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We present a novel approach to address the challenges of variable occupation numbers in direct optimization of density functional theory (DFT). By parameterizing both the eigenfunctions and the occupation matrix, our method minimizes the free energy with respect to these parameters. As the stationary conditions require the occupation matrix and the Kohn-Sham Hamiltonian to be simultaneously diagonalizable, this leads to the concept of ``self-diagonalization,'' where, by assuming a diagonal occupation matrix without loss of generality, the Hamiltonian matrix naturally becomes diagonal at stationary points. Our method incorporates physical constraints on both the eigenfunctions and the occupations into the parameterization, transforming the constrained optimization into an fully differentiable unconstrained problem, which is solvable via gradient descent. Implemented in JAX, our method was tested on aluminum and silicon, confirming that it achieves efficient self-diagonalization, produces the correct Fermi-Dirac distribution of the occupation numbers and yields band structures consistent with those obtained with SCF methods in Quantum Espresso.

Large language models (LLMs) have shown remarkable progress in encoding clinical knowledge and responding to complex medical queries with appropriate clinical reasoning. However, their applicability in subspecialist or complex medical settings remains underexplored. In this work, we probe the performance of AMIE, a research conversational diagnostic AI system, in the subspecialist domain of breast oncology care without specific fine-tuning to this challenging domain. To perform this evaluation, we curated a set of 50 synthetic breast cancer vignettes representing a range of treatment-naive and treatment-refractory cases and mirroring the key information available to a multidisciplinary tumor board for decision-making (openly released with this work). We developed a detailed clinical rubric for evaluating management plans, including axes such as the quality of case summarization, safety of the proposed care plan, and recommendations for chemotherapy, radiotherapy, surgery and hormonal therapy. To improve performance, we enhanced AMIE with the inference-time ability to perform web search retrieval to gather relevant and up-to-date clinical knowledge and refine its responses with a multi-stage self-critique pipeline. We compare response quality of AMIE with internal medicine trainees, oncology fellows, and general oncology attendings under both automated and specialist clinician evaluations. In our evaluations, AMIE outperformed trainees and fellows demonstrating the potential of the system in this challenging and important domain. We further demonstrate through qualitative examples, how systems such as AMIE might facilitate conversational interactions to assist clinicians in their decision making. However, AMIE's performance was overall inferior to attending oncologists suggesting that further research is needed prior to consideration of prospective uses.

We investigate the coevolution of metals and dust for 173 galaxies at 4.010 by JWST. Besides, we observe that the metallicity of galaxies at z>8 presents a metal-to-stellar mass ratio larger than a few 10^-3, above a floor. This suggests a very fast rise of metals at high redshift, impacting the tentative detections of population III objects., Comment: This paper is submitted. It contains a main paper with 5 figures and 2 tables, plus supplementary materials

It has recently been demonstrated empirically that in-context learning emerges in transformers when certain distributional properties are present in the training data, but this ability can also diminish upon further training. We provide a new theoretical understanding of these phenomena by identifying simplified distributional properties that give rise to the emergence and eventual disappearance of in-context learning. We do so by first analyzing a simplified model that uses a gating mechanism to choose between an in-weight and an in-context predictor. Through a combination of a generalization error and regret analysis we identify conditions where in-context and in-weight learning emerge. These theoretical findings are then corroborated experimentally by comparing the behaviour of a full transformer on the simplified distributions to that of the stylized model, demonstrating aligned results. We then extend the study to a full large language model, showing how fine-tuning on various collections of natural language prompts can elicit similar in-context and in-weight learning behaviour.

To achieve efficient long-range wireless power transfer (WPT), large antenna systems are necessary spanning lengths of tens to thousands of meters in one dimension. This creates an array in the order of at least hundreds of thousands to billions of elements. This makes the implementation of beamforming control a challenge. Various works focus on iterative optimization or channel estimation to maintain high efficiency in a time-varying environment requiring complex processing capabilities. A simpler alternative is the both-sides retrodirective antenna array (BS-RDAA) system where iterative optimization or channel estimation is not required. In a previous study, it was observed that this system achieves maximum WPT efficiency if the system is marginally stable. Thus, there is a need to regulate the system to maintain marginal stability regardless of the transmission channel conditions. In this paper, we present a plant model for the system and design a control system to drive it to marginal stability. The result is a WPT implementation that is not dependent on complex processing capabilities present in other established high efficiency methods. We also confirmed the ability of the proposed design to maintain maximum efficiency in a dynamic environment through the results of an electromagnetic simulator and a time domain simulator., Comment: 12 pages, 14 figure, this work has been submitted to the IEEE for possible publication

Effective monitoring of manufacturing processes is crucial for maintaining product quality and operational efficiency. Modern manufacturing environments generate vast amounts of multimodal data, including visual imagery from various perspectives and resolutions, hyperspectral data, and machine health monitoring information such as actuator positions, accelerometer readings, and temperature measurements. However, interpreting this complex, high-dimensional data presents significant challenges, particularly when labeled datasets are unavailable. This paper presents a novel approach to multimodal sensor data fusion in manufacturing processes, inspired by the Contrastive Language-Image Pre-training (CLIP) model. We leverage contrastive learning techniques to correlate different data modalities without the need for labeled data, developing encoders for five distinct modalities: visual imagery, audio signals, laser position (x and y coordinates), and laser power measurements. By compressing these high-dimensional datasets into low-dimensional representational spaces, our approach facilitates downstream tasks such as process control, anomaly detection, and quality assurance. We evaluate the effectiveness of our approach through experiments, demonstrating its potential to enhance process monitoring capabilities in advanced manufacturing systems. This research contributes to smart manufacturing by providing a flexible, scalable framework for multimodal data fusion that can adapt to diverse manufacturing environments and sensor configurations.

Let $\mathcal I=\{I_n\}$ be a divisorial filtration on a two dimensional normal excellent local ring $(R,m_R)$. Let $R[\mathcal I]=\oplus_{n\ge 0}I_n$ be the Rees algebra of $\mathcal I$ and $\tau:\mbox{Proj}R[\mathcal I])\rightarrow \mbox{Spec}(R)$ be the natural morphism. The reduced fiber cone of $\mathcal I$ is the $R$-algebra $R[\mathcal I]/\sqrt{m_RR[\mathcal I]}$, and the reduced exceptional fiber of $\tau$ is $\mbox{Proj}(R[\mathcal I]/\sqrt{m_RR[\mathcal I]})$. We give an explicit description of the scheme structure of $\mbox{Proj}(R[\mathcal I])$. As a corollary, we obtain a new proof of a theorem of F. Russo, showing that $\mbox{Proj}(R[\mathcal I])$ is always Noetherian and that $R[\mathcal I]$ is Noetherian if and only if $\mbox{Proj}(R[\mathcal I])$ is a proper $R$-scheme. We give an explicit description of the scheme structure of the reduced exceptional fiber $\mbox{Proj}(R[\mathcal I]/\sqrt{m_RR[\mathcal I]})$ of $\tau$, in terms of the possible values 0, 1 or 2 of the analytic spread $\ell(\mathcal I)=\dim R[\mathcal I]/m_RR[\mathcal I]$. In the case that $\ell(\mathcal I)=0$, $\tau^{-1}(m_R)$ is the emptyset; this case can only occur if $R[\mathcal I]$ is not Noetherian., Comment: Corrected typos in introduction. 26 pages

Recent advances in aligning large language models with human preferences have corroborated the growing importance of best-of-N distillation (BOND). However, the iterative BOND algorithm is prohibitively expensive in practice due to the sample and computation inefficiency. This paper addresses the problem by revealing a unified game-theoretic connection between iterative BOND and self-play alignment, which unifies seemingly disparate algorithmic paradigms. Based on the connection, we establish a novel framework, WIN rate Dominance (WIND), with a series of efficient algorithms for regularized win rate dominance optimization that approximates iterative BOND in the parameter space. We provides provable sample efficiency guarantee for one of the WIND variant with the square loss objective. The experimental results confirm that our algorithm not only accelerates the computation, but also achieves superior sample efficiency compared to existing methods.

Over the past decade, several millimeter interferometer programs have mapped the nearby star-forming galaxy M51 at a spatial resolution of ${\le}170$ pc. This study combines observations from three major programs: the PdBI Arcsecond Whirlpool Survey (PAWS), the SMA M51 large program (SMA-PAWS), and the Surveying the Whirlpool at Arcseconds with NOEMA (SWAN). The dataset includes the (1-0) and (2-1) rotational transitions of $^{12}$CO, $^{13}$CO, and C$^{18}$O isotopologues. The observations cover the $r{<}\rm 3\,kpc$ region including center and part of the disk, thereby ensuring strong detections of the weaker $^{13}$CO and C$^{18}$O lines. All observations are convolved in this analysis to an angular resolution of 4$''$, corresponding to a physical scale of ${\sim}$170 pc. We investigate empirical line ratio relations and quantitatively evaluate molecular gas conditions such as temperature, density, and the CO-to-H$_2$ conversion factor ($\alpha_{\rm CO}$). We employ two approaches to study the molecular gas conditions: (i) assuming local thermal equilibrium (LTE) to analytically determine the CO column density and $\alpha_{\rm CO}$, and (ii) using non-LTE modeling with RADEX to fit physical conditions to observed CO isotopologue intensities. We find that the $\alpha_{\rm CO}$ values {in the center and along the inner spiral arm} are $\sim$0.5 dex (LTE) and ${\sim}$0.1 dex (non-LTE) below the Milky Way inner disk value. The average non-LTE $\alpha_{\rm CO}$ is $2.4{\pm}0.5$ M$_\odot$ pc$^{-2}$ (K km s$^{-1}$)$^{-1}$. While both methods show dispersion due to underlying assumptions, the scatter is larger for LTE-derived values. This study underscores the necessity for robust CO line modeling to accurately constrain the molecular ISM's physical and chemical conditions in nearby galaxies., Comment: accepted for publication in AJ; 31 pages, 16 figures, 7 tables

Deep neural networks can struggle to learn continually in the face of non-stationarity. This phenomenon is known as loss of plasticity. In this paper, we identify underlying principles that lead to plastic algorithms. In particular, we provide theoretical results showing that linear function approximation, as well as a special case of deep linear networks, do not suffer from loss of plasticity. We then propose deep Fourier features, which are the concatenation of a sine and cosine in every layer, and we show that this combination provides a dynamic balance between the trainability obtained through linearity and the effectiveness obtained through the nonlinearity of neural networks. Deep networks composed entirely of deep Fourier features are highly trainable and sustain their trainability over the course of learning. Our empirical results show that continual learning performance can be drastically improved by replacing ReLU activations with deep Fourier features. These results hold for different continual learning scenarios (e.g., label noise, class incremental learning, pixel permutations) on all major supervised learning datasets used for continual learning research, such as CIFAR10, CIFAR100, and tiny-ImageNet.

As the signatories of this manifesto, we denounce the attention economy as inhumane and a threat to our sociopolitical and ecological well-being. We endorse policymakers' efforts to address the negative consequences of the attention economy's technology, but add that these approaches are often limited in their criticism of the systemic context of human attention. Starting from Buddhist philosophy, we advocate a broader approach: an ecology of attending, that centers on conceptualizing, designing, and using attention (1) in an embedded way and (2) focused on the alleviating of suffering. With 'embedded' we mean that attention is not a neutral, isolated mechanism but a meaning-engendering part of an 'ecology' of bodily, sociotechnical and moral frameworks. With 'focused on the alleviation of suffering' we explicitly move away from the (often implicit) conception of attention as a tool for gratifying desires., Comment: 21 pages, 1 figure

In this work we present source-tailored WISE mid-infrared photometry (at 3.4$\mu$m, 4.6$\mu$m, 12$\mu$m, and 23$\mu$m) of 2812 galaxies in the extended Spitzer Survey of Stellar Structure in Galaxies (S$^4$G) sample, and characterise the mid-infrared colors and dust properties of this legacy nearby galaxy data set. Informed by the relative emission between W3 (12$\mu$ m) and W4 (23$\mu$ m), we re-derive star formation rate (SFR) scaling relations calibrated to L$_{\rm TIR}$, which results in improved agreement between the two tracers. By inverse-variance weighting the W3 and W4-derived SFRs, we generate a combined mid-infrared SFR that is a broadly robust measure of star formation activity in dusty, star-forming galaxies in the nearby Universe. In addition, we investigate the use of a W3-derived dust density metric, $\Sigma_{\rm 12\mu m}$ (L$_\odot$/kpc$^2$), to estimate the SFR deficit of low mass, low dust galaxies. This is achieved by combining WISE with existing GALEX ultraviolet (UV) photometry, which we further use to explore the relationship between dust and UV emission as a function of morphology. Finally, we use our derived SFR prescriptions to examine the location of galaxies in the log SFR - log M$_\textrm{stellar}$ plane, as a function of morphological type, which underscores the complexity of dust-derived properties seen in galaxies of progressively earlier type., Comment: Accepted to ApJ

Consider the model of computation where we start with two halves of a $2n$-qubit maximally entangled state. We get to apply a universal quantum computation on one half, measure both halves at the end, and perform classical postprocessing. This model, which we call $\frac12$BQP, was defined in STOC 2017 [ABKM17] to capture the power of permutational computations on special input states. As observed in [ABKM17], this model can be viewed as a natural generalization of the one-clean-qubit model (DQC1) where we learn the content of a high entropy input state only after the computation is completed. An interesting open question is to characterize the power of this model, which seems to sit nontrivially between DQC1 and BQP. In this paper, we show that despite its limitations, this model can carry out many well-known quantum computations that are candidates for exponential speed-up over classical computations (and possibly DQC1). In particular, $\frac12$BQP can simulate Instantaneous Quantum Polynomial Time (IQP) and solve the Deutsch-Jozsa problem, Bernstein-Vazirani problem, Simon's problem, and period finding. As a consequence, $\frac12$BQP also solves Order Finding and Factoring outside of the oracle setting. Furthermore, $\frac12$BQP can solve Forrelation and the corresponding oracle problem given by Raz and Tal [RT22] to separate BQP and PH. We also study limitations of $\frac12$BQP and show that similarly to DQC1, $\frac12$BQP cannot distinguish between unitaries which are close in trace distance, then give an oracle separating $\frac12$BQP and BQP. Due to this limitation, $\frac12$BQP cannot obtain the quadratic speedup for unstructured search given by Grover's algorithm [Gro96]. We conjecture that $\frac12$BQP cannot solve $3$-Forrelation., Comment: Authors are listed in alphabetical order

JWST spectroscopy has discovered a population of $z \gtrsim 3.5$ galaxies with broad Balmer emission lines, and narrow forbidden lines, that are consistent with hosting active galactic nuclei (AGN). Many of these systems, now known as ``little red dots" (LRDs), are compact and have unique colors that are very red in the optical/near-infrared and blue in the ultraviolet. The relative contribution of galaxy starlight and AGN to these systems remains uncertain, especially for the galaxies with unusual blue+red spectral energy distributions. In this work, we use Balmer decrements to measure the independent dust attenuation of the broad and narrow emission-line components of a sample of 29 broad-line AGN identified from three public JWST spectroscopy surveys: CEERS, JADES, and RUBIES. Stacking the narrow components from the spectra of 25 sources with broad H$\rm{\alpha}$ and no broad H$\rm{\beta}$ results in a median narrow H$\rm{\alpha}$/H$\rm{\beta}$ = $2.47^{+0.05}_{-0.05}$ (consistent with $A_{v} = 0$) and broad H$\rm{\alpha}$/H$\rm{\beta}$ $> 8.85$ ($A_{v} > 3.63$). The narrow and broad Balmer decrements imply little-to-no attenuation of the narrow emission lines, which are consistent with being powered by star formation and located on larger physical scales. Meanwhile, the lower limit in broad H$\rm{\alpha}$/H$\rm{\beta}$ decrement, with broad H$\rm{\beta}$ undetected in the stacked spectrum of 25 broad-H$\rm{\alpha}$ AGN, implies significant dust attenuation of the broad-line emitting region that is presumably associated with the central AGN. Our results indicate that these systems, on average, are consistent with heavily dust-attenuated AGN powering the red parts of their SED while their blue UV emission is powered by unattenuated star formation in the host galaxy., Comment: 4 figures, 3 tables

Combining Atacama Large Millimeter/sub-millimeter Array CO(2-1) mapping and JWST near- and mid-infrared imaging, we characterize the relationship between CO(2-1) and polycyclic aromatic hydrocarbon (PAH) emission at ~100 pc resolution in 66 nearby star-forming galaxies, expanding the sample size from previous ~100 pc resolution studies by more than an order of magnitude. Focusing on regions of galaxies where most of the gas is likely to be molecular, we find strong correlations between CO(2-1) and 3.3 micron, 7.7 micron, and 11.3 micron PAH emission, estimated from JWST's F335M, F770W, and F1130W filters. We derive power law relations between CO(2-1) and PAH emission, which have indices in the range 0.8-1.2, implying relatively weak variations in the observed CO-to-PAH ratios across the regions that we study. We find that CO-to-PAH ratios and scaling relationships near HII regions are similar to those in diffuse sight lines. The main difference between the two types of regions is that sight lines near HII regions show higher intensities in all tracers. Galaxy centers, on the other hand, show higher overall intensities and enhanced CO-to-PAH ratios compared to galaxy disks. Individual galaxies show 0.19 dex scatter in the normalization of CO at fixed I_PAH and this normalization anti-correlates with specific star formation rate (SFR/M*) and correlates with stellar mass. We provide a prescription that accounts for these galaxy-to-galaxy variations and represents our best current empirical predictor to estimate CO(2-1) intensity from PAH emission, which allows one to take advantage of JWST's excellent sensitivity and resolution to trace cold gas., Comment: 21 pages, 4 figures, 3 tables. Submitted to ApJ

We show that autoregressive decoding of a transformer-based language model can realize universal computation, without external intervention or modification of the model's weights. Establishing this result requires understanding how a language model can process arbitrarily long inputs using a bounded context. For this purpose, we consider a generalization of autoregressive decoding where, given a long input, emitted tokens are appended to the end of the sequence as the context window advances. We first show that the resulting system corresponds to a classical model of computation, a Lag system, that has long been known to be computationally universal. By leveraging a new proof, we show that a universal Turing machine can be simulated by a Lag system with 2027 production rules. We then investigate whether an existing large language model can simulate the behaviour of such a universal Lag system. We give an affirmative answer by showing that a single system-prompt can be developed for gemini-1.5-pro-001 that drives the model, under deterministic (greedy) decoding, to correctly apply each of the 2027 production rules. We conclude that, by the Church-Turing thesis, prompted gemini-1.5-pro-001 with extended autoregressive (greedy) decoding is a general purpose computer., Comment: 32 pages

The structure and chemistry of the dusty interstellar medium (ISM) are shaped by complex processes that depend on the local radiation field, gas composition, and dust grain properties. Of particular importance are Polycyclic Aromatic Hydrocarbons (PAHs), which emit strong vibrational bands in the mid-infrared, and play a key role in the ISM energy balance. We recently identified global correlations between PAH band and optical line ratios across three nearby galaxies, suggesting a connection between PAH heating and gas ionization throughout the ISM. In this work, we perform a census of the PAH heating -- gas ionization connection using $\sim$700,000 independent pixels that probe scales of 40--150 pc in nineteen nearby star-forming galaxies from the PHANGS survey. We find a universal relation between $\log$PAH(11.3 \mic/7.7 \mic) and $\log$([SII]/H$\alpha$) with a slope of $\sim$0.2 and a scatter of $\sim$0.025 dex. The only exception is a group of anomalous pixels that show unusually high (11.3 \mic/7.7 \mic) PAH ratios in regions with old stellar populations and high starlight-to-dust emission ratios. Their mid-infrared spectra resemble those of elliptical galaxies. AGN hosts show modestly steeper slopes, with a $\sim$10\% increase in PAH(11.3 \mic/7.7 \mic) in the diffuse gas on kpc scales. This universal relation implies an emerging simplicity in the complex ISM, with a sequence that is driven by a single varying property: the spectral shape of the interstellar radiation field. This suggests that other properties, such as gas-phase abundances, gas ionization parameter, and grain charge distribution, are relatively uniform in all but specific cases., Comment: resubmitted to ApJ after addressing referee report; Figure 12 summarizes the results

The scarcity of subspecialist medical expertise, particularly in rare, complex and life-threatening diseases, poses a significant challenge for healthcare delivery. This issue is particularly acute in cardiology where timely, accurate management determines outcomes. We explored the potential of AMIE (Articulate Medical Intelligence Explorer), a large language model (LLM)-based experimental AI system optimized for diagnostic dialogue, to potentially augment and support clinical decision-making in this challenging context. We curated a real-world dataset of 204 complex cases from a subspecialist cardiology practice, including results for electrocardiograms, echocardiograms, cardiac MRI, genetic tests, and cardiopulmonary stress tests. We developed a ten-domain evaluation rubric used by subspecialists to evaluate the quality of diagnosis and clinical management plans produced by general cardiologists or AMIE, the latter enhanced with web-search and self-critique capabilities. AMIE was rated superior to general cardiologists for 5 of the 10 domains (with preference ranging from 9% to 20%), and equivalent for the rest. Access to AMIE's response improved cardiologists' overall response quality in 63.7% of cases while lowering quality in just 3.4%. Cardiologists' responses with access to AMIE were superior to cardiologist responses without access to AMIE for all 10 domains. Qualitative examinations suggest AMIE and general cardiologist could complement each other, with AMIE thorough and sensitive, while general cardiologist concise and specific. Overall, our results suggest that specialized medical LLMs have the potential to augment general cardiologists' capabilities by bridging gaps in subspecialty expertise, though further research and validation are essential for wide clinical utility.

We present a chemical analysis of selected HII regions from the PHANGS-MUSE nebular catalogue. Our intent is to empirically re-calibrate strong-line diagnostics of gas-phase metallicity, applicable across a wide range of metallicities within nearby star-forming galaxies. To ensure reliable measurements of auroral line fluxes, we carried out a new spectral fitting procedure whereby only restricted wavelength regions around the emission lines of interest are taken into account: this assures a better fit for the stellar continuum. No prior cuts to nebulae luminosity were applied to limit biases in auroral line detections. Ionic abundances of O+, O++, N+, S+, and S++ were estimated by applying the direct method. We integrated the selected PHANGS-MUSE sample with other existing auroral line catalogues, appropriately re-analysed to obtain a homogeneous dataset. This was used to derive strong-line diagnostic calibrations that span from 12+log(O/H) = 7.5 to 8.8. We investigate their dependence on the ionisation parameter and conclude that it is likely the primary cause of the significant scatter observed in these diagnostics. We apply our newly calibrated strong-line diagnostics to the total sample of HII regions from the PHANGS-MUSE nebular catalogue, and we exploit these indirect metallicity estimates to study the radial metallicity gradient within each of the 19 galaxies of the sample. We compare our results with the literature and find good agreement, validating our procedure and findings. With this paper, we release the full catalogue of auroral and nebular line fluxes for the selected HII regions from the PHANGS-MUSE nebular catalogue. This is the first catalogue of direct chemical abundance measurements carried out with PHANGS-MUSE data., Comment: 20 pages, 14 figures, 6 tables; accepted for publication in Astronomy & Astrophysics

The James Webb Space Telescope (JWST) has enabled us to uncover faint galaxies and active galactic nuclei (AGNs) in the early universe. Taking advantage of the unique filter combination used in the Cosmic Evolution Early Release Science Survey (CEERS) program, we perform an extensive photometric search of galaxies emitting strong H$\beta+$[O III] and H$\alpha$ lines. The redshift range of the galaxies is limited to $5.03\leq z\leq 5.26$ by requiring photometric excesses in NIRCam's F277W and F410M images. A total of 261 H$\beta+$[O III] and H$\alpha$ dual-line emitters are found over the absolute UV magnitude $-22\lesssim M_{\mathrm{UV}}\lesssim -17$, with a mean rest-frame equivalent width of 1010 A for H$\beta+$[O III] and 1040 A for H$\alpha$. This population accounts for $\sim 40\%$ of the Lyman break galaxies at this redshift range. Intriguingly, there are 58 objects (22% of the whole sample) that exhibit compact morphology at the rest-UV or optical wavelength. With an assumption that these compact dual-line emitters are dominated by AGN, their AGN bolometric luminosities are in the range of $2\times 10^{43} \lesssim L_{\rm bol}/({\rm erg~s}^{-1})\lesssim 3\times 10^{44}$. Their number density is two orders of magnitude higher than the extrapolation from the UV-selected luminous quasars, which is in good agreement with previous JWST studies of broad-line AGNs, requiring a $\sim 10\%$ of the AGN duty cycle. Moreover, our dual-line emitter sample reaches the faint end of the H$\alpha$ and [O III] luminosity functions down to $\lesssim 10^{42}~{\rm erg~s}^{-1}$. Spectroscopic follow-up observations are planned in an approved JWST Cycle 3 program, in which we aim to confirm their nature, characterize their black hole activity, and construct their mass distribution at $10^6\lesssim M_{\rm BH}/M_\odot \lesssim 10^8$., Comment: 24 pages, 16 figures, 5 tables. submitted to ApJ

A key challenge for computationally intensive state-of-the-art Earth-system models is to distinguish global warming signals from interannual variability. Recently machine learning models have performed better than state-of-the-art numerical weather prediction models for medium-range forecasting. Here we introduce DLESyM, a parsimonious deep learning model that accurately simulates the Earth's current climate over 1000-year periods with negligible drift. DLESyM simulations equal or exceed key metrics of seasonal and interannual variability--such as tropical cyclone genesis and intensity, and mid-latitude blocking frequency--for historical simulations from four leading models from the 6th Climate Model Intercomparison Project. DLESyM, trained on both historical reanalysis data and satellite observations, is a key step toward an accurate highly efficient model of the coupled Earth system, empowering long-range sub-seasonal and seasonal forecasts., Comment: 24 Pages, 20 figures

A multitude of JWST studies reveal a surprising over-abundance of over-massive accreting super-massive blackholes (SMBHs) -- leading to a deepening tension between theory and observation in the first billion years of cosmic time. Across X-ray to infrared wavelengths, models built off of pre-JWST predictions fail to easily reproduce observed AGN signatures (or lack thereof), driving uncertainty around the true nature of these sources. Using a sample of JWST AGN identified via their broadened Halpha emission and covered by the deepest X-ray surveys, we find neither any measurable X-ray emission nor any detection of high-ionization emission lines frequently associated with accreting SMBHs. We propose that these sources are accreting at or beyond the Eddington limit, which reduces the need for efficient production of heavy SMBH seeds at cosmic dawn. Using a theoretical model of super-Eddington accretion, we can produce the observed relative dearth of both X-ray and ultraviolet emission, as well as the high Balmer decrements, without the need for significant dust attenuation. This work indicates that super-Eddington accretion is easily achieved through-out the early Universe, and further study is required to determine what environments are required to trigger this mode of black hole growth., Comment: Submitted

For many equation-theoretical questions about modular lattices, Hall and Dilworth give a useful construction: Let $L_0$ be a lattice with largest element $u_0$, $L_1$ be a lattice disjoint from $L_0$ with smallest element $v_1$, and $a \in L_0$, $b \in L_1$ such that the intervals $[a, u_0]$ and $[v_1, b]$ are isomorphic. Then, after identifying those intervals you obtain $L_0 \cup L_1$, a lattice structure whose partial order is the transitive relation generated by the partial orders of $L_0$ and $L_1$. It is modular if $L_0$ and $L_1$ are modular. Since in this construction the index set $\{0, 1\}$ is essentially a chain, this work presents a method -- termed S-glued -- whereby a general family $L_x\ (x \in S)$ of lattices can specify a lattice with the small-scale lattice structure determined by the $L_x$ and the large-scale structure determined by $S$. A crucial application is representing finite-length modular lattices using projective geometries., Comment: 16 pages, 3 figures. German original by Christian Herrmann, English translation by Dale R. Worley. Added a footnote 1 to give more detail about Dilworth's work on the subject

This Report summarizes findings and recommendations from the Kavli-IAU workshop on "Probing the Universe from far-infrared to millimeter wavelengths: future facilities and their synergies" which took place from 26 to 28 March 2024 in Pasadena, CA, USA. The workshop aimed to define the needs and potential synergies for different facilities at wavelengths from 30 {\mu}m to a few cm in the post-2030 era, considering both financial and programmatic limitations and exploring how to maximize the scientific insights from the data they will yield in the coming decades. This wavelength range provides unique probes of relatively cool, dense interstellar material central to studying the physics and chemistry of nascent stars, proton-planetary disks, and young forming exoplanets. On larger scales, these facilities probe dust and dense gas in galaxies and around highly obscured accreting supermassive black holes and are thus essential for characterizing feedback processes and galaxy evolution out to the highest redshifts. Solar system and time domain studies are also addressed. The main recommendations include the need for ALMA to develop an ALMA2040 vision; for ngVLA to maintain its momentum and schedule and further develop international partnerships; for far-IR astronomy to pursue a space-based observatory with urgency; and for large aperture, wide field millimeter/submillimeter telescopes to continue studies to mature science and technology.

We present a sample of 50 H-alpha detected broad-line active galactic nuclei (BLAGN) at redshifts 3.50), independent of the contributions of emission lines to the broadband photometry. We construct the black hole (BH) mass function at 3.5

Generative models trained at scale can now produce text, video, and more recently, scientific data such as crystal structures. In applications of generative approaches to materials science, and in particular to crystal structures, the guidance from the domain expert in the form of high-level instructions can be essential for an automated system to output candidate crystals that are viable for downstream research. In this work, we formulate end-to-end language-to-structure generation as a multi-objective optimization problem, and propose Generative Hierarchical Materials Search (GenMS) for controllable generation of crystal structures. GenMS consists of (1) a language model that takes high-level natural language as input and generates intermediate textual information about a crystal (e.g., chemical formulae), and (2) a diffusion model that takes intermediate information as input and generates low-level continuous value crystal structures. GenMS additionally uses a graph neural network to predict properties (e.g., formation energy) from the generated crystal structures. During inference, GenMS leverages all three components to conduct a forward tree search over the space of possible structures. Experiments show that GenMS outperforms other alternatives of directly using language models to generate structures both in satisfying user request and in generating low-energy structures. We confirm that GenMS is able to generate common crystal structures such as double perovskites, or spinels, solely from natural language input, and hence can form the foundation for more complex structure generation in near future., Comment: https://generative-materials.github.io/

Fusion is a potentially transformational energy technology, which promises limitless clean energy. Yet, it requires continued scientific and technological development to realize its potential. The conditions necessary for fusion energy gain in terms of the product of plasma pressure $P$ and confinement time $\tau$ have been known for many decades. An underappreciated fact is that pulsed magnetic fusion has demonstrated $P \tau$ performance on par with laser-driven ICF and tokamaks despite receiving only a small fraction of investment relative to those concepts. In light of this demonstrated performance, well-established scaling relations, and opportunities for further innovations, here we advocate for pulsed magnetic fusion as the most attractive path towards commercialization of fusion energy.

Lack of low phonon energy glass waveguides delays the progress of integrated photonics in the mid-infrared wavelength. This perspective provides insights on the history of waveguide production in the past and what to expect in the near future., Comment: 4 Pages, 3 figures

The ``Little Red Dots'' (LRDs) are red and compact galaxies detected in JWST deep fields, mainly in the redshift range $z=4-8$. Given their compactness and the inferred stellar masses in the hypothesis that LRDs are starburst galaxies, the implied stellar densities are immense. This Research Note uses an extensive catalog of LRDs from the PRIMER and the COSMOS-Web surveys to investigate these densities. We find a median (upper limit) on the effective radius of $80$ pc, which leads to median (lower limit) values of the core density of $\sim 10^4 \, \rm M_\odot \, pc^{-3}$, and individual densities as high as $\sim 10^8 \, \rm M_\odot \, pc^{-3}$, which is $\sim 10$ times higher than the density necessary for runaway collisions to take place. For $\sim 35\%$ of the LRDs investigated, the lower limits are higher than the highest stellar densities observed in any system in any redshift range., Comment: Accepted on Research Notes of the AAS. This is a concise note (3 pages, 1 figure) regarding the inferred stellar densities for 475 LRDs from the PRIMER and the COSMOS-Web surveys

The use of multiple modalities (e.g., face and fingerprint) or multiple algorithms (e.g., three face comparators) has shown to improve the recognition accuracy of an operational biometric system. Over time a biometric system may evolve to add new modalities, retire old modalities, or be merged with other biometric systems. This can lead to scenarios where there are missing scores corresponding to the input probe set. Previous work on this topic has focused on either the verification or identification tasks, but not both. Further, the proportion of missing data considered has been less than 50%. In this work, we study the impact of missing score data for both the verification and identification tasks. We show that the application of various score imputation methods along with simple sum fusion can improve recognition accuracy, even when the proportion of missing scores increases to 90%. Experiments show that fusion after score imputation outperforms fusion with no imputation. Specifically, iterative imputation with K nearest neighbors consistently surpasses other imputation methods in both the verification and identification tasks, regardless of the amount of scores missing, and provides imputed values that are consistent with the ground truth complete dataset., Comment: 2022 26th International Conference on Pattern Recognition (ICPR)

The performance of a particle detector derived from nitride read-only memory (NROM) technology is evaluated, with immediate applications in space-based heavy ion radiation monitoring and detection. Irradiation exposures are performed using 40 MeV/u $^{78}$Kr and 10 MeV/u $^4$He particle beams at the Texas A&M University Cyclotron Institute. The results show a strong sensitivity to high-Z heavy ions, and medium sensitivity to low-Z heavy ions.

Storm-scale convection-allowing models (CAMs) are an important tool for predicting the evolution of thunderstorms and mesoscale convective systems that result in damaging extreme weather. By explicitly resolving convective dynamics within the atmosphere they afford meteorologists the nuance needed to provide outlook on hazard. Deep learning models have thus far not proven skilful at km-scale atmospheric simulation, despite being competitive at coarser resolution with state-of-the-art global, medium-range weather forecasting. We present a generative diffusion model called StormCast, which emulates the high-resolution rapid refresh (HRRR) model-NOAA's state-of-the-art 3km operational CAM. StormCast autoregressively predicts 99 state variables at km scale using a 1-hour time step, with dense vertical resolution in the atmospheric boundary layer, conditioned on 26 synoptic variables. We present evidence of successfully learnt km-scale dynamics including competitive 1-6 hour forecast skill for composite radar reflectivity alongside physically realistic convective cluster evolution, moist updrafts, and cold pool morphology. StormCast predictions maintain realistic power spectra for multiple predicted variables across multi-hour forecasts. Together, these results establish the potential for autoregressive ML to emulate CAMs -- opening up new km-scale frontiers for regional ML weather prediction and future climate hazard dynamical downscaling.

We present new JWST observations of the nearby, prototypical edge-on, spiral galaxy NGC 891. The northern half of the disk was observed with NIRCam in its F150W and F277W filters. Absorption is clearly visible in the mid-plane of the F150W image, along with vertical dusty plumes that closely resemble the ones seen in the optical. A $\sim 10 \times 3~{\rm kpc}^2$ area of the lower circumgalactic medium (CGM) was mapped with MIRI F770W at 12 pc scales. Thanks to the sensitivity and resolution of JWST, we detect dust emission out to $\sim 4$ kpc from the disk, in the form of filaments, arcs, and super-bubbles. Some of these filaments can be traced back to regions with recent star formation activity, suggesting that feedback-driven galactic winds play an important role in regulating baryonic cycling. The presence of dust at these altitudes raises questions about the transport mechanisms at play and suggests that small dust grains are able to survive for several tens of million years after having been ejected by galactic winds in the disk-halo interface. We lay out several scenarios that could explain this emission: dust grains may be shielded in the outer layers of cool dense clouds expelled from the galaxy disk, and/or the emission comes from the mixing layers around these cool clumps where material from the hot gas is able to cool down and mix with these cool cloudlets. This first set of data and upcoming spectroscopy will be very helpful to understand the survival of dust grains in energetic environments, and their contribution to recycling baryonic material in the mid-plane of galaxies., Comment: Accepted for publication in Astronomy & Astrophysics; 16 pages, 8 figures

Combining match scores from different biometric systems via fusion is a well-established approach to improving recognition accuracy. However, missing scores can degrade performance as well as limit the possible fusion techniques that can be applied. Imputation is a promising technique in multibiometric systems for replacing missing data. In this paper, we evaluate various score imputation approaches on three multimodal biometric score datasets, viz. NIST BSSR1, BIOCOP2008, and MIT LL Trimodal, and investigate the factors which might influence the effectiveness of imputation. Our studies reveal three key observations: (1) Imputation is preferable over not imputing missing scores, even when the fusion rule does not require complete score data. (2) Balancing the classes in the training data is crucial to mitigate negative biases in the imputation technique towards the under-represented class, even if it involves dropping a substantial number of score vectors. (3) Multivariate imputation approaches seem to be beneficial when scores between modalities are correlated, while univariate approaches seem to benefit scenarios where scores between modalities are less correlated., Comment: Proc. of IEEE International Workshop on Information Forensics and Security (WIFS), (Nuremberg, Germany), December 2023

We present a near infrared (NIR) candidate star cluster catalog for the central kiloparsec of M82 based on new JWST NIRCam images. We identify star cluster candidates using the F250M filter, finding 1357 star cluster candidates with stellar masses $>10^4$ M$_\odot$. Compared to previous optical catalogs, nearly all (87%) of the candidates we identify are new. The star cluster candidates have a median intrinsic cluster radius of $\approx$1 pc and have stellar masses up to $10^6$ M$_\odot$. By comparing the color-color diagram to dust-free yggdrasil stellar population models, we estimate that the star cluster candidates have A$_{\rm V}\sim3-24$ mag, corresponding to A$_{\rm 2.5\mu m}\sim0.3-2.1$ mag. There is still appreciable dust extinction towards these clusters into the NIR. We measure the stellar masses of the star cluster candidates, assuming ages of 0 and 8 Myr. The slope of the resulting cluster mass function is $\beta=1.9\pm0.2$, in excellent agreement with studies of star clusters in other galaxies., Comment: Resubmitted to ApJL

The two main competing theories proposed to explain the formation of massive ($>10$M$_\odot$) stars -- competitive accretion and monolithic core collapse -- make different observable predictions for the environment of the massive stars during, and immediately after, their formation. Proponents of competitive accretion have long predicted that the most massive stars should have a different spatial distribution to lower-mass stars, either through the stars being mass segregated, or being in areas of higher relative densities, or sitting deeper in gravitational potential wells. We test these predictions by analysing a suite of SPH simulations where star clusters form massive stars via competitive accretion with and without feedback. We find that the most massive stars have higher relative densities, and sit in deeper potential wells, only in simulations in which feedback is not present. When feedback is included, only half of the simulations have the massive stars residing in deeper potential wells, and there are no other distinguishing signals in their spatial distributions. Intriguingly, in our simple models for monolithic core collapse, the massive stars may also end up in deeper potential wells, because if massive cores fragment the stars are still massive, and dominate their local environs. We find no robust diagnostic test in the spatial distributions of massive stars that can distinguish their formation mechanisms, and so other predictions for distinguishing between competitive accretion and monolithic collapse are required., Comment: 18 pages, 7 figures, with an Appendix containing a further 18 figures showing all the SPH simulation results. Accepted for publication in ApJ

We investigate the scattering properties and bound states of a quasi-two-dimensional (q2D) spin-polarized Fermi gas near a $p$-wave Feshbach resonance. Strong confinement promotes the out-of-plane spatial wave functions to a discrete, gapped orbital degree of freedom. Exchange-antisymmetric orbital pair wave functions are predicted to give rise to low-energy q2D interactions with $s$-wave symmetry. Using radiofrequency (rf) spectroscopy, we observe the signature power-law scaling and the dimensional-crossover feature anticipated for the emergent $s$-wave channel. Additionally, we demonstrate that two types of low-energy dimers, with either $s$-wave and $p$-wave symmetry, could be formed via rf spin-flip association from an orbital mixture. These findings illustrate how gapped orbital degrees of freedom can provide additional control over scattering symmetries in strongly confined ultracold gases.

A crucial yet challenging task in galaxy evolution studies is the identification of distant merging galaxies, a task which suffers from a variety of issues ranging from telescope sensitivities and limitations to the inherently chaotic morphologies of young galaxies. In this paper, we use random forests and convolutional neural networks to identify high-redshift JWST CEERS galaxy mergers. We train these algorithms on simulated $3

Asteroseismology of dwarf stars cooler than the Sun is very challenging due to the low amplitudes and rapid timescales of oscillations. Here, we present the asteroseismic detection of solar-like oscillations at 4-minute timescales ($\nu_{\mathrm{max}}\sim4300\mu$Hz) in the nearby K-dwarf $\sigma$ Draconis using extreme precision Doppler velocity observations from the Keck Planet Finder and 20-second cadence photometry from NASA's Transiting Exoplanet Survey Satellite. The star is the coolest dwarf star to date with both velocity and luminosity observations of solar-like oscillations, having amplitudes of $5.9\pm0.8\,$cm$\,\text{s}^{-1}$ and $0.8\pm0.2$ ppm, respectively. These measured values are in excellent agreement with established luminosity-velocity amplitude relations for oscillations and provide further evidence that mode amplitudes for stars with $T_{\mathrm{eff}}<\,5500\,$K diminish in scale following a $(L/M)^{1.5}$ relation. By modeling the star's oscillation frequencies from photometric data, we measure an asteroseismic age of $4.5\pm0.9\,\rm{(ran)} \pm 1.2\,\rm{(sys)}$ Gyr. The observations demonstrate the capability of next-generation spectrographs and precise space-based photometry to extend observational asteroseismology to nearby cool dwarfs, which are benchmarks for stellar astrophysics and prime targets for directly imaging planets using future space-based telescopes., Comment: Accepted for publication in The Astrophysical Journal

Low-dimensional ultracold gases are created in the laboratory by confining three-dimensional (3D) gases inside highly anisotropic trapping potentials. Such trap geometries not only provide access to simulating one-dimensional (1D) and two-dimensional (2D) physics, but also can be used to study how the system crosses over towards a 3D system in the limit of weak confinement. In this work, we study the signature in radio-frequency (RF) spectroscopy for both the 1D-to-3D and the 2D-to-3D crossovers, in spin-polarized Fermi gases. We solve the two-body scattering T-matrix in the presence of strong harmonic confinement and use it to evaluate the two-body bound state and the RF spectroscopy transfer rate in the high frequency limit, covering both the quasi-low-dimensional and 3D limits. We find that in order to understand the dimensional crossover for spin-polarized Fermi gases with p-wave interactions, one needs to take into account an emergent s-wave interaction., Comment: 16 pages, 5 figures, 4 appendices

Background: Low vitamin D intake and high prevalence of vitamin D deficiency (serum 25-hydroxyvitamin D concentration < 50 nmol/L) among Aboriginal and Torres Strait Islander peoples highlight a need for public health strategies to improve vitamin D status. As few foods contain naturally occurring vitamin D, fortification strategies may be needed to improve vitamin D intake and status among Aboriginal and Torres Strait Islander peoples. Objective: We aimed to model vitamin D food fortification scenarios among Aboriginal and Torres Strait Islander peoples. Methods: We used nationally representative food consumption data (n=4,109) and vitamin D food composition data to model four food fortification scenarios. The modelling for Scenario 1 included foods and maximum vitamin D concentrations permitted for fortification in Australia: i) dairy products and alternatives, ii) butter/margarine/oil spreads, iii) formulated beverages, and iv) selected ready-to-eat breakfast cereals. The modelling for Scenarios 2a-c included some vitamin D concentrations higher than permitted in Australia; Scenario 2c included bread, which is not permitted for vitamin D fortification in Australia. Scenario 2a: i) dairy products and alternatives, ii) butter/margarine/oil spreads, iii) formulated beverages. Scenario 2b: as per Scenario 2a plus selected ready-to-eat breakfast cereals. Scenario 2c: as per Scenario 2b plus bread. Results: Vitamin D fortification of a range of staple foods could potentially increase vitamin D intake among Aboriginal and Torres Strait Islander peoples by ~ 3-6 {\mu}g/day. Scenario 2c showed the highest potential median vitamin D intake increase to ~ 8 {\mu}g/day. Across all modelled scenarios, none of the participants had vitamin D intake above the Australian upper level of intake of 80 {\mu}g/day.

How can intelligent machines assess their competencies in completing tasks? This question has come into focus for autonomous systems that algorithmically reason and make decisions under uncertainty. It is argued here that machine self-confidence - a form of meta-reasoning based on self-assessments of an agent's knowledge about the state of the world and itself, as well as its ability to reason about and execute tasks - leads to many eminently computable and useful competency indicators for such agents. This paper presents a culmination of work on this concept in the form of a computational framework called Factorized Machine Self-confidence (FaMSeC), which provides a holistic engineering-focused description of factors driving an algorithmic decision-making process, including: outcome assessment, solver quality, model quality, alignment quality, and past experience. In FaMSeC, self confidence indicators are derived from hierarchical `problem-solving statistics' embedded within broad classes of probabilistic decision-making algorithms such as Markov decision processes. The problem-solving statistics are obtained by evaluating and grading probabilistic exceedance margins with respect to given competency standards, which are specified for each of the various decision-making competency factors by the informee (e.g. a non-expert user or an expert system designer). This approach allows `algorithmic goodness of fit' evaluations to be easily incorporated into the design of many kinds of autonomous agents in the form of human-interpretable competency self-assessment reports. Detailed descriptions and application examples for a Markov decision process agent show how two of the FaMSeC factors (outcome assessment and solver quality) can be computed and reported for a range of possible tasking contexts through novel use of meta-utility functions, behavior simulations, and surrogate prediction models., Comment: 59 pages, 22 figures, draft to be submitted for journal review

In this paper we investigate some properties of Rees algebras of divisorial filtrations and their analytic spread. A classical theorem of McAdam shows that the analytic spread of an ideal $I$ in a formally equidimensional local ring is equal to the dimension of the ring if and only if the maximal ideal is an associated prime of $R/\overline{I^n}$ for some $n$. We show in Theorem 1.6 that McAdam's theorem holds for $\mathbb Q$-divisorial filtrations in an equidimensional local ring which is essentially of finite type over a field. This generalizes an earlier result for $\mathbb Q$-divisorial filtrations in an equicharacteristic zero excellent local domain by the author. This theorem does not hold for more general filtrations. We consider the question of the asymptotic behavior of the function $n\mapsto \lambda_R(R/I_n)$ for a $\mathbb Q$-divisorial filtration $\mathcal I=\{I_n\}$ of $m_R$-primary ideals on a $d$-dimensional normal excellent local ring. It is known from earlier work of the author that the multiplicity $$ e(\mathcal I)=d! \lim_{n\rightarrow\infty}\frac{\lambda_R(R/I_n)}{n^d} $$ can be irrational. We show in Lemma 4.1 that the limsup of the first difference function $$ \limsup_{n\rightarrow\infty}\frac{\lambda_R(I_n/I_{n+1})}{n^{d-1}} $$ is always finite for a $\mathbb Q$-divisorial filtration. We then give an example in Section 4 showing that this limsup may not exist as a limit. In the final section, we give an example of a symbolic filtration $\{P^{(n)}\}$ of a prime ideal $P$ in a normal two dimensional excellent local ring which has the property that the set of Rees valuations of all the symbolic powers $P^{(n)}$ of $P$ is infinite., Comment: 25 pages