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Throughout the research conducted for WestEd's recent brief, "Schools Can't Do It Alone: Developing Sustainable Systems of Care for School-Based Behavioral Health in California," local educational agencies (LEAs) expressed a need for technical assistance (TA) in a variety of areas to improve student behavioral health outcomes. Needed assistance includes TA to build school staff's capacity to support student behavioral health (e.g., by using trauma-informed and restorative practices) and TA on how to develop infrastructure to collaborate with external partners such as health care providers and community-based organizations to provide school-based care. This companion brief envisions how a statewide system might be structured to improve LEAs' and their local partners' access to TA focused on supporting students' behavioral health. The brief explores two potential models for such a statewide system of TA. Model 1 would involve integrating TA on school-based behavioral health into California's existing Statewide System of Support (SSOS). Model 2 would entail creating a separate structure, analogous and connected to the SSOS and focused on school-based behavioral health.

Through its "Master Plan for Kids' Mental Health," California has invested $4.7 billion in youth mental and behavioral health since the start of the COVID-19 pandemic. Expanding behavioral health services on school campuses has been a powerful way to ensure equitable access to this support. However, most of the state's new behavioral health funding is short term, and local educational agencies (LEAs) cannot sustain or expand necessary services without making substantial reductions in other services. Collaborating with external partners--particularly from the health care sector--to create integrated, school-based systems of care offers a promising strategy for sustaining and expanding behavioral health support for students. This research brief investigates how well the state's recent short-term investments are aligned with LEAs' highest priority needs for supporting mental and behavioral health and are aligned with the state's goal of advancing integrated, school-based systems of care to support students in the long term. The brief describes how LEAs' investments of recent short-term funds seem well-aligned with students' most pressing behavioral health needs. However, LEAs have largely used these funds to hire behavioral health staff rather than to invest in long-term, cross-sector partnerships to expand services on campus, which presents a funding sustainability concern. The brief offers recommendations for how state, county, and health plan leaders can maximize the effectiveness of future investments and strengthen partnerships between local education and health care systems to sustain school-based behavioral health supports. A companion brief, "Schools Can't Do It Alone: Envisioning a Statewide System of Support to Advance School-Based Behavioral Health in California," explores the availability of technical assistance on this topic and how a statewide system to support school-based behavioral health might be structured.

The accumulation of potassium in the extracellular space surrounding nerve cells is a fundamental aspect of biophysics that has garnered significant attention in recent research. This phenomenon holds implications for various neurological conditions, including spreading depression, migraine, certain types of epilepsy, and potentially, learning processes. A quantitative analysis is essential for understanding the dynamics of potassium clearance following a series of action potentials. This clearance process involves multiple structures along the nerve, including glia, the extracellular space, axons, and the perivascular space, necessitating a spatially distributed systems approach akin to the cable equations of nerve physiology. In this study, we propose a multi-domain model for the optic nerve to investigate potassium accumulation and clearance dynamics. The model accounts for the convection, diffusion, and electrical migration of fluid and ions, revealing the significant roles of glia and the perivascular space in potassium buffering. Specifically, our findings suggest that potassium clearance primarily occurs through convective flow within the syncytia of glia, driven by osmotic pressure differences. Additionally, the perivascular space serves as a crucial pathway for potassium buffering and fluid circulation, further contributing to the overall clearance process. Importantly, our model's adaptability allows for its application to diverse structures with distinct channel and transporter distributions across the six compartments, extending its utility beyond the optic nerve.

An exciting recent development for deep-tissue imaging with cellular resolution is three-photon fluorescence microscopy (3PM) with excitation at long wavelengths (1300 and 1700 nm). In the last few years, long-wavelength 3PM has driven rapid progress in deep-tissue imaging beyond the depth limit of two-photon microscopy, with impacts in neuroscience, immunology, and cancer biology. However, wide adoption of 3PM faces challenges. Three-photon excitation (3PE) is naturally weaker than two-photon excitation, which places a premium on ultrashort pulses with high peak power. The inefficiency, complexity, and cost of current sources of these pulses present major barriers to the use of 3PM in typical biomedical research labs. Here, we describe a fiber-based source of femtosecond pulses with multi-megawatt peak power, tunable from 850 nm to 1700 nm. Compressed pulses from a fiber amplifier at 1030~nm are launched into an antiresonant hollow-core fiber filled with argon. By varying only the gas pressure, pulses with hundreds of nanojoules of energy and sub-100 fs duration are obtained at wavelengths between 850 and 1700 nm. This approach is a new route to an efficient, robust, and potentially low-cost source for multiphoton deep-tissue imaging. In particular, 960-nJ and 50-fs pulses are generated at 1300 nm with a conversion efficiency of 10\%. The nearly 20-MW peak power is an order of magnitude higher than the previous best from femtosecond fiber source at 1300~nm. As an example of the capabilities of the source, these pulses are used to image structure and neuronal activity in mouse brain as deep as 1.1 mm below the dura., Comment: Supplement appended at end

It is certain that electrical properties-whether slow (sec) or fast (nsec), even optical (fsec)-are described by Maxwell's equations, and there are terms that depend on the rate of change of the electric and magnetic fields. In particular, Maxwell's equation for the curl of the magnetic field contains both the steady current and a term depending upon the temporal derivative of the electric displacement field. The latter is referred to as displacement current, and is generally believed to have been included originally by Maxwell himself, although there is evidence it was earlier considered by Kirchhoff. Maxwell's equations and Kirchoff's circuit laws both are important over the wide range of frequencies with which electronics traditionally deals. And, displacement current is an important contribution to these in both classical and quantum mechanics. Here, the development of displacement current, its importance in both classical and quantum mechanics, and some applications are provided to illustrate the fundamental role that it plays in the dynamics of a wide range of systems.

We consider an insurance company which faces financial risk in the form of insurance claims and market-dependent surplus fluctuations. The company aims to simultaneously control its terminal wealth (e.g. at the end of an accounting period) and the ruin probability in a finite time interval by purchasing reinsurance. The target functional is given by the expected utility of terminal wealth perturbed by a modified Gerber-Shiu penalty function. We solve the problem of finding the optimal reinsurance strategy and the corresponding maximal target functional via neural networks. The procedure is illustrated by a numerical example, where the surplus process is given by a Cram\'er-Lundberg model perturbed by a mean-reverting Ornstein-Uhlenbeck process., Comment: 18 pages with 4 figures

For mathematical and experimental ease, models with time varying parameters are often simplified to assume constant parameters. However, this simplification can potentially lead to identifiability issues (lack of uniqueness of parameter estimates). Methods have been developed to algebraically and numerically determine the identifiability of a model, as well as resolve identifiability issues. This specific type of simplification presents an alternate opportunity to instead use this information to resolve the unidentifiability. Given that re-parameterizing, collecting more data, and adding inputs can be potentially costly or impractical, this could present new alternatives. We present a method for resolving unidentifiability in a system by introducing a new data stream correlated with a parameter of interest. First, we demonstrate how and when non-constant input data can be introduced into any rational function ODE system without worsening the model identifiability. Then, we prove when these input functions improve structural and potentially also practical identifiability for a given model and relevant data. By utilizing pre-existing data streams, these methods can potentially reduce experimental costs, while still answering key questions. By connecting mathematical proofs to application, our framework removes guesswork from when, where, and how researchers can best introduce new data to improve model outcomes.

Sample Return Capsules (SRCs) entering Earth's atmosphere at hypervelocity from interplanetary space are a valuable resource for studying meteor phenomena. The 24 September 2023 arrival of the OSIRIS-REx (Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer) SRC provided an unprecedented chance for geophysical observations of a well-characterized source with known parameters, including timing and trajectory. A collaborative effort involving researchers from 16 institutions executed a carefully planned geophysical observational campaign at strategically chosen locations, deploying over 400 ground-based sensors encompassing infrasound, seismic, distributed acoustic sensing (DAS), and GPS technologies. Additionally, balloons equipped with infrasound sensors were launched to capture signals at higher altitudes. This campaign (the largest of its kind so far) yielded a wealth of invaluable data anticipated to fuel scientific inquiry for years to come. The success of the observational campaign is evidenced by the near-universal detection of signals across instruments, both proximal and distal. This paper presents a comprehensive overview of the collective scientific effort, field deployment, and preliminary findings. The early findings have the potential to inform future space missions and terrestrial campaigns, contributing to our understanding of meteoroid interactions with planetary atmospheres. Furthermore, the dataset collected during this campaign will improve entry and propagation models as well as augment the study of atmospheric dynamics and shock phenomena generated by meteoroids and similar sources., Comment: 87 pages, 14 figures

The Interval poset of a permutation is an effective way of capturing all the intervals of the permutation and the inclusions between them and was introduced recently by Tenner. Thi paper explores the geometric interpretation of interval posets of permutations. We present a bijection between tree interval posets and convex polygons with non-crossing diagonals, offering a novel geometric perspective on this purely combinatorial concept. Additionally, we provide an enumeration of interval posets using this bijection and demonstrate its application to block-wise simple permutations., Comment: In Proceedings GASCom 2024, arXiv:2406.14588

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark.

Measuring the impact of an environmental point source exposure on the risk of disease, like cancer or childhood asthma, is well-developed. Modeling how an environmental health hazard that is extensive in space, like a wastewater canal, impacts disease risk is not. We propose a novel Bayesian generative semiparametric model for characterizing the cumulative spatial exposure to an environmental health hazard that is not well-represented by a single point in space. The model couples a dose-response model with a log-Gaussian Cox process integrated against a distance kernel with an unknown length-scale. We show that this model is a well-defined Bayesian inverse model, namely that the posterior exists under a Gaussian process prior for the log-intensity of exposure, and that a simple integral approximation adequately controls the computational error. We quantify the finite-sample properties and the computational tractability of the discretization scheme in a simulation study. Finally, we apply the model to survey data on household risk of childhood diarrheal illness from exposure to a system of wastewater canals in Mezquital Valley, Mexico.

The melting temperature is important for materials design because of its relationship with thermal stability, synthesis, and processing conditions. Current empirical and computational melting point estimation techniques are limited in scope, computational feasibility, or interpretability. We report the development of a machine learning methodology for predicting melting temperatures of binary ionic solid materials. We evaluated different machine-learning models trained on a data set of the melting points of 476 non-metallic crystalline binary compounds, using materials embeddings constructed from elemental properties and density-functional theory calculations as model inputs. A direct supervised-learning approach yields a mean absolute error of around 180~K but suffers from low interpretability. We find that the fidelity of predictions can further be improved by introducing an additional unsupervised-learning step that first classifies the materials before the melting-point regression. Not only does this two-step model exhibit improved accuracy, but the approach also provides a level of interpretability with insights into feature importance and different types of melting that depend on the specific atomic bonding inside a material. Motivated by this finding, we used a symbolic learning approach to find interpretable physical models for the melting temperature, which recovered the best-performing features from both prior models and provided additional interpretability., Comment: 21 pages and 3 figures for the main manuscript, and 11 pages and 15 figures of supplementary information

Thomas Schelling introduced his agent-based model of segregation in 1971 and concluded that even when there is a low amount of intolerance within society that segregation will develop if people follow their individual preferences. A large body of literature building of this framework has been built and has bolstered this claim. This paper aims to take the same framework but instead look for ways to get to an integrated state. We focus on Allport's contact hypothesis that states that if there is equal status among groups, common goals among groups, and an institutional mechanism supporting intergroup contact then intergroup contact can reduce prejudice. We incorporate the contact hypothesis by having individuals adjust their intolerance based on their current neighborhood composition and the ease of conforming to their surroundings. Furthermore, we add in positive and negative media effects, as individuals are likely to get information about an outgroup from the media (e.g., news, TV, movies, etc.) that they consume. We find that having a society composed of individuals who do not easily conform to their surroundings and displaying positive examples of both groups in media promote integration within society., Comment: 14 pages, 9 figures

Fusing information from different modalities can enhance data analysis tasks, including clustering. However, existing multi-view clustering (MVC) solutions are limited to specific domains or rely on a suboptimal and computationally demanding two-stage procedure of representation and clustering. We propose an end-to-end deep learning-based MVC framework for general data (image, tabular, etc.). Our approach involves learning meaningful fused data representations with a novel permutation-based canonical correlation objective. Concurrently, we learn cluster assignments by identifying consistent pseudo-labels across multiple views. We demonstrate the effectiveness of our model using ten MVC benchmark datasets. Theoretically, we show that our model approximates the supervised linear discrimination analysis (LDA) representation. Additionally, we provide an error bound induced by false-pseudo label annotations.

Quantum metrology has been shown to surpass classical limits of correlation, resolution, and sensitivity. It has been introduced to interferometric Radar schemes, with intriguing preliminary results. Even quantum-inspired detection of classical signals may be advantageous in specific use cases. Following ideas demonstrated so far only in the optical domain, where practically no thermal background photons exist, we realize room-temperature microwave frequency super-resolved phase measurements with trillions of photons, while saturating the Cramer-Rao bound of sensitivity. We experimentally estimate the interferometric phase using the expectation value of the Parity operator by two methods. We achieve super-resolution up to 1200 times better than the wavelength with 25ns integration time and 56dB SNR.

Control over the joint spectral amplitude of a photon pair has proved highly desirable for many quantum applications, since it contains the spectral quantum correlations, and has crucial effects on the indistinguishability of photons, as well as promising emerging applications involving complex quantum functions and frequency encoding of qudits. Until today, this has been achieved by engineering a single degree of freedom, either by custom poling nonlinear crystal or by shaping the pump pulse. We present a combined approach where two degrees of freedom, the phase-matching function, and the pump spectrum, are controlled. This approach enables the two-dimensional control of the joint spectral amplitude, generating a variety of spectrally encoded quantum states - including frequency uncorrelated states, frequency-bin Bell states, and biphoton qudit states. In addition, the joint spectral amplitude is controlled by photon bunching and anti-bunching, reflecting the symmetry of the phase-matching function.

Numerical models allow the investigation of phenomena that cannot exist in a laboratory. Computational simulations are therefore essential for advancing our knowledge of astrophysics, however, the very nature of simulation requires making assumptions that can substantially affect their outcome. Here, we present the challenges faced when simulating dim thermonuclear explosions, Type Iax supernovae. This class of dim events produce a slow moving, sparse ejecta that presents challenges for simulation. We investigate the limitations of the equation of state and its applicability to the expanding, cooling ejecta. We also discuss how the "fluff", i.e. the low-density gas on the grid in lieu of vacuum, inhibits the ejecta as it expands. We explore how the final state of the simulation changes as we vary the character of the burning, which influences the outcome of the explosion. These challenges are applicable to a wide range of astrophysical simulations, and are important to discuss and overcome as a community., Comment: 10 pages, 7 figures, submitted to the Proceedings of 15th International Conference on Numerical Modeling of Space Plasma Flows (AstroNum)

Resilient operation of interdependent infrastructures against compound hazard events is essential for maintaining societal well-being. To address consequence assessment challenges in this problem space, we propose a novel tri-level optimization model applied to a proof-of-concept case study with fuel distribution and transportation networks -- encompassing one realistic network; one fictitious, yet realistic network; as well as networks drawn from three synthetic distributions. Mathematically, our approach takes the form of a defender-attacker-defender (DAD) model -- a multi-agent tri-level optimization, comprised of a defender, attacker, and an operator acting in sequence. Here, our notional operator may choose proxy actions to operate an interdependent system comprised of fuel terminals and gas stations (functioning as supplies) and a transportation network with traffic flow (functioning as demand) to minimize unmet demand at gas stations. A notional attacker aims to hypothetically disrupt normal operations by reducing supply at the supply terminals, and the notional defender aims to identify best proxy defense policy options which include hardening supply terminals or allowing alternative distribution methods such as trucking reserve supplies. We solve our DAD formulation at a metropolitan scale and present practical defense policy insights against hypothetical compound hazards. We demonstrate the generalizability of our framework by presenting results for a realistic network; a fictitious, yet realistic network; as well as for three networks drawn from synthetic distributions. Additionally, we demonstrate the scalability of the framework by investigating runtime performance as a function of the network size. Steps for future research are also discussed.

As AI systems' sophistication and proliferation have increased, awareness of the risks has grown proportionally (Sorkin et al. 2023). In response, calls have grown for stronger emphasis on disclosure and transparency in the AI industry (NTIA 2023; OpenAI 2023b), with proposals ranging from standardizing use of technical disclosures, like model cards (Mitchell et al. 2019), to yet-unspecified licensing regimes (Sindhu 2023). Since the AI value chain is complicated, with actors representing various expertise, perspectives, and values, it is crucial that consumers of a transparency disclosure be able to understand the risks of the AI system the disclosure concerns. In this paper we propose a risk profiling standard which can guide downstream decision-making, including triaging further risk assessment, informing procurement and deployment, and directing regulatory frameworks. The standard is built on our proposed taxonomy of AI risks, which reflects a high-level categorization of the wide variety of risks proposed in the literature. We outline the myriad data sources needed to construct informative Risk Profiles and propose a template-based methodology for collating risk information into a standard, yet flexible, structure. We apply this methodology to a number of prominent AI systems using publicly available information. To conclude, we discuss design decisions for the profiles and future work.

The dimmest and most numerous outlier of the Type Ia supernova population, Type Iax events, is increasingly being found in the results of observational campaigns. There is currently no single accepted model to describe these events. This 2D study explores the viability of modeling Type Iax events as a hybrid C/O/Ne white dwarf progenitor undergoing a deflagration using the multi-physics software FLASH. This hybrid was created using the stellar evolution code MESA, and its C-depleted core and mixed structure have demonstrated lower yields than traditional C/O progenitors in previous deflagration-to-detonation studies. To generate a sample, 30 "realizations" of this simulation were performed, the only difference being the shape of the initial matchhead used to start the deflagration. As consistent with earlier work, these realizations produce the familiar hot dense bound remnant surrounded by sparse ejecta. Our results indicate the majority of the star remains unburned (~70%) and bound (>90%). Our realizations produce total ejecta yields on the order of 10$^{-2}$ - 10$^{-1}$ solar masses, ejected $^{56}$Ni yields on the order of 10$^{-4}$ - 10$^{-2}$ solar masses, and ejecta kinetic energies on the order of 10$^{48}$ - 10$^{49}$ ergs. Compared to yields inferred from recent observations of the dimmest Type Iax events - SN 2007qd, SN 2008ha, SN 2010ae, SN 2019gsc, SN 2019muj, SN 2020kyg, and SN 2021fcg - our simulation produces comparable $^{56}$Ni yields, but too-small total yields and kinetic energies. Reignition of the remnant is also seen in some realizations., Comment: 43 pages, 15 figures, 4 tables. To be published in ApJ