Journal articles on the topic 'Conditional Search Space'
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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.
Abstract:
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 a subset of the properties, but in order to provide a standardized comparison we present several generalizations of planning graph heuristics that are used in a single planner. We compliment our belief state distance estimate framework by also investigating efficient planning graph data structures that incorporate BDDs to compute the most effective heuristics. We developed two planners to serve as test-beds for our investigation. The first, CAltAlt, is a conformant regression planner that uses A* search. The second, POND, is a conditional progression planner that uses AO* search. We show the relative effectiveness of our heuristic techniques within these planners. We also compare the performance of these planners with several state of the art approaches in conditional planning.
2
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.
Abstract:
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.
3
Park, Gunju, and Youngmin Yi. "CondNAS: Neural Architecture Search for Conditional CNNs." Electronics 11, no. 7 (March 31, 2022): 1101. http://dx.doi.org/10.3390/electronics11071101.
Abstract:
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 accuracy loss or with negligible loss, has been proposed. In this paper, we propose a NAS technique for the conditional CNN architecture, CondNAS, which efficiently finds a near-optimal conditional CNN architecture for the target platform using GA. By attaching auxiliary classifiers through adaptive pooling, OFA’s SuperNet is successfully extended, such that it incorporates the various conditional CNN sub-networks. In addition, we devise machine learning-based prediction models for the accuracy and latency of an arbitrary conditional CNN, which are used in the GA of CondNAS to efficiently explore the large search space. The experimental results show that the conditional CNNs from CondNAS is 2.52× and 1.75× faster than the CNNs from OFA for Galaxy Note10+ GPU and CPU, respectively.
4
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 (June 26, 2023): 3349–57. http://dx.doi.org/10.1609/aaai.v37i3.25442.
Abstract:
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., different classes of images are coupled in the search space. Besides, the widely used cross-entropy loss in these attacks suffers from gradient vanishing. To address these problems, we propose Pseudo Label-Guided MI (PLG-MI) attack via conditional GAN (cGAN). At first, a top-n selection strategy is proposed to provide pseudo-labels for public data, and use pseudo-labels to guide the training of the cGAN. In this way, the search space is decoupled for different classes of images. Then a max-margin loss is introduced to improve the search process on the subspace of a target class. Extensive experiments demonstrate that our PLG-MI attack significantly improves the attack success rate and visual quality for various datasets and models, notably, 2 ∼ 3× better than state-of-the-art attacks under large distributional shifts. Our code is available at: https://github.com/LetheSec/PLG-MI-Attack.
5
MALI, AMOL DATTATRAYA, and MINH TANG. "STATE-SPACE PLANNING WITH VARIANTS OF A*." International Journal on Artificial Intelligence Tools 15, no. 03 (June 2006): 433–64. http://dx.doi.org/10.1142/s0218213006002758.
Abstract:
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-phase heuristic evaluation. MAWA* applies node-dependent weighting to a subset of the nodes in the fringe, after the two-phase evaluation. One novel idea in AWA*-AC-LE is lazy heuristic evaluation which does not construct relaxed plans to compute heuristic values for all nodes. We report on an empirical comparison of AWA*, MAWA*, AWA*-AC, AWA*-PD, and AWA*-AC-LE with classical planners AltAlt, FF, HSP-2 and STAN 4. Our variants of A* outperform these planners on several problems. The empirical evaluation shows that heuristic search planning is significantly benefitted by node-dependent weighting, conditional two-phase heuristic evaluation and lazy evaluation. We report on the insights about inferior performance of our planners in some domains using the notion of waiting time. We discuss many other variants of A*, state-space planners and directions for future work.
6
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 (April 3, 2020): 10226–34. http://dx.doi.org/10.1609/aaai.v34i06.6584.
Abstract:
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 parallelizes the conditional likelihood computations. We also extend the proposed algorithms into depth-first parallel search schemes. Our experiments on difficult benchmarks demonstrate the effectiveness of the parallel search algorithms against current sequential methods for solving Marginal MAP exactly.
7
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 (December 5, 2019): 173–90. http://dx.doi.org/10.1093/biomet/asz064.
Abstract:
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 a Laplace approximation on the latent variables of the logistic Gaussian process model which marginalizes the parameters in each partition element, allowing an efficient search of the approximate posterior distribution of the tessellation. The method is consistent when the density is piecewise constant in the covariate space or when the density is Lipschitz continuous with respect to the covariates. In simulation and application to wind turbine data, the model successfully estimates the partition structure and conditional distribution.
8
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.
Abstract:
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 accuracy, a wrapper approach based on naive Bayesian classifier is used to search the optimal feature subset in the space of a candidate feature subset which is selected by filter model. Finally, the proposed algorithms are tested on several UCI datasets compared with other classical feature selection methods. The results show that our approach obtains not only high classification accuracy, but also the least number of selected features.
9
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 (June 22, 2011): 270–85. http://dx.doi.org/10.2166/hydro.2011.120.
Abstract:
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 investigates robustness of the Pareto-optimal set by developing a model characterization framework (MCF). The MCF uses cluster analysis to examine the distribution of solutions in parameter space and objective space, and conditional probability to combine linkages between the distributions of solutions in both spaces. The MCF has been illustrated for calibration output generated from application of the Non-dominated Sorting Genetic Algorithm-II to calibrate the Soil and Water Assessment Tool for streamflow in the Fairchild Creek watershed in southern Ontario. Our results show that not all non-dominated solutions found at the calibration phase perform the same for different validation periods. The MCF illustrates that robust solutions – non-dominated solutions which cluster in similar locations in parameter space and objective space – performed consistently well for several validation periods.
10
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.
Abstract:
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 reformulation method invented in the context of classical AI planning.
Star-topology decoupled state space search, short decoupled search, addresses planning tasks where a single center component interacts with several leaf components. The search exploits a form of conditional independence arising in this setting: given a fixed path p of transitions by the center, the possible leaf moves compliant with p are independent across the leaves. Decoupled search thus searches over center paths only, maintaining the compliant paths for each leaf separately. This avoids the enumeration of combined states across leaves.
Just like standard search, decoupled search is adversely affected by commutative parts of its search space. The adaptation of strong stubborn set pruning is challenging due to the more complex structure of the search space, and the resulting ways in which action dependencies may affect the search. We spell out how to address this challenge, designing optimality-preserving decoupled strong stubborn set (DSSS) pruning methods. We introduce a design for star topologies in full generality, as well as simpler design variants for the practically relevant fork and inverted fork special cases. We show that there are cases where DSSS pruning is exponentially more effective than both, decoupled search and SSS pruning, exhibiting true synergy where the whole is more than the sum of its parts. Empirically, DSSS pruning reliably inherits the best of its components, and sometimes outperforms both.
11
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 (April 24, 2020): 492. http://dx.doi.org/10.3390/e22040492.
Abstract:
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 algorithm optimality, the UCS required time in computational assays was exponential on the number of features. Here, we report that such scalability issue arises due to the fact that the U-curve problem is NP-hard. In the sequence, we introduce the Parallel U-Curve Search (PUCS), a new algorithm for the U-curve problem. In PUCS, we present a novel way to partition the search space into smaller Boolean lattices, thus rendering the algorithm highly parallelizable. We also provide computational assays with both synthetic data and Machine Learning datasets, where the PUCS performance was assessed against UCS and other golden standard algorithms in feature selection.
12
Esposito, Joel M. "Conditional Density Growth (CDG) model: a simplified model of RRT coverage for kinematic systems." Robotica 31, no. 5 (January 25, 2013): 733–46. http://dx.doi.org/10.1017/s0263574712000690.
Abstract:
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 configuration spaces, we derive closed-form solutions for the expected value and variance of configuration space coverage, which only depend on two easily computable parameters. Using a grid-based coverage measurement, we present experimental evidence supporting this model across a range of dimensions, obstacle densities, and parameter choices. Collecting data from 1000 RRTs, we provide evidence that configuration space coverage concentrates tightly around the expected coverage predicted by the model; and the results of the Chi-squared test suggest that the distribution of coverage across these runs is highly Gaussian. Together these results enable one to predict the expected coverage, along with a confidence interval, after a certain number of nodes have been added to the tree. We also applied the model to an example with extremely narrow passages and to a system with non-holonomic kinematics. The expected value prediction is still qualitatively accurate; but the rate constant is reduced and the variance is higher. Overall, in addition to its theoretical value, the model may find future application as an online measure of search-progress and problem difficulty, useful for adaptive variants of the basic RRT algorithm.
13
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.
Abstract:
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 Set of Partial Permutations in the form of a graph, which significantly reduces the search path for the optimal solution, as evidenced by the practical example considered.
14
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 (July 21, 2020): 1088–98. http://dx.doi.org/10.1177/0020294020932347.
Abstract:
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 difficult to be tuned. In this article, a new automatic machine learning based HPO, named adaptive Tree Pazen Estimator (ATPE), is proposed for workpiece quality prediction in high dimension. In the proposed method, it can iteratively search the best combination of hyperparameters in the automatic way. During the warm-up process for ATPE, it can adaptively adjust the hyperparameter interval to guide the search. The proposed ATPE is tested on sparse stack autoencoder based MNIST and XGBoost based WorkpieceQuality dataset, and the results show that ATPE provides the state-of-the-art performances in high-dimensional space and can search the hyperparameters in reasonable range by comparing with Tree Pazen Estimator, annealing, and random search, showing its potential in the field of workpiece quality prediction.
15
Wang, Junwei, Kun Gao, Zhenzhou Zhang, Chong Ni, Zibo Hu, Dayu Chen, and Qiong Wu. "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.
Abstract:
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 super-resolved image from discriminatively trained networks with lp norm loss is an average of the infinite possible HR images, thus, always has low perceptual quality. Though this issue can be mitigated by generative adversarial network (GAN), it is still hard to search in the whole solution-space and find the best solution. In this paper, we focus on real-world application and introduce a new multisensor dataset for real-world remote sensing satellite imagery super-resolution. In addition, we propose a novel conditional GAN scheme for SISR task which can further reduce the solution-space. Therefore, the super-resolved images have not only high fidelity, but high perceptual quality as well. Extensive experiments demonstrate that networks trained on the introduced dataset can obtain better performances than those trained on simulated data. Additionally, the proposed conditional GAN scheme can achieve better perceptual quality while obtaining comparable fidelity over the state-of-the-art methods.
16
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 (April 2002): 267–81. http://dx.doi.org/10.1017/s0308210500001621.
Abstract:
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, the models predict a form of conditional persistence where small populations go extinct but large populations persist. This is somewhat surprising because logistic equations without diffusion or with non-aggregative diffusion predict either unconditional persistence or unconditional extinction. Furthermore, in the aggregative models, the minimum patch size needed to sustain an existing population at moderate to high densities may be smaller than the minimum patch size needed for invasibility by a small population. The tradeoff is that if a population is inhabiting a large patch whose size is reduced below the size needed to sustain any population, then the population on the patch can be expected to experience a sudden crash rather than a steady decline.
17
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 (March 4, 2021): 2665–75. http://dx.doi.org/10.1093/mnras/stab504.
Abstract:
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 periodic object search tool capable of searching for generic periodic signals based on the conditional entropy algorithm. This new implementation allows for a grid search over both the period (P) and the time derivative of the period ($\dot{P}$). To demonstrate the usage of this tool, we use a small, hand-picked subset of a UCB population generated from the population synthesis code cosmic , as well as a custom catalogue for varying periods at fixed intrinsic parameters. We simulate light curves as likely to be observed by future time domain surveys by using an existing eclipsing binary light-curve model accounting for the change in orbital period due to gravitational radiation. We find that a search with $\dot{P}$ values is necessary for detecting binaries at orbital periods less than ∼10 min. We also show it is useful in finding and characterizing binaries with longer periods, but at a higher computational cost. Our code is called gce (GPU-accelerated Conditional Entropy). It is available on Github (https://github.com/mikekatz04/gce).
18
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 (June 20, 2024): 1730–54. http://dx.doi.org/10.1145/3656447.
Abstract:
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 synthesis paradigm, to unlock the potential of program synthesis to overcome the exploration challenges. We develop a novel hierarchical synthesis algorithm with decomposed search space for loops, on-demand synthesis of conditional statements, and curriculum synthesis for procedure calls, to effectively compress the exploration space for long-horizon, multi-stage, and procedural robot-control tasks that are difficult to address by conventional RL techniques. Experiment results demonstrate that ReGuS significantly outperforms state-of-the-art RL algorithms and standard program synthesis baselines on challenging robot tasks including autonomous driving, locomotion control, and object manipulation.
19
Shugrina, Maria, Chin-Ying Li, and Sanja Fidler. "Neural Brushstroke Engine." ACM Transactions on Graphics 41, no. 6 (November 30, 2022): 1–18. http://dx.doi.org/10.1145/3550454.3555472.
Abstract:
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 artists direct control of stroke shape, color and thickness. We show that the latent space learned by our model generalizes to unseen drawing and more experimental styles (e.g. beads) by embedding real styles into the latent space. We explore other applications of the continuous latent space, such as optimizing brushes to enable painting in the style of an existing artwork, automatic line drawing stylization, brush interpolation, and even natural language search over a continuous space of drawing tools. Our prototype received positive feedback from a small group of digital artists.
20
Li, Zhiyuan, Wenshuai Zhao, Lijun Wu, and Joni Pajarinen. "Backpropagation Through Agents." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 12 (March 24, 2024): 13718–26. http://dx.doi.org/10.1609/aaai.v38i12.29277.
Abstract:
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 sequential execution, it does not take full advantage of the dependency during learning. In particular, the subsequent agents do not give the preceding agents feedback about their decisions. In this paper, we propose a new framework Back-Propagation Through Agents (BPTA) that directly accounts for both agents' own policy updates and the learning of their dependent counterparts. This is achieved by propagating the feedback through action chains. With the proposed framework, our Bidirectional Proximal Policy Optimisation (BPPO) outperforms the state-of-the-art methods. Extensive experiments on matrix games, StarCraftII v2, Multi-agent MuJoCo, and Google Research Football demonstrate the effectiveness of the proposed method.
21
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.
Abstract:
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 four dozen professional directors who later became theater managers and directors of Ukrainian theaters, teachers. The director created Ukrainian theater and cinema school of acting; his innovative artistic ideas still remain relevant today. He developed his own aesthetic-theatrical concept of conditional-metaphorical theater based on life itself. Being the man of art, Kurbas influenced the formation of stage constructivism in Ukrainian theatrical art. In addition to the positive memories of his contemporaries, we also encounter some legends, various testimonies and assessments of events of that time, which create a certain mythological space around the artist. Hence, today we need to carefully analyze numerous documentary evidence, facts, memoirs, literary and theatrical sources, as well as try to be objective in reconstructing events and reflecting on the fate and work of the director.
22
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.
Abstract:
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 of compiler transformations is based on the correlation between program features, speed-up, and the analysis of the code generated and a small number of passes of iterative compilation. Our preliminary experimental results show that proposed technique attains performance improvements the best ~ 6x using loops in the TSVC benchmark suite on the state-of-the-art Intel Core i3 processor.
23
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 (September 28, 2022): 2029. http://dx.doi.org/10.3390/sym14102029.
Abstract:
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 paper. First, we introduce a memristive chaotic system, which has a property of conditional symmetry. We use the sequences generated by the memristive chaotic system to initialize the location of the sparrows. Then, MCSSA is applied before K-means for finding the optimal locations in the search space. Those locations are used as initial cluster centroids for the K-means algorithm to find final data clusters. Finally, the improved clustering algorithm is applied to the analysis of college students’ academic data, demonstrating the value and viability of the approach suggested in this paper. Through empirical research, it is also confirmed that this method can be promoted and applied.
24
Lee, Won-Seok, and Hyoung-Kyu Song. "Efficient Channel Feedback Scheme for Multi-User MIMO Hybrid Beamforming Systems." Sensors 21, no. 16 (August 5, 2021): 5298. http://dx.doi.org/10.3390/s21165298.
Abstract:
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 of the devices is impractically high. To reduce the overhead, this paper uses common sparsity of channel and nonlinear quantization. To find a common sparse part of a wide frequency band, the proposed system uses minimum mean squared error orthogonal matching pursuit (MMSE-OMP). After the search of the common sparse basis, sparse vectors of subcarriers are searched by using the basis. The sparse vectors are quantized by a nonlinear codebook that is generated by conditional random vector quantization (RVQ). For the conditional RVQ, the Linde–Buzo–Gray (LBG) algorithm is used in conditional vector space. Typically, elements of sparse vectors are sorted according to magnitude by the OMP algorithm. The proposed quantization scheme considers the property for the conditional RVQ. For feedback, indices of the common sparse basis and the quantized sparse vectors are delivered and the channel is recovered at a transmitter for precoding of MU-MIMO. The simulation results show that the proposed scheme achieves lower MMSE for the recovered channel than that of the linear quantization scheme. Furthermore, the transmitter can adopt analog and digital precoding matrix freely by the recovered channel and achieve higher sum rate than that of conventional codebook-based MU-MIMO precoding schemes.
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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 (October 20, 2012): 802–10. http://dx.doi.org/10.20965/jrm.2012.p0802.
Abstract:
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) algorithm was developed to automate fall motion analysis. Using the CV algorithm, fall motion data were accumulated into a database. The database allows a user to perform conditional searches for fall data by inputting search conditions, such as a child’s attributes, and fall situations.
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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.
Abstract:
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 given as a result; after that a search and scoring algorithm is operated based on the candidate sets to find the final structure of the network. Simulation results show that the algorithm proposed in this paper is more efficient than similar algorithms with the same learning precision.
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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 (January 9, 2023): 2048–78. http://dx.doi.org/10.1145/3571263.
Abstract:
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 recursive programs, pruning candidates that are inconsistent with the blocks. Our method is based on an efficient version space learning, thereby effectively dealing with a possibly enormous number of blocks. In addition, we present a method that uses sampled input-output behaviors of library functions to enable a goal-directed search for a recursive program using the library. We have implemented our approach in a system called Trio and evaluated it on synthesis tasks from prior work and on new tasks. Our experiments show that Trio outperforms prior work by synthesizing a solution to 98% of the benchmarks in our benchmark suite.
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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.
Abstract:
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 condition is that the points in the flow state space belong to the stochastic inhomogeneity subdomains.We consider a space-time Poisson flow whose spatial domain contains stochastic subdomains of inhomogeneity. An equality criterion to zero occurrence probability generated by this flow in the inhomogeneity subdomains is used to derive an expression for the flow intensity density.The case has been considered when positions of inhomogeneity subdomain centers are random, and their angular positions relative to these centers and their shapes are defined and unchanged at analysis interval.To describe them, we used the probability densities of the inhomogeneity subdomain centers, which are time variant. A theorem is proved that substantiates the structure of the intensity density of the space-time Poisson flow with a stochastic inhomogeneous spatial domain of definition. The relationship of this characteristic with the probabilistic characteristics of inhomogeneity subdomain parameters is shown.Examples are given to illustrate a procedure for determining the intensity densities of space-time Poisson flows for both stochastic and deterministic structures of inhomogeneity subdomains. It is shown that for the stochastic case, taking into account the random nature of their location leads to a solution significantly different from the singular case.The scope of possible practical use of the results obtained for tasks related to the search for objects of observation is determined.
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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 (September 28, 2021): 6473. http://dx.doi.org/10.3390/s21196473.
Abstract:
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 measurements. A seedless search heuristic is also provided to find the most likely pose estimate in light of these measurements. The method is demonstrated to provide for pose estimation (2D and 3D shape poses as well as joint-space searches), object identification/classification, and platform localisation. Furthermore, the method is shown to be robust in cluttered or non-segmented point cloud data as well as being robust to measurement uncertainty and extrinsic sensor calibration.
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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 (April 29, 2024): 462–74. http://dx.doi.org/10.52783/jes.2674.
Abstract:
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 balance exploration and exploitation in search space while avoiding local optimization. A Caviar model estimates the parameter over time by autoregressive process and determine a parameter by regression analysis. The advantages of these two optimization algorithms lead to better monetary policy forecasts. The experimental results show that Caviar-HGSO_DeepLSTM performs better regarding MAE, MSE, and RMSE, namely 0.438, 0.103, and 0.351.
31
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.
Abstract:
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 input is first translated into an alternative representation called multi-valued planning tasks, which makes many of the implicit constraints of a propositional planning task explicit. Exploiting this alternative representation, Fast Downward uses hierarchical decompositions of planning tasks for computing its heuristic function, called the causal graph heuristic, which is very different from traditional HSP-like heuristics based on ignoring negative interactions of operators. In this article, we give a full account of Fast Downward's approach to solving multi-valued planning tasks. We extend our earlier discussion of the causal graph heuristic to tasks involving axioms and conditional effects and present some novel techniques for search control that are used within Fast Downward's best-first search algorithm: preferred operators transfer the idea of helpful actions from local search to global best-first search, deferred evaluation of heuristic functions mitigates the negative effect of large branching factors on search performance, and multi-heuristic best-first search combines several heuristic evaluation functions within a single search algorithm in an orthogonal way. We also describe efficient data structures for fast state expansion (successor generators and axiom evaluators) and present a new non-heuristic search algorithm called focused iterative-broadening search, which utilizes the information encoded in causal graphs in a novel way. Fast Downward has proven remarkably successful: It won the "classical'' (i.e., propositional, non-optimising) track of the 4th International Planning Competition at ICAPS 2004, following in the footsteps of planners such as FF and LPG. Our experiments show that it also performs very well on the benchmarks of the earlier planning competitions and provide some insights about the usefulness of the new search enhancements.
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Doerge, R. W., and G. A. Churchill. "Permutation Tests for Multiple Loci Affecting a Quantitative Character." Genetics 142, no. 1 (January 1, 1996): 285–94. http://dx.doi.org/10.1093/genetics/142.1.285.
Abstract:
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 conditioning on markers linked to major QTL. It allows for general nonadditive interactions among QTL, but its practical application is restricted to regions of the genome that are unlinked to the major QTL. The RET assumes a structural model for the effect of major QTL, and a threshold is constructed using residuals from this structural model. The search space for minor QTL is unrestricted, and RET-based tests may be more powerful than the CET-based test when the structural model is approximately true.
33
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.
Abstract:
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 train and infer the joint model, which has not been solved previously. We design a dynamic programming algorithm for both training and inference, whose complexity is O(n·4h), where n is the sentence length and h is the height limit. In addition, we derive a pruning algorithm for the joint model, which further prunes 99.9% of the search space with 2% loss of the ground truth data. Experimental results on the OntoNotes 4.0 dataset have demonstrated that the proposed model outperforms the state-of-the-art method by 2.79 points in the F1-measure.
34
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 (March 31, 2022): 1–21. http://dx.doi.org/10.1145/3487018.
Abstract:
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 capability for conditional constraints, we develop efficient search-space reduction techniques to achieve practical scalability. Experiments demonstrate that our SMT framework achieves 10× higher scalability than ILP (Integer Linear Programming) with 931.1× (ranges from 2.2× to 1532.1×) and 1237.1× (ranges from 4× to 4373.8×) faster average runtimes for finding optimum solutions on 2D and 3D SMART NoCs and our 2D and 3D extensions of the SMT framework with mixed dimension-order routing also maintain the improved scalability with the extended and diversified routing paths, resulting in reduced application schedule lengths throughout various application benchmarks.
35
Troumbis, Andreas, Cleo Gagani, and Haralambos Sideropoulos. "Probabilistic Wildfire Risk Assessment and Modernization Transitions: The Case of Greece." Fire 6, no. 4 (April 14, 2023): 158. http://dx.doi.org/10.3390/fire6040158.
Abstract:
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 evolution in the past decades and a prediction of its trends in Greece. We use power law distributions of the fire frequency–magnitude relationship to estimate the basic SOC parameters and the Weibull reliability method to calculate large-size wildfires’ conditional probability as a time function. We use automatic linear modeling to search for the most accurate relationship between wildfire metrics and the best subset of modernization predictors. The discussion concentrates on reframing the political debate on fire prevention vs. suppression, its flaws and limitations, and the core challenges for adopting more efficient wildfire management policies in Greece.
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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 (September 26, 2021): 1013. http://dx.doi.org/10.3390/life11101013.
Abstract:
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 adversarial networks model to get an ECG segment generator, and thus to increase the number of training data. Then, we pre-train a deep quality assessment model based on a training dataset composed of real and generated ECG. Finally, we fine-tune the proposed model using real ECG and validate it on two different datasets composed of real ECG. The proposed system has a generalized performance on the two validation datasets. The model’s accuracy is 97.1% and 96.4%, respectively for the two datasets. The proposed method outperforms a shallow neural network model, and also a deep neural network models without being pre-trained by generated ECG. The proposed system demonstrates improved performance in the ECG quality assessment, and it has the potential to be an initial ECG quality screening tool in clinical practice.
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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 (November 24, 2021): 5992–6007. http://dx.doi.org/10.1093/mnras/stab3372.
Abstract:
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 for line-like features in patches of the sky. In this paper, we describe the via machinae algorithm in detail and demonstrate our approach on the prominent stream GD-1. Though some parts of the algorithm are tuned to increase sensitivity to cold streams, the via machinae technique itself does not rely on astrophysical assumptions, such as the potential of the Milky Way or stellar isochrones. This flexibility suggests that it may have further applications in identifying other anomalous structures within the Gaia data set, for example debris flow and globular clusters.
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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 (May 13, 2019): 489. http://dx.doi.org/10.3390/e21050489.
Abstract:
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 sorting strategy and extend KDB from a single restricted network to unrestricted ensemble networks, i.e., unrestricted Bayesian classifier (UKDB), in terms of Markov blanket analysis and target learning. Target learning is a framework that takes each unlabeled testing instance P as a target and builds a specific Bayesian model Bayesian network classifiers (BNC) P to complement BNC T learned from training data T . UKDB respectively introduced UKDB P and UKDB T to flexibly describe the change in dependence relationships for different testing instances and the robust dependence relationships implicated in training data. They both use UKDB as the base classifier by applying the same learning strategy while modeling different parts of the data space, thus they are complementary in nature. The extensive experimental results on the Wisconsin breast cancer database for case study and other 10 datasets by involving classifiers with different structure complexities, such as Naive Bayes (0-dependence), Tree augmented Naive Bayes (1-dependence) and KDB (arbitrary k-dependence), prove the effectiveness and robustness of the proposed approach.
39
Vennos, Amy, Kiernan George, and Alan Michaels. "Attacks and Defenses for Single-Stage Residue Number System PRNGs." IoT 2, no. 3 (June 25, 2021): 375–400. http://dx.doi.org/10.3390/iot2030020.
Abstract:
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 equivalently key, of the RNS algorithm. We develop and demonstrate a conditional entropy analysis that permits extraction of the key given a priori knowledge of state transitions as well as reverse engineering of the RNS PRNG algorithm and parameters (but not the key) in problems where the multiplicative RNS characteristic is too large to obtain a priori state transitions. We then discuss multiple defenses and perturbations for the RNS system that fool the original attack algorithm, including deliberate noise injection and code hopping. We present a modification to the algorithm that accounts for deliberate noise, but rapidly increases the search space and complexity. Lastly, we discuss memory requirements and time required for the attacker and defender to maintain these defenses.
40
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.
Abstract:
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. Иными словами, поселение, которое в одном регионе по своему масштабу, сложности планиграфии и архитектуры и геоэкономическому потенциалу является центром и может считаться городом, в других условиях было очевидной периферией, заурядной деревней (селом). Задачей истории, археологии, антропологии, социологии и исторической географии является не поиск условных критериев города, а реконструкция многоуровневой структуры урбанизации территорий и анализ систем описания и номинации поселений в документах и нарративах без их принудительного перевода в современную систему терминов и обиходных лексем. The article proposes to abandon attempts to identify “objective” quantitative and qualitative criteria for a “real city”. The main distinguishing feature of settlements, which can be conventionally designated as “cities,” is the concentration of demographic, material, and virtual (power, sacredness, knowledge, & etc.) resources. The status of a “city” for any stationary settlement of people is determined only by its dominant position in the socio-political space relative to other settlements, which allows it to accumulate resources. Consequently, “city” is a conditional and relative category. In other words, a settlement, which in one region in terms of its scale, complexity of planning and architecture, and geo-economic potential can be considered as a “city”, in other circumstances would be an obvious periphery, an ordinary village (town). The task of history, archeology, anthropology, sociology, and historical geography is not to search for conventional criteria of a “city”, but to reconstruct the multi-level structure of urbanization of territories and analyze systems for describing and nominating settlements in documents and narratives without their forced translation into the modern system of terms and everyday lexemes.
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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 (February 2023): 95–115. http://dx.doi.org/10.31107/2075-1990-2023-1-95-115.
Abstract:
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 take into account the features of the Ethereum blockchain in the price dynamics of the Ether cryptocurrency?" This question is also directly related to the search for potential fundamental factors that can explain the price dynamics of Ether. The main econometric method used in the study is generalized autoregressive conditional heteroskedasticity (GARCH) models. Having evaluated about 15 thousand different specifications of GARCH models, where various Ethereum blockchain usage metrics were used as explanatory variables, we obtained the results that Ethereum network usage metrics do not significantly correlate with Ether cryptocurrency returns. Moreover, these metrics are also unable to explain the relative strengthening/weakening of Ether relative to Bitcoin. Thus, we conclude that despite the presence of a number of special functional properties of the Ethereum blockchain, the price dynamics of the Ether cryptocurrency does not reflect them.
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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.
Abstract:
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) is one of the popular methods for parameter optimization. The novel GWO-based parameters selection approach for GARCH and ARIMA models aims to improve stock price prediction accuracy by optimizing the parameters of ARIMA and GARCH models. The hierarchical structure of GWO comprises four distinct categories: alpha (α), beta (β), delta (δ) and omega (ω). The predatory conduct of wolves primarily encompasses the act of pursuing and closing in on the prey, tracing the movements of the prey, and ultimately launching an attack on the prey. In the proposed context, attacking prey is a selection of the best parameters for GARCH and ARIMA models. The GWO algorithm iteratively updates the positions of wolves to provide potential solutions in the search space in GARCH and ARIMA models. The proposed model is evaluated using root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE). The GWO-based parameter selection for GARCH and ARIMA improves the performance of the model by 5% to 8% compared to existing traditional GARCH and ARIMA models.
43
Mykhalchuk, Nataliia, Ернест Івашкевич, Лариса Гронь, and Наталія Кваснецька. "CHALLENGES OF ONLINE STUDYING: THE THEORY AND THE PRACTICE." Інноватика у вихованні, no. 18 (December 6, 2023): 96–106. http://dx.doi.org/10.35619/iiu.v1i18.561.
Abstract:
In the article we’d the aim to study challenges of on-line studying, to show the technical characteristics of online learning, to single out the pedagogical and psychological features of online learning. So, we described teaching method which are in a great degree enriched by the integration of information technologies into them; also, we defined the concept of “information and communication technology”; we showed the most commonly used information and communication technology’s method for assessing learner’s listening skills and giving feedback; we presented the field of information and different communication technologies.
The technical characteristics of online learning are: virtuality / conventionality of educational space and time; interactivity of educational methods and non-linearity of the communicative process, dispersion of its structure, synchronicity of communicative acts; the presence of an electronic signal, which determines the possibility of constant access to the Internet; distance and indirectness of online learning; preservation of the transcript of the educational process, the opportunity for teachers and students to reflect on their own activities; ease of updating and changing the content of text material and perceived educational material, which determine the efficiency of students’ performance of tasks and the ways of solving problems; more effective process of subject-subject interaction, openness in communication; conditional socialization; the possibility of simultaneous electronic search and syndication and the process of perceiving new information.
We also singled out the pedagogical and psychological features of online learning, such as: significant limitation of the person’s sensory experience to visual and auditory ones; a sense of a space and time, which is more different from a real image of the world; the presence of students in a state of continuous distracted attention, the feeling of being constantly overloaded with unnecessary educational material; the appearance of negative emotions in the case of rather limited access to wider cyberspace; the implementation of permanent recording of the results of cyber activity; multifacetedness and multiplicity of personality, virtual identity, disinhibition of “online self”; alignment of personal statuses and profiles, unlimited access to various interactions; change of personal priorities and behavior, feeling of complete “browsing”, “web surfing”, “filter bubble”.
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Barysheva, Elena V. "‘Galvanize the Streets to Life with Joy of Celebration.’ V. E. Meyerhold and Demonstration Decoration." Herald of an archivist, no. 4 (2018): 1103–14. http://dx.doi.org/10.28995/2073-0101-2018-4-1103-1114.
Abstract:
The avant-garde director V. E. Meyerhold accepted the ideas of the October Revolution of 1917 with enthusiasm. His search for new theatrical forms seemed interconnected with agitation mass art and grandiose festivities on the days of revolutionary holidays in particular. The article sequentially reviews several documents on preparation of the 15th anniversary of the October Revolution (1932) and activities of the Central Art Commission (the October Commission) under the Moscow Soviet. V. Meyerhold took part in the work of the Commission. In his report ‘On artistic design of demonstrations,’ Meyerhold underscored main features of the demonstration, he assessed stage space for theatrical presentation of the holiday within the urban landscape and conditional and imaginative idiom of the theatrical performance scenography as parts of the festive culture stylistics. A tradition and rituals of celebrating the revolution anniversaries had already taken shape by 1930s. V. Meyerhold evinced the causes of mistakes inherent in all festive events of 1930s, such as passivity and even apathy of participants, clich?s in design, redundancy of slogans. Moreover, he made recommendations for decorating festive demonstrations and even city squares, streets, and houses. Following the meeting of the Moscow City Committee of the VKP(B) and of the Mossovet (Moscow Soviet of People's Deputies), a series of methodological recommendations and guidelines on the procedure for conducting and decorating holiday demonstrations was written. The main idea was to involve the demonstrators themselves in the festive events, to engage massoviks (organizers of popular cultural and recreational activities), athletes, entertainers, harmonists, chorus, orchestra, to use allegorical signs and emblems. And yet all activities were controlled and coordinated by central and district commissions, whose function was to instruct factories to organize demonstration columns with stylistic and topical continuity.
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Ankhym, O., and M. Ankhym. "BERTOLT BRECHT AND THE TRANSNATIONAL THEATRE." Brecht-Magazine: Articles, Essays, Translations, no. 9 (December 26, 2023): 14–24. http://dx.doi.org/10.35433/brecht.9.2023.14-24.
Abstract:
Transnationality and transculturality, which have been attracting increasing attention within the scientific community in recent decades, have become an integral part of modern literature. Decolonization and globalization, as well as the mobility and migration caused by them, have become triggers for the blurring of ethnic, national, and cultural boundaries. These processes have also found their embodiment in literature. The transnational turn observed in literature since the 1990s shows that the understanding of literary works within national frameworks no longer corresponds to modern reality, and nations, cultures, and identities are featured with convention, fragmentation, and hybridity. And although consideration of transnationality is mostly focused on lyric and epic works, its manifestations take place in drama and theatre as well.
This article examines the influence of Bertolt Brecht on contemporary transnational and/or transcultural theatre. It is noted that the manifestation of transnational elements in the playwright's work occurs both at the level of his plays and performances. In particular, the effects of alienation used by B. Brecht to depict the strange, foreign and unusual in one's own self have a significant impact on the formation of transnational theatre. Another manifestation of the transnational is the playwright's departure from the idea of closed nations and cultures in favour of open, conditional and fluid ones. An important aspect is also the intertextual references of his plays, which are artistic interpretive models of famous works of the past and thus enable their existence through space and time. Focusing on the processes of alienation, B. Brecht's epic theatre shows the hybridity and fragmentation not only of cultures and nations, but also of identities, therefore encouraging readers and viewers to search for the strange in themselves. At the same time, topics and problems raised in his plays and performances are not limited to local or national contexts, but are transnational in their nature.
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Chen, Chaohui, Guohua Gao, Ruijian Li, Richard Cao, Tianhong Chen, Jeroen C. Vink, and Paul Gelderblom. "Global-Search Distributed-Gauss-Newton Optimization Method and Its Integration With the Randomized-Maximum-Likelihood Method for Uncertainty Quantification of Reservoir Performance." SPE Journal 23, no. 05 (May 11, 2018): 1496–517. http://dx.doi.org/10.2118/182639-pa.
Abstract:
Summary Although it is possible to apply traditional optimization algorithms together with the randomized-maximum-likelihood (RML) method to generate multiple conditional realizations, the computation cost is high. This paper presents a novel method to enhance the global-search capability of the distributed-Gauss-Newton (DGN) optimization method and integrates it with the RML method to generate multiple realizations conditioned to production data synchronously. RML generates samples from an approximate posterior by minimizing a large ensemble of perturbed objective functions in which the observed data and prior mean values of uncertain model parameters have been perturbed with Gaussian noise. Rather than performing these minimizations in isolation using large sets of simulations to evaluate the finite-difference approximations of the gradients used to optimize each perturbed realization, we use a concurrent implementation in which simulation results are shared among different minimization tasks whenever these results are helping to converge to the global minimum of a specific minimization task. To improve sharing of results, we relax the accuracy of the finite-difference approximations for the gradients with more widely spaced simulation results. To avoid trapping in local optima, a novel method to enhance the global-search capability of the DGN algorithm is developed and integrated seamlessly with the RML formulation. In this way, we can improve the quality of RML conditional realizations that sample the approximate posterior. The proposed work flow is first validated with a toy problem and then applied to a real-field unconventional asset. Numerical results indicate that the new method is very efficient compared with traditional methods. Hundreds of data-conditioned realizations can be generated in parallel within 20 to 40 iterations. The computational cost (central-processing-unit usage) is reduced significantly compared with the traditional RML approach. The real-field case studies involve a history-matching study to generate history-matched realizations with the proposed method and an uncertainty quantification of production forecasting using those conditioned models. All conditioned models generate production forecasts that are consistent with real-production data in both the history-matching period and the blind-test period. Therefore, the new approach can enhance the confidence level of the estimated-ultimate-recovery (EUR) assessment using production-forecasting results generated from all conditional realizations, resulting in significant business impact.
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Loveykin, Vyacheslav, Dmitriy Mishchuk, and Yeuvgen Mishchuk. "Implementation of optimal energy displacements of the working tool of the two-link manipulator along the parabolic trajectory." Mechanics and Advanced Technologies 6, no. 1 (May 31, 2022): 14–23. http://dx.doi.org/10.20535/2521-1943.2022.6.1.253476.
Abstract:
The intensive spread of automated and robotic systems in the construction industry poses a number of problematic and unsolved problems related to the efficiency and reliability of their use, namely: reducing dynamic loads in the structural elements of robots and manipulators, reducing energy costs to perform a given process by a robotic system. Particular attention is paid to the quality of control, in particular, in a limited working space when moving working bodies with hydraulically actuated manipulators, which are dominant in construction.
Problems: For welding of metal structures or when laying building elements using handling systems, the technology for performing such work involves the use of the tasks of moving special working bodies along parabolic trajectories. To implement the tasks set by manipulators, it is necessary to determine the control laws for the drive system. One of the ways to find the necessary functions for the control system is the use of optimization problems according to energy criteria and imposed geometric restrictions.
Purpose: to develop and investigate the modes of movement of the drive mechanism of a hydraulic manipulator with the implementation of an energy-intensive mode of operation of a mechanical system in a given space of movement of the working body along a hyperbolic trajectory.
Methodology: To achieve the goals of the study, it is proposed to use the optimization problem of minimizing energy consumption in the boom system of a two-link manipulator on a given parabolic trajectory of movement of its working body in a limited working space. In this paper, we consider the problem of conditional optimization, where the restrictions of the working space are imposed by the conditions of movement of the working body and the limiting restrictions on the movement of actuators. The objective optimization function is formed in the form of Lagrange equations from the components of energy consumption and the equation of a parabola that specifies the movement of the manipulator grip.
Results: To implement the optimal control of a two-link manipulator on a given parabolic trajectory, it is necessary to determine the extremals of the objective function functional in the form of the Lagrange equation for the components, which in this study were convolutions from the dependencies of energy consumption and the given equation for the trajectory of movement of the working body. The search for the minimum of the objective function is obtained in numerical form, based on which the form of the polynomial of the analytical dependence of the generalized coordinates on time is determined.
Conclusions: In further research, it is desirable to consider criteria that take into account various force loads, in particular, the root-mean-square value of the drive force and the intensity of its change over time, and it is also necessary to develop polynomial functions that can be used to express numerical solutions to optimization problems.
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Плєтньов, Михайло Васильович. "ТРАНСФОРМАЦІЯ ЕКОНОМІЧНОГО ПРОСТОРУ УКРАЇНИ ЧЕРЕЗ ВІЙНУ." TIME DESCRIPTION OF ECONOMIC REFORMS, no. 2 (July 9, 2022): 25–30. http://dx.doi.org/10.32620/cher.2022.2.04.
Abstract:
During the war, the spatial features of Ukraine developed independently, but the impact of the war on the economy requires a comprehensive analysis and assessment of economic, human and infrastructural losses at the regional level. Such an analysis also involves determining changes in the quality of life of the population in different regions of Ukraine and monitoring the existing changes taking place in the economic space of the country. The aim of the article is to determine the features of the transformation of Ukraine's economy during the war / after the war, taking into account the spatial aspect. Methods used in the research: comparison, systematization, sociological survey, etc. The hypothesis of the research was the assumption that due to the great losses and destruction of Ukraine's economy is the transformation of the economic space, and for its restoration it is appropriate to form areas of priority development along borders. Presenting the main material. According to the results of research on the peculiarities of the transformation of Ukraine's economy during the war / after the war, taking into account the spatial aspect, it was found that the war in Ukraine intensified migration processes, people move to the West or abroad. Due to the destruction of transport infrastructure, Ukraine's transport potential has been destroyed. Ukraine's economic losses also occurred due to damage and destruction of industrial infrastructure in Ukraine's industrial cities. It is established that the territories differ significantly in the degree of destruction, even in the scale of individual regions and cities. Therefore, the article presents a conditional gradation of the social quality of life of Ukrainians because of the war. In order to improve the quality of the population and despite the continuation of the war, it is necessary to plan the restoration and development of the regions, which are divided into three macro-regions. It is established that the destroyed regions need to be restored by introducing priority development areas for which state support areas have been identified. The originality and practical significance of the research lies in the formation of areas of priority development in the postwar period. Conclusions. It requires changes or approval of a new version of the State Strategy for Regional Development until 2027, changes in the formation and use of funds from the State Fund for Reconstruction and Regional Development, the search for donors in the EU and more. The further researches should be aimed at adjusting regulations on regional development
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Dorosh-Kizym, M., O. Dadak, and T. Gachek. "Logistics services as an inalienable complex of the electronic commerce." Scientific Messenger of LNU of Veterinary Medicine and Biotechnologies 20, no. 91 (November 16, 2018): 51–56. http://dx.doi.org/10.32718/nvlvet9111.
Abstract:
In recent years, e-commerce has been able to penetrate practically in all spheres of life of the population and in Ukraine. The domestic e-commerce market is only at the inception stage, but at the same time it has a significant potential for development. The Internet and related technologies continue to actively and effectively interfere with logistics. Modern logistics technologies are inextricably linked with intensive information exchange. Due to the timely receipt of information, high accuracy, speed and consistency of goods exchange in logistic chains are ensured. Over the past few years, the structure of the logistics space of the Internet has significantly changed, which was reflected in the transformation of the content of logistics-oriented sites. If advertising information dominated previously, now, often in interactive mode, organizational, reference and design services are presented. Business commerce, advertising, production and modern technologies of the Internet are all more vibrant in all strata of human life. Today, the penetration of Internet technologies into business and economics is growing rapidly. The Internet is now – a huge market with a multi-level structure with abyss of opportunities that has an incredible potential for solving problems and business. This penetration also affects logistics. Logistics technologies are unthinkable without intensive information exchange. It is owing to the timely receipt of information that provides high accuracy, speed and consistency of turnover in logistics chains. Therefore, logistics as a modern scientific and practical direction of commodity distribution is also rapidly mastering these technologies and in its own way is equipped on the network. The Internet as a technology of global open networks is the best means to attract a wide range of logistics service users. With the help of this technology can be provided: advertising company; providing a list of services and price lists; account of regular clients and companions; providing consumers with the necessary documents on a paid and free basis; interactive advisory service; counteragent search service; registries of logistics companies and the database of information and logistics resources in the network; electronic freight; monitoring of goods and vehicles; virtual agency and forwarding. Today, the process of accumulation of logistics resources in the Internet has reached a level that allows us to talk about the process of forming virtual logistics centers (commercial or conditional commercial). This, in turn, with further development, can form a single logistics information space on the Internet.
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Kvasnytsia, Roksoliana. "Scenography as a Design Component of Presentation spaces for Fashion Shows." Bulletin of Lviv National Academy of Arts, no. 41 (December 26, 2019): 57–64. http://dx.doi.org/10.37131/2524-0943-2019-41-05.
Abstract:
Competition for the attention of consumers in the modern fashion industry makes the problem of finding new effective means of marketing communications a topical issue. Fashion designers are interested in creating conditions of high quality for a vivid demonstration of new collections, it enables to increase the popularity of the brand, to strengthen the prestige on the market, to attract the attention of potential customers to the products. Modern trends demonstrate how often designers prefer original and unusual locations for defile, which are oriented on maximally spectacular design planning of the presentation space, in order to stand out against the background of the general information flow in any way. Various studies were devoted to this problem directly or indirectly, its various aspects were studied in many papers, but the peculiarities of forming scenography for fashion defile in different types of locations were not brought together as one of the design planning problems, which determined the topic of the study. The purpose of the article is to demonstrate the significance of scenography in modern design planning of demonstration spaces, to reveal the peculiarities of the monumental and decorative design of fashion shows. The article deals with the main types of scenery for outdoor and indoor location: interior, landscape, special and project ones. It has been defined what kind of differences exist between the scenery for fashion shows and theatrical and cinematic scenery. It has been determined under which principles the decorative design of presentation spaces is formed. • The scenery on the demonstration site shall look flawless, since the audience often stands very close and interacts with objects, unlike theatrical stage or cinema, where it may look “conditional”. And there is, also, photo and video shooting. Thus, performance, use of materials and detalization of decoration require high quality. • Additional equipment, namely stage structures, illumination systems, sound equipment, is visually open to the audience on the fashion shows and it also requires a comprehensive approach with decorative design. In the cinema and theater, these things remain off screen, are not visually active. • Installation and disassembly of scenery in a short time frame, since it is installed only for the duration of the show, often in the leased spaces. It is usually one-time. • Scenery at the outdoor locations in the open air on fashion shows requires special decorative objects, scale observed towards space, and professional illumination, due to photo and video shooting. • Objective decorative filling shall provide ecological, functional and aesthetic comfortability, these factors determine the general comfort. Hence, the decorative design of the environment facilitates the disclosure of the main theme of the collection, the concept as a whole, provides performance with a certain form, enhances the impact of the show on the audience, determines the style of the performance. It is an indisputable fact that the decorative design, created by highly skilled experts for fashion shows, does not cease to astonish and captivate. Theatricalization of fashion shows and the development of modern technologies have led to the creative search and implementation of the most unexpected ideas of scenery, stage-properties and props. This article opens the following prospects in the study of the design of demonstration spaces: to study the specifics of use of new technologies in modern scenography and their influence on the formation of the artistic image of Ukrainian fashion defile.