Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios

Recurrent State-space models (RSSMs) are highly expressive models for learning patterns in time series data and system identification. However, these models assume that the dynamics are fixed and unchanging, which is rarely the case in real-world scenarios. Many control applications often exhibit tasks with similar but not identical dynamics which can be modeled as a latent variable. We introduce the Hidden Parameter Recurrent State Space Models (HiP-RSSMs), a framework that parametrizes a family of related dynamical systems with a low-dimensional set of latent factors. We present a simple and effective way of learning and performing inference over this Gaussian graphical model that avoids approximations like variational inference. We show that HiP-RSSMs outperforms RSSMs and competing multi-task models on several challenging robotic benchmarks both on real-world systems and simulations

Stock structure analysis of Nemipterus bipunctatus (Valenciennes, 1830) from three locations along the Indian coast

1888-1895Present study was done to identify the occurrence of various stocks of Nemipterus bipunctatus along the Indian coast, based on their body and skull shape morphometrics. Fish samples were collected from three locations along the Indian coast viz. Chennai along the East coast and Mumbai and Veraval on the West coast. Twenty truss distances from nine-point truss network of body and twenty-one truss distances from eleven-point truss network of the skull were measured from each fish sample. The canonical discriminant analysis showed that the truss distances belong to the anterior region and caudal peduncle of body and olfactory region of skull were significant in separating the fish stocks. The artificial neural network analysis revealed 91.4 % and 86.14 % well classification of the specimen, based on the truss distances of body and skull respectively. The results from the study indicated that there is a significant difference among the stocks of N. bipunctatus

The DESI One-Percent Survey: Modelling the clustering and halo occupation of all four DESI tracers with Uchuu

We present results from a set of high-fidelity simulated lightcones for the DESI One-Percent Survey, created from the Uchuu simulation. This 8 (Gpc/h)^3 N-body simulation comprises 2.1 trillion particles and provides high-resolution dark matter (sub)haloes in the framework of the Planck base-LCDM cosmology. Employing the subhalo abundance matching (SHAM) technique, we populate the Uchuu (sub)haloes with all four DESI tracers (BGS, LRG, ELG and QSO) to z = 2.1. Our method accounts for redshift evolution as well as the clustering dependence on luminosity and stellar mass. The two-point clustering statistics of the DESI One-Percent Survey align reasonably well with our predictions from Uchuu across scales ranging from 0.1 Mpc/h to 100 Mpc/h. Some discrepancies arise due to cosmic variance, incompleteness in the massive end of the stellar mass function, and a simplified galaxy-halo connection model. We find that the Uchuu BGS and LRG samples are adequately described using the standard 5-parameter halo occupation distribution model, while the ELGs and QSOs show agreement with an adopted Gaussian distribution for central halos with a power law for satellites. We observe a fair agreement in the large-scale bias measurements between data and mock samples, although the data exhibits smaller bias values, likely due to cosmic variance. The bias dependence on absolute magnitude, stellar mass and redshift aligns with that of previous surveys. These results improve simulated lightcone construction from cosmological models and enhance our understanding of the galaxy-halo connection, with pivotal insights from the first DESI data for the success of the final survey.Comment: 23 pages, 15 figures, 5 tables, submitted to MNRAS. The Uchuu-DESI lightcones will be available at https://data.desi.lbl.go

Tests of Violation of Inverse Square Law on Radio Pulsar Fluxes

Like most other astrophysical phenomenon, it is implicitly assumed that the pulsar fluxes obey the inverse-square law. In this project, effort was made to study the claims for inverse Square law violation in the flux of Radio Pulsars as claimed by Singleton et al[1], in based on the assertion that the relative convergence factor for an inverse square law (1/r2) obeying flux of a radio pulsar is 105 times larger than the case of 1/r obeying flux emission, using the latest data obtained from Australian Telescope National Facility (ATNF) Pulsar Catalogue[2]. After running several simulations, it was found that there is no conclusive evidence suggesting any form of Inverse Square Law Violation for radio pulsar fluxes

Not Available

Not AvailableAvailable with full textNot Availabl

Not Available

Not AvailablePresent study was done to identify the occurrence of various stocks of Nemipterus bipunctatus along the Indian coast, based on their body and skull shape morphometrics. Fish samples were collected from three locations along the Indian coast viz. Chennai along the East coast and Mumbai and Veraval on the West coast. Twenty truss distances from nine-point truss network of body and twenty-one truss distances from eleven-point truss network of the skull were measured from each fish sample. The canonical discriminant analysis showed that the truss distances belong to the anterior region and caudal peduncle of body and olfactory region of skull were significant in separating the fish stocks. The artificial neural network analysis revealed 91.4 per cent and 86.14 per cent well classification of the specimen, based on the truss distances of body and skull respectively. The results from the study indicated that there is a significant difference among the stocks of N. bipunctatus.Not Availabl