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K-means is a popular clustering algorithm with significant applications in numerous scientific and engineering areas. One drawback of K-means is its inability to identify non-linearly separable clusters, which may lead to inaccurate solutions in certain cases. Kernel K-means is a variant of classical K-means that can find non-linearly separable clusters. However, it scales quadratically with respect to the size of the dataset, taking several minutes to cluster even medium-sized datasets on traditional CPU-based machines. In this paper, we present a formulation of Kernel K-means using sparse-dense matrix multiplication (SpMM) and sparse matrix-vector multiplication (SpMV), and we show that our formulation enables the rapid implementation of a fast GPU-based version of Kernel K-means with little programming effort. Our implementation, named Popcorn, is the first open-source GPU-based implementation of Kernel K-means. Popcorn achieves a speedup of up to 123.8x over a CPU implementation of Kernel K-means and a speedup of up to 2.6x over a GPU implementation of Kernel K-means that does not use sparse matrix computations. Our results support the effectiveness of sparse matrices as tools for efficient parallel programming.

We argue that the Declarative Self-improving Python (DSPy) optimizers are a way to align the large language model (LLM) prompts and their evaluations to the human annotations. We present a comparative analysis of five teleprompter algorithms, namely, Cooperative Prompt Optimization (COPRO), Multi-Stage Instruction Prompt Optimization (MIPRO), BootstrapFewShot, BootstrapFewShot with Optuna, and K-Nearest Neighbor Few Shot, within the DSPy framework with respect to their ability to align with human evaluations. As a concrete example, we focus on optimizing the prompt to align hallucination detection (using LLM as a judge) to human annotated ground truth labels for a publicly available benchmark dataset. Our experiments demonstrate that optimized prompts can outperform various benchmark methods to detect hallucination, and certain telemprompters outperform the others in at least these experiments., Comment: 7 pages, 10 tables, two-column format

In this work we provide a model-independent experimental evaluation of the degree of isospin equilibration taking place in $^{58,64}$Ni+$^{58,64}$Ni collisions at 32 MeV/nucleon across varying reaction centralities. This result has been obtained by combining the complementary information provided by two different datasets, sharing common characteristics. The first dataset has been acquired with the INDRA setup and has been used to implement a model-independent reconstruction of the impact parameter. The second dataset has been acquired in the first experimental campaign of the coupled INDRA-FAZIA apparatus at GANIL. The neutron-to-proton content of the quasiprojectile remnant measured by FAZIA has been employed as isospin observable. The effect of isospin diffusion has been evidenced by means of the isospin transport ratio, reported as a function of the impact parameter of the collision. The evolution towards isospin equilibration from semiperipheral to more central collisions is clearly extracted. This experimental result, expanding our previous works (Phys. Rev. C 106, 024603 (2022) and Phys. Rev. C 108, 054611 (2023)), can be compared with the predictions of any transport model, and can thus be used to set constraints on the behavior of the symmetry energy term of the nuclear Equation of State at sub- to saturation densities.

Taking a quotient roughly means changing the notion of equality on a given object, set or type. In a quantitative setting, equality naturally generalises to a distance, measuring how much elements are similar instead of just stating their equivalence. Hence, quotients can be understood quantitatively as a change of distance. In this paper, we show how, combining Lawvere's doctrines and the calculus of relations, one can unify quantitative and usual quotients in a common picture. More in detail, we introduce relational doctrines as a functorial description of (the core of) the calculus of relations. Then, we define quotients and a universal construction adding them to any relational doctrine, generalising the quotient completion of existential elementary doctrine and also recovering many quantitative examples. This construction deals with an intensional notion of quotient and breaks extensional equality of morphisms. Then, we describe another construction forcing extensionality, showing how it abstracts several notions of separation in metric and topological structures. Combining these two constructions, we get the extensional quotient completion, whose essential image is characterized through the notion of projective cover. As an application, we show that, under suitable conditions, relational doctrines of algebras arise as the extensional quotient completion of free algebras. Finally, we compare relational doctrines to other categorical structures where one can model the calculus of relations.

To ensure and monitor large language models (LLMs) reliably, various evaluation metrics have been proposed in the literature. However, there is little research on prescribing a methodology to identify a robust threshold on these metrics even though there are many serious implications of an incorrect choice of the thresholds during deployment of the LLMs. Translating the traditional model risk management (MRM) guidelines within regulated industries such as the financial industry, we propose a step-by-step recipe for picking a threshold for a given LLM evaluation metric. We emphasize that such a methodology should start with identifying the risks of the LLM application under consideration and risk tolerance of the stakeholders. We then propose concrete and statistically rigorous procedures to determine a threshold for the given LLM evaluation metric using available ground-truth data. As a concrete example to demonstrate the proposed methodology at work, we employ it on the Faithfulness metric, as implemented in various publicly available libraries, using the publicly available HaluBench dataset. We also lay a foundation for creating systematic approaches to select thresholds, not only for LLMs but for any GenAI applications., Comment: 16 pages, 8 figures, 4 tables. 2-columns

Peer analysis is a critical component of investment management, often relying on expert-provided categorization systems. These systems' consistency is questioned when they do not align with cohorts from unsupervised clustering algorithms optimized for various metrics. We investigate whether unsupervised clustering can reproduce ground truth classes in a labeled dataset, showing that success depends on feature selection and the chosen distance metric. Using toy datasets and fund categorization as real-world examples we demonstrate that accurately reproducing ground truth classes is challenging. We also highlight the limitations of standard clustering evaluation metrics in identifying the optimal number of clusters relative to the ground truth classes. We then show that if appropriate features are available in the dataset, and a proper distance metric is known (e.g., using a supervised Random Forest-based distance metric learning method), then an unsupervised clustering can indeed reproduce the ground truth classes as distinct clusters., Comment: 9 pages, 4 tables 28 figures

The explainability of black-box machine learning algorithms, commonly known as Explainable Artificial Intelligence (XAI), has become crucial for financial and other regulated industrial applications due to regulatory requirements and the need for transparency in business practices. Among the various paradigms of XAI, Explainable Case-Based Reasoning (XCBR) stands out as a pragmatic approach that elucidates the output of a model by referencing actual examples from the data used to train or test the model. Despite its potential, XCBR has been relatively underexplored for many algorithms such as tree-based models until recently. We start by observing that most XCBR methods are defined based on the distance metric learned by the algorithm. By utilizing a recently proposed technique to extract the distance metric learned by Random Forests (RFs), which is both geometry- and accuracy-preserving, we investigate various XCBR methods. These methods amount to identify special points from the training datasets, such as prototypes, critics, counter-factuals, and semi-factuals, to explain the predictions for a given query of the RF. We evaluate these special points using various evaluation metrics to assess their explanatory power and effectiveness., Comment: 8 pages, 2 figures, 5 tables

Extraction and interpretation of intricate information from unstructured text data arising in financial applications, such as earnings call transcripts, present substantial challenges to large language models (LLMs) even using the current best practices to use Retrieval Augmented Generation (RAG) (referred to as VectorRAG techniques which utilize vector databases for information retrieval) due to challenges such as domain specific terminology and complex formats of the documents. We introduce a novel approach based on a combination, called HybridRAG, of the Knowledge Graphs (KGs) based RAG techniques (called GraphRAG) and VectorRAG techniques to enhance question-answer (Q&A) systems for information extraction from financial documents that is shown to be capable of generating accurate and contextually relevant answers. Using experiments on a set of financial earning call transcripts documents which come in the form of Q&A format, and hence provide a natural set of pairs of ground-truth Q&As, we show that HybridRAG which retrieves context from both vector database and KG outperforms both traditional VectorRAG and GraphRAG individually when evaluated at both the retrieval and generation stages in terms of retrieval accuracy and answer generation. The proposed technique has applications beyond the financial domain, Comment: 9 pages, 2 figures, 5 tables

The cowpea chlorotic mottle virus (CCMV) has emerged as an exemplary model system to assess the balance between electrostatic and topological features of ssRNA viruses, specifically in the context of the viral self-assembly process. Yet, in spite of its biophysical significance, little structural data of the RNA content of the CCMV virion is currently available. Here, the conformational dynamics of the RNA2 fragment of CCMV was assessed via coarse-grained molecular dynamics simulations, employing the oxRNA2 model. The behavior of RNA2 has been characterized both as a freely-folding molecule and within a mean-field depiction of a CCMV-like capsid. For the latter, a multi-scale approach was employed, to derive a radial potential profile of the viral cavity, from atomistic structures of the CCMV capsid in solution. The conformational ensembles of the encapsidated RNA2 were significantly altered with respect to the freely-folding counterparts, as shown by the emergence of long-range motifs and pseudoknots in the former case. Finally, the role of the N-terminal tails of the CCMV subunits (and ionic shells thereof) is highlighted as a critical feature in the construction of a proper electrostatic model of the CCMV capsid.

In many real-world classification or recognition tasks, it is often difficult to collect training examples that exhaust all possible classes due to, for example, incomplete knowledge during training or ever changing regimes. Therefore, samples from unknown/novel classes may be encountered in testing/deployment. In such scenarios, the classifiers should be able to i) perform classification on known classes, and at the same time, ii) identify samples from unknown classes. This is known as open-set recognition. Although random forest has been an extremely successful framework as a general-purpose classification (and regression) method, in practice, it usually operates under the closed-set assumption and is not able to identify samples from new classes when run out of the box. In this work, we propose a novel approach to enabling open-set recognition capability for random forest classifiers by incorporating distance metric learning and distance-based open-set recognition. The proposed method is validated on both synthetic and real-world datasets. The experimental results indicate that the proposed approach outperforms state-of-the-art distance-based open-set recognition methods.

We investigate the stellar Mass-Metallicity Relation (MZR) using a sample of 637 quiescent galaxies with 10.4 <= log(M*/M ) < 11.7 selected from the LEGA-C survey at 0.6 <= z <= 1. We derive mass-weighted stellar metallicities using full-spectral fitting. We find that while lower-mass galaxies are both metal -rich and -poor, there are no metal-poor galaxies at high masses, and that metallicity is bounded at low values by a mass-dependent lower limit. This lower limit increases with mass, empirically defining a MEtallicity-Mass Exclusion (MEME) zone. We find that the spectral index MgFe = \sqrt{Mgb \times Fe4383}, a proxy for the stellar metallicity, also shows a mass-dependent lower limit resembling the MEME relation. Crucially, MgFe is independent of stellar population models and fitting methods. By constructing the Metallicity Enrichment Histories, we find that, after the first Gyr, the Star Formation History of galaxies has a mild impact on the observed metallicity distribution. Finally, from the average formation times, we find that galaxies populate differently the metallicity-mass plane at different cosmic times, and that the MEME limit is recovered by galaxies that formed at z >= 3. Our work suggests that the stellar metallicity of quiescent galaxies is bounded by a lower limit which increases with the stellar mass. On the other hand, low-mass galaxies can have metallicities as high as galaxies ~1 dex more massive. This suggests that, at log(M*/M ) >= 10.4, rather than lower-mass galaxies being systematically less metallic, the observed MZR might be a consequence of the lack of massive, metal-poor galaxies., Comment: Accepted for publication in A&A

When falling into a galaxy cluster, galaxies experience a loss of gas due to ram pressure stripping. In particular, disk galaxies lose gas from their disks and very large tentacles of gas can be formed. Because of the morphology of these stripped galaxies they have been referred to as Jellyfish galaxies. It has been found that star formation is triggered not only in the disk, but also in the tentacles of such Jellyfish galaxies. The observed star forming regions located in the tentacles of those galaxies have been found to be as massive as $3\times10^7$ M$_{\odot}$ and with sizes $> 100$ pc. Interestingly, these parameters in mass and size agree with those of dwarf galaxies. In this work we make use of the state of the art magneto-hydrodynamical cosmological simulation Illustris TNG-50, to study massive jellyfish galaxies with long tentacles. We find that, in the tentacles of TNG-50 Jellyfish galaxies, the star formation regions (gas+stars) formed could be as massive as $\sim2\times10^8$ M$_{\odot}$. A particular star forming region was analyzed. This region has a star formation rate of $0.04$ M$_{\odot}$/yr, it is metal rich, has an average age of $0.46$ Gyr, and has a half mass radius of $\sim1$ kpc, typical of standard dwarf galaxies. Most importantly, this region is gravitationally self-bound. All and all, we identify a new type of dwarf galaxy being born from the gas tentacles of jellyfish galaxies, that by construction lacks a dark matter (hereafter DM) halo., Comment: 11 pages, 6 figures, ApJ Accepted

We present new H- and K-band spectroscopy for the bulge of M31, taken with the LUCI spectrograph at the Large Binocular Telescope (LBT). We studied radial trends of CO absorption features (namely, CO1.58, CO1.60, CO1.64, CO1.66, CO1.68, CO2.30, CO2.32, CO2.35) in the bulge of M31, out to a galactocentric distance of 100'' (380pc). We find that most COs do not exhibit a strong radial gradient, despite the strong metallicity gradient inferred from the optical spectral range, except for CO1.64, showing a steep increase in the center. We compared the observed line strengths to predictions of different state-of-the-art stellar population models, including an updated version of EMILES models, which also uses the extended IRTF spectral library. The observed COs are close to models' predictions, but in some models they turn out to be underestimated. We find that the lack of radial gradients is due to the combination of increasing CO strength with metallicity and C abundance, and decreasing CO strength with IMF slope and O abundance. We speculate that the steep gradient of CO1.64 might be due to Na overabundance. Remarkably, we were able to fit, at the same time, optical indices and all the NIR COs except for CO1.68, leaving abundance ratios (i.e., [C/Fe], [O/Fe], and [Mg/Fe]) as free-fitting parameters, imposing age and metallicity constraints from the optical, with no significant contribution from intermediate-age populations. For the majority of the bulge, we find [Mg/Fe]~0.15dex, [O/Fe] larger than [Mg/Fe] (by ~0.1dex), and C abundance consistent with that of Mg. In the central (few arcsec) region, we still find an enhancement of O and Mg, but significantly lower [C/Fe]. We find that the COs' line strengths of the bulge are significantly lower than those of massive galaxies, possibly because of a difference in carbon abundance, as well as, to some extent, total metallicity., Comment: 20 pages, 10 figures, accepted for publication on A&A

Ribonucleic acids (RNA) are unique in that they can store genetic information, replicate and perform catalysis. Importantly, RNA molecules are highly dynamic, and thus determining the ensemble of conformations that they populate is crucial not only to elucidate their biological functions, but also for their potential use as therapeutic targets. Computational and experimental techniques provide complementary views on RNA dynamics, and their integration is fundamental to improve the accuracy of computations and the resolution of experiments. Recent exciting developments in this field, were discussed at the CECAM workshop ``RNA dynamics from experimental and computational approaches'', in Paris, June 26-28, 2023. This report outlines key `take-home' messages that emerged during this workshop from the presentations and discussions.

Congenital heart defects (CHD) are the most prevalent birth defects in the United States and surgical outcomes vary considerably across the country. The outcomes of treatment for CHD differ for specific patient subgroups, with non-Hispanic Black and Hispanic populations experiencing higher rates of mortality and morbidity. A valid comparison of outcomes within racial/ethnic subgroups is difficult given large differences in case-mix and small subgroup sizes. We propose a causal inference framework for outcome assessment and leverage advances in transfer learning to incorporate data from both target and source populations to help estimate causal effects while accounting for different sources of risk factor and outcome differences across populations. Using the Society of Thoracic Surgeons' Congenital Heart Surgery Database (STS-CHSD), we focus on a national cohort of patients undergoing the Norwood operation from 2016-2022 to assess operative mortality and morbidity outcomes across U.S. geographic regions by race/ethnicity. We find racial and ethnic outcome differences after controlling for potential confounding factors. While geography does not have a causal effect on outcomes for non-Hispanic Caucasian patients, non-Hispanic Black patients experience wide variability in outcomes with estimated 30-day mortality ranging from 5.9% (standard error 2.2%) to 21.6% (4.4%) across U.S. regions., Comment: 26 pages

Lawvere's generalised the notion of complete metric space to the field of enriched categories: an enriched category is said to be Cauchy-complete if every left adjoint bimodule into it is represented by an enriched functor. Looking at this definition from a logical standpoint, regarding bimodules as an abstraction of relations and functors as an abstraction of functions, Cauchy-completeness resembles a formulation of the rule of unique choice. In this paper, we make this analogy precise, using the language of relational doctrines, a categorical tool that provides a functorial description of the calculus of relations, in the same way Lawvere's hyperdoctrines give a functorial description of predicate logic. Given a relational doctrine, we define Cauchy-complete objects as those objects of the domain category satisfying the rule of unique choice. Then, we present a universal construction that completes a relational doctrine with the rule of unique choice, that is, producing a new relational doctrine where all objects are Cauchy-complete. We also introduce relational doctrines with singleton objects and show that these have the minimal structure needed to build the reflector of the full subcategory of its domain on Cauchy-complete objects. The main result is that this reflector exists if and only if the relational doctrine has singleton objects and this happens if and only if its restriction to Cauchy-complete objects is equivalent to its completion with the rule of unique choice. We support our results with many examples, also falling outside the scope of standard doctrines, such as complete metric spaces, Banach spaces and compact Hausdorff spaces in the general context of monoidal topology, which are all shown to be Cauchy-complete objects for appropriate relational doctrines.

The emission of the pre-equilibrium particles during nuclear collisions at moderate beam energies is still an open question. This influences the properties of the compound nucleus but also changes the interpretation of the quasi-fission process. A systematic analysis of the data obtained by the FAZIA collaboration during a recent experiment with a neutron rich projectile is presented. The full range of charged particles detected in the experiment is within the limit of isotopic resolution of the FAZIA detector. Quasi-projectile (QP) fragments were detected in majority thanks to the forward angular acceptance of the experimental setup which was confirmed by introducing cuts based on the HIPSE event generator calculations. The main goal was to compare the experimental results with the HIPSE simulations after introducing these cuts to investigate the influence of the n-rich entrance channel on the QP fragment properties. More specifically, the lowering of N/Z of QP fragments with beam energy was found to be present since the initial phase of the reaction. Thus, pre-equilibrium emissions might be a possible candidate to explain such an effect., Comment: 10 pages, 10 figures

A detailed knowledge of the decay properties of the so called Hoyle state in the $^{12}$C nucleus ($E_x=7.654$ MeV, $0^+$) is required to calculate the rate at which carbon is forged in typical red-giant stars. This paper reports on a new almost background-free measurement of the radiative decay branching ratio of the Hoyle state using advanced charged particle coincidence techniques. The exploitation, for the first time in a similar experiment, of a bidimensional map of the coincidence efficiency allows to reach an unitary value and, consequently, to strongly reduce sources of systematic uncertainties. The present results suggest a value of the radiative branching ratio of $\Gamma_{rad}/\Gamma_{tot}=4.2(6)\cdot10^{-4}$. This finding helps to resolve the tension between recent data published in the literature.

The shapes of galaxies, in particular their outer regions, are important guideposts to their formation and evolution. Here we report on the discovery of strongly box-shaped morphologies of the, otherwise well-studied, elliptical and lenticular galaxies NGC 720 and NGC 2768 from deep imaging. The boxiness is strongly manifested in the shape parameter $A_4/a$ of $-0.04$ in both objects, and also significant center shifts of the isophotes of $\sim$ 2--4 kpc are seen. One reason for such asymmetries commonly stated in the literature is a merger origin, although the number of such cases is still sparse and the exact properties of the individual boxy objects is highly diverse. Indeed, for NGC 2768, we identify a progenitor candidate (dubbed Pelops) in the residual images, which appears to be a dwarf satellite that is currently merging with NGC 2768. At its absolute magnitude of M$_r$ of $-$12.2 mag, the corresponding Sersic radius of 2.4 kpc is more extended than those of typical dwarf galaxies from the literature. However, systematically larger radii are known to occur in systems that are in tidal disruption. This finding is bolstered by the presence of a tentative tidal stream feature on archival GALEX data. Finally, further structures in the fascinating host galaxy comprise rich dust lanes and a vestigial X-shaped bulge component., Comment: 6 pages, 8 figures, accepted for publication in Astronomy & Astrophysics

We initiate a novel approach to explain the predictions and out of sample performance of random forest (RF) regression and classification models by exploiting the fact that any RF can be mathematically formulated as an adaptive weighted K nearest-neighbors model. Specifically, we employ a recent result that, for both regression and classification tasks, any RF prediction can be rewritten exactly as a weighted sum of the training targets, where the weights are RF proximities between the corresponding pairs of data points. We show that this linearity facilitates a local notion of explainability of RF predictions that generates attributions for any model prediction across observations in the training set, and thereby complements established feature-based methods like SHAP, which generate attributions for a model prediction across input features. We show how this proximity-based approach to explainability can be used in conjunction with SHAP to explain not just the model predictions, but also out-of-sample performance, in the sense that proximities furnish a novel means of assessing when a given model prediction is more or less likely to be correct. We demonstrate this approach in the modeling of US corporate bond prices and returns in both regression and classification cases., Comment: 5 pages, 6 figures

For a financial analyst, the question and answer (Q\&A) segment of the company financial report is a crucial piece of information for various analysis and investment decisions. However, extracting valuable insights from the Q\&A section has posed considerable challenges as the conventional methods such as detailed reading and note-taking lack scalability and are susceptible to human errors, and Optical Character Recognition (OCR) and similar techniques encounter difficulties in accurately processing unstructured transcript text, often missing subtle linguistic nuances that drive investor decisions. Here, we demonstrate the utilization of Large Language Models (LLMs) to efficiently and rapidly extract information from earnings report transcripts while ensuring high accuracy transforming the extraction process as well as reducing hallucination by combining retrieval-augmented generation technique as well as metadata. We evaluate the outcomes of various LLMs with and without using our proposed approach based on various objective metrics for evaluating Q\&A systems, and empirically demonstrate superiority of our method., Comment: 4 pages + references. Accepted for publication in Workshop on Generative AI at the 3rd International Conference on AI-ML Systems 2023, Bengaluru, India

Using MUSE data, we investigate the radial gradients of stellar population properties (namely age, [M/H], and the abundance ratio of $\alpha$ elements [$\alpha$/Fe]) for a sample of nine dwarf early-type (dE) galaxies with log(M$_{\star}$/M$_{\odot}$) $\sim$ 9.0 and an infall time onto the Virgo cluster of 2-3Gyr ago. We followed a similar approach as in Bidaran et al. (2022) to derive their stellar population properties and star formation histories (SFHs) through fitting observed spectral indices and full spectral fitting, respectively. We find that these nine dE galaxies have truncated [Mg/Fe]vs.[Fe/H] profiles than equally-massive Virgo dE galaxies with longer past infall times. Short profiles of three dE galaxies are the result of their intense star formation which has been quenched long before their accretion onto the Virgo cluster, possibly as a result of their group environment. In the remaining six dE galaxies, profiles mainly trace a recent episode of star burst within 0.4R$_{\rm e}$ which results in higher light-weighted [$\alpha$/Fe] values. The latter SFH peak can be due to ram pressure exerted by the Virgo cluster at the time of the accretion of the dE galaxies. Also, we show that younger, more metal-rich and less $\alpha$-enhanced stellar populations dominate their inner regions (i.e., < 0.4R$_{\rm e}$) resulting in mainly flat $\nabla_{\rm age}$, negative $\nabla_{\rm [M/H]}$ and positive $\nabla_{\rm [\alpha/Fe]}$. We find that with increasing log($\sigma_{\rm Re}$) of dE galaxies, $\nabla_{\rm age}$ and $\nabla_{\rm [\alpha/Fe]}$ flatten, and the latter correlation persists even after including early-type galaxies up to log($\sigma_{\rm Re}$ $\sim$ 2.5), possibly due to the more extended star formation activity in the inner regions of dEs, as opposed to more massive early-type galaxies., Comment: 18 pages, 8 figures, accepted in MNRAS

An investigation of the quasiprojectile breakup channel in semiperipheral and peripheral collisions of $^{58,64}$Ni+$^{58,64}$Ni at 32 and 52 MeV/nucleon is presented. Data have been acquired in the first experimental campaign of the INDRA-FAZIA apparatus in GANIL. The effect of isospin diffusion between projectile and target in the two asymmetric reactions has been highlighted by means of the isospin transport ratio technique, exploiting the neutron-to-proton ratio of the quasiprojectile reconstructed from the two breakup fragments. We found evidence that, for the same reaction centrality, a higher degree of relaxation of the initial isospin imbalance is achieved in the breakup channel with respect to the more populated binary output, possibly indicating the indirect selection of specific dynamical features. We have proposed an interpretation based on different average projectile-target contact times related to the two exit channels under investigation, with a longer interaction for the breakup channel. The time information has been extracted from AMD simulations of the studied systems coupled to GEMINI++: the model calculations support the hypothesis hereby presented.

Using a sample of $>200$ clusters, each with typically $100-200$ spectroscopically confirmed cluster members, we search for a signal of alignment between the Position Angle (PA) of the Brightest Cluster Galaxy (BCG) and the distribution of cluster members on the sky about the cluster centre out to projected distances of 3~R$_{200}$. The deep spectroscopy, combined with corrections for spectroscopic incompleteness, makes our sample ideal to determine alignment signal strengths. We also use an SDSS based skeleton of the filamentary Large Scale Structure (LSS), and measure BCG alignment with the location of the LSS skeleton segments on the sky out to projected distances of 10~R$_{200}$. The alignment signal is measured using three separate statistical measures; Rao's spacing test (U), Kuiper's V parameter (V), and the Binomial probability test (P). The significance of the BCG alignment signal with both cluster members and LSS segments is extremely high (1 in a million chance or less to be drawn randomly from a uniform distribution). We investigate a wide set of parameters that may influence the strength of the alignment signal. Clusters with more elliptical-shaped BCGs show stronger alignment with both their cluster members and LSS segments. Also, selecting clusters with closely connected filaments, or using a luminosity-weighted LSS skeleton, increases the alignment signal significantly. Alignment strength decreases with increasing projected distance. Combined, these results provide strong evidence for the growth of clusters and their BCGs by preferential feeding along the direction of the filaments in which they are embedded., Comment: 21 pages, 5 tables, 5 figures ,Accepted to MNRAS, August 2023

Identifying companies with similar profiles is a core task in finance with a wide range of applications in portfolio construction, asset pricing and risk attribution. When a rigorous definition of similarity is lacking, financial analysts usually resort to 'traditional' industry classifications such as Global Industry Classification System (GICS) which assign a unique category to each company at different levels of granularity. Due to their discrete nature, though, GICS classifications do not allow for ranking companies in terms of similarity. In this paper, we explore the ability of pre-trained and finetuned large language models (LLMs) to learn company embeddings based on the business descriptions reported in SEC filings. We show that we can reproduce GICS classifications using the embeddings as features. We also benchmark these embeddings on various machine learning and financial metrics and conclude that the companies that are similar according to the embeddings are also similar in terms of financial performance metrics including return correlation., Comment: 8 pages, 2 figures, 2 tables

Mutual fund categorization has become a standard tool for the investment management industry and is extensively used by allocators for portfolio construction and manager selection, as well as by fund managers for peer analysis and competitive positioning. As a result, a (unintended) miscategorization or lack of precision can significantly impact allocation decisions and investment fund managers. Here, we aim to quantify the effect of miscategorization of funds utilizing a machine learning based approach. We formulate the problem of miscategorization of funds as a distance-based outlier detection problem, where the outliers are the data-points that are far from the rest of the data-points in the given feature space. We implement and employ a Random Forest (RF) based method of distance metric learning, and compute the so-called class-wise outlier measures for each data-point to identify outliers in the data. We test our implementation on various publicly available data sets, and then apply it to mutual fund data. We show that there is a strong relationship between the outlier measures of the funds and their future returns and discuss the implications of our findings., Comment: 8 pages, 5 tables, 8 figures

We measure the [${\alpha}$/Fe] abundances for 183 quiescent galaxies at z = 0.60 - 0.75 with stellar masses ranging 10.4 \leq log10 10.4 $\leq$ log10 (M$_*$ /M$_\odot$) $\leq$ 11.6 selected from the LEGA-C survey. We estimate [${\alpha}$/Fe] from the ratio of the spectral indices Mgb (${\lambda} \sim 5177$ {\AA}) and Fe4383, compared to predictions of simple stellar population models. We find that 91% of quiescents in our sample have supersolar [${\alpha}$/Fe], with an average value of [${\alpha}$/Fe] = +0.24 $\pm$ 0.01. We find no significant correlation between [${\alpha}$/Fe] and stellar metallicity, mass, velocity dispersion, and average formation time. Galaxies that formed the bulk of their stellar mass on time scales shorter than 1 Gyr follow the same [${\alpha}$/Fe] distribution as those which formed on longer time scales. In comparison to local early-type galaxies and to stacked spectra of quiescent galaxies at z = 0.38 and z = 0.07, we find that the average [${\alpha}$/Fe] has not changed between z = 0.75 and the present time. Our work shows that the vast majority of massive quiescent galaxies at z $\sim$ 0.7 are ${\alpha}$-enhanced, and that no detectable evolution of the average [${\alpha}$/Fe] has taken place over the last $\sim$ 6.5 Gyr., Comment: 12 pages, 10 figures, 2 tables, 1 appendix