Herding Cats: Modelling, Simulation, Testing, and Data Mining for Weak Memory

We propose an axiomatic generic framework for modelling weak memory. We show how to instantiate this framework for SC, TSO, C++ restricted to release-acquire atomics, and Power. For Power, we compare our model to a preceding operational model in which we found a flaw. To do so, we define an operational model that we show equivalent to our axiomatic model. We also propose a model for ARM. Our testing on this architecture revealed a behaviour later acknowl-edged as a bug by ARM, and more recently 31 additional anomalies. We offer a new simulation tool, called herd, which allows the user to specify the model of his choice in a concise way. Given a specification of a model, the tool becomes a simulator for that model. The tool relies on an axiomatic description; this choice allows us to outperform all previous simulation tools. Additionally, we confirm that verification time is vastly improved, in the case of bounded model checking. Finally, we put our models in perspective, in the light of empirical data obtained by analysing the C and C++ code of a Debian Linux distribution. We present our new analysis tool, called mole, which explores a piece of code to find the weak memory idioms that it uses

ECG, EEG and IMU data for local motion artefact removal

ECG and EEG data collected from a single participant using our custom device with inertial measurement units (IMUs) placed on each electrode, to enable recording of local motion activity (accelerometer and gyroscope) during electrophysiological recordings. This is to enable improved motion artefact removal. Please see paper for description of the device, placement of the electrodes and testing procedure. Data is in CSV format, with a sample rate of 220 Hz. If using this data please cite:C. Beach, M. Li, E. Balaban, and A. J. Casson, “Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering,” Healthc. Technol. Lett., vol. 8, no. 5, pp. 128–138, 2021, doi: 10.1049/htl2.12016. The columns of the csv files correspond as follows: ECG:x: Internal system timestampeeg1: V1 electrodeeeg2: V2 electrodeax1: x-axis of accelerometer on V1ay1: y-axis of accelerometer on V1az1: z-axis of accelerometer on V1gx1: x-axis of gyroscope on V1gy1: y-axis of gyroscope on V1gz1: z-axis of gyroscope on V1ax2: x-axis of accelerometer on V2ay2: y-axis of accelerometer on V2az2: z-axis of accelerometer on V2gx2: x-axis of gyroscope on V2gy2: y-axis of gyroscope on V2gz2: z-axis of gyroscope on V2ax3: x-axis of accelerometer on REFay3: y-axis of accelerometer on REFaz3: z-axis of accelerometer on REFgx3: x-axis of gyroscope on REFgy3: y-axis of gyroscope on REFgz3: z-axis of gyroscope on REFax4: x-axis of accelerometer on DRLay4: y-axis of accelerometer on DRLaz4: z-axis of accelerometer on DRLgx4: x-axis of gyroscope on DRLgy4: y-axis of gyroscope on DRLgz4: z-axis of gyroscope on DRL EEG:x: Internal system timestampeeg1: T3 electrodeeeg2: T5 electrodeax1: x-axis of accelerometer on T3ay1: y-axis of accelerometer on T3az1: z-axis of accelerometer on T3gx1: x-axis of gyroscope on T3gy1: y-axis of gyroscope on T3gz1: z-axis of gyroscope on T3ax2: x-axis of accelerometer on T5ay2: y-axis of accelerometer on T5az2: z-axis of accelerometer on T5gx2: x-axis of gyroscope on T5gy2: y-axis of gyroscope on T5gz2: z-axis of gyroscope on T5ax3: x-axis of accelerometer on REFay3: y-axis of accelerometer on REFaz3: z-axis of accelerometer on REFgx3: x-axis of gyroscope on REFgy3: y-axis of gyroscope on REFgz3: z-axis of gyroscope on REFax4: x-axis of accelerometer on DRLay4: y-axis of accelerometer on DRLaz4: z-axis of accelerometer on DRLgx4: x-axis of gyroscope on DRLgy4: y-axis of gyroscope on DRLgz4: z-axis of gyroscope on DRL In our paper we take plot the following sections of data: ECG:Lines 1939:5039 (for the good filtering case)Lines 7100:10200 (for the poor filtering case)EEG:Lines 7279:1043