Is It Worth It? Comparing Six Deep and Classical Methods for Unsupervised Anomaly Detection in Time Series

Detecting anomalies in time series data is important in a variety of fields, including system monitoring, healthcare and cybersecurity. While the abundance of available methods makes it difficult to choose the most appropriate method for a given application, each method has its strengths in detecting certain types of anomalies. In this study, we compare six unsupervised anomaly detection methods of varying complexity to determine whether more complex methods generally perform better and if certain methods are better suited to certain types of anomalies. We evaluated the methods using the UCR anomaly archive, a recent benchmark dataset for anomaly detection. We analyzed the results on a dataset and anomaly-type level after adjusting the necessary hyperparameters for each method. Additionally, we assessed the ability of each method to incorporate prior knowledge about anomalies and examined the differences between point-wise and sequence-wise features. Our experiments show that classical machine learning methods generally outperform deep learning methods across a range of anomaly types

Factors affecting the structure and elasticity of thoracic aorta

Wstęp Celem pracy była ocena za pomocą echokardiograficznego badania przezprzełykowego (TEE) czynników wpływających na strukturę i elastyczność aorty piersiowej. Materiał i metody Badaniami objęto 102 chorych (26 kobiet) w wieku 57 &plusmn; 10 lat. Na podstawie danych z wywiadu analizowano obecność czynników ryzyka miażdżycy. U wszystkich chorych wykonano ze wskazań klinicznych badanie TEE, które rozszerzono o ocenę aorty piersiowej. Analizowano średnicę aorty, grubość błony środkowo-wewnętrznej oraz wskaźniki elastyczności aorty. Wyniki Średnica aorty oraz grubość błony środkowo- wewnętrznej była dodatnio skorelowana z wiekiem (odpowiednio: r = 0,22, p = 0,03; r = 0,27, p = 0,005). Moduł Younga i moduł elastyczności &#946; również były dodatnio skorelowane z wiekiem (odpowiednio: r = 0,42, p < 0,0001; r = 0,37, p = 0,0002). Chorzy z nadciśnieniem tętniczym charakteryzowali się grubszą błoną środkowo-wewnętrzną (0,10 &plusmn; 0,03 vs. 0,13 &plusmn; 0.04, p < 0,05). Zaawansowana miażdżyca wiązała się ze zwiększoną sztywnością jej ściany. Wniosek Głównymi czynnikami determinującymi sztywność aorty są wiek i zaawansowanie miażdżycy.Background The purpose of the study was to evaluate by means of transesophageal echocardiography (TEE) the factors affecting the structure and elasticity of the thoracic aorta. Material and methods The study population consisted of 102 patients (26 women) aged 57 &plusmn; 10 years. Information of risk factors of atherosclerosis was obtained by interview. All patients underwent TEE for other reasons, examination was completed by imaging of the thoracic aorta. Results Aortic diameter, the intima-media thickness and distensibility indices were measured. Diameter and thickness of thoracic aorta were positively correlated with age (r = 0.22, p = 0.03; r = 0.27 p = 0.005 respectively). Young&#8217;s modulus and B modulus were also related to age (r = 0.42, p < 0.0001; r = 0.37, p = 0.0002 respectively). Hypertensive subjects had higher wall thickness (0.10 &plusmn; 0.03 vs. 0.13 &plusmn; 0.04, p < 0.05). Advanced atherosclerosis of the aorta was related to higher stiffness of the aorta. Conclusion Aortic elasticity is related mostly to age and atherosclerosis

Estimating Uncertainty of Deep Learning Multi-Label Classifications using Laplace Approximation

With the huge successes of deep learning and its application in critical areas such as medical diagnosis or autonomous driving and in fields with noisy and very varying data such as remote sensing, the need for reliable confidence statements about such model's predictions becomes apparent. Therefore, uncertainty estimation methods for neural networks have raised rising interest in the machine learning community. While various methods for regression and multi-class classification tasks have been published, the field of multi-label classification has hardly been considered yet. In this work, we derive the Kronecker-factored Laplace approximation in the multi-label setting, a method to approximate the intractable posterior distribution over the parameters of neural networks. We employ this method in the remote sensing domain and estimate the model uncertainty of eight deep neural networks that have been trained on an aerial scene classification dataset. By comparing the probabilistic classifiers to their deterministic counterparts, we evaluate the potential for using the uncertainty estimates to improve the calibration of those classifiers as well as the out-of-distribution detection. We found that we can improve the calibration for overconfident classifiers whereas for underconfident ones, this method might not be beneficial. Furthermore, the ability to improve the separation from in- and out-of-distribution data seems to be depending on the depth of the neural network within one model family

Über Kumulene, XXII. Die Struktur des Triphenylallen-Dimeren

Aus Triphenylallen entsteht in Gegenwart von Säure durch eine C-3→C-2-Verknüpfung zweier Moleküle und Indenringschluß 2-[1.3.3-Triphenyl-allyl]-1.3-diphenyl-inden (1)

Machine Learning for Space Gardening

Sustained human presence in space requires the development of new technologies to maintain environment control, provide water, oxygen, food and to keep astronauts healthy and psychologically fit. The EDEN NEXT GEN project works along the roadmap of building a flight-ready bio-regenerative life support system within this decade. Being part of that project, we are concerned with detecting unhealthy system states and plant stress in the context of extraterrestrial horticulture. In this talk, I will introduce different classical and deep-learning-based methods for finding anomalies in time series and present our latest results on differences regarding the types of anomalies these methods can find

ESTIMATING UNCERTAINTY OF DEEP LEARNING MULTI-LABEL CLASSIFICATIONS USING LAPLACE APPROXIMATION

Deep learning methods have become valuable tools in remote sensing for tasks like aerial scene classification or land cover analysis. Dealing with noisy and very varying data, the need for reliable confidence statements becomes apparent. While deep learning models are known to yield overconfident pre- dictions, quantifying the model uncertainty of those classi- fiers can help mitigating that effect. Although uncertainty es- timation methods for multi-class classification have been pub- lished, multi-label classification - the task of labelling data with multiple class labels simultaneously - has hardly been considered yet. In this study, we use multi-label Laplace Ap- proximation to estimate the model uncertainty of deep multi- label classifiers and show how this method can improve cali- bration and out-of-distribution detection in the remote sensing domain

Über hochacide Kohlenwasserstoffe, VII. Die Lichtabsorption der Anionen hochacider Kohlenwasserstoffe

Die Anion-Spektren von Phenyl- und Biphenylen-substituierten Alkenen wurden in Dimethylsulfoxyd bzw. Dimethylformamid im Bereich von 300—800 mμ aufgenommen. Die Abhängigkeit der Lage der längstwelligen Absorptionsbande von der Struktur wird diskutiert. — Für einen Vergleich mit den Anion-Spektren wurden in einigen Fällen auch Lösungen der entsprechenden Carboniumsalze und der freien Radikale vermessen. Die Darstellung von 1.1.5.5-Tetraphenyl- und 1.1.5.5-Bis-biphenylen-pentadienyl-perchlorat aus Tetraarylpentadienolen wird beschrieben. Die z. T. sehr instabilen Kationen anderer Kohlenwasserstoffe entstehen aus den entsprechenden C-Radikalen durch Disproportionierung in Gegenwart von Perchlorsäure. Die ESR-Spektren der freien Radikale werden kurz diskutiert

Über hochacide Kohlenwasserstoffe, IX. pK-Werte hochacider Kohlenwasserstoffe

Die relative Acidität der Kohlenwasserstoffe 1–18 wird in geeigneten Lösungsmittelsystemen spektralphotometrisch bestimmt. Mittels 9-Cyan-fluoren (pwK = 11.4) werden auf Wasser bezogene pK-Werte abgeleitet. Für Tris-(7H-dibenzo[c.g]fluorenylidenmethyl)-methan (18), den bisher acidesten Kohlenwasserstoff, ergibt sich ein pwK-Wert < 7. Die Ergebnisse der pK-Messungen werden diskutiert und theoretischen Erwartungen gegenübergestellt