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In this paper, we explore the bounded cohomology of quandles and its applications to knot theory. We establish two key results that provide sufficient conditions for the infinite dimensionality of the second bounded cohomology of quandles. The first condition involves a subspace of homogeneous group quasimorphisms on the inner automorphism group of the quandle, whereas the second condition concerns the vanishing of the stable commutator length on a subgroup of this inner automorphism group. As topological applications, we show that the second bounded cohomology of the quandle of any non-split link whose link group is non-solvable as well as the quandle of any split link, is infinite dimensional. From these results, we conclude that the second bounded cohomology of the knot quandle can be used to detect the unknot. On the algebraic side, we prove that the second bounded cohomology of a free product of quandles is infinite dimensional if the inner automorphism group of at least one of the free factors is amenable. This leads to the result that the second bounded cohomology of free quandles of rank greater than one, as well as their canonical quotients, is infinite dimensional., Comment: 15 pages

Galaxy morphology in stellar light can be described by a series of "non-parametric" or "morphometric" parameters, such as concentration-asymmetry-smoothness, Gini, $M_{20}$, and Sersic fit. These parameters can be applied to column density maps of atomic hydrogen (HI). The HI distribution is susceptible to perturbations by environmental effects, e.g. inter-galactic medium pressure and tidal interactions. Therefore, HI morphology can potentially identify galaxies undergoing ram-pressure stripping or tidal interactions. We explore three fields in the WALLABY Pilot HI survey and identify perturbed galaxies based on a k-nearest Neighbor (kNN) algorithm using an HI morphometric feature space. For training, we used labeled galaxies in the combined NGC 4808 and NGC 4636 fields with six HI morphometrics to train and test a kNN classifier. The kNN classification is proficient in classifying perturbed galaxies with all metrics -- accuracy, precision and recall -- at 70-80%. By using the kNN method to identify perturbed galaxies in the deployment field, the NGC 5044 mosaic, we find that in most regards, the scaling relations of perturbed and unperturbed galaxies have similar distribution in the scaling relations of stellar mass vs star formation rate and the Baryonic Tully-Fisher relation, but the HI and stellar mass relation flatter than of the unperturbed galaxies. Our results for NGC 5044 provide a prediction for future studies on the fraction of galaxies undergoing interaction in this catalogue and to build a training sample to classify such galaxies in the full WALLABY survey., Comment: 21 pages, 15 figures, 8 tables, accepted by PASA

This paper introduces a high-performance Soft-Core Processor based data acquisition system designed for handling Resistive Plate Chambers (RPCs). The DAQ consist of FPGA-based hardware equipped with Soft-Core Processor and embedded hardwired Ethernet controllers named RPC-DAQ, offering a versatile and fast network-enabled data acquisition solution. A soft processor, NIOS, is instantiated within an Intel Cyclone IV FPGA, overseeing control, communication, and data transfer with remote processing units. These integrated RPC-DAQ units, in substantial numbers, connect to a limited set of high-end processing units via LAN switches. This paper provides a detailed account of the software implementation scheme for the NIOS processor in the RPC-DAQ system. A remarkable 28,800 RPC-DAQ units will be deployed in proximity to the RPCs, serving the proposed INO-ICAL experiment in Theni-Madurai, Tamil Nadu. The network-enabled RPC-DAQ units controlled by the soft processor offloads FPGA tasks including event data acquisition, periodic health monitoring of RPCs, command interfaces, high voltage control, and data transfer to back-end data concentrators. Communication and data transfer are executed efficiently via TCP and UDP protocols over a 100 Mbps Ethernet interface. This system provides innovative solutions to improve data acquisition and control in large-scale scientific experiments.

Precision AC resonant capacitance bridges, with a planar printed circuit board transformer using an ungapped MnZn ferrite core, have shown excellent noise performance in high-precision measurements. As many applications use an ensemble of bridges, consistency of performance is critical to the functionality of the systems. If part of the same manufacturing batch and scaled to the same frequency, the noise performance of the ferrite cores at 293 K has a weighted mean variance of <0.3%, with all the cores within a +/- 1% band. At 140 K the weighted mean variance is <0.6% and the 90% inclusion band is +/- 1.5%. Ten cores of SIFERRIT Material N41, from TDK Ferrites Accessories, were tested at room temperature, 293 K, and in a liquid nitrogen dewar at 140 K. Fitted to a parabolic function, to + 3dB on both frequency sides, the weighted mean of the noise minima at 293 K was 0.3027 aF/rtHz, with a variance of 0.0012 aF/rtHz spanning a frequency range of 85.0 +/- 2.7 kHz. Scaled to 100 kHz, the weighted mean and variance were 0.2788 aF/rtHz and 0.0008 aF/rtHz. Corresponding noise values at 140 K were 0.1815 aF/rtHz with a variance of 0.0016 aF/rtHz for the range of 152.7 +/- 6.3 kHz, and 0.1835 aF/rtHz with a variance of 0.0011 aF/rtHz when scaled to 150 kHz. Scaled for resonant frequency and temperature these results are consistent with a previous measurement of another ungapped core (same supplier and type, different manufacturing batch) to within 9% and 6% at 293 K and 140 K respectively.

The ability to differentiate between different models of inflation through the imprint of primordial non-Gaussianity (PNG) requires a tight constraint on the local PNG parameter $f_{\text{NL}}^{\text{loc}}$. Future large scale structure surveys like \textit{Euclid}, Vera C. Rubin Observatory, and the Spectro-Photometer for the History of the Universe, Epoch of Reionization, and Ices Explorer (SPHEREx) will play a crucial role in advancing our understanding of the inflationary epoch. In this light, we present forecasts on PNG with tomographic angular power spectrum from simulations of SPHEREx. We put forward the effects of redshift bin mismatch of galaxies as a source of systematic error when estimating $f_{\text{NL}}^{\text{loc}}$ and galaxy linear halo bias. We %use \texttt{GLASS} to simulate $500$ SPHEREx-like galaxy density fields, and divide the galaxies into $13$ redshift bins assuming Gaussian photometric redshift errors. We show that the misclassification of galaxies in redshift bins can result in strong apparent tensions on $f_{\text{NL}}^{\text{loc}}$ up to $\sim 3\sigma$ and up to $\sim 9\sigma$ on galaxy bias. We propose a scattering matrix formalism to mitigate bin mismatch of galaxies and to obtain unbiased estimates of cosmological parameters from tomographic angular clustering measurements., Comment: Submitted to PASA. 11 Pages, 10 figures, 1 table

3D Gaussian Splatting (3DGS) has recently emerged as a state-of-the-art 3D reconstruction and rendering technique due to its high-quality results and fast training and rendering time. However, pixels covered by the same Gaussian are always shaded in the same color up to a Gaussian falloff scaling factor. Furthermore, the finest geometric detail any individual Gaussian can represent is a simple ellipsoid. These properties of 3DGS greatly limit the expressivity of individual Gaussian primitives. To address these issues, we draw inspiration from texture and alpha mapping in traditional graphics and integrate it with 3DGS. Specifically, we propose a new generalized Gaussian appearance representation that augments each Gaussian with alpha~(A), RGB, or RGBA texture maps to model spatially varying color and opacity across the extent of each Gaussian. As such, each Gaussian can represent a richer set of texture patterns and geometric structures, instead of just a single color and ellipsoid as in naive Gaussian Splatting. Surprisingly, we found that the expressivity of Gaussians can be greatly improved by using alpha-only texture maps, and further augmenting Gaussians with RGB texture maps achieves the highest expressivity. We validate our method on a wide variety of standard benchmark datasets and our own custom captures at both the object and scene levels. We demonstrate image quality improvements over existing methods while using a similar or lower number of Gaussians., Comment: Project website: https://textured-gaussians.github.io/

Decarbonization of transport sector through adoption of cleaner vehicle options will depend on the environmental awareness of consumers and their priorities. This work develops and uses a system dynamics approach to understand possible adoption pathways of novel vehicle options, i.e., ethanol-blended fuel (E85), electric, and compressed natural gas (CNG) vehicles in India. A system dynamics model using a multi-nominal logit model to capture consumer choices has been previously developed for the private road transport sector of India. The model has been modified to also include annual vehicular emissions as a decisionmaking attribute in addition to the annual cost. Four different classes of consumers with different priorities to cost and annual emissions are modeled. The model coefficients are identified using historical data. Model simulations over a period of 30 years till 2050 are performed to determine possible vehicle adoption trends. Different scenarios of changing environmental awareness, policy interventions, and technology development were analyzed to achieve targets such as COP26 greenhouse gas emission reduction, ethanol blending, and EV adoption. Simulation results showed that an increase in environmental awareness resulted in the adoption of novel vehicle options by 67.42% and 22.3% of the car and two-wheeler stocks, respectively. However, rising environmental awareness was not enough to meet the target values. Scenario analysis showed that a greater share of renewables in the electricity grid, carbon tax on transport fuel, and reduced vehicle driving together can possibly achieve the GHG emissions target in 2030. Battery-related GHG emissions were shown to be very important and led to counter-intuitive trends in EV adoption. 30% EV sales by 2030 could be achieved with a greener electricity grid and carbon tax., Comment: 32 Pages with 14 figures

We measured the harmonic-space power spectrum of galaxy clustering auto-correlation from the Evolutionary Map of the Universe Pilot Survey 1 data (EMU PS1) and its cross-correlation with the lensing convergence map of cosmic microwave background (CMB) from Planck Public Release 4 at the linear scale range from $\ell=2$ to 500. We applied two flux density cuts at $0.18$ and $0.4$mJy on the radio galaxies observed at 944MHz and considered two source detection algorithms. We found the auto-correlation measurements from the two algorithms at the 0.18mJy cut to deviate for $\ell\gtrsim250$ due to the different criteria assumed on the source detection and decided to ignore data above this scale. We report a cross-correlation detection of EMU PS1 with CMB lensing at $\sim$5.5$\sigma$, irrespective of flux density cut. In our theoretical modelling we considered two redshift distribution simulation models that yield consistent results, a linear and a non-linear matter power spectrum, and two linear galaxy bias models. That is a constant redshift-independent galaxy bias $b(z)=b_g$ and a constant amplitude galaxy bias $b(z)=b_g/D(z)$. By fixing a cosmology model and considering a non-linear matter power spectrum, we measured a constant galaxy bias at $0.18$mJy ($0.4$mJy) with $b_g=2.32^{+0.41}_{-0.33}$ ($2.18^{+0.17}_{-0.25}$) and a constant amplitude bias with $b_g=1.72^{+0.31}_{-0.21}$ ($1.78^{+0.22}_{-0.15}$). When $\sigma_8$ is a free parameter for the same models at $0.18$mJy ($0.4$mJy) with the constant model we found $\sigma_8=0.68^{+0.16}_{-0.14}$ ($0.82\pm0.10$), while with the constant amplitude model we measured $\sigma_8=0.61^{+0.18}_{-0.20}$ ($0.78^{+0.11}_{-0.09}$), respectively. Our results agree at $1\sigma$ with the measurements from Planck CMB and the weak lensing surveys and also show the potential of cosmology studies with future radio continuum survey data., Comment: To be submitted to PASA. 22 pages, 9 figures, 4 tables

Cryptocurrency lending pools are services that allow lenders to pool together assets in one cryptocurrency and loan it out to borrowers who provide collateral worth more (than the loan) in a separate cryptocurrency. Borrowers can repay their loans to reclaim their collateral unless their loan was liquidated, which happens when the value of the collateral dips significantly. Interest rates for these pools are currently set via supply and demand heuristics, which have several downsides, including inefficiency, inflexibility, and being vulnerable to manipulation. Here, we reduce lending pools to options, and then use ideas from options pricing to search for fair interest rates for lending pools. In a simplified model where the loans have a fixed duration and can only be repaid at the end of the term, we obtain analytical pricing results. We then consider a more realistic model, where loans can be repaid dynamically and without expiry. Our main theoretical contribution is to show that fair interest rates do not exist in this setting. We then show that impossibility results generalize even to models of lending pools which have no obvious reduction to options. To address these negative results, we introduce a model of lending pools with fixed fees, and model the ability of borrowers to top-up their loans to reduce the risk of liquidation. As a proof of concept, we use simulations to show how our model's predicted interest rates compare to interest rates in practice.

In this paper, we investigate the problem of "generation supervision" in large language models, and present a novel bicameral architecture to separate supervision signals from their core capability, helpfulness. Doppelg\"anger, a new module parallel to the underlying language model, supervises the generation of each token, and learns to concurrently predict the supervision score(s) of the sequences up to and including each token. In this work, we present the theoretical findings, and leave the report on experimental results to a forthcoming publication.

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

We introduce a novel technique for enhancing the robustness of light-pulse atom interferometers against the pulse infidelities that typically limit their sensitivities. The technique uses quantum optimal control to favorably harness the multipath interference of the stray trajectories produced by imperfect atom-optics operations. We apply this method to a resonant atom interferometer and achieve thousand-fold phase amplification, representing a fifty-fold improvement over the performance observed without optimized control. Moreover, we find that spurious interference can arise from the interplay of spontaneous emission and many-pulse sequences and demonstrate optimization strategies to mitigate this effect. Given the ubiquity of spontaneous emission in quantum systems, these results may be valuable for improving the performance of a diverse array of quantum sensors. We anticipate our findings will significantly benefit the performance of matter-wave interferometers for a variety of applications, including dark matter, dark energy, and gravitational wave detection.

Large language models (LLMs) have demonstrated strong potential in performing automatic scoring for constructed response assessments. While constructed responses graded by humans are usually based on given grading rubrics, the methods by which LLMs assign scores remain largely unclear. It is also uncertain how closely AI's scoring process mirrors that of humans, or if it adheres to the same grading criteria. To address this gap, this paper uncovers the grading rubrics that LLMs used to score students' written responses to science tasks and their alignment with human scores. We also examine whether enhancing the alignments can improve scoring accuracy. Specifically, we prompt LLMs to generate analytic rubrics that they use to assign scores and study the alignment gap with human grading rubrics. Based on a series of experiments with various configurations of LLM settings, we reveal a notable alignment gap between human and LLM graders. While LLMs can adapt quickly to scoring tasks, they often resort to shortcuts, bypassing deeper logical reasoning expected in human grading. We found that incorporating high-quality analytical rubrics designed to reflect human grading logic can mitigate this gap and enhance LLMs' scoring accuracy. These results caution against the simplistic application of LLMs in science education and highlight the importance of aligning LLM outputs with human expectations to ensure efficient and accurate automatic scoring., Comment: Non-archival Presenting at EDM 2024 Workshop on Large Language Models

It is well-known that the cohomology of symmetric quandles generates robust cocycle invariants for unoriented classical and surface links. Expanding on the recently introduced module-theoretic generalized cohomology for symmetric quandles, we derive a four-term exact sequence that relates 1-cocycles, second cohomology, and a specific group of automorphisms associated with the extensions of symmetric quandles. This exact sequence shows that the obstruction to lifting and extending automorphisms is found in the second symmetric quandle cohomology. Additionally, some general aspects of dynamical cocycles and extensions are discussed., Comment: 18 pages

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated.

We study the importance of precise modelling of the photometric redshift error distributions on the estimation of parameters from cross correlation measurements and present a working example of the scattering matrix formalism to correct for the redshift bin mismatch of objects in tomographic cross correlation analysis. We measured the angular galaxy auto-power spectrum and cross-power spectrum in four tomographic bins with redshift intervals $z = [0.0,0.3,0.45,0.6,0.8]$ from the cross correlation of Planck Cosmic Microwave Background lensing potential and photometric galaxy catalogue from the Dark Energy Spectroscopic Instrument Legacy Imaging Survey Data Release 8. We estimated galaxy linear bias and amplitude of cross correlation using maximum likelihood estimation to put constraints on the $\sigma_{8}$ parameter. We show that the modified Lorentzian function used to fit the photometric redshift error distribution performs well only near the peaks of the distribution. We adopt a sum of Gaussians model to capture the broad tails of the error distribution. Our sum of Gaussians model yields $\sim 2-5\,\sigma$ smaller values of cross correlation amplitude compared to the $\Lambda$CDM expectations. We compute the $\sigma_{8}$ parameter after correcting for the redshift bin mismatch of objects following the scattering matrix approach. The $\sigma_{8}$ parameter becomes consistent with $\Lambda$CDM model in the last tomographic bin but shows $\sim 1-3\,\sigma$ tension in other redshift bins., Comment: 20 pages, 17 figures, 4 tables, published in Astronomy and Astrophysics

Recent advancements in LLMs have shown their significant potential in tasks like text summarization and generation. Yet, they often encounter difficulty while solving complex physics problems that require arithmetic calculation and a good understanding of concepts. Moreover, many physics problems include images that contain important details required to understand the problem's context. We propose an LMM-based chatbot to answer multimodal physics MCQs. For domain adaptation, we utilize the MM-PhyQA dataset comprising Indian high school-level multimodal physics problems. To improve the LMM's performance, we experiment with two techniques, RLHF (Reinforcement Learning from Human Feedback) and Image Captioning. In image captioning, we add a detailed explanation of the diagram in each image, minimizing hallucinations and image processing errors. We further explore the integration of Reinforcement Learning from Human Feedback (RLHF) methodology inspired by the ranking approach in RLHF to enhance the human-like problem-solving abilities of the models. The RLHF approach incorporates human feedback into the learning process of LLMs, improving the model's problem-solving skills, truthfulness, and reasoning capabilities, minimizing the hallucinations in the answers, and improving the quality instead of using vanilla-supervised fine-tuned models. We employ the LLaVA open-source model to answer multimodal physics MCQs and compare the performance with and without using RLHF.

While Large Language Models (LLMs) can achieve human-level performance in various tasks, they continue to face challenges when it comes to effectively tackling multi-step physics reasoning tasks. To identify the shortcomings of existing models and facilitate further research in this area, we curated a novel dataset, MM-PhyQA, which comprises well-constructed, high schoollevel multimodal physics problems. By evaluating the performance of contemporary LLMs that are publicly available, both with and without the incorporation of multimodal elements in these problems, we aim to shed light on their capabilities. For generating answers for questions consisting of multimodal input (in this case, images and text) we employed Zero-shot prediction using GPT-4 and utilized LLaVA (LLaVA and LLaVA-1.5), the latter of which were fine-tuned on our dataset. For evaluating the performance of LLMs consisting solely of textual input, we tested the performance of the base and fine-tuned versions of the Mistral-7B and LLaMA2-7b models. We also showcased the performance of the novel Multi-Image Chain-of-Thought (MI-CoT) Prompting technique, which when used to train LLaVA-1.5 13b yielded the best results when tested on our dataset, with superior scores in most metrics and the highest accuracy of 71.65% on the test set.

Empirical studies of the relationship between baryonic matter in galaxies and the gravitational potential of their host halos are important to constrain our theoretical framework for galaxy formation and evolution. One such relation, between the atomic hydrogen (HI) mass of central galaxies ($M_{\rm{HI,c}}$) and the total mass of their host halos ($M_{\rm{halo}}$), has attracted significant interest in the last few years. In this work, we use the extended GALEX Arecibo SDSS Survey to examine the scatter of the HI-halo mass relation for a representative sample of central galaxies. Our findings reveal a flat median relation at $\rm{log}_{10}$$(M_{\rm{HI,c}}/\rm{M}_{\odot}) \approx 9.40$, across $11.1 < \rm{log}_{10}$$(M_{\rm{halo}}/\rm{M_{\odot}}) < 14.1$. This flat relation stems from the statistical dominance of star-forming, disc galaxies at low $M_{\rm{halo}}$ in combination with the increasing prevalence of passive, high stellar-concentration systems at higher $M_{\rm{halo}}$. The scatter of this relation and the stellar specific angular momentum of centrals have a strong link (Spearman's rank correlation coefficient $\geq 0.5$). Comparisons with simulations suggest that the kinematic state of host halos may be primarily driving this scatter. Our findings highlight that the HI-halo mass parameter space is too complex to be completely represented by simple median or average relations and we show that tensions with previous works are most likely due to selection biases. We recommend that future observational studies, and their comparisons with theoretical models, bin central galaxies also by their secondary properties to enable a statistically robust understanding of the processes regulating the cold gas content within central galaxies of dark-matter halos., Comment: 11 pages, 6 figures (4 additional figures in appendix)

Reference resolution is an important problem, one that is essential to understand and successfully handle context of different kinds. This context includes both previous turns and context that pertains to non-conversational entities, such as entities on the user's screen or those running in the background. While LLMs have been shown to be extremely powerful for a variety of tasks, their use in reference resolution, particularly for non-conversational entities, remains underutilized. This paper demonstrates how LLMs can be used to create an extremely effective system to resolve references of various types, by showing how reference resolution can be converted into a language modeling problem, despite involving forms of entities like those on screen that are not traditionally conducive to being reduced to a text-only modality. We demonstrate large improvements over an existing system with similar functionality across different types of references, with our smallest model obtaining absolute gains of over 5% for on-screen references. We also benchmark against GPT-3.5 and GPT-4, with our smallest model achieving performance comparable to that of GPT-4, and our larger models substantially outperforming it., Comment: Accepted at SIGDIAL 2024 (Oral presentation)

In this paper, we investigate residual finiteness and subquandle separability of quandles. The existence of these finiteness properties implies the solvability of the word problem and the generalised word problem for quandles. We prove that the fundamental $n$-quandle of any link in the 3-sphere is residually finite for each $n \ge 2$. This supplements the recent result on residual finiteness of link quandles and the classification of links whose fundamental $n$-quandles are finite for some $n$. We also establish several general results on these finiteness properties and give many families of quandles admitting them., Comment: 16 pages, comments are welcomed

The paper presents a new approach of stability evaluation of the approximate Riemann solvers based on the direct Lyapunov method. The present methodology offers a detailed understanding of the origins of numerical shock instability in the approximate Riemann solvers. The pressure perturbation feeding the density and transverse momentum perturbations is identified as the cause of the numerical shock instabilities in the complete approximate Riemann solvers, while the magnitude of the numerical shock instabilities are found to be proportional to the magnitude of the pressure perturbations. A shock-stable HLLEM scheme is proposed based on the insights obtained from this analysis about the origins of numerical shock instability in the approximate Riemann solvers. A set of numerical test cases are solved to show that the proposed scheme is free from numerical shock instability problems of the original HLLEM scheme at high Mach numbers., Comment: arXiv admin note: text overlap with arXiv:2207.06830

We present a comprehensive analysis of the detailed chemical abundances for a sample of 11 metal-poor, very metal-poor and extremely metal-poor stars ([Fe/H] = -1.65 to [Fe/H] = -3.0) as part of the HESP-GOMPA (Galactic survey Of Metal Poor stArs) survey. The abundance determinations encompass a range of elements, including C, Na, Mg, Al, Si, Ca, Sc, Ti, Cr, Mn, Fe, Co, Ni, Cu, Zn, Sr, and Ba, with a subset of the brighter objects allowing for the measurement of additional key elements. Notably, the abundance analysis of a relatively bright highly r-process-enhanced (r-II) star (SDSS J0019+3141) exhibits a predominantly main r-process signature and variations in the lighter r-process elements. Moreover, successful measurements of thorium in this star facilitate stellar age determinations. We find a consistent odd-even nucleosynthesis pattern in these stars, aligning with expectations for their respective metallicity levels, thus implicating Type II supernovae as potential progenitors. From the interplay between the light and heavy r-process elements, we infer a diminishing relative production of light r-process elements with increasing Type II supernova contributions, challenging the notion that Type II supernovae are the primary source of these light r-process elements in the early Milky Way. A chemodynamical analysis based on Gaia astrometric data and our derived abundances indicates that all but one of our program stars are likely to be of accreted origin. Additionally, our examination of alpha-poor stars underscores the occurrence of an early accretion event from a satellite on a prograde orbit, similar to that of the Galactic disc., Comment: 22 pages, 12 figures, Accepted for publication in MNRAS