Exploring functions of and recollections with photos in the age of smartphone cameras

People often report taking photos to aid memory. Two mixed-method surveys were used to investigate participants’ reasons for taking photos, focusing specifically on memory-related reasons, which were split into two sub-types: photos taken as mementos, and photos taken as a means of offloading information. Participants reported their motivations for taking a sample of photos and then rated their recollective experience of each photographed event. Across both studies, participants reported recollecting events associated with a memento goal more vividly, more positively, and with more emotional intensity than events associated with an offloading goal. As expected, events photographed with a memento goal were also rated by participants to be more reflective of a shared memory system between the participants and the camera than were events photographed with an offloading goal. These findings suggest that people’s motivations when taking photos tend to be associated with different types of recollective experiences, as well as different judgments about where personal information is located in a blended human-camera memory system.

Efficiency in Parallel Computing of FDS Model for Compartment Fire Simulation: Shared Memory System

This study evaluates the computational efficiency based on the parallel processing mode and domain decomposition method of the FDS model to enhance the computational performance of fire simulation. A single compartment of dimensions 12.0 m × 3.8 m × 3.0 m is considered along with a rectangular fire source (0.4 m × 0.4 m) fueled by n-Heptane. The computational domain was divided into 136,000 cells forming a grid size of 0.1 m, and the computational efficiency for each calculation was evaluated by the wall clock time for a simulation time of 300 s using a computational framework with 24 cores of a single CPU and a 256 GB shared memory system. The MPI and hybrid mode in FDS parallel offers a greater speed-up capability than the OpenMP mode, and the domain decomposition method used greatly affects the computational efficiency. The maximum speed-up with the OpenMP mode was less than 1.5 for a single computational domain, which indicates that there is an optimal condition for thread assignment and domain decomposition in the OpenMP mode. The present study is expected to contribute toward obtaining effective fire simulation results with limited computing power and time in fire protection engineering.

Optimizing Neural Network Training through TensorFlow Profile Analysis in a Shared Memory System

On the one hand, Deep Neural Networks have emerged as a powerful tool for solving complex problems in image and text analysis. On the other, they are sophisticated learning machines that require deep programming and math skills to be understood and implemented. Therefore, most researchers employ toolboxes and frameworks to design and implement such architectures. This paper performs an execution analysis of TensorFlow, one of the most used deep network frameworks available, on a shared memory system. To do so, we chose a text classification problem based on tweets sentiment analysis. The focus of this work is to identify the best environment configuration for training neural networks on a shared memory system. We set five different configurations using environment variables to modify the TensorFlow execution behavior. The results on an Intel Xeon Platinum 8000 processors series show that the default environment configuration of the TensorFlow can increase the speed up to 5.8. But, fine-tuning this environment can improve the speedup at least 37%.