The aim of the present study was to evaluate cardiac autonomic function by pupillometry in male athletes. Fifteen elite endurance- (END) and eleven power-trained (POWER) athletes and fifteen sedentary individuals (CONTROL) were studied. All subjects underwent three pupillometric measurements: at rest, peak exercise testing and recovery phase. The pupillometric indices studied were: baseline pupil radius (R1), minimum pupil radius (R2), maximum constriction velocity (VC(max)), maximum constriction acceleration (AC(max)), amplitude (AMP, R1-R2), constriction ratio (AMP%). During exercise, RR intervals were obtained for each subject with a Polar S810i for time and frequency domain heart rate variability (HRV) analysis. The following parameters of HRV were measured: standard deviation of all NN intervals (SDNN), the mean square successive differences (rMSSD), percent of NN intervals differing >50 ms from the preceding NN (pNN50), low (LF)- and high (HF)- frequency components of the autoregressive power spectrum of the NN intervals and their ratio (LF/HF). At rest and recovery, END showed significantly increased VC(max) and AC(max) compared to POWER and CONTROL. AMP% was significantly greater in END at rest, peak exercise and recovery compared to POWER and CONTROL. END and POWER had significantly greater AMP at rest and recovery compared to CONTROL. Moreover, all HRV indices were significantly increased in END compared to POWER and CONTROL. However, POWER showed significantly increased rMSSD and LF compared to CONTROL. HRV parameters were significantly correlated with pupillometric parameters during exercise. Our results indicated that any kind of exercise training and mainly endurance one affects autonomic regulation of pupillary light reflex.

Abstract. We report on practical experiences over the last 2.5 years related to the engineering of GPUVerify, a static verification tool for OpenCL and CUDA GPU kernels, plotting the progress of GPUVerify from a prototype to a fully functional and relatively efficient analysis tool. Our hope is that this experience report will serve the verification community by helping to inform future tooling efforts.

The aim of the study was to evaluate the cardiac autonomic activity in free diving (FD) athletes. Thirteen Greek male free divers (group I, aged 33.4 ± 6.3 years, 6.6 ± 4.5 years of training experience) volunteered to participate while 13 age-matched sedentary subjects served as control group (group II). All subjects were submitted to ambulatory 24-h ECG recording for heart rate variability (HRV) analysis on a day of regular activities with no exercise or training. The results showed that group I had significantly lower minimum and mean heart rate by 23.9% (P < 0.001) and 20.6% (P < 0.001), respectively. All the measured time and frequency domain indices of HRV which reflect cardiac parasympathetic activity were higher in group I than in group II by 37.6% to 146% (P < 0.001). Conclusively, the resting cardiac autonomic activity and especially the parasympathetic branch was significantly increased in free divers compared to untrained subjects. This finding should be rather attributed to the accumulated effect of both exercise training and frequent exposure to FD stimulus.

Abstract-Concurrency errors, such as data races, make device drivers notoriously hard to develop and debug without automated tool support. We present WHOOP, a new automated approach that statically analyzes drivers for data races. WHOOP is empowered by symbolic pairwise lockset analysis, a novel analysis that can soundly detect all potential races in a driver. Our analysis avoids reasoning about thread interleavings and thus scales well. Exploiting the race-freedom guarantees provided by WHOOP, we achieve a sound partial-order reduction that significantly accelerates CORRAL, an industrial-strength bug-finder for concurrent programs. Using the combination of WHOOP and CORRAL, we analyzed 16 drivers from the Linux 4.0 kernel, achieving 1.5-20× speedups over standalone CORRAL.

Programming efficient asynchronous systems is challenging because it can often be hard to express the design declaratively, or to defend against data races and interleaving-dependent assertion violations. Previous work has only addressed these challenges in isolation, by either designing a new declarative language, a new data race detection tool or a new testing technique. We present P#, a language for high-reliability asynchronous programming co-designed with a static data race analysis and systematic concurrency testing infrastructure. We describe our experience using P# to write several distributed protocols and port an industrial-scale system internal to Microsoft, showing that the combined techniques, by leveraging the design of P#, are effective in finding bugs.