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What are the fundamental principles that inform representation in the primate visual brain? While objects have become an intuitive framework for studying neurons in many parts of cortex, it is possible that neurons follow a more expressive organizational principle, such as encoding generic features present across textures, places, and objects. In this study, we used multielectrode arrays to record from neurons in the early (V1/V2), middle (V4), and later [posterior inferotemporal (PIT) cortex] areas across the visual hierarchy, estimating each neuron’s local operation across natural scene via “heatmaps.” We found that, while populations of neurons with foveal receptive fields across V1/V2, V4, and PIT responded over the full scene, they focused on salient subregions within object outlines. Notably, neurons preferentially encoded animal features rather than general objects, with this trend strengthening along the visual hierarchy. These results show that the monkey ventral stream is partially organized to encode local animal features over objects, even as early as primary visual cortex. 

ABSTRACT Climate change can influence host–parasite dynamics by altering the abundance and distribution of hosts and their parasites as well as the physiology of both parasite and host. While the physiological effects of hosting parasites have been extensively studied in aquatic and laboratory model systems, these dynamics have been much less studied in wild terrestrial vertebrates, such as ectotherms that live in tropical forests. These organisms are particularly vulnerable to climate change because they have limited scope for behavioral buffering of stressful temperatures while already living at body temperatures close to their heat tolerance limits. Thus, it is imperative to understand how parasitism and tolerance to stressful thermal conditions, both of which are changing under climate warming, might interact to shape survival of non-model organisms. We measured heat tolerance and assessed endoparasites and ectoparasites in slender anole lizards (Anolis apletophallus; a lowland tropical forest species from central Panama). We then treated lizards with the antiparasitic drugs ivermectin and praziquantel and measured changes in immune function and heat tolerance compared with an unmanipulated control group. Immune function was not altered by treatment; however, heat tolerance increased in treated lizards. Additionally, higher endoparasite and ectoparasite abundance was associated with lower heat tolerance in a separate set of wild-caught lizards. Our results suggest that increasing environmental temperatures may have especially severe effects on host survival when parasites are present and highlight the need to consider interactions between thermal physiology and host–parasite dynamics when forecasting the responses of tropical animals to climate change. 

Synopsis The trajectory of evolution is impacted by molecular constraints and biases that are difficult to validate experimentally. Repeated evolution of similar traits across the Tree of Life serves as a natural experiment to discern common factors that drive the evolution of these traits. The architecture of genomes in one-dimensional, two-dimensional, and three-dimensional space is emerging as a potential factor that may predict repeated phenotypic evolution. For example, chromatin packaging and the 3D organization of the genome within the nucleus can impose evolutionary constraints by predisposing genomic regions for particular types of mutations, while the evolution of genome sequence can also drive reorganization of chromatin. With the explosion of new library preparation and sequencing technologies that are accessible for non-model species, we envision a great opportunity to understand how genome architecture across phylogenetically disparate species may impact repeated phenotypic evolution. We provide examples of the known and potential avenues of phenotypic convergence at each level of genome architecture and how integration of these data can provide unique insights into the constraints, trajectory, and predictability of evolution. 

Quantum machine learning algorithms promise to deliver near-term, applicable quantum computation on noisy, intermediate-scale systems. While most of these algorithms leverage quantum circuits for generic applications, a recent set of proposals, called analog quantum machine learning (AQML) algorithms, breaks away from circuit-based abstractions and favors leveraging the natural dynamics of quantum systems for computation, promising to be noise-resilient and suited for specific applications such as quantum simulation. Recent AQML studies have called for determining best ansatz selection practices and whether AQML algorithms have trap-free landscapes based on theory from quantum optimal control (QOC). We address this call by systematically studying AQML landscapes on two models: those admitting black-boxed expressivity and those tailored to simulating a specific unitary evolution. Numerically, the first kind exhibits local traps in their landscapes, while the second kind is trap-free. However, both kinds violate QOC theory’s key assumptions for guaranteeing trap-free landscapes. We propose a methodology to co-design AQML algorithms for unitary evolution simulation using the ansatz’s Magnus expansion. Our methodology guarantees the algorithm has an amenable dynamical Lie algebra with independently tunable terms. We show favorable convergence in simulating dynamics with applications to metrology and quantum chemistry. We conclude that such co-design is necessary to ensure the applicability of AQML algorithms. 

Since the terahertz frequency band (0.1–1 THz) has attracted considerable attention for the upcoming sixth-generation (6G) wireless communication systems, accurate models for multipath propagation in this frequency range need to be established. Such models advantageously use the fact that multi-path components (MPCs) occur typically in clusters, i.e., groups of MPCs that have similar delays and angles. In this paper, we first analyze the limitations of a widely used clustering algorithm, Kernel-Power-Density (KPD), in evaluating an extensive THz outdoor measurement campaign at 145–146 GHz, particularly its inability to detect small clusters. We introduce a modified version, which we term multi-level KPD (ML-KPD), iteratively applying KPD to detect whether a cluster determined in the previous round is made up of multiple clusters. We first apply the method to synthetic channels to demonstrate its efficacy and select suitable values for the adaptive hyperparameters. Then, multi-level KPD is applied to our channel measurements in line-of-sight (LOS) and non-line-of-sight (NLOS) environments to determine statistics for the number of clusters and the cluster spreads.