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Introduction and application of a new approach for model-based optical bidirectional measurements
No full textAdvection-Based Tracking and Analysis of Salinity Movement in the Indian Ocean
No full textThe Bay of Bengal (BoB) has maintained its salinity distribution over the years despite a continuous flow of fresh water entering it through rivers on the northern coast, which is capable of diluting the salinity. This can be attributed to the cyclic flow of high salinity water (>= 35 psu), coming from Arabian sea and entering BoB from the south, which moves northward and mixes with this fresh water. The movement of this high salinity water has been studied and analyzed in previous work (Singh et al., 2022). This paper extends the computational methods and analysis of salinity movement. Specifically, we introduce an advection based feature definition that represents the movement of high salinity water, and describe algorithms to track their evolution over time. This method allows us to trace the movement of high salinity water caused due to ocean currents. The method is validated via comparison with established observations on the flow of high salinity water in the BoB, including its entry from the Arabian Sea and its movement near Sri Lanka. Further, the visual analysis and tracking framework enables us to compare it with previous work and analyze the contribution of advection to salinity transport
Co-evolving networks for opinion and social dynamics in agent-based models
No full textThe rise of digital social media has strengthened the coevolution of public opinions and social interactions, that shape social structures and collective outcomes in increasingly complex ways. Existing literature often explores this interplay as a one-directional influence, focusing on how opinions determine social ties within adaptive networks. However, this perspective overlooks the intrinsic dynamics driving social interactions, which can significantly influence how opinions form and evolve. In this work, we address this gap, by introducing the co-evolving opinion and social dynamics using stochastic agent-based models. Agents' mobility in a social space is governed by both their social and opinion similarity with others. Similarly, the dynamics of opinion formation is driven by the opinions of agents in their social vicinity. We analyze the underlying social and opinion interaction networks and explore the mechanisms influencing the appearance of emerging phenomena, like echo chambers and opinion consensus. To illustrate the model's potential for real-world analysis, we apply it to General Social Survey data on political identity and public opinion regarding governmental issues. Our findings highlight the model's strength in capturing the coevolution of social connections and individual opinions over time
Data for Training the DeepOrientation Model: Simulated cryo-ET tomogram patches
No full textA major restriction to applying deep learning methods in cryo-electron tomography is the lack of annotated data. Many large learning-based models cannot be applied to these images due to the lack of adequate experimental ground truth. One appealing alternative solution to the time-consuming and expensive experimental data acquisition and annotation is the generation of simulated cryo-ET images. In this context, we exploit a public cryo-ET simulator called PolNet to generate three datasets of two macromolecular structures, namely the ribosomal complex 4v4r and Thermoplasma acidophilum 20S proteasome, 3j9i. We select these two specific particles to test whether our models work for macromolecular structures with and without rotational symmetry. The three datasets contain 50, 150, and 450 tomograms with a voxel size of 10 ̊A, respectively. Here, we publish patches of size 40 × 40 × 40 extracted from the medium-sized dataset with 26,703 samples of 4v4r and 40,671 samples of 3j9i. The original tomograms from which the samples were extracted are of size 500 × 500 × 250. Finally, it should be noted that the currently published test dataset is employed for reporting the results of our paper titled ”DeepOrientation: Deep Orientation Estimation of Macromolecules in Cryo-electron tomography” paper
SHREC 2024: Recognition Of Dynamic Hand Motions Molding Clay
No full textGesture recognition is a tool to enable novel interactions with different techniques and
applications, like Mixed Reality and Virtual Reality environments. With all the recent
advancements in gesture recognition from skeletal data, it is still unclear how well state-of-
the-art techniques perform in a scenario using precise motions with two hands. This
paper presents the results of the SHREC 2024 contest organized to evaluate methods
for their recognition of highly similar hand motions using the skeletal spatial coordinate
data of both hands. The task is the recognition of 7 motion classes given their spatial
coordinates in a frame-by-frame motion. The skeletal data has been captured using
a Vicon system and pre-processed into a coordinate system using Blender and Vicon
Shogun Post. We created a small, novel dataset with a high variety of durations in
frames. This paper shows the results of the contest, showing the techniques created
by the 5 research groups on this challenging task and comparing them to our baseline
method
FELRec: Efficient Handling of Item Cold-Start With Dynamic Representation Learning in Recommender Systems
No full textISOKANN.jl
No full textJulia pacarkge implementing the ISOKANN algorithm for the computation of invariant subspaces of Koopman operator
