Ranking the economic importance of countries and industries

In the current era of worldwide market interdependencies, the global financial village has become increasingly vulnerable to systemic collapse. The global financial crisis has highlighted the necessity of understanding and quantifying the interdependencies among the world’s economies; developing new, effective approaches for risk evaluation; and providing mitigating solutions. We present a methodological framework for quantifying interdependencies in the global market and for evaluating risk levels in the worldwide financial network. The resulting information will enable policy and decision makers to better measure, understand and maintain financial stability. We use this methodology to rank the economic importance of each industry and country according to the global damage that would result from its failure. Our quantitative results shed new light on China’s increasing economic dominance over other economies, including that of the United States, as well as the global economy

ASI: Accuracy-Stability Index for Evaluating Deep Learning Models

In the context of deep learning research, where model introductions continually occur, the need for effective and efficient evaluation remains paramount. Existing methods often emphasize accuracy metrics, overlooking stability. To address this, the paper introduces the Accuracy-Stability Index (ASI), a quantitative measure incorporating both accuracy and stability for assessing deep learning models. Experimental results demonstrate the application of ASI, and a 3D surface model is presented for visualizing ASI, mean accuracy, and coefficient of variation. This paper addresses the important issue of quantitative benchmarking metrics for deep learning models, providing a new approach for accurately evaluating accuracy and stability of deep learning models. The paper concludes with discussions on potential weaknesses and outlines future research directions.Comment: 6 pages, 3 figure

Active task design in adaptive control of redundant robotic systems

This paper seeks to use robots' kinematic redundancy to excite the system persistently, through actively designing a secondary task in the null space of a primary task. Resulted convergence of unknown parameters in adaptive control leads to better system stability and performance. A measure in Grassmannian, referred to as Subspace Discrepancy Measure (SDM), is proposed for evaluating the additional benefit from the secondary task in converging unknown parameters to their true values. This measure evaluates the angles among subspaces that the parameter estimations are converging to, given different secondary tasks. The subspaces are obtained from Principal Component Analysis (PCA) on a small amount of samples of parameter estimations. The SDM is used to determine the choice of the secondary task online through a trial-and-evaluation procedure actively. Numerical simulations demonstrated that the secondary task chosen by SDM enhances the parameter convergence

Comparing hard and overlapping clusterings

Similarity measures for comparing clusterings is an important component, e.g., of evaluating clustering algorithms, for consensus clustering, and for clustering stability assessment. These measures have been studied for over 40 years in the domain of exclusive hard clusterings (exhaustive and mutually exclusive object sets). In the past years, the literature has proposed measures to handle more general clusterings (e.g., fuzzy/probabilistic clusterings). This paper provides an overview of these new measures and discusses their drawbacks. We ultimately develop a corrected-for-chance measure (13AGRI) capable of comparing exclusive hard, fuzzy/probabilistic, non-exclusive hard, and possibilistic clusterings. We prove that 13AGRI and the adjusted Rand index (ARI, by Hubert and Arabie) are equivalent in the exclusive hard domain. The reported experiments show that only 13AGRI could provide both a fine-grained evaluation across clusterings with different numbers of clusters and a constant evaluation between random clusterings, showing all the four desirable properties considered here. We identified a high correlation between 13AGRI applied to fuzzy clusterings and ARI applied to hard exclusive clusterings over 14 real data sets from the UCI repository, which corroborates the validity of 13AGRI fuzzy clustering evaluation. 13AGRI also showed good results as a clustering stability statistic for solutions produced by the expectation maximization algorithm for Gaussian mixture

New methods to assess cotton varietal stability and indentify discriminating environments

Studies were conducted in 2001-2004 evaluating genotype by environment interactions in cotton (Gossypium hirsutum L.). Genotype by Environment interactions were characterized using GGE Biplot for conventional cotton cultivars and their transgenic derivatives. Significant interactions existed for several non-target traits. Transgenic cultivars were taller, had greater height to node ratios, larger seed, and lower lint percentages. Transgenic cultivars containing the Bollgard gene yielded more than their conventional parents and STV4691B was the highest yielding, most stable cultivar. In 2002-2004, GGE Biplot was used to identify two levels (high/low) of discriminating locations for three distinct selection criteria. Crosses were made with parents recommended by a least squares means analysis for each population criteria and F2 plants were planted in the high and low discriminating locations for each population. Gains by selection (h2) were calculated by regressing the F2:3 plants on their F2 parents. Genotypic variance was greater among F2:3 progeny in discriminating environments compared to non-discriminating environments, regardless of population. Heritability was greater in the population containing fiber traits compared to yield. In 2004, GGE Biplot was compared to other widely-accepted stability analysis tools. Correlation coefficients between GGE biplot (stability evaluation) and the Cultivar Superiority Measure, Shukla\u27s Stability Variance, the Eberhart-Russell regression model, Kang\u27s yield stability statistic, and AMMI were 0.54, 0.91, 0.86, 0.63, and 0.55, respectively. Correlation coefficients between GGE biplot (mean performance + stability evaluation) and the Cultivar Superiority Measure, the Eberhart-Russell regression model, Kang\u27s yield stability statistic, and AMMI were 0.95, 0.60, 0.85, and -0.33, respectively. Based on the results of this study and our experience using GGE Biplot, Model 3 with an entry-focused scaling is the most valuable analysis for breeders engaged in cultivar development. GGE Biplot was used with the 1993-2003 Louisiana Official Variety Trials to identify the most desirable (discriminating and representative) test locations in Louisiana for yield and fiber length. St. Joseph loam was ranked 1st for yield, Winnsboro irrigated was ranked 1st for fiber length, and St. Joseph loam was ranked 1st to simultaneously select for both traits. Winnsboro non-irrigated should not be used to select for yield or fiber length

The Role of Measuring and Evaluating Performance in Achieving Control Objectives- Case Study of "Islamic University"

The study aimed to identify the role of measuring and evaluating performance in achieving the objectives of control and the performance of the job at the Islamic University in Gaza Strip. To achieve the objectives of the research, the researchers used the descriptive analytical approach to collect information which is the questionnaire that consisted of (22) phrases were distributed to three categories of employees of the Islamic University (Faculty Members and Their Assistants, Members of the Administrative Board, Senior Management). A random sample of (314) employees was selected and 276 responses were retrieved with a recovery rate of 88.1%. The Statistical Analysis Program (SPSS) was used to enter process and analyze the data. The results of the research showed a positive role between measuring and evaluating the performance and achieving the objectives of the control of performance in the Islamic University from the point of view of the members (senior management, faculty and their assistants, and members of administrative board). The researchers also recommended a number of recommendations, most notably the provision of an appropriate level of the elements of the control systems today through the modernization and continuous development of performance measures and the need to provide the physical and financial resources necessary to continue the development and achievement within the university, to expand the development of technology in the various activities of the university through the construction of a complete and integrated system to support supervision systems in the university to suit the size of the university. The researchers also recommended following up and reviewing the performance measures and work to modify them in line with the mission and the goals of the university that it seeks to reach

Evaluating Teachers: The Important Role of Value-Added

Outlines issues for evaluating teachers based on value added -- their contribution to student learning -- and the use of value added information, implications of classifying teachers, and reliability compared with other fields and evaluations