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The U.S. education system is facing severe challenges, including student learning loss, declining academic performance, a youth mental health crisis, and increasing absenteeism. Yet these challenges also provide a catalyst for transformational change. But a new paradigm for education cannot be done without the support for transformative school leaders. This paper advocates for reimagining the role of principal as pivotal agents of change, moving beyond outdated managerial frameworks to empower them as visionary leaders. It outlines innovative policy solutions, or "big bets," aimed at reshaping principal preparation, support systems, and responsibilities. These proposals call on educators, policymakers, and communities to embrace new models of school leadership that can drive lasting success for students and schools.

We introduce the Granite Embedding models, a family of encoder-based embedding models designed for retrieval tasks, spanning dense-retrieval and sparse retrieval architectures, with both English and Multilingual capabilities. This report provides the technical details of training these highly effective 12 layer embedding models, along with their efficient 6 layer distilled counterparts. Extensive evaluations show that the models, developed with techniques like retrieval oriented pretraining, contrastive finetuning, knowledge distillation, and model merging significantly outperform publicly available models of similar sizes on both internal IBM retrieval and search tasks, and have equivalent performance on widely used information retrieval benchmarks, while being trained on high-quality data suitable for enterprise use. We publicly release all our Granite Embedding models under the Apache 2.0 license, allowing both research and commercial use at https://huggingface.co/collections/ibm-granite.

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis; improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and ultimately enabling AI-empowered virtual cells., Comment: 28 pages, 2 figures, perspective in AI proteomics

We consider a Bayesian adaptive optimal stochastic control problem where a hidden static signal has a non-separable influence on the drift of a noisy observation. Being allowed to control the specific form of this dependence, we aim at optimising a cost functional depending on the posterior distribution of the hidden signal. Expressing the dynamics of this posterior distribution in the observation filtration, we embed our problem into a genuinely infinite-dimensional stochastic control problem featuring so-called measure-valued martingales. We address this problem by use of viscosity theory and approximation arguments. Specifically, we show equivalence to a corresponding weak formulation, characterise the optimal value of the problem in terms of the unique continuous viscosity solution of an associated HJB equation, and construct a piecewise constant and arbitrarily-close-to-optimal control to our main problem of study.

We introduce Granite Vision, a lightweight large language model with vision capabilities, specifically designed to excel in enterprise use cases, particularly in visual document understanding. Our model is trained on a comprehensive instruction-following dataset, including document-related tasks, such as content extraction from tables, charts, diagrams, sketches, and infographics, as well as general image tasks. The architecture of Granite Vision is centered around visual modality alignment with a decoder-only, 2 billion parameter Granite large language model. Additionally, we introduce a dedicated safety classification approach in test-time that leverages a sparse set of attention vectors to identify potential harmful inputs. Despite its lightweight architecture, Granite Vision achieves strong results in standard benchmarks related to visual document understanding, as well as on the LiveXiv benchmark, which is designed to avoid test set contamination by using a constantly updated corpus of recently published Arxiv papers. We are releasing the model under the Apache-2 license, allowing for both research and commercial use, while offering complete visibility into the training data and other relevant details. See https://huggingface.co/ibm-granite/ for model weights.

We present the analysis of 15 X-ray observations of Mrk 477, a nearby Seyfert 2 active galactic nucleus, with the objective to monitor its obscuring column density variability. The full dataset consists of five archival observations, split into two XMM-Newton, two NuSTAR and one Chandra observation, plus two dedicated monitoring campaigns. The monitoring campaigns were performed with Swift-XRT and NuSTAR, containing five observations each. We performed a simultaneous analysis using self-consistent torus models, deriving geometric properties of the torus as well as the obscuration along the line of sight. Mrk 477 is best modeled with a torus with large covering factor yet low column density (on average). Its line of sight column density oscillates between $1.5-7\times10^{23}$~cm$^{-2}$. Mrk~477 presents frequent obscuring column density variability, on timescales as short as $\sim2$~weeks. The probability of drawing a pair of obscuration-variable observations for Mrk~477 when having 2, 3, and 4 observations is 40\%, 78\% and 95\%, respectively. Adding the results of this work to those of another 26 sources, we find a trend of increasing obscuration variability with time (from $\sim20$\% at $\Delta t<10$~days, to $\sim60-70$\% at timescales larger than 5 years). We discuss whether this is compatible with the majority of obscuration variability coming from broad line region clouds., Comment: 16 pages, 7 figures, 4 tables. Accepted to ApJ

Molecular dynamics (MD) simulations are essential for understanding biomolecular systems but remain challenging to automate. Recent advances in large language models (LLM) have demonstrated success in automating complex scientific tasks using LLM-based agents. In this paper, we introduce MDCrow, an agentic LLM assistant capable of automating MD workflows. MDCrow uses chain-of-thought over 40 expert-designed tools for handling and processing files, setting up simulations, analyzing the simulation outputs, and retrieving relevant information from literature and databases. We assess MDCrow's performance across 25 tasks of varying required subtasks and difficulty, and we evaluate the agent's robustness to both difficulty and prompt style. \texttt{gpt-4o} is able to complete complex tasks with low variance, followed closely by \texttt{llama3-405b}, a compelling open-source model. While prompt style does not influence the best models' performance, it has significant effects on smaller models.

We present [NII] 205 $\mu$m fine structure line observations of three submillimeter galaxies (SMGs) and three quasar host galaxies at 4$\lesssim$z$\lesssim$6 using the Institut de radioastronomie millim\'etrique (IRAM) interferometer. The [NII] emission is detected in three sources, and we report detections of the underlying dust continuum emission in all sources. The observed [NII]-to-infrared luminosity ratio spans at least 0.5 dex for our sources. Comparing our estimates with sources detected in the [NII] 205 $\mu$m at similar redshifts shows that the overall [NII]-to-IR luminosity ratio spans over a dex in magnitude from L$_{[NII]}$/L$_{IR}$ ~ 10$^{-4}$ - 10$^{-5}$ and follows the trend of the so-called [NII] fine structure line deficit observed in (ultra)-luminous infrared galaxies in the local Universe. The [CII]-to-[NII] luminosity ratio is >10 for most of our sources, indicating that the bulk of the [CII] 158 $\mu$m line emission (f([CII]$^{PDR}$)>75%) arises from the neutral medium. From our analysis, we do not find significant differences in the [NII] 205 $\mu$m emission and the respective ratios between SMGs and QSOs, suggesting a negligible contribution to the boosting of [NII] 205 $\mu$m emission due to the active galactic nucleus (AGN) photoionization. Future investigations involving other fine structure lines and optical diagnostics will provide further insight into a suite of ionized medium properties and reveal the diversity between AGN and non-AGN environments., Comment: 14 pages, 6 figures, accepted for publication in A&A; typo corrected for number of figures

Although indicators based on scholarly citations are widely used to support the evaluation of academic journals, alternatives are needed for scholarly book acquisitions. This article assesses the value of research quality scores from ChatGPT 4o-mini for 9,830 social sciences, arts, and humanities books from 2019 indexed in Scopus, based on their titles and descriptions but not their full texts. Although most books scored the same (3* on a 1* to 4* scale), the citation rates correlate positively but weakly with ChatGPT 4o-mini research quality scores in both the social sciences and the arts and humanities. Part of the reason for the differences was the inclusion of textbooks, short books, and edited collections, all of which tended to be less cited and lower scoring. Some topics also tend to attract many/few citations and/or high/low ChatGPT scores. Descriptions explicitly mentioning theory and/or some methods also associated with higher scores and more citations. Overall, the results provide some evidence that both ChatGPT scores and citation counts are weak indicators of the research quality of books. Whilst not strong enough to support individual book quality judgements, they may help academic librarians seeking to evaluate new book collections, series, or publishers for potential acquisition., Comment: Journal of Academic Librarianship, to appear

The role of Large Language Models (LLMs) has not been extensively explored in analog circuit design, which could benefit from a reasoning-based approach that transcends traditional optimization techniques. In particular, despite their growing relevance, there are no benchmarks to assess LLMs' reasoning capability about circuits. Therefore, we created the CIRCUIT dataset consisting of 510 question-answer pairs spanning various levels of analog-circuit-related subjects. The best-performing model on our dataset, GPT-4o, achieves 48.04% accuracy when evaluated on the final numerical answer. To evaluate the robustness of LLMs on our dataset, we introduced a unique feature that enables unit-test-like evaluation by grouping questions into unit tests. In this case, GPT-4o can only pass 27.45% of the unit tests, highlighting that the most advanced LLMs still struggle with understanding circuits, which requires multi-level reasoning, particularly when involving circuit topologies. This circuit-specific benchmark highlights LLMs' limitations, offering valuable insights for advancing their application in analog integrated circuit design.

The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20\% increase in the efficiency of sub-1\,cm vertex reconstruction across all neutrino flavours., Comment: 32 pages, 18 figures

Context: Numerous studies of diffuse interstellar band (DIB) profiles have detected substructures, implying large molecules as their carriers. However, some of the narrowest DIBs generally do not show such substructure, suggesting the possibility of very small carriers. Aims: Based on the previously found tight correlation of the three narrow DIBs at 6196, 6440 and 6623 A and the present detection of weaker side DIBs to each of them in the extensive data set from the ESO Diffuse Interstellar Bands Large Exploration Survey, we investigate whether they may stem from small linear carrier molecules. This approach can lead to concrete DIB carrier suggestions, which can be tested in laboratory measurements in future studies. Methods: We suggest that the DIBs we study here represent individual rotational transitions of a small molecule. We determined the molecular constants from observation and compared them with data from a large set of quantum-chemical calculations to confine possible carrier candidates. Furthermore, we determined rotational temperatures by fitting line ratios using the fitted molecular models. Results: We determined molecular constants for three DIB systems and the corresponding transition types. The fitted rotational temperatures lie within the range of known interstellar diatomic molecules. We identified several DIB carrier candidates, almost all of them molecular ions. Some of them are metastable species, indicating the possibility of collision complexes as DIB carriers. Conclusions: If our hypothesis holds, this would be a major step toward the identification of a carrier molecule of the 6196 A DIB, the strongest among the narrow DIBs., Comment: 16 pages, 12 figures, Accepted for publication in Astronomy & Astrophysics

Context. Multiple systems are common in field stars, and the frequency is found to be higher in early evolutionary stages. Thus, the study of young multiple systems during the embedded stages is key to have a comprehensive understanding of star formation. In particular, how material accretes from the large-scale envelope into the inner region and how this flow interacts with the system physically and chemically has not yet been well characterized observationally. Aims. We aim to provide a snapshot of the forming protobinary system SVS13A, consisting of VLA4A and VLA4B. This includes clear pictures of its kinematic structures, physical conditions, and chemical properties. Methods. We conducted ALMA observations toward SVS13A targeting CH3CN and CH3-13CN J=12-11 K-ladder line emission with a high spatial resolution of ~30 au at a spectral resolution of ~0.08 km s-1 Results. We perform LTE radiative transfer models to fit the spectral features of the line emission. We find the two-layer LTE radiative model including dust absorption is essential to interpret the CH3CN and CH3-13-CN line emission. We identify two major and four small kinematic components, and derive their physical and chemical properties. Conclusions. We find a possible infalling signature toward the bursting secondary source VLA4A, which may be fed by an infalling streamer from the large-scale envelope. The mechanical heating in the binary system, as well as the infalling shocked gas, likely play a role in the thermal structure of the protobinary system. By accumulating mass from the streamer, the system might have experienced a gravitationally unstable phase before the accretion outburst. Finally, the derived CH3CN/CH3-13-CN ratio is lower than the canonical ratio in the ISM and is different between VLA4A and VLA4B., Comment: 14 pages, 13 figures

Large language models (LLMs) have demonstrated remarkable reasoning capabilities across diverse domains. Recent studies have shown that increasing test-time computation enhances LLMs' reasoning capabilities. This typically involves extensive sampling at inference time guided by an external LLM verifier, resulting in a two-player system. Despite external guidance, the effectiveness of this system demonstrates the potential of a single LLM to tackle complex tasks. Thus, we pose a new research problem: Can we internalize the searching capabilities to fundamentally enhance the reasoning abilities of a single LLM? This work explores an orthogonal direction focusing on post-training LLMs for autoregressive searching (i.e., an extended reasoning process with self-reflection and self-exploration of new strategies). To achieve this, we propose the Chain-of-Action-Thought (COAT) reasoning and a two-stage training paradigm: 1) a small-scale format tuning stage to internalize the COAT reasoning format and 2) a large-scale self-improvement stage leveraging reinforcement learning. Our approach results in Satori, a 7B LLM trained on open-source models and data. Extensive empirical evaluations demonstrate that Satori achieves state-of-the-art performance on mathematical reasoning benchmarks while exhibits strong generalization to out-of-domain tasks. Code, data, and models will be fully open-sourced.

In this note, we recall the definition of the binary branching model with Moran type interactions (BBMMI) introduced in [8]. In this interacting particle system, particles evolve, reproduce and die independently and, with a probability that may depend on the configuration of the whole system, the death of a particle may trigger the reproduction of another particle, while a branching event may trigger the death of another particle. We recall its relation to the Feynman-Kac semigroup of the underlying Markov evolution and improve on the L 2 distance between their normalisations proved in [8], when additional regularity is assumed on the process., Comment: arXiv admin note: text overlap with arXiv:2207.03323

The cosmic X-ray background (CXB) is dominated by the obscured and unobscured coronal light of active galactic nuclei (AGN). At energies below 10 keV, the CXB can be well explained by models taking into account the known AGN and the observed distribution of their obscuring, line-of-sight column densities, $N_{\rm H,l.o.s}$. However, at energies around the Compton reflection hump ($\sim30$ keV), the models fall short of the data. This suggests the existence of a population of as yet undetected Compton-thick AGN ($N_{\rm H,l.o.s}>1.5\times10^{24}$ cm$^{-2}$) whose X-ray spectra are dominated by the light that has been reprocessed by the obscuring material. In this work, we continue the effort to find and catalog all local ($z<0.05$) Compton-thick (CT) AGN. To this end, we obtained soft X-ray data with Chandra for six local BAT detected sources lacking ROSAT (0.1-2.4 keV) counterparts, indicating potential obscuration. We fit their spectra with Bayesian and least squares methods using two different models, borus02 and UXCLUMPY. We compare the results of the different models and methods and find that the $N_{\rm H,l.o.s}$ is consistently measured in each case. Three of the sources also were observed with XMM-Newton allowing the opportunity to search for variability in soft X-ray flux or $N_{\rm H,l.o.s}$. From this sample, we find one strong CT candidate (NGC 5759) and one weaker CT candidate (CGCG 1822.3+2053). Furthermore, we find tentative evidence of $N_{\rm H,l.o.s}$ variability in 2MASX J17253053-4510279, which has $N_{\rm H,l.o.s}<10^{22}$ cm$^{-2}$., Comment: 32 pages, 17 figures, 9 tables; Published in ApJ

In the pursuit of identifying rare two-particle events within the GADGET II Time Projection Chamber (TPC), this paper presents a comprehensive approach for leveraging Convolutional Neural Networks (CNNs) and various data processing methods. To address the inherent complexities of 3D TPC track reconstructions, the data is expressed in 2D projections and 1D quantities. This approach capitalizes on the diverse data modalities of the TPC, allowing for the efficient representation of the distinct features of the 3D events, with no loss in topology uniqueness. Additionally, it leverages the computational efficiency of 2D CNNs and benefits from the extensive availability of pre-trained models. Given the scarcity of real training data for the rare events of interest, simulated events are used to train the models to detect real events. To account for potential distribution shifts when predominantly depending on simulations, significant perturbations are embedded within the simulations. This produces a broad parameter space that works to account for potential physics parameter and detector response variations and uncertainties. These parameter-varied simulations are used to train sensitive 2D CNN object detectors. When combined with 1D histogram peak detection algorithms, this multi-modal detection framework is highly adept at identifying rare, two-particle events in data taken during experiment 21072 at the Facility for Rare Isotope Beams (FRIB), demonstrating a 100% recall for events of interest. We present the methods and outcomes of our investigation and discuss the potential future applications of these techniques.

An increasingly important class of estimators has members whose asymptotic distribution is non-Gaussian, yet characterizable as the argmax of a Gaussian process. This paper presents high-level sufficient conditions under which such asymptotic distributions admit a continuous distribution function. The plausibility of the sufficient conditions is demonstrated by verifying them in three prominent examples, namely maximum score estimation, empirical risk minimization, and threshold regression estimation. In turn, the continuity result buttresses several recently proposed inference procedures whose validity seems to require a result of the kind established herein. A notable feature of the high-level assumptions is that one of them is designed to enable us to employ the celebrated Cameron-Martin theorem. In a leading special case, the assumption in question is demonstrably weak and appears to be close to minimal.

The next generation of radar systems will include advanced digital front-end technology in the apertures allowing for spatially subdividing radar tasks over the array, the so-called split-aperture phased array (SAPA) concept. The goal of this paper is to introduce radar resource management for the SAPA concept and to demonstrate the added benefit of the SAPA concept for active tracking tasks. To do so, the radar resource management problem is formulated and solved by employing the quality of service based resource allocation model (Q-RAM) framework. As active tracking tasks may be scheduled simultaneously, the resource allocation of tasks becomes dependent on the other tasks. The solution to the resource allocation problem is obtained by introducing the adaptive fast traversal algorithm combined with a three dimensional strip packing algorithm to handle task dependencies. It will be demonstrated by a simulation example that the SAPA concept can significantly increase the number of active tracks of a multifunction radar system compared to scheduling tasks sequentially., Comment: 12 pages, 9 figures, 3 algorithms. arXiv admin note: text overlap with arXiv:2402.17607

Power Doppler ultrasound is in widespread clinical use for non-invasive vascular imaging but the most common current method - Delay and Sum (DAS) beamforming - suffers from limited resolution and high side-lobes. Here we propose the Sub-Aperture Angular Multiply and Sum (SAMAS) algorithm; it combines the advantages of two recent non-linear beamformers, Frame Multiply and Sum (FMAS) which uses signal temporal coherence and the acoustic sub-aperture (ASAP) algorithm, which uses signal spatial coherence, respectively. Following in vitro experiments to optimise the algorithm, particularly the use of phase information and sub-aperture pairing, it was evaluated in vivo, first in a rabbit kidney and then in human lymph node, using ultrafast ultrasound images obtained with intravenous contrast agents. The SAMAS algorithm improved the CNR and SNR across all tests, on average raising the CNR by 11 dB and the SNR by 18 dB over DAS in vivo. This work demonstrates a promising vascular imaging method that could have widespread clinical utility.

Spin states in semiconductors provide exceptionally stable and noise-resistant environments for qubits, positioning them as optimal candidates for reliable quantum computing technologies. The proposal to use nuclear and electronic spins of donor atoms in silicon, introduced by Kane in 1998, sparked a new research field focused on the precise positioning of individual impurity atoms for quantum devices, utilising scanning tunnelling microscopy and ion implantation. This roadmap article reviews the advancements in the 25 years since Kane's proposal, the current challenges, and the future directions in atomic-scale semiconductor device fabrication and measurement. It covers the quest to create a silicon-based quantum computer and expands to include diverse material systems and fabrication techniques, highlighting the potential for a broad range of semiconductor quantum technological applications. Key developments include phosphorus in silicon devices such as single-atom transistors, arrayed few-donor devices, one- and two-qubit gates, three-dimensional architectures, and the development of a toolbox for future quantum integrated circuits. The roadmap also explores new impurity species like arsenic and antimony for enhanced scalability and higher-dimensional spin systems, new chemistry for dopant precursors and lithographic resists, and the potential for germanium-based devices. Emerging methods, such as photon-based lithography and electron beam manipulation, are discussed for their disruptive potential. This roadmap charts the path toward scalable quantum computing and advanced semiconductor quantum technologies, emphasising the critical intersections of experiment, technological development, and theory., Comment: 94 pages

We present the Cosmic Evolution Early Release Science (CEERS) Survey, a 77.2 hour Director's Discretionary Early Release Science Program. CEERS demonstrates, tests, and validates efficient extragalactic surveys using coordinated, overlapping parallel observations with the JWST instrument suite, including NIRCam and MIRI imaging, NIRSpec low (R~100) and medium (R~1000) resolution spectroscopy, and NIRCam slitless grism (R~1500) spectroscopy. CEERS targets the Hubble Space Telescope-observed region of the Extended Groth Strip (EGS) field, supported by a rich set of multiwavelength data. CEERS facilitated immediate community science in both of the extragalactic core JWST science drivers ``First Light" and ``Galaxy Assembly," including: 1) The discovery and characterization of large samples of galaxies at z >~ 10 from ~90 arcmin^2 of NIRCam imaging, constraining their abundance and physical nature; 2) Deep spectra of >1000 galaxies, including dozens of galaxies at 63; and 4) Characterizing galaxy mid-IR emission with MIRI to study dust-obscured star-formation and supermassive black hole growth at z~1-3. As a legacy product for the community, the CEERS team has provided several data releases, accompanied by detailed notes on the data reduction procedures and notebooks to aid in reproducibility. In addition to an overview of the survey and quality of the data, we provide science highlights from the first two years with CEERS data., Comment: 38 pages, 13 figures, 6 tables

The world of nanoscales in fluidics is the frontier where the continuum of fluid mechanics meets the atomic, and even quantum, nature of matter. While water dynamics remains largely classical under extreme confinement, several experiments have recently reported coupling between water transport and the electronic degrees of freedom of the confining materials. This avenue prompts us to reconsider nanoscale hydrodynamic flows under the perspective of interacting excitations, akin to condensed matter frameworks. Here we show, using a combination of many-body theory and molecular simulations, that the flow of a liquid can induce the flow of another liquid behind a separating wall, at odds with the prediction of continuum hydrodynamics. We further show that the range of this 'flow tunnelling' can be tuned through the solid's electronic excitations, with a maximum occurring when these are at resonance with the liquid's charge density fluctuations. Flow tunnelling is expected to play a role in global transport across nanoscale fluidic networks, such as lamellar graphene oxide or MXene membranes. It further suggests exploiting the electronic properties of the confining walls for manipulating liquids via their dielectric spectra, beyond the nature and characteristics of individual molecules.

American youth face an acute crisis of belonging, marked by increasing alienation, anxiety, and absenteeism among students, that jeopardizes their educational experiences, mental health, and future success. Educators, policymakers, and community leaders are called upon to develop a strategic and comprehensive response. This brief contains research-backed context on the belonging crisis and key recommendations on how schools can foster student belonging so every child feels valued, understood, and connected.

In this paper we develop a systematic calculus for the Duistermaat index, a symplectic invariant defined for triples of Lagrangian subspaces. Introduced nearly half a century ago, this index has lately been the subject of renewed attention, due to its central role in eigenvalue interlacing problems on quantum graphs (and more abstractly for self-adjoint extensions of symmetric operators). Here we give an axiomatic characterization of the index that leads to elementary proofs of its fundamental properties. We also relate the index to other quantities often appearing in symplectic geometry, such as the H\"ormander--Kashiwara--Wall index and the Maslov index. Among other things, this leads to a curious formula for the Morse index of a difference of Hermitian matrices., Comment: 15 pages

Solving complex real-world tasks requires cycles of actions and observations. This is particularly true in science, where tasks require many cycles of analysis, tool use, and experimentation. Language agents are promising for automating intellectual tasks in science because they can interact with tools via natural language or code. Yet their flexibility creates conceptual and practical challenges for software implementations, since agents may comprise non-standard components such as internal reasoning, planning, tool usage, as well as the inherent stochasticity of temperature-sampled language models. Here, we introduce Aviary, an extensible gymnasium for language agents. We formalize agents as policies solving language-grounded partially observable Markov decision processes, which we term language decision processes. We then implement five environments, including three challenging scientific environments: (1) manipulating DNA constructs for molecular cloning, (2) answering research questions by accessing scientific literature, and (3) engineering protein stability. These environments were selected for their focus on multi-step reasoning and their relevance to contemporary biology research. Finally, with online training and scaling inference-time compute, we show that language agents backed by open-source, non-frontier LLMs can match and exceed both frontier LLM agents and human experts on multiple tasks at up to 100x lower inference cost.

Deconstructibility is an often-used sufficient condition on a class $\mathcal{C}$ of modules that allows one to carry out homological algebra \emph{relative to $\mathcal{C}$}. The principle \textbf{Maximum Deconstructibility (MD)} asserts that a certain necessary condition for a class to be deconstructible is also sufficient. MD implies, for example, that the classes of Gorenstein Projective modules, Ding Projective modules, their relativized variants, and all torsion classes are deconstructible over any ring. MD was known to follow from Vop\v{e}nka's Principle and imply the existence of an $\omega_1$-strongly compact cardinal. We prove that MD is equivalent to Vop\v{e}nka's Principle, and to the assertion that each torsion class of abelian groups is generated by a single group within the class (yielding the converse of a theorem of G\"obel and Shelah)., Comment: minor edits and corrections

We establish weak convergence rates for spectral Galerkin approximations of the stochastic viscous Burgers equation driven by additive trace-class noise. Our results complement the known results regarding strong convergence; we obtain essential weak convergence rate 2. As expected, this is twice the known strong rate. The main ingredients of the proof are novel regularity results on the solutions of the associated Kolmogorov equations.

We present a new scenario of freeze-in, where the dark matter is produced exclusively via the annihilation of photons. We study fermionic and scalar dark matter models with a focus on cosmological histories with low reheat temperatures. Photonic freeze-in can be probed via direct detection, dark matter production in SN1987A, and via the production of new electromagnetically charged states at the Large Hadron Collider. We briefly discuss UV-complete models that can realise this scenario., Comment: 11 pages, 4 figures

Graph burning models the spread of information or contagion in a graph. At each time step, two events occur: neighbours of already burned vertices become burned, and a new vertex is chosen to be burned. The big conjecture is known as the {\it burning number conjecture}: for any connected graph on $n$ vertices, all $n$ vertices can be burned after at most $\lceil \sqrt{n}\ \rceil$ time steps. It is well-known that to prove the conjecture, it suffices to prove it for trees. We prove the conjecture for sufficiently large $p$-caterpillars.

We measured the $\beta$-delayed neutron emission from $^{25}$F for the first time at the Facility for Rare Isotope Beams (FRIB). Using combined neutron and $\gamma$-ray detector systems of the FRIB Decay Station Initiator (FDSi), we observed $\beta$-decay transitions populating neutron unbound states between 4.2 and 8 MeV in $^{25}$Ne. The experimental results led to the revision of the $\beta$-decay half-life and $\beta$-delayed neutron-emission probability of $^{25}$F. The $\beta$-decay strength distribution of $^{25}$F extracted from the data agrees with the shell-model predictions using the USDB and SDPF-M effective interactions. This result indicates that the spherical neutron $N = 16$ shell gap persists in $^{25}$F and $^{25}$Ne., Comment: 7 pages

Wireless power transfer (WPT) technologies hold promise for enhancing device autonomy, particularly for energy-limited IoT systems. This paper presents experimental results on coherent and non-coherent transmit diversity approaches for WPT, tested in the near field using the Techtile testbed. We demonstrate that a fully synchronized beamfocusing system achieves a 14 dB gain over non-coherent transmission, consistent with the theoretical 14.9 dB gain for a 31-element array. Additionally, phase alignment errors below 20{\deg} result in less than 1 dB of gain loss, while errors exceeding 40{\deg} lead to losses over 3 dB. These findings suggest that phase coherency requirements for WPT can be relaxed, and that scaling the number of antennas is a promising strategy for improving power transfer efficiency.

In materials with one-dimensional electronic bands, electron-electron interactions can produce intriguing quantum phenomena, including spin-charge separation and charge density waves (CDW). Most of these systems, however, are non-magnetic, motivating a search for anisotropic materials where the coupling of charge and spin may affect emergent quantum states. Here, electron doping the van der Waals magnetic semiconductor CrSBr induces an electronically driven quasi-1D CDW, which survives above room temperature. Lithium intercalation also increases the magnetic ordering temperature to 200 K and changes its interlayer magnetic coupling from antiferromagnetic to ferromagnetic. The spin-polarized nature of the anisotropic bands that give rise to this CDW enforces an intrinsic coupling of charge and spin. The coexistence and interplay of ferromagnetism and charge modulation in this exfoliatable material provides a promising platform for studying tunable quantum phenomena across a range of temperatures and thicknesses., Comment: Main text: 30 pages, 4 figures, 1 table. Supplementary Information: 21 pages, 11 figures, 2 tables

We report on a search for millicharged particles (mCPs) produced in cosmic ray proton atmospheric interactions using data collected during the first science run of the LUX-ZEPLIN experiment. The mCPs produced by two processes -- meson decay and proton bremsstrahlung -- are considered in this study. This search utilized a novel signature unique to liquid xenon (LXe) time projection chambers (TPCs), allowing sensitivity to mCPs with masses ranging from 10 to 1000 MeV/c$^2$ and fractional charges between 0.001 and 0.02 of the electron charge e. With an exposure of 60 live days and a 5.5 tonne fiducial mass, we observed no significant excess over background. This represents the first experimental search for atmospheric mCPs and the first search for mCPs using an underground LXe experiment.

Using the time-of-flight technique, we measured the beta-delayed neutron emission of $^{132}$Cd. From our large-scale shell model (LSSM) calculation using the N$^3$LO interaction [Z.Y. Xu et al., Phys. Rev. Lett. 131, 022501 (2023)], we suggest the decay is dominated by the transformation of a neutron in the $g_{7/2}$ orbital, deep below the Fermi surface, into a proton in the $g_{9/2}$ orbital. We compare the beta-decay half-lives and neutron branching ratios of nuclei with $Z<50$ and $N\geq82$ obtained with our LSSM with those of leading "global" models. Our calculations match known half-lives and neutron branching ratios well and suggest that current leading models overestimate the yet-to-be-measured half-lives. Our model, backed by the $^{132}$Cd decay data presented here, offers robust predictive power for nuclei of astrophysical interest such as $r$-process waiting points., Comment: 7 pages, 5 figures

State-space models are a popular statistical framework for analysing sequential data. Within this framework, particle filters are often used to perform inference on non-linear state-space models. We introduce a new method, StateMixNN, that uses a pair of neural networks to learn the proposal distribution and transition distribution of a particle filter. Both distributions are approximated using multivariate Gaussian mixtures. The component means and covariances of these mixtures are learnt as outputs of learned functions. Our method is trained targeting the log-likelihood, thereby requiring only the observation series, and combines the interpretability of state-space models with the flexibility and approximation power of artificial neural networks. The proposed method significantly improves recovery of the hidden state in comparison with the state-of-the-art, showing greater improvement in highly non-linear scenarios.

State-space models (SSMs) are a powerful statistical tool for modelling time-varying systems via a latent state. In these models, the latent state is never directly observed. Instead, a sequence of observations related to the state is available. The state-space model is defined by the state dynamics and the observation model, both of which are described by parametric distributions. Estimation of parameters of these distributions is a very challenging, but essential, task for performing inference and prediction. Furthermore, it is typical that not all states of the system interact. We can therefore encode the interaction of the states via a graph, usually not fully connected. However, most parameter estimation methods do not take advantage of this feature. In this work, we propose GraphGrad, a fully automatic approach for obtaining sparse estimates of the state interactions of a non-linear state-space model via a polynomial approximation. This novel methodology unveils the latent structure of the data-generating process, allowing us to infer both the structure and value of a rich and efficient parameterisation of a general state-space model. Our method utilises a differentiable particle filter to optimise a Monte Carlo likelihood estimator. It also promotes sparsity in the estimated system through the use of suitable proximity updates, known to be more efficient and stable than subgradient methods. As shown in our paper, a number of well-known dynamical systems can be accurately represented and recovered by our method, providing basis for application to real-world scenarios., Comment: corrected author names

Understanding of the formation and evolution of the Solar System requires understanding key and common materials found on and in planetary bodies. Mineral mixing and its implications on planetary body formation is a topic of high interest to the planetary science community. Previous work establishes a case for the use of Optical PhotoThermal InfraRed (O-PTIR) in planetary science and introduces and demonstrates the technique's capability to study planetary materials. In this paper, we performed a measurement campaign on granular materials relevant to planetary science, such as minerals found in lunar and martian soils. These laboratory measurements serve to start a database of O-PTIR measurements. We also present FTIR absorption measurements of the materials we observed in O-PTIR for comparison purposes. We find that the O-PTIR technique suffers from granular orientation effects similar to other IR techniques, but in most cases, is is directly comparable to commonly used absorption spectroscopy techniques. We conclude that O-PTIR would be an excellent tool for the purpose of planetary material identification during in-situ investigations on regolith and bedrock surfaces., Comment: This paper follows, "Photothermal Spectroscopy for Planetary Sciences: Mid-IR Absorption Made Easy," (arXiv:2409.11626) and is part 2 of a series. This work catalogues O-PTIR Mid-IR measurements of various planetary materials. This is a pre-print

The precise characterization and mitigation of systematic effects is one of the biggest roadblocks impeding the detection of the fluctuations of cosmological 21cm signals. Missing data in radio cosmological experiments, often due to radio frequency interference (RFI), poses a particular challenge to power spectrum analysis as it could lead to the ringing of bright foreground modes in Fourier space, heavily contaminating the cosmological signals. Here we show that the problem of missing data becomes even more arduous in the presence of systematic effects. Using a realistic numerical simulation, we demonstrate that partially flagged data combined with systematic effects can introduce significant foreground ringing. We show that such an effect can be mitigated through inpainting the missing data. We present a rigorous statistical framework that incorporates the process of inpainting missing data into a quadratic estimator of the 21cm power spectrum. Under this framework, the uncertainties associated with our inpainting method and its impact on power spectrum statistics can be understood. These results are applied to the latest Phase II observations taken by the Hydrogen Epoch of Reionization Array, forming a crucial component in power spectrum analyses as we move toward detecting 21cm signals in the ever more noisy RFI environment., Comment: 25 pages, 11 figures; Replaced to match accepted ApJ version. New version contains small editorial changes throughout in response to referee comments, no changes to results

In this work, we introduce and experimentally demonstrate a photonic frequency-multiplexed next generation reservoir computer (FM-NGRC) capable of performing real-time inference at GHz speed. NGRCs apply a feed-forward architecture to produce a feature vector directly from the input data over a fixed number of time steps. This feature vector, analogous to the reservoir state in a conventional RC, is used to perform inference by applying a decision layer trained by linear regression. Photonic NGRC provides a flexible platform for real-time inference by forgoing the need for explicit feedback loops inherent to a physical reservoir. The FM-NGRC introduced here defines the memory structure using an optical frequency comb and dispersive fiber while the sinusoidal response of electro-optic Mach-Zehnder interferometers controls the nonlinear transform applied to elements of the feature vector. A programmable waveshaper modulates each comb tooth independently to apply the trained decision layer weights in the analog domain. We apply the FM-NGRC to solve the benchmark nonlinear channel equalization task; after theoretically determining feature vectors that enable high-accuracy distortion compensation, we construct an FM-NGRC that generates these vectors to experimentally demonstrate real-time channel equalization at 5 GS/s with a symbol error rate of $\sim 2\times 10^{-3}$., Comment: 23 pages, 6 figures

We investigate the emergence of self-hybridized thermal magnetoplasmons in doped graphene nanodisks at finite temperatures when subjected to an external magnetic field. Using a semianalytical approach, which fully describes the eigenmodes and polarizability of the graphene nanodisks, we show that the hybridization originates from the coupling of transitions between thermally populated Landau levels and localized magnetoplasmon resonances of the nanodisks. Owing to their origin, these modes combine the extraordinary magneto-optical response of graphene with the strong field enhancement of plasmons, making them an ideal tool for achieving strong chiral light-matter interactions, with the additional advantage of being tunable through carrier concentration, magnetic field, and temperature. As a demonstration of their capabilities, we show that the thermal magnetoplasmons supported by an array of graphene nanodisks enable chiral perfect absorption and chiral thermal emission., Comment: 22 pages, 11 figures

As urbanization and climate change progress, urban heat becomes a priority for climate adaptation efforts. High temperatures concentrated in urban heat can drive increased risk of heat-related death and illness as well as increased energy demand for cooling. However, estimating the effects of urban heat is an ongoing field of research typically burdened by an imprecise description of the built environment, significant computational cost, and a lack of high-resolution estimates of the impacts of climate change. Here, we present open-source, computationally efficient machine learning methods that can improve the accuracy of urban temperature estimates when compared to historical reanalysis data. These models are applied to residential buildings in Los Angeles, and we compare the energy benefits of heat mitigation strategies to the impacts of climate change. We find that cooling demand is likely to increase substantially through midcentury, but engineered high-albedo surfaces could lessen this increase by more than 50%. The corresponding increase in heating demand complicates this narrative, but total annual energy use from combined heating and cooling with electric heat pumps in the Los Angeles urban climate is shown to benefit from the engineered cooling strategies under both current and future climates.

This article presents the latest results of our ALMA program to study circumstellar disk characteristics as a function of orbital and stellar properties in a sample of young binary star systems known to host at least one disk. Optical and infrared observations of the eccentric, ~48-year period binary DF Tau indicated the presence of only one disk around the brighter component. However, our 1.3 mm ALMA thermal continuum maps show two nearly-equal brightness components in this system. We present these observations within the context of updated stellar and orbital properties which indicate that the inner disk of the secondary is absent. Because the two stars likely formed together, with the same composition, in the same environment, and at the same time, we expect their disks to be co-eval. However the absence of an inner disk around the secondary suggests uneven dissipation. We consider several processes which have the potential to accelerate inner disk evolution. Rapid inner disk dissipation has important implications for planet formation, particularly in the terrestrial-planet-forming region., Comment: 18 pages, 8 figures, accepted to AJ