Homage to Richard M. Elliott

No abstract available

Transfer learning for time series classification

Transfer learning for deep neural networks is the process of first training a base network on a source dataset, and then transferring the learned features (the network's weights) to a second network to be trained on a target dataset. This idea has been shown to improve deep neural network's generalization capabilities in many computer vision tasks such as image recognition and object localization. Apart from these applications, deep Convolutional Neural Networks (CNNs) have also recently gained popularity in the Time Series Classification (TSC) community. However, unlike for image recognition problems, transfer learning techniques have not yet been investigated thoroughly for the TSC task. This is surprising as the accuracy of deep learning models for TSC could potentially be improved if the model is fine-tuned from a pre-trained neural network instead of training it from scratch. In this paper, we fill this gap by investigating how to transfer deep CNNs for the TSC task. To evaluate the potential of transfer learning, we performed extensive experiments using the UCR archive which is the largest publicly available TSC benchmark containing 85 datasets. For each dataset in the archive, we pre-trained a model and then fine-tuned it on the other datasets resulting in 7140 different deep neural networks. These experiments revealed that transfer learning can improve or degrade the model's predictions depending on the dataset used for transfer. Therefore, in an effort to predict the best source dataset for a given target dataset, we propose a new method relying on Dynamic Time Warping to measure inter-datasets similarities. We describe how our method can guide the transfer to choose the best source dataset leading to an improvement in accuracy on 71 out of 85 datasets.Comment: Accepted at IEEE International Conference on Big Data 201

Adversarial Attacks on Deep Neural Networks for Time Series Classification

Time Series Classification (TSC) problems are encountered in many real life data mining tasks ranging from medicine and security to human activity recognition and food safety. With the recent success of deep neural networks in various domains such as computer vision and natural language processing, researchers started adopting these techniques for solving time series data mining problems. However, to the best of our knowledge, no previous work has considered the vulnerability of deep learning models to adversarial time series examples, which could potentially make them unreliable in situations where the decision taken by the classifier is crucial such as in medicine and security. For computer vision problems, such attacks have been shown to be very easy to perform by altering the image and adding an imperceptible amount of noise to trick the network into wrongly classifying the input image. Following this line of work, we propose to leverage existing adversarial attack mechanisms to add a special noise to the input time series in order to decrease the network's confidence when classifying instances at test time. Our results reveal that current state-of-the-art deep learning time series classifiers are vulnerable to adversarial attacks which can have major consequences in multiple domains such as food safety and quality assurance.Comment: Accepted at IJCNN 201

Deep learning for time series classification: a review

Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data availability, hundreds of TSC algorithms have been proposed. Among these methods, only a few have considered Deep Neural Networks (DNNs) to perform this task. This is surprising as deep learning has seen very successful applications in the last years. DNNs have indeed revolutionized the field of computer vision especially with the advent of novel deeper architectures such as Residual and Convolutional Neural Networks. Apart from images, sequential data such as text and audio can also be processed with DNNs to reach state-of-the-art performance for document classification and speech recognition. In this article, we study the current state-of-the-art performance of deep learning algorithms for TSC by presenting an empirical study of the most recent DNN architectures for TSC. We give an overview of the most successful deep learning applications in various time series domains under a unified taxonomy of DNNs for TSC. We also provide an open source deep learning framework to the TSC community where we implemented each of the compared approaches and evaluated them on a univariate TSC benchmark (the UCR/UEA archive) and 12 multivariate time series datasets. By training 8,730 deep learning models on 97 time series datasets, we propose the most exhaustive study of DNNs for TSC to date.Comment: Accepted at Data Mining and Knowledge Discover

Adipositaschirurgie—Was nützt, was schadet?

Zusammenfassung: Die morbide Adipositas hat in den letzten Jahren in der Bevölkerung der westlichen Welt dramatisch zugenommen und stellt ein erhebliches gesundheitliches und ökonomisches Problem dar. Da die konservative Therapie bis heute keine wesentlichen Erfolge aufweisen kann, erlangt die Chirurgie in der Behandlung der morbiden Adipositas zunehmend eine zentrale Bedeutung. Diese Entwicklung wurde durch die raschen Fortschritte in der Laparoskopie zusätzlich beschleunigt. Die vorliegende Arbeit stellt die heute gebräuchlichen chirurgischen Techniken mit ihren Vor- und Nachteilen dar und soll gleichzeitig den aktuellen Stellenwert der bariatrischen Chirurgie in der Behandlungsstrategie dieser Erkrankung vermittel

Schlüssel zur Einteilung von Neophyten in der Schweiz in die Schwarze Liste oder die Watch-Liste

No Abstrac

Laparoscopic Versus Open Splenectomy for Nontraumatic Diseases

Background: Laparoscopic splenectomy (LS) is the standard procedure for normal size or moderately enlarged spleens; open splenectomy (OS) is preferred in cases of splenomegaly. In this study, indications for and complications of open and laparoscopic splenectomy were analyzed, with the aim to identify patients who will benefit from either technique. Method: A consecutive series of 52 patients undergoing elective open or laparoscopic splenectomy between January 2001 and December 2006 was analyzed. Spleen volume was calculated as length×width×depth from the pathologist's measurements. Results: LS was performed in 25 patients with a median age of 41years (range=24-65). OS was performed in 27 patients with a median age of 60years (range=24-86) (p<0.001). Conversion to OS was necessary in two patients (8%). Operation time was significantly shorter in LS (p<0.05). Spleen volume was significantly greater in patients who underwent open (median=2520ml, range=150-16,800ml) versus laparoscopic (median=648ml, range=150-4860ml) splenectomy (p=0.001). In 36% of all laparoscopic procedures, spleen volume exceeded 1000ml. The underlying disease was mainly immunothrombocytopenia in LS patients and lymphoma and osteomyelofibrosis in OS patients. Five patients died after OS. Significantly more patients were hospitalized longer than 7days following OS than following LS (p<0.05). Overall complication rate was higher after OS (LS, 8; OS, 13 patients; p<0.05). Conclusions: LS was preferred in younger patients with moderate splenomegaly, while massive splenomegaly mostly led to OS. In view of the absence of technique-related differences, LS can primarily be attempted in all patient

Quality of Life After Bariatric Surgery—A Comparative Study of Laparoscopic Banding vs. Bypass

 : Laparoscopic gastric banding and Roux-en-Y gastric bypass are widely used for the treatment of morbid obesity. The impact of these two procedures on health-related quality of life has not been analyzed in comparative studies. Methods: A matched-pair analysis of a prospectively collected database was performed. Fifty-two consecutive patients with laparoscopic gastric bypass were randomly matched to fifty-two patients with laparoscopic gastric banding according to age, BMI, and gender. Quality of life was assessed using two standardized questionnaires (SF 36 and Moorehead-Ardelt II). Results: Mean preoperative BMI was 45.7kg/m2 for the bypass patients and 45.3kg/m2 for the banding patients. Mean BMI after 3years follow-up of was 30.4kg/m2 and 33.1kg/m2 (p = 0.036). In the SF 36 questionnaire, gastric bypass patients yielded a mean total score of 613 versus 607 points in the gastric banding group (p = 0.543), which is comparable to the normal population in Europe. In the Moorhead-Ardelt II questionnaire, the gastric bypass patients scored a mean total of 1.35 points and the gastric banding patients 1.28 points (p = 0.747). Of the patients, 97% with a gastric bypass and 83% with a gastric banding were satisfied with the result of the operation (p = 0.145). Conclusion: The patients after laparoscopic gastric bypass and laparoscopic gastric banding have a high level of satisfaction 3years after the operation and have similar quality of life scores compared to the normal population. Quality of life indexes were not different between the two procedures and were independent of weight loss in successfully operated patient