Deep saliency models learn low-, mid-, and high-level features to predict scene attention

Deep saliency models represent the current state-of-the-art for predicting where humans look in real-world scenes. However, for deep saliency models to inform cognitive theories of attention, we need to know how deep saliency models prioritize different scene features to predict where people look. Here we open the black box of three prominent deep saliency models (MSI-Net, DeepGaze II, and SAM-ResNet) using an approach that models the association between attention, deep saliency model output, and low-, mid-, and high-level scene features. Specifically, we measured the association between each deep saliency model and low-level image saliency, mid-level contour symmetry and junctions, and high-level meaning by applying a mixed effects modeling approach to a large eye movement dataset. We found that all three deep saliency models were most strongly associated with high-level and low-level features, but exhibited qualitatively different feature weightings and interaction patterns. These findings suggest that prominent deep saliency models are primarily learning image features associated with high-level scene meaning and low-level image saliency and highlight the importance of moving beyond simply benchmarking performance.

Laughing Heads: Can Transformers Detect What Makes a Sentence Funny?

The automatic detection of humor poses a grand challenge for natural language processing. Transformer-based systems have recently achieved remarkable results on this task, but they usually (1) were evaluated in setups where serious vs humorous texts came from entirely different sources, and (2) focused on benchmarking performance without providing insights into how the models work. We make progress in both respects by training and analyzing transformer-based humor recognition models on a recently introduced dataset consisting of minimal pairs of aligned sentences, one serious, the other humorous. We find that, although our aligned dataset is much harder than previous datasets, transformer-based models recognize the humorous sentence in an aligned pair with high accuracy (78\%). In a careful error analysis, we characterize easy vs hard instances. Finally, by analyzing attention weights, we obtain important insights into the mechanisms by which transformers recognize humor. Most remarkably, we find clear evidence that one single attention head learns to recognize the words that make a test sentence humorous, even without access to this information at training time.

Conventional, High-Resolution and Imaging Flow Cytometry: Benchmarking Performance in Characterisation of Extracellular Vesicles

Flow cytometry remains a commonly used methodology due to its ability to characterise multiple parameters on single particles in a high-throughput manner. In order to address limitations with lacking sensitivity of conventional flow cytometry to characterise extracellular vesicles (EVs), novel, highly sensitive platforms, such as high-resolution and imaging flow cytometers, have been developed. We provided comparative benchmarks of a conventional FACS Aria III, a high-resolution Apogee A60 Micro-PLUS and the ImageStream X Mk II imaging flow cytometry platform. Nanospheres were used to systematically characterise the abilities of each platform to detect and quantify populations with different sizes, refractive indices and fluorescence properties, and the repeatability in concentration determinations was reported for each population. We evaluated the ability of the three platforms to detect different EV phenotypes in blood plasma and the intra-day, inter-day and global variabilities in determining EV concentrations. By applying this or similar methodology to characterise methods, researchers would be able to make informed decisions on choice of platforms and thereby be able to match suitable flow cytometry platforms with projects based on the needs of each individual project. This would greatly contribute to improving the robustness and reproducibility of EV studies.