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

Less spatial exploration is associated with poorer spatial memory in midlife adults

IntroductionDespite its importance for navigation, very little is known about how the normal aging process affects spatial exploration behavior. We aimed to investigate: (1) how spatial exploration behavior may be altered early in the aging process, (2) the relationship between exploration behavior and subsequent spatial memory, and (3) whether exploration behavior can classify participants according to age.MethodsFifty healthy young (aged 18–28) and 87 healthy midlife adults (aged 43–61) freely explored a desktop virtual maze, learning the locations of nine target objects. Various exploration behaviors (object visits, distance traveled, turns made, etc.) were measured. In the test phase, participants navigated from one target object to another without feedback, and their wayfinding success (% correct trials) was measured.ResultsIn the exploration phase, midlife adults exhibited less exploration overall compared to young adults, and prioritized learning target object locations over maze layout. In the test phase, midlife adults exhibited less wayfinding success when compared to the young adults. Furthermore, following principal components analysis (PCA), regression analyses indicated that both exploration quantity and quality components were associated with wayfinding success in the midlife group, but not the young adults. Finally, we could classify participants according to age with similar accuracy using either their exploration behavior or wayfinding success scores.DiscussionOur results aid in the understanding of how aging impacts spatial exploration, and encourages future investigations into how pathological aging may affect spatial exploration behavior

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

The Early Data Release of the Dark Energy Spectroscopic Instrument

\ua9 2024. The Author(s). Published by the American Astronomical Society. The Dark Energy Spectroscopic Instrument (DESI) completed its 5 month Survey Validation in 2021 May. Spectra of stellar and extragalactic targets from Survey Validation constitute the first major data sample from the DESI survey. This paper describes the public release of those spectra, the catalogs of derived properties, and the intermediate data products. In total, the public release includes good-quality spectral information from 466,447 objects targeted as part of the Milky Way Survey, 428,758 as part of the Bright Galaxy Survey, 227,318 as part of the Luminous Red Galaxy sample, 437,664 as part of the Emission Line Galaxy sample, and 76,079 as part of the Quasar sample. In addition, the release includes spectral information from 137,148 objects that expand the scope beyond the primary samples as part of a series of secondary programs. Here, we describe the spectral data, data quality, data products, Large-Scale Structure science catalogs, access to the data, and references that provide relevant background to using these spectra

Validation of the Scientific Program for the Dark Energy Spectroscopic Instrument

The Dark Energy Spectroscopic Instrument (DESI) was designed to conduct a survey covering 14,000 deg$^2$ over five years to constrain the cosmic expansion history through precise measurements of Baryon Acoustic Oscillations (BAO). The scientific program for DESI was evaluated during a five month Survey Validation (SV) campaign before beginning full operations. This program produced deep spectra of tens of thousands of objects from each of the stellar (MWS), bright galaxy (BGS), luminous red galaxy (LRG), emission line galaxy (ELG), and quasar target classes. These SV spectra were used to optimize redshift distributions, characterize exposure times, determine calibration procedures, and assess observational overheads for the five-year program. In this paper, we present the final target selection algorithms, redshift distributions, and projected cosmology constraints resulting from those studies. We also present a `One-Percent survey' conducted at the conclusion of Survey Validation covering 140 deg$^2$ using the final target selection algorithms with exposures of a depth typical of the main survey. The Survey Validation indicates that DESI will be able to complete the full 14,000 deg$^2$ program with spectroscopically-confirmed targets from the MWS, BGS, LRG, ELG, and quasar programs with total sample sizes of 7.2, 13.8, 7.46, 15.7, and 2.87 million, respectively. These samples will allow exploration of the Milky Way halo, clustering on all scales, and BAO measurements with a statistical precision of 0.28% over the redshift interval $z<1.1$, 0.39% over the redshift interval $1.1<z<1.9$, and 0.46% over the redshift interval $1.9<z<3.5$.Comment: 42 pages, 18 figures, accepted by A

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