Can lies be faked? Comparing low-stakes and high-stakes deception video datasets from a Machine Learning perspective

Despite the great impact of lies in human societies and a meager 54% human accuracy for Deception Detection (DD), Machine Learning systems that perform automated DD are still not viable for proper application in real-life settings due to data scarcity. Few publicly available DD datasets exist and the creation of new datasets is hindered by the conceptual distinction between low-stakes and high-stakes lies. Theoretically, the two kinds of lies are so distinct that a dataset of one kind could not be used for applications for the other kind. Even though it is easier to acquire data on low-stakes deception since it can be simulated (faked) in controlled settings, these lies do not hold the same significance or depth as genuine high-stakes lies, which are much harder to obtain and hold the practical interest of automated DD systems. To investigate whether this distinction holds true from a practical perspective, we design several experiments comparing a high-stakes DD dataset and a low-stakes DD dataset evaluating their results on a Deep Learning classifier working exclusively from video data. In our experiments, a network trained in low-stakes lies had better accuracy classifying high-stakes deception than low-stakes, although using low-stakes lies as an augmentation strategy for the high-stakes dataset decreased its accuracy.Comment: 11 pages, 3 figure

Formation of the postmitotic nuclear envelope from extended ER cisternae precedes nuclear pore assembly

During mitosis, the nuclear envelope merges with the endoplasmic reticulum (ER), and nuclear pore complexes are disassembled. In a current model for reassembly after mitosis, the nuclear envelope forms by a reshaping of ER tubules. For the assembly of pores, two major models have been proposed. In the insertion model, nuclear pore complexes are embedded in the nuclear envelope after their formation. In the prepore model, nucleoporins assemble on the chromatin as an intermediate nuclear pore complex before nuclear envelope formation. Using live-cell imaging and electron microscope tomography, we find that the mitotic assembly of the nuclear envelope primarily originates from ER cisternae. Moreover, the nuclear pore complexes assemble only on the already formed nuclear envelope. Indeed, all the chromatin-associated Nup 107–160 complexes are in single units instead of assembled prepores. We therefore propose that the postmitotic nuclear envelope assembles directly from ER cisternae followed by membrane-dependent insertion of nuclear pore complexes

Unsupervised learning in second-order neural networks for motion analysis

This paper demonstrates how unsupervised learning based on Hebb-like mechanisms is sufficient for training second-order neural networks to perform different types of motion analysis. The paper studies the convergence properties of the network in several conditions, including different levels of noise and motion coherence and different network configurations. We demonstrate the effectiveness of a novel variability dependent learning mechanism, which allows the network to learn under conditions of large feature similarity thresholds, which is crucial for noise robustness. The paper demonstrates the particular relevance of second-order neural networks and therefore correlation based approaches as contributing mechanisms for directional selectivity in the retina