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We analyse the stellar distributions on the [Fe/H]-[Mg/Fe] plane for 11 Milky Way-mass galaxies from the FIRE-2 cosmological baryonic zoom-in simulations. Alpha-element bimodality, in the form of two separate sequences on the [Fe/H]-[Mg/Fe] plane, is not a universal feature of disk galaxies. Five galaxies demonstrate double sequences with the $\alpha$-enriched one being older and kinematically hotter, in qualitative agreement with the high-$\alpha$ and low-$\alpha$ populations in the Milky Way disk; three galaxies have unimodal distribution, two show weakly-bimodal features where low-$\alpha$ sequence is visible only over a short range of metallicities, and one show strong bimodality with a different slope of high-$\alpha$ population. We examine the galaxies' gas accretion history over the last 8 Gyr, when bimodal sequences emerge, and demonstrate that the presence of the low-$\alpha$ sequence in the bimodal galaxies is related to the recent infall of metal-poor gas from the circumgalactic medium that joins the galaxy in the outskirts and induces significant growth of the gas disks compared to their non-bimodal counterparts. We also analyse the sources of the accreted gas and illustrate that both gas-rich mergers and smooth accretion of ambient gas can be the source of the accreted gas, and create slightly different bimodal patterns.

In some recent papers, researchers have found two very good methods for reordering columns within supernodes in sparse Cholesky factors; these reorderings can be very useful for certain factorization methods. The first of these reordering methods is based on modeling the underlying problem as a traveling salesman problem (TSP), and the second of these methods is based on partition refinement (PR). In this paper, we devise a fair way to compare the two methods. While the two methods are virtually the same in the quality of the reorderings that they produce, PR should be the method of choice because PR reorderings can be computed using far less time and storage than TSP reorderings.

Detection of the weak cosmological signal from high-redshift hydrogen demands careful data analysis and an understanding of the full instrument signal chain. Here we use the WODEN simulation pipeline to produce realistic data from the Murchison Widefield Array Epoch of Reionisation experiment, and test the effects of different instrumental systematics through the AusEoRPipe analysis pipeline. The simulations include a realistic full sky model, direction-independent calibration, and both random and systematic instrumental effects. Results are compared to matched real observations. We find that, (i) with a sky-based calibration and power spectrum approach we have need to subtract more than 90% of all unresolved point source flux (10mJy apparent) to recover 21-cm signal in the absence of instrumental effects; (ii) when including diffuse emission in simulations, some k-modes cannot be accessed, leading to a need for some diffuse emission removal; (iii) the single greatest cause of leakage is an incomplete sky model; (iv) other sources of errors, such as cable reflections, flagged channels and gain errors, impart comparable systematic power to one another, and less power than the incomplete skymodel., Comment: 13 pages, 11 figures, 1 table Accepted for publication to PASA

With recent advances in Large Language Models (LLMs), Agentic AI has become phenomenal in real-world applications, moving toward multiple LLM-based agents to perceive, learn, reason, and act collaboratively. These LLM-based Multi-Agent Systems (MASs) enable groups of intelligent agents to coordinate and solve complex tasks collectively at scale, transitioning from isolated models to collaboration-centric approaches. This work provides an extensive survey of the collaborative aspect of MASs and introduces an extensible framework to guide future research. Our framework characterizes collaboration mechanisms based on key dimensions: actors (agents involved), types (e.g., cooperation, competition, or coopetition), structures (e.g., peer-to-peer, centralized, or distributed), strategies (e.g., role-based or model-based), and coordination protocols. Through a review of existing methodologies, our findings serve as a foundation for demystifying and advancing LLM-based MASs toward more intelligent and collaborative solutions for complex, real-world use cases. In addition, various applications of MASs across diverse domains, including 5G/6G networks, Industry 5.0, question answering, and social and cultural settings, are also investigated, demonstrating their wider adoption and broader impacts. Finally, we identify key lessons learned, open challenges, and potential research directions of MASs towards artificial collective intelligence.

Single-enzyme catalysis offers a promising approach for unravelling the dynamic behaviour of individual enzymes as they undergo a reaction, revealing the complex heterogeneity that is lost in the averaged ensembles. Here we demonstrate real-time, label-free monitoring of the electrical transduction of single-protein enzymatic activity for two redox enzymes, cytochrome P450cam and glutathione reductase, trapped in an electrochemically controlled nanoscale tunnelling junction immersed in the aqueous enzymatic mixture. The conductance switching signal observed in individual transients of the electrical current flowing through the single-protein junction shows that the tunnelling conductance is modulated by the enzymatic reaction; subtle changes of the enzyme redox state occurring during the chemical catalysis process result in fluctuations of the enzyme junction conductivity, which are captured as a switching signal. At the applied electrochemical reducing potential for electrocatalysis, the transient oxidation of the trapped enzyme in every catalytic cycle opens an additional redox-mediated electron tunnelling channel in the single protein junction that results in a temporary current jump, contributing to the observed conductance switching features. The latter is experimentally assessed via electrochemically controlled conductance measurements of the single-protein junction. The statistical analysis of the switching events captured over long time periods results in average frequencies that correlate well with the reported catalytic turnover values of both enzymes obtained in standard bulk assays. The single-enzyme experiments reveal the acute heterogenous behaviour of enzymatic catalysis and the quantification of single enzyme turnover frequencies.

Negative binomial (NB) regression is a popular method for identifying differentially expressed genes in genomics data, such as bulk and single-cell RNA sequencing data. However, NB regression makes stringent parametric and asymptotic assumptions, which can fail to hold in practice, leading to excess false positive and false negative results. We propose the permuted score test, a new strategy for robust regression based on permuting score test statistics. The permuted score test provably controls type-I error across a much broader range of settings than standard NB regression while nevertheless approximately matching standard NB regression with respect to power (when the assumptions of standard NB regression obtain) and computational efficiency. We accelerate the permuted score test by leveraging emerging techniques for sequential Monte-Carlo testing and novel algorithms for efficiently computing GLM score tests. We apply the permuted score test to real and simulated RNA sequencing data, finding that it substantially improves upon the error control of existing NB regression implementations, including DESeq2. The permuted score test could enhance the reliability of differential expression analysis across diverse biological contexts.

Following the remarkable success of Large Language Models (LLMs) in NLP tasks, there is increasing interest in extending their capabilities to speech -- the most common form in communication. To integrate speech into LLMs, one promising approach is dense feature prepending (DFP) which prepends the projected speech representations to the textual representations, allowing end-to-end training with the speech encoder. However, DFP typically requires connecting a text decoder to a speech encoder. This raises questions about the importance of having a sophisticated speech encoder for DFP, and how its performance compares with a standard encoder-decoder (i.e. cross-attention) architecture. In order to perform a controlled architectural comparison, we train all models from scratch, rather than using large pretrained models, and use comparable data and parameter settings, testing speech-to-text recognition (ASR) and translation (ST) on MuST-C v1.0 and CoVoST2 datasets. We study the influence of a speech encoder in DFP. More importantly, we compare DFP and cross-attention under a variety of configurations, such as CTC compression, sequence-level knowledge distillation, generation speed and GPU memory footprint on monolingual, bilingual and multilingual models. Despite the prevalence of DFP over cross-attention, our overall results do not indicate a clear advantage of DFP., Comment: Submitted to ARR October 2024

The testing and quality assurance of cryogenic superconducting detectors is a time- and labor-intensive process. As experiments deploy increasingly larger arrays of detectors, new methods are needed for performing this testing quickly. Here, we propose a process for flagging under-performing detector wafers before they are ever tested cryogenically. Detectors are imaged under an optical microscope, and computer vision techniques are used to analyze the images, searching for visual defects and other predictors of poor performance. Pipeline performance is verified via a suite of images with simulated defects, yielding a detection accuracy of 98.6%. Lastly, results from running the pipeline on prototype microwave kinetic inductance detectors from the planned SPT-3G+ experiment are presented., Comment: 24 pages, 14 figures. To be submitted to JINST

We examined the anomalies in the light curves of the lensing events MOA-2022-BLG-033, KMT-2023-BLG-0119, and KMT-2023-BLG-1896. We conducted detailed modeling of the light curves to uncover the nature of the anomalies. This modeling revealed that all signals originated from planetary companions to the primary lens. The planet-to-host mass ratios are very low: $q\sim 7.5\times 10^{-5}$ for MOA-2022-BLG-033, $q\sim 3.6\times 10^{-4}$ for KMT-2023-BLG-0119, and $q\sim 6.9\times 10^{-5}$ for KMT-2023-BLG-1896. The anomalies occurred as the source passed through the negative deviation region behind the central caustic along the planet-host axis. The solutions are subject to a common inner-outer degeneracy, resulting in variations in estimating the projected planet-host separation. For KMT-2023-BLG-1896, although the planetary scenario provides the best explanation of the anomaly, the binary companion scenario is marginally possible. We estimate the physical parameters of the planetary systems through Bayesian analyses based on the lensing observables. The analysis identifies MOA-2022-BLG-033L as a planetary system with an ice giant, approximately 12 times the mass of Earth, orbiting an early M dwarf star. The companion of KMT-2023-BLG-1896L is also an ice giant, with a mass around 16 Earth masses, orbiting a mid-K-type main-sequence star. The companion of KMT-2023-BLG-0119L, which has a mass about the mass of Saturn, orbits a mid-K-type dwarf star. The lens for MOA-2022-BLG-033 is highly likely to be located in the disk, whereas for the other events, the probabilities of the lens being in the disk or the bulge are roughly comparable., Comment: 9 pages, 10 figures, 6 tables

The matter power spectrum, $P(k)$, is one of the fundamental quantities in the study of large-scale structure in cosmology. Here, we study its small-scale asymptotic limit, and show that for cold dark matter in $d$ spatial dimensions, $P(k)$ has a universal $k^{-d}$ asymptotic scaling with the wave-number $k$, for $k \gg k_{\rm nl}$, where $k_{\rm nl}^{-1}$ denotes the length scale at which non-linearities in gravitational interactions become important. We propose a theoretical explanation for this scaling, based on a non-perturbative analysis of the system's phase-space structure. Gravitational collapse is shown to drive a turbulent phase-space flow of the quadratic Casimir invariant, where the linear and non-linear time scales are balanced, and this balance dictates the $k$ dependence of the power spectrum. A parallel is drawn to Batchelor turbulence in hydrodynamics, where large scales mix smaller ones via tidal interactions. The $k^{-d}$ scaling is also derived by expressing $P(k)$ as a phase-space integral in the framework of kinetic field theory, which is analysed by the saddle-point method; the dominant critical points of this integral are precisely those where the time scales are balanced. The coldness of the dark-matter distribution function -- its non-vanishing only on a $d$-dimensional sub-manifold of phase-space -- underpins both approaches. The theory is accompanied by $1\mathrm{D}$ Vlasov--Poisson simulations, which confirm it., Comment: Submitted, comments welcome

Information theory has provided foundations for the theories of several application areas critical for modern society, including communications, computer storage, and AI. A key aspect of Shannon's 1948 theory is a sharp lower bound on the number of bits needed to encode and communicate a string of symbols. When he introduced the theory, Shannon famously excluded any notion of semantics behind the symbols being communicated. This semantics-free notion went on to have massive impact on communication and computing technologies, even as multiple proposals for reintroducing semantics in a theory of information were being made, notably one where Carnap and Bar-Hillel used logic and reasoning to capture semantics. In this paper we present, for the first time, a Shannon-style analysis of a communication system equipped with a deductive reasoning capability, implemented using logical inference. We use some of the most important techniques developed in information theory to demonstrate significant and sometimes surprising gains in communication efficiency availed to us through such capability, demonstrated also through practical codes. We thus argue that proposals for a semantic information theory should include the power of deductive reasoning to magnify the value of transmitted bits as we strive to fully unlock the inherent potential of semantics.

Laser-based displays are highly sought after for their superior brightness and color performance, especially in advanced applications like augmented reality (AR). However, their broader adoption has been hindered by bulky projector designs and complex optical module assemblies. Here, we introduce a new laser display architecture enabled by large-scale visible photonic integrated circuits (PICs) to address these challenges. Unlike previous projector-style laser displays, this architecture features an ultra-thin, flat-panel form factor, replacing bulky free-space illumination modules with a single, high-performance photonic chip. Centimeter-scale PIC devices, which integrate thousands of distinct optical components on-chip, are carefully tailored to achieve high display uniformity, contrast, and efficiency. We demonstrate a 2 mm-thick flat-panel laser display combining the PIC with a liquid-crystal-on-silicon (LCoS) panel, achieving 211% of the color gamut and more than 80% volume reduction compared to traditional LCoS displays. We further showcase its application in a see-through AR system. Our work represents a major advancement in the integration of nanophotonics with display technology, enabling a range of new display concepts, from high-performance immersive displays to slim-panel 3D holography.

Large Multimodal Models (LMMs) have demonstrated impressive performance across numerous academic benchmarks. However, fine-tuning still remains essential to achieve satisfactory performance on downstream tasks, while the task-specific tuning samples are usually not readily available or expensive and time-consuming to obtain. To address this, we propose an error-driven data-efficient tuning framework that aims to efficiently adapt generic LMMs to newly emerging tasks without requiring any task-specific training samples. In our approach, a generic LMM, acting as a student model, is first evaluated on a small validation set of the target task, and then a more powerful model, acting as a teacher model, identifies the erroneous steps within the student model's reasoning steps and analyzes its capability gaps from fully addressing the target task. Based on these gaps, targeted training samples are further retrieved from existing task-agnostic datasets to tune the student model and tailor it to the target task. We perform extensive experiments across three different training data scales and seven tasks, demonstrating that our training paradigm significantly and efficiently improves LMM's performance on downstream tasks, achieving an average performance boost of 7.01%., Comment: 16 pages, 6 figures

This summary of the second Terrestrial Very-Long-Baseline Atom Interferometry (TVLBAI) Workshop provides a comprehensive overview of our meeting held in London in April 2024, building on the initial discussions during the inaugural workshop held at CERN in March 2023. Like the summary of the first workshop, this document records a critical milestone for the international atom interferometry community. It documents our concerted efforts to evaluate progress, address emerging challenges, and refine strategic directions for future large-scale atom interferometry projects. Our commitment to collaboration is manifested by the integration of diverse expertise and the coordination of international resources, all aimed at advancing the frontiers of atom interferometry physics and technology, as set out in a Memorandum of Understanding signed by over 50 institutions., Comment: Summary of the second Terrestrial Very-Long-Baseline Atom Interferometry Workshop held at Imperial College London: https://indico.cern.ch/event/1369392/

The density-density response in optimally doped Bi$_2$Sr$_2$CaCu$_2$O$_{8+x}$ has recently been shown to exhibit conformal symmetry. Using, the experimentally inferred conformal dynamic susceptibility, we compute the resultant quantum Fisher information (QFI), a witness to multi-partite entanglement. In contrast to a Fermi liquid in which the QFI is approximately temperature independent much below the Fermi energy scale, we find that the QFI increases as a power law at low temperatures but ultimately extrapolates to a constant at $T=0$. The constant is of the form, $\omega_g^{2\Delta}$, where $\Delta$ is the conformal dimension and $\omega_g$ is the UV cutoff which is on the order of the pseudogap. As this constant {depends on both UV and IR properties}, it illustrates that multipartite entanglement in a strange metal exhibits UV-IR mixing, a benchmark feature of doped Mott insulators as exemplified by dynamical spectral weight transfer. We conclude with a discussion of the implication of our results for low-energy reductions of the Hubbard model., Comment: For an issue of Physica C dedicated to Jan Zaanen

Generic sentences express generalisations about the world without explicit quantification. Although generics are central to everyday communication, building a precise semantic framework has proven difficult, in part because speakers use generics to generalise properties with widely different statistical prevalence. In this work, we study the implicit quantification and context-sensitivity of generics by leveraging language models as models of language. We create ConGen, a dataset of 2873 naturally occurring generic and quantified sentences in context, and define p-acceptability, a metric based on surprisal that is sensitive to quantification. Our experiments show generics are more context-sensitive than determiner quantifiers and about 20% of naturally occurring generics we analyze express weak generalisations. We also explore how human biases in stereotypes can be observed in language models., Comment: Accepted at CoLing 2025

Gravitational-wave observatories like LIGO are large-scale, terrestrial instruments housed in infrastructure that spans a multi-kilometer geographic area and which must be actively controlled to maintain operational stability for long observation periods. Despite exquisite seismic isolation, they remain susceptible to seismic noise and other terrestrial disturbances that can couple undesirable vibrations into the instrumental infrastructure, potentially leading to control instabilities or noise artifacts in the detector output. It is, therefore, critical to characterize the seismic state of these observatories to identify a set of temporal patterns that can inform the detector operators in day-to-day monitoring and diagnostics. On a day-to-day basis, the operators monitor several seismically relevant data streams to diagnose operational instabilities and sources of noise using some simple empirically-determined thresholds. It can be untenable for a human operator to monitor multiple data streams in this manual fashion and thus a distillation of these data-streams into a more human-friendly format is sought. In this paper, we present an end-to-end machine learning pipeline for features-based multivariate time series clustering to achieve this goal and to provide actionable insights to the detector operators by correlating found clusters with events of interest in the detector., Comment: 8 pages, 6 figures, Accepted to The 5th International Workshop on Big Data & AI Tools, Methods, and Use Cases for Innovative Scientific Discovery (BTSD 2024)

Given recent increases in ocean water levels brought on by climate change, this investigation decomposed changes in coastal water levels into its fundamental components to predict maximum water levels for a given coastal location. The study focused on Virginia Key, Florida, in the United States, located near the coast of Miami. Hourly mean lower low water (MLLW) levels were obtained from the National Data Buoy Center from January 28, 1994, through December 31, 2023. In the temporal dimension, Kolmogorov-Zurbenko filters were used to extract long-term trends, annual and daily tides, and higher frequency harmonics, while in the spectral dimension, Kolmogorov-Zurbenko periodograms with DiRienzo-Zurbenko algorithm smoothing were used to confirm known tidal frequencies and periods. A linear model predicted that the long-term trend in water level will rise 2.02 feet from January 1994 to December 2050, while a quadratic model predicted a rise of 5.91 during the same period. In addition, the combined crests of annual tides, daily tides, and higher frequency harmonics increase water levels up to 2.16 feet, yielding a combined total of 4.18 feet as a lower bound and a combined total of 8.09 feet as an upper bound. These findings provide a foundation for more accurate prediction of coastal flooding during severe weather events and provide an impetus for policy choices with respect to residential communities, businesses, and wildlife habitats. Further, using Kolmogorov-Zurbenko analytic methods to study coastal sites throughout the world could draw a more comprehensive picture of the impact climate change is having on coastal waters globally., Comment: 23 pages, 7 figures, this article draws from arXiv:2007.03031v3 and arXiv:2412.07735v1

Both encoder-only models (e.g., BERT, RoBERTa) and large language models (LLMs, e.g., Llama3) have been widely used for text classification tasks. However, there is a lack of systematic studies comparing the performance of encoder-based models and LLMs in text classification, particularly when fine-tuning is involved. This study employed a diverse range of models and methods, varying in size and architecture, and including both fine-tuned and pre-trained approaches. We first assessed the performances of these LLMs on the 20 Newsgroups (20NG) and MASSIVE datasets, comparing them to encoder-only RoBERTa models. Additionally, we explored the multi-task capabilities of both model types by combining multiple classification tasks, including intent detection and slot-filling, into a single model using data from both datasets. Our results indicate that fully fine-tuned Llama3-70B models outperform RoBERTa-large and other decoder LLMs across various classification tasks and datasets. Moreover, the consolidated multi-task fine-tuned LLMs matched the performance of dual-model setups in both tasks across both datasets. Overall, our study provides a comprehensive benchmark of encoder-only and LLM models on text classification tasks and demonstrates a method to combine two or more fully fine-tuned decoder LLMs for reduced latency and equivalent performance., Comment: 9 pages, 3 tables

Confining electrons or holes in quantum dots formed in the channel of industry-standard fully depleted silicon-on-insulator CMOS structures is a promising approach to scalable qubit architectures. In this communication, we present measurement results of a commercial nanostructure fabricated using the GlobalFoundries 22FDX(TM) industrial process. We demonstrate here that quantum dots are formed in the device channel by applying a combination of a back- and gate voltages. We report our results on an effective detuning of the energy levels in the quantum dots by varying the barrier gate voltages in combination with the back-gate voltage. Given the need and importance of scaling to larger numbers of qubits, we demonstrate here the feasibility of single-electron box sensors at the edge of the quantum dot array for effective charge sensing in different operation modes -- sensing charge transitions in a single- and double quantum dots forming the quantum dot array. We also report measurement results demonstrating bias triangle pair formation and precise control over coupled quantum dots with variations in the inter-dot barrier. The reported measurement results demonstrate the ability to control the formation and coupling of multiple quantum dots in a quantum dot array and to sense their charge state via a Single Electron Box sensor in a commercial process for the first time.

This investigation establishes the theoretical and practical limits of signal strength estimate precision for Kolmogorov-Zurbenko periodograms with dynamic smoothing and compares them to those of standard log-periodograms with static smoothing. Previous research has established the sensitivity, accuracy, resolution, and robustness of Kolmogorov-Zurbenko periodograms with dynamic smoothing in estimating signal frequencies. However, the precision with which they estimate signal strength has never been evaluated. To this point, the width of the confidence interval for a signal strength estimate can serve as a criterion for assessing the precision of such estimates: the narrower the confidence interval, the more precise the estimate. The statistical background for confidence intervals of periodograms is presented, followed by candidate functions to compute and plot them when using Kolmogorov-Zurbenko periodograms with dynamic smoothing. Given an identified signal frequency, a static smoothing window and its smoothing window width can be selected such that its confidence interval is narrower and, thus, its signal strength estimate more precise, than that of dynamic smoothing windows, all while maintaining a level of frequency resolution as good as or better than that of a dynamic smoothing window. These findings suggest the need for a two-step protocol in spectral analysis: computation of a Kolmogorov-Zurbenko periodogram with dynamic smoothing to detect, identify, and separate signal frequencies, followed by computation of a Kolmogorov-Zurbenko periodogram with static smoothing to precisely estimate signal strength and compute its confidence intervals., Comment: 32 pages, 8 figures, this article draws from arXiv:2007.03031v3

The growing demand for accurate, continuous, and non-invasive health monitoring has propelled multi-sensor data fusion to the forefront of healthcare technology. This review aims to provide an overview of the development of fusion frameworks in the literature and common terminology used in fusion literature. The review introduces the fusion classification standards and methods that are most relevant from an algorithm development perspective. Applications of the reviewed fusion frameworks in fields such as defense, autonomous driving, robotics, and image fusion are also discussed to provide contextual information on the various fusion methodologies that have been developed in this field. This review provides a comprehensive analysis of multi-sensor data fusion methods applied to health monitoring systems, focusing on key algorithms, applications, challenges, and future directions. We examine commonly used fusion techniques, including Kalman filters, Bayesian networks, and machine learning models. By integrating data from various sources, these fusion approaches enhance the reliability, accuracy, and resilience of health monitoring systems. However, challenges such as data quality and differences in acquisition systems exist, calling for intelligent fusion algorithms in recent years. The review finally converges on applications of fusion algorithms in biomedical inference tasks like heartbeat detection, respiration rate estimation, sleep apnea detection, arrhythmia detection, and atrial fibrillation detection.

We define the triple Riordan group, whose elements consist of $4$-tuples of power series $(g, f_1, f_2, f_3)$ with $g\in \mathbf{R}[[x^3]]$, and $f_1, f_2, f_3 \in x\mathbf{R}[[x^3]]$, for an appropriate ring $\mathbf{R}$. The construction of this group generalizes that of the double Riordan group, and lays the pattern for further generalizations., Comment: 10 pages

Table extraction from document images is a challenging AI problem, and labelled data for many content domains is difficult to come by. Existing table extraction datasets often focus on scientific tables due to the vast amount of academic articles that are readily available, along with their source code. However, there are significant layout and typographical differences between tables found across scientific, financial, and other domains. Current datasets often lack the words, and their positions, contained within the tables, instead relying on unreliable OCR to extract these features for training modern machine learning models on natural language processing tasks. Therefore, there is a need for a more general method of obtaining labelled data. We present SynFinTabs, a large-scale, labelled dataset of synthetic financial tables. Our hope is that our method of generating these synthetic tables is transferable to other domains. To demonstrate the effectiveness of our dataset in training models to extract information from table images, we create FinTabQA, a layout large language model trained on an extractive question-answering task. We test our model using real-world financial tables and compare it to a state-of-the-art generative model and discuss the results. We make the dataset, model, and dataset generation code publicly available., Comment: 12 pages, 8 figures

Large language models (LLMs) are increasingly being adapted to achieve task-specificity for deployment in real-world decision systems. Several previous works have investigated the bias transfer hypothesis (BTH) by studying the effect of the fine-tuning adaptation strategy on model fairness to find that fairness in pre-trained masked language models have limited effect on the fairness of models when adapted using fine-tuning. In this work, we expand the study of BTH to causal models under prompt adaptations, as prompting is an accessible, and compute-efficient way to deploy models in real-world systems. In contrast to previous works, we establish that intrinsic biases in pre-trained Mistral, Falcon and Llama models are strongly correlated (rho >= 0.94) with biases when the same models are zero- and few-shot prompted, using a pronoun co-reference resolution task. Further, we find that bias transfer remains strongly correlated even when LLMs are specifically prompted to exhibit fair or biased behavior (rho >= 0.92), and few-shot length and stereotypical composition are varied (rho >= 0.97). Our findings highlight the importance of ensuring fairness in pre-trained LLMs, especially when they are later used to perform downstream tasks via prompt adaptation.

This paper presents a characterization of a two-qubit processor in a 6-quantum dot array in SiGe, from the perspective of its quantum information processing capabilities. The analysis includes randomized benchmarking of single- and two-qubit gates, SPAM characterization, and Bell's state tomography; all basic functionality required for universal quantum computation. In light of our efforts to combine spin qubits with integrated cryogenic electronics, we evaluate the qubits' performance metrics at 300mK and 740mK. The latter temperature lies within the realistic thermal budget for integrated cryogenic electronics, making it particularly relevant for assessing qubit performance in practical scenarios., Comment: 13 pages, 7 figures, 4 tables

We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors of the atomic environment, MALA models efficiently predict key electronic observables, including local density of states, electronic density, density of states, and total energy. The package integrates data sampling, model training and scalable inference into a unified library, while ensuring compatibility with standard DFT and molecular dynamics codes. We demonstrate MALA's capabilities with examples including boron clusters, aluminum across its solid-liquid phase boundary, and predicting the electronic structure of a stacking fault in a large beryllium slab. Scaling analyses reveal MALA's computational efficiency and identify bottlenecks for future optimization. With its ability to model electronic structures at scales far beyond standard DFT, MALA is well suited for modeling complex material systems, making it a versatile tool for advanced materials research.

The widespread use of generative AI has led to multiple applications of AI-generated text and media to potentially enhance learning outcomes. However, there are a limited number of well-designed experimental studies investigating the impact of learning gains and affective feedback from AI-generated media compared to traditional media (e.g., text from documents and human recordings of video). The current study recruited 500 participants to investigate adult learners recall and recognition performances as well as their affective feedback on the AI-generated synthetic video, using a mixed-methods approach with a pre-and post-test design. Specifically, four learning conditions, AI-generated framing of human instructor-generated text, AI-generated synthetic videos with human instructor-generated text, human instructor-generated videos, and human instructor-generated text frame (baseline), were considered. The results indicated no statistically significant difference amongst conditions on recall and recognition performance. In addition, the participants affective feedback was not statistically significantly different between the two video conditions. However, adult learners preferred to learn from the video formats rather than text materials., Comment: 13 pages, 9 figures

SPT-3G+ is the next-generation camera for the South Pole Telescope (SPT). SPT is designed to measure the cosmic microwave background (CMB) and the mm/sub-mm sky. The planned focal plane consists of 34,000 microwave kinetic inductance detectors (MKIDs), divided among three observing bands centered at 220, 285, and 345 GHz. Each readout line is designed to measure 800 MKIDs over a 500 MHz bandwidth, which places stringent constraints on the accuracy of the frequency placement required to limit resonator collisions that reduce the overall detector yield. To meet this constraint, we are developing a two-step process that first optically maps the resonance to a physical pixel location, and then next trims the interdigitated capacitor (IDC) to adjust the resonator frequency. We present a cryogenic LED apparatus operable at 300 mK for the optical illumination of SPT-3G+ detector arrays. We demonstrate integration of the LED controls with the GHz readout electronics (RF-ICE) to take data on an array of prototype SPT-3G+ detectors. We show that this technique is useful for characterizing defects in the resonator frequency across the detector array and will allow for improvements in the detector yield., Comment: 5 pages, 10 figures, Applied Superconductivity Conference (ASC 2024) proceedings, submitted to IEEE Transactions on Applied Superconductivity (IEEE TAS)

The molecular anion C$_2^-$ has been of interest in the last few years as a candidate for laser cooling due to its electronic structure and favourable branching ratios to the ground electronic and vibrational state. Molecular hydrogen has been used by the Wester group in Innsbruck as a buffer gas to cool the molecule's internal ro-vibrational motion. In the present work, we generate a new, five dimensional (5D) interaction potential for the system by considering the H$_2$ as a rigid rotor and the C$_2^-$ as a rotating-vibrating diatomic molecule. We thereafter calculate the cross sections and rate coefficients for ro-vibrational inelastic collisions of C$_2^-$ with both para- and ortho-H$_2$ on this new 5D \textit{ab initio} potential energy surface using quantum scattering theory for the dynamics. The rates for vibrational quenching are obtained over the range of temperatures which covers the single value measured by the experiments. A comparison is also made with the earlier results using a simpler 3D interaction potential. Furthermore, para-H$_2$ is found to be more efficient than ortho-H$_2$ (with or without undergoing rotational excitation) in cooling C$_2^-$. The rate coefficients for cooling the anions has been computed by appropriately weighting the ortho- and para-H$_2$ and compared with the available experimental result at 20 K. When the vibrational de-excitation rate coefficients are taken to be the ones not causing any concurrent rotational excitations in the final C$_2^-$ anions, the properly averaged results are found to get smaller and to become very close to the experimental measurements. The implications of these new results for laser cooling of C$_2^-$ are analyzed and discussed., Comment: 21 pages

A skip graph is a resilient application-layer routing structure that supports range queries of distributed k-dimensional data. By sorting deterministic keys into groups based on locally computed random membership vectors, nodes in a standard skip graph can optimize range query performance in mobile networks such as unmanned aerial vehicle swarms. We propose a skip graph extension that inverts the key and membership vector roles and bases group membership on deterministic vectors derived from the z-ordering of k-dimensional data and sorting within groups is based on locally computed random keys., Comment: archival pre-print of this k-D distributed prefix-free distributed encoding technique ahead of another arXiv release which will cite this work, specifically with respect to using a more extensive prefix-free encoding technique to localize token partitions in an arbitrary input/output context of an LLM, SSM, or other k-D tokenized model

Galaxy mergers represent the most transformative and dramatic avenue for galaxy and supermassive black hole (SMBH) evolution. Multi-active galactic nuclei (multi-AGNs) are expected to ignite, grow, and evolve alongside the host galaxies, and these represent different evolutionary stages of the SMBHs over the merger sequence. However, no comprehensive census exists of observed multi-AGN systems. Here we present The Big Multi-AGN Catalog (The Big MAC), the first literature-complete catalog of all known (confirmed and candidate) multi-AGN systems, which includes dual AGNs (separations $\sim0.03-110$ kpc), binary AGNs (gravitationally bound, $\lesssim30$ pc), recoiling AGNs, and N-tuple AGNs (involving three or more AGNs), gleaned from hundreds of literature articles spanning the years 1970-2020. The Big MAC is the first archive to assemble all multi-AGN systems and candidates across all selection methods, redshifts, and galaxy mass ratios, and this catalog offers a solid foundation for archival and targeted multiwavelength follow-up investigations. In this work, we provide an overview of the creation of the multi-AGN literature library and the catalog itself, present definitions for different multi-AGN classes (including new definitions for dual AGNs derived from galaxy pairs in Illustris-TNG100), describe the general properties of the catalog as a function of redshift space and separation, and we provide a thorough examination of selection and confirmation method usage within the literature. We also discuss best practices for the multi-AGN literature, and we emphasize that a diverse, multiwavelength array of selection approaches is crucial for a complete understanding of multi-AGNs and - by extension - answering long-standing, open questions regarding the importance of AGNs and galaxy mergers., Comment: Under review for ApJS. 67 pages, including: 24 figures, 11 tables, 10 pages of references. Feedback is welcomed and appreciated! Big MAC website and repository (still undergoing updates) located here: https://thatastroguy.github.io/thebigmac/ . Supplemental (complete) description of the multi-AGN literature (1970-2020) is forthcoming

We use the Monte Carlo For AGN (active galactic nucleus) Channel Testing and Simulation (McFACTS, https://www.github.com/mcfacts/mcfacts) code to study the effect of AGN disk and nuclear star cluster parameters on predicted mass distributions for LIGO-Virgo-KAGRA (LVK) compact binaries forming in AGN disks. The assumptions we vary include the black hole (BH) initial mass function, disk model, disk size, disk lifetime, and the prograde-to-retrograde fraction of newly formed black hole binaries. Broadly we find that dense, moderately short-lived AGN disks are preferred for producing a $(q,\chi_{\rm eff})$ anti-correlation like those identified from existing gravitational wave (GW) observations. Additionally, a BH initial mass function (MF $\propto M^{-2}$) is preferred over a more top-heavy MF ($M^{-1}$). The preferred fraction of prograde-to-retrograde is $>90\%$, to produce results consistent with observations., Comment: Submitted to ApJ, 18 pages, 9 figures, 2 tables

We present analytical expressions for the fluxes of energy and angular momentum from a point mass on an inclined spherical orbit about a Kerr black hole. The expressions are obtained using the method of Mano, Suzuki and Takasugi to construct analytical solutions of the Teukolsky equation, and are given as post-Newtonian expansions valid through 12PN, with arbitrary values for the inclination parameter $x$ and black hole spin $a$. We characterize the structure of the PN expansions in terms of their dependence on $x$ and $a$, and we validate our results against numerical calculations.

Most existing work on event extraction has focused on sentence-level texts and presumes the identification of a trigger-span -- a word or phrase in the input that evokes the occurrence of an event of interest. Event arguments are then extracted with respect to the trigger. Indeed, triggers are treated as integral to, and trigger detection as an essential component of, event extraction. In this paper, we provide the first investigation of the role of triggers for the more difficult and much less studied task of document-level event extraction. We analyze their usefulness in multiple end-to-end and pipelined neural event extraction models for three document-level event extraction datasets, measuring performance using triggers of varying quality (human-annotated, LLM-generated, keyword-based, and random). Our research shows that trigger effectiveness varies based on the extraction task's characteristics and data quality, with basic, automatically-generated triggers serving as a viable alternative to human-annotated ones. Furthermore, providing detailed event descriptions to the extraction model helps maintain robust performance even when trigger quality degrades. Perhaps surprisingly, we also find that the mere existence of trigger input, even random ones, is important for prompt-based LLM approaches to the task.

We carried out a project involving the systematic analysis of microlensing data from the Korea Microlensing Telescope Network survey. The aim of this project is to identify lensing events with complex anomaly features that are difficult to explain using standard binary-lens or binary-source models. Our investigation reveals that the light curves of microlensing events KMT-2021-BLG-0284, KMT-2022-BLG-2480, and KMT-2024-BLG-0412 display highly complex patterns with three or more anomaly features. These features cannot be adequately explained by a binary-lens (2L1S) model alone. However, the 2L1S model can effectively describe certain segments of the light curve. By incorporating an additional source into the modeling, we identified a comprehensive model that accounts for all the observed anomaly features. Bayesian analysis, based on constraints provided by lensing observables, indicates that the lenses of KMT-2021-BLG-0284 and KMT-2024-BLG-0412 are binary systems composed of M dwarfs. For KMT-2022-BLG-2480, the primary lens is an early K-type main-sequence star with an M dwarf companion. The lenses of KMT-2021-BLG-0284 and KMT-2024-BLG-0412 are likely located in the bulge, whereas the lens of KMT-2022-BLG-2480 is more likely situated in the disk. In all events, the binary stars of the sources have similar magnitudes due to a detection bias favoring binary source events with a relatively bright secondary source star, which increases detection efficiency., Comment: 9 pages, 9 figures

This paper reports on the shared tasks organized by the 21st IWSLT Conference. The shared tasks address 7 scientific challenges in spoken language translation: simultaneous and offline translation, automatic subtitling and dubbing, speech-to-speech translation, dialect and low-resource speech translation, and Indic languages. The shared tasks attracted 18 teams whose submissions are documented in 26 system papers. The growing interest towards spoken language translation is also witnessed by the constantly increasing number of shared task organizers and contributors to the overview paper, almost evenly distributed across industry and academia., Comment: IWSLT 2024; 59 pages

It is common practice, both in dynamical modelling and in idealised numerical simulations, to assume that galaxies and/or dark matter haloes are spherical and have isotropic velocity distributions, such that their distribution functions are ergodic. However, there is no good reason to assume that this assumption is accurate. In this paper we use idealised $N$-body simulations to study the tidal evolution of subhaloes that are anisotropic at infall. We show that the detailed velocity anisotropy has a large impact on the subhalo's mass loss rate. In particular, subhaloes that are radially anisotropic experience much more mass loss than their tangentially anisotropic counterparts. In fact, in the former case, the stripping of highly radial orbits can cause a rapid cusp-to-core transformation, without having to resort to any baryonic feedback processes. Once the tidal radius becomes comparable to the radius of the core thus formed, the subhalo is tidally disrupted. Subhaloes that at infall are tangentially anisotropic are far more resilient to tidal stripping, and are never disrupted when simulated with sufficient resolution. We show that the preferential stripping of more radial orbits, combined with re-virialisation post stripping, causes an isotropisation of the subhalo's velocity distributions. This implies that subhaloes that have experienced significant mass loss are expected to be close to isotropic, which may alleviate the mass-anisotropy degeneracies that hamper the dynamical modelling of Milky Way satellites., Comment: 16 pages, 16 figures

In this paper, we construct and compare algorithmic approaches to solve the Preference Consistency Problem for preference statements based on hierarchical models. Instances of this problem contain a set of preference statements that are direct comparisons (strict and non-strict) between some alternatives, and a set of evaluation functions by which all alternatives can be rated. An instance is consistent based on hierarchical preference models, if there exists an hierarchical model on the evaluation functions that induces an order relation on the alternatives by which all relations given by the preference statements are satisfied. Deciding if an instance is consistent is known to be NP-complete for hierarchical models. We develop three approaches to solve this decision problem. The first involves a Mixed Integer Linear Programming (MILP) formulation, the other two are recursive algorithms that are based on properties of the problem by which the search space can be pruned. Our experiments on synthetic data show that the recursive algorithms are faster than solving the MILP formulation and that the ratio between the running times increases extremely quickly., Comment: Longer Version of IJCAI'16 publication https://www.ijcai.org/Proceedings/16/Papers/157.pdf

We present measurements of the microlensing optical depth and event rate toward the Galactic bulge using the dataset from the 2006--2014 MOA-II survey, which covers 22 bulge fields spanning ~42 deg^2 between -5 deg < l < 10 deg and -7 deg < b < -1 deg. In the central region with |l|<5 deg, we estimate an optical depth of {\tau} = [1.75+-0.04]*10^-6exp[(0.34+-0.02)(3 deg-|b|)] and an event rate of {\Gamma} = [16.08+-0.28]*10^-6exp[(0.44+-0.02)(3 deg-|b|)] star^-1 year^-1 using a sample consisting of 3525 microlensing events, with Einstein radius crossing times of tE < 760 days and source star magnitude of IsWe confirm our results are consistent with the latest measurements from OGLE-IV 8 year dataset (Mr\'oz et al. 2019). We find our result is inconsistent with a prediction based on Galactic models, especially in the central region with |b|<3 deg. These results can be used to improve the Galactic bulge model, and more central regions can be further elucidated by future microlensing experiments, such as The PRime-focus Infrared Microlensing Experiment (PRIME) and Nancy Grace Roman Space Telescope.

We use JWST/NIRISS slitless spectroscopy from the Next Generation Deep Extragalactic Exploratory Public (NGDEEP) Survey to investigate the physical condition of star-forming galaxies at $1.7 < z < 3.4$. At these redshifts, the deep NGDEEP NIRISS slitless spectroscopy covers the [O II]$\lambda\lambda$3726,3729, [O III]$\lambda\lambda$4959,5007, H$\beta$ and H$\alpha$ emission features for galaxies with stellar masses $\log(\mathrm{M_\ast/M_\odot}) \gtrsim 7$, nearly a factor of a hundred lower than previous studies. We focus on the [O III]/[O II] (O$_{32}$) ratio which is primarily sensitive to the ionization state and with a secondary dependence on the gas-phase metallicity of the interstellar medium. We find significant ($\gtrsim5\sigma$) correlations between the O$_{32}$ ratio and galaxy properties as O$_{32}$ increases with decreasing stellar mass, decreasing star formation rate (SFR), increasing specific SFR (sSFR$\equiv \mathrm{SFR}/M_*$), and increasing equivalent width (EW) of H$\beta$ and H$\alpha$. These trends suggest a tight connection between the ionization parameter and these galaxy properties. Galaxies at $z\sim2-3$ exhibit a higher O$_{32}$ than local normal galaxies with the same stellar masses and SFRs, indicating that they have a higher ionization parameter and lower metallicity than local normal galaxies. In addition, we observe an evolutionary trend in the O$_{32}$ -- EW(H$\beta$) relation from $z\sim0$ and $z\gtrsim5$, such that higher redshift galaxies have higher EW(H$\beta$) and higher O$_{32}$ at fixed EW. We argue that both the enhanced recent star formation activity and the higher star formation surface density may contribute to the increase in O$_{32}$ and the ionization parameter., Comment: 27 pages, 14 figures