Search

Among the most puzzling early discoveries of JWST are "Little Red Dots" -- compact red sources that host broad Balmer emission lines and, in many cases, exhibit a "V shaped" change in slope in the rest-optical. The physical properties of Little Red Dots currently have order-of-magnitude uncertainties, because models to explain the continuum of these sources differ immensely. Here, we leverage the complete selection of red sources in the RUBIES program, supplemented with public PRISM spectra, to study the origin of this "V shape". By fitting a broken power law with a flexible inflection point, we find that a large fraction (20/44, nearly all spatially unresolved) of extremely red H$\alpha$ emitters at $2

This paper presents a methodology for training a transformer-based model to classify lexical and morphosyntactic features of Skolt Sami, an endangered Uralic language characterized by complex morphology. The goal of our approach is to create an effective system for understanding and analyzing Skolt Sami, given the limited data availability and linguistic intricacies inherent to the language. Our end-to-end pipeline includes data extraction, augmentation, and training a transformer-based model capable of predicting inflection classes. The motivation behind this work is to support language preservation and revitalization efforts for minority languages like Skolt Sami. Accurate classification not only helps improve the state of Finite-State Transducers (FSTs) by providing greater lexical coverage but also contributes to systematic linguistic documentation for researchers working with newly discovered words from literature and native speakers. Our model achieves an average weighted F1 score of 1.00 for POS classification and 0.81 for inflection class classification. The trained model and code will be released publicly to facilitate future research in endangered NLP., Comment: IWCLUL 2024

Understanding the tail behavior of distributions is crucial in statistical theory. For instance, the tail of a distribution plays a ubiquitous role in extreme value statistics, where it is associated with the likelihood of extreme events. There are several ways to characterize the tail of a distribution based on how the tail function, $\bar{F}(x) = P(X>x)$, behaves when $x\to\infty$. However, for unimodal distributions, where does the core of the distribution end and the tail begin? This paper addresses this unresolved question and explores the usage of delimiting points obtained from the derivatives of the density function of continuous random variables, namely, the inflection point and the point of maximum curvature. These points are used to delimit the bulk of the distribution from its tails. We discuss the estimation of these delimiting points and compare them with other measures associated with the tail of a distribution, such as the kurtosis and extreme quantiles. We derive the proposed delimiting points for several known distributions and show that it can be a reasonable criterion for defining the starting point of the tail of a distribution., Comment: 21 pages, 17 figures, 2 tables

Linguistic fieldwork is an important component in language documentation and preservation. However, it is a long, exhaustive, and time-consuming process. This paper presents a novel model that guides a linguist during the fieldwork and accounts for the dynamics of linguist-speaker interactions. We introduce a novel framework that evaluates the efficiency of various sampling strategies for obtaining morphological data and assesses the effectiveness of state-of-the-art neural models in generalising morphological structures. Our experiments highlight two key strategies for improving the efficiency: (1) increasing the diversity of annotated data by uniform sampling among the cells of the paradigm tables, and (2) using model confidence as a guide to enhance positive interaction by providing reliable predictions during annotation.

In this paper, we investigate the behaviour of a minimally coupled tachyonic scalar field at inflection points in an accelerating universe. We consider the different expansion factors and obtain potentials of tachyonic scalar field. Inflection points of homogeneous tachyonic scalar field is calculated for these potentials. We employ the tools of dynamical system analysis for the considered potentials and obtain the stable points.

The emergence of Large Language Models (LLMs) has renewed the debate on the important issue of "technology displacement". While prior research has investigated the effect of information technology in general on human labor from a macro perspective, this paper complements the literature by examining the impact of LLMs on freelancers from a micro perspective. Specifically, we leverage the release of ChatGPT to investigate how AI influences freelancers across different online labor markets (OLMs). Employing the Difference-in-Differences method, we discovered two distinct scenarios following ChatGPT's release: 1) the displacement effect of LLMs, featuring reduced work volume and earnings, as is exemplified by the translation & localization OLM; 2) the productivity effect of LLMs, featuring increased work volume and earnings, as is exemplified by the web development OLM. To shed light on the underlying mechanisms, we developed a Cournot-type competition model to highlight the existence of an inflection point for each occupation which separates the timeline of AI progress into a honeymoon phase and a substitution phase. Before AI performance crosses the inflection point, human labor benefits each time AI improves, resulting in the honeymoon phase. However, after AI performance crosses the inflection point, additional AI enhancement hurts human labor. Further analyzing the progression from ChatGPT 3.5 to 4.0, we found three effect scenarios (i.e., productivity to productivity, displacement to displacement, and productivity to displacement), consistent with the inflection point conjecture. Heterogeneous analyses reveal that U.S. web developers tend to benefit more from the release of ChatGPT compared to their counterparts in other regions, and somewhat surprisingly, experienced translators seem more likely to exit the market than less experienced translators after the release of ChatGPT., Comment: 41 pages, 3 figures, 13 tables

Large language models (LLMs) are deep learning algorithms being used to perform natural language processing tasks in various fields, from social sciences to finance and biomedical sciences. Developing and training a new LLM can be very computationally expensive, so it is becoming a common practice to take existing LLMs and finetune them with carefully curated datasets for desired applications in different fields. Here, we present BioFinBERT, a finetuned LLM to perform financial sentiment analysis of public text associated with stocks of companies in the biotechnology sector. The stocks of biotech companies developing highly innovative and risky therapeutic drugs tend to respond very positively or negatively upon a successful or failed clinical readout or regulatory approval of their drug, respectively. These clinical or regulatory results are disclosed by the biotech companies via press releases, which are followed by a significant stock response in many cases. In our attempt to design a LLM capable of analyzing the sentiment of these press releases,we first finetuned BioBERT, a biomedical language representation model designed for biomedical text mining, using financial textual databases. Our finetuned model, termed BioFinBERT, was then used to perform financial sentiment analysis of various biotech-related press releases and financial text around inflection points that significantly affected the price of biotech stocks.

In his Theory of Light and Colours, presented to the Royal Society in November 1801, Thomas Young defended a mechanical explanation of the coloured fringes observed outside of the shadow of an opaque object-the so-called 'colours by inflection'-that was based on the hypothesis of an ethereal density gradient surrounding all material bodies. However, two years later, he publicly rejected that hypothesis, without giving much detail on his reasons. Now, although Geoffrey Cantor demonstrated the crucial role of mechanical and astronomical arguments in explaining the withdrawal of this fundamental hypothesis long ago, the purpose of this article is to draw deeper attention on a set of experiments performed by David Brewster on the inflection of light, described in a letter he addressed to the Royal Society in January 1802, but which kept retained in the hands of William Herschel and never reached its original destination. For the hypothesis that will be evaluated here is that these unpublished experiments of Brewster's were eventually known to Young through the mediation of William Herschel, and eventually played a significant role in his rejection of his own ether distribution hypothesis too., Comment: Accept{\'e} en octobre 2023 pour publication dans Annals of Science, revue publi{\'e}e par Taylor and Francis

We present an asymptotic and numerical study of the evolution of an incoming wavefield which has a caustic close to a curve with an inflection point. Our results reveal the emergence of a wavefield which resembles that of a shadow boundary but has a maximum amplitude along the tangent at the inflection point.

This brief reports the analytical modeling and measurements of the inflection in the MOSFET transfer characteristics at cryogenic temperatures. Inflection is the inward bending of the drain current versus gate voltage, which reduces the current in weak and moderate inversion at a given gate voltage compared to the drift-diffusion current. This phenomenon is explained by introducing a Gaussian distribution of localized states centered around the band edge. The localized states are attributed to disorder and interface traps. The proposed model allows to extract the density of localized states at the interface from the dc current measurements.

In this work, we investigate the physical aspects of the inflection-point inflation scenario and assess its observational viability in light of current Cosmic Microwave Background (CMB) data. The model we consider encapsulates the inflaton with a pseudo-scalar (the dark matter candidate) in a complex neutral scalar singlet. The cosmological constraints on the parameters of inflation derived at a high energy scale are translated to a low energy scale by running these parameters. Ensuring the entire Lagrangian to be invariant under a $Z_3$ symmetry with the adequate transformation of the fields, the imaginary part of the singlet decouples from the other scalars of the model. We then investigate if the observational viability of inflation is also compatible with this pseudo-scalar being the dark matter component.We show that the CMB constraints on the inflationary parameters assure that the pseudo-scalar is stable and provides the correct relic dark matter abundance only when the pseudo-scalar is thermally produced., Comment: This manuscript contains 17 pages, 17 figures and 3 tables. Version accepted for publication

When translating words referring to the speaker, speech translation (ST) systems should not resort to default masculine generics nor rely on potentially misleading vocal traits. Rather, they should assign gender according to the speakers' preference. The existing solutions to do so, though effective, are hardly feasible in practice as they involve dedicated model re-training on gender-labeled ST data. To overcome these limitations, we propose the first inference-time solution to control speaker-related gender inflections in ST. Our approach partially replaces the (biased) internal language model (LM) implicitly learned by the ST decoder with gender-specific external LMs. Experiments on en->es/fr/it show that our solution outperforms the base models and the best training-time mitigation strategy by up to 31.0 and 1.6 points in gender accuracy, respectively, for feminine forms. The gains are even larger (up to 32.0 and 3.4) in the challenging condition where speakers' vocal traits conflict with their gender., Comment: Accepted at EMNLP 2023

The frozen mode regime is a unique slow-light scenario in periodic structures, where the flat-bands (zero group velocity) are associated with the formation of high-order stationary points (aka exceptional points). The formation of exceptional points is accompanied by enhancement of various optical properties such as gain, Q-factor and absorption, which are key properties for the realization of wide variety of devices such as switches, modulators and lasers. Here we present and study a new integrated optical periodic structure consisting of three waveguides coupled via micro-cavities and directional coupler. We study this design theoretically, demonstrating that a proper choice of parameters yields a third order stationary inflection point (SIP). We also show that the structure can be designed to exhibit two almost-overlapping SIPs at the center of the Brillouin Zone. We study the transmission and reflection of light propagating through realistic devices comprising a finite number of unit-cells and investigate their spectral properties in the vicinity of the stationary points. Finally, we analyze the lasing frequencies and threshold level of finite structures (as a function of the number of unit-cells) and show that it outperforms conventional lasers utilizing regular band edge lasing (such as DFB lasers)., Comment: 11 pages, 10 figures

A stationary inflection point (SIP) in the Bloch dispersion relation of a periodic waveguide is an exceptional point degeneracy where three Bloch eigenmodes coalesce forming the so-called frozen mode with a divergent amplitude and vanishing group velocity of its propagating component. We have developed a theoretical framework to study the time evolution of wavepackets centered at an SIP. Analysis of the evolution of statistical moments distribution of linear pulses shows a strong deviation from the conventional ballistic wavepacket dynamics in dispersive media. The presence of nonlinear interactions dramatically changes the situation, resulting in a mostly ballistic propagation of nonlinear wavepackets with the speed and even the direction of propagation essentially dependent on the wavepacket amplitude. Such a behavior is unique to nonlinear wavepackets centered at an SIP and can be used for the realization of a novel family of beam power routers for classical waves., Comment: 9 pages, 5 figures

Recent years have brought great advances into solving morphological tasks, mostly due to powerful neural models applied to various tasks as (re)inflection and analysis. Yet, such morphological tasks cannot be considered solved, especially when little training data is available or when generalizing to previously unseen lemmas. This work explores effects on performance obtained through various ways in which morphological models get access to subcharacter phonological features that are the targets of morphological processes. We design two methods to achieve this goal: one that leaves models as is but manipulates the data to include features instead of characters, and another that manipulates models to take phonological features into account when building representations for phonemes. We elicit phonemic data from standard graphemic data using language-specific grammars for languages with shallow grapheme-to-phoneme mapping, and we experiment with two reinflection models over eight languages. Our results show that our methods yield comparable results to the grapheme-based baseline overall, with minor improvements in some of the languages. All in all, we conclude that patterns in character distributions are likely to allow models to infer the underlying phonological characteristics, even when phonemes are not explicitly represented., Comment: ACL 2023 main conference; 8 pages, 1 figure

With a growing focus on morphological inflection systems for languages where high-quality data is scarce, training data noise is a serious but so far largely ignored concern. We aim at closing this gap by investigating the types of noise encountered within a pipeline for truly unsupervised morphological paradigm completion and its impact on morphological inflection systems: First, we propose an error taxonomy and annotation pipeline for inflection training data. Then, we compare the effect of different types of noise on multiple state-of-the-art inflection models. Finally, we propose a novel character-level masked language modeling (CMLM) pretraining objective and explore its impact on the models' resistance to noise. Our experiments show that various architectures are impacted differently by separate types of noise, but encoder-decoders tend to be more robust to noise than models trained with a copy bias. CMLM pretraining helps transformers, but has lower impact on LSTMs., Comment: ACL 2023 Findings

Morphological inflection is a popular task in sub-word NLP with both practical and cognitive applications. For years now, state-of-the-art systems have reported high, but also highly variable, performance across data sets and languages. We investigate the causes of this high performance and high variability; we find several aspects of data set creation and evaluation which systematically inflate performance and obfuscate differences between languages. To improve generalizability and reliability of results, we propose new data sampling and evaluation strategies that better reflect likely use-cases. Using these new strategies, we make new observations on the generalization abilities of current inflection systems., Comment: To appear at ACL 2023

Data augmentation techniques are widely used in low-resource automatic morphological inflection to overcome data sparsity. However, the full implications of these techniques remain poorly understood. In this study, we aim to shed light on the theoretical aspects of the prominent data augmentation strategy StemCorrupt (Silfverberg et al., 2017; Anastasopoulos and Neubig, 2019), a method that generates synthetic examples by randomly substituting stem characters in gold standard training examples. To begin, we conduct an information-theoretic analysis, arguing that StemCorrupt improves compositional generalization by eliminating spurious correlations between morphemes, specifically between the stem and the affixes. Our theoretical analysis further leads us to study the sample efficiency with which StemCorrupt reduces these spurious correlations. Through evaluation across seven typologically distinct languages, we demonstrate that selecting a subset of datapoints with both high diversity and high predictive uncertainty significantly enhances the data-efficiency of StemCorrupt. However, we also explore the impact of typological features on the choice of the data selection strategy and find that languages incorporating a high degree of allomorphy and phonological alternations derive less benefit from synthetic examples with high uncertainty. We attribute this effect to phonotactic violations induced by StemCorrupt, emphasizing the need for further research to ensure optimal performance across the entire spectrum of natural language morphology., Comment: EMNLP 2023 camera-ready

Transformer-based encoder-decoder models that generate outputs in a left-to-right fashion have become standard for sequence-to-sequence tasks. In this paper, we propose a framework for decoding that produces sequences from the "outside-in": at each step, the model chooses to generate a token on the left, on the right, or join the left and right sequences. We argue that this is more principled than prior bidirectional decoders. Our proposal supports a variety of model architectures and includes several training methods, such as a dynamic programming algorithm that marginalizes out the latent ordering variable. Our model sets state-of-the-art (SOTA) on the 2022 and 2023 shared tasks, beating the next best systems by over 4.7 and 2.7 points in average accuracy respectively. The model performs particularly well on long sequences, can implicitly learn the split point of words composed of stem and affix, and performs better relative to the baseline on datasets that have fewer unique lemmas (but more examples per lemma).

Maltese is often described as having a hybrid morphological system resulting from extensive contact between Semitic and Romance language varieties. Such a designation reflects an etymological divide as much as it does a larger tradition in the literature to consider concatenative and non-concatenative morphological patterns as distinct in the language architecture. Using a combination of computational modeling and information theoretic methods, we quantify the extent to which the phonology and etymology of a Maltese singular noun may predict the morphological process (affixal vs. templatic) as well as the specific plural allomorph (affix or template) relating a singular noun to its associated plural form(s) in the lexicon. The results indicate phonological pressures shape the organization of the Maltese lexicon with predictive power that extends beyond that of a word's etymology, in line with analogical theories of language change in contact., Comment: Presented at the Annual Meeting of the Society for Computation in Linguistics 2023 (SCiL 2023)