argued that high-level semantic concepts are encoded "linearly" in the representation space of large language models. In this work, we study the origins of such linear representations. To that end, we introduce a simple latent variable model to abstract and formalize the concept dynamics of the next token prediction. We use this formalism to show that the next token prediction objective (softmax with cross-entropy) and the implicit bias of gradient descent together promote the linear representation of concepts. Experiments show that linear representations emerge when learning from data matching the latent variable model, confirming that this simple structure already suffices to yield linear representations. We additionally confirm some predictions of the theory using the LLaMA-2 large language model, giving evidence that the simplified model yields generalizable insights. Copyright 2024 by the author(s)

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Optimal bounds for p sensitivity sampling via 2 augmentation 41

Optimal bounds for p sensitivity sampling via 2 augmentation

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41st International Conference on machine learning, ICML 2024

作者： Munteanu, Alexander Omlor, Simon Dortmund Data Science Center Faculties of Statistics and Computer Science TU Dortmund University Dortmund Germany Faculty of Statistics TU Dortmund University Dortmund Germany Lamarr-Institute for Machine Learning and Artificial Intelligence Dortmund Germany

data subsampling is one of the most natural methods to approximate a massively large data set by a small representative proxy. In particular, sensitivity sampling received a lot of attention, which samples points proportional to an individual importance measure called sensitivity. This framework reduces in very general settings the size of data to roughly the VC dimension d times the total sensitivity S while providing strong (1 ± Ε) guarantees on the quality of approximation. The recent work of Woodruff & Yasuda (2023c) improved substantially over the general Õ(Ε−2Sd) bound for the important problem of p subspace embeddings to Õ(Ε−2S2/p) for p ∈ [1, 2]. Their result was subsumed by an earlier Õ(Ε−2Sd1−p/2) bound which was implicitly given in the work of Chen & Derezinski (2021). We show that their result is tight when sampling according to plain p sensitivities. We observe that by augmenting the p sensitivities by 2 sensitivities, we obtain better bounds improving over the aforementioned results to optimal linear Õ(Ε−2(S+d)) = Õ(Ε−2d) sampling complexity for all p ∈ [1, 2]. In particular, this resolves an open question of Woodruff & Yasuda (2023c) in the affirmative for p ∈ [1, 2] and brings sensitivity subsampling into the regime that was previously only known to be possible using Lewis weights (Cohen & Peng, 2015). As an application of our main result, we also obtain an Õ(Ε−2µd) sensitivity sampling bound for logistic regression, where µ is a natural complexity measure for this problem. This improves over the previous Õ(Ε−2µ2d) bound of Mai et al. (2021) which was based on Lewis weights subsampling. Copyright 2024 by the author(s)

关键词： Importance sampling

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Turnstile p leverage score sampling with applications 41

Turnstile p leverage score sampling with applications

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41st International Conference on machine learning, ICML 2024

The turnstile data stream model offers the most flexible framework where data can be manipulated dynamically, i.e., rows, columns, and even single entries of an input matrix can be added, deleted, or updated multiple times in a data stream. We develop a novel algorithm for sampling rows ai of a matrix A ∈ Rn×d, proportional to their p norm, when A is presented in a turnstile data stream. Our algorithm not only returns the set of sampled row indexes, it also returns slightly perturbed rows ãi ≈ ai, and approximates their sampling probabilities up to Ε relative error. When combined with preconditioning techniques, our algorithm extends to p leverage score sampling over turnstile data streams. With these properties in place, it allows us to simulate subsampling constructions of coresets for important regression problems to operate over turnstile data streams with very little overhead compared to their respective off-line subsampling algorithms. For logistic regression, our framework yields the first algorithm that achieves a (1 + Ε) approximation and works in a turnstile data stream using polynomial sketch/subsample size, improving over O(1) approximations, or exp(1/Ε) sketch size of previous work. We compare experimentally to plain oblivious sketching and plain leverage score sampling algorithms for p and logistic regression. Copyright 2024 by the author(s)

关键词： data streams

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An Efficient Doubly-Robust Test for the Kernel Treatment Effect 37

An Efficient Doubly-Robust Test for the Kernel Treatment Eff...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Martinez-Taboada, Diego Ramdas, Aaditya Kennedy, Edward H. Department of Statistics and Data Science Carnegie Mellon University PittsburghPA15213 United States Department of Statistics and Data Science Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)9781713899921

The average treatment effect, which is the difference in expectation of the counterfactuals, is probably the most popular target effect in causal inference with binary treatments. However, treatments may have effects beyond the mean, for instance decreasing or increasing the variance. We propose a new kernel-based test for distributional effects of the treatment. It is, to the best of our knowledge, the first kernel-based, doubly-robust test with provably valid type-I error. Furthermore, our proposed algorithm is computationally efficient, avoiding the use of permutations. © 2023 Neural information processing systems foundation. All rights reserved.

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Optimizing AUV Positioning and data Transmission with Actor Critic (A3C) Algorithm in Underwater Wireless Sensor Networks 4

Optimizing AUV Positioning and Data Transmission with Actor ...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Kodukulla, Aruna Gayatri Amanullah, M. Kalaiselvan, S.A. Saveetha School of Engineering Simats Department of Computer Science & Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331529635

In recent years, UWSNs have emerged as a game-changer in many aquatic applications, including exploration, environmental monitoring, target tracking for underwater navigation, terrain-assisted navigation, and many more. The intricacy of the underwater environment presents several obstacles for the deployment of sensor nodes, and UWSNs are dynamic networks, unlike terrestrial wireless sensor networks. Instead of transferring data from every node to a destination node, autonomous underwater vehicles (AUVs) provide a more reliable option for data collecting in UWSNs. Nevertheless, a major challenge in data collection for UWSNs is the development of AUV positioning algorithms that are both delay-aware and energy-efficient. A trustworthy AUV-based data-collection routing strategy employed for UWSNs with the goal of decreasing network latency and increasing network lifespan. A positioning mechanism is used by the proposed protocol to gather data utilizing AUVs. With the goal of lowering power usage, the sink node coordinates AUV data collecting from the sensor nodes. To improve scalability, sensor nodes are first grouped into clusters, and then an AUV is assigned to each cluster. Second, in order to ensure the trustworthy gathering of data, the Actor 3 Critic (A3C) for every AUV is designed using the Markov decision process (MDP). In regards to energy consumption, delay, coverage percentage, and dependability, the simulation results validate the efficacy and efficiency of the proposed method. © 2024 IEEE.

关键词： Sensor nodes

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Convergence Analysis of Probability Flow ODE for Score-Based Generative Models

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IEEE Transactions on Information Theory 2025年第6期71卷 4581-4601页

作者： Huang, Daniel Zhengyu Huang, Jiaoyang Lin, Zhengjiang Peking University Beijing International Center for Mathematical Research Center for Machine Learning Research Beijing100871 China University of Pennsylvania Department of Statistics and Data Science PhiladelphiaPA19104 United States Massachusetts Institute of Technology Department of Mathematics CambridgeMA02139 United States

Score-based generative models have emerged as a powerful approach for sampling high-dimensional probability distributions. Despite their effectiveness, their theoretical underpinnings remain relatively underdeveloped. In this work, we study the convergence properties of deterministic samplers based on probability flow ODEs from both theoretical and numerical perspectives. Assuming access to L2 -accurate estimates of the score function, we prove the total variation between the target and the generated data distributions can be bounded above by Q(d3/4 δ1/2) in the continuous time level, where d denotes the data dimension and δ represents the L2 -score matching error. For practical implementations using a p-th order Runge-Kutta integrator with step size h, we establish error bounds of Q(d3/4 δ1/2 + d·(dhp) athe discrete level. Finally, we present numerical studies on problems up to 128 dimensions to verify our theory. © 1963-2012 IEEE.

关键词： Continuous time systems

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