More Like this

1. The Continuum Self-Similar Tree

   https://doi.org/10.1007/978-1-4020-6899-7  

   Bonk, Mario; Tran, Huy (January 2021, Progress in probability)

   Freiberg, U; null (Ed.)
2. Molecular Geometry‐Directed Self‐Recognition in the Self‐Assembly of Giant Amphiphiles

   https://doi.org/10.1002/marc.202200216  

   Zhou, Yifan; Luo, Jiancheng; Liu, Tong; Wen, Tao; Williams‐Pavlantos, Kayla; Wesdemiotis, Chrys; Cheng, Stephen Z. D.; Liu, Tianbo (May 2022, Macromolecular Rapid Communications)

   Abstract Three sets of polyoxometalate (POM)‐based amphiphilic hybrid macromolecules with different rigidity in their organic tails are used as models to understand the effect of molecular rigidity on their possible self‐recognition feature during self‐assembly processes. Self‐recognition is achieved in the mixed solution of two structurally similar, sphere‐rigid T‐shape‐linked oligofluorene(TOF₄) rod amphiphiles, with the hydrophilic clusters being Anderson (Anderson‐TOF₄) and Dawson (Dawson‐TOF₄), respectively. Anderson‐TOF₄is observed to self‐assemble into onion‐like multilayer structures and Dawson‐TOF₄forms multilayer vesicles. The self‐assembly is controlled by the interdigitation of hydrophobic rods and the counterion‐mediated attraction among charged hydrophilic inorganic clusters. When the hydrophobic blocks are less rigid, e.g., partially rigid polystyrene and fully flexible alkyl chains, self‐recognition is not observed, attributing to the flexible conformation of hydrophobic molecules in the solvophobic domain. This study reveals that the self‐recognition among amphiphiles can be achieved by the geometrical limitation of the supramolecular structure due to the rigidity of solvophobic domains. 

   more » « less
3. The social self in the developing brain.

   https://doi.org/10.1016/j.neubiorev.2025.106023  

   Grossmann, T (February 2025, Neuroscience biobehavioral reviews)

   The notion that the self is fundamentally social in nature and develops through social interactions has a long tradition in philosophy, sociology, and psychology. However, to date, the early development of the social self and its brain bases in infancy has received relatively little attention. This presents a review and synthesis of existing neuroimaging research, showing that infants recruit brain systems, involved in self-processing and social cognition in adults, when responding to self-relevant cues during social interactions. Moreover, this review draws on recent research, demonstrating the early developmental emergence and social embeddedness/dependency of the default-mode network in infancy, a brain network considered of critical importance to the sense of self and social cognition. This stands in contrast to research pointing to the relatively late ontogenetic emergence of the conceptual self, by about 18-24 months of age, as seen in the mirror-self recognition test. Based on this review and synthesis, the social self first hypothesis (SSFH) is formulated, presenting an integrated view, arguing for the early ontogenetic emergence of the social self and its brain basis. This developmental account informs and extends existing evolutionary thinking, emphasizing the primary role that social interdependence has played in the evolution of the human mind. 

   more » « less
4. Simulating other people changes the self.

   https://doi.org/10.1037/xge0000565  

   Meyer, Meghan L.; Zhao, Zidong; Tamir, Diana I. (November 2019, Journal of Experimental Psychology: General)
5. Self-Supervised Learning across the Spectrum

   https://doi.org/10.3390/rs16183470  

   Shenoy, Jayanth; Zhang, Xingjian Davis; Tao, Bill; Mehrotra, Shlok; Yang, Rem; Zhao, Han; Vasisht, Deepak (September 2024, Remote Sensing)

   Satellite image time series (SITS) segmentation is crucial for many applications, like environmental monitoring, land cover mapping, and agricultural crop type classification. However, training models for SITS segmentation remains a challenging task due to the lack of abundant training data, which requires fine-grained annotation. We propose S4, a new self-supervised pretraining approach that significantly reduces the requirement for labeled training data by utilizing two key insights of satellite imagery: (a) Satellites capture images in different parts of the spectrum, such as radio frequencies and visible frequencies. (b) Satellite imagery is geo-registered, allowing for fine-grained spatial alignment. We use these insights to formulate pretraining tasks in S4. To the best of our knowledge, S4 is the first multimodal and temporal approach for SITS segmentation. S4’s novelty stems from leveraging multiple properties required for SITS self-supervision: (1) multiple modalities, (2) temporal information, and (3) pixel-level feature extraction. We also curate m2s2-SITS, a large-scale dataset of unlabeled, spatially aligned, multimodal, and geographic-specific SITS that serves as representative pretraining data for S4. Finally, we evaluate S4 on multiple SITS segmentation datasets and demonstrate its efficacy against competing baselines while using limited labeled data. Through a series of extensive comparisons and ablation studies, we demonstrate S4’s ability as an effective feature extractor for downstream semantic segmentation. 

   more » « less