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California has a two-tiered credential structure for education administrators. The first tier is the Preliminary Administrative Services Credential. An individual who has completed all requirements for a preliminary credential but does not have an offer of employment in an administrative position may apply for a Certificate of Eligibility, which does not expire. Once an individual secures an administrative position, the holder of a Certificate of Eligibility may apply for the Preliminary Administrative Services Credential. The second tier is the Clear Administrative Services Credential. To receive the Clear Administrative Services Credential, individuals must successfully complete a 2-year induction program. The state's 2-year induction model focuses on job-embedded, individualized support that includes: (1) coaching with a trained coach for at least 40 hours per year, (2) personalized professional development for at least 20 hours per year, and (3) multiple assessments that capture competency toward the practices outlined in the state's professional standards for administrators. These assessments include an initial assessment to inform a participant's individual induction plan, benchmark assessments to measure progress over the course of the program, and a summative assessment to demonstrate that participants have completed program requirements and to support programs in recommending participants for the clear credential. This report explores the landscape of new administrator induction in California using statewide survey data and case studies of administrator induction programs. The California Commission on Teacher Credentialing (CTC), which accredits and oversees all administrator induction programs, conducts a program completer survey asking about administrators' experiences with their induction programs and overall preparedness when they apply for their clear credential. This report analyzes 4 years of survey data from 6,812 administrators who completed induction between September 2017 and August 2021. It also draws on qualitative data from six case studies of administrator induction programs that were highly rated on the program completer surveys to highlight exemplary structures and practices.

Teachers of color often work in schools with few colleagues from the same racial or ethnic background. This "racial isolation" may affect their work experiences and important job outcomes, including retention. Using longitudinal administrative and survey data, we investigate the degree to which Tennessee teachers who are more racially isolated are more likely to turn over. Accounting for other factors, we find that racially isolated Black teachers are more likely to leave their schools than less isolated teachers. This turnover is driven by transfers to a different district and exiting the profession altogether. Consistent with an explanation that isolated teachers' work experiences differ, they also report less collaboration with colleagues and receive lower observation scores.

Teaching performance assessments (TPAs) can be used to assess the readiness of potential teachers because they require candidates to provide evidence of their planning and teaching skills through classroom videos accompanied by commentaries Ensuring that teaching candidates are well prepared to enter the classroom is a critical mission for teacher preparation programs and state agencies that approve programs and set teacher licensure standards. Teaching performance assessments (TPAs) can be used to assess the readiness of potential teachers because they require candidates to provide evidence of their teaching knowledge and skills through classroom videos, lesson plans, student work, and analysis of teaching and learning. Multiple studies have found that TPA scores predict effectiveness once candidates enter the classroom as licensed teachers. TPAs have been adopted in at least 16 states as a requirement of either teacher preparation program completion or initial licensure. California, the focus of this study, was one of the first states to adopt a TPA as a licensure requirement for beginning teachers. The report on which this brief is based explored whether certain preparation experiences predicted success on the California Teaching Performance Assessment (CalTPA) or the educative Teaching Performance Assessment (edTPA), the two widely available TPAs used across California preparation programs. Focusing on the 2021-22 and 2022-23 school years, this analysis examined whether TPA success for more than 18,000 California teaching candidates varied by preparation pathway, program, and the nature of preparation experiences as reported by respondents on the annual survey administered to all those completing preparation and applying for their preliminary teaching credential with the California Commission on Teacher Credentialing (CTC).

Preparing the ground state of the Fermi-Hubbard model is challenging, in part due to the exponentially large Hilbert space, which complicates efficiently finding a path from an initial state to the ground state using the variational principle. In this work, we propose an approach for ground state preparation of interacting models by involving a classical reservoir, simplified to the instantaneous-response limit, which can be described using a Hamiltonian formalism. The resulting time evolution operator consist of spin-adapted nearest-neighbor hopping and on-site interaction terms similar to those in the Hubbard model, without expanding the Hilbert space. We can engineer the coupling to rapidly drive the system from an initial product state to its interacting ground state by numerically minimizing the final state energy. This ansatz closely resembles the Hamiltonian variational ansatz, offering a fresh perspective on it.

Leaf-cutting ants, notorious for causing defoliation in commercial forest plantations, significantly contribute to biomass and productivity losses, impacting forest producers in Brazil. These ants construct complex underground nests, highlighting the need for advanced monitoring tools to extract subsurface information across large areas. Synthetic Aperture Radar (SAR) systems provide a powerful solution for this challenge. This study presents the results of electromagnetic simulations designed to detect leaf-cutting ant nests in industrial forests. The simulations modeled nests with 6 to 100 underground chambers, offering insights into their radar signatures. Following these simulations, a field study was conducted using a drone-borne SAR operating in the P-band. A helical flight pattern was employed to generate high-resolution ground tomography of a commercial eucalyptus forest. A convolutional neural network (CNN) was implemented to detect ant nests and estimate their sizes from tomographic data, delivering remarkable results. The method achieved an ant nest detection accuracy of 100%, a false alarm rate of 0%, and an average error of 21% in size estimation. These outcomes highlight the transformative potential of integrating Synthetic Aperture Radar (SAR) systems with machine learning to enhance monitoring and management practices in commercial forestry., Comment: 8 pages, 22 figures, 4 tables

The resonance region of $^{11}$B covering excitation energies from 8.4 MeV to 13.6 MeV was investigated with the $(d,p)$ reaction performed on an enriched $^{10}$B target at the Florida State University Super-Enge Split-Pole Spectrograph of the John D. Fox Superconducting Linear Accelerator Laboratory. Complementary measurements were performed with a target enriched in $^{11}$B to identify possible $^{12}$B contaminants in the $(d,p)$ reaction. Four strongly populated $^{11}$B states were observed above the $\alpha$-decay threshold. Angular distributions were measured and compared to DWBA calculations to extract angular momentum transfers and $^{10}\mathrm{B}\left(3^+\right)+n$ spectroscopic factors. The recently observed and heavily discussed resonance at 11.4 MeV in $^{11}$B was not observed in this work. This result is consistent with the interpretation that it is predominantly a $^{10}\mathrm{Be}\left(0^+\right)+p$ resonance with a possible additional $^{7}\mathrm{Li}+\alpha$ contribution. The predicted $^{10}\mathrm{B}\left(3^+\right)+n$ resonance at 11.6 MeV, analogous to the 11.4-MeV proton resonance, was not observed either. Upper limits for the $^{10}\mathrm{B}\left(3^+\right)+n$ spectroscopic factors of the 11.4-MeV and 11.6-MeV states were determined. In addition, supporting configuration interaction shell model calculations with the effective WBP interaction are presented., Comment: 7 pages, 3 figures, accepted for publication in Physical Review C as regular article

We report new experimental data for excited states of $^{72,74}$Se obtained from proton removal from $^{73,75}$Br secondary beams on a proton target. The experiments were performed with the Ursinus-NSCL Liquid Hydrogen Target and the combined GRETINA+S800 setup at the Coupled Cyclotron Facility of the National Superconducting Cyclotron Laboratory at Michigan State University. Within uncertainties, the inclusive cross sections for proton removal from $^{73,75}$Br on a proton target are identical suggesting that the same single-particle orbitals contribute to the proton-removal reaction. In addition, details of the partial cross section fragmentation are discussed. The data might suggest that $l = 1, 2, 3$, and 4 angular momentum transfers are important to understand the population of excited states of $^{72,74}$Se in proton removal. Available data for excited states of $^{74}$Ge populated through the $^{75}$As$(d,{}^{3}{\mathrm{He}}){}^{74}$Ge proton-removal reaction in normal kinematics suggest indeed that the $fp$ and $sd$ shell as well as the $1g_{9/2}$ orbital contribute. A comparison to data available for odd-$A$ nuclei supports that the bulk of the spectroscopic strengths could be found at lower energies in the even-even Se isotopes than in, for instance, the even-even Ge isotopes. In addition, the population of high-$J$ states seems to indicate that multi-step processes contribute to proton-removal reactions at intermediate energies in these collective nuclei., Comment: 9 pages, 5 figures

We present 850 $\mu$m polarization observations of the IC 348 star-forming region in the Perseus molecular cloud as part of the B-fields In STar-forming Region Observation (BISTRO) survey. We study the magnetic properties of two cores (HH 211 MMS and IC 348 MMS) and a filamentary structure of IC 348. We find that the overall field tends to be more perpendicular than parallel to the filamentary structure of the region. The polarization fraction decreases with intensity, and we estimate the trend by power-law and the mean of the Rice distribution fittings. The power indices for the cores are much smaller than 1, indicative of possible grain growth to micron size in the cores. We also measure the magnetic field strengths of the two cores and the filamentary area separately by applying the Davis-Chandrasekhar-Fermi method and its alternative version for compressed medium. The estimated mass-to-flux ratios are 0.45-2.20 and 0.63-2.76 for HH 211 MMS and IC 348 MMS, respectively, while the ratios for the filament is 0.33-1.50. This result may suggest that the transition from subcritical to supercritical conditions occurs at the core scale ($\sim$ 0.05 pc) in the region. In addition, we study the energy balance of the cores and find that the relative strength of turbulence to the magnetic field tends to be stronger for IC 348 MMS than HH 211 MMS. The result could potentially explain the different configurations inside the two cores: a single protostellar system in HH 211 MMS and multiple protostars in IC 348 MMS., Comment: Accepted for publication in ApJ. 21 pages, 12 figures

We provide a classification of all dynamical Lie algebras generated by 2-local spin interactions on undirected graphs. Building on our previous work where we provided such a classification for spin chains, here we consider the more general case of undirected graphs. As it turns out, the one-dimensional case is special; for any other graph, the dynamical Lie algebra solely depends on whether the graph is bipartite or not. An important consequence of this result is that the cases where the dynamical Lie algebra is polynomial in size are special and restricted to one dimension., Comment: 12+8 pages

Adiabatic state preparation aims to prepare the ground state of a target Hamiltonian starting from the easily prepared ground state of an initial Hamiltonian. While effective for time-dependent Hamiltonians with a significant energy gap to the first coupled excited state, this process becomes exceedingly slow as the gap becomes small. To accelerate it, we allow controlled diabatic excitations during the evolution and optimize the path to remove those excitations at the end of the evolution. This is done via a modulated time evolution (dynamically scaling the Hamiltonian) in addition to a field similar to the one used in local adiabatic time evolution, with the target to optimize the final energy of the unscaled Hamiltonian. This method is easy to implement and requires no complex counter-diabatic Hamiltonians and no prior knowledge of the system's energy gap. When the time evolution is further Trotterized, separating the spin-spin coupling terms and the magnetic-field terms into distinct factors, it becomes identical in structure to the quantum approximate optimization algorithm (QAOA). Compared to QAOA, the modulated time evolution often achieves the same level of performance with fewer layers (time steps).

Recruiting and retaining a well-prepared, stable, and diverse teacher workforce is a critical endeavor to advance student learning and development. However, the persistent teacher shortages across the nation's schools challenge this mission. The teaching profession has been characterized by relatively lower levels of compensation compared to other professions that require equivalent levels of educational attainment, and salaries are often cited both as a reason why college students do not pursue teaching and as a driver of attrition among current teachers. Another barrier to entering and staying in the teaching profession is the cost of investments to become a teacher, along with the cost of attaining a higher degree or additional credential-requirements that have become not only more common for career advancement but also more expensive over time. This report analyzes one condition that is intertwined with teacher compensation, preparation and credentialing, and job-related stress: student loan debt. Against the backdrop of the current state of teaching, the strains arising from student loans and the return to repayment could pose additional challenges not only for teachers but also for policymakers and school leaders who want to support a highly qualified, stable, and diverse teaching workforce. Using data from the National Teacher and Principal Survey from 2020-21, this report describes the state of student loan borrowing and repayment among full-time, public school teachers and explores whether student loan burdens differ by teacher characteristics. The analyses also examine the extent to which student loan debt is associated with teachers' well-being and employment decisions. The report concludes with recommendations that support the expansion of programs that alleviate or minimize the costs of comprehensive teacher preparation and credentialing; improve teaching conditions; and promote high-quality preparation, career advancement, and retention. [This research was supported by Skyline Foundation.]

Subspace methods are powerful, noise-resilient methods that can effectively prepare ground states on quantum computers. The challenge is to get a subspace with a small condition number that spans the states of interest using minimal quantum resources. In this work, we will use eigenvector continuation (EC) to build a subspace from the low-lying states of a set of Hamiltonians. The basis vectors are prepared using truncated versions of standard state preparation methods such as imaginary time evolution (ITE) and adiabatic state preparation (ASP). By using these truncated methods combined with eigenvector continuation, we can directly improve upon them, obtaining more accurate ground state energies at a reduced cost. We use several spin systems to demonstrate convergence even when methods like ITE and ASP fail, such as ASP in the presence of level crossings and ITE with vanishing energy gaps. We also showcase the noise resilience of this approach beyond the gains already made by having a shallower quantum circuit. Our findings suggest that eigenvector continuation can be used to improve existing state preparation methods in the near term., Comment: 17 pages, 18 figures

Although recent advances in higher-order Graph Neural Networks (GNNs) improve the theoretical expressiveness and molecular property predictive performance, they often fall short of the empirical performance of models that explicitly use fragment information as inductive bias. However, for these approaches, there exists no theoretic expressivity study. In this work, we propose the Fragment-WL test, an extension to the well-known Weisfeiler & Leman (WL) test, which enables the theoretic analysis of these fragment-biased GNNs. Building on the insights gained from the Fragment-WL test, we develop a new GNN architecture and a fragmentation with infinite vocabulary that significantly boosts expressiveness. We show the effectiveness of our model on synthetic and real-world data where we outperform all GNNs on Peptides and have 12% lower error than all GNNs on ZINC and 34% lower error than other fragment-biased models. Furthermore, we show that our model exhibits superior generalization capabilities compared to the latest transformer-based architectures, positioning it as a robust solution for a range of molecular modeling tasks.

Understanding the physics of strongly correlated materials is one of the grand challenge problems for physics today. A large class of scientifically interesting materials, from high-$T_c$ superconductors to spin liquids, involve medium to strong correlations, and building a holistic understanding of these materials is critical. Doing so is hindered by the competition between the kinetic energy and Coulomb repulsion, which renders both analytic and numerical methods unsatisfactory for describing interacting materials. Fault-tolerant quantum computers have been proposed as a path forward to overcome these difficulties, but this potential capability has not yet been fully assessed. Here, using the multi-orbital Fermi-Hubbard model as a representative model and a source of scalable problem specifications, we estimate the resource costs needed to use fault-tolerant quantum computers for obtaining experimentally relevant quantities such as correlation function estimation. We find that advances in quantum algorithms and hardware will be needed in order to reduce quantum resources and feasibly address utility-scale problem instances.

In the current framework of Geometric Quantum Machine Learning, the canonical method for constructing a variational ansatz that respects the symmetry of some group action is by forcing the circuit to be equivariant, i.e., to commute with the action of the group. This can, however, be an overzealous constraint that greatly limits the expressivity of the circuit, especially in the case of continuous symmetries. We propose an alternative paradigm for the symmetry-informed construction of variational quantum circuits, based on homogeneous spaces, relaxing the overly stringent requirement of equivariance. We achieve this by introducing horizontal quantum gates, which only transform the state with respect to the directions orthogonal to those of the symmetry. We show that horizontal quantum gates are much more expressive than equivariant gates, and thus can solve problems that equivariant circuits cannot. For instance, a circuit comprised of horizontal gates can find the ground state of an $\mathrm{SU}(2)$-symmetric model where the ground state spin sector is unknown--a task where equivariant circuits fall short. Moreover, for a particular subclass of horizontal gates based on symmetric spaces, we can obtain efficient circuit decompositions for our gates through the KAK theorem. Finally, we highlight a particular class of horizontal quantum gates that behave similarly to general $\mathrm{SU}(4)$ gates, while achieving a quadratic reduction in the number of parameters for a generic problem.

Recent research interest in the learning-based processing of garments, from virtual fitting to generation and reconstruction, stumbles on a scarcity of high-quality public data in the domain. We contribute to resolving this need by presenting the first large-scale synthetic dataset of 3D made-to-measure garments with sewing patterns, as well as its generation pipeline. GarmentCodeData contains 115,000 data points that cover a variety of designs in many common garment categories: tops, shirts, dresses, jumpsuits, skirts, pants, etc., fitted to a variety of body shapes sampled from a custom statistical body model based on CAESAR, as well as a standard reference body shape, applying three different textile materials. To enable the creation of datasets of such complexity, we introduce a set of algorithms for automatically taking tailor's measures on sampled body shapes, sampling strategies for sewing pattern design, and propose an automatic, open-source 3D garment draping pipeline based on a fast XPBD simulator, while contributing several solutions for collision resolution and drape correctness to enable scalability. Project Page: https://igl.ethz.ch/projects/GarmentCodeData/, Comment: Accepted to ECCV 2024. Sept 4th, 2024: release of GarmentCodeData(v2)

Conversational recommendation (ConvRec) systems must understand rich and diverse natural language (NL) expressions of user preferences and intents, often communicated in an indirect manner (e.g., "I'm watching my weight"). Such complex utterances make retrieving relevant items challenging, especially if only using often incomplete or out-of-date metadata. Fortunately, many domains feature rich item reviews that cover standard metadata categories and offer complex opinions that might match a user's interests (e.g., "classy joint for a date"). However, only recently have large language models (LLMs) let us unlock the commonsense connections between user preference utterances and complex language in user-generated reviews. Further, LLMs enable novel paradigms for semi-structured dialogue state tracking, complex intent and preference understanding, and generating recommendations, explanations, and question answers. We thus introduce a novel technology RA-Rec, a Retrieval-Augmented, LLM-driven dialogue state tracking system for ConvRec, showcased with a video, open source GitHub repository, and interactive Google Colab notebook.

The Collaborative Minnesota Partnerships to Advance Student Success (COMPASS), a statewide education system designed to support schools' and districts' efforts to accelerate student learning, offers professional development opportunities with various levels of support. Through COMPASS school districts and charter schools are provided a variety of resources, evidence-based practices, and facilitated guidance to support schools in areas of Literacy, Math, and School Climate and Mental Health using the Minnesota Multi-Tiered System of Supports (MnMTSS) continuous improvement framework. As part of the federally-funded Region 10 Comprehensive Center (R10CC), the Center for Applied Research and Educational Improvement (CAREI) at the University of Minnesota has collaborated with the Minnesota Department of Education (MDE) to develop a wealth of written resources to support implementation of MnMTSS across Minnesota schools and districts. This brief summarizes what was learned from three Minnesota school districts who participated in COMPASS cohort learning experiences. The report introduces each district and describes how their teams engaged in the cohort courses, how their participation impacted local MnMTSS development and implementation, and what lessons they learned along the way.

High-quality teacher preparation is a critical building block of an effective and stable teacher workforce. In California, the Commission on Teacher Credentialing (CTC) currently oversees teacher preparation programs (TPPs) at more than 100 institutions, and these programs graduate more than 10,000 teacher candidates per year. Over the past decade, California has considerably revised its statewide licensing and accreditation systems that set standards for teacher preparation and performance expectations for beginning teachers. These new teaching standards emphasize teachers' abilities to teach to the more ambitious student learning standards the state adopted in 2010. Given policy changes and financial investments, what is the state of teacher preparation in California? Based on survey responses from almost 60,000 completers of TPPs across California from 2016-17 to 2020-21, this report describes changes in the pool of newly credentialed teachers over this period of time, how well prepared completers of TPPs feel in different domains of practice, what kind of learning opportunities they encountered, what aspects of preparation explain how prepared completers feel to enter the profession, and how access to preparation experiences varies among different groups of teachers. This analysis also examines the extent to which perceptions of TPP completers align with survey responses from more than 5,000 cooperating teachers working with student teachers and more than 1,500 employers hiring these new teachers. This report begins by describing the landscape of teacher preparation in California. The authors then explain the data and methods used in this report and outline how the composition of the pool of newly credentialed teachers has changed between 2016-17 and 2020-21. They then detail the findings from the analysis of CTC surveys. These findings are organized into three sections: (1) overall perceptions of preparation effectiveness, (2) differences in perceptions of effectiveness by characteristics of preparation, and (3) differential access to highly rated preparation. The report concludes with policy considerations for California and implications for other states. [For the Research Brief, see ED630346.]

Mid-infrared emission features probe the properties of ionized gas, and hot or warm molecular gas. The Orion Bar is a frequently studied photodissociation region (PDR) containing large amounts of gas under these conditions, and was observed with the MIRI IFU aboard JWST as part of the "PDRs4All" program. The resulting IR spectroscopic images of high angular resolution (0.2") reveal a rich observational inventory of mid-IR emission lines, and spatially resolve the substructure of the PDR, with a mosaic cutting perpendicularly across the ionization front and three dissociation fronts. We extracted five spectra that represent the ionized, atomic, and molecular gas layers, and measured the most prominent gas emission lines. An initial analysis summarizes the physical conditions of the gas and the potential of these data. We identified around 100 lines, report an additional 18 lines that remain unidentified, and measured the line intensities and central wavelengths. The H I recombination lines originating from the ionized gas layer bordering the PDR, have intensity ratios that are well matched by emissivity coefficients from H recombination theory, but deviate up to 10% due contamination by He I lines. We report the observed emission lines of various ionization stages of Ne, P, S, Cl, Ar, Fe, and Ni, and show how certain line ratios vary between the five regions. We observe the pure-rotational H$_2$ lines in the vibrational ground state from 0-0 S(1) to 0-0 S(8), and in the first vibrationally excited state from 1-1 S(5) to 1-1 S(9). We derive H$_2$ excitation diagrams, and approximate the excitation with one thermal (~700 K) component representative of an average gas temperature, and one non-thermal component (~2700 K) probing the effect of UV pumping. We compare these results to an existing model for the Orion Bar PDR and highlight the differences with the observations., Comment: 26 pages, 12 figures, 3 tables. Submitted to A&A, under review (1st revision)

In 1993, just about a century after the epoch of Classical Invariant Theory and almost 30 years after Mumford's seminal book on Geometric Invariant Theory, Bernd Sturmfels approached the subject from a new, algorithmic perspective in his book on Algorithms in Invariant Theory. This article aims to highlight some of the developments that followed the book. Inspired by Bernd's style of teaching mathematics, the goal is neither comprehensiveness nor maximal generality, but to emphasize the main ideas and to convey the beauty of the subject. The article is intended as an invitation to invariant theory, and in particular to Bernd's work and the ideas inspired by it.

Imaginary-time response functions of finite-temperature quantum systems are often obtained with methods that exhibit stochastic or systematic errors. Reducing these errors comes at a large computational cost -- in quantum Monte Carlo simulations, the reduction of noise by a factor of two incurs a simulation cost of a factor of four. In this paper, we relate certain imaginary-time response functions to an inner product on the space of linear operators on Fock space. We then show that data with noise typically does not respect the positive definiteness of its associated Gramian. The Gramian has the structure of a Hankel matrix. As a method for denoising noisy data, we introduce an alternating projection algorithm that finds the closest positive definite Hankel matrix consistent with noisy data. We test our methodology at the example of fermion Green's functions for continuous-time quantum Monte Carlo data and show remarkable improvements of the error, reducing noise by a factor of up to 20 in practical examples. We argue that Hankel projections should be used whenever finite-temperature imaginary-time data of response functions with errors is analyzed, be it in the context of quantum Monte Carlo, quantum computing, or in approximate semianalytic methodologies., Comment: 5 figures

We present point-source photometry from the Spitzer Space Telescope's final survey of the Small Magellanic Cloud (SMC). We mapped 30 square degrees in two epochs in 2017, with the second extending to early 2018 at 3.6 and 4.5 microns using the Infrared Array Camera. This survey duplicates the footprint from the SAGE-SMC program in 2008. Together, these surveys cover a nearly 10 yr temporal baseline in the SMC. We performed aperture photometry on the mosaicked maps produced from the new data. We did not use any prior catalogs as inputs for the extractor in order to be sensitive to any moving objects (e.g., foreground brown dwarfs) and other transient phenomena (e.g., cataclysmic variables or FU Ori-type eruptions). We produced a point-source catalog with high-confidence sources for each epoch as well as combined-epoch catalog. For each epoch and the combined-epoch data, we also produced a more complete archive with lower-confidence sources. All of these data products will be available to the community at the Infrared Science Archive., Comment: 16 pages, 11 figures, 6 tables

The White House Executive Order on Artificial Intelligence highlights the risks of large language models (LLMs) empowering malicious actors in developing biological, cyber, and chemical weapons. To measure these risks of malicious use, government institutions and major AI labs are developing evaluations for hazardous capabilities in LLMs. However, current evaluations are private, preventing further research into mitigating risk. Furthermore, they focus on only a few, highly specific pathways for malicious use. To fill these gaps, we publicly release the Weapons of Mass Destruction Proxy (WMDP) benchmark, a dataset of 3,668 multiple-choice questions that serve as a proxy measurement of hazardous knowledge in biosecurity, cybersecurity, and chemical security. WMDP was developed by a consortium of academics and technical consultants, and was stringently filtered to eliminate sensitive information prior to public release. WMDP serves two roles: first, as an evaluation for hazardous knowledge in LLMs, and second, as a benchmark for unlearning methods to remove such hazardous knowledge. To guide progress on unlearning, we develop RMU, a state-of-the-art unlearning method based on controlling model representations. RMU reduces model performance on WMDP while maintaining general capabilities in areas such as biology and computer science, suggesting that unlearning may be a concrete path towards reducing malicious use from LLMs. We release our benchmark and code publicly at https://wmdp.ai, Comment: See the project page at https://wmdp.ai

In this work, a microscopic quantum mechanical model for gravitationally induced decoherence introduced by Blencowe and Xu is investigated in the context of neutrino oscillations. The focus is on the comparison with existing phenomenological models and the physical interpretation of the decoherence parameters in such models. The results show that for neutrino oscillations in vacuum gravitationally induced decoherence can be matched with phenomenological models with decoherence parameters of the form $\Gamma_{ij}\sim \Delta m^4_{ij}E^{-2}$. When matter effects are included, the decoherence parameters show a dependence on matter effects, which vary in the different layers of the Earth, that can be explained with the form of the coupling between neutrinos and the gravitational wave environment inspired by linearised gravity. Consequently, in the case of neutrino oscillations in matter, the microscopic model does not agree with many existing phenomenological models that assume constant decoherence parameters in matter, and their existing bounds cannot be used to further constrain the model considered here. The probabilities for neutrino oscillations with constant and varying decoherence parameters are compared and it is shown that the deviations can be up to 10%. On a theoretical level, these different models can be characterised by a different choice of Lindblad operators, with the model with decoherence parameters that do not include matter effects being less suitable from the point of view of linearised gravity., Comment: 27 pages, 8 figures, updated the color of the axis in figure 1, extended the discussion in the conclusion and updated some references

Understanding the physical processes that regulate star formation and galaxy evolution are major areas of activity in modern astrophysics. Nearby galaxies offer unique opportunities to inspect interstellar medium (ISM), star formation (SF), radiative, dynamic and magnetic physics in great detail from sub-galactic (kpc) scales to sub-cloud (sub-pc) scales, from quiescent galaxies to starbursts, and from field galaxies to overdensities. In this case study, we discuss the major breakthroughs in this area of research that will be enabled by the Atacama Large Aperture Submillimeter Telescope (AtLAST), a proposed 50-m single-dish submillimeter telescope. The new discovery space of AtLAST comes from its exceptional sensitivity, in particular to extended low surface brightness emission, a very large 2 degree field of view, and correspondingly high mapping efficiency. This paper focuses on four themes which will particularly benefit from AtLAST: 1) the LMC and SMC, 2) extragalactic magnetic fields, 3) the physics and chemistry of the interstellar medium, and 4) star formation and galaxy evolution. With ~1000-2000h surveys each, AtLAST could deliver deep dust continuum maps of the entire LMC and SMC fields at parsec-scale resolution, high-resolution maps of the magnetic field structure, gas density, temperature and composition of the dense and diffuse ISM in ~100 nearby galaxies, as well as the first large-scale blind CO survey in the nearby Universe, delivering molecular gas masses for up to 10^6 galaxies (3 orders of magnitude more than current samples). Through such observing campaigns, AtLAST will have a profound impact on our understanding of the baryon cycle and star formation across a wide range of environments., Comment: 29 pages, 11 figues, submitted to Open Research Europe as part of the AtLAST collection: https://open-research-europe.ec.europa.eu/collections/atlast/about

This paper deals with the problem of reconstructing the path of a vehicle in an unknown environment consisting of planar structures using sound. Many systems in the literature do this by using a loudspeaker and microphones mounted on a vehicle. Symmetries in the environment lead to solution ambiguities for such systems. We propose to resolve this issue by placing the loudspeaker at a fixed location in the environment rather than on the vehicle. The question of whether this will remove ambiguities regardless of the environment geometry leads to a question about breaking symmetries that can be phrased in purely mathematical terms. We solve this question in the affirmative if the geometry is in dimension three or bigger, and give counterexamples in dimension two. Excluding the rare situations where the counterexamples arise, we also give an affirmative answer in dimension two. Our results lead to a simple path reconstruction algorithm for a vehicle carrying four microphones navigating within an environment in which a loudspeaker at a fixed position emits short bursts of sounds. This algorithm could be combined with other methods from the literature to construct a path tracking system for vehicles navigating within a potentially symmetric environment.

Most low-mass stars form in stellar clusters that also contain massive stars, which are sources of far-ultraviolet (FUV) radiation. Theoretical models predict that this FUV radiation produces photo-dissociation regions (PDRs) on the surfaces of protoplanetary disks around low-mass stars, impacting planet formation within the disks. We report JWST and Atacama Large Millimetere Array observations of a FUV-irradiated protoplanetary disk in the Orion Nebula. Emission lines are detected from the PDR; modelling their kinematics and excitation allows us to constrain the physical conditions within the gas. We quantify the mass-loss rate induced by the FUV irradiation, finding it is sufficient to remove gas from the disk in less than a million years. This is rapid enough to affect giant planet formation in the disk.