scholarly journals Robust Data-Driven Inference for Density-Weighted Average Derivatives

2009 ◽  
Author(s):  
Matias D. Cattaneo ◽  
Richard K. Crump ◽  
Michael Jansson
Keyword(s):  
Weighted Average ◽  
Data Driven

scholarly journals Data-Driven Handover Optimization in Next Generation Mobile Communication Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Po-Chiang Lin ◽  
Lionel F. Gonzalez Casanova ◽  
Bakary K. S. Fatty
Keyword(s):  
Communication Networks ◽  
Mobile Communication ◽  
Performance Indicator ◽  
Weighted Average ◽  
Data Driven ◽  
Next Generation ◽  
Ping Pong ◽  
Measurement Results ◽  
Mobile Communication Networks ◽  
The Relationship

Network densification is regarded as one of the important ingredients to increase capacity for next generation mobile communication networks. However, it also leads to mobility problems since users are more likely to hand over to another cell in dense or even ultradense mobile communication networks. Therefore, supporting seamless and robust connectivity through such networks becomes a very important issue. In this paper, we investigate handover (HO) optimization in next generation mobile communication networks. We propose a data-driven handover optimization (DHO) approach, which aims to mitigate mobility problems including too-late HO, too-early HO, HO to wrong cell, ping-pong HO, and unnecessary HO. The key performance indicator (KPI) is defined as the weighted average of the ratios of these mobility problems. The DHO approach collects data from the mobile communication measurement results and provides a model to estimate the relationship between the KPI and features from the collected dataset. Based on the model, the handover parameters, including the handover margin and time-to-trigger, are optimized to minimize the KPI. Simulation results show that the proposed DHO approach could effectively mitigate mobility problems.

scholarly journals Deep attention networks reveal the rules of collective motion in zebrafish

2018 ◽  
Author(s):  
Francisco J.H. Heras ◽  
Francisco Romero-Ferrero ◽  
Robert C. Hinz ◽  
Gonzalo G. de Polavieja
Keyword(s):  
Danio Rerio ◽  
Collective Behavior ◽  
Collective Motion ◽  
Weighted Average ◽  
Data Driven ◽  
List Type ◽  
Attention Networks ◽  
Interaction Information ◽  
Large Groups ◽  
Simple Models

AbstractA variety of simple models has been proposed to understand the collective motion of animals. These models can be insightful but lack important elements necessary to predict the motion of each individual in the collective. Adding more detail increases predictability but can make models too complex to be insightful. Here we report that deep attention networks can obtain in a data-driven way a model of collective behavior that is simultaneously predictive and insightful thanks to an organization in modules. The model obtains that interactions between two zebrafish, Danio rerio, in a large groups of 60-100, can be approximately be described as repulsive, attractive or as alignment, but only when moving slowly. At high velocities, interactions correspond only to alignment or alignment mixed with repulsion at close distances. The model also shows that each zebrafish decides where to move by aggregating information from the group as a weighted average over neighbours. Weights are higher for neighbours that are close, in a collision path or moving faster in frontal and lateral locations. These weights effectively select 5 relevant neighbours on average, but this number is dynamical, changing between a single neighbour to up to 12, often in less than a second. Our results suggest that each animal in a group decides by dynamically selecting information from the group.HighlightsAt 30 days postfertilization, zebrafish, Danio rerio, can move in very cohesive and predictable large groupsDeep attention networks obtain a predictive and understadable model of collective motionWhen moving slowly, interations between pairs of zebrafish have clear components of repulsion, attraction and alignmentWhen moving fast, interactions correspond to alignment and a mixture of alignment and repulsion at close distancesZebrafish turn left or right depending on a weighted average of interaction information with other fish, with weights higher for close fish, those in a collision path or those moving fast in front or to the sidesAggregation is dynamical, oscillating between 1 and 12 neighbouring fish, with 5 on average

scholarly journals High resolution data-driven model of the mouse connectome

2018 ◽  
Author(s):  
Joseph E. Knox ◽  
Kameron Decker Harris ◽  
Nile Graddis ◽  
Jennifer D. Whitesell ◽  
Hongkui Zeng ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Structural Connectivity ◽  
Weighted Average ◽  
Data Driven ◽  
Scale Model ◽  
Wild Type ◽  
High Resolution Data ◽  
Spatial Connectivity ◽  
Voxel Model ◽  
Region Information

AbstractKnowledge of mesoscopic brain connectivity is important for understanding inter- and intra-region information processing. Models of structural connectivity are typically constructed and analyzed with the assumption that regions are homogeneous. We instead use the Allen Mouse Brain Connectivity Atlas to construct a model of whole brain connectivity at the scale of 100 µm voxels. The dataset used consists of 366 anterograde tracing experiments in wild type C7BL/6 mice, mapping fluorescently-labeled neuronal projections brain-wide. Inferring spatial connectivity with this dataset remains underdetermined, since the approximately 2 × 105 source voxels outnumber the number of experiments. To address this, we assume that connection patterns and strengths vary smoothly across major brain divisions. We model the connectivity at each voxel as a radial basis kernel-weighted average of the projection patterns of nearby injections. The voxel model outperforms a previous regional model in predicting held-out experiments and compared to a human-curated dataset. This voxel-scale model of the mouse connectome permits researchers to extend their previous analyses of structural connectivity to unprecedented levels of resolution, and allows for comparison with functional imaging and other datasets.

scholarly journals High-resolution data-driven model of the mouse connectome

2019 ◽  
Vol 3 (1) ◽  
pp. 217-236 ◽  
Author(s):  
Joseph E. Knox ◽  
Kameron Decker Harris ◽  
Nile Graddis ◽  
Jennifer D. Whitesell ◽  
Hongkui Zeng ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Structural Connectivity ◽  
Weighted Average ◽  
Regional Model ◽  
Data Driven ◽  
Scale Model ◽  
Wild Type ◽  
High Resolution Data ◽  
Spatial Connectivity ◽  
Voxel Model

Knowledge of mesoscopic brain connectivity is important for understanding inter- and intraregion information processing. Models of structural connectivity are typically constructed and analyzed with the assumption that regions are homogeneous. We instead use the Allen Mouse Brain Connectivity Atlas to construct a model of whole-brain connectivity at the scale of 100 μm voxels. The data consist of 428 anterograde tracing experiments in wild type C57BL/6J mice, mapping fluorescently labeled neuronal projections brain-wide. Inferring spatial connectivity with this dataset is underdetermined, since the approximately 2 × 105 source voxels outnumber the number of experiments. To address this issue, we assume that connection patterns and strengths vary smoothly across major brain divisions. We model the connectivity at each voxel as a radial basis kernel-weighted average of the projection patterns of nearby injections. The voxel model outperforms a previous regional model in predicting held-out experiments and compared with a human-curated dataset. This voxel-scale model of the mouse connectome permits researchers to extend their previous analyses of structural connectivity to much higher levels of resolution, and it allows for comparison with functional imaging and other datasets.

scholarly journals Robust Data-Driven Inference for Density-Weighted Average Derivatives

2010 ◽  
Vol 105 (491) ◽  
pp. 1070-1083 ◽  
Author(s):  
Matias D. Cattaneo ◽  
Richard K. Crump ◽  
Michael Jansson
Keyword(s):  
Weighted Average ◽  
Data Driven

Spatial and Temporal Resolution in Data-Driven Process Modeling of an Integrated Newsprint Mill

2007 ◽  
Vol 2 (1) ◽  
Author(s):  
Robert P Harrison ◽  
Paul Stuart
Keyword(s):  
Wood Chip ◽  
Weighted Average ◽  
Data Driven ◽  
Industrial Case Study ◽  
Unit Operations ◽  
Using Data ◽  
Important Design ◽  
Average Filter ◽  
Paper Quality

Like many globalized industries, the pulp and paper sector finds itself with an increasingly demanding clientele, who continually expect a better and cheaper product. An important design strategy being employed to address this objective is through an analysis of the vast quantity of process and product data accumulated in plant-wide data historians, in order to improve operations. Mill processes are multivariate, meaning that the interactions between the variables are as important as the variables themselves. Process relationships must therefore be modeled as a group, using an appropriate simulation technique like Multivariate Analysis (MVA), with suitable data pre-processing to account for process upsets and other disturbances. In a previous paper, using an Eastern Canadian newsprint mill as an industrial case study, we showed that it was possible to find statistically significant correlations between wood chip refiner operation, intermediate pulp quality, and final paper quality using data-driven models. This was true even though some important process parameters went unmeasured, process lags changed with time, and the operation of key equipment items changed gradually with use. The present study compares the use of different timescales and combinations of unit operations to determine which ones yield the best MVA simulations. Because plant operating data were used, and experimental design was not practical, it is possible that some of the correlations found could be attributable to coincidence. We therefore added and removed variables and time periods to explore the validity of the models. The best MVA models were obtained by using a shorter (1-hour) data timescale, although use of a weighted-average filter helped to bridge the gap between these faster readings and the slower paper quality trends.

The data-driven classroom: Scholarly teaching and scholarship of teaching

2010 ◽  
Author(s):  
Julie S. Lyon ◽  
Marcus W. Dickson
Keyword(s):  
Data Driven ◽  
Scholarship Of Teaching ◽  
Scholarly Teaching
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