scholarly journals Online Multikernel Learning Based on a Triple-Norm Regularizer for Semantic Image Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Shuangping Huang ◽  
Lianwen Jin ◽  
Yunyu Li
Keyword(s):  
Scale Up ◽  
Model Complexity ◽  
New Approach ◽  
Online Mode ◽  
Kernel Weights ◽  
Hinge Loss ◽  
Fast Convergence Rate ◽  
Benchmark Datasets ◽  
Primal Dual ◽  
Kernel Combination

Currently image classifiers based on multikernel learning (MKL) mostly use batch approach, which is slow and difficult to scale up for large datasets. In the meantime, standard MKL model neglects the correlations among examples associated with a specific kernel, which makes it infeasible to adjust the kernel combination coefficients. To address these issues, a new and efficient multikernel multiclass algorithm called TripleReg-MKL is proposed in this work. Taking the principle of strong convex optimization into consideration, we propose a new triple-norm regularizer (TripleReg) to constrain the empirical loss objective function, which exploits the correlations among examples to tune the kernel weights. It highlights the application of multivariate hinge loss and a conservative updating strategy to filter noisy samples, thereby reducing the model complexity. This novel MKL formulation is then solved in an online mode using a primal-dual framework. A theoretical analysis of the complexity and convergence of TripleReg-MKL is presented. It shows that the new algorithm has a complexity ofOCMTand achieves a fast convergence rate ofOlogT/T. Extensive experiments on four benchmark datasets demonstrate the effectiveness and robustness of this new approach.

scholarly journals A New Approach to Compensator Design Based on Multi-Loop Technique and Scalable Forward Model Complexity

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3049
Author(s):  
Rafał Osypiuk
Keyword(s):  
Process Model ◽  
Position Control ◽  
Control Method ◽  
Scale Up ◽  
Model Complexity ◽  
Linear Processes ◽  
New Approach ◽  
Special Cases ◽  
Non Linear ◽  
Forward Process

Using a compensator in the structure is one of the simplest ways to achieve efficient control of a non-linear process. Unfortunately, accessing the inverse process model is not a trivial issue. Except for some special cases, it is much easier to determine the forward process model than the inverse one. For this reason, it would be interesting to propose an alternative solution to the well-known feedforward control method. In this paper, a simple multi-loop concept will be introduced. The main idea is based on the natural (but limited) robustness offered by a single PID loop and the ability to scale up the complexity of the forward process model. The proposed structure multiplies a single PID loop including forward models with increasing complexity to calculate the resultant non-linear control value. This new approach produces a comparable performance to the feedforward method but does not require access to the inverse properties of the process. The idea was evaluated in terms of stability and robustness to parameter changes. In addition, a simulation study was carried out using two coupled non-linear processes, i.e., the position control of a robot manipulator with force interaction. The selection of this process was no casual choice. On the one hand, it is extremely complex; however, on the other hand, it provides the possibility to determine both the inverse and the forward dynamic model. This capability was helpful to perform an effective comparison of the proposed solution with the known feedforward structure.

Solid Lipid Nanoparticles: A Promising Drug Delivery Technology

2009 ◽  
Vol 2 (2) ◽  
pp. 509-516
Author(s):  
S. Pragati ◽  
S. Kuldeep ◽  
S. Ashok ◽  
M. Satheesh
Keyword(s):  
Drug Delivery ◽  
Solid Lipid Nanoparticles ◽  
Large Scale ◽  
Colloidal Particles ◽  
Scale Up ◽  
Lipid Nanoparticles ◽  
Scale Production ◽  
Research Activity ◽  
New Approach ◽  
Solid Lipid

One of the situations in the treatment of disease is the delivery of efficacious medication of appropriate concentration to the site of action in a controlled and continual manner. Nanoparticle represents an important particulate carrier system, developed accordingly. Nanoparticles are solid colloidal particles ranging in size from 1 to 1000 nm and composed of macromolecular material. Nanoparticles could be polymeric or lipidic (SLNs). Industry estimates suggest that approximately 40% of lipophilic drug candidates fail due to solubility and formulation stability issues, prompting significant research activity in advanced lipophile delivery technologies. Solid lipid nanoparticle technology represents a promising new approach to lipophile drug delivery. Solid lipid nanoparticles (SLNs) are important advancement in this area. The bioacceptable and biodegradable nature of SLNs makes them less toxic as compared to polymeric nanoparticles. Supplemented with small size which prolongs the circulation time in blood, feasible scale up for large scale production and absence of burst effect makes them interesting candidates for study. In this present review this new approach is discussed in terms of their preparation, advantages, characterization and special features.

scholarly journals Real-Time Environment Monitoring Using a Lightweight Image Super-Resolution Network

2021 ◽  
Vol 18 (11) ◽  
pp. 5890
Author(s):  
Qiang Yu ◽  
Feiqiang Liu ◽  
Long Xiao ◽  
Zitao Liu ◽  
Xiaomin Yang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Super Resolution ◽  
Model Complexity ◽  
Practical Application ◽  
Single Image ◽  
Feature Maps ◽  
Benchmark Datasets ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Deep-learning (DL)-based methods are of growing importance in the field of single image super-resolution (SISR). The practical application of these DL-based models is a remaining problem due to the requirement of heavy computation and huge storage resources. The powerful feature maps of hidden layers in convolutional neural networks (CNN) help the model learn useful information. However, there exists redundancy among feature maps, which can be further exploited. To address these issues, this paper proposes a lightweight efficient feature generating network (EFGN) for SISR by constructing the efficient feature generating block (EFGB). Specifically, the EFGB can conduct plain operations on the original features to produce more feature maps with parameters slightly increasing. With the help of these extra feature maps, the network can extract more useful information from low resolution (LR) images to reconstruct the desired high resolution (HR) images. Experiments conducted on the benchmark datasets demonstrate that the proposed EFGN can outperform other deep-learning based methods in most cases and possess relatively lower model complexity. Additionally, the running time measurement indicates the feasibility of real-time monitoring.

Lesson Study to Scale Up Research-Based Knowledge: A Randomized, Controlled Trial of Fractions Learning

2017 ◽  
Vol 48 (3) ◽  
pp. 261-299 ◽  
Author(s):  
Catherine Lewis ◽  
Rebecca Perry
Keyword(s):  
Randomized Controlled Trial ◽  
Elementary Teachers ◽  
Teacher Leadership ◽  
Lesson Study ◽  
Controlled Trial ◽  
Scale Up ◽  
Linear Representation ◽  
The United States ◽  
New Approach ◽  
Randomized Controlled

An understanding of fractions eludes many U.S. students, and research-based knowledge about fractions, such as the utility of linear representation, has not broadly influenced instruction. This randomized trial of lesson study supported by mathematical resources assigned 39 educator teams across the United States to locally managed lesson study supported by a fractions lesson study resource kit or to 1 of 2 control conditions. Educators (87% of whom were elementary teachers) self-managed learning over a 3-month period. HLM analyses indicated significantly greater improvement of educators' and students' fractions knowledge for teams randomly assigned to lesson study with resource kits. Results suggest that integrating researchbased resources into lesson study offers a new approach to the problem of “scale-up” by combining the strengths of teacher leadership and research-based knowledge.

scholarly journals Inundation mapping based on reach-scale effective geometry

2018 ◽  
Vol 22 (11) ◽  
pp. 5967-5985 ◽  
Author(s):  
Cédric Rebolho ◽  
Vazken Andréassian ◽  
Nicolas Le Moine
Keyword(s):  
Model Performance ◽  
Model Complexity ◽  
Hydraulic Geometry ◽  
Future Event ◽  
New Approach ◽  
Flow Equations ◽  
Geometrical Properties ◽  
Inundation Maps ◽  
Extreme Flood ◽  
Inundation Mapping

Abstract. The production of spatially accurate representations of potential inundation is often limited by the lack of available data as well as model complexity. We present in this paper a new approach for rapid inundation mapping, MHYST, which is well adapted for data-scarce areas; it combines hydraulic geometry concepts for channels and DEM data for floodplains. Its originality lies in the fact that it does not work at the cross section scale but computes effective geometrical properties to describe the reach scale. Combining reach-scale geometrical properties with 1-D steady-state flow equations, MHYST computes a topographically coherent relation between the “height above nearest drainage” and streamflow. This relation can then be used on a past or future event to produce inundation maps. The MHYST approach is tested here on an extreme flood event that occurred in France in May–June 2016. The results indicate that it has a tendency to slightly underestimate inundation extents, although efficiency criteria values are clearly encouraging. The spatial distribution of model performance is discussed and it shows that the model can perform very well on most reaches, but has difficulties modelling the more complex, urbanised reaches. MHYST should not be seen as a rival to detailed inundation studies, but as a first approximation able to rapidly provide inundation maps in data-scarce areas.

Automated design process for hybrid regression modeling with a one-class SVM

2019 ◽  
Vol 67 (10) ◽  
pp. 843-852 ◽  
Author(s):  
Moritz Böhland ◽  
Wolfgang Doneit ◽  
Lutz Gröll ◽  
Ralf Mikut ◽  
Markus Reischl
Keyword(s):  
Design Process ◽  
Regression Models ◽  
Data Science ◽  
Automated Design ◽  
Model Complexity ◽  
Support Vector ◽  
Local Data ◽  
Benchmark Datasets ◽  
Data Coverage ◽  
First Time

Abstract The accuracy of many regression models suffers from inhomogeneous data coverage. Models loose accuracy because they are unable to locally adapt the model complexity. This article develops and evaluates an automated design process for the generation of hybrid regression models from arbitrary submodels. For the first time, these submodels are weighted by a One-Class Support Vector Machine, taking local data coverage into account. Compared to reference regression models, the newly developed hybrid models achieve significant better results in nine out of ten benchmark datasets. To enable straightforward usage in data science, an implementation is integrated in the open source MATLAB toolbox SciXMiner.

Sparse additive machine with ramp loss

2020 ◽  
pp. 1-20
Author(s):  
Hong Chen ◽  
Changying Guo ◽  
Huijuan Xiong ◽  
Yingjie Wang
Keyword(s):  
Descent Method ◽  
Misclassification Error ◽  
High Dimensional ◽  
Block Coordinate Descent ◽  
Regularization Scheme ◽  
Hinge Loss ◽  
Dimensional Classification ◽  
Benchmark Datasets ◽  
Ramp Loss ◽  
Error Decomposition

Sparse additive machines (SAMs) have attracted increasing attention in high dimensional classification due to their representation flexibility and interpretability. However, most of existing methods are formulated under Tikhonov regularization scheme with the hinge loss, which are susceptible to outliers. To circumvent this problem, we propose a sparse additive machine with ramp loss (called ramp-SAM) to tackle classification and variable selection simultaneously. Misclassification error bound is established for ramp-SAM with the help of detailed error decomposition and constructive hypothesis error analysis. To solve the nonsmooth and nonconvex ramp-SAM, a proximal block coordinate descent method is presented with convergence guarantees. The empirical effectiveness of our model is confirmed on simulated and benchmark datasets.

Is China's dairy processing industry oligopolistic and/or oligopsonistic?

2014 ◽  
Vol 6 (4) ◽  
pp. 644-653 ◽  
Author(s):  
Jiawu Dai ◽  
Xiuqing Wang
Keyword(s):  
Market Power ◽  
Food Industry ◽  
Dairy Farm ◽  
Raw Milk ◽  
Processing Industry ◽  
New Approach ◽  
Content Type ◽  
Dairy Processing ◽  
Primal Dual ◽  
Oligopsony Power

Purpose – Complaints about lower agricultural farm-gate price and higher consumer price have emerged in China in recent years. The large gap between dairy farm-gate price and consumer price gives rise to worries that China's dairy industry is characterized by strong degree of oligopoly. The purpose of this paper is to take the dairy processing industry as an epitome of China's food industry, and use a new approach to investigate whether it is oligopolistic and/or oligopsonistic. Design/methodology/approach – Based on a new proposed Primal-Dual Solow Residual model, the authors first test the hypothesis that there are significant oligopoly and oligopsony powers in China's dairy sector, and the latter is stronger. The authors then turn to measure these two kinds of market power using regressions of the model. Findings – The estimation results show that firms in the sector have both strong oligopoly and oligopsony power, but the latter is stronger than the former. Meanwhile, with the continuous reinforcement of competition in China's dairy sector, market power in both the upstream and downstream has decreased slightly. Originality/value – This paper is the first to simultaneously test oligopoly and oligopsony power in China's dairy sector. The empirical results explicitly imply that more attention should be paid to the raw milk purchase market.

scholarly journals Microscale to manufacturing scale‐up of cell‐free cytokine production—a new approach for shortening protein production development timelines

2011 ◽  
Vol 108 (7) ◽  
pp. 1570-1578 ◽  
Author(s):  
James F. Zawada ◽  
Gang Yin ◽  
Alexander R. Steiner ◽  
Junhao Yang ◽  
Alpana Naresh ◽  
...  
Keyword(s):  
Protein Production ◽  
Cytokine Production ◽  
Scale Up ◽  
New Approach ◽  
Production Development
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