Academic literature on the topic 'Multiple disjoint supports'
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Journal articles on the topic "Multiple disjoint supports"
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Jin, Junwei, and Sanghyun Ahn. "A Multipath Routing Protocol Based on Bloom Filter for Multihop Wireless Networks." Mobile Information Systems 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/8151403.
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On-demand multipath routing in a wireless ad hoc network is effective in achieving load balancing over the network and in improving the degree of resilience to mobility. In this paper, the salvage capable opportunistic node-disjoint multipath routing (SNMR) protocol is proposed, which forms multiple routes for data transmission and supports packet salvaging with minimum overhead. The proposed mechanism constructs a primary path and a node-disjoint backup path together with alternative paths for the intermediate nodes in the primary path. It can be achieved by considering the reverse route back to the source stored in the route cache and the primary path information compressed by a Bloom filter. Our protocol presents higher capability in packet salvaging and lower overhead in forming multiple routes. Simulation results show that SNMR outperforms the compared protocols in terms of packet delivery ratio, normalized routing load, and throughput.
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Wu, Yuncheng, Naili Xing, Gang Chen, Tien Tuan Anh Dinh, Zhaojing Luo, Beng Chin Ooi, Xiaokui Xiao, and Meihui Zhang. "Falcon: A Privacy-Preserving and Interpretable Vertical Federated Learning System." Proceedings of the VLDB Endowment 16, no. 10 (June 2023): 2471–84. http://dx.doi.org/10.14778/3603581.3603588.
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Federated learning (FL) enables multiple data owners to collaboratively train machine learning (ML) models without disclosing their raw data. In the vertical federated learning (VFL) setting, the collaborating parties have data from the same set of users but with disjoint attributes. After constructing the VFL models, the parties deploy the models in production systems to infer prediction requests. In practice, the prediction output itself may not be convincing for party users to make the decisions, especially in high-stakes applications. Model interpretability is therefore essential to provide meaningful insights and better comprehension on the prediction output. In this paper, we propose Falcon, a novel privacy-preserving and interpretable VFL system. First, Falcon supports VFL training and prediction with strong and efficient privacy protection for a wide range of ML models, including linear regression, logistic regression, and multi-layer perceptron. The protection is achieved by a hybrid strategy of threshold partially homomorphic encryption (PHE) and additive secret sharing scheme (SSS), ensuring no intermediate information disclosure. Second, Falcon facilitates understanding of VFL model predictions by a flexible and privacy-preserving interpretability framework, which enables the implementation of state-of-the-art interpretable methods in a decentralized setting. Third, Falcon supports efficient data parallelism of VFL tasks and optimizes the parallelism factors to reduce the overall execution time. Falcon is fully implemented, and on which, we conduct extensive experiments using six real-world and multiple synthetic datasets. The results demonstrate that Falcon achieves comparable accuracy to non-private algorithms and outperforms three secure baselines in terms of efficiency.
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Salas, F. R., E. Boldrini, D. R. Maidment, S. Nativi, and B. Domenico. "Crossing the digital divide: an interoperable solution for sharing time series and coverages in Earth sciences." Natural Hazards and Earth System Sciences 12, no. 10 (October 2, 2012): 3013–29. http://dx.doi.org/10.5194/nhess-12-3013-2012.
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Abstract. In a world driven by the Internet and the readily accessible information it provides, there exists a high demand to easily discover and collect vast amounts of data available over several scientific domains and numerous data types. To add to the complexity, data is not only available through a plethora of data sources within disparate systems but also represents differing scales of space and time. One clear divide that exists in the world of information science and technology is the disjoint relationship between hydrologic and atmospheric science information. These worlds have long been split between observed time series at discrete geographical features in hydrologic science and modeled or remotely sensed coverages or grids over continuous space and time domains in atmospheric science. As more information becomes widely available through the Web, data are being served and published as Web services using standardized implementations and encodings. This paper illustrates a framework that utilizes Sensor Observation Services, Web Feature Services, Web Coverage Services, Catalog Services for the Web and GI-cat Services to index and discover data offered through different classes of information. This services infrastructure supports multiple servers of time series and gridded information, which can be searched through multiple portals, using a common set of time, space and concept query filters.
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Малиновский, Г. "Multiplying logical values." Logical Investigations 18 (May 3, 2012): 292–308. http://dx.doi.org/10.21146/2074-1472-2012-18-0-292-308.
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The modern history of many-valuedness starts with Lukasiewicz’s construction of three-valued logic. This pioneering, philosophically motivated and matrix based construction, first presented in 1918, was in 1922 extended to n-valued cases, including two infinite ones. Soon several constructions of many-valued logic appeared and the history of the topic became rich and interesting. However, as it is widely known, the problem of interpretation of multiple values is still among vexed questions of contemporary logic. With the paper, which essentially groups my earlier settlements, from [3], [4], [7] and [8], I intend to put a new thread into discussion on the nature of logical many-valuedness. The topics, touched upon, are: matrices, tautological and non-tautological many-valuedness, Tarski’s structural consequence and the Lindenbaum–Wojcicki completeness result, which supports the Suszko’s claim on logical two-valuedness of any structural logic. Consequently, two facets of many-valuedness — referential and inferential — are unravelled. The first, fits the standard approach and it results in multiplication of semantic correlates of sentences, and not logical values in a proper sense. The second many-valuedness is a metalogical property of inference and refers to partition of the matrix universe into more than two disjoint subsets, used in the definition of inference.
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Bakhshi Kiadehi, Katayoun, Amir Masoud Rahmani, and Amir Sabbagh Molahosseini. "Increasing fault tolerance of data plane on the internet of things using the software-defined networks." PeerJ Computer Science 7 (May 27, 2021): e543. http://dx.doi.org/10.7717/peerj-cs.543.
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Considering the Internet of Things (IoT) impact in today’s world, uninterrupted service is essential, and recovery has received more attention than ever before. Fault-tolerance (FT) is an essential aspect of network resilience. Fault-tolerance mechanisms are required to ensure high availability and high reliability in systems. The advent of software-defined networking (SDN) in the IoT plays a significant role in providing a reliable communication platform. This paper proposes a data plane fault-tolerant architecture using the concepts of software-defined networks for IoT environments. In this work, a mathematical model called Shared Risk Link Group (SRLG) calculates redundant paths as the primary and backup non-overlapping paths between network equipment. In addition to the fault tolerance, service quality was considered in the proposed schemes. Putting the percentage of link bandwidth usage and the rate of link delay in calculating link costs makes it possible to calculate two completely non-overlapping paths with the best condition. We compare our two proposed dynamic schemes with the hybrid disjoint paths (Hybrid_DP) method and our previous work. IoT developments, wireless and wired equipment are now used in many industrial and commercial applications, so the proposed hybrid dynamic method supports both wired and wireless devices; furthermore multiple link failures will be supported in the two proposed dynamic schemes. Simulation results indicate that, while reducing the error recovery time, the two proposed dynamic designs lead to improved service quality parameters such as packet loss and delay compared to the Hybrid_DP method. The results show that in case of a link failure in the network, the proposed hybrid dynamic scheme’s recovery time is approximately 12 ms. Furthermore, in the proposed hybrid dynamic scheme, on average, the recovery time, the packet loss, and the delay improved by 22.39%, 8.2%, 5.66%, compared to the Hybrid_DP method, respectively.
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Cox, Nicholas J. "Speaking Stata: From Rounding to Binning." Stata Journal: Promoting communications on statistics and Stata 18, no. 3 (September 2018): 741–54. http://dx.doi.org/10.1177/1536867x1801800311.
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This is a basic review of how to bin variables in Stata, meaning how to divide their range or support into disjoint intervals. I survey rounding functions with emphasis on floor and ceiling functions as tools to get clearly defined intervals of equal width. Using a specific display format is usually a better idea than rounding to multiples of a fraction. Quantile binning is popular in several fields. I give tips and tricks on how to produce such bins and also on how to show their limitations. Experimentation with the display command or Mata is a good way to learn about functions and to test binning rules.
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Atamian, Rubik, and Hossein Mansouri. "Student Career Preferences: In Support Of A New Learning Paradigm." Contemporary Issues in Education Research (CIER) 6, no. 2 (March 27, 2013): 223. http://dx.doi.org/10.19030/cier.v6i2.7732.
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According to the Bureau of Labor Statistics, throughout their careers, college graduates change multiple jobs and several careers, often remotely related to one another or to their major field of study. Experts project that the majority of newly created jobs requiring college education would involve extensive and prolonged on-the-job training of new hires, with soft skills gaining more prominence as determinants of professional success. Conversely, over the past several decades, higher education has followed a trend of compartmentalization of college education into narrowly defined disjointed disciplines each with a strict degree program. Such one-size-fits-all educational programs are unlikely to prepare prospective professionals for gainful employment in the emerging economy considering the new success indicators. This study presents a comparative exploratory analysis of accounting students career preferences by gender, age, grade point average, and academic classification. The study reveals notable differences in career preferences among students enrolled in the same academic program due to differences in gender, age, and academic classification.
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Gallina, Valentina, Silvia Torresan, Alex Zabeo, Andrea Critto, Thomas Glade, and Antonio Marcomini. "A Multi-Risk Methodology for the Assessment of Climate Change Impacts in Coastal Zones." Sustainability 12, no. 9 (May 2, 2020): 3697. http://dx.doi.org/10.3390/su12093697.
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Climate change threatens coastal areas, posing significant risks to natural and human systems, including coastal erosion and inundation. This paper presents a multi-risk approach integrating multiple climate-related hazards and exposure and vulnerability factors across different spatial units and temporal scales. The multi-hazard assessment employs an influence matrix to analyze the relationships among hazards (sea-level rise, coastal erosion, and storm surge) and their disjoint probability. The multi-vulnerability considers the susceptibility of the exposed receptors (wetlands, beaches, and urban areas) to different hazards based on multiple indicators (dunes, shoreline evolution, and urbanization rate). The methodology was applied in the North Adriatic coast, producing a ranking of multi-hazard risks by means of GIS maps and statistics. The results highlight that the higher multi-hazard score (meaning presence of all investigated hazards) is near the coastline while multi-vulnerability is relatively high in the whole case study, especially for beaches, wetlands, protected areas, and river mouths. The overall multi-risk score presents a trend similar to multi-hazard and shows that beaches is the receptor most affected by multiple risks (60% of surface in the higher multi-risk classes). Risk statistics were developed for coastal municipalities and local stakeholders to support the setting of adaptation priorities and coastal zone management plans.
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Gawas, Mahadev A., Lucy J. Gudino, and K. R. Anupama. "Congestion-Adaptive and Delay-Sensitive Multirate Routing Protocol in MANETs: A Cross-Layer Approach." Journal of Computer Networks and Communications 2019 (May 29, 2019): 1–13. http://dx.doi.org/10.1155/2019/6826984.
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With a growing demand of multimedia communication over MANETs, to support quality of service (QoS), the MAC standards such as 802.11a/b/g operate with multiple data rates to efficiently utilize the limited resources. Since the wireless channel is shared among the neighbors in MANETs, determining delay-sensitive and congestion-aware routes using the IEEE 802.11 MAC is still a challenging problem. This paper proposes a novel cross-layer approach called congestion-adaptive and delay-sensitive multirate (CADM) routing protocol in MANETs. The CADM protocol exploits the cross-layer interaction between the network layer, MAC, and physical layer. The CADM accesses the correlation between data rate, congestion metric, and MAC delay in delay-sensitive applications to provide enhanced network efficiency in MANETs. The protocol discovers multiple node-disjoint routes and facilitates optimal data rates between the links based on the estimated delay to admit a flow with the certain delay requirement in multirate MANETs. The proposed CADM protocol discovers the route through less congested nodes and also actively handles the congestion if it occurs. The performance of the CADM protocol is comprehensively assessed through the simulation, which highlights the advantages of our cross-layer mechanism.
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Pettersson, Lars Winkler, Andreas Kjellin, Mats Lind, and Stefan Seipel. "On the Role of Visual References in Collaborative Visualization." Information Visualization 9, no. 2 (May 14, 2009): 98–114. http://dx.doi.org/10.1057/ivs.2009.2.
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Multi-Viewer Display Environments (MVDE) provide unique opportunities to present personalized information to several users concurrently in the same physical display space. MVDEs can support correct 3D visualizations to multiple users, present correctly oriented text and symbols to all viewers and allow individually chosen subsets of information in a shared context. MVDEs aim at supporting collaborative visual analysis, and when used to visualize disjoint information in partitioned visualizations they even necessitate collaboration. When solving visual tasks collaboratively in a MVDE, overall performance is affected not only by the inherent effects of the graphical presentation but also by the interaction between the collaborating users. We present results from an empirical study where we compared views with lack of shared visual references in disjoint sets of information to views with mutually shared information. Potential benefits of 2D and 3D visualizations in a collaborative task were investigated and the effects of partitioning visualizations both in terms of task performance, interaction behavior and clutter reduction. In our study of a collaborative task that required only a minimum of information to be shared, we found that partitioned views with a lack of shared visual references were significantly less efficient than integrated views. However, the study showed that subjects were equally capable of solving the task at low error levels in partitioned and integrated views. An explorative analysis revealed that the amount of visual clutter was reduced heavily in partitioned visualization, whereas verbal and deictic communication between subjects increased. It also showed that the type of the visualization (2D/3D) affects interaction behavior strongly. An interesting result is that collaboration on complex geo-time visualizations is actually as efficient in 2D as in 3D.
Conference papers on the topic "Multiple disjoint supports"
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Azevedo, Marcelo Moraes de, Shahram Latifi, and Nader Bagherzadeh. "On Packing and Embedding Hypercubes into Star Graphs." In Simpósio de Arquitetura de Computadores e Processamento de Alto Desempenho. Sociedade Brasileira de Computação, 1994. http://dx.doi.org/10.5753/sbac-pad.1994.21873.
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Packing is a graph simulation technique hy which pk node-disjoint copies of a guest graph G(k) are embedded into a host graph H(n). Many advantages result from this technique as opposed to a simple embedding of G(k) into H(n). The multiple copies of G(k) can execute different instances of any algorithm designed to run in G(k), providing high throughput via an efficient, low-expansion utilization of H(n). Task migration mechanisms between the multiple copies of G(k) also become possible, allowing a proper allocation of the processors of H(n), load balancing and support of fault tolerance. Other advantages that arise from a well-devised packing technique are variable-dilation embeddings and multiple-sized packings. A variable-dilation embedding consists of connecting c copies of a graph G(k), packed into a host graph H(n) wilh dilation d, such as to obtain an emhedding of a graph G(k+l), l > 0, into H(n). The resulting embedding has dilation d when the nodes of G(k+l) communicate over the first k dimensions of G(k+l), and dilation di > d when a dimension i, k < i ≤ k + l, is used. Since many parallel algorithms use a restricted number of dimensions of the guest graph at any given step (e.g., SIMD-based algorithms), the resulting communication slowdown can be made significantly small on the average. We also extend the concept of connecting node-disjoint copies of a graph G(k) to obtain multiple-sized packings, in which graphs G(k), G(k + 1), ... , G(k + l) of various sizes are packed into a host graph H(n). Multiple-sized packings allow tasks with different processor requirements to be allocated proper guest graphs G(k + j) in H(n) (variable-dilation embeddings result when j > 0). This paper focuses on the problem of packing hypercubes Q(n-2) and Q(n-1) into a star graph S(n) with dilation 3. We show that 3 · [n/2]! · [(n-1)/2]! copies of Q(n-2) or [n/2]! · [(n-1)/2]! copies of Q(n-1) can be packed into S(n), with expansion n!/3 · [n/2]! · ((n-1)/2]! · 2n-2 and n!/ [n/2]! · [(n-1)/2]! · 2n-1, respectively. We also show how to connect packed Q(n-1)'s to obtain a variable-dilation embedding of Q(n - 1 + l), l ≤ [log2(ln/2]! · [(n-1)/2]!)], into S(n). Such an emhedding has dilation 3 for the first (n-1) dimensions of Q(n - 1 + l) and guarantees a minimal slowdown by using a slightly higher dilation (4 in most cases) for the remaining dimensions of Q(n - 1 + l). Finally, we also address the issue of multiple-sized packings of hypercubes into S(n).
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Malak, Richard J., and Christiaan J. J. Paredis. "Using Support Vector Machines to Formalize the Valid Input Domain of Models in Data-Driven Predictive Modeling for Systems Design." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87376.
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Predictive modeling can be a valuable tool for systems designers, allowing them to capture and reuse knowledge from a set of observed data related to their system. An important challenge associated with predictive modeling is that of describing the domain over which model predictions are valid. This is necessary to avoid extrapolating beyond the original data, particularly when designers use predictive models in concert with optimizers or other computational routines that search a model’s input space automatically. The general problem of domain description is complicated by the characteristics of observational data sets, which can contain small numbers of samples, can have nonlinear associations among the variables, can be non-convex, and can occur in largely disjoint clusters. Support Vector Machine (SVM) techniques, developed originally in the machine learning community, offer a solution to this problem. This paper is a description of a kernel-based SVM approach that yields a formal mathematical description of the valid input domain of a predictive model. The approach also provides for cluster analysis, which can lead to improved model accuracy through the decomposition of a data set into multiple subsets that designers can model independently. The paper includes a mathematical presentation of kernel-based SVM methods, an explanation of the procedure for applying the approach to predictive modeling problems, and illustrative examples for applying and using the approach in systems design.