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Journal articles on the topic 'Conditional Search Space'

Author: Grafiati

Published: 7 July 2024

Last updated: 31 July 2025

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1

Bryce, D., S. Kambhampati, and D. E. Smith. "Planning Graph Heuristics for Belief Space Search." Journal of Artificial Intelligence Research 26 (May 30, 2006): 35–99. http://dx.doi.org/10.1613/jair.1869.

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Some recent works in conditional planning have proposed reachability heuristics to improve planner scalability, but many lack a formal description of the properties of their distance estimates. To place previous work in context and extend work on heuristics for conditional planning, we provide a formal basis for distance estimates between belief states. We give a definition for the distance between belief states that relies on aggregating underlying state distance measures. We give several techniques to aggregate state distances and their associated properties. Many existing heuristics exhibit

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Li, Jiaxing, Wei Liu, Chao Xue, et al. "Modeling All Response Surfaces in One for Conditional Search Spaces." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 18323–32. https://doi.org/10.1609/aaai.v39i17.34016.

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Bayesian Optimization (BO) is a sample-efficient black-box optimizer commonly used in search spaces where hyperparameters are independent. However, in many practical AutoML scenarios, there will be dependencies among hyperparameters, forming a conditional search space, which can be partitioned into structurally distinct subspaces. The structure and dimensionality of hyperparameter configurations vary across these subspaces, challenging the application of BO. Some previous BO works have proposed solutions to develop multiple Gaussian Process models in these subspaces. However, these approaches

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BOKHONSKY, A. I. "SEARCH EXTREME FUNCTIONAL." Fundamental and Applied Problems of Engineering and Technology 4, no. 1 (2020): 144–49. http://dx.doi.org/10.33979/2073-7408-2020-342-4-1-144-149.

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Examples illustrate the developed procedure for checking the sufficient condition for the extremum of a functional (construction of its mathematical image); in the problem of the calculus of variations on a conditional extremum, the role of the Lagrange multiplier is estimated and the surface of the functional is constructed in terms of the parameters of variations of admissible functions of a normalized linear space.

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Park, Gunju, and Youngmin Yi. "CondNAS: Neural Architecture Search for Conditional CNNs." Electronics 11, no. 7 (2022): 1101. http://dx.doi.org/10.3390/electronics11071101.

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As deep learning has become prevalent and adopted in various application domains, the need for efficient convolution neural network (CNN) inference on diverse target platforms has increased. To address the need, a neural architecture search (NAS) technique called once-for-all, or OFA, which aims to efficiently find the optimal CNN architecture for the given target platform using genetic algorithm (GA), has recently been proposed. Meanwhile, a conditional CNN architecture, which allows early exits with auxiliary classifiers in the middle of a network to achieve efficient inference without accur

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Yuan, Xiaojian, Kejiang Chen, Jie Zhang, Weiming Zhang, Nenghai Yu, and Yang Zhang. "Pseudo Label-Guided Model Inversion Attack via Conditional Generative Adversarial Network." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (2023): 3349–57. http://dx.doi.org/10.1609/aaai.v37i3.25442.

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Model inversion (MI) attacks have raised increasing concerns about privacy, which can reconstruct training data from public models. Indeed, MI attacks can be formalized as an optimization problem that seeks private data in a certain space. Recent MI attacks leverage a generative adversarial network (GAN) as an image prior to narrow the search space, and can successfully reconstruct even the high-dimensional data (e.g., face images). However, these generative MI attacks do not fully exploit the potential capabilities of the target model, still leading to a vague and coupled search space, i.e.,

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MALI, AMOL DATTATRAYA, and MINH TANG. "STATE-SPACE PLANNING WITH VARIANTS OF A*." International Journal on Artificial Intelligence Tools 15, no. 03 (2006): 433–64. http://dx.doi.org/10.1142/s0218213006002758.

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Significant advances have occurred in heuristic search for planning in the last eleven years. Many of these planners use A*-style search. We report on five sound and complete domain-independent forward state-space STRIPS planners in this paper. The planners are AWA* (Adjusted Weighted A*), MAWA* (Modified AWA*), AWA*-AC (AWA* with action conflict-based adjustment), AWA*-PD (AWA* with deleted preconditions-based adjustment), and AWA*-AC-LE (AWA*-AC with lazy evaluation). AWA* is the first planner to use node-dependent weighting in A*. MAWA*, AWA*-AC, AWA*-PD, and AWA*-AC-LE use conditional two-

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Marinescu, Radu, Akihiro Kishimoto, and Adi Botea. "Parallel AND/OR Search for Marginal MAP." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 06 (2020): 10226–34. http://dx.doi.org/10.1609/aaai.v34i06.6584.

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Marginal MAP is a difficult mixed inference task for graphical models. Existing state-of-the-art algorithms for solving exactly this task are based on either depth-first or best-first sequential search over an AND/OR search space. In this paper, we explore and evaluate for the first time the power of parallel search for exact Marginal MAP inference. We introduce a new parallel shared-memory recursive best-first AND/OR search algorithm that explores the search space in a best-first manner while operating with limited memory. Subsequently, we develop a complete parallel search scheme that only p

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Payne, R. D., N. Guha, Y. Ding, and B. K. Mallick. "A conditional density estimation partition model using logistic Gaussian processes." Biometrika 107, no. 1 (2019): 173–90. http://dx.doi.org/10.1093/biomet/asz064.

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Summary Conditional density estimation seeks to model the distribution of a response variable conditional on covariates. We propose a Bayesian partition model using logistic Gaussian processes to perform conditional density estimation. The partition takes the form of a Voronoi tessellation and is learned from the data using a reversible jump Markov chain Monte Carlo algorithm. The methodology models data in which the density changes sharply throughout the covariate space, and can be used to determine where important changes in the density occur. The Markov chain Monte Carlo algorithm involves

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Zeng, Zilin, Hongjun Zhang, Rui Zhang, and Youliang Zhang. "A Hybrid Feature Selection Method Based on Rough Conditional Mutual Information and Naive Bayesian Classifier." ISRN Applied Mathematics 2014 (March 30, 2014): 1–11. http://dx.doi.org/10.1155/2014/382738.

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We introduced a novel hybrid feature selection method based on rough conditional mutual information and Naive Bayesian classifier. Conditional mutual information is an important metric in feature selection, but it is hard to compute. We introduce a new measure called rough conditional mutual information which is based on rough sets; it is shown that the new measure can substitute Shannon’s conditional mutual information. Thus rough conditional mutual information can also be used to filter the irrelevant and redundant features. Subsequently, to reduce the feature and improve classification accu

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Gnad, Daniel, Jörg Hoffmann, and Martin Wehrle. "Strong Stubborn Set Pruning for Star-Topology Decoupled State Space Search." Journal of Artificial Intelligence Research 65 (July 17, 2019): 343–92. http://dx.doi.org/10.1613/jair.1.11576.

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Analyzing reachability in large discrete transition systems is an important sub-problem in several areas of AI, and of CS in general. State space search is a basic method for conducting such an analysis. A wealth of techniques have been proposed to reduce the search space without affecting the existence of (optimal) solution paths. In particular, strong stubborn set (SSS) pruning is a prominent such method, analyzing action dependencies to prune commutative parts of the search space. We herein show how to apply this idea to star-topology decoupled state space search, a recent search reformulat

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Dumedah, Gift, Aaron A. Berg, and Mark Wineberg. "Pareto-optimality and a search for robustness: choosing solutions with desired properties in objective space and parameter space." Journal of Hydroinformatics 14, no. 2 (2011): 270–85. http://dx.doi.org/10.2166/hydro.2011.120.

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Multi-objective genetic algorithms are increasingly being applied to calibrate hydrological models by generating several competitive solutions usually referred to as a Pareto-optimal set. The Pareto-optimal set comprises non-dominated solutions at the calibration phase but it is usually unknown whether all or only a subset of non-dominated solutions at the calibration phase remains non-dominated at the validation phase. In practice, users would like to know solutions (and their associated properties) which remain non-dominated at both the calibration and validation phases. This study investiga

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Vasquez-Iglesias, Philip, Amelia E. Pizarro, David Zabala-Blanco, et al. "A Hyper-Parameter Optimizer Algorithm Based on Conditional Opposition Local-Based Learning Forbidden Redundant Indexes Adaptive Artificial Bee Colony Applied to Regularized Extreme Learning Machine." Electronics 13, no. 23 (2024): 4652. http://dx.doi.org/10.3390/electronics13234652.

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Finding the best configuration of a neural network’s hyper-parameters may take too long to be feasible using an exhaustive search, especially when the cardinality of the search space has a big combinatorial number of possible solutions with various hyper-parameters. This problem is aggravated when we also need to optimize the parameters of the neural network, such as the weight of the hidden neurons and biases. Extreme learning machines (ELMs) are part of the random weights neural network family, in which parameters are randomly initialized, and the solution, unlike gradient-descent-based algo

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Estrela, Gustavo, Marco Dimas Gubitoso, Carlos Eduardo Ferreira, Junior Barrera, and Marcelo S. Reis. "An Efficient, Parallelized Algorithm for Optimal Conditional Entropy-Based Feature Selection." Entropy 22, no. 4 (2020): 492. http://dx.doi.org/10.3390/e22040492.

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In Machine Learning, feature selection is an important step in classifier design. It consists of finding a subset of features that is optimum for a given cost function. One possibility to solve feature selection is to organize all possible feature subsets into a Boolean lattice and to exploit the fact that the costs of chains in that lattice describe U-shaped curves. Minimization of such cost function is known as the U-curve problem. Recently, a study proposed U-Curve Search (UCS), an optimal algorithm for that problem, which was successfully used for feature selection. However, despite of the

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Esposito, Joel M. "Conditional Density Growth (CDG) model: a simplified model of RRT coverage for kinematic systems." Robotica 31, no. 5 (2013): 733–46. http://dx.doi.org/10.1017/s0263574712000690.

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SUMMARYIt has been shown before that the Rapidly Exploring Random Tree (RRT) algorithm is probabilistically and resolution complete; and that the probability of finding a particular solution path can be related to the number of nodes. However, little analysis exists on the rate at which the tree covers the configuration space. In this paper, we present a stochastic difference equation which models how the tree covers the configuration space as a function of the number of nodes in the tree. Using two simplifying assumptions, appropriate for holonomic, kinematic systems in expansive configuratio

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Koliechkina, L. M., and A. M. Nahirna. "Finding the Optimal Solution to the Problem of Conditional Optimization on the Graph of the set of Placements." Control Systems and Computers, no. 6 (290) (December 2020): 29–34. http://dx.doi.org/10.15407/csc.2020.06.029.

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The model of the problem of conditional optimization on the set of partial permutations is formulated. The linear form of the objective function is obtained by interpreting the elements of the set of partial permutations as points of the Euclidean space. A combinatorial polytope of allocations is considered for which there is a graph of the set of partial permutations An algorithm for solving this problem is proposed and its practical applicability is demonstrated. The proposed algorithm for solving the conditional optimization problem provides for the representation of the admissible of the S

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Wen, Long, Xingchen Ye, and Liang Gao. "A new automatic machine learning based hyperparameter optimization for workpiece quality prediction." Measurement and Control 53, no. 7-8 (2020): 1088–98. http://dx.doi.org/10.1177/0020294020932347.

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Workpiece quality prediction is very important in modern manufacturing industry. However, traditional machine learning methods are very sensitive to their hyperparameters, making the tuning of the machine learning methods essential to improve the prediction performance. Hyperparameter optimization (HPO) approaches are applied attempting to tune hyperparameters, such as grid search and random search. However, the hyperparameters space for workpiece quality prediction model is high dimension and it consists with continuous, combinational and conditional types of hyperparameters, which is difficu

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Wang, Junwei, Kun Gao, Zhenzhou Zhang, et al. "Multisensor Remote Sensing Imagery Super-Resolution with Conditional GAN." Journal of Remote Sensing 2021 (September 8, 2021): 1–11. http://dx.doi.org/10.34133/2021/9829706.

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Despite the promising performance on benchmark datasets that deep convolutional neural networks have exhibited in single image super-resolution (SISR), there are two underlying limitations to existing methods. First, current supervised learning-based SISR methods for remote sensing satellite imagery do not use paired real sensor data, instead operating on simulated high-resolution (HR) and low-resolution (LR) image-pairs (typically HR images with their bicubic-degraded LR counterparts), which often yield poor performance on real-world LR images. Second, SISR is an ill-posed problem, and the su

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Anzalone, Luca, Simranjit Singh Chhibra, Benedikt Maier, Nadezda Chernyavskaya, and Maurizio Pierini. "Triggering dark showers with conditional dual auto-encoders." Machine Learning: Science and Technology 5, no. 3 (2024): 035064. http://dx.doi.org/10.1088/2632-2153/ad652b.

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Abstract We present a family of conditional dual auto-encoders (CoDAEs) for generic and model-independent new physics searches at colliders. New physics signals, which arise from new types of particles and interactions, are considered in our study as anomalies causing deviations in data with respect to expected background events. In this work, we perform a normal-only anomaly detection, which employs only background samples, to search for manifestations of a dark version of strong force applying (variational) auto-encoders on raw detector images, which are large and highly sparse, without leve

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Cantrell, Robert Stephen, and Chris Cosner. "Conditional persistence in logistic models via nonlinear diffusion." Proceedings of the Royal Society of Edinburgh: Section A Mathematics 132, no. 2 (2002): 267–81. http://dx.doi.org/10.1017/s0308210500001621.

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A nonlinear diffusion process modelling aggregative dispersal is combined with local (in space) population dynamics given by a logistic equation and the resulting growth-dispersal model is analysed. The nonlinear diffusion process models aggregation via a diffusion coefficient, which is decreasing with respect to the population density at low densities. This mechanism is similar to area-restricted search, but it is applied to conspecifics rather than prey. The analysis shows that in some cases the models predict a threshold effect similar to an Allee effect. That is, for some parameter ranges,

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Katz, Michael L., Olivia R. Cooper, Michael W. Coughlin, Kevin B. Burdge, Katelyn Breivik, and Shane L. Larson. "GPU-accelerated periodic source identification in large-scale surveys: measuring P and P." Monthly Notices of the Royal Astronomical Society 503, no. 2 (2021): 2665–75. http://dx.doi.org/10.1093/mnras/stab504.

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ABSTRACT Many inspiraling and merging stellar remnants emit both gravitational and electromagnetic radiation as they orbit or collide. These gravitational wave events together with their associated electromagnetic counterparts provide insight about the nature of the merger, allowing us to further constrain properties of the binary. With the future launch of the Laser Interferometer Space Antenna (LISA), follow-up observations and models are needed of ultracompact binary (UCB) systems. Current and upcoming long baseline time domain surveys will observe many of these UCBs. We present a new fast

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Cui, Guofeng, Yuning Wang, Wenjie Qiu, and He Zhu. "Reward-Guided Synthesis of Intelligent Agents with Control Structures." Proceedings of the ACM on Programming Languages 8, PLDI (2024): 1730–54. http://dx.doi.org/10.1145/3656447.

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Deep reinforcement learning (RL) has led to encouraging successes in numerous challenging robotics applications. However, the lack of inductive biases to support logic deduction and generalization in the representation of a deep RL model causes it less effective in exploring complex long-horizon robot-control tasks with sparse reward signals. Existing program synthesis algorithms for RL problems inherit the same limitation, as they either adapt conventional RL algorithms to guide program search or synthesize robot-control programs to imitate an RL model. We propose ReGuS, a reward-guided synth

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Shugrina, Maria, Chin-Ying Li, and Sanja Fidler. "Neural Brushstroke Engine." ACM Transactions on Graphics 41, no. 6 (2022): 1–18. http://dx.doi.org/10.1145/3550454.3555472.

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We propose Neural Brushstroke Engine, the first method to apply deep generative models to learn a distribution of interactive drawing tools. Our conditional GAN model learns the latent space of drawing styles from a small set (about 200) of unlabeled images in different media. Once trained, a single model can texturize stroke patches drawn by the artist, emulating a diverse collection of brush styles in the latent space. In order to enable interactive painting on a canvas of arbitrary size, we design a painting engine able to support real-time seamless patch-based generation, while allowing ar

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Li, Zhiyuan, Wenshuai Zhao, Lijun Wu, and Joni Pajarinen. "Backpropagation Through Agents." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 12 (2024): 13718–26. http://dx.doi.org/10.1609/aaai.v38i12.29277.

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A fundamental challenge in multi-agent reinforcement learning (MARL) is to learn the joint policy in an extremely large search space, which grows exponentially with the number of agents. Moreover, fully decentralized policy factorization significantly restricts the search space, which may lead to sub-optimal policies. In contrast, the auto-regressive joint policy can represent a much richer class of joint policies by factorizing the joint policy into the product of a series of conditional individual policies. While such factorization introduces the action dependency among agents explicitly in

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Demeshchenko, Violeta. "From Theater to Cinematography: Creative Search of Les Kurbas." Culturology Ideas, no. 18 (2'2020) (2020): 109–19. http://dx.doi.org/10.37627/2311-9489-18-2020-2.109-119.

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The article examines the artistic path and creative pursuits of Les Kurbas, the Ukrainian and soviet director, who undoubtedly remains an outstanding figure in the history of Ukrainian cultural life in the 1920s and 1930s. He was the founder of Ukrainian political theater, and later philosophical theater; also fruitfully worked in early cinematography. The article emphasizes the relevance of studying the creative work of the director, his original creative method of educating actors nowadays. In his own way, Kurbas became a standalone theatrical institute for young people, raised more than fou

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Zhu, Qi Shen. "Improving Program Performance via Auto-Vectorization of Loops with Conditional Statements with GCC Compiler Setting." Applied Mechanics and Materials 433-435 (October 2013): 1410–14. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.1410.

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The GCC is an auto-vectorization compiler across iterations of loops to parallelism data. Turning GCC compiler optimizations flags for auto-vectorization is a way to improve the performance ability, which is a popular approach to speed up program performance. However, there are many options in GCC compiler and selecting the best combination of these options to improve program performance through vectorization is non-trivial ( as the search space is very large ).In this work we focus on the selection of compiler transformations to auto-vectorize loops with conditional statements. The selection

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Wan, Yilin, Qi Xiong, Zhiwei Qiu, and Yaohan Xie. "K-Means Clustering Algorithm Based on Memristive Chaotic System and Sparrow Search Algorithm." Symmetry 14, no. 10 (2022): 2029. http://dx.doi.org/10.3390/sym14102029.

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With the advent of the big data era, it is vital to explore the information involved in this type of data. With the continuous development of higher education, the K-means clustering algorithm is widely used to analyze students’ academic data. However, a significant drawback of this method is that it is seriously affected by initial centroids of clustering and easily falls into local optima. Motivated by the fact that the chaos and swarm intelligence algorithm are frequently combined, we propose an approach for data clustering by Memristive Chaotic Sparrow Search Algorithm (MCSSA) in this pape

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Lee, Won-Seok, and Hyoung-Kyu Song. "Efficient Channel Feedback Scheme for Multi-User MIMO Hybrid Beamforming Systems." Sensors 21, no. 16 (2021): 5298. http://dx.doi.org/10.3390/s21165298.

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This paper proposes an efficient channel information feedback scheme to reduce the feedback overhead of multi-user multiple-input multiple-output (MU-MIMO) hybrid beamforming systems. As massive machine type communication (mMTC) was considered in the deployments of 5G, a transmitter of the hybrid beamforming system should communicate with multiple devices at the same time. To communicate with multiple devices in the same time and frequency slot, high-dimensional channel information should be used to control interferences between the receivers. Therefore, the feedback overhead for the channels

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Kakara, Hiroyuki, Yoshifumi Nishida, Sang Min Yoon, Hiroshi Mizoguchi, and Tatsuhiro Yamanaka. "Development of Database of Children’s Fall Dynamics Using Daily Behavior Observing System." Journal of Robotics and Mechatronics 24, no. 5 (2012): 802–10. http://dx.doi.org/10.20965/jrm.2012.p0802.

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This paper describes the development of a fall database for biomechanical simulation. First, data on children’s daily activities were collected at a “sensor home,” which is a imitation daily living space. The sensor-based home comprises a video-surveillance system embedded into a daily-living environment and a wearable acceleration-gyro sensor. Falls were then detected from sensor data using a fall detection algorithm that we developed, and videos of detected falls were extracted from long-time recorded video. Extracted videos were used for fall motion analysis. A new Computer Vision (CV) algo

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Liu, Guangyi, Ou Li, Dalong Zhang, and Tao Song. "Upper-Lower Bounds Candidate Sets Searching Algorithm for Bayesian Network Structure Learning." Mathematical Problems in Engineering 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/625173.

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Bayesian network is an important theoretical model in artificial intelligence field and also a powerful tool for processing uncertainty issues. Considering the slow convergence speed of current Bayesian network structure learning algorithms, a fast hybrid learning method is proposed in this paper. We start with further analysis of information provided by low-order conditional independence testing, and then two methods are given for constructing graph model of network, which is theoretically proved to be upper and lower bounds of the structure space of target network, so that candidate sets are

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Lee, Woosuk, and Hangyeol Cho. "Inductive Synthesis of Structurally Recursive Functional Programs from Non-recursive Expressions." Proceedings of the ACM on Programming Languages 7, POPL (2023): 2048–78. http://dx.doi.org/10.1145/3571263.

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We present a novel approach to synthesizing recursive functional programs from input-output examples. Synthesizing a recursive function is challenging because recursive subexpressions should be constructed while the target function has not been fully defined yet. We address this challenge by using a new technique we call block-based pruning. A block refers to a recursion- and conditional-free expression (i.e., straight-line code) that yields an output from a particular input. We first synthesize as many blocks as possible for each input-output example, and then we explore the space of recursiv

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Pechenizkiy, Mykola, Alexey Tsymbal, Seppo Puuronen, and David Patterson. "Feature Extraction for Dynamic Integration of Classifiers." Fundamenta Informaticae 77, no. 3 (2007): 243–75. https://doi.org/10.3233/fun-2007-77304.

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Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. In this paper, we present an algorithm for the dynamic integration of classifiers in the space of extracted features (FEDIC). It is based on the technique of dynamic integration, in which local accuracy estimates are calculated for each base classifier of an ensemble, in the neighborhood of a new instance to be processed. Generally, the whole space of original features is used to find the neighborhood of a new instance for local accuracy estimates in

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Khutortsev, V. V. "Space-time Poisson Flow Intensity Density with Zero Occurrence Probability on Stochastic Subsets of Its Spatial Definition Domain." Mathematics and Mathematical Modeling, no. 3 (September 20, 2020): 15–28. http://dx.doi.org/10.24108/mathm.0320.0000214.

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Object of research: space-time Poisson flows.Subject of research: influence patterns of the stochastic subset characteristics of the spatial definition domain of the space-time Poisson flow on its intensity density.Work objective: to determine a relationship between the space-time Poisson flow intensity density and the characteristics of the inhomogeneity subdomains of the spatial definition domain where this flow is specified.A problem to be solved: to determine the space-time Poisson flow intensity density to meet a selected criterion, i.e. a conditional intensity density, where the conditio

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Phillips, Tyson, Tim D’Adamo, and Peter McAree. "Maximum Sum of Evidence—An Evidence-Based Solution to Object Pose Estimation in Point Cloud Data." Sensors 21, no. 19 (2021): 6473. http://dx.doi.org/10.3390/s21196473.

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The capability to estimate the pose of known geometry from point cloud data is a frequently arising requirement in robotics and automation applications. This problem is directly addressed by Iterative Closest Point (ICP), however, this method has several limitations and lacks robustness. This paper makes the case for an alternative method that seeks to find the most likely solution based on available evidence. Specifically, an evidence-based metric is described that seeks to find the pose of the object that would maximise the conditional likelihood of reproducing the observed range measurement

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Man Jing. "Application of Time Series Analysis and Regression Algorithm in Assessing the Effectiveness of Monetary Policy in the Era of Digital Economy." Journal of Electrical Systems 20, no. 6s (2024): 462–74. http://dx.doi.org/10.52783/jes.2674.

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Monetary policy is the series of actions to manage country's money supply and to attain economic growth. An objection to using interest rates in monetary policy is that the use of interest rates has the problem of time uncertainty. Therefore, this research introduces Conditional Autoregressive Value-at-Risk and Henry Gas Solubility Optimization (Caviar-HGSO) with Deep Long Short Term Memory (DeepLSTM) to monetary policy forecasting to demonstrate its effectiveness. Here, Caviar-HGSO is a hybrid of Caviar and HGSO used to tune DeepLSTM weights. The HGSO algorithm follows the wind state to balan

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Helmert, M. "The Fast Downward Planning System." Journal of Artificial Intelligence Research 26 (July 12, 2006): 191–246. http://dx.doi.org/10.1613/jair.1705.

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Fast Downward is a classical planning system based on heuristic search. It can deal with general deterministic planning problems encoded in the propositional fragment of PDDL2.2, including advanced features like ADL conditions and effects and derived predicates (axioms). Like other well-known planners such as HSP and FF, Fast Downward is a progression planner, searching the space of world states of a planning task in the forward direction. However, unlike other PDDL planning systems, Fast Downward does not use the propositional PDDL representation of a planning task directly. Instead, the inpu

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Cristofor, Dana, and Dan Simovici. "Finding Median Partitions Using Information-Theoretical-Based Genetic Algorithms." JUCS - Journal of Universal Computer Science 8, no. (2) (2002): 153–72. https://doi.org/10.3217/jucs-008-02-0153.

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In a database with categorical attributes, each attribute defines a partition whose classes can be regarded as natural clusters of rows. In this paper we focus on finding a partition of the rows of a given database, that is as close as possible to the partitions associated to each attribute. We evaluate the closeness of two partitions by using a generalization of the classical conditional entropy. From this perspective, we wish to construct a partition (referred to as the median partition) such that the sum of the dissimilarities between this partition and all the partitions determined by the

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Doerge, R. W., and G. A. Churchill. "Permutation Tests for Multiple Loci Affecting a Quantitative Character." Genetics 142, no. 1 (1996): 285–94. http://dx.doi.org/10.1093/genetics/142.1.285.

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The problem of detecting minor quantitative trait loci (QTL) responsible for genetic variation not explained by major QTL is of importance in the complete dissection of quantitative characters. Two extensions of the permutation-based method for estimating empirical threshold values are presented. These methods, the conditional empirical threshold (CET) and the residual empirical threshold (RET), yield critical values that can be used to construct tests for the presence of minor QTL effects while accounting for effects of known major QTL. The CET provides a completely nonparametric test through

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Wang, Rui, Xin Xin, Wei Chang, Kun Ming, Biao Li, and Xin Fan. "Chinese NER with Height-Limited Constituent Parsing." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7160–67. http://dx.doi.org/10.1609/aaai.v33i01.33017160.

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In this paper, we investigate how to improve Chinese named entity recognition (NER) by jointly modeling NER and constituent parsing, in the framework of neural conditional random fields (CRF). We reformulate the parsing task to heightlimited constituent parsing, by which the computational complexity can be significantly reduced, and the majority of phrase-level grammars are retained. Specifically, an unified model of neural semi-CRF and neural tree-CRF is proposed, which simultaneously conducts word segmentation, part-ofspeech (POS) tagging, NER, and parsing. The challenge comes from how to tr

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Lee, Daeyeal, Bill Lin, and Chung-Kuan Cheng. "SMT-Based Contention-Free Task Mapping and Scheduling on 2D/3D SMART NoC with Mixed Dimension-Order Routing." ACM Transactions on Architecture and Code Optimization 19, no. 1 (2022): 1–21. http://dx.doi.org/10.1145/3487018.

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SMART NoCs achieve ultra-low latency by enabling single-cycle multiple-hop transmission via bypass channels. However, contention along bypass channels can seriously degrade the performance of SMART NoCs by breaking the bypass paths. Therefore, contention-free task mapping and scheduling are essential for optimal system performance. In this article, we propose an SMT (Satisfiability Modulo Theories)-based framework to find optimal contention-free task mappings with minimum application schedule lengths on 2D/3D SMART NoCs with mixed dimension-order routing. On top of SMT’s fast reasoning capabil

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Troumbis, Andreas, Cleo Gagani, and Haralambos Sideropoulos. "Probabilistic Wildfire Risk Assessment and Modernization Transitions: The Case of Greece." Fire 6, no. 4 (2023): 158. http://dx.doi.org/10.3390/fire6040158.

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Wildfire is the primary cause of deforestation in fire-prone environments, disrupting the forest transition process generated by multiple social-ecological drivers of modernization. Given the positive feedback between climate change and wildfire-driven deforestation, it seems necessary to abstract the primary- or micro-characteristics of wildfire event(s) and focus on the general behavior of the phenomenon across time and space. This paper intends to couple wildfire self-organizing criticality theory (SOC) and modernization statistics to propose a verisimilar explanation of the phenomenon’s ev

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Chen, Kai, Xin Song, Ye Wang, et al. "LLM-DR: A Novel LLM-Aided Diffusion Model for Rule Generation on Temporal Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 11 (2025): 11481–89. https://doi.org/10.1609/aaai.v39i11.33249.

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Among various temporal knowledge graph (TKG) extrapolation methods, rule-based approaches stand out for their explicit rules and transparent reasoning paths. However, the vast search space for rule extraction poses a challenge in identifying high-quality logic rules. To navigate this challenge, we explore the use of generation models to generate new rules, thereby enriching our rule base and enhancing our reasoning capabilities. In this paper, we introduce LLM-DR, an innovative rule-based method for TKG extrapolation, which harnesses diffusion models to generate rules that are consistent with

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Zhou, Xue, Xin Zhu, Keijiro Nakamura, and Mahito Noro. "Electrocardiogram Quality Assessment with a Generalized Deep Learning Model Assisted by Conditional Generative Adversarial Networks." Life 11, no. 10 (2021): 1013. http://dx.doi.org/10.3390/life11101013.

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The electrocardiogram (ECG) is widely used for cardiovascular disease diagnosis and daily health monitoring. Before ECG analysis, ECG quality screening is an essential but time-consuming and experience-dependent work for technicians. An automatic ECG quality assessment method can reduce unnecessary time loss to help cardiologists perform diagnosis. This study aims to develop an automatic quality assessment system to search qualified ECGs for interpretation. The proposed system consists of data augmentation and quality assessment parts. For data augmentation, we train a conditional generative a

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Shih, David, Matthew R. Buckley, Lina Necib, and John Tamanas. "via machinae: Searching for stellar streams using unsupervised machine learning." Monthly Notices of the Royal Astronomical Society 509, no. 4 (2021): 5992–6007. http://dx.doi.org/10.1093/mnras/stab3372.

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ABSTRACT We develop a new machine learning algorithm, via machinae, to identify cold stellar streams in data from the Gaia telescope. via machinae is based on ANODE, a general method that uses conditional density estimation and sideband interpolation to detect local overdensities in the data in a model agnostic way. By applying ANODE to the positions, proper motions, and photometry of stars observed by Gaia, via machinae obtains a collection of those stars deemed most likely to belong to a stellar stream. We further apply an automated line-finding method based on the Hough transform to search

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Wang, Limin, Yang Liu, Musa Mammadov, Minghui Sun, and Sikai Qi. "Discriminative Structure Learning of Bayesian Network Classifiers from Training Dataset and Testing Instance." Entropy 21, no. 5 (2019): 489. http://dx.doi.org/10.3390/e21050489.

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Over recent decades, the rapid growth in data makes ever more urgent the quest for highly scalable Bayesian networks that have better classification performance and expressivity (that is, capacity to respectively describe dependence relationships between attributes in different situations). To reduce the search space of possible attribute orders, k-dependence Bayesian classifier (KDB) simply applies mutual information to sort attributes. This sorting strategy is very efficient but it neglects the conditional dependencies between attributes and is sub-optimal. In this paper, we propose a novel

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Vennos, Amy, Kiernan George, and Alan Michaels. "Attacks and Defenses for Single-Stage Residue Number System PRNGs." IoT 2, no. 3 (2021): 375–400. http://dx.doi.org/10.3390/iot2030020.

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This paper explores the security of a single-stage residue number system (RNS) pseudorandom number generator (PRNG), which has previously been shown to provide extremely high-quality outputs when evaluated through available RNG statistical test suites or in using Shannon and single-stage Kolmogorov entropy metrics. In contrast, rather than blindly performing statistical analyses on the outputs of the single-stage RNS PRNG, this paper provides both white box and black box analyses that facilitate reverse engineering of the underlying RNS number generation algorithm to obtain the residues, or eq

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Щавелев, А. С. "“City” as an assemblage point of social space and a center of resource concentration in archaic societies." Историческая география, no. 5 (January 31, 2022): 7–23. http://dx.doi.org/10.21267/aquilo.2022.71.42.001.

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В статье предлагается отказаться от попыток выделить «объективные» количественные и качественные критерии «настоящего города». Главная отличительная черта поселений, которые можно условно обозначить как «города», — концентрация демографических, материальных и виртуальных (власти, сакральности, знаний и т.п.) ресурсов. Статус «город» для любого стационарного поселения людей определяется только его доминирующим положением в социально-политическом пространстве относительно других поселений, которое позволяет ему аккумулировать ресурсы. Следовательно, «город» — категория условная и относительная.

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Shilov, Kirill D., and Andrei V. Zubarev. "Factors of Ethereum Profitability as a Platform for Creating Decentrilized Applications." Financial Journal 15, no. 1 (2023): 95–115. http://dx.doi.org/10.31107/2075-1990-2023-1-95-115.

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By now, cryptocurrencies have almost become a part of the modern financial asset space, but the cryptocurrency market itself is not homogeneous, and individual cryptocurrencies can differ significantly in their properties and functions. For example, the cryptocurrency Ether is second in capitalization after Bitcoin, but the Ethereum and Bitcoin blockchains differ significantly in their properties and functions. In particular, Ethereum is the most popular digital platform for creating decentralized applications (dApps). The purpose of this work is to try to answer the question "Does the market

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DARSHANA, WAJEKAR, and EKTA UKEY PROF. "FIDOOP – FIM: DATA SEGREGATION USING FREQUENT ITEM SETS MINING AND MAP REDUCE ALGORITHM." JournalNX - A Multidisciplinary Peer Reviewed Journal 3, no. 3 (2017): 87–91. https://doi.org/10.5281/zenodo.1462697.

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Hadoop is an open-source platform of the MapReduce programming model. As data size is increasing gradually day by day, the improvement and security of data structure in Hadoop have become a critical issue. So far, algorithms have lacked of mechanisms like data distribution, fault tolerance, load balancing and input-output overhead. Hence, in order to overcome these discrepancies, the most effective the new method is the FiDoop method using a Map Reduce programming model and FIM algorithm. FiDoop includes the Frequent Item set Ulta metric Tree rather than conventional FP-trees which avoid the n

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MERKUSHOV, STANISLAV F. "THE APOCALYPTIC SEMASIOLOGY OF ALEXANDER BASHLACHEV’S SHORT TEXT (“OATMEAL FOREHEADS” AND UNOFFICIAL POETIC FRAGMENTS)." Челябинский гуманитарий 70, no. 1 (2025): 37–44. https://doi.org/10.47475/1999-5407-2025-70-1-37-44.

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The article continues the study of Alexander Bashlachev’s short text from the point of view of semasiology2 (in terms of form) and apocalypticism (in terms of content). The article examines the multidirectional artistic communication of three texts: one that is included in the inclusive space of the poet’s archive (“Oatmeal foreheads” (1984)), and two that are not included (conditional names on the first lines - “Fear has shackled human faces...” (1977) and “I love you, Alice...” (1985)). These texts, with all their natural self-su ciency, to one degree or another, of course, form an isomorphi

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Bagalkot, Sneha S., Dinesha H. A, and Nagaraj Naik. "Novel grey wolf optimizer based parameters selection for GARCH and ARIMA models for stock price prediction." PeerJ Computer Science 10 (January 2, 2024): e1735. http://dx.doi.org/10.7717/peerj-cs.1735.

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Stock price data often exhibit nonlinear patterns and dynamics in nature. The parameter selection in generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive integrated moving average (ARIMA) models is challenging due to stock price volatility. Most studies examined the manual method for parameter selection in GARCH and ARIMA models. These procedures are time-consuming and based on trial and error. To overcome this, we considered a GWO method for finding the optimal parameters in GARCH and ARIMA models. The motivation behind considering the grey wolf optimizer (GWO)

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