Many methods have been proposed to detect concept drift, i.e., the change in the distribution of streaming data, due to concept drift causes a decrease in the prediction accuracy of algorithms. However, the most of current detection methods are based on the assessment of the degree of change in the data distribution, cannot identify the type of concept drift. In this paper, we propose Active Drift Detection with Meta learning (Meta-ADD), a novel framework that learns to classify concept drift by tracking the changed pattern of error rates. Specifically, in the training phase, we extract meta-features based on the error rates of various concept drift, after which a meta-detector is developed via a prototypical neural network by representing various concept drift classes as corresponding prototypes. In the detection phase, the learned meta-detector is fine-tuned to adapt to the corresponding data stream via stream-based active learning. Hence, Meta-ADD uses machine learning to learn to detect concept drifts and identify their types automatically, which can directly support drift understand. The experiment results verify the effectiveness of Meta-ADD.

Since the depletion of fossil fuels, the world has started to rely heavily on renewable sources of energy. With every passing year, our dependency on the renewable sources of energy is increasing exponentially. As a result, complex and hybrid generation systems are being designed and developed to meet the energy demands and ensure energy security in a country. The continual improvement in the technology and an effort towards the provision of uninterrupted power to the end-users is strongly dependent on an effective and fault resilient Operation and Maintenance (O&M) system. Ingenious algorithms and techniques are hence been introduced aiming to minimize equipment and plant downtime. Efforts are being made to develop robust Prognostic Maintenance systems that can identify the faults before they occur. To this aim, complex Data Analytics and Machine Learning (ML) techniques are being used to increase the overall efficiency of these prognostic maintenance systems. This paper provides an overview of the predictive/prognostic maintenance frameworks reported in the literature. We pay a particular focus to the approaches, challenges including data-related issues, such as the availability and quality of the data and data auditing, feature engineering, interpretability, and security issues. Being a key aspect of ML-based solutions, we also discuss some of the commonly used publicly available datasets in the domain. The paper also identifies key future research directions. We believe such detailed analysis will provide a baseline for future research in the domain.

The scope of data-driven fault diagnosis models is greatly improved through deep learning (DL). However, the classical convolution and recurrent structure have their defects in computational efficiency and feature representation, while the latest Transformer architecture based on attention mechanism has not been applied in this field. To solve these problems, we propose a novel time-frequency Transformer (TFT) model inspired by the massive success of standard Transformer in sequence processing. Specially, we design a fresh tokenizer and encoder module to extract effective abstractions from the time-frequency representation (TFR) of vibration signals. On this basis, a new end-to-end fault diagnosis framework based on time-frequency Transformer is presented in this paper. Through the case studies on bearing experimental datasets, we constructed the optimal Transformer structure and verified the performance of the diagnostic method. The superiority of the proposed method is demonstrated in comparison with the benchmark model and other state-of-the-art methods.

Depending on the application, radiological diagnoses can be associated with high inter- and intra-rater variabilities. Most computer-aided diagnosis (CAD) solutions treat such data as incontrovertible, exposing learning algorithms to considerable and possibly contradictory label noise and biases. Thus, managing subjectivity in labels is a fundamental problem in medical imaging analysis. To address this challenge, we introduce auto-decoded deep latent embeddings (ADDLE), which explicitly models the tendencies of each rater using an auto-decoder framework. After a simple linear transformation, the latent variables can be injected into any backbone at any and multiple points, allowing the model to account for rater-specific effects on the diagnosis. Importantly, ADDLE does not expect multiple raters per image in training, meaning it can readily learn from data mined from hospital archives. Moreover, the complexity of training ADDLE does not increase as more raters are added. During inference each rater can be simulated and a 'mean' or 'greedy' virtual rating can be produced. We test ADDLE on the problem of liver steatosis diagnosis from 2D ultrasound (US) by collecting 46 084 studies along with clinical US diagnoses originating from 65 different raters. We evaluated diagnostic performance using a separate dataset with gold-standard biopsy diagnoses. ADDLE can improve the partial areas under the curve (AUCs) for diagnosing severe steatosis by 10.5% over standard classifiers while outperforming other annotator-noise approaches, including those requiring 65 times the parameters.

Many decisions involve choosing an uncertain course of actions in deep and wide decision trees, as when we plan to visit an exotic country for vacation. In these cases, exhaustive search for the best sequence of actions is not tractable due to the large number of possibilities and limited time or computational resources available to make the decision. Therefore, planning agents need to balance breadth (exploring many actions at each level of the tree) and depth (exploring many levels in the tree) to allocate optimally their finite search capacity. We provide efficient analytical solutions and numerical analysis to the problem of allocating finite sampling capacity in one shot to large decision trees. We find that in general the optimal policy is to allocate few samples per level so that deep levels can be reached, thus favoring depth over breadth search. In contrast, in poor environments and at low capacity, it is best to broadly sample branches at the cost of not sampling deeply, although this policy is marginally better than deep allocations. Our results provide a theoretical foundation for the optimality of deep imagination for planning and show that it is a generally valid heuristic that could have evolved from the finite constraints of cognitive systems.

Agtech capabilities are bringing traditional farming into the 21st century. These solutions range from sprawling LED-equipped indoor farming facilities to robotically plucking ripe produce off the vine using computer vision and artificial intelligence (AI). On Thursday, John Deere and Intel announced a pilot program that relies on AI and computer vision to detect defects in manufacturing related to the welding process. "Welding is a complicated process. This AI solution has the potential to help us produce our high-quality machines more efficiently than before," said Andy Benko, quality director at John Deere Construction and Forestry Division.

The hardware or software that operates medical devices can be biased. A biased device is one that operates in a manner that disadvantages certain demographic groups and influences health inequity. As one measure of fairness, reducing bias is related to increasing fairness in the operation of a medical device. Initiatives to promote fairness are rapidly growing in a range of technical disciplines, but this growth is not rapid enough for medical engineering. Although computer science companies terminate lucrative but biased facial recognition systems, biased medical devices continue to be sold as commercial products. It is important to address bias in medical devices now. This can be achieved by studying where and how bias arises, and understanding these can inform mitigation strategies. Bias in medical devices can be divided into three broad forms (see the figure). A medical device can exhibit physical bias, where physical principles are biased against certain demographics. Once data are collected, computational bias, which pertains to the distribution, processing, and computation of data that are used to operate a device, must be considered. Subsequent implementation in clinical settings can lead to interpretation bias, where clinical staff or other users may interpret device outputs differently based on demographics. The physical working principle of a medical device is biased when it exhibits an undesirable performance variation across demographic groups. An example of physical bias occurs in the context of optical biosensors that use light to monitor vital signs. A pulse oximeter uses two colors of light (one in near-infrared and the other in visible light) to measure blood oxygenation. Through the pulse oximeter, it is possible to diagnose occult hypoxemia, low levels of arterial oxygen saturation that are not detectable from symptoms. However, a recent study found that Black patients had about three times the frequency of undiagnosed occult hypoxemia as measured by pulse oximeters ([ 1 ][1]). Dark skin tones respond differently to these wavelengths of light, particularly visible light. Because hypoxemia relates to mortality, such a biased medical device could lead to disparate mortality outcomes for Black and dark-skinned patients. Physical bias is not restricted to skin color. For example, the mechanical design of implants for hip replacement exhibits a potentially troubling gender disparity. The three-dimensional models used to design hip-joint implants sometimes do not account for the distinct bone structure of female hips ([ 2 ][2]). This could lead to alignment issues and relatively poor outcomes for affected females. This problem was one motivation for the development of gender-specific implants. Fortunately, physical challenges can also be addressed through unexpected technical innovation, such as in the example of the remote plethysmograph. This device measures heart rate through visual changes in skin color. Because visual cues are biased, researchers developed an alternative approach using motion cues to estimate heart rate. Because motions are visible on the surface of skin, the technique is less biased by subsurface melanin content ([ 3 ][3]). With the goal of promoting fairness, an exciting technical direction of studying motion cues instead of color cues has been advanced. ![Figure][4] Measuring fairness Fairness can be quantified based on ϵ-bias. Fairness is maximized when ϵ = 0, achieving a state of 0-bias. GRAPHIC: N. DESAI/ SCIENCE Computational workflows are becoming more tightly coupled with devices, which increases the number of entry points where computational bias can invade medical technologies. An aspect of computational bias is dataset bias. Consider the following example from x-ray imaging: Diagnostic algorithms can learn patterns from x-ray imaging datasets of thoracic conditions. However, these imaging datasets often contain a surprising imbalance, where females are underrepresented. For example, despite having a sample size of more than 100,000 images, frequently used chest x-ray databases are ∼60% male and ∼40% female ([ 4 ][5]). This imbalance worsens the quality of diagnosis for female patients. A solution is to ensure that datasets are balanced. Somewhat unexpectedly, balancing the gender representation to 50% female boosts diagnostic performance not only for females but also for males ([ 4 ][5]). Despite best efforts, demographic balancing of a dataset might not be possible. This could be due to conditions that present more often in one sex than the other. In such cases where balancing a dataset is truly infeasible, transfer learning can be used as a step toward a longer-term solution ([ 5 ][6]). Transfer learning could repurpose design parameters from task A (based on a balanced dataset) to task B (with an unbalanced dataset). In the future, it might be possible to balance a dataset using a human digital twin. These are computational models that can be programmed to reflect a desired race, sex, or morphological trait. Another form of computational bias is algorithm bias, where the mathematics of data processing disadvantages certain groups. Now, software algorithms are able to process video streams to detect the spontaneous blink rate of a human subject. This is helpful in diagnosing a variety of neurological disorders, including Parkinson's disease ([ 6 ][7]) and Tourette syndrome ([ 7 ][8]). Unfortunately, traditional image-processing systems have particular difficulty in detecting blinks for Asian individuals ([ 8 ][9]). The use of such poorly designed and biased algorithms ([ 9 ][10]) could produce or exacerbate health disparities between racial groups. Interpretation bias occurs when a medical device is subject to biased inference of readings. An example of a misinterpreted medical device is the spirometer, which measures lung capacity. The interpretation of spirometry data creates unfairness because certain ethnic groups, such as Black or Asian, are assumed to have lower lung capacity than white people: 15% lower for Black people and about 5% lower for Asian people. This assumption is based on earlier studies that may have incorrectly estimated innate lung capacity ([ 10 ][11]). Unfortunately, these “correction factors,” based on questionable assumptions, are applied to the interpretation of spirometer data. For example, before “correction,” a Black person's lung capacity might be measured to be lower than the lung capacity of a white person. After “correction” to a smaller baseline lung capacity, treatment plans would prioritize the white person, because it is expected that a Black person should have lower lung capacity, and so their capacity must be much lower than that of a white person before their reduction is considered a priority. ![Figure][4] Bias in medical devices A device can be biased if its design disadvantages certain groups on the basis of their physical attributes, such as skin color. For example, pulse oximeters (see the photo) detect changes in light passed through skin and are less effective in people with dark skin. Computational techniques are biased if training datasets are not representative of the population. Interpretation of results may be biased according to demographic groups, for example, with the use of “correction factors.” CREDIT: N. DESAI/ SCIENCE However well intentioned, errors in “correction” for race (or sex) can disadvantage the groups it seeks to protect. In the spirometer example, the device designers conflated a racial group's healthy lung capacity with their average lung capacity. This assumption does not account for socioeconomic distinctions across race: Individuals who live near motorways exhibit reduced lung capacity, and these individuals are often from disadvantaged ethnic groups. The spirometer is just one of several examples of systemic racism in medicine ([ 11 ][12]). If our society desires fair medical devices, it must reward a fair approach to innovation. It is inspiring to observe the speed at which the artificial intelligence (AI) community has recognized fairness in its endeavors. Authors can be encouraged by journals to address the societal implications of their technologies and include a “broader impacts” statement that is considered in peer review. This has already been introduced at an AI journal to encourage consideration of the diversity of potential users of their software ([ 12 ][13]). Fairness research in AI is increasingly garnering scholarly acclaim. For example, a seminal report highlighted the widespread problem of bias in face recognition, which found that darker-skinned females are misclassified at rates up to 34.7% while the maximum error rate for lighter-skinned males is only 0.8% ([ 13 ][14]). In response to concerns of fairness, action is being taken. For example, Amazon Inc. has recently banned the use of its facial-recognition products by police until bias concerns can be resolved. There is still a long way to go in addressing bias in AI, but some of the lessons learned can be repurposed to medical devices. A “fairness” statement for the evaluation of studies of medical devices could use the three categories of bias as a rubric: physical bias, computational bias, and interpretation bias. A medical-device study does not need to be perfectly unbiased to be reported. Indeed, it may not always be possible to remove all sources of bias. For example, an oximeter reliant on an optical sensor is likely to remain biased against dark skin ([ 1 ][1]). The fairness statement can consist of technical explanations for how attempts to mitigate bias failed and suggest technical compensations for disadvantaged groups (e.g., collect additional data points for dark-skinned people). This is consistent with the introduction of “positive biases,” where race-aware and gender-aware methodologies are explicitly designed to counteract negative bias ([ 14 ][15]). Additionally, the inclusion of fairness metrics in studies of medical devices could be considered. Choosing the right fairness metric of an algorithm is a quantitatively challenging computer science exercise ([ 15 ][16]) and can be abstracted here as “ϵ-bias,” where ϵ quantifies the degree of bias across subgroups. For example, 0-bias would be seen as perfectly fair. Achieving 0-bias on its own is trivial: Simply return a measurement that is consistently useless across demographics. The problem is to maximize performance and minimize ϵ-bias. This may present a Pareto trade-off, where maximizing the performance and minimizing bias are objectives at odds with each other. A Pareto curve can quantitatively display how changing device configuration varies the balance between performance and fairness (see the graph). Such analyses might be a useful inclusion in medical-device studies. Achieving fairness in medical devices is a key piece of the puzzle, but a piece nonetheless. Even if one manages to engineer a fair medical device, it could be used by a clinical provider who has conscious or subconscious bias. And even a fair medical device from an engineering perspective might be inaccessible to a range of demographic groups, owing to socioeconomic reasons. Several open questions remain. What is an acceptable trade-off between device performance and fairness? It is also important to consider how biases that are not easy to predict or easy to observe at scale can be dealt with. Race and sex are also part of human biology. How can positive biases be properly encoded into medical-device design? Diversity and inclusion have gained increasing attention, and the era of fair medical devices is only just beginning. 1. [↵][17]1. M. W. Sjoding et al ., N. Engl. J. Med. 383, 2477 (2020). [OpenUrl][18][CrossRef][19][PubMed][20] 2. [↵][21]1. C. W. Hartman et al ., Semin. Arthroplasty 20, 62 (2009). [OpenUrl][22] 3. [↵][23]1. G. Balakrishnan, 2. F. Durand, 3. J. Guttag , in Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition (IEEE Computer Society, 2013), pp. 3430–3437. 4. [↵][24]1. A. J. Larrazabal, 2. N. Nieto, 3. V. Peterson, 4. D. H. Milone, 5. E. Ferrante , Proc. Natl. Acad. Sci. U.S.A. 117, 12592 (2020). 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I am supported by a National Science Foundation CAREER grant (IIS-2046737), Google Faculty Award, and Sony Imaging Young Faculty Award. 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Would you like to build predictive models using machine learning? That s precisely what you will learn in this course "Decision Trees, Random Forests and Gradient Boosting in R." My name is Carlos Martínez, I have a Ph.D. in Management from the University of St. Gallen in Switzerland. I have presented my research at some of the most prestigious academic conferences and doctoral colloquiums at the University of Tel Aviv, Politecnico di Milano, University of Halmstad, and MIT. Furthermore, I have co-authored more than 25 teaching cases, some of them included in the case bases of Harvard and Michigan. This is a very comprehensive course that includes presentations, tutorials, and assignments. The course has a practical approach based on the learning-by-doing method in which you will learn decision trees and ensemble methods based on decision trees using a real dataset.

The growing integration of distributed energy resources (DERs) in distribution grids raises various reliability issues due to DER's uncertain and complex behaviors. With a large-scale DER penetration in distribution grids, traditional outage detection methods, which rely on customers report and smart meters' last gasp signals, will have poor performance, because the renewable generators and storages and the mesh structure in urban distribution grids can continue supplying power after line outages. To address these challenges, we propose a data-driven outage monitoring approach based on the stochastic time series analysis with a theoretical guarantee. Specifically, we prove via power flow analysis that the dependency of time-series voltage measurements exhibits significant statistical changes after line outages. This makes the theory on optimal change-point detection suitable to identify line outages. However, existing change point detection methods require post-outage voltage distribution, which is unknown in distribution systems. Therefore, we design a maximum likelihood estimator to directly learn the distribution parameters from voltage data. We prove that the estimated parameters-based detection also achieves the optimal performance, making it extremely useful for fast distribution grid outage identifications. Furthermore, since smart meters have been widely installed in distribution grids and advanced infrastructure (e.g., PMU) has not widely been available, our approach only requires voltage magnitude for quick outage identification. Simulation results show highly accurate outage identification in eight distribution grids with 14 configurations with and without DERs using smart meter data.

When confirming on Wednesday that the investigation had been concluded, he added: "We have reached out to Tiger Woods and his personnel and there are some privacy issues on releasing information on the investigation.