SCOOP: Self-Supervised Correspondence and Optimization-Based Scene Flow

Scene flow estimation is a long-standing problem in computer vision, where the goal is to find the 3D motion of a scene from its consecutive observations. Recently, there have been efforts to compute the scene flow from 3D point clouds. A common approach is to train a regression model that consumes source and target point clouds and outputs the per-point translation vectors. An alternative is to learn point matches between the point clouds concurrently with regressing a refinement of the initial correspondence flow. In both cases, the learning task is very challenging since the flow regression is done in the free 3D space, and a typical solution is to resort to a large annotated synthetic dataset. We introduce SCOOP, a new method for scene flow estimation that can be learned on a small amount of data without employing ground-truth flow supervision. In contrast to previous work, we train a pure correspondence model focused on learning point feature representation and initialize the flow as the difference between a source point and its softly corresponding target point. Then, in the run-time phase, we directly optimize a flow refinement component with a self-supervised objective, which leads to a coherent and accurate flow field between the point clouds. Experiments on widespread datasets demonstrate the performance gains achieved by our method compared to existing leading techniques while using a fraction of the training data. Our code is publicly available at https://github.com/itailang/SCOOP

Global and Local Features of Semantic Networks: Evidence from the Hebrew Mental Lexicon

BACKGROUND: Semantic memory has generated much research. As such, the majority of investigations have focused on the English language, and much less on other languages, such as Hebrew. Furthermore, little research has been done on search processes within the semantic network, even though they are abundant within cognitive semantic phenomena. METHODOLOGY/PRINCIPAL FINDINGS: We examine a unique dataset of free association norms to a set of target words and make use of correlation and network theory methodologies to investigate the global and local features of the Hebrew lexicon. The global features of the lexicon are investigated through the use of association correlations--correlations between target words, based on their association responses similarity; the local features of the lexicon are investigated through the use of association dependencies--the influence words have in the network on other words. CONCLUSIONS/SIGNIFICANCE: Our investigation uncovered Small-World Network features of the Hebrew lexicon, specifically a high clustering coefficient and a scale-free distribution, and provides means to examine how words group together into semantically related 'free categories'. Our novel approach enables us to identify how words facilitate or inhibit the spread of activation within the network, and how these words influence each other. We discuss how these properties relate to classical research on spreading activation and suggest that these properties influence cognitive semantic search processes. A semantic search task, the Remote Association Test is discussed in light of our findings

Pride and prejudice: using ethnic-sounding names and inter-ethnic marriages to identify labor market discrimination

We use non-random sorting into interethnic marriage and salient differences between Sephardic and Ashkenazi surnames to evaluate the causal impact of Sephardic affiliation on wages. Using the 1995 Israeli Census, we estimate the effect of a Sephardic affiliation on wages. We first compare the wages of Israeli Jewish males born to Sephardic fathers and Ashkenazi mothers (SA), who are more likely to carry a Sephardic surname, with the wages of Israeli Jewish males born to Ashkenazi fathers and Sephardic mothers (AS). We find that SA workers earn significantly less than their AS counterparts. We then exploit the custom of women to adopt their husbands. surnames to disentangle actual ethnicity from the ethnicity perceived by the market. Consistent with our interpretation of the results for males, we find that it is father-in-law’s ethnicity - rather than father’s ethnicity - that shapes female wage rates, yet only for daughters of interethnic couples and others with mild skin tone who have equal chances to be perceived either as an Ashkenazi or as a Sephardic group member

Dynamic Stability of Off-Road Vehicles Considering a Longitudinal Terramechanics Model

Abstract-Dynamic stability reflects the vehicle's ability to traverse uneven terrain at high speeds. It is determined from the set of admissible speeds and tangential accelerations of the center of mass along the path, subject to the ground force and geometric path constraints. This paper presents an analytical method for computing the stability margins of a planar allwheel drive vehicle that accounts for soil parameters. It consists of mapping the ground force constraints to constraints on the vehicle's speeds and accelerations along the path. The boundaries of the set of admissible speeds and accelerations determines the static and dynamic stability margins, used to gage the traversability of the vehicle along the path. The first is the maximum feasible acceleration at zero speed, whereas the second is the maximum feasible speed. Both stability margins are demonstrated for a planar vehicle moving on a sinusoidal path