Tissue deformation is a critical issue in soft-tissue surgery, particularly during tumor resection, as it causes landmark displacement, complicating tissue orientation. The authors conducted an experimental study on 45 pig head cadavers to simulate tissue deformation, approved by the Mannheim Veterinary Office (DE 08 222 1019 21). We used 3D cameras and head-mounted displays to capture tissue shapes before and after controlled deformation induced by heating. The data were processed using software such as Meshroom, MeshLab, and Blender to create and evaluate 2½D meshes. The dataset includes different levels of deformation, noise, and outliers, generated using the same approach as the SynBench dataset. 1. Deformation_Level: 10 different deformation levels are considered. 0.1 and 0.7 are representing minimum and maximum deformation, respectively. Source and target files are available in each folder. The deformation process is just applied to target files. For simplicity, the corresponding source files to the target ones are available in this folder with the same name, but source ones start with Source_ and the target files start with Target_. The number after Source_ and Target_ represents the primitive object in the “Data” folder. For example, Target_3 represents that this file is generated from object number 3 in the “Data” folder. The two other numbers in the file name represent the percentage number of control points and the width of the Gaussian radial basis function, respectively. 2. Noisy_Data For all available files in the “Deformation_Level” folder (for all deformation levels), Noisy data is generated. They are generated in 4 different noise levels namely, 0.01, 0.02, 0.03, and 0.04 (More explanation about implementation can be found in the paper). The name of the files is the same as the files in the “Deformation_Level” folder. 3. Outlier_Data For all available files in the “Deformation_Level” folder (for all deformation levels), data with outliers is generated. They are generated in different outlier levels, in 5 categories, namely, 5%, 15%, 25%, 35%, and 45% (More explanation about implementation can be found in the paper). The name of the files is the same as the files in the “Deformation_Level” folder. Furthermore, for each file, there is one additional file with the same name but is started with “Outlier_”. This represents a matrix with the coordinates of outliers. Then, it would be possible to use these files as benchmarks to check the validity of future algorithms. Additional notes: Considering the fact that all challenges are generated under small to large deformation levels, the DeformedTissue dataset makes it possible for users to select their desired data based on the ability of their proposed method, to show how robust to complex challenges their methods are.

In the recent years, there has been a growing interest in identifying anomalous structure within multivariate data sequences. We consider the problem of detecting collective anomalies, corresponding to intervals where one, or more, of the data sequences behaves anomalously. We first develop a test for a single collective anomaly that has power to simultaneously detect anomalies that are either rare, that is affecting few data sequences, or common. We then show how to detect multiple anomalies in a way that is computationally efficient but avoids the approximations inherent in binary segmentation-like approaches. This approach is shown to consistently estimate the number and location of the collective anomalies—a property that has not previously been shown for competing methods. Our approach can be made robust to point anomalies and can allow for the anomalies to be imperfectly aligned. We show the practical usefulness of allowing for imperfect alignments through a resulting increase in power to detect regions of copy number variation. Supplemental files for this article are available online.

Radar cars with outsourced driving cover eight eight regions of metropolitan France: Normandy, Brittany, Pays-de-la-Loire, and Centre-Val de Loire, Nouvelle-Aquitaine, Bourgogne-Franche-Comté, Grand-Est, Hauts de France. The other five metropolitan areas are checked by radar cars driven by a police officer or gendarme. Radar cars with outsourced driving run on routes and time slots set by the prefects according to local accident criteria. Such vehicles with an automated control system shall remain the property of the State. They have equipment capable of reading speed limitation signs allowing the radar to operate autonomously, without any intervention from the driver of the vehicle. The published dataset shall provide the measurement of the speeds of vehicles controlled on the move and recorded by the on-board ceinemometer which shall incorporate the technical margin of the equipment. This dataset partially covers the roads of the metropolitan territory. Metadata: These data correspond to the checks carried out by radar cars with outsourced driving. These checks shall record the speed of all vehicles crossed in approach and overtaking. Each statement shall contain: the speed of the vehicle checked, including the technical margin of the equipment, the maximum authorised speed at the checkpoint and the date/time and location of the check. Only the finding of the infringement is automated. The validation of the established offence and the drawing up of the corresponding report remain the responsibility of a judicial police officer under the supervision of the officer of the Public Prosecutor’s Office and the Public Prosecutor’s Office of the RENNES Judicial Court. Measures outside the control area: The GPS receiver of the control device is likely to generate some technical errors. These very small numbers of errors are not corrected in the dataset and can therefore lead to outliers. The checks carried out are therefore excluded from criminal treatment. **Measures observed in Ile de France: ** The road network in the Ile de France region is not controlled by outsourced driving radar cars. The data concerning the department of Val d'Oise (95) correspond to data from vehicles tested by the industrialist in that department, and do not ultimately generate any notice of contravention.