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Gas metallicity, ionization parameter, and gas pressure can affect the observed ratios of specific strong emission lines within galaxies. While the theoretical strong lines diagnostics for gas metallicity, ionization parameters, and gas pressure in star-forming regions are well-established, theoretical diagnostics for active galactic nuclei (AGNs) narrow line regions are still lacking. In Zhu et al. (2023), we presented a new AGN model that provides the best predictions for observations spanning the UV, optical, and infrared wavelengths. This paper presents a suite of theoretical diagnostics for the gas metallicity, ionization parameter, gas pressure, and the peak energy in AGN ionizing radiation field $E_{peak}$ for AGN narrow-line regions spanning the UV and optical wavelengths. We investigate the model dependency on the ionization parameter, gas pressure, $E_{peak}$, and the nitrogen scaling relation and make recommendations on metallicity diagnostics that are most robust against these parameters. We test our new AGN metallicity diagnostics using optical galaxy spectra from Sloan Digital Sky Survey DR16. These tests show that the metallicities measured from different diagnostics in this paper are consistent within $\sim0.3$ dex. We compare consistent HII and AGN diagnostics and demonstrate that HII and AGN diagnostics should not be used interchangeably. With a wide wavelength coverage, we anticipate that these AGN diagnostics will enable new metallicity studies of galaxies dominated by AGN., Comment: 33 pages, 22 figures, 7 tables, Accepted for publication in ApJ

We present $[OIII]/H_{\rm \beta}$ emision line flux ratio predictions for galaxies at $z \sim 7-9$ using the MAPPINGS V v5.2.0 photoionization modelling code combined with an analytic galaxy formation model. Properties such as pressure and ionization parameter that determine emission line properties are thought to evolve towards high redshift. In order to determine the range of expected interstellar conditions we extend previous modelling of the Star Formation Rate Density (SFRD) function to calculate the metallicity and ionization parameter, and incorporate the potential impact of turbulence on the density of the ISM. To validate our emission line predictions we calculate the [OIII] line luminosity and its dependence on UV luminosity, as well as the flux ratio $[OIII]/H_{\rm \beta}$ and its variation with the line luminosity, finding that both reproduce recent JWST observations from the FRESCO survey. We also use our model to predict the number counts of emission line galaxies across a range of redshift as well as the dependence of $[OIII]/H_{\rm \beta}$ on ionization parameter and metallicity. Finally, we show that the dependence of flux ratio on luminosity may provide a diagnostic of turbulent motion in galactic discs., Comment: 12 pages, 17 figures, Submitted to MNRAS

We discuss a general technique for using quantum logic spectroscopy to perform quantum non-demolition (QND) measurements that determine which of two subspaces a logic ion is in. We then show how to use the scheme to perform high fidelity state preparation and measurement (SPAM) and non-destructive leakage detection, as well as how this would reduce the engineering task associated with building larger quantum computers. Using a magnetic field gradient, we can apply a geometric phase interaction to map the magnetic sensitivity of the logic into onto the spin-flip probability of a readout ion. In the `low quantization field' regime, where the Zeeman splitting of the sublevels in the hyperfine ground state manifold are approximately integer multiples of each other, we show how to we can use the technique to distinguish between $2^{K}$ hyperfine sublevels in only $K$ logic/readout measurement sequences--opening the door for efficient state preparation of the logic ion. Finally, we show how the binary nature of the scheme, as well as its potential for high QND purity, let us improve SPAM fidelities by detecting and correcting errors rather than preventing them.

High-redshift protoclusters consisting of dusty starbursts are thought to play an important role in galaxy evolution. Their dusty nature makes them bright in the FIR/submm but difficult to find in optical/NIR surveys. Radio observations are an excellent way to study these dusty starbursts, as dust is transparent in the radio and there is a tight correlation between the FIR and radio emission of a galaxy. Here, we present MeerKAT 1.28 GHz radio imaging of 3 Herschel candidate protoclusters, with a synthesised beam size of ~$7.5'' \times 6.6''$ and a central thermal noise down to $4.35~\mu$Jy/beam. Our source counts are consistent with other radio counts with no evidence of overdensities. Around 95% of the Herschel sources have 1.28 GHz IDs. Using the Herschel 250 micron primary beam size as the searching radius, we find 54.2% Herschel sources have multiple 1.28 GHz IDs. Our average FIR-radio correlation coefficient $q_{250\mu m}$ is $2.33\pm 0.26$. Adding $q_{250\mu m}$ as a new constraint, the probability of finding chance-aligned sources is reduced by a factor of ~6, but with the risk of discarding true identifications of radio-loud/quiet sources. With accurate MeerKAT positions, we cross-match our Herschel sources to optical/NIR data followed by photometric redshift estimations. By removing $z<1$ sources, the density contrasts of two of the candidate protoclusters increase, suggestive of them being real protoclusters at $z>1$. There is also potentially a $0.9

We consider a class of BSM models where a generic scalar electroweak multiplet obtains a significant fraction of its mass from a coupling to the Higgs. Such models are non-decoupling: their new states are necessarily at the TeV scale or below, they can significantly alter the electroweak phase transition, and they have a pattern of low energy effects that are distinct from those predicted by SMEFT. Using their minimal gauge and Higgs couplings, we show that a future precision lepton collider (such as FCC-ee, CEPC, ILC, or CLIC) can probe all the non-decoupling parameter space of scalar electroweak multiplets, providing fundamental information on the mechanism of electroweak symmetry breaking., Comment: 27 pages, 4 figures

Obtaining compositional mappings is important for the model to generalize well compositionally. To better understand when and how to encourage the model to learn such mappings, we study their uniqueness through different perspectives. Specifically, we first show that the compositional mappings are the simplest bijections through the lens of coding length (i.e., an upper bound of their Kolmogorov complexity). This property explains why models having such mappings can generalize well. We further show that the simplicity bias is usually an intrinsic property of neural network training via gradient descent. That partially explains why some models spontaneously generalize well when they are trained appropriately., Comment: 4 pages

The Hilbert-Schmidt Independence Criterion (HSIC) is a powerful tool for nonparametric detection of dependence between random variables. It crucially depends, however, on the selection of reasonable kernels; commonly-used choices like the Gaussian kernel, or the kernel that yields the distance covariance, are sufficient only for amply sized samples from data distributions with relatively simple forms of dependence. We propose a scheme for selecting the kernels used in an HSIC-based independence test, based on maximizing an estimate of the asymptotic test power. We prove that maximizing this estimate indeed approximately maximizes the true power of the test, and demonstrate that our learned kernels can identify forms of structured dependence between random variables in various experiments.

This study evaluates the productivity improvements achieved using a generative artificial intelligence personal assistant tool (PAT) developed by Trane Technologies. The PAT, based on OpenAI's GPT 3.5 model, was deployed on Microsoft Azure to ensure secure access and protection of intellectual property. To assess the tool's productivity effectiveness, an experiment was conducted comparing the completion times and content quality of four common office tasks: writing an email, summarizing an article, creating instructions for a simple task, and preparing a presentation outline. Sixty-three (63) participants were randomly divided into a test group using the PAT and a control group performing the tasks manually. Results indicated significant productivity enhancements, particularly for tasks involving summarization and instruction creation, with improvements ranging from 3.3% to 69%. The study further analyzed factors such as the age of users, response word counts, and quality of responses, revealing that the PAT users generated more verbose and higher-quality content. An 'LLM-as-a-judge' method employing GPT-4 was used to grade the quality of responses, which effectively distinguished between high and low-quality outputs. The findings underscore the potential of PATs in enhancing workplace productivity and highlight areas for further research and optimization., Comment: 18 pages, 9 figures

Previous measurements of neutron energy using fast plastic scintillators while operating the Fusion Z Pinch Experiment (FuZE) constrained the energy of any yield-producing deuteron beams to less than $4.65 keV$. FuZE has since been operated at increasingly higher input power, resulting in increased plasma current and larger fusion neutron yields. A detailed experimental study of the neutron energy isotropy in these regimes applies more stringent limits to possible contributions from beam-target fusion. The FuZE device operated at $-25~kV$ charge voltage has resulted in average plasma currents of $370~kA$ and D-D fusion neutron yields of $4\times10^7$ neutrons per discharge. Measurements of the neutron energy isotropy under these operating conditions demonstrates the energy of deuteron beams is less than $7.4 \pm 5.6^\mathrm{(stat)} \pm 3.7^\mathrm{(syst)}~keV$. Characterization of the detector response has reduced the number of free parameters in the fit of the neutron energy distribution, improving the confidence in the forward-fit method. Gamma backgrounds have been measured and the impact of these contributions on the isotropy results have been studied. Additionally, a time dependent measurement of the isotropy has been resolved for the first time, indicating increases to possible deuteron beam energies at late times. This suggests the possible growth of $m$=0 instabilities at the end of the main radiation event but confirms that the majority of the neutron production exhibits isotropy consistent with thermonuclear origin., Comment: 16 pages, 11 figures, submitted to Journal of Nuclear Fusion

Machine learning techniques are an active area of research for speech enhancement for hearing aids, with one particular focus on improving the intelligibility of a noisy speech signal. Recent work has shown that feature encodings from self-supervised speech representation models can effectively capture speech intelligibility. In this work, it is shown that the distance between self-supervised speech representations of clean and noisy speech correlates more strongly with human intelligibility ratings than other signal-based metrics. Experiments show that training a speech enhancement model using this distance as part of a loss function improves the performance over using an SNR-based loss function, demonstrated by an increase in HASPI, STOI, PESQ and SI-SNR scores. This method takes inference of a high parameter count model only at training time, meaning the speech enhancement model can remain smaller, as is required for hearing aids., Comment: Accepted for EUSIPCO 2024

We present a theoretical explanation of the ``grokking'' phenomenon, where a model generalizes long after overfitting,for the originally-studied problem of modular addition. First, we show that early in gradient descent, when the ``kernel regime'' approximately holds, no permutation-equivariant model can achieve small population error on modular addition unless it sees at least a constant fraction of all possible data points. Eventually, however, models escape the kernel regime. We show that two-layer quadratic networks that achieve zero training loss with bounded $\ell_{\infty}$ norm generalize well with substantially fewer training points, and further show such networks exist and can be found by gradient descent with small $\ell_{\infty}$ regularization. We further provide empirical evidence that these networks as well as simple Transformers, leave the kernel regime only after initially overfitting. Taken together, our results strongly support the case for grokking as a consequence of the transition from kernel-like behavior to limiting behavior of gradient descent on deep networks., Comment: Accepted by ICML 2024

The ProbCover method of Yehuda et al. is a well-motivated algorithm for active learning in low-budget regimes, which attempts to "cover" the data distribution with balls of a given radius at selected data points. We demonstrate, however, that the performance of this algorithm is extremely sensitive to the choice of this radius hyper-parameter, and that tuning it is quite difficult, with the original heuristic frequently failing. We thus introduce (and theoretically motivate) a generalized notion of "coverage," including ProbCover's objective as a special case, but also allowing smoother notions that are far more robust to hyper-parameter choice. We propose an efficient greedy method to optimize this coverage, generalizing ProbCover's algorithm; due to its close connection to kernel herding, we call it "MaxHerding." The objective can also be optimized non-greedily through a variant of $k$-medoids, clarifying the relationship to other low-budget active learning methods. In comprehensive experiments, MaxHerding surpasses existing active learning methods across multiple low-budget image classification benchmarks, and does so with less computational cost than most competitive methods., Comment: Accepted to ECCV2024

Learning dynamics, which describes how the learning of specific training examples influences the model's predictions on other examples, gives us a powerful tool for understanding the behavior of deep learning systems. We study the learning dynamics of large language models during different types of finetuning, by analyzing the step-wise decomposition of how influence accumulates among different potential responses. Our framework allows a uniform interpretation of many interesting observations about the training of popular algorithms for both instruction tuning and preference tuning. In particular, we propose a hypothetical explanation of why specific types of hallucination are strengthened after finetuning, e.g., the model might use phrases or facts in the response for question B to answer question A, or the model might keep repeating similar simple phrases when generating responses. We also extend our framework and highlight a unique "squeezing effect" to explain a previously observed phenomenon in off-policy direct preference optimization (DPO), where running DPO for too long makes even the desired outputs less likely. This framework also provides insights into where the benefits of on-policy DPO and other variants come from. The analysis not only provides a novel perspective of understanding LLM's finetuning but also inspires a simple, effective method to improve alignment performance.

We discuss dressing trapped ions with the near field of a trap integrated wire. Ramping a dressing field on/off adiabatically before/after an operation changes its effective Hamiltonian. The amplitude and detuning of the dressing field act as tunable degrees of freedom we can use to `customize' the properties of any operation. We propose three use cases for this general tool. First, we can generate `artificial' clock states, where we eliminate the (assumed to be small) linear sensitivity of a qubit. Second, we can break the degeneracies that often complicate shelving at low quantization fields\textemdash allowing us to implement operations with linearly polarized microwaves that would, otherwise, require circular polarization. Finally, we can implement laser-free single qubit gates on a set of `target' ions using fields that are separated from the rest of the computer in frequency space.

Despite the growing body of research on how to support older readers, few studies consider whether U.S. teachers can identify students who have difficulty reading instructional materials across content areas or whether teachers feel like they have the knowledge and resources to support those students. Such information could lay the groundwork for states and school systems to provide better supports to teachers to address students' reading difficulties. In this report, researchers explore U.S. public school teachers' perceptions of students' difficulties with reading in grades 3-8, those teachers' knowledge about how students learn to read, their experiences supporting these readers, and what they need to help students become proficient readers. The findings in this report highlight the importance of including teachers in upper elementary and middle school grades in resource development and allocation and offer guidance to policymakers designing or implementing reading instruction reforms. [The research described in this report was sponsored by the Advanced Education Research and Development Fund (AERDF).]

The orange carotenoid protein (OCP) is the water-soluble mediator of non-photochemical quenching in cyanobacteria, a crucial photoprotective mechanism in response to excess illumination. OCP converts from a globular, inactive state (OCPo) to an extended, active conformation (OCPr) under high-light conditions, resulting in a concomitant redshift in the absorption of the bound carotenoid. Here, OCP was trapped in either the active or inactive state by fixing each protein conformation in trehalose-sucrose glass. Glass-encapsulated OCPo did not convert under intense illumination and OCPr did not convert in darkness, allowing the optical properties of each conformation to be determined at room temperature. We measured pump wavelength-dependent transient absorption of OCPo in glass films and found that initial OCP photoproducts are still formed, despite the glass preventing completion of the photocycle. By comparison to the pump wavelength dependence of the OCPo to OCPr photoconversion yield in buffer, we show that the long-lived carotenoid singlet-like feature (S*) is associated with ground-state heterogeneity within OCPo, rather than triggering OCP photoconversion.

In this study, we introduce Generative Manufacturing Systems (GMS) as a novel approach to effectively manage and coordinate autonomous manufacturing assets, thereby enhancing their responsiveness and flexibility to address a wide array of production objectives and human preferences. Deviating from traditional explicit modeling, GMS employs generative AI, including diffusion models and ChatGPT, for implicit learning from envisioned futures, marking a shift from a model-optimum to a training-sampling decision-making. Through the integration of generative AI, GMS enables complex decision-making through interactive dialogue with humans, allowing manufacturing assets to generate multiple high-quality global decisions that can be iteratively refined based on human feedback. Empirical findings showcase GMS's substantial improvement in system resilience and responsiveness to uncertainties, with decision times reduced from seconds to milliseconds. The study underscores the inherent creativity and diversity in the generated solutions, facilitating human-centric decision-making through seamless and continuous human-machine interactions.

With the widespread adoption of Large Language Models (LLMs), the prevalence of iterative interactions among these models is anticipated to increase. Notably, recent advancements in multi-round self-improving methods allow LLMs to generate new examples for training subsequent models. At the same time, multi-agent LLM systems, involving automated interactions among agents, are also increasing in prominence. Thus, in both short and long terms, LLMs may actively engage in an evolutionary process. We draw parallels between the behavior of LLMs and the evolution of human culture, as the latter has been extensively studied by cognitive scientists for decades. Our approach involves leveraging Iterated Learning (IL), a Bayesian framework that elucidates how subtle biases are magnified during human cultural evolution, to explain some behaviors of LLMs. This paper outlines key characteristics of agents' behavior in the Bayesian-IL framework, including predictions that are supported by experimental verification with various LLMs. This theoretical framework could help to more effectively predict and guide the evolution of LLMs in desired directions.

Galaxies' stellar masses, gas-phase oxygen abundances (metallicity), and star formation rates (SFRs) obey a series of empirical correlations, most notably the mass-metallicity relation (MZR) and fundamental metallicity relation (FZR), which relates oxygen abundance to a combination of stellar mass and SFR. However, due to the difficulty of measuring oxygen abundances and SFRs in galaxies that host powerful active galactic nuclei (AGN), to date it is unknown to what extent AGN-host galaxies also follow these correlations. In this work, we apply Bayesian methods to the MaNGA integral field spectrographic (IFS) survey that allow us to measure oxygen abundances and SFRs in AGN hosts, and use these measurements to explore how the MZR and FZR differ between galaxies that do and do not host AGN. We find similar MZRs at stellar masses above $10^{10.5} \mathrm{M}_\odot$, but that at lower stellar masses AGN hosts show up to $\sim 0.2$ dex higher oxygen abundances. The offset in the FZR is significantly smaller, suggesting that the larger deviation in the MZR is a result of AGN-host galaxies having systematically lower SFRs at fixed stellar mass. However, within the AGN-host sample there is little correlation between SFR and oxygen abundance. These findings support a scenario in which an AGN can halt efficient gas accretion, which drives non-AGN host galaxies to both higher SFR and lower oxygen abundance, resulting in the galaxy evolving off the star-forming main sequence (SFMS). As a consequence, as the SFR declines for an individual system its metallicity remains mostly unchanged., Comment: 18 pages, 14 figures, accepted for publication in MNRAS

In recent demonstrations of the quantum charge-coupled device (QCCD) computer architecture, circuit times are dominated by cooling. Some motional modes of multi-ion crystals take orders-of-magnitude longer to cool than others because of low coolant ion participation. Here we demonstrate a new technique, which we call phonon rapid adiabatic passage (phrap), that avoids this issue by coherently exchanging the thermal populations of selected modes on timescales short compared to direct cooling. Analogous to adiabatic rapid passage, we quasi-statically couple these slow-cooling modes with fast-cooling ones using DC electric fields. When the crystal is then adiabatically ramped through the resultant avoided crossing, nearly-complete phonon population exchange results. We demonstrate this on two-ion crystals, and show the indirect ground-state cooling of all radial modes--achieving an order of magnitude speedup compared to direct cooling. We also show the technique's insensitivity to trap potential and control field fluctuations, and find that it still achieves sub-quanta temperatures starting as high as n~200.