Music Structure Analysis is an open research task in Music Information Retrieval (MIR). In the past, there have been several works that attempt to segment music into the audio and symbolic domains, however, the identification and segmentation of the music structure at different levels is still an open research problem in this area. In this work we propose three methods, two of which are novel graph-based algorithms that aim to segment symbolic music by its form or structure: Norm, G-PELT and G-Window. We performed an ablation study with two public datasets that have different forms or structures in order to compare such methods varying their parameter values and comparing the performance against different music styles. We have found that encoding symbolic music with graph representations and computing the novelty of Adjacency Matrices obtained from graphs represent the structure of symbolic music pieces well without the need to extract features from it. We are able to detect the boundaries with an online unsupervised changepoint detection method with a F_1 of 0.5640 for a 1 bar tolerance in one of the public datasets that we used for testing our methods. We also provide the performance results of the algorithms at different levels of structure, high, medium and low, to show how the parameters of the proposed methods have to be adjusted depending on the level. We added the best performing method with its parameters for each structure level to musicaiz, an open source python package, to facilitate the reproducibility and usability of this work. We hope that this methods could be used to improve other MIR tasks such as music generation with structure, music classification or key changes detection.

Various faults can occur during the operation of PV arrays, and both the dust-affected operating conditions and various diode configurations make the faults more complicated. However, current methods for fault diagnosis based on I-V characteristic curves only utilize partial feature information and often rely on calibrating the field characteristic curves to standard test conditions (STC). It is difficult to apply it in practice and to accurately identify multiple complex faults with similarities in different blocking diodes configurations of PV arrays under the influence of dust. Therefore, a novel fault diagnosis method for PV arrays considering dust impact is proposed. In the preprocessing stage, the Isc-Voc normalized Gramian angular difference field (GADF) method is presented, which normalizes and transforms the resampled PV array characteristic curves from the field including I-V and P-V to obtain the transformed graphical feature matrices. Then, in the fault diagnosis stage, the model of convolutional neural network (CNN) with convolutional block attention modules (CBAM) is designed to extract fault differentiation information from the transformed graphical matrices containing full feature information and to classify faults. And different graphical feature transformation methods are compared through simulation cases, and different CNN-based classification methods are also analyzed. The results indicate that the developed method for PV arrays with different blocking diodes configurations under various operating conditions has high fault diagnosis accuracy and reliability.

This paper focuses on the application of one type of empirical methods, namely statistical causal inference (SCI, see section 2). Such methods have their roots in a number of applied fields (from AI to econometrics) and aim to provide a framework for making valid inferences about causal effects based on interventional or observational data. More specifically, we focus on SCI methods that use graphical models as developed by Pearl and colleagues [1, 2]. This framework has been shown to be equivalent of the potential-outcomes framework (also called the Neyman-Rubin Causal Model [3]) but enriches it by making use of an explicit causal structure called a graphical causal model. Making assumptions about causal effects explicit through a graphical structure has several advantages. First, it helps determine whether causal effects can be estimated and how they might be estimated (see section 2).

In real-world phenomena which involve mutual influence or causal effects between interconnected units, equilibrium states are typically represented with cycles in graphical models. An expressive class of graphical models, relational causal models, can represent and reason about complex dynamic systems exhibiting such cycles or feedback loops. Existing cyclic causal discovery algorithms for learning causal models from observational data assume that the data instances are independent and identically distributed which makes them unsuitable for relational causal models. At the same time, causal discovery algorithms for relational causal models assume acyclicity. In this work, we examine the necessary and sufficient conditions under which a constraint-based relational causal discovery algorithm is sound and complete for cyclic relational causal models. We introduce relational acyclification, an operation specifically designed for relational models that enables reasoning about the identifiability of cyclic relational causal models. We show that under the assumptions of relational acyclification and $\sigma$-faithfulness, the relational causal discovery algorithm RCD (Maier et al. 2013) is sound and complete for cyclic models. We present experimental results to support our claim.

Although artificial intelligence (AI) systems have been shown to improve the accuracy of initial melanoma diagnosis, the lack of transparency in how these systems identify melanoma poses severe obstacles to user acceptance. Explainable artificial intelligence (XAI) methods can help to increase transparency, but most XAI methods are unable to produce precisely located domain-specific explanations, making the explanations difficult to interpret. Moreover, the impact of XAI methods on dermatologists has not yet been evaluated. Extending on two existing classifiers, we developed an XAI system that produces text and region based explanations that are easily interpretable by dermatologists alongside its differential diagnoses of melanomas and nevi. To evaluate this system, we conducted a three-part reader study to assess its impact on clinicians' diagnostic accuracy, confidence, and trust in the XAI-support. We showed that our XAI's explanations were highly aligned with clinicians' explanations and that both the clinicians' trust in the support system and their confidence in their diagnoses were significantly increased when using our XAI compared to using a conventional AI system. The clinicians' diagnostic accuracy was numerically, albeit not significantly, increased. This work demonstrates that clinicians are willing to adopt such an XAI system, motivating their future use in the clinic.

The performance of fault diagnosis systems is highly affected by data quality in cyber-physical power systems. These systems generate massive amounts of data that overburden the system with excessive computational costs. Another issue is the presence of noise in recorded measurements, which prevents building a precise decision model. Furthermore, the diagnostic model is often provided with a mixture of redundant measurements that may deviate it from learning normal and fault distributions. This paper presents the effect of feature engineering on mitigating the aforementioned challenges in cyber-physical systems. Feature selection and dimensionality reduction methods are combined with decision models to simulate data-driven fault diagnosis in a 118-bus power system. A comparative study is enabled accordingly to compare several advanced techniques in both domains. Dimensionality reduction and feature selection methods are compared both jointly and separately. Finally, experiments are concluded, and a setting is suggested that enhances data quality for fault diagnosis.

Acoustic-based fault detection has a high potential to monitor the health condition of mechanical parts. However, the background noise of an industrial environment may negatively influence the performance of fault detection. Limited attention has been paid to improving the robustness of fault detection against industrial environmental noise. Therefore, we present the Lenze production background-noise (LPBN) real-world dataset and an automated and noise-robust auditory inspection (ARAI) system for the end-of-line inspection of geared motors. An acoustic array is used to acquire data from motors with a minor fault, major fault, or which are healthy. A benchmark is provided to compare the psychoacoustic features with different types of envelope features based on expert knowledge of the gearbox. To the best of our knowledge, we are the first to apply time-varying psychoacoustic features for fault detection. We train a state-of-the-art one-class-classifier, on samples from healthy motors and separate the faulty ones for fault detection using a threshold. The best-performing approaches achieve an area under curve of 0.87 (logarithm envelope), 0.86 (time-varying psychoacoustics), and 0.91 (combination of both).

Abstract: ntroduction: Mild cognitive impairment (MCI) is regarded as a prodrome to dementia. Various cognitive tests can help with diagnosis; meta-analysis of diagnostic accuracy studies would assist clinicians in choosing optimal tests. Methods: We searched online databases for "mild cognitive impairment" and "diagnosis" or "screening" from 01/01/1999 to 01/07/2017. Articles assessing the diagnostic accuracy of a cognitive test compared with standard diagnostic criteria were extracted. Risk of bias was assessed.

The epidemiological characteristics and clinical examination methods of thyroid nodules: Thyroid nodules are widespread clinically, and the incidence continues to rise worldwide, with an autopsy study estimating that 50% to 60% of adults may have thyroid nodules (1, 2). High-resolution ultrasound (US) can detect thyroid nodules in 19%- 68% (3) of randomly selected individuals, of which thyroid cancer occurs in 7% to 15% (4). Thyroid cancer is the most common endocrine malignancy in the United States (5) and the fifth most common cancer among women (6). The benign thyroid nodules without surgical indications generally do not require special treatment. In contrast, malignant thyroid nodules should be elective surgical treatment once diagnosed, and neck dissection should be performed if lymph node metastases are present. Some patients need to be treated with Iodine-131 nuclide after the operation (7) and predict the prognosis. Papillary thyroid carcinoma (PTC) is the most common pathological type of thyroid cancer. It usually has a good prognosis, but relapse patients have a poor prognosis. About 10%-15% of PTC will relapse, and recurrent PTC has aggressive characteristics such as extra-thyroid extension (ETE), invasive cell subtypes, lateral neck lymphatic metastasis, resistance to therapy, and distant metastases (8). The challenge for clinicians is to balance treatment approaches so that patients with low-risk or benign thyroid nodules are not over-treated, while patients with high-risk or malignant thyroid nodules need more aggressive therapies. Therefore, the differential diagnosis of thyroid nodules and the risk stratification are essential and helpful for the subsequent individualized treatment.

Autism Spectrum Disorder (autism) is a neurodevelopmental delay which affects at least 1 in 44 children. Like many neurological disorder phenotypes, the diagnostic features are observable, can be tracked over time, and can be managed or even eliminated through proper therapy and treatments. Yet, there are major bottlenecks in the diagnostic, therapeutic, and longitudinal tracking pipelines for autism and related delays, creating an opportunity for novel data science solutions to augment and transform existing workflows and provide access to services for more affected families. Several prior efforts conducted by a multitude of research labs have spawned great progress towards improved digital diagnostics and digital therapies for children with autism. We review the literature of digital health methods for autism behavior quantification using data science. We describe both case-control studies and classification systems for digital phenotyping. We then discuss digital diagnostics and therapeutics which integrate machine learning models of autism-related behaviors, including the factors which must be addressed for translational use. Finally, we describe ongoing challenges and potent opportunities for the field of autism data science. Given the heterogeneous nature of autism and the complexities of the relevant behaviors, this review contains insights which are relevant to neurological behavior analysis and digital psychiatry more broadly.