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:Machine LearningPublish Year:2019Machine Learning for Aggregate Computing: a Research Roadmap Abstract: Aggregate computing is a macro-approach for programming collective intelligence and self
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· Aggregation of wind turbines (WTs) in wind farms (WFs) can reduce modeling and computation burden, but it may also reduce accuracy. Furthermore, it may be difficult to accurately determine the dynamic behaviors of WTs under power system disturbances. This paper proposes a novel aggregation modeling method of WFs for
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LAG can increase maximum throughput, and allow for network redundancy. It does this by splitting traffic across multiple ports instead of forcing clients to use a single uplink port on a switch. Note that these performance improvements will only occur when multiple clients are passing traffic simultaneously through the LAG.
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· Data aggregation involves collecting and processing data from multiple sources into a single source for data analysis. Data mining involves uncovering patterns, trends and insights from large datasets to aid decision-making. The two processes are related, but they have different goals and approaches.
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· Residual Tree Aggregation of Layers for Neural Machine Translation. Although attention-based Neural Machine Translation has achieved remarkable progress in recent layers, it still suffers from issue of making insufficient use of the output of each layer. In transformer, it only uses the top layer of encoder and decoder in the subsequent
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Third, the in-network aggregation primitive is. protocols and ML frameworks to provide a robust, eficient solution that speeds up training by up to 300%, and at least by 20% for a number of real-world benchmark models. an all-to-all primitive that does not provide workers with the ability to recognize the loss of individual packets.
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· Differentially private aggregation of distributed time-series with transformation and encryption. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data. ACM, 735--746.
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· Now a machine learning approach has been used to identify small molecule inhibitors of α-synuclein aggregation, a process implicated in Parkinson’s disease and other synucleinopathies.
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So we propose a verifiable aggregation scheme that can effectively verify the results of server aggregation. Specifically, we follow the classic double mask aggregation scheme, and use Paillier homomorphic encryption algorithm to implement the message authentication code with additive homomorphic property.
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: 354The distribution of wind speed is not uniform within the wind farm because of the terrain and wake, which limits the accuracy of wind farm turbines’ aggregation equivalence. To improve precision, a wind farm turbine aggregation equivalent method based on probability clustering is proposed considering the uneven wind speed distribution. Based on copula
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· Training machine learning models in parallel is an increasingly important workload. We accelerate distributed parallel training by designing a communication primitive that uses a programmable switch dataplane to execute a key step of the training process. Our approach, SwitchML, reduces the volume of exchanged data by aggregating the
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· Secure aggregation is a cryptographic protocol that securely computes the aggregation of its inputs. It is pivotal in keeping model updates private in federated learning. Indeed, the use of secure aggregation prevents the server from learning the value and the source of the individual model updates provided by the users, hampering
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· Aggregation-induced emission (AIE) is another photophysical phenomenon associated with chromophore aggregation. The concept of AIE has been introduced by a group of researchers in 2001.
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· To explore data aggregation strategies in distributed learning, we considered phrases like “Aggregation Methods,” “Aggregation Algorithms,” and “Aggregation Techniques.” For practical insights into FL implementation, we included terms such as “Development Tools,” “Software,” “Platforms,” “FL Frameworks”, and “Datasets”.
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· This paper presents Weighted Dynamic aggregation (WD agg) method to obtain an equivalent Wind Turbine Generator (WTG) for an induction machine-based wind farm using its dynamic model. The suggested approach obtains the equivalent d-q model of the induction generators considering the contribution of each unit in the model. The
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· Branched kinetic model describes antibody aggregation in a broad range of temperatures. (A) Aggregation time course of mAb1 measured at different temperatures and concentrations in 20 mM histidine
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· [email protected]. Google. 1600 Amphitheatre Parkway. Mountain View, California 94043. ABSTRACT. We design a no vel, communication-e cient, failure-robust proto-. col for secure aggregation of high
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:Data AggregationModel Aggregation Machine Learning · Random Forest is one of the most popular and most powerful machine learning algorithms. It is a type of ensemble machine learning algorithm called Bootstrap Aggregation or bagging. In this
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LAG can increase maximum throughput, and allow for network redundancy. It does this by splitting traffic across multiple ports instead of forcing clients to use a single uplink port on a switch. Note that these performance improvements will only occur when multiple clients are passing traffic simultaneously through the LAG.
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· Based on diversified datasets generated from the original set of observations, Salman et al. [] implemented a general ensemble framework in which the feature importance scores were generated by multiple feature selection techniques and aggregated using two methods: Within Aggregation Method (WAM) which refers to
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· Here, we report our multiscale machine learning accelerated photodynamics simulations for classical AIE molecules in aggregate state. Our results reproduce the fluorescence enhancement reported in experiments and reveal that the substituents block the π CC torsions in the conjugated cores, which significantly reduces
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· Learning Aggregation Functions. Giovanni Pellegrini, Alessandro Tibo, Paolo Frasconi, Andrea Passerini, Manfred Jaeger. Learning on sets is increasingly gaining attention in the machine learning community, due to its widespread applicability. Typically, representations over sets are computed by using fixed aggregation functions such as
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:Aggregate ComputingAggregate Features Machine LearningA Systematic Review of Data Aggregation using Machine Learning Techniques Abstract: Wireless Sensor Networks (WSNs) are usually employed to address the data transfer
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Bootstrap aggregating, also called bagging (from bootstrap aggregating), is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used
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The distribution of wind speed is not uniform within the wind farm because of the terrain and wake, which limits the accuracy of wind farm turbines’ aggregation equivalence. To improve precision, a wind farm turbine aggregation equivalent method based on probability clustering is proposed considering the uneven wind speed distribution. Based on copula
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· Random Forest is one of the most popular and most powerful machine learning algorithms. It is a type of ensemble machine learning algorithm called Bootstrap Aggregation or bagging. In this post you will discover the Bagging ensemble algorithm and the Random Forest algorithm for predictive modeling. After reading this post you will
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In short, pharma aggregation is the process of creating a hierarchical relationship between unique identifiers assigned to packaging containers. Aggregation in pharma is the next logical step in Track and Trace requirements, and many companies that started the process (or finished) of implementation of the pharma serialization are foreseeing
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· aggregation models，Ensemble Learning ，Averaging、Weighting、Stacking、Bagging、BoostingEnsemble Learning， Decision TreeAggregation Models，Ensemble Learning ，Decision Tree
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· We report the development and deployment of a coarse-graining method that is well suited for computer simulations of aggregation and phase separation of pr Kiersten M. Ruff, Tyler S. Harmon, Rohit V. Pappu; CAMELOT: A machine learning approach for coarse-grained simulations of aggregation of block-copolymeric protein
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