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Journal articles on the topic 'Preallocation'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Greatbatch, David. "A turn-taking system for British news interviews." Language in Society 17, no. 3 (September 1988): 401–30. http://dx.doi.org/10.1017/s0047404500012963.

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ABSTRACTThe British news interview turn-taking system operates through a simple form of turn-type preallocation. This article shows that a large number of the systematic differences between the news interview and mundane conversation are a product of these constraints on the production of types of turns. It then explores the relationship of turn-type preallocation in news interviews to the background legal and institutional restrictions on British broadcast journalists. In so doing, it notes how the organisation of turn-taking in two other types of broadcast interview can differ from that in the news interview due to differences between the institutionalized footings that the interviewers are conventionally required to maintain within them. (Conversation analysis, mass communication, British speech, turn-taking systems, institutional talk)

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2

Gheorghioiu, Ovidiu, Alexandru Salcianu, and Martin Rinard. "Interprocedural compatibility analysis for static object preallocation." ACM SIGPLAN Notices 38, no. 1 (January 15, 2003): 273–84. http://dx.doi.org/10.1145/640128.604154.

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3

Elsesser, Gary W., Viet N. Ngo, Sourav Bhattacharya, and Wei Tek Tsai. "Processor preallocation and load balancing of DOALL loops." Journal of Supercomputing 8, no. 2 (June 1994): 135–61. http://dx.doi.org/10.1007/bf01204659.

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4

Belim, S. Yu. "KEY PREALLOCATION PROTOCOL BASED ON A SECRET SHARING SCHEME." Applied Mathematics and Fundamental Informatics 7, no. 2 (2020): 018–22. http://dx.doi.org/10.25206/2311-4908-2020-7-2-18-22.

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The symmetric encryption session key generation protocol based on the Shamir secret sharing scheme and the Blom key predistribution scheme is proposed. Predistribution of key materials through secure channels is used. Key calculation is based on symmetric polynomials from three variables. Key calculation is based on threshold scheme (3,4). The basic protocol for two participants was considered. A scheme for an arbitrary number of users has been summarized.

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5

Chen, Albert Y., Tsung-Yu Lu, Matthew Huei-Ming Ma, and Wei-Zen Sun. "Demand Forecast Using Data Analytics for the Preallocation of Ambulances." IEEE Journal of Biomedical and Health Informatics 20, no. 4 (July 2016): 1178–87. http://dx.doi.org/10.1109/jbhi.2015.2443799.

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6

Guo, Yu, Zhenqiang Mi, Yang Yang, Huan Ma, and Mohammad S. Obaidat. "Efficient Network Resource Preallocation on Demand in Multitenant Cloud Systems." IEEE Systems Journal 13, no. 4 (December 2019): 4027–38. http://dx.doi.org/10.1109/jsyst.2019.2910276.

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7

Mu, Rongjun, and Xin Zhang. "Control Allocation Design of Reaction Control System for Reusable Launch Vehicle." Abstract and Applied Analysis 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/541627.

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During the early stage of reusable launch vehicle (RLV) reentry flight, reaction control system (RCS) is the major attitude control device. RCS, which is much different from the atmospheric steer’s control, requires a well designed control allocation system to fit the attitude control in high altitude. In this paper, an indexed control method was proposed for RCS preallocation, a 0-1 integer programming algorithm was designed for RCS allocation controller, and then this RCS scheme’s effect was analyzed. Based on the specified flight mission simulation, the results show that the control system is satisfied. Moreover, several comparisons between the attitude control effect and RCS relevant parameters were studied.

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8

Shobaki, Ghassan, Maxim Shawabkeh, and Najm Eldeen Abu Rmaileh. "Preallocation instruction scheduling with register pressure minimization using a combinatorial optimization approach." ACM Transactions on Architecture and Code Optimization 10, no. 3 (September 16, 2013): 1–31. http://dx.doi.org/10.1145/2512432.

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9

He, Shuibing, Yuanhua Yang, Xianbin Xu, and Xiaohua Xu. "MGPA: a multi-granularity space preallocation algorithm for object-based storage devices." International Journal of Embedded Systems 8, no. 2/3 (2016): 237. http://dx.doi.org/10.1504/ijes.2016.076116.

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10

Chuan-Ching Sue and Sy-Yen Kuo. "Design and analysis of accelerative preallocation protocol for WDM star-coupled networks." Journal of Lightwave Technology 20, no. 3 (March 2002): 338–49. http://dx.doi.org/10.1109/50.988982.

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11

Nao, Marion. "The preallocation of student topic nomination and negotiated compliance in conversation-for-learning." Journal of Applied Linguistics and Professional Practice 7, no. 3 (February 11, 2013): 251–72. http://dx.doi.org/10.1558/japl.v7i3.251.

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12

Luan, Siliang, Qingfang Yang, Wei Wang, Zhongtai Jiang, Ruru Xing, and Ruijuan Chu. "Random Regret-Minimization Model for Emergency Resource Preallocation at Freeway Accident Black Spots." Journal of Advanced Transportation 2018 (October 16, 2018): 1–13. http://dx.doi.org/10.1155/2018/3513058.

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The preallocation of emergency resources is a mechanism increasing preparedness for uncertain traffic accidents under different weather conditions. This paper introduces the concept of accident probability of black spots and an improved accident frequency method to identify accident black spots and obtain the accident probability. At the same time, we propose a three-stage random regret-minimization (RRM) model to minimize the regret value of the attribute of overall response time, cost, and demand, which allocates limited emergency resources to more likely to happen accident spots. Due to the computational complexity of our model, a genetic algorithm is developed to solve a large-scale instance of the problem. A case study focuses on three-year rainy accidents’ data in Weifang, Linyi, and Rizhao of China to test the correctness and validity of the application of the model.

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13

Changho Yun and Kiseon Kim. "Performance analysis of modified accelerative preallocation MAC protocol for passive star-coupled WDMA networks." Journal of Lightwave Technology 24, no. 4 (April 2006): 1663–73. http://dx.doi.org/10.1109/jlt.2006.870970.

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14

Wen, Yean-Fu, and Wanjiun Liao. "Spectrum Section Preallocation for Cooperative Sensing and Transmission in Cognitive Radio Ad Hoc Networks." IEEE Transactions on Vehicular Technology 66, no. 10 (October 2017): 8910–25. http://dx.doi.org/10.1109/tvt.2017.2700200.

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15

Xiao, Kaile, Weisong Shi, Zhipeng Gao, Congcong Yao, and Xuesong Qiu. "DAER: A Resource Preallocation Algorithm of Edge Computing Server by Using Blockchain in Intelligent Driving." IEEE Internet of Things Journal 7, no. 10 (October 2020): 9291–302. http://dx.doi.org/10.1109/jiot.2020.2984553.

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16

de la Garza, Jesús M., Apirath Prateapusanond, and Nikhil Ambani. "Preallocation of Total Float in the Application of a Critical Path Method Based Construction Contract." Journal of Construction Engineering and Management 133, no. 11 (November 2007): 836–45. http://dx.doi.org/10.1061/(asce)0733-9364(2007)133:11(836).

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17

Cooney, Gus, Daniel T. Gilbert, and Timothy D. Wilson. "When fairness matters less than we expect." Proceedings of the National Academy of Sciences 113, no. 40 (September 16, 2016): 11168–71. http://dx.doi.org/10.1073/pnas.1606574113.

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Do those who allocate resources know how much fairness will matter to those who receive them? Across seven studies, allocators used either a fair or unfair procedure to determine which of two receivers would receive the most money. Allocators consistently overestimated the impact that the fairness of the allocation procedure would have on the happiness of receivers (studies 1–3). This happened because the differential fairness of allocation procedures is more salient before an allocation is made than it is afterward (studies 4 and 5). Contrary to allocators’ predictions, the average receiver was happier when allocated more money by an unfair procedure than when allocated less money by a fair procedure (studies 6 and 7). These studies suggest that when allocators are unable to overcome their own preallocation perspectives and adopt the receivers’ postallocation perspectives, they may allocate resources in ways that do not maximize the net happiness of receivers.

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18

Zheng, Fei, Zhao Pi, Zou Zhou, and Kaixuan Wang. "LEO Satellite Channel Allocation Scheme Based on Reinforcement Learning." Mobile Information Systems 2020 (December 12, 2020): 1–10. http://dx.doi.org/10.1155/2020/8868888.

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Delay, cost, and loss are low in Low Earth Orbit (LEO) satellite networks, which play a pivotal role in channel allocation in global mobile communication system. Due to nonuniform distribution of users, the existing channel allocation schemes cannot adapt to load differences between beams. On the basis of the satellite resource pool, this paper proposes a network architecture of LEO satellite that utilizes a centralized resource pool and designs a combination allocation of fixed channel preallocation and dynamic channel scheduling. The dynamic channel scheduling can allocate or recycle free channels according to service requirements. The Q-Learning algorithm in reinforcement learning meets channel requirements between beams. Furthermore, the exponential gradient descent and information intensity updating accelerate the convergence speed of the Q-Learning algorithm. The simulation results show that the proposed scheme improves the system supply-demand ratio by 14%, compared with the fixed channel allocation (FCA) scheme and by 18%, compared with the Lagrange algorithm channel allocation (LACA) scheme. The results also demonstrate that our allocation scheme can exploit channel resources effectively.

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19

Ni, Bin, Remy Colin, Hannes Link, Robert G. Endres, and Victor Sourjik. "Growth-rate dependent resource investment in bacterial motile behavior quantitatively follows potential benefit of chemotaxis." Proceedings of the National Academy of Sciences 117, no. 1 (December 23, 2019): 595–601. http://dx.doi.org/10.1073/pnas.1910849117.

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Microorganisms possess diverse mechanisms to regulate investment into individual cellular processes according to their environment. How these regulatory strategies reflect the inherent trade-off between the benefit and cost of resource investment remains largely unknown, particularly for many cellular functions that are not immediately related to growth. Here, we investigate regulation of motility and chemotaxis, one of the most complex and costly bacterial behaviors, as a function of bacterial growth rate. We show with experiment and theory that in poor nutritional conditions,Escherichia coliincreases its investment in motility in proportion to the reproductive fitness advantage provided by the ability to follow nutrient gradients. Since this growth-rate dependent regulation of motility genes occurs even when nutrient gradients are absent, we hypothesize that it reflects an anticipatory preallocation of cellular resources. Notably, relative fitness benefit of chemotaxis could be observed not only in the presence of imposed gradients of secondary nutrients but also in initially homogeneous bacterial cultures, suggesting that bacteria can generate local gradients of carbon sources and excreted metabolites, and subsequently use chemotaxis to enhance the utilization of these compounds. This interplay between metabolite excretion and their chemotaxis-dependent reutilization is likely to play an important general role in microbial communities.

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20

Okazaki, Kohei, Eiichi Shimada, and Iwao Sasase. "Preferential transmission of multicast packets using a preallocation scheme in combination with a wavelength monitoring scheme on a RingO network." Electronics and Communications in Japan (Part II: Electronics) 90, no. 7 (2007): 56–68. http://dx.doi.org/10.1002/ecjb.20375.

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21

Sun, Ying, and Lei Bai. "Examination on Security Performance Analysis Model of Internet of Things Assigned Based on Composite Security Key." Wireless Communications and Mobile Computing 2021 (October 18, 2021): 1–14. http://dx.doi.org/10.1155/2021/8548981.

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Rapid development of Internet of Things technology makes the Internet interact and communicate with objects in the real world. The diversity of Internet of Things’ system architectures and the diversity of networks determine the complexity of their security issues. A key step in building an Internet of Things’ security system is to build a model and evaluate threats of security. First, this paper studies the distribution of key security about mobile ad hoc networks and analyzed the characteristics of the scene. To promote the certification of the key exchange strategy of both sides, it supports key exchange between the core node and multiple nodes for existing problems that management scheme of random key preallocation is limited by the storage space of the node through combination of key matrix elements which has less information to generate a large number of keys, and each sensor node only needs to store related parameters and key matrix. It saves the memory space of node greatly. The results of simulation show that the program has a strong security; it can fight against the common attacks of wireless sensor networks, such as node forgery attacks, message replay attacks, and denial of service attacks. At the same time, it is superior to traditional solutions in terms of network connectivity and nondestructibility, which can apply clustered wireless sensor network massively.

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22

De Nijs, Frits, Matthijs Spaan, and Mathijs De Weerdt. "Preallocation and Planning Under Stochastic Resource Constraints." Proceedings of the AAAI Conference on Artificial Intelligence 32, no. 1 (April 26, 2018). http://dx.doi.org/10.1609/aaai.v32i1.11592.

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Resource constraints frequently complicate multi-agent planning problems. Existing algorithms for resource-constrained, multi-agent planning problems rely on the assumption that the constraints are deterministic. However, frequently resource constraints are themselves subject to uncertainty from external influences. Uncertainty about constraints is especially challenging when agents must execute in an environment where communication is unreliable, making on-line coordination difficult. In those cases, it is a significant challenge to find coordinated allocations at plan time depending on availability at run time. To address these limitations, we propose to extend algorithms for constrained multi-agent planning problems to handle stochastic resource constraints. We show how to factorize resource limit uncertainty and use this to develop novel algorithms to plan policies for stochastic constraints. We evaluate the algorithms on a search-and-rescue problem and on a power-constrained planning domain where the resource constraints are decided by nature. We show that plans taking into account all potential realizations of the constraint obtain significantly better utility than planning for the expectation, while causing fewer constraint violations.

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23

Hofstetter, Emily. "Achieving Preallocation: Turn Transition Practices in Board Games." Discourse Processes, September 21, 2020, 1–21. http://dx.doi.org/10.1080/0163853x.2020.1816401.

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24

Nao, Marion. "The preallocation of student topic nomination and negotiated compliance in conversation-for-learning." Journal of Applied Linguistics and Professional Practice 1, no. 1 (May 7, 2013). http://dx.doi.org/10.1558/japl.v1.i1.17292.

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25

Li, Boda, Y. Chen, Wei Wei, Shengwei Mei, Yunhe Hou, and Shanshan Shi. "Preallocation of Electric Buses for Resilient Restoration of Distribution Network: A Data-Driven Robust Stochastic Optimization Method." IEEE Systems Journal, 2021, 1–12. http://dx.doi.org/10.1109/jsyst.2021.3123623.

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26

Tran, Brandon, Brennan Schaffner, Joseph M. Myre, Jason Sawin, and David Chiu. "Exploring Means to Enhance the Efficiency of GPU Bitmap Index Query Processing." Data Science and Engineering, November 30, 2020. http://dx.doi.org/10.1007/s41019-020-00148-8.

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AbstractOnce exotic, computational accelerators are now commonly available in many computing systems. Graphics processing units (GPUs) are perhaps the most frequently encountered computational accelerators. Recent work has shown that GPUs are beneficial when analyzing massive data sets. Specifically related to this study, it has been demonstrated that GPUs can significantly reduce the query processing time of database bitmap index queries. Bitmap indices are typically used for large, read-only data sets and are often compressed using some form of hybrid run-length compression. In this paper, we present three GPU algorithm enhancement strategies for executing queries of bitmap indices compressed using word aligned hybrid compression: (1) data structure reuse (2) metadata creation with various type alignment and (3) a preallocated memory pool. The data structure reuse greatly reduces the number of costly memory system calls. The use of metadata exploits the immutable nature of bitmaps to pre-calculate and store necessary intermediate processing results. This metadata reduces the number of required query-time processing steps. Preallocating a memory pool can reduce or entirely remove the overhead of memory operations during query processing. Our empirical study showed that performing a combination of these strategies can achieve 32.4$$\times$$ × to 98.7$$\times$$ × speedup over the current state-of-the-art implementation. Our study also showed that by using our enhancements, a common gaming GPU can achieve a $$15.0\times$$ 15.0 × speedup over a more expensive high-end CPU.

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