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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

In this paper we consider three-dimensional steady water waves with vorticity, under the action of gravity and surface tension; in particular we consider so-called Beltrami flows, for which the velocity field and the vorticity are collinear. We discuss a recent variational formulation of the problem which involves a generalisation of the classical Dirichlet-Neumann operator. We study this operator in detail, extending some well-known results for the classical Dirichlet-Neumann operator, such as the Taylor expansion in homogeneous powers of the wave profile, the computation of its differential and the asymptotic expansion of its associated symbol. A new formulation of the problem as a single equation for the wave profile is also presented and discussed in a similar vein. As an application of these results we rigorously prove existence of doubly periodic gravity-capillary steady waves and construct approximate doubly periodic gravity steady waves.

Large language models (LLMs) have significantly advanced the field of natural language processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of applications. However, directly applying LLMs to solve sophisticated problems in specific domains meets many hurdles, caused by the heterogeneity of domain data, the sophistication of domain knowledge, the uniqueness of domain objectives, and the diversity of the constraints (e.g., various social norms, cultural conformity, religious beliefs, and ethical standards in the domain applications). Domain specification techniques are key to make large language models disruptive in many applications. Specifically, to solve these hurdles, there has been a notable increase in research and practices conducted in recent years on the domain specialization of LLMs. This emerging field of study, with its substantial potential for impact, necessitates a comprehensive and systematic review to better summarize and guide ongoing work in this area. In this article, we present a comprehensive survey on domain specification techniques for large language models, an emerging direction critical for large language model applications. First, we propose a systematic taxonomy that categorizes the LLM domain-specialization techniques based on the accessibility to LLMs and summarizes the framework for all the subcategories as well as their relations and differences to each other. Second, we present an extensive taxonomy of critical application domains that can benefit dramatically from specialized LLMs, discussing their practical significance and open challenges. Last, we offer our insights into the current research status and future trends in this area.

The LHCb upgrade represents a major change of the experiment. The detectors have been almost completely renewed to allow running at an instantaneous luminosity five times larger than that of the previous running periods. Readout of all detectors into an all-software trigger is central to the new design, facilitating the reconstruction of events at the maximum LHC interaction rate, and their selection in real time. The experiment's tracking system has been completely upgraded with a new pixel vertex detector, a silicon tracker upstream of the dipole magnet and three scintillating fibre tracking stations downstream of the magnet. The whole photon detection system of the RICH detectors has been renewed and the readout electronics of the calorimeter and muon systems have been fully overhauled. The first stage of the all-software trigger is implemented on a GPU farm. The output of the trigger provides a combination of totally reconstructed physics objects, such as tracks and vertices, ready for final analysis, and of entire events which need further offline reprocessing. This scheme required a complete revision of the computing model and rewriting of the experiment's software., Comment: All figures and tables, along with any supplementary material and additional information, are available at http://lhcbproject.web.cern.ch/lhcbproject/Publications/LHCbProjectPublic/LHCb-DP-2022-002.html (LHCb public pages)

Textual health records of cancer patients are usually protracted and highly unstructured, making it very time-consuming for health professionals to get a complete overview of the patient's therapeutic course. As such limitations can lead to suboptimal and/or inefficient treatment procedures, healthcare providers would greatly benefit from a system that effectively summarizes the information of those records. With the advent of deep neural models, this objective has been partially attained for English clinical texts, however, the research community still lacks an effective solution for languages with limited resources. In this paper, we present the approach we developed to extract procedures, drugs, and diseases from oncology health records written in European Portuguese. This project was conducted in collaboration with the Portuguese Institute for Oncology which, besides holding over $10$ years of duly protected medical records, also provided oncologist expertise throughout the development of the project. Since there is no annotated corpus for biomedical entity extraction in Portuguese, we also present the strategy we followed in annotating the corpus for the development of the models. The final models, which combined a neural architecture with entity linking, achieved $F_1$ scores of $88.6$, $95.0$, and $55.8$ per cent in the mention extraction of procedures, drugs, and diseases, respectively.

We study analytically the dynamics of a $d$-dimensional Klein-Gordon lattice with periodic boundary conditions, for $d \leq 3$. We consider initial data supported on one low-frequency Fourier mode. We show that, in the continuous approximation, the resonant normal form of the system is given by a small-dispersion nonlinear Schr\"odinger (NLS) equation. By exploiting a result about the growth of Sobolev norms for solutions of small-dispersion NLS, we are able to describe an energy cascade phenomenon for the Klein-Gordon lattice, where part of the energy is transferred to modes associated to higher frequencies. Such phenomenon holds within the time-scale for which we can ensure the validity of the continuous approximation., Comment: Changes with respect to previous version: changes to the presentation of the main result in Section 2 and in Section 6, changes to Section 3 and Appendix A, some comments added, some typos corrected. arXiv admin note: text overlap with arXiv:1911.12648

Four different reactions, $^{32}$S+$^{12}$C and $^{20}$Ne+$^{12}$C at 25 and 50 MeV/nucleon, have been measured with the FAZIA detector capable of full isotopic identification of most forward emitted reaction products. Fragment multiplicities, angular distributions and energy spectra have been measured and compared with Monte Carlo simulations, i.e. the antisymmetrized molecular dynamics (AMD) and the heavy-ion phase space exploration (HIPSE) models. These models are combined with two different afterburner codes (HF$l$ and SIMON) to describe the decay of the excited primary fragments. In the case of AMD, the effect of including the clustering and inter-clustering processes to form bound particles and fragments is discussed. A clear confirmation of the role of cluster aggregation in the reaction dynamics and particle production for these light systems, for which the importance of the clustering process increases with bombarding energy, is obtained., Comment: 15 pages, 14 PDF figures

An analysis of the asymmetric reactions $^{40,48}$Ca+$^{12}$C at 25 and 40 MeV/nucleon is presented. Data have been collected with six modules of the FAZIA array. The analysis is focused on the breakup channel of sources produced in dissipative collisions, partially corresponding to incomplete fusion processes. The study has been performed both on detected fragments and on some resonances reconstructed by means of particle-fragment correlations, with a focus on the evolution of the breakup channel with the beam energy and the neutron content of the system, looking in particular at the relative velocity between the breakup fragments. Results show that also Carbon fragments reconstructed by means of particle correlations can be in large part interpreted as the light partner of a scission.

Carbon nanotube (CNT) wires approach copper's specific conductivity and surpass carbon fiber's strength, with further improvement anticipated with greater aspect ratios and incorporation of dopants with long-range structural order. Fullerenes assemble into multitudes of process-dependent supramolecular crystals and, while initially insulating, they become marginally conductive (up to 0.05 MSm$^{-1}$) and superconductive ($T_c=18^\circ$K with K and 28$^\circ$K with Rb) after doping. These were small (100's $\mu$m long), soft (hardness comparable to indium), and typically unaligned, which hindered development of fullerene based wires. Individual fullerenes were previously incorporated into CNT fibers, although randomly without self-assembly into supramolecules. Here, a simple variation in established CNT acid extrusion creates a fiber composed of uniaxial chains of aligned fullerene supramolecules, self-assembled between aligned few-walled CNT bundles. This will provide a testbed for novel fullerene wire transport and prospects in CNT wire advancement.

There is an emerging recognition that successful utilization of chiral degrees of freedom can bring new scientific and technological opportunities to diverse research areas. Hence, methods are being sought for creating artificial matter with controllable chirality in an uncomplicated and reproducible manner. Here, we report the development of two straightforward methods for fabricating wafer-scale chiral architectures of ordered carbon nanotubes (CNTs) with tunable and giant circular dichroism (CD). Both methods employ simple approaches, (i) mechanical rotation and (ii) twist-stacking, based on controlled vacuum filtration and do not involve any sophisticated nanofabrication processes. We used a racemic mixture of CNTs as the starting material, so the intrinsic chirality of chiral CNTs is not responsible for the observed chirality. In particular, by controlling the stacking angle and handedness in (ii), we were able to maximize the CD response and achieve a record-high deep-ultraviolet ellipticity of 40 $\pm$ 1 mdeg/nm. Our theoretical simulations using the transfer matrix method reproduce the salient features of the experimentally observed CD spectra and further predict that a film of twist-stacked CNTs with an optimized thickness will exhibit an ellipticity as high as 150 mdeg/nm. The created wafer-scale objects represent a new class of synthetic chiral matter consisting of ordered quantum wires whose macroscopic properties are governed by nanoscopic electronic signatures such as van Hove singularities. These artificial structures with engineered chirality will not only provide playgrounds for uncovering new chiral phenomena but also open up new opportunities for developing high-performance chiral photonic and optoelectronic devices., Comment: Main text: 4 figures, 21 pages; SI: 9 figures, 1 table, 11 pages

We derived stellar ages and metallicities [Z/H] for $\sim$70 passive early type galaxies (ETGs) selected from VANDELS survey over the redshift range 1.0$<$$z$$<$1.4 and stellar mass range 10$<$log(M$_*$/M$_\odot$)$<$11.6. We find significant systematics in their estimates depending on models and wavelength ranges considered. Using the full-spectrum fitting technique, we find that both [Z/H] and age increase with mass as for local ETGs. Age and metallicity sensitive spectral indices independently confirm these trends. According to EMILES models, for 67 per cent of the galaxies we find [Z/H]$>$0.0, a percentage which rises to $\sim$90 per cent for log(M$_*$/M$_\odot$)$>$11 where the mean metallicity is [Z/H]=0.17$\pm$0.1. A comparison with homogeneous measurements at similar and lower redshift does not show any metallicity evolution over the redshift range 0.0$11.0 host stellar populations with [Z/H]>0.05, formed over short timescales ($\Delta{t50}$$<$1 Gyr) at early epochs (t$_{form}$$<$2 Gyr), implying high star formation rates (SFR$>$100 M$_\odot$/yr) in high mass density regions (log($\Sigma_{1kpc}$)$>$10 M$_\odot$/kpc$^2$). This sharp picture tends to blur at lower masses: log(M$_*$/M$_\odot$)$\sim$10.6 galaxies can host either old stars with [Z/H]$<$0.0 or younger stars with [Z/H]$>$0.0, depending on the duration ($\Delta{t50}$) of the SF. The relations between galaxy mass, age and metallicities are therefore largely set up ab initio as part of the galaxy formation process. Mass, SFR and SF time-scale all contribute to shape up the stellar mass-metallicity relation with the mass that modulates metals retention., Comment: 18 pages + Appendix, 16 figures, 5 tables. Accepted for publication in MNRAS

We analyse the quenched fractions, gas content, and star formation histories of ~1200 satellite galaxies with $M_* \geq 5 \times 10^6~{\rm M}_\odot$ around 198 Milky Way- (MW) and Andromeda-like (M31) hosts in TNG50, the highest-resolution simulation of IllustrisTNG. Satellite quenched fractions are larger for smaller masses, for smaller distances to their host galaxy, and in the more massive M31-like compared to MW-like hosts. As satellites cross their host's virial radius, their gas content drops: most satellites within 300 kpc lack detectable gas reservoirs at $z=0$, unless they are massive like the Magellanic Clouds and M32. Nevertheless, their stellar assembly exhibits a large degree of diversity. On average, the cumulative star formation histories are more extended for brighter, more massive satellites with a later infall, and for those in less massive hosts. Based on these relationships, we can even infer infall periods for observedMWand M31 dwarfs: e.g. 0-4 Gyr ago for the Magellanic Clouds and Leo I, 4-8 and 0-2 Gyr ago for M32 and IC 10, respectively. Ram pressure stripping (in combination with tidal stripping) deprives TNG50 satellites of their gas reservoirs and ultimately quenches their star formation, even though only a few per cent of the present-day satellites around the 198 TNG50 MW/M31-like hosts appear as jellyfish. The typical time since quenching for currently quenched TNG50 satellites is $6.9^{+2.5}_{-3.3}~{\rm Gyr}$ ago. The TNG50 results are consistent with the quenched fractions and stellar assembly of observed MW and M31 satellites, however, satellites of the SAGA survey with $M_* \sim 10^{8-9}~{\rm M}_\odot$ exhibit lower quenched fractions than TNG50 and other, observed analogues., Comment: Accepted for publication in MNRAS

Understanding non-linear relationships among financial instruments has various applications in investment processes ranging from risk management, portfolio construction and trading strategies. Here, we focus on interconnectedness among stocks based on their correlation matrix which we represent as a network with the nodes representing individual stocks and the weighted links between pairs of nodes representing the corresponding pair-wise correlation coefficients. The traditional network science techniques, which are extensively utilized in financial literature, require handcrafted features such as centrality measures to understand such correlation networks. However, manually enlisting all such handcrafted features may quickly turn out to be a daunting task. Instead, we propose a new approach for studying nuances and relationships within the correlation network in an algorithmic way using a graph machine learning algorithm called Node2Vec. In particular, the algorithm compresses the network into a lower dimensional continuous space, called an embedding, where pairs of nodes that are identified as similar by the algorithm are placed closer to each other. By using log returns of S&P 500 stock data, we show that our proposed algorithm can learn such an embedding from its correlation network. We define various domain specific quantitative (and objective) and qualitative metrics that are inspired by metrics used in the field of Natural Language Processing (NLP) to evaluate the embeddings in order to identify the optimal one. Further, we discuss various applications of the embeddings in investment management., Comment: 8 pages, 2 column format, 3 figure, 7 tables

Using MUSE spectra, we investigate how pre-processing and accretion onto a galaxy cluster affect the integrated stellar population properties of dwarf early-type galaxies (dEs). We analyze a sample of nine dEs with stellar masses of $\rm \sim 10^9 \, M_\odot$, which were accreted ($\sim$ 2-3 Gyr ago) onto the Virgo cluster as members of a massive galaxy group. We derive their stellar population properties, namely age, metallicity ([M/H]), and the abundance ratio of $\alpha$ elements ([$\alpha$/Fe]), by fitting observed spectral indices with a robust, iterative procedure, and infer their star formation history (SFH) by means of full spectral fitting. We find that these nine dEs are more metal-poor (at the 2-3$\sigma$ level) and significantly more $\alpha$-enhanced than dEs in the Virgo and Coma clusters with similar stellar mass, cluster-centric distance, and infall time. Moreover, for six dEs, we find evidence for a recent episode of star formation during or right after the time of accretion onto Virgo. We interpret the high [$\alpha$/Fe] of our sample of dEs as the result of the previous exposure of these galaxies to an environment hostile to star formation, and/or the putative short burst of star formation they underwent after infall into Virgo. Our results suggest that the stellar population properties of low-mass galaxies may be the result of the combined effect of pre-processing in galaxy groups and environmental processes (such as ram-pressure triggering star formation) acting during the early phases of accretion onto a cluster., Comment: 17 pages, 10 figures, Accepted for publication in MNRAS

Categorization of mutual funds or Exchange-Traded-funds (ETFs) have long served the financial analysts to perform peer analysis for various purposes starting from competitor analysis, to quantifying portfolio diversification. The categorization methodology usually relies on fund composition data in the structured format extracted from the Form N-1A. Here, we initiate a study to learn the categorization system directly from the unstructured data as depicted in the forms using natural language processing (NLP). Positing as a multi-class classification problem with the input data being only the investment strategy description as reported in the form and the target variable being the Lipper Global categories, and using various NLP models, we show that the categorization system can indeed be learned with high accuracy. We discuss implications and applications of our findings as well as limitations of existing pre-trained architectures in applying them to learn fund categorization., Comment: 8 pages, 5 figures, 2-column format

Financial literature consists of ample research on similarity and comparison of financial assets and securities such as stocks, bonds, mutual funds, etc. However, going beyond correlations or aggregate statistics has been arduous since financial datasets are noisy, lack useful features, have missing data and often lack ground truth or annotated labels. However, though similarity extrapolated from these traditional models heuristically may work well on an aggregate level, such as risk management when looking at large portfolios, they often fail when used for portfolio construction and trading which require a local and dynamic measure of similarity on top of global measure. In this paper we propose a supervised similarity framework for corporate bonds which allows for inference based on both local and global measures. From a machine learning perspective, this paper emphasis that random forest (RF), which is usually viewed as a supervised learning algorithm, can also be used as a similarity learning (more specifically, a distance metric learning) algorithm. In addition, this framework proposes a novel metric to evaluate similarities, and analyses other metrics which further demonstrate that RF outperforms all other methods experimented with, in this work., Comment: A few minor typos corrected, 1 figure added. Conclusions unchanged. Matching with the accepted version

Atomically detailed simulations of RNA folding have proven very challenging in view of the difficulties of developing realistic force fields and the intrinsic computational complexity of sampling rare conformational transitions. To tackle both these issues, we extend to RNA an enhanced path sampling method previously successfully applied to proteins. In this scheme, the information about the RNA's native structure is harnessed by a soft history-dependent biasing force, which is added to the atomistic force field, thus promoting the generation of productive folding trajectories. Here, we report on the results of simulations in explicit solvent of RNA molecules from 20 to 47 nucleotides long and increasing topological complexity. From a statistical analysis of the folding pathways we infer that, differently from proteins, the underlying free energy landscape is significantly frustrated, even for relatively small chains with a simple topology. The folding mechanism and the thermodynamics are in agreement with the available experiments and some of the existing coarse-grained models. This scheme provides a fully microscopic characterization of RNA folding, relating the kinetics and dynamics of the transition to the chemistry of the chain and its solvent. Therefore, it provides a transferable framework that sets the stage for future translational applications.

An investigation of the isospin equilibration process in the reactions $^{58,64}$Ni+$^{58,64}$Ni at two bombarding energies in the Fermi regime ($32\,$MeV/nucleon and $52\,$MeV/nucleon) is presented. Data have been acquired during the first experimental campaign of the coupled INDRA-FAZIA apparatus in GANIL. Selecting from peripheral to semi-central collisions, both the neutron content of the quasiprojectile residue and that of the light ejectiles coming from the quasiprojectile evaporation have been used as probes of the dynamical process of isospin diffusion between projectile and target for the asymmetric systems. The isospin transport ratio technique has been employed. The relaxation of the initial isospin imbalance with increasing centrality has been clearly evidenced. The isospin equilibration appears stronger for the reactions at $32\,$MeV/nucleon, as expected due to the longer projectile-target interaction time than at $52\,$MeV/nucleon. Coherent indications of isospin equilibration come from the quasiprojectile residue characteristics and from particles ascribed to the quasiprojectile decay.

It has been well established that dwarf early-type galaxies (ETGs) can often exhibit a complex morphology, whereby faint spiral arms, bars, edge-on disks or clumps are embedded in their main, brighter diffuse body. In our first paper (Brought to Light I: Michea et al. 2021), we developed a new method for robustly identifying and extracting substructures in deep imaging data of dwarf ETGs in the Virgo galaxy cluster. Here we apply our method to a sample of 23 dwarf ETGs in the Fornax galaxy cluster, out of which 9 have disk-like and 14 have clump-like substructures. According to Fornax Deep Survey (FDS) data, our sample constitutes $12\%$ of all dwarf ETGs in Fornax brighter than $\text{M}_{r}=-13$ mag, and contains all cases that unequivocally exhibit substructure features. We use $g$ and $r$-band FDS images to measure the relative contribution of the substructures to the total galaxy light and to estimate their $g-r$ colors. We find that substructures typically contribute $8.7\%$ and $5.3\%$ of the total galaxy light in the $g$ and $r$ bands, respectively, within two effective radii. Disk substructures are usually found in dwarf ETGs with redder global colors, and they can be either as red as or bluer than their galaxy's diffuse component. In contrast, clump substructures are found in comparatively bluer dwarf ETGs, and they are always bluer than their galaxy's diffuse component. These results provide further evidence that dwarf ETGs can hide diverse complex substructures, with stellar populations that can greatly differ from those of the dominant diffuse light in which they are embedded., Comment: 35 pages, 12 figures, 5 tables. Accepted to AJ

The elementary quotient completion of an elementary doctrine in the sense of Lawvere was introduced in previous work by the first and third authors. It generalises the exact completion of a category with finite products and weak equalisers. In this paper we characterise when an elementary quotient completion is a quasi-topos. We obtain as a corollary a complete characterisation of when an elementary quotient completions is an elementary topos. As a byproduct we determine also when the elementary quotient completion of a tripos is equivalent to the doctrine obtained via the tripos-to-topos construction. Our results are reminiscent of other works regarding exact completions and put those under a common scheme: in particular, Carboni and Vitale's characterisation of exact completions in terms of their projective objects, Carboni and Rosolini's characterisation of locally cartesian closed exact completions, also in the revision by Emmenegger, and Menni's characterisation of the exact completions which are elementary toposes.

We present a novel approach to the riddle of star cluster multiple populations. Stars form from molecular cores. But not all cores form stars. Following their initial compression, such 'failed' cores re-expand, rather than collapsing. We propose that their formation and subsequent dispersal regulate the gas density of cluster-forming clumps and, therefore, their core and star formation rates. Clumps for which failed cores are the dominant core type experience star formation histories with peaks and troughs. In contrast, too few failed cores results in smoothly decreasing star formation rates. We identify three main parameters shaping the star formation history of a clump: the star and core formation efficiencies per free-fall time, and the time-scale on which failed cores return to the clump gas. The clump mass acts as a scaling factor. We use our model to constrain the density and mass of the Orion Nebula Cluster progenitor clump, and to caution that the star formation histories of starburst clusters may contain close-by peaks concealed by stellar age uncertainties. Our model generates a great variety of star formation histories. Intriguingly, the chromosome maps and O-Na anti-correlations of old globular clusters also present diverse morphologies. This prompts us to discuss our model in the context of globular cluster multiple stellar populations. More massive globular clusters exhibit stronger multiple stellar population patterns, which our model can explain if the formation of the polluting stars requires a given stellar mass threshold., Comment: 23 pages, 9 figures, accepted by ApJ

Substructural logics naturally support a quantitative interpretation of formulas, as they are seen as consumable resources. Distances are the quantitative counterpart of equivalence relations: they measure how much two objects are similar, rather than just saying whether they are equivalent or not. Hence, they provide the natural choice for modelling equality in a substructural setting. In this paper, we develop this idea, using the categorical language of Lawvere's doctrines. We work in a minimal fragment of Linear Logic enriched by graded modalities, which are needed to write a resource sensitive substitution rule for equality, enabling its quantitative interpretation as a distance. We introduce both a deductive calculus and the notion of Lipschitz doctrine to give it a sound and complete categorical semantics. The study of 2-categorical properties of Lipschitz doctrines provides us with a universal construction, which generates examples based for instance on metric spaces and quantitative realisability. Finally, we show how to smoothly extend our results to richer substructural logics, up to full Linear Logic with quantifiers.

A multi-layer trigger system, based on programmable logic devices hosted on VME boards, has been implemented. It is completely scalable and suitable for handling from a few up to a few hundred channels. Custom software for monitoring and controlling the trigger system has been developed., Comment: 23 pages, 4 figures, Published on Nuclear Instruments and Methods in Physics Research A. For associated software/firmware package see https://github.com/NUCLEX-Fi/FTBOX.git

We investigate how cosmic web environment impacts the average properties of central galaxies in the Sloan Digital Sky Survey (SDSS). We analyse how the average specific star-formation rate, stellar age, metallicity and element abundance ratio [$\alpha$/Fe] of SDSS central galaxies depend on distance from the cosmic web nodes, walls and filaments identified by DisPerSE. In our approach we control for galaxy stellar mass and local density differentiated between field and group environment. Our results confirm the known trend whereby galaxies exhibit lower specific star-formation rates with decreasing distance to the cosmic web features. Furthermore, we show that centrals closer to either nodes, walls or filaments are on average older, metal richer and $\alpha$-enhanced compared to their equal mass counterparts at larger distances. The identified property gradients appear to have the same amplitude for central galaxies in the field as for those in groups. Our findings support a cosmic web quenching that stems from nurture effects, such as ram pressure stripping and strangulation, and/or nature effects linked to the intrinsic properties of the cosmic web., Comment: 15 pages, 7 figures, 4 tables, accepted for publication in MNRAS

Printed electronics rely on the deposition of conductive liquid inks, typically onto polymeric or paper substrates. Among available conductive fillers for use in electronic inks, carbon nanotubes (CNTs) have high conductivity, low density, processability at low temperatures, and intrinsic mechanical flexibility. However, the electrical conductivity of printed CNT structures has been limited by CNT quality and concentration, and by the need for nonconductive modifiers to make the ink stable and extrudable. This study introduces a polymer-free, printable aqueous CNT ink, and presents the relationships between printing resolution, ink rheology, and ink-substrate interactions. A model is constructed to predict printed feature sizes on impermeable substrates based on Wenzel wetting. Printed lines have conductivity up to 10,000 S/m. The lines are flexible, with < 5% change in DC resistance after 1,000 bending cycles, and <3% change in DC resistance with a bending radius down to 1 mm. Demonstrations focus on (i) conformality, via printing CNTs onto stickers that can be applied to curved surfaces, (ii) interactivity using a CNT-based button printed onto folded paper structure, and (iii) capacitive sensing of liquid wicking into the substrate itself. Facile integration of surface mount components on printed circuits is enabled by the intrinsic adhesion of the wet ink., Comment: Published in Advanced Functional Materials

Charge transport in organic semiconductors is notoriously extremely sensitive to the presence of disorder, both internal and external (i.e. related to the interactions with the dielectric layer), especially for n-type materials. Internal dynamic disorder stems from large thermal fluctuations both in intermolecular transfer integrals and (molecular) site energies in weakly interacting van der Waals solids and sources transient localization of the charge carriers. The molecular vibrations that drive transient localization typically operate at low-frequency (< a-few-hundred cm-1), which renders it difficult to assess them experimentally. Hitherto, this has prevented the identification of clear molecular design rules to control and reduce dynamic disorder. In addition, the disorder can also be external, being controlled by the gate insulator dielectric properties. Here we report on a comprehensive study of charge transport in two closely related n-type molecular organic semiconductors using a combination of temperature-dependent inelastic neutron scattering and photoelectron spectroscopy corroborated by electrical measurements, theory and simulations. We provide unambiguous evidence that ad hoc molecular design enables to free the electron charge carriers from both internal and external disorder to ultimately reach band-like electron transport., Comment: arXiv admin note: text overlap with arXiv:1909.05344

Using new, homogeneous, long-slit spectroscopy in the wavelength range from ~0.35 to ~1micron, we study radial gradients of optical and near-infrared (NIR) IMF-sensitive features along the major axis of the bulge of M31, out to a galacto-centric distance of ~200'' (~800pc). Based on state-of-the-art stellar population synthesis models with varying Na abundance ratio, we fit a number of spectral indices, from different chemical species (including TiO's, Ca, and Na indices), to constrain the low-mass (<0.5M_Sun) end slope (i.e. the fraction of low-mass stars) of the stellar IMF, as a function of galacto-centric distance. Outside a radial distance of ~10'', we infer an IMF similar to a Milky-Way-like distribution, while at small galacto-centric distances, an IMF radial gradient is detected, with a mildly bottom-heavy IMF in the few inner arcsec. We are able to fit Na features (both NaD and NaI8190), without requiring extremely high Na abundance ratios. [Na/Fe] is ~0.4dex for most of the bulge, rising up to ~0.6dex in the innermost radial bins. Our results imply an overall, luminosity-weighted, IMF and mass-to-light ratio for the M31 bulge, consistent with those for a Milky-Way-like distribution, in contrast to results obtained, in general, for most massive early-type galaxies., Comment: accepted for publication in MNRAS, 22 pages, 16 figures

In our companion paper (Brought to Light I: Michea et al. 2021), we reveal spectacular spiral galaxy-like features in deep optical imaging of nine Virgo early-type dwarf galaxies, hidden beneath a dominating smooth stellar disk. Using a new combination of approaches, we find that bar- and spiral-like features contribute 2.2-6.4% of the total flux within 2 R$_{\rm{eff}}$. In this study, we conduct high resolution simulations of cluster harassment of passive dwarf galaxies. Following close pericenter passages of the cluster core, tidal triggering generates features in our model disks that bear a striking resemblance to the observed features. However, we find the disks must be highly rotationally supported (V$_{\rm{peak}}/\sigma_0 \sim 3$), much higher than typically observed. We propose that some early-type dwarfs may contain a few percent of their mass in a cold, thin disk which is buried in the light of a hot, diffuse disk, and only revealed when they undergo tidal triggering. The red optical colors of our sample do not indicate any recent significant star formation, and our simulations show that very plunging pericenter passages (r$_{\rm{peri}}<0.25$r$_{\rm{vir}}$) are required for tidal triggering. Thus, many cluster early-type dwarfs with less plunging orbits may host a yet undetected cold stellar disk component. We discuss possible origin scenarios and consider why similar mass star-forming galaxies in the field are significantly more thin disk dominated than in our cluster sample., Comment: 20 pages, 12 figures, 2 tables. Accepted to ApJ. Companion paper: Brought to Light I (Michea et al. 2021) accepted to AJ, also listed on arXiv today (1st of April, 2021)

Dwarf early-type galaxies (ETGs) display a rich diversity in their photometric, structural, and dynamical properties. In this work, we address their structural complexity by studying with deep imaging a sample of nine dwarf ETGs from the Virgo galaxy cluster, characterized by having faint disk features, such as bars and spiral arms, which lie mostly hidden within the bright diffuse light of the galaxies. We present a new, robust method that aims to identify and extract the disk substructure embedded in these dwarf ETGs. The method consists in an iterative procedure that gradually separates a galaxy image into two components; the bright, dominant diffuse component, and the much fainter, underlying disk component. By applying it to the dwarf ETG sample, we quantify their disk substructure and find that its relative contribution to the total galaxy light ranges between 2.2 to 6.4% within two effective radii. We test the reliability of the method, and prove that it is accurate in recovering the disk substructure we introduce in mock galaxy images, even at low disk-to-total light fractions of a few percent. As a potential application of the method, we perform a Fourier analysis on the extracted disk substructures and measure the orientation, length, and strength of the bars, and the pitch angle and strength of the spiral arms. We also briefly discuss a scenario based on the numerical simulations presented in our companion paper, Brought to Light II: Smith et al. 2021, in which we investigate the origins of the substructure in such dwarf systems., Comment: 31 pages, 15 figures, 6 tables. Accepted to AJ. Companion paper: Brought to Light II (Smith et al. 2021) accepted to ApJ, also listed on arXiv today (1st of April, 2021)