Adapting Pretrained ASR Models to Low-resource Clinical Speech using Epistemic Uncertainty-based Data Selection

While there has been significant progress in ASR, African-accented clinical ASR has been understudied due to a lack of training datasets. Building robust ASR systems in this domain requires large amounts of annotated or labeled data, for a wide variety of linguistically and morphologically rich accents, which are expensive to create. Our study aims to address this problem by reducing annotation expenses through informative uncertainty-based data selection. We show that incorporating epistemic uncertainty into our adaptation rounds outperforms several baseline results, established using state-of-the-art (SOTA) ASR models, while reducing the required amount of labeled data, and hence reducing annotation costs. Our approach also improves out-of-distribution generalization for very low-resource accents, demonstrating the viability of our approach for building generalizable ASR models in the context of accented African clinical ASR, where training datasets are predominantly scarce

A Fuzzy Analytic Hierarchical Method to Reduce Imprecision and Uncertainty in Drilling Operation’s Factor Selection Process for Unidirectional Carbon Fibre Reinforced Plastic Composite Plates

Parametric selection in machining processes is recently understood as a route to reducing waste generation in drilling activities and achieving a robust resource distribution in drilling activities. However, the selection methods dominant in the literature lack competence in reducing uncertainties and imprecision associated with the drilling process. The purpose of this research is to reduce the uncertainty and imprecision in previously analyzed data that used the analytic hierarchy process (AHP) method. This paper adjusts the uncertainty and imprecision by introducing a geometric mean-based fuzzy analytic hierarchy process. The selection method influences the drilling expert's preferences by imposing the fuzzy theory in a triangular member function that converts the crisp numerical values into fuzzy members and adequately suppresses the imprecision and uncertainty in the elements. The thrust force was positioned first in ranking with a FAHP method's weight of 0.415, which matched the literature value of 0.413 for the AHP method. It was found that the use of the FAHP method has corrected the imprecision and uncertainty introduced by the AHP method. It was found that the thrust force and torque were overestimated by or 0.48% and 3.95%, respectively and was accordingly corrected. Besides, no errors were found with the measurement of eccentricity response. Furthermore, the entry delamination, exit delamination and surface roughness were underestimated by -8.11%, -3.33% and -6.96%, respectively, and therefore corrected by the FAHP method. The usefulness of this effort is to enhance cost-effective decisions and the effectiveness in the distribution of scarce drilling resources

What can systems and control theory do for agricultural science?

Abstract: While many professionals with a background in agricultural and bio-resource sciences work with models, only few have been exposed to systems and control theory. The purpose of this paper is to elucidate a selection of methods from systems theory that can be beneficial to quantitative agricultural science. The state space representation of a dynamical system is the corner stone in the mainstream of systems theory. It is not well known in agro-modelling that linearization followed by evaluation of eigenvalues and eigenvectors of the system matrix is useful to obtain dominant time constants and dominant directions in state space, and offers opportunities for science-based model reduction. The continuous state space description is also useful in deriving truly equivalent discrete time models, and clearly shows that parameters obtained with discrete models must be interpreted with care when transferred to another model code environment. Sensitivity analysis of dynamic models reveals that sensitivity is time and input dependent. Identifiability and sensitivity are essential notions in the design of informative experiments, and the idea of persistent excitation, leading to dynamic experiments rather than the usual static experiments can be very beneficial. A special branch of systems theory is control theory. Obviously, control plays an important part in agricultural and bio-systems engineering, but it is argued that also agronomists can profit from notions from the world of control, even if practical control options are restricted to alleviating growth limiting conditions, rather than true crop control. The most important is the idea of reducing uncertainty via feed-back. On the other hand, the systems and control community is challenged to do more to address the problems of real life, such as spatial variability, measurement delays, lacking data, environmental stochasticity, parameter variability, unavoidable model uncertainty, discrete phenomena, variable system structures, the interaction of technical ad living systems, and, indeed, the study of the functioning of life itself

Issues related to uncertainty in projections of hazardous and mixed waste volumes in the U.S. Department of Energy`s environmental restoration program

Projected volumes of contaminated media and debris at US Department of Energy (DOE) environmental restoration sites that are potentially subject to the hazardous waste provisions of the Resource Conservation and Recovery Act are needed to support programmatic planning. Such projections have been gathered in various surveys conducted under DOE`s environmental restoration and waste management programs. It is expected that reducing uncertainty in the projections through review of existing site data and process knowledge and through further site characterization will result in substantially lowered projections. If promulgated, the US Environmental Protection Agency`s Hazardous Waste Identification Rule would result in potentially even greater reductions in the projections when site conditions are reviewed under the provisions of the new rule. Reducing uncertainty in projections under current and future waste identification rules may be necessary to support effective remediation planning. Further characterization efforts that may be conducted should be designed to limit uncertainty in identifying volumes of wastes to the extent needed to support alternative selection and to minimize costs of remediation

Economics and Environmental Markets: Lessons from Water-quality Trading

Water-quality trading is an area of active development in environmental markets. Unlike iconic national-scale air-emission trading programs, water-quality trading programs address local or regional water quality and are largely the result of innovations in water-pollution regulation by state or substate authorities rather than by national agencies. This article examines lessons from these innovations about the "real world" meaning of trading and its mechanisms, the economic merits of alternative institutional designs, utilization of economic research in program development, and research needed to improve the success of environmental markets for water quality

Co-evolutionary dynamics in strategic alliances : the influence of the industry lifecycle

This study examines the application of the co-evolution literature to strategic alliance formation in SME’s in the UK and Australia in two differing industries at different stages of the industry life-cycle. Extending the framework developed by Das and Teng (2002) and that of Wilson and Hynes (2009), it engages with wider industry and environmental characteristics present in these two countries, specifically examining whether different theories of alliance formation are better suited to different stages of an industry life cycle. The issues discussed above are explored and developed through the use of a qualitative case study approach. Findings indicate strong resource-based drivers for alliance formation in both industries, with firms dependent on the co-evolution of their alliances and indeed selected by the results of their alliance participation. However, differences emerged in the strategic use of alliances in these two industries. The influence of the stage of the industry life cycle on this is discussed

Design project planning, monitoring and re-planning through process simulation

Effective management of design schedules is a major concern in industry, since timely project delivery can have a significant influence on a company’s profitability. Based on insights gained through a case study of planning practice in aero-engine component design, this paper examines how task network simulation models can be deployed in a new way to support design process planning. Our method shows how simulation can be used to reconcile a description of design activities and information flows with project targets such as milestone delivery dates. It also shows how monitoring and re-planning can be supported using the non-ideal metrics which the case study revealed are used to monitor processes in practice. The approach is presented as a theoretical contribution which requires further work to implement and evaluate in practice

Load-aware Channel Selection for 802.11 WLANs with Limited Measurement

It has been known that load unaware channel selection in 802.11 networks results in high level interference, and can significantly reduce the network throughput. In current implementation, the only way to determine the traffic load on a channel is to measure that channel for a certain duration of time. Therefore, in order to find the best channel with the minimum load all channels have to be measured, which is costly and can cause unacceptable communication interruptions between the AP and the stations. In this paper, we propose a learning based approach which aims to find the channel with the minimum load by measuring only limited number of channels. Our method uses Gaussian Process Regressing to accurately track the traffic load on each channel based on the previous measured load. We confirm the performance of our algorithm by using experimental data, and show that the time consumed for the load measurement can be reduced up to 46% compared to the case where all channels are monitored.Comment: accepted to IC

Green Cellular Networks: A Survey, Some Research Issues and Challenges

Energy efficiency in cellular networks is a growing concern for cellular operators to not only maintain profitability, but also to reduce the overall environment effects. This emerging trend of achieving energy efficiency in cellular networks is motivating the standardization authorities and network operators to continuously explore future technologies in order to bring improvements in the entire network infrastructure. In this article, we present a brief survey of methods to improve the power efficiency of cellular networks, explore some research issues and challenges and suggest some techniques to enable an energy efficient or "green" cellular network. Since base stations consume a maximum portion of the total energy used in a cellular system, we will first provide a comprehensive survey on techniques to obtain energy savings in base stations. Next, we discuss how heterogeneous network deployment based on micro, pico and femto-cells can be used to achieve this goal. Since cognitive radio and cooperative relaying are undisputed future technologies in this regard, we propose a research vision to make these technologies more energy efficient. Lastly, we explore some broader perspectives in realizing a "green" cellular network technologyComment: 16 pages, 5 figures, 2 table