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1. Efficient Vision Transformer for Human Pose Estimation via Patch Selection. British Machine Vision Conference

   Kinfu, Kaleab; Vidal, Rene (September 2023, British Machine Vision Conference)

   While Convolutional Neural Networks (CNNs) have been widely successful in 2D human pose estimation, Vision Transformers (ViTs) have emerged as a promising alternative to CNNs, boosting state-of-the-art performance. However, the quadratic computational complexity of ViTs has limited their applicability for processing high-resolution images. In this paper, we propose three methods for reducing ViT’s computational complexity, which are based on selecting and processing a small number of most informative patches while disregarding others. The first two methods leverage a lightweight pose estimation network to guide the patch selection process, while the third method utilizes a set of learnable joint tokens to ensure that the selected patches contain the most important information about body joints. Experiments across six benchmarks show that our proposed methods achieve a significant reduction in computational complexity, ranging from 30% to 44%, with only a minimal drop in accuracy between 0% and 3.5%. 

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2. Comprehensive Social Trait Judgments From Faces in Autism Spectrum Disorder

   https://doi.org/10.1177/09567976231192236  

   Cao, Runnan; Zhang, Na; Yu, Hongbo; Webster, Paula J.; Paul, Lynn K.; Li, Xin; Lin, Chujun; Wang, Shuo (October 2023, Psychological Science)

   Processing social information from faces is difficult for individuals with autism spectrum disorder (ASD). However, it remains unclear whether individuals with ASD make high-level social trait judgments from faces in the same way as neurotypical individuals. Here, we comprehensively addressed this question using naturalistic face images and representatively sampled traits. Despite similar underlying dimensional structures across traits, online adult participants with self-reported ASD showed different judgments and reduced specificity within each trait compared with neurotypical individuals. Deep neural networks revealed that these group differences were driven by specific types of faces and differential utilization of features within a face. Our results were replicated in well-characterized in-lab participants and partially generalized to more controlled face images (a preregistered study). By investigating social trait judgments in a broader population, including individuals with neurodevelopmental variations, we found important theoretical implications for the fundamental dimensions, variations, and potential behavioral consequences of social cognition. 

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3. DNA conformational equilibrium enables continuous changing of curvatures

   https://doi.org/10.1039/D2NR05404C  

   Mao, Dake; Paluzzi, Victoria E.; Zhang, Cuizheng; Mao, Chengde (January 2023, Nanoscale)

   Assembly of complex structures from a small set of tiles is a common theme in biology. For example, many copies of identical proteins make up polyhedron-shaped, viral capsids and tubulin can make long microtubules. This inspired the development of tile-based DNA self-assembly for nanoconstruction, particularly for structures with high symmetries. In the final structure, each type of motif will adopt the same conformation, either rigid or with defined flexibility. For structures that have no symmetry, their assembly remains a challenge from a small set of tiles. To meet this challenge, algorithmic self-assembly has been explored driven by computational science, but it is not clear how to implement this approach to one-dimensional (1D) structures. Here, we have demonstrated that a constant shift of a conformational equilibrium could allow 1D structures to evolve. As shown by atomic force microscopy imaging, one type of DNA tile successfully assembled into DNA spirals and concentric circles, which became less and less curved from the structure's center outward. This work points to a new direction for tile-based DNA assembly. 

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4. Combinatorial decision-making driven by multicomponent surface condensates

   https://doi.org/10.1073/pnas.2527873123  

   Zentner, Aidan; Halingstad, Ethan V; Chalk, Cameron; Brenner, Michael P; Murugan, Arvind; Winfree, Erik; Shrinivas, Krishna (July 2026, Proceedings of the National Academy of Sciences)

   Living organisms rely on molecular networks, such as gene circuits and signaling pathways, for information processing and robust decision-making in crowded, noisy environments. Recent advances show that interacting biomolecules self-organize by phase transitions into coexisting spatial compartments called condensates, often on cellular surfaces such as chromatin and membranes. In this paper, we demonstrate that multicomponent fluids can be designed to recruit distinct condensates to surfaces with differing compositions, performing a form of surface classification by condensation. We draw an analogy to multidimensional classification in machine learning and explore how hidden species, analogous to hidden nodes, expand the expressivity and capacity of these interacting ensembles to facilitate complex decision boundaries. By simply changing levels of individual species, we find that the same molecular repertoire can be reprogrammed to solve new tasks. Together, our findings suggest that the physical processes underlying biomolecular condensates can encode and drive adaptive information processing beyond compartmentalization. 

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5. Argus: Rapid Tracking of Wildfires from Unlabeled Satellite Images

   Saptashwa Mitra; Paahuni Khandelwal; Shrideep Pallickara; Sangmi Pallickara (July 2023, International Conference on Cloud Computing (CLOUD))

   Interactive visual analytics over distributed systems housing voluminous datasets is hindered by three main factors - disk and network I/O, and data processing overhead. Requests over geospatial data are prone to erratic query load and hotspots due to users’ simultaneous interest over a small sub-domain of the overall data space at a time. Interactive analytics in a distributed setting is further hindered in cases of voluminous datasets with large/high-dimensional data objects, such as multi-spectral satellite imagery. The size of the data objects prohibits efficient caching mechanisms that could significantly reduce response latencies. Additionally, extracting information from these large data objects incurs significant data processing overheads and they often entail resource-intensive computational methods. Here, we present our framework, ARGUS, that extracts low- dimensional representation (embeddings) of high-dimensional satellite images during ingestion and houses them in the cache for use in model-driven analysis relating to wildfire detection. These embeddings are versatile and are used to perform model- based extraction of analytical information for a set of dif- ferent scenarios, to reduce the high computational costs that are involved with typical transformations over high-dimensional datasets. The models for each such analytical process are trained in a distributed manner in a connected, multi-task learning fashion, along with the encoder network that generates the original embeddings. 

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