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Abstract Accurate mesh stiffness calculation is crucial for developing reliable dynamic models and analyzing vibratory behavior in hypoid gears. Current studies often use a hypoid gear pair, incorporating the misalignment effects of the hypoid gear system model, as a substitute for the full hypoid gear system model to reduce simulation costs. However, this simplification overlooks the boundary problem, particularly the influence of housing on the hypoid gear system. This discrepancy can lead to deviations in mesh stiffness calculation and affect the accuracy of dynamic response predictions. To address this issue, we established a three-dimensional static mesh model to calculate the mesh stiffness under different boundary conditions, i.e., the gear pair model constrained at the base and the gear system model constrained at the housing, based on finite element results. Then, we introduce a 14-degree-of-freedom dynamic model to examine the influence of mesh stiffness differences on system dynamics. Finally, a numerical case study evaluates key factors, including unloaded and loaded transmission error, mesh points, line of action, and static mesh force, to assess their impact on mesh stiffness and the resultant impact on dynamic behavior. The findings provide insights into the selection of an appropriate calculation model for accurate gear design and simulation. 

Abstract How the brain encodes, recognizes, and memorizes general visual objects is a fundamental question in neuroscience. Here, we investigated the neural processes underlying visual object perception and memory by recording from 3173 single neurons in the human amygdala and hippocampus across four experiments. We employed both passive-viewing and recognition memory tasks involving a diverse range of naturalistic object stimuli. Our findings reveal a region-based feature code for general objects, where neurons exhibit receptive fields in the high-level visual feature space. This code can be validated by independent new stimuli and replicated across all experiments, including fixation-based analyses with large natural scenes. This region code explains the long-standing visual category selectivity, preferentially enhances memory of encoded stimuli, predicts memory performance, encodes image memorability, and exhibits intricate interplay with memory contexts. Together, region-based feature coding provides an important mechanism for visual object processing in the human brain. 

Information complexity is one of the most powerful techniques to prove information-theoretical lower bounds, in which Shannon entropy plays a central role. Though Shannon entropy has some convenient properties, such as the chain rule, it still has inherent limitations. One of the most notable barriers is the square-root loss, which appears in the square-root gap between entropy gaps and statistical distances, e.g., Pinsker’s inequality. To bypass this barrier, we introduce a new method based on min-entropy analysis. Building on this new method, we prove the following results. - An Ω(N^{∑_i α_i - max_i {α_i}}/k) randomized communication lower bound of the k-party set-intersection problem where the i-th party holds a random set of size ≈ N^{1-α_i}. - A tight Ω(n/k) randomized lower bound of the k-party Tree Pointer Jumping problems, improving an Ω(n/k²) lower bound by Chakrabarti, Cormode, and McGregor (STOC 08). - An Ω(n/k+√n) lower bound of the Chained Index problem, improving an Ω(n/k²) lower bound by Cormode, Dark, and Konrad (ICALP 19). Since these problems served as hard problems for numerous applications in streaming lower bounds and cryptography, our new lower bounds directly improve these streaming lower bounds and cryptography lower bounds. On the technical side, min-entropy does not have nice properties such as the chain rule. To address this issue, we enhance the structure-vs-pseudorandomness decomposition used by Göös, Pitassi, and Watson (FOCS 17) and Yang and Zhang (STOC 24); both papers used this decomposition to prove communication lower bounds. In this paper, we give a new breath to this method in the multi-party setting, presenting a new toolkit for proving multi-party communication lower bounds.