A Byzantine Fault-Tolerant (BFT) protocol protects a distributed system from faulty participants. To provide both liveness and safety, many such protocols assume they are dealing with a partially-synchronous network, which will eventually stabilize after a global stabilization time (GST). In a real-world network environment, however, there is no such guarantee of bounded transmission time for network packets. For this reason, even if a BFT protocol is mathematically proven to achieve both liveness and safety, its overall performance is difficult to analyze theoretically, especially if there are bad network conditions or adversarial behaviors. Accordingly, we propose a simulator for evaluating the performance of BFT protocols under various network conditions and attacks, and we implement it to empirically compare the performance of eight representative protocols. Experiment results show that our simulator can simulate 16 times as many nodes as an existing simulator supports (512 vs. 32), and it is over 500 times faster when simulating 32 nodes (38 milliseconds vs. 19.4 seconds).

Visual search is a ubiquitous and often challenging daily task, exemplified by looking for the car keys at home or a friend in a crowd. An intriguing property of some classical search tasks is an asymmetry such that finding a target A among distractors B can be easier than finding B among A. To elucidate the mechanisms responsible for asymmetry in visual search, we propose a computational model that takes a target and a search image as inputs and produces a sequence of eye movements until the target is found. The model integrates eccentricity-dependent visual recognition with target-dependent top-down cues. We compared the model against human behavior in six paradigmatic search tasks that show asymmetry in humans. Without prior exposure to the stimuli or task-specific training, the model provides a plausible mechanism for search asymmetry. We hypothesized that the polarity of search asymmetry arises from experience with the natural environment. We tested this hypothesis by training the model on augmented versions of ImageNet where the biases of natural images were either removed or reversed. The polarity of search asymmetry disappeared or was altered depending on the training protocol. This study highlights how classical perceptual properties can emerge in neural network models, without the need for task-specific training, but rather as a consequence of the statistical properties of the developmental diet fed to the model. All source code and data are publicly available at https://github.com/kreimanlab/VisualSearchAsymmetry.

Existing literature reveals little information about the relationship between microsaccade rate and the average change in pupil size. There is a need to investigate this relationship and how the microsaccade rate may be relevant to cognitive load. In our study, we compared the microsaccade rate to the average change in pupil size during eight experimental conditions. Four of them were considered fixation conditions (subjects look at a fixation cross in each visual scene) and four were free-viewing conditions (subjects are free to move their eyes over the visual scene). We analyzed the change in pupil size and microsaccade rate for the first part of each task and as well as the entire task in all conditions. We discovered a significant correlation between the microsaccade rate and the average change in pupil size during the first part of each task, and comparable characteristics throughout the entire task. Then we measured the data for only one of the experimental conditions in free-viewing that involves a search task to understand comparable characteristics related to cognitive load. We found that there is a correlation between the microsaccade and pupil data. We hope that this finding will help further the understanding of the relative function of microsaccades and use it to support cognitive load response and pupil measurement.

Existing literature reveals that during reading, gaze fixations within a word are not necessarily close to the optimal reading position. The optimal position for reading represents the gaze fixation near the center of each word for which recognition time is minimal. In our study, we examined the match between optimal position and initial fixation position, which has not been studied before. We did this in conventional reading and in two text-viewing tasks. For the text-viewing tasks, we employed multiple words that were non-isolated in the presentation but could be examined individually in rhythm and linguistic context. We discovered that for the text-viewing tasks, the initial fixation position tends to be close to the optimal position, which was in contrast to the conventional reading task. This finding will help us understand the relationship between optimal position and reading context and inform the development of new reading applications.