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In this work we present an a priori error analysis for solving the unsteady advection equation on cut cell meshes along a straight ramp in two dimensions. The space discretization uses a lowest order upwind-type discontinuous Galerkin scheme involving a \textit{Domain of Dependence} (DoD) stabilization to correct the update in the neighborhood of small cut cells. Thereby, it is possible to employ explicit time stepping schemes with a time step length that is independent of the size of the very small cut cells. Our error analysis is based on a general framework for error estimates for first-order linear partial differential equations that relies on consistency, boundedness, and discrete dissipation of the discrete bilinear form. We prove these properties for the space discretization involving DoD stabilization. This allows us to prove, for the fully discrete scheme, a quasi-optimal error estimate of order one half in a norm that combines the $L^\infty$-in-time $L^2$-in-space norm and a seminorm that contains velocity weighted jumps. We also provide corresponding numerical results.

Important advances have recently been made in the search for materials with complex multi-phase landscapes that host photoinduced metastable collective states with exotic functionalities. In almost all cases so far, the desired phases are accessed by exploiting light-matter interactions via the imaginary part of the dielectric function through above-bandgap or resonant mode excitation. Nonresonant Raman excitation of coherent modes has been experimentally observed and proposed for dynamic material control, but the resulting atomic excursion has been limited to perturbative levels. Here, we demonstrate that it is possible to overcome this challenge by employing nonresonant ultrashort pulses with low photon energies well below the bandgap. Using mid-infrared pulses, we induce ferroelectric reversal in lithium niobate and phase switching in tin selenide and characterize the large-amplitude mode displacements through femtosecond Raman scattering, second harmonic generation, and x-ray diffraction. This approach, validated by first-principle calculations, defines a novel method for synthesizing hidden phases with unique functional properties and manipulating complex energy landscapes at reduced energy consumption and ultrafast speeds., Comment: 5 figures

AIs can beat humans in game environments; however, how helpful those agents are to human remains understudied. We augment CICERO, a natural language agent that demonstrates superhuman performance in Diplomacy, to generate both move and message advice based on player intentions. A dozen Diplomacy games with novice and experienced players, with varying advice settings, show that some of the generated advice is beneficial. It helps novices compete with experienced players and in some instances even surpass them. The mere presence of advice can be advantageous, even if players do not follow it., Comment: 9 pages, 3 figures

Minimoons are asteroids that become temporarily captured by the Earth-Moon system. We present the discovery of 2024 PT$_5$, a minimoon discovered by the Asteroid Terrestrial-impact Last Alert System (ATLAS) Sutherland telescope on 2024 August 7. The minimoon with heliocentric semi-major axis, $a$$\sim$1.01 au, and perihelion, $q$$\sim$0.99 au, became captured by the Earth-Moon system on 2024 September 29 and left on 2024 November 25 UTC. Visible g, r, i, and Z spectrophotometry was obtained using Gemini North/Gemini Multi-Object Spectrograph (GMOS) on 2024 September 27. The color indices are g-r = 0.58$\pm$0.04, r-i = 0.29$\pm$0.04, i-Z = -0.27$\pm$0.06, and the spectrum best matches lunar rock samples followed by S-complex asteroids. Assuming an albedo of 0.21 and using our measured absolute magnitude of 28.64$\pm$0.04, 2024 PT$_5$ has a diameter of 5.4$\pm$1.3 m. We also detect variations in the lightcurve of 2024 PT$_5$ with a 0.28$\pm$0.07 magnitude amplitude and a double-peaked period of $\sim$2600$\pm$500 s. We improve the orbital solution of 2024 PT$_5$ with our astrometry and estimate the effect of radiation pressure on its deriving an area-to-mass ratio of 7.02$\pm$2.05$\times$10$^{-5}$ m$^2$/kg, implying a density of $\sim$3.9$\pm$2.1 g/cm$^3$, compatible with having a rocky composition. If we assume 2024 PT$_5$ is from the NEO population, its most likely sources are resonances in the inner Main Belt by comparing its orbit with the NEO population model, though this does not exclude a lunar origin., Comment: 23 pages, 7 figures, 3 tables

Trapped ions provide a highly controlled platform for quantum sensors, clocks, simulators, and computers, all of which depend on cooling ions close to their motional ground state. Existing methods like Doppler, resolved sideband, and dark resonance cooling balance trade-offs between the final temperature and cooling rate. A traveling polarization gradient has been shown to cool multiple modes quickly and in parallel, but utilizing a stable polarization gradient can achieve lower ion energies, while also allowing more tailorable light-matter interactions in general. In this paper, we demonstrate cooling of a trapped ion below the Doppler limit using a phase-stable polarization gradient created using trap-integrated photonic devices. At an axial frequency of $2\pi\cdot1.45~ \rm MHz$ we achieve $\langle n \rangle = 1.3 \pm 1.1$ in $500~\mu \rm s$ and cooling rates of ${\sim}0.3 \, \rm quanta/\mu s$. We examine ion dynamics under different polarization gradient phases, detunings, and intensities, showing reasonable agreement between experimental results and a simple model. Cooling is fast and power-efficient, with improved performance compared to simulated operation under the corresponding running wave configuration., Comment: 16 pages, 11 figures

Large Language Models (LLMs) have demonstrated remarkable capabilities in conversational tasks. Embodying an LLM as a virtual human allows users to engage in face-to-face social interactions in Virtual Reality. However, the influence of person- and task-related factors in social interactions with LLM-controlled agents remains unclear. In this study, forty-six participants interacted with a virtual agent whose persona was manipulated as extravert or introvert in three different conversational tasks (small talk, knowledge test, convincing). Social-evaluation, emotional experience, and realism were assessed using ratings. Interactive engagement was measured by quantifying participants' words and conversational turns. Finally, we measured participants' willingness to ask the agent for help during the knowledge test. Our findings show that the extraverted agent was more positively evaluated, elicited a more pleasant experience and greater engagement, and was assessed as more realistic compared to the introverted agent. Whereas persona did not affect the tendency to ask for help, participants were generally more confident in the answer when they had help of the LLM. Variation of personality traits of LLM-controlled embodied virtual agents, therefore, affects social-emotional processing and behavior in virtual interactions. Embodied virtual agents allow the presentation of naturalistic social encounters in a virtual environment., Comment: 11 pages, 5 figures

Human writers plan, then write. For large language models (LLMs) to play a role in longer-form article generation, we must understand the planning steps humans make before writing. We explore one kind of planning, source-selection in news, as a case-study for evaluating plans in long-form generation. We ask: why do specific stories call for specific kinds of sources? We imagine a generative process for story writing where a source-selection schema is first selected by a journalist, and then sources are chosen based on categories in that schema. Learning the article's plan means predicting the schema initially chosen by the journalist. Working with professional journalists, we adapt five existing schemata and introduce three new ones to describe journalistic plans for the inclusion of sources in documents. Then, inspired by Bayesian latent-variable modeling, we develop metrics to select the most likely plan, or schema, underlying a story, which we use to compare schemata. We find that two schemata: stance and social affiliation best explain source plans in most documents. However, other schemata like textual entailment explain source plans in factually rich topics like "Science". Finally, we find we can predict the most suitable schema given just the article's headline with reasonable accuracy. We see this as an important case-study for human planning, and provides a framework and approach for evaluating other kinds of plans. We release a corpora, NewsSources, with annotations for 4M articles., Comment: 9 pages

The deployment of ML models on edge devices is challenged by limited computational resources and energy availability. While split computing enables the decomposition of large neural networks (NNs) and allows partial computation on both edge and cloud devices, identifying the most suitable split layer and hardware configurations is a non-trivial task. This process is in fact hindered by the large configuration space, the non-linear dependencies between software and hardware parameters, the heterogeneous hardware and energy characteristics, and the dynamic workload conditions. To overcome this challenge, we propose DynaSplit, a two-phase framework that dynamically configures parameters across both software (i.e., split layer) and hardware (e.g., accelerator usage, CPU frequency). During the Offline Phase, we solve a multi-objective optimization problem with a meta-heuristic approach to discover optimal settings. During the Online Phase, a scheduling algorithm identifies the most suitable settings for an incoming inference request and configures the system accordingly. We evaluate DynaSplit using popular pre-trained NNs on a real-world testbed. Experimental results show a reduction in energy consumption up to 72% compared to cloud-only computation, while meeting ~90% of user request's latency threshold compared to baselines.

Multi-document (MD) processing is crucial for LLMs to handle real-world tasks such as summarization and question-answering across large sets of documents. While LLMs have improved at processing long inputs, MD contexts still present challenges, such as managing inter-document dependencies, redundancy, and incoherent structures. We introduce MDCure, a scalable and effective fine-tuning pipeline to enhance the MD capabilities of LLMs without the computational cost of pre-training or reliance on human annotated data. MDCure is based on generation of high-quality synthetic MD instruction data from sets of related articles via targeted prompts. We further introduce MDCureRM, a multi-objective reward model which filters generated data based on their training utility for MD settings. With MDCure, we fine-tune a variety of LLMs, from the FlanT5, Qwen2, and LLAMA3.1 model families, up to 70B parameters in size. Extensive evaluations on a wide range of MD and long-context benchmarks spanning various tasks show MDCure consistently improves performance over pre-trained baselines and over corresponding base models by up to 75.5%. Our code, datasets, and models are available at https://github.com/yale-nlp/MDCure.

Oscillating clouds of ultralight bosons can grow around spinning black holes through superradiance, extracting energy and angular momentum, and eventually dissipating through gravitational radiation. Gravitational wave detectors like LIGO, Virgo, KAGRA, and LISA can thus probe the existence of ultralight bosons. In this study, we use fully general-relativistic solutions of the black hole-boson cloud systems to study the self-gravity effects of scalar and vector boson clouds, making only the simplifying assumption that the spacetime is axisymmetric (essentially corresponding to taking an oscillation average). We calculate the self-gravity shift in the cloud oscillation frequency, which determines the frequency evolution of the gravitational wave signal, finding that this effect can be up to twice as large in the relativistic regime compared to non-relativistic estimates. We use this to improve the superrad waveform model, and estimate that this reduces the theoretical phase error to a few cycles over the characteristic timescale of the gravitational wave emission timescale for the louder vector boson signals. We also perform an analysis of the spacetime geometry of these systems, calculating how the cloud changes the innermost stable circular orbit and light-ring around the black hole., Comment: 20 pages

Complex news events, such as natural disasters and socio-political conflicts, require swift responses from the government and society. Relying on historical events to project the future is insufficient as such events are sparse and do not cover all possible conditions and nuanced situations. Simulation of these complex events can help better prepare and reduce the negative impact. We develop a controllable complex news event simulator guided by both the event schema representing domain knowledge about the scenario and user-provided assumptions representing case-specific conditions. As event dynamics depend on the fine-grained social and cultural context, we further introduce a geo-diverse commonsense and cultural norm-aware knowledge enhancement component. To enhance the coherence of the simulation, apart from the global timeline of events, we take an agent-based approach to simulate the individual character states, plans, and actions. By incorporating the schema and cultural norms, our generated simulations achieve much higher coherence and appropriateness and are received favorably by participants from a humanitarian assistance organization., Comment: Accepted as EMNLP 2024 Demo

We prove the asymptotic stability of the high speed solitary waves to the Benjamin equation. This is done by establishing a Liouville property for the nonlinear evolution of the Benjamin equation around these solitary waves. To do this, inspired by Kenig-Martel-Robbiano 2011, we make use of the KdV limit of the Benjamin equation together with known rigidity property of the KdV flow. The main difficulties are linked to the presence of the Hilbert transform, that is a non-local operator, as well as the non-positivity of the quadratic part of the energy in the case $ \gamma<0$ which is the physical case.

We describe the cryogenic system being developed for Taurus: a super-pressure balloon-borne microwave polarimeter scheduled to fly in 2027. The Taurus cryogenic system consists of a 660L liquid helium cryostat which achieves a base temperature of <100mK with the help of a capillary-fed superfluid tank and a closed cycle dilution refrigerator. The main tank is supported with fiberglass flexures and is encased in two layers of vapor-cooled shields which allow Taurus to make full use of the extended flight time offered by the super-pressure balloon platform. The Taurus cryostat is projected to hold for over 50 days while weighing under 1000lbs. We present the design, testing, and thermal analysis of the Taurus cryogenic systems.

In twisted van der Waals materials, local atomic relaxation can significantly alter the underlying electronic structure and properties. Characterizing the lattice reconstruction and the impact of strain is essential to better understand and harness the resulting emergent electronic states. Here, we use a scanning single-electron transistor to image spatial modulations in the electronic structure of helical trilayer graphene, demonstrating relaxation into a superstructure of large domains with uniform moire periodicity. We further show that the supermoire domain size is enhanced by strain and can even be altered in subsequent measurements of the same device, while nevertheless maintaining the same local electronic properties within each domain. Finally, we observe higher conductance at the boundaries between domains, consistent with the prediction that they host counter-propagating topological edge modes. Our work confirms that lattice relaxation can produce moire-periodic order in twisted multilayers, demonstrates strain-engineering as a viable path for designing topological networks at the supermoire scale, and paves the way to direct imaging of correlation-driven topological phases and boundary modes.

This article investigates equivariant parametrized cellular cohomology, a cohomology theory introduced by Costenoble-Waner for spaces with an action by a compact Lie group $G$. The theory extends the $RO(G)$-graded cohomology of a $G$-space $B$ to a cohomology graded by $RO(\Pi B)$, the representations of the equivariant fundamental groupoid of $B$. This paper is meant to serve as a guide to this theory and contains some new computations. We explain the key ingredients for defining parametrized cellular cohomology when $G$ is a finite group, with particular attention to the case of the cyclic group $G=C_2$. We compute some examples and observe that $RO(\Pi B)$ is not always free. When $G$ is the trivial group, we explain how to identify equivariant parametrized cellular cohomology with cellular cohomology in local coefficients. Finally, we illustrate the theory with some new computations of parametrized cellular cohomology for several spaces with $G = C_2$ and $G=C_4$.

Faced with an expensive human annotation process, creators of NLP systems increasingly turn to synthetic data generation. While this method shows promise, the extent to which synthetic data can replace human annotation is poorly understood. We investigate the use of synthetic data in Fact Verification (FV) and Question Answering (QA) by studying the effects of incrementally replacing human generated data with synthetic points on eight diverse datasets. Strikingly, replacing up to 90% of the training data only marginally decreases performance, but replacing the final 10% leads to severe declines. We find that models trained on purely synthetic data can be reliably improved by including as few as 125 human generated data points. We show that matching the performance gain of just a little additional human data (only 200 points) requires an order of magnitude more synthetic data and estimate price ratios at which human annotation would be a more cost-effective solution. Our results suggest that even when human annotation at scale is infeasible, there is great value to having a small proportion of the dataset being human generated.

This paper presents a novel approach termed Layer-of-Thoughts Prompting (LoT), which utilizes constraint hierarchies to filter and refine candidate responses to a given query. By integrating these constraints, our method enables a structured retrieval process that enhances explainability and automation. Existing methods have explored various prompting techniques but often present overly generalized frameworks without delving into the nuances of prompts in multi-turn interactions. Our work addresses this gap by focusing on the hierarchical relationships among prompts. We demonstrate that the efficacy of thought hierarchy plays a critical role in developing efficient and interpretable retrieval algorithms. Leveraging Large Language Models (LLMs), LoT significantly improves the accuracy and comprehensibility of information retrieval tasks., Comment: Presented at NeLaMKRR@KR, 2024 (arXiv:2410.05339)

Automated wildlife surveys based on drone imagery and object detection technology are a powerful and increasingly popular tool in conservation biology. Most detectors require training images with annotated bounding boxes, which are tedious, expensive, and not always unambiguous to create. To reduce the annotation load associated with this practice, we develop POLO, a multi-class object detection model that can be trained entirely on point labels. POLO is based on simple, yet effective modifications to the YOLOv8 architecture, including alterations to the prediction process, training losses, and post-processing. We test POLO on drone recordings of waterfowl containing up to multiple thousands of individual birds in one image and compare it to a regular YOLOv8. Our experiments show that at the same annotation cost, POLO achieves improved accuracy in counting animals in aerial imagery., Comment: Published in the CV4Ecology workshop at ECCV 2024

The ability to coherently control and read out qubits with long coherence times in a scalable system is a crucial requirement for any quantum processor. Nuclear spins in the solid state have shown great promise as long-lived qubits. Control and readout of individual nuclear spin qubit registers has made major progress in the recent years using individual electron spin ancilla addressed either electrically or optically. Here, we present a new platform for quantum information processing, consisting of $^{183}$W nuclear spin qubits adjacent to an Er$^{3+}$ impurity in a CaWO$_4$ crystal, interfaced via a superconducting resonator and detected using a microwave photon counter at 10mK. We study two nuclear spin qubits with $T_2^*$ of $0.8(2)~$s and $1.2(3)~$s, $T_2$ of $3.4(4)~$s and $4.4(6)~$ s, respectively. We demonstrate single-shot quantum non-demolition readout of each nuclear spin qubit using the Er$^{3+}$ spin as an ancilla. We introduce a new scheme for all-microwave single- and two-qubit gates, based on stimulated Raman driving of the coupled electron-nuclear spin system. We realize single- and two-qubit gates on a timescale of a few milliseconds, and prepare a decoherence-protected Bell state with 88% fidelity and $T_2^*$ of $1.7(2)~$s. Our results are a proof-of-principle demonstrating the potential of solid-state nuclear spin qubits as a promising platform for quantum information processing. With the potential to scale to tens or hundreds of qubits, this platform has prospects for the development of scalable quantum processors with long-lived qubits., Comment: 14 pages, 4 main figures, 7 supplementary figures

In one-dimensional quantum materials, conducting electrons and the underlying lattices can undergo a spontaneous translational symmetry breaking, known as Peierls transition. For nearly a century, the Peierls transition has been understood within the paradigm of electron-electron interactions mediated by longitudinal acoustic phonons. This classical picture has recently been revised in topological semimetals, where transverse acoustic phonons can couple with conducting p-orbital electrons and give rise to an unconventional Fermi surface instability, dubbed the transverse Peierls transition (TPT). Most interestingly, the TPT induced lattice distortions can further break rotation or mirror/inversion symmetries, leading to nematic or chiral charge density waves (CDWs). Quantum materials that host the TPT, however, have not been experimentally established. Here, we report the experimental discovery of an incommensurate TPT in the tetragonal Dirac semimetal EuAl$_4$. Using inelastic x-ray scattering with meV resolution, we observe the complete softening of a transverse acoustic phonon at the CDW wavevector upon cooling, whereas the longitudinal acoustic phonon is nearly unchanged. Combining with first principles calculations, we show that the incommensurate CDW wavevector matches the calculated charge susceptibility peak and connects the nested Dirac bands with Al 3$p_{x}$ and 3$p_{y}$ orbitals. Supplemented by second harmonic generation measurements, we show that the CDW induced lattice distortions break all vertical and diagonal mirrors whereas the four-fold rotational symmetry is retained below the CDW transition. Our observations strongly suggest a chiral CDW in EuAl$_4$ and highlight the TPT as a new avenue for chiral quantum states., Comment: Supplementary materials are available upon request

We present the first emission spectrum of the hot Jupiter WASP-17 b using one eclipse observation from the JWST Near Infrared Imager and Slitless Spectrograph (NIRISS) Single Object Slitless Spectroscopy (SOSS) mode. Covering a wavelength range of 0.6 to 2.8 microns, our retrieval analysis reveals a strong detection of H2O in WASP-17b dayside atmosphere (6.4sigma). Our retrievals consistently favor a super-solar dayside H2O abundance and a non-inverted temperature-pressure profile over a large pressure range. Additionally, our examination of the brightness temperature reveals excess emission below 1 microns, suggesting the possibility of a high internal temperature (600 to 700 K) and/or contributions from reflected light. We highlight that JWST emission spectroscopy retrieval results can be sensitive to whether negative eclipse depths are allowed at optical wavelengths during light curve fitting. Our findings deepen our understanding of WASP-17b atmospheric composition while also highlighting the sensitivity of our results to pressure-temperature profile parameterizations. This work is part of a series of studies by our JWST Telescope Scientist Team (JWST-TST), in which we will use Guaranteed Time Observations to perform Deep Reconnaissance of Exoplanet Atmospheres through Multi-instrument Spectroscopy (DREAMS)., Comment: Accepted for Publication in the Astronomical Journal (AJ) October 4, 2024

We present the first spectroscopic characterisation of the dayside atmosphere of WASP-17b in the mid-infrared using a single JWST MIRI/LRS eclipse observation. From forward-model fits to the 5-12 $\mu$m emission spectrum, we tightly constrain the heat redistribution factor of WASP-17b to be 0.92$\pm$0.02 at the pressures probed by this data, indicative of inefficient global heat redistribution. We also marginally detect a supersolar abundance of water, consistent with previous findings for WASP-17b, but note our weak constraints on this parameter. These results reflect the thermodynamically rich but chemically poor information content of MIRI/LRS emission data for high-temperature hot Jupiters. Using the eclipse mapping method, which utilises the signals that the spatial emission profile of an exoplanet imprints on the eclipse light curve during ingress/egress due to its partial occultation by the host star, we also construct the first eclipse map of WASP-17b, allowing us to diagnose its multidimensional atmospheric dynamics for the first time. We find a day-night temperature contrast of order 1000 K at the pressures probed by this data, consistent with our derived heat redistribution factor, along with an eastward longitudinal hotspot offset of $18.7^{+11.1\deg}_{-3.8}$, indicative of the presence of an equatorial jet induced by day-night thermal forcing being the dominant redistributor of heat from the substellar point. These dynamics are consistent with general circulation model predictions for WASP-17b. This work is part of a series of studies by the JWST Telescope Scientist Team (JWST-TST), in which we use Guaranteed Time Observations to perform Deep Reconnaissance of Exoplanet Atmospheres through Multi-instrument Spectroscopy (DREAMS)., Comment: 28 pages, 8 figures. Published in The Astronomical Journal

Mental health disorders are one of the most serious diseases in the world. Most people with such a disease lack access to adequate care, which highlights the importance of training models for the diagnosis and treatment of mental health disorders. However, in the mental health domain, privacy concerns limit the accessibility of personalized treatment data, making it challenging to build powerful models. In this paper, we introduce MentalArena, a self-play framework to train language models by generating domain-specific personalized data, where we obtain a better model capable of making a personalized diagnosis and treatment (as a therapist) and providing information (as a patient). To accurately model human-like mental health patients, we devise Symptom Encoder, which simulates a real patient from both cognition and behavior perspectives. To address intent bias during patient-therapist interactions, we propose Symptom Decoder to compare diagnosed symptoms with encoded symptoms, and dynamically manage the dialogue between patient and therapist according to the identified deviations. We evaluated MentalArena against 6 benchmarks, including biomedicalQA and mental health tasks, compared to 6 advanced models. Our models, fine-tuned on both GPT-3.5 and Llama-3-8b, significantly outperform their counterparts, including GPT-4o. We hope that our work can inspire future research on personalized care. Code is available in https://github.com/Scarelette/MentalArena/tree/main, Comment: Technical Report; 27 pages

As large language models (LLMs) demonstrate increasingly advanced capabilities, aligning their behaviors with human values and preferences becomes crucial for their wide adoption. While previous research focuses on general alignment to principles such as helpfulness, harmlessness, and honesty, the need to account for individual and diverse preferences has been largely overlooked, potentially undermining customized human experiences. To address this gap, we train LLMs that can ''interact to align'', essentially cultivating the meta-skill of LLMs to implicitly infer the unspoken personalized preferences of the current user through multi-turn conversations, and then dynamically align their following behaviors and responses to these inferred preferences. Our approach involves establishing a diverse pool of 3,310 distinct user personas by initially creating seed examples, which are then expanded through iterative self-generation and filtering. Guided by distinct user personas, we leverage multi-LLM collaboration to develop a multi-turn preference dataset containing 3K+ multi-turn conversations in tree structures. Finally, we apply supervised fine-tuning and reinforcement learning to enhance LLMs using this dataset. For evaluation, we establish the ALOE (ALign With CustOmized PrEferences) benchmark, consisting of 100 carefully selected examples and well-designed metrics to measure the customized alignment performance during conversations. Experimental results demonstrate the effectiveness of our method in enabling dynamic, personalized alignment via interaction., Comment: The code and dataset are made public at https://github.com/ShujinWu-0814/ALOE

Megathrust earthquakes are the largest on Earth, capable of causing strong ground shaking and generating tsunamis. Physical models used to understand megathrust earthquake hazard are limited by existing uncertainties about material properties and governing processes in subduction zones. A key quantity in megathrust hazard assessment is the distance between the updip and downdip rupture limits. The thermal structure of a subduction zone exerts a first-order control on the extent of rupture. We simulate temperature for profiles of the Cascadia, Nankai and Hikurangi subduction zones using a 2D coupled kinematic-dynamic thermal model. We then build reduced-order models (ROMs) for temperature using the interpolated Proper Orthogonal Decomposition (iPOD). The resulting ROMs are data-driven, model agnostic, and computationally cheap to evaluate. Using the ROMs, we can efficiently investigate the sensitivity of temperature to input parameters, physical processes, and modeling choices. We find that temperature, and by extension the potential rupture extent, is most sensitive to variability in parameters that describe shear heating on the slab interface, followed by parameters controlling the thermal structure of the incoming lithosphere and coupling between the slab and the mantle. We quantify the effect of using steady-state vs. time-dependent models, and of uncertainty in the choice of isotherm representing the downdip rupture limit. We show that variability in input parameters translates to significant differences in estimated moment magnitude. Our analysis highlights the strong effect of variability in the apparent coefficient of friction, with previously published ranges resulting in pronounced variability in estimated rupture limit depths.

So far, even the highest resolution galaxy formation simulations with gravitational softening have failed to reproduce realistic life cycles of star clusters. We present the first star-by-star galaxy models of star cluster formation to account for hydrodynamics, star formation, stellar evolution and collisional gravitational interactions between stars and compact remnants using the updated SPHGAL+KETJU code, part of the GRIFFIN-project. Gravitational dynamics in the vicinity of $>3$ M$_\odot$ stars and their remnants are solved with a regularised integrator (KETJU) without gravitational softening. Comparisons of idealised star cluster evolution with SPHGAL+KETJU and direct N-body show broad agreement and the failure of simulations that use gravitational softening. In the hydrodynamical dwarf galaxy simulations run with SPHGAL+KETJU, clusters up to $\sim900$ M$_\odot$ are formed compact (effective radii $0.1-1$ pc) and their sizes increase by up to a factor of ten in agreement with previous N-body simulations and the observed sizes of exposed star clusters. The sizes increase rapidly once the clusters become exposed due to photoionising radiation. On average $63\%$ of the gravitationally bound clusters disrupt during the first $100$ Myr of evolution in the galactic tidal field. The addition of collisional dynamics reduces the fraction of supernovae in bound clusters by a factor of $\sim 2.6$, however the global star formation and outflow histories change by less than $30\%$. We demonstrate that the accurate treatment of gravitational encounters with massive stars enables more realistic star cluster life cycles from the earliest stages of cluster formation until disruption in simulated low-mass galaxies., Comment: 20 pages, 12 figures, submitted to MNRAS, comments welcome

We study real and positive tropicalizations of the varieties of low rank symmetric matrices over real or complex Puiseux series. We show that real tropicalization coincides with complex tropicalization for rank two and corank one cases. We also show that the two notions of positive tropicalization introduced by Speyer and Williams coincide for symmetric rank two matrices, but they differ for symmetric corank one matrices.

The dual phosphorylation network provides an essential component of intracellular signaling, affecting the expression of phenotypes and cell metabolism. For particular choices of kinetic parameters, this system exhibits multistationarity, a property that is relevant in the decision-making of cells. Determining which reaction rate constants correspond to monostationarity and which produce multistationarity is an open problem. The system's monostationarity is linked to the nonnegativity of a specific polynomial. A previous study by Feliu et al. provides a sufficient condition for monostationarity via a decomposition of this polynomial into nonnegative circuit polynomials. However, this decomposition is not unique. We extend their work by a systematic approach to classifying such decompositions in the dual phosphorylation network. Using this result classification, we provide a qualitative comparison of the decompositions into nonnegative circuit polynomials via empirical experiments and improve on previous conditions for the region of monostationarity., Comment: 20 pages, 9 figures, 2 tables

Current instruction-tuned language models are exclusively trained with textual preference data and thus are often not aligned with the unique requirements of other modalities, such as speech. To better align language models with the speech domain, we explore (i) prompting strategies grounded in radio-industry best practices and (ii) preference learning using a novel speech-based preference data of 20K samples, generated with a wide spectrum of prompts that induce varying dimensions of speech-suitability and labeled by annotators who listen to response pairs. Both human and automatic evaluation show that both prompting and preference learning increase the speech-suitability of popular instruction-tuned LLMs. Interestingly, we find that prompting and preference learning can be additive; combining them achieves the best win rates in head-to-head comparison, resulting in responses that are preferred or tied to the base model in 76.2% of comparisons on average. Lastly, we share lexical, syntactical, and qualitative analyses to showcase how each method contributes to improving the speech-suitability of generated responses., Comment: EMNLP2024

Propaganda plays a critical role in shaping public opinion and fueling disinformation. While existing research primarily focuses on identifying propaganda techniques, it lacks the ability to capture the broader motives and the impacts of such content. To address these challenges, we introduce propainsight, a conceptual framework grounded in foundational social science research, which systematically dissects propaganda into techniques, arousal appeals, and underlying intent. propainsight offers a more granular understanding of how propaganda operates across different contexts. Additionally, we present propagaze, a novel dataset that combines human-annotated data with high-quality synthetic data generated through a meticulously designed pipeline. Our experiments show that off-the-shelf LLMs struggle with propaganda analysis, but training with propagaze significantly improves performance. Fine-tuned Llama-7B-Chat achieves 203.4% higher text span IoU in technique identification and 66.2% higher BertScore in appeal analysis compared to 1-shot GPT-4-Turbo. Moreover, propagaze complements limited human-annotated data in data-sparse and cross-domain scenarios, showing its potential for comprehensive and generalizable propaganda analysis., Comment: 8 pages

Flavor development in the food industry is increasingly challenged by the need for rapid innovation and precise flavor profile creation. Traditional flavor research methods typically rely on iterative, subjective testing, which lacks the efficiency and scalability required for modern demands. This paper presents three contributions to address the challenges. Firstly, we define a new problem domain for scientific agents in flavor science, conceptualized as the generation of hypotheses for flavor profile sourcing and understanding. To facilitate research in this area, we introduce the FoodPuzzle, a challenging benchmark consisting of 978 food items and 1,766 flavor molecules profiles. We propose a novel Scientific Agent approach, integrating in-context learning and retrieval augmented techniques to generate grounded hypotheses in the domain of food science. Experimental results indicate that our model significantly surpasses traditional methods in flavor profile prediction tasks, demonstrating its potential to transform flavor development practices.

We study the constant $\mathscr{C}_p$ defined as the smallest constant $C$ such that $|f(0)|^p \leq C\|f\|_p^p$ holds for every function $f$ in the Paley-Wiener space $PW^p$. Brevig, Chirre, Ortega-Cerd\`a, and Seip have recently shown that $\mathscr{C}_p

2$. We improve this bound for $2

We study the implications of relaxing the requirement for ultralight axions to account for all dark matter in the Universe by examining mixed dark matter (MDM) cosmologies with axion fractions $f \leq 0.3$ within the fuzzy dark matter (FDM) window $10^{-25}$ eV $\lesssim m \lesssim 10^{-23}$ eV. Our simulations, using a new MDM gravity solver implemented in AxiREPO, capture wave dynamics across various scales with high accuracy down to redshifts $z\approx 1$. We identify halos with Rockstar using the CDM component and find good agreement of inferred halo mass functions (HMFs) and concentration-mass relations with theoretical models across redshifts $z=1-10$. This justifies our halo finder approach a posteriori as well as the assumptions underlying the MDM halo model AxionHMcode. Using the inferred axion halo mass - cold halo mass relation $M_{\text{a}}(M_{\text{c}})$ and calibrating a generalised smoothing parameter $\alpha$ to our MDM simulations, we present a new version of AxionHMcode. The code exhibits excellent agreement with simulations on scales $k< 20 \ h$ cMpc$^{-1}$ at redshifts $z=1-3.5$ for $f\leq 0.1$ around the fiducial axion mass $m = 10^{-24.5}$ eV $ = 3.16\times 10^{-25}$ eV, with maximum deviations remaining below 10%. For axion fractions $f\leq 0.3$, the model maintains accuracy with deviations under 20% at redshifts $z\approx 1$ and scales $k< 10 \ h$ cMpc$^{-1}$, though deviations can reach up to 30% for higher redshifts when $f=0.3$. Reducing the run-time for a single evaluation of AxionHMcode to below $1$ minute, these results highlight the potential of AxionHMcode to provide a robust framework for parameter sampling across MDM cosmologies in Bayesian constraint and forecast analyses., Comment: 20 pages, 10 figures, 5 Tables, comments welcome

We propose fractional anomalous Hall crystals (FAHCs) as possible ground states of strongly interacting electrons in parent bands with Berry curvature. FAHCs are exotic states of matter that spontaneously break continuous translation symmetry to form a fractional Chern insulator. We construct a unified family of variational wavefunctions that describe FAHCs and their competing states in the presence of uniform parent Berry curvature. We calculate their variational energy with Coulomb interactions semi-analytically in the thermodynamic limit. Our analysis reveals that FAHCs can be energetically favorable over both Wigner crystals and integer anomalous Hall crystals for sufficiently strong interactions or flat dispersion.

Elastodynamic Green's functions are an essential ingredient in seismology as they form the connection between direct observations of seismic waves and the earthquake source. They are also fundamental to various seismological techniques including physics-based ground motion prediction and kinematic or dynamic source inversions. In regions with established 3D models of the Earth's elastic structure, 3D Green's functions can be computed using numerical simulations of seismic wave propagation. However, such simulations are computationally expensive which poses challenges for real-time ground motion prediction. Here, we use a reduced-order model (ROM) approach that enables the rapid evaluation of approximate Green's functions. The ROM technique developed approximates three-component surface velocity wavefields obtained from numerical simulations of seismic wave propagation. We apply our ROM approach to a 50 km x 40 km area in the greater Los Angeles area accounting for topography, site effects, 3D subsurface velocity structure, and viscoelastic attenuation. The ROM constructed for this region enables rapid computation (0.001 CPU hours) of complete, high-resolution, 0.5 Hz surface velocity wavefields that are accurate for a shortest wavelength of 1.0 km. Using leave-one-out cross validation, we measure the accuracy of our Green's functions in both the time-domain and frequency-domain. Averaged across all sources and receivers, the error in the rapid seismograms is less than 0.01 cm/s. We demonstrate that the ROM can accurately and rapidly reproduce simulated seismograms for generalized moment tensor sources in our region, as well as kinematic sources by using a finite fault model of the 1987 Mw 5.9 Whittier Narrows earthquake as an example. We envision that our rapid, approximate Green's functions will be useful for constructing rapid ground motion synthetics with high spatial resolution.

We study transits of several ``hot Jupiter'' systems - including WASP-12 b, WASP-43 b, WASP-103 b, HAT-P-23 b, KELT-16 b, WD 1856+534 b, and WTS-2 b - with the goal of detecting tidal orbital decay and extending the baselines of transit times. We find no evidence of orbital decay in any of the observed systems except for that of the extensively studied WASP-12 b. Although the orbit of WASP-12 b is unequivocally decaying, we find no evidence for acceleration of said orbital decay, with measured $\ddot{P} = (-7 \pm 8) \times 10^{-14} \rm ~s^{-1}$, against the expected acceleration decay of $\ddot{P} \approx -10^{-23} \rm ~s^{-1}$. In the case of WD 1856+534 b, there is a tentative detection of orbital growth with $\dot{P} = (5.0 \pm 1.5) \times 10^{-10}$. While statistically significant, we err on the side of caution and wait for longer follow-up observations to consider the measured $\dot{P}$ real. For most systems, we provide a 95\%-confidence lower limit on the tidal quality factor, $Q_\star'$. The possibility of detecting orbital decay in hot Jupiters via long-term radial velocity (RV) measurements is also explored. We find that $\sim 1 \rm ~m~s^{-1}$ precision in RVs will be required to detect orbital decay of WASP-12 b with only 3 yr of observations. Currently available RV measurements and precision are unable to detect orbital decay in any of the systems studied here., Comment: 14 pages, 11 figures; Accepted in MNRAS on 2024 August 30. Received 2024 August 29; in original form 2024 February 13

The integration of Large Language Models (LLMs) into the drug discovery and development field marks a significant paradigm shift, offering novel methodologies for understanding disease mechanisms, facilitating drug discovery, and optimizing clinical trial processes. This review highlights the expanding role of LLMs in revolutionizing various stages of the drug development pipeline. We investigate how these advanced computational models can uncover target-disease linkage, interpret complex biomedical data, enhance drug molecule design, predict drug efficacy and safety profiles, and facilitate clinical trial processes. Our paper aims to provide a comprehensive overview for researchers and practitioners in computational biology, pharmacology, and AI4Science by offering insights into the potential transformative impact of LLMs on drug discovery and development.

In early 2020, the global COVID-19 pandemic closed schools for the rest of the 2019-20 school year (fiscal year 2020 or FY20). The United States Congress deployed funds to help K-12 schools adjust and plan for reopening via the Coronavirus Aid, Relief, and Economic Security (CARES) Act, including the initial $13.2 billion installment through the Elementary and Secondary School Emergency Relief Fund (ESSER I) and initial $2.95 billion installment through the Governor's Emergency Education Relief (GEER I) Fund. Private sector and non-profit organizations, including charter schools, were also eligible for loans through the Small Business Administration's Paycheck Protection Program (PPP), most of which were partially or fully forgiven. In this report, we extend our analysis of school funding during FY20 from the 2023 report, "Charter School Funding: Little Progress Toward Equity in the City." In that report, we found that, on average, charter schools receive about 30 percent ($7,147) less funding per pupil compared to traditional public schools (TPS). That analysis excluded COVID relief funding. Here we compare the initial emergency COVID relief funds received by TPS and charter schools in 18 US Cities: Atlanta, Georgia; Boston, Massachusetts; Camden, New Jersey; Chicago, Illinois; Denver, Colorado; Detroit, Michigan; Houston, Texas; Indianapolis, Indiana; Little Rock, Arkansas; Memphis, Tennessee; New Orleans, Louisiana; New York City, New York; Oakland, California; Phoenix, Arizona; San Antonio, Texas; Tulsa, Oklahoma; and Washington, DC. We use data from federal and state sources to address the following research questions: (1) did emergency COVID relief funds allocated to publicly-funded schools in FY20 widen or narrow the preexisting charter school funding gap? and (2) was initial COVID relief funding (allocated for FY20) distributed equitably relative to student poverty?