Robust crowd bias correction via dual knowledge transfer from multiple overlapping sources

PurposeThis study reviews strategies organizational leaders and human resource practitioners can deploy to develop knowledge transfer and retention succession planning strategies for older employees to mitigate generational organizational knowledge loss prior to retirement.Design/methodology/approachThis study used a questionnaire with 28 baby boomer employees and leaders of baby boomers at a large federal agency. Purposive and snowball sampling techniques were used. Questions asked participants about knowledge transfer, retention strategies and how challenges to counter baby boomer knowledge loss are addressed in the workplace.FindingsDevelop succession plans using standard operating procedures and job aids to reduce knowledge loss and enhance retention. Deploy hands-on training to share historical knowledge, enhance relationship building, mentoring programs, cross-training opportunities, retention incentives and document process improvement. The strategies are supported by organizational learning and knowledge management theories.Originality/valueThis study contributes to organizational leaders’ and human resource practitioners’ knowledge transfer and retention succession planning strategies to counter generational knowledge loss.

Selection of genes that are differentially expressed and critical to a particular biological process has been a major challenge in post-array analysis. Recent development in bioinformatics has made various data sources available such as mRNA and miRNA expression profiles, biological pathway and gene annotation, etc. Efficient and effective integration of multiple data sources helps enrich our knowledge about the involved samples and genes for selecting genes bearing significant biological relevance. In this work, we studied a novel problem of multi-source gene selection: given multiple heterogeneous data sources (or data sets), select genes from expression profiles by integrating information from various data sources. We investigated how to effectively employ information contained in multiple data sources to extract an intrinsic global geometric pattern and use it in covariance analysis for gene selection. We designed and conducted experiments to systematically compare the proposed approach with representative methods in terms of statistical and biological significance, and showed the efficacy and potential of the proposed approach with promising findings.

The growth of available information sources in the industry requires new efficient methods for data access by end-users whose ability to analyse data is at the core of making business decisions. Data analysis suffer from end-users' ability to efficiently clean and integrate and query data spread across a multitude of different data sources. Current centralised approaches, where an IT expert translates the requirements of domain experts into Extract-Transform-Load (ETL) processes to integrate the data and to apply predefined analytical reporting tools, are too heavy-weight and inflexible. To support interactive and efficient data exploration directly by domain experts, without IT expert involvement, a more flexible and modular information systems architecture is needed. The Optique platform reduces the time and cost of data access dramatically by automating the process of translating an information requirement into the retrieval of the relevant data. The key to this automated translation is Ontology-Based Data Access (OBDA), where the central idea is to use an ontology, a semantically rich conceptual model of the problem domain, and mappings, that describe the relationship between the ontology and the data sources, to allow users to formulate queries and receive answers in a domain-centric and intelligible form, without any knowledge of the implementational details of the data sources. We present the Optique platform as a next generation OBDA system featuring user-friendly query formulation interface(s), automated query translation over multiple sources using novel ontology and mapping management components, and distributed query optimisation and execution exploiting cloud resources for scale-out. At Statoil Exploration, the Optique platform integrates multiple large and complex data sources from different vendors, and allows G&G experts to execute and present detailed information needs in the subsurface domain directly into existing expert client tools and also into web portals for easy information distribution of central assets across the enterprise.

Surrogate-assisted evolutionary algorithms provide an effective means for complex and computationally expensive optimization problems. However, due to the scarcity of training samples, the prediction accuracy of frequently-used regression surrogate models can hardly be guaranteed as the difficulty of the problem increases, resulting in performance degradation of the whole algorithm. Since real-world problems rarely exist in isolation, it is expected to alleviate the above issue by properly exploiting the knowledge shared across different problems. In this context, this study proposes a novel evolutionary multitasking optimization algorithm assisted by a classifier rather than a regression model for expensive multitasking problems, where the accuracy of the classifier is boosted by knowledge transfer. To be specific, a support vector classifier (SVC) is first developed and integrated into a classic evolutionary algorithm, i.e., covariance matrix adaptation evolution strategy (CMA-ES). With a low computational cost, it helps CMA-ES to prescreen parent solutions from the current population. Following that, a knowledge transfer strategy is designed to enrich the training samples for each task-oriented classifier by sharing high-quality solutions among different tasks, where a PCA-based subspace alignment technique is employed. Extensive experiments indicate that the SVC-assisted CMA-ES gains significant superiority over general CMA-ES in terms of both robustness and scalability, and the knowledge transfer strategy further helps it earn a competitive edge over some state-of-the-art algorithms on expensive multitasking optimization problems.

In this paper we explore the possibility of deriving consensus rankings by solving consensus optimization problems, characterizing consensus rankings as suitable complete order relations minimizing the average Kemeny-Snell distance to the individual rankings. This optimization problem can be expressed as a binary programming (BP) problem which can typically be solved reasonably efficiently. The underlying theory is discussed in Sect. 1. Applications of the proposed method given in Sect. 2 include a comparison to other mathematical programming (MP) approaches using the data set of Tse [9] and establishing a consensus ranking of marketing journals identified by domain experts from a subset of the Harzing journal quality list [2]. In Sect. 3 we discuss computational details and present the results of a benchmark experiment comparing the performance of the commercial solver CPLEX to three open source mixed integer linear programming (MILP) solvers

Need for a strategic approach to knowledge transfer and exchangeLate-phase clinical trials and systematic reviews find results that have the potential to improve health outcomes for people. However, there are often delays in these results influencing clinical practice. We developed a knowledge transfer and exchange strategy to support research teams, aiming to identify activities along the research process to maximise and accelerate the research impact.Our knowledge transfer and exchange strategyThe strategy has five strands of activity across the life-course of our research:1. Partnerships with external stakeholders (including patient and public involvement, charities, policymakers, healthcare professionals, professional bodies, regulators and industry)2. Communication (including the development of research impact strategies and use of communication tools and channels)3. Maximising the scientific value of our studies (including open access, data and sample sharing, and incorporating multi-disciplinary components within studies)4. Strengthening capacity (including building internal and partner capacity to communicate effectively, and strengthening the capacity of external stakeholders to understand and apply our research).5. Learning and sharing (evaluating the impact of research, sharing lessons learnt internally and externally)The strategy has helped trial teams think systematically about impact and was easy to use.ConclusionsOur strategy helps researchers systematically identify activities which may improve the usefulness and uptake of their study results. While developed in a single trials unit, we think it may be of use to others.

Accurate records of high‐resolution rainfall fields are essential in urban hydrology, and are lacking in many areas. We develop a high‐resolution (15 min, 1 km2) radar rainfall data set for Charlotte, North Carolina during the 2001‐2010 period using the Hydro‐NEXRAD system with radar reflectivity from the National Weather Service Weather Surveillance Radar 1988 Doppler weather radar located in Greer, South Carolina. A dense network of 71 rain gages is used for estimating and correcting radar rainfall biases. Radar rainfall estimates with daily mean field bias (MFB) correction accurately capture the spatial and temporal structure of extreme rainfall, but bias correction at finer timescales can improve cold‐season and tropical cyclone rainfall estimates. Approximately 25 rain gages are sufficient to estimate daily MFB over an area of at least 2,500 km2, suggesting that robust bias correction is feasible in many urban areas. Conditional (rain‐rate dependent) bias can be removed, but at the expense of other performance criteria such as mean square error. Hydro‐NEXRAD radar rainfall estimates are also compared with the coarser resolution (hourly, 16 km2) Stage IV operational rainfall product. Stage IV is adequate for flood water balance studies but is insufficient for applications such as urban flood modeling, in which the temporal and spatial scales of relevant hydrologic processes are short. We recommend the increased use of high‐resolution radar rainfall fields in urban hydrology.

The increasing availability of numerous heterogeneous but related sources of data arises in many fields, such as bioinformatics, robotics, computer vision, information retrieval, and many others. Multiple sources of data can be viewed as different, related views of the same learning problem, where dependencies between the views could potentially take on complex structures. This gives rise to interesting and challenging machine learning problems where data sources are combined for learning. This framework encompasses several data fusion tasks and related topics, such as transfer learning, multitask learning, multiview learning, and learning under covariate shift. Often the general concept is to augment training data for each problem of interest with data from other partially related learning problems. Several approaches for inferring and exploiting complex relationships between sources have been presented. The advantages of the multiple source learning paradigm can be seen in situations where individual data sources are noisy, incomplete, and learning from more than one source can filter out problem-independent noise. Additionally, another aspect of multiple source learning is that data sources need only contain partially relevant information to the desired learning problem, making it possible to exploit a large number of data sources. This special issue on “Learning from Multiple Sources” was inspired by a successful (eponymous) NIPS workshop which took place in Whistler (Canada) on December 13, 2008. The workshop received twenty-three submissions, from which seven contributed talks and

Abstract. Dynamically downscaled precipitation fields from regional climate models (RCMs) often cannot be used directly for regional climate studies. Due to their inherent biases, i.e., systematic over- or underestimations compared to observations, several correction approaches have been developed. Most of the bias correction procedures such as the quantile mapping approach employ a transfer function that is based on the statistical differences between RCM output and observations. Apart from such transfer function-based statistical correction algorithms, a stochastic bias correction technique, based on the concept of Copula theory, is developed here and applied to correct precipitation fields from the Weather Research and Forecasting (WRF) model. For dynamically downscaled precipitation fields we used high-resolution (7 km, daily) WRF simulations for Germany driven by ERA40 reanalysis data for 1971–2000. The REGNIE (REGionalisierung der NIEderschlagshöhen) data set from the German Weather Service (DWD) is used as gridded observation data (1 km, daily) and aggregated to 7 km for this application. The 30-year time series are split into a calibration (1971–1985) and validation (1986–2000) period of equal length. Based on the estimated dependence structure (described by the Copula function) between WRF and REGNIE data and the identified respective marginal distributions in the calibration period, separately analyzed for the different seasons, conditional distribution functions are derived for each time step in the validation period. This finally allows to get additional information about the range of the statistically possible bias-corrected values. The results show that the Copula-based approach efficiently corrects most of the errors in WRF derived precipitation for all seasons. It is also found that the Copula-based correction performs better for wet bias correction than for dry bias correction. In autumn and winter, the correction introduced a small dry bias in the northwest of Germany. The average relative bias of daily mean precipitation from WRF for the validation period is reduced from 10% (wet bias) to −1% (slight dry bias) after the application of the Copula-based correction. The bias in different seasons is corrected from 32% March–April–May (MAM), −15% June–July–August (JJA), 4% September–October–November (SON) and 28% December–January–February (DJF) to 16% (MAM), −11% (JJA), −1% (SON) and −3% (DJF), respectively. Finally, the Copula-based approach is compared to the quantile mapping correction method. The root mean square error (RMSE) and the percentage of the corrected time steps that are closer to the observations are analyzed. The Copula-based correction derived from the mean of the sampled distribution reduces the RMSE significantly, while, e.g., the quantile mapping method results in an increased RMSE for some regions.

In this paper, we focus on the visualization of heterogeneous semantic networks obtained from multiple data sources. A semantic network comprising a set of entities and relationships is often used for representing knowledge derived from textual data or database records. Although the semantic networks created for the same domain at different data sources may cover a similar set of entities, these networks could also be very different because of naming conventions, coverage, view points, and other reasons. Since digital libraries often contain data from multiple sources, we propose a visualization tool to integrate and analyze the differences among multiple social networks. Through a case study on two terrorism-related semantic networks derived from Wikipedia and Terrorism Knowledge Base (TKB) respectively, the effectiveness of our proposed visualization tool is demonstrated.

In recent years, huge different biological databases have been stored in various locations. Using distinct data sets from multiple sources results in more reliable data analysis. However, it is so difficult to combine heterogeneous data in one single server. The most obvious reasons include data privacy, large data sizes, costs, and different geographical locations of data sources. In this paper, we present two new algorithms for clustering data from multiple remote data sources using kernel k-means. The first algorithm is the center-based algorithm built on k-means algorithm. The second algorithm uses distributed kernel k-means over multiple data sources. In the distributed scheme, clustering methods are executed only on their local data sources themselves. Partial clustering results are synched between data sources. To evaluate performance of our proposed algorithms, we merged all data from different sources into one large data set to perform kernel k-means. The results showed that our center-based algorithm greatly reduced transmission data between data sources while still yielding acceptable clustering results. Our distributed kernel k-means algorithm achieved even better performance. The clustering results are very close to those generated by kernel k-means on one merged data set.

Detecting functional modules from protein-protein interaction (PPI) networks is an active research area with many practical applications. However, there is always a critical concern on the false PPI interactions which are derived from the high-throughput experiments and the unsatisfactory results obtained from single PPI network with severe information insufficiency. To address this problem, we propose a Collective Non-negative Matrix Factorization (CoNMF) based soft clustering method which efficiently integrates information of gene ontology (GO), gene expression data and PPI networks. In our method, the three data sources are formed into two graphs with similarity adjacency matrices and these graphs are approximated by a matrix factorization with their common factor which provides the straight-forward interpretation of clustering results. Extensive experiments show that we can improve the module detection performance by integrating multiple biological data sources and that CoNMF yields superior results compared to other multiple data sources fusion methods by identifying a larger number of more precise protein modules with actual biological meaning and certain degree of overlapping.

This paper summarizes and replicates multiple classical and cutting-edge knowledge transfer methods, including Factor Transfer (FT), Knowledge Distillation (KD), Deep Mutual Learning (DML), Contrastive Representation Distillation (CRD), and Born-Again Self-Distillation (BSS). Additionally, we studied three advanced knowledge transfer methods: Relational Knowledge Distillation (RKD), Similarity-Preserving (SP), and Attention-based Feature Distillation (AFD), successfully replicating an optimized version of KD, namely RKD. Based on these methods, a flexible model library was constructed in Pycharm, allowing the quick integration of multiple knowledge transfer strategies. The experimental results are visualized through a user-friendly interface, enabling intuitive comparisons of model training speed and performance across different methods. This research provides valuable insights into the challenge of building a reusable framework that efficiently integrates various knowledge transfer strategies into deep neural networks.

This study employed a qualitative methodology to explore small family farm owners' strategiesto transfer knowledge and preserve critical expertise, skills, and organizational insights. Given the challenges inherent in small family farms, including the lack of standardized processes for knowledge transfer, qualitative research allows for an in-depth exploration of these complexities. The study aimed to investigate knowledge transfer strategies used by retired small family farm owners in the northeastern U.S. A total of 13 were interviewed using a semi-structured format, focusing on strategies employed to facilitate knowledge retention. Data was analyzed through coding and theme identification to provide comprehensive insights into the barriers, facilitators, and situational factors affecting knowledge transfer. Findings contribute valuable knowledge to the existing literature on knowledge transfer and offer practical recommendations for improving knowledge retention in small family farms.

It is challenging to identify meaningful gene networks because biological interactions are often condition-specific and confounded with external factors. It is necessary to integrate multiple sources of genomic data to facilitate network inference. For example, one can jointly model expression datasets measured from multiple tissues with molecular marker data in so-called genetical genomic studies. In this paper, we propose a joint conditional Gaussian graphical model (JCGGM) that aims for modeling biological processes based on multiple sources of data. This approach is able to integrate multiple sources of information by adopting conditional models combined with joint sparsity regularization. We apply our approach to a real dataset measuring gene expression in four tissues (kidney, liver, heart, and fat) from recombinant inbred rats. Our approach reveals that the liver tissue has the highest level of tissue-specific gene regulations among genes involved in insulin responsive facilitative sugar transporter mediated glucose transport pathway, followed by heart and fat tissues, and this finding can only be attained from our JCGGM approach.