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An innovative component of the Federally-sponsored Bingham County career education project is the Programed Activities for Career Exploration (PACE) Center, a mobile unit offering programed student activities to assist individual students in career planning. The mobile center visits each high school in the county; the sophomore year is selected as the target grade for the career exploration activities, which are limited in size to groups of 12. A variety of media formats geared to a wide range of academic capabilities (sound filmstrips, taped interviews, microfilm, books, and pamphlet files) are available to students in separate learning stations. The program consists of six components: (1) interest identification (Kuder E General Interest Survey), (2) exploration activities (exploration of 15-20 occupations), (3) self-appraisel activity, (4) decision-making activities (identification of the career that is of most interest to the individual), (5) career planning (in the PACE center or in small groups), and (6) career guidance (continuing contact between student and counselor). The report also discusses administrational details of the program such as scheduling, staff, budget, and physical facilities. More than two-thirds of the document consists of supplementary exhibits within the appendixes--information and worksheets, PACE questionnaire, facility layouts, equipment, and instructional materials. (EA)

As AI systems become increasingly powerful, the need for safe AI has become more pressing. Humans are an attractive model for AI safety: as the only known agents capable of general intelligence, they perform robustly even under conditions that deviate significantly from prior experiences, explore the world safely, understand pragmatics, and can cooperate to meet their intrinsic goals. Intelligence, when coupled with cooperation and safety mechanisms, can drive sustained progress and well-being. These properties are a function of the architecture of the brain and the learning algorithms it implements. Neuroscience may thus hold important keys to technical AI safety that are currently underexplored and underutilized. In this roadmap, we highlight and critically evaluate several paths toward AI safety inspired by neuroscience: emulating the brain's representations, information processing, and architecture; building robust sensory and motor systems from imitating brain data and bodies; fine-tuning AI systems on brain data; advancing interpretability using neuroscience methods; and scaling up cognitively-inspired architectures. We make several concrete recommendations for how neuroscience can positively impact AI safety., Comment: 133 pages, 19 figures

Computer models are used as a way to explore complex physical systems. Stationary Gaussian process emulators, with their accompanying uncertainty quantification, are popular surrogates for computer models. However, many computer models are not well represented by stationary Gaussian processes models. Deep Gaussian processes have been shown to be capable of capturing non-stationary behaviors and abrupt regime changes in the computer model response. In this paper, we explore the properties of two deep Gaussian process formulations within the context of computer model emulation. For one of these formulations, we introduce a new parameter that controls the amount of smoothness in the deep Gaussian process layers. We adapt a stochastic variational approach to inference for this model, allowing for prior specification and posterior exploration of the smoothness of the response surface. Our approach can be applied to a large class of computer models, and scales to arbitrarily large simulation designs. The proposed methodology was motivated by the need to emulate an astrophysical model of the formation of binary black hole mergers., Comment: 31 pages, 16 figures, 38 pages including Supplementary Materials

In modular Bayesian analyses, complex models are composed of distinct modules, each representing different aspects of the data or prior information. In this context, fully Bayesian approaches can sometimes lead to undesirable feedback between modules, compromising the integrity of the inference. This paper focuses on the "cut-distribution" which prevents unwanted influence between modules by "cutting" feedback. The multiple imputation (DS) algorithm is standard practice for approximating the cut-distribution, but it can be computationally intensive, especially when the number of imputations required is large. An enhanced method is proposed, the Emulating the Conditional Posterior (ECP) algorithm, which leverages emulation to increase the number of imputations. Through numerical experiment it is demonstrated that the ECP algorithm outperforms the traditional DS approach in terms of accuracy and computational efficiency, particularly when resources are constrained. It is also shown how the DS algorithm can be improved using ideas from design of experiments. This work also provides practical recommendations on algorithm choice based on the computational demands of sampling from the prior and cut-distributions.

An important task in the study of fast radio bursts (FRBs) remains the automatic classification of repeating and non-repeating sources based on their morphological properties. We propose a statistical model that considers a modified logistic regression to classify FRB sources. The classical logistic regression model is modified to accommodate the small proportion of repeaters in the data, a feature that is likely due to the sampling procedure and duration and is not a characteristic of the population of FRB sources. The weighted logistic regression hinges on the choice of a tuning parameter that represents the true proportion $\tau$ of repeating FRB sources in the entire population. The proposed method has a sound statistical foundation, direct interpretability, and operates with only 5 parameters, enabling quicker retraining with added data. Using the CHIME/FRB Collaboration sample of repeating and non-repeating FRBs and numerical experiments, we achieve a classification accuracy for repeaters of nearly 75\% or higher when $\tau$ is set in the range of $50$ to $60$\%. This implies a tentative high proportion of repeaters, which is surprising, but is also in agreement with recent estimates of $\tau$ that are obtained using other methods., Comment: 16 pages, 7 figures. Submitted to ApJ

This paper introduces an approach to analyze nonhomogeneous Poisson processes (NHPP) observed with noise, focusing on previously unstudied second-order characteristics of the noisy process. Utilizing a hierarchical Bayesian model with noisy data, we estimate hyperparameters governing a physically motivated NHPP intensity. Simulation studies demonstrate the reliability of this methodology in accurately estimating hyperparameters. Leveraging the posterior distribution, we then infer the probability of detecting a certain number of events within a given radius, the $k$-contact distance. We demonstrate our methodology with an application to observations of fast radio bursts (FRBs) detected by the Canadian Hydrogen Intensity Mapping Experiment's FRB Project (CHIME/FRB). This approach allows us to identify repeating FRB sources by bounding or directly simulating the probability of observing $k$ physically independent sources within some radius in the detection domain, or the $\textit{probability of coincidence}$ ($P_{\text{C}}$). The new methodology improves the repeater detection $P_{\text{C}}$ in 86% of cases when applied to the largest sample of previously classified observations, with a median improvement factor (existing metric over $P_{\text{C}}$ from our methodology) of $\sim$ 3000., Comment: 24 pages, 8 figures, submitted to the Annals of Applied Statistics. Feedback/comments welcome

A high-power laser pulse at normal incidence onto a plane solid target will generate odd harmonics of its frequency. However, the spacing of the harmonic lines in this configuration is fixed. Here, we study harmonic generation using two laser beams incident on a plane target at small, opposite angles to the target normal, via particle-in-cell simulations. When looking at the harmonic radiation in a specific direction via a narrow slit or pinhole, we select an angle-dependent subset of the harmonic spectrum. This way, we obtain a harmonic frequency comb that we control via the observation angle and the input laser frequency. The divergence of the harmonic radiation will be reduced by using wider laser spots, thus increasing the efficacy of the scheme. We will discuss extensions to this scheme, such as using beams with unequal frequencies, a slight tilt of the target, or employing more than two beams., Comment: 10 pages, 5 figures. This version provides a more detailed description of the laser pulses used in the simulations

We use extensive molecular dynamics simulations to calculate thermal conductivity and thermal diffusivity in two common molten salts, LiF and KCl. Our analysis includes the total thermal conductivity and intrinsic conductivity involving mass currents measured experimentally. The latter shows good agreement with the experimental data. We also calculate their key thermodynamic properties such as constant-pressure and constant-volume specific heats. We subsequently compare the results to the lower bound for thermal diffusivity expressed in terms of fundamental physical constants. Using this comparison and recent theoretical insights into thermodynamic and transport properties in liquids, we interpret thermal properties on the basis of atomistic dynamics and phonon excitations. We finally find that thermal diffusivity of molten salts is close to their kinematic viscosity., Comment: 8 pages, 4 figures

Hallucination has been a major problem for large language models and remains a critical challenge when it comes to multimodality in which vision-language models (VLMs) have to deal with not just textual but also visual inputs. Despite rapid progress in VLMs, resources for evaluating and addressing multimodal hallucination are limited and mostly focused on evaluation. This work introduces HaloQuest, a novel visual question answering dataset that captures various aspects of multimodal hallucination such as false premises, insufficient contexts, and visual challenges. A novel idea from HaloQuest is to leverage synthetic images, apart from real ones, to enable dataset creation at scale. With over 7.7K examples spanning across a wide variety of categories, HaloQuest was designed to be both a challenging benchmark for VLMs and a fine-tuning dataset for advancing multimodal reasoning. Our experiments reveal that current models struggle with HaloQuest, with all open-source VLMs achieving below 36% accuracy. On the other hand, fine-tuning on HaloQuest significantly reduces hallucination rates while preserving performance on standard reasoning tasks. Our results discover that benchmarking with generated images is highly correlated (r=0.97) with real images. Last but not least, we propose a novel Auto-Eval mechanism that is highly correlated with human raters (r=0.99) for evaluating VLMs. In sum, this work makes concrete strides towards understanding, evaluating, and mitigating hallucination in VLMs, serving as an important step towards more reliable multimodal AI systems in the future., Comment: Accepted as a main conference paper at ECCV 2024 (https://github.com/google/haloquest)

On the path towards high-gain inertial confinement fusion ignition, foams are being considered to tamp the hohlraum wall-motion, and mitigate laser backscattering from Stimulated Raman Scattering (SRS) and Stimulated Brillouin Scattering (SBS). Here we present the results from an experimental campaign on foam-filled hohlraums conducted at the OMEGA laser facility. SiO2 foam-fills, with densities as low as 1 mg/cm3, successfully reduce the gold wall expansion, with laser backscattering comparable to gas-fill.

Scalable surrogate models enable efficient emulation of computer models (or simulators), particularly when dealing with large ensembles of runs. While Gaussian Process (GP) models are commonly employed for emulation, they face limitations in scaling to truly large datasets. Furthermore, when dealing with dense functional output, such as spatial or time-series data, additional complexities arise, requiring careful handling to ensure fast emulation. This work presents a highly scalable emulator for functional data, building upon the works of Kennedy and O'Hagan (2001) and Higdon et al. (2008), while incorporating the local approximate Gaussian Process framework proposed by Gramacy and Apley (2015). The emulator utilizes global GP lengthscale parameter estimates to scale the input space, leading to a substantial improvement in prediction speed. We demonstrate that our fast approximation-based emulator can serve as a viable alternative to the methods outlined in Higdon et al. (2008) for functional response, while drastically reducing computational costs. The proposed emulator is applied to quickly calibrate the multiphysics continuum hydrodynamics simulator FLAG with a large ensemble of 20000 runs. The methods presented are implemented in the R package FlaGP.

There is renewed interest in direct-drive inertial confinement fusion, following the milestone December 2022 3.15 MJ ignition result on the National Ignition Facility. A key obstacle is the control of the two plasmon decay instability. Here, recent advances in inhomogeneous turbulence theory are applied to the broadband parametric instability problem for the first time. A novel dispersion relation is derived for the two plasmon decay in a uniform plasma valid under broad-bandwidth laser fields with arbitrary power spectra. The effects of temporal incoherence on the instability are then studied. In the limit of large bandwidth, the well-known scaling relations for the growth rate are recovered, but it is shown that the result is more sensitive to the spectral shape of the laser pulse rather than to its coherence time. The range of wavenumbers of the excited plasma waves is shown to be substantially broadened, suggesting that the absolute instability is favoured in regions further away from the quarter critical density. The intermediate bandwidth regime is explored numerically -- the growth rate is reduced to half its monochromatic value for laser intensities of $10^{15} \, \text{W}/\text{cm}^{2}$ and relatively modest bandwidths of $5 \, \text{THz}$. The instability-quenching properties of a spectrum of discrete lines spread over some bandwidth have also been studied. The reduction in the growth rate is found to be somewhat lower compared to the continuous case but is still significant, despite the fact that, formally, the coherence time of such a laser pulse is infinite., Comment: 22 pages, 3 figures, Accepted for publication at the Journal of Plasma Physics

We present new exclusion bounds obtained at the European X-ray Free Electron Laser facility (EuXFEL) on axion-like particles (ALPs) in the mass range 10^{-3} eV < m_a < 10^{4} eV. Our experiment exploits the Primakoff effect via which photons can, in the presence of a strong external electric field, decay into axions, which then convert back into photons after passing through an opaque wall. While similar searches have been performed previously at a 3^rd generation synchrotron [1], our work demonstrates improved sensitivity, exploiting the higher brightness of X-rays at EuXFEL., Comment: 7 pages, 6 figures

In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.

Many practical problems involve estimating low dimensional statistical quantities with high-dimensional models and datasets. Several approaches address these estimation tasks based on the theory of influence functions, such as debiased/double ML or targeted minimum loss estimation. This paper introduces \textit{Monte Carlo Efficient Influence Functions} (MC-EIF), a fully automated technique for approximating efficient influence functions that integrates seamlessly with existing differentiable probabilistic programming systems. MC-EIF automates efficient statistical estimation for a broad class of models and target functionals that would previously require rigorous custom analysis. We prove that MC-EIF is consistent, and that estimators using MC-EIF achieve optimal $\sqrt{N}$ convergence rates. We show empirically that estimators using MC-EIF are at parity with estimators using analytic EIFs. Finally, we demonstrate a novel capstone example using MC-EIF for optimal portfolio selection.

Diverse demonstration datasets have powered significant advances in robot learning, but the dexterity and scale of such data can be limited by the hardware cost, the hardware robustness, and the ease of teleoperation. We introduce ALOHA 2, an enhanced version of ALOHA that has greater performance, ergonomics, and robustness compared to the original design. To accelerate research in large-scale bimanual manipulation, we open source all hardware designs of ALOHA 2 with a detailed tutorial, together with a MuJoCo model of ALOHA 2 with system identification. See the project website at aloha-2.github.io., Comment: Project website: aloha-2.github.io

Most existing medication recommendation models learn representations for medical concepts based on electronic health records (EHRs) and make recommendations with learnt representations. However, most medications appear in the dataset for limited times, resulting in insufficient learning of their representations. Medical ontologies are the hierarchical classification systems for medical terms where similar terms are in the same class on a certain level. In this paper, we propose OntoMedRec, the logically-pretrained and model-agnostic medical Ontology Encoders for Medication Recommendation that addresses data sparsity problem with medical ontologies. We conduct comprehensive experiments on benchmark datasets to evaluate the effectiveness of OntoMedRec, and the result shows the integration of OntoMedRec improves the performance of various models in both the entire EHR datasets and the admissions with few-shot medications. We provide the GitHub repository for the source code on https://anonymous.4open.science/r/OntoMedRec-D123

Recently, it was realised that liquid viscosity has a lower bound which is nearly constant for all liquids and is governed by fundamental physical constants. This was supported by experimental data in noble and molecular liquids. Here, we perform large-scale molecular dynamics simulations to ascertain this bound in two other important liquid types: the ionic molten salt system LiF and metallic Pb. We find that these ionic and metallic systems similarly have lower viscosity bounds corresponding to the minimum of kinematic viscosity of about 10$^{-7}$ $\frac{{\rm m}^2}{\rm s}$. We show that this agrees with experimental data in other systems with different structures and bonding types, including noble, molecular, metallic and covalent liquids. This expands the universality of viscosity bounds into the main system types known., Comment: 6 pages, 7 figures

In this paper, we show that the Bruhat order on the sects of the symmetric space of type $AIII$ are lexicographically shellable. Our proof proceeds from a description of these posets as rook placements in an arbitrary partition shape which allows us to extend an $EL$-labelling of the rook monoid given by Can to an arbitrary sect. As a special case, our result implies that the Bruhat order on matrix Schubert varieties is lexicographically shellable., Comment: 17 pages, 4 figures, comments are welcome

Relativistic electron-positron plasmas are ubiquitous in extreme astrophysical environments such as black holes and neutron star magnetospheres, where accretion-powered jets and pulsar winds are expected to be enriched with such pair plasmas. Their behaviour is quite different from typical electron-ion plasmas due to the matter-antimatter symmetry of the charged components and their role in the dynamics of such compact objects is believed to be fundamental. So far, our experimental inability to produce large yields of positrons in quasi-neutral beams has restricted the understanding of electron-positron pair plasmas to simple numerical and analytical studies which are rather limited. We present first experimental results confirming the generation of high-density, quasi-neutral, relativistic electron-positron pair beams using the 440 GeV/c beam at CERN's Super Proton Synchrotron (SPS) accelerator. The produced pair beams have a volume that fills multiple Debye spheres and are thus able to sustain collective plasma oscillations. Our work opens up the possibility of directly probing the microphysics of pair plasmas beyond quasi-linear evolution into regimes that are challenging to simulate or measure via astronomical observations., Comment: 14 pages, 8 figures

We analyze the local dynamics of magnetotail reconnection onset using Magnetospheric Multiscale (MMS) data. In conjunction with MMS, the macroscopic dynamics of this event were captured by a number of other ground and space-based observatories, as is reported in a companion paper. We find that the local dynamics of the onset were characterized by the rapid thinning of the cross-tail current sheet below the ion inertial scale, accompanied by the growth of flapping waves and the subsequent onset of electron tearing. Multiple kinetic-scale magnetic islands were detected coincident with the growth of an initially sub-Alfv\'enic, demagnetized tailward ion exhaust. The onset and rapid enhancement of parallel electron inflow at the exhaust boundary was a remote signature of the intensification of reconnection Earthward of the spacecraft. Two secondary reconnection sites are found embedded within the exhaust from a primary X-line. The primary X-line was designated as such on the basis that (1) while multiple jet reversals were observed in the current sheet, only one reversal of the electron inflow was observed at the high-latitude exhaust boundary, (2) the reconnection electric field was roughly 5 times larger at the primary X-line than the secondary X-lines, and (3) energetic electron fluxes increased and transitioned from anti-field-aligned to isotropic during the primary X-line crossing, indicating a change in magnetic topology. The results are consistent with the idea that a primary X-line mediates the reconnection of lobe magnetic field lines and accelerates electrons more efficiently than its secondary X-line counterparts., Comment: In press, JGR Space Physics, JGRA58162

We analyze a magnetotail reconnection onset event on 3 July 2017 that was observed under otherwise quiescent magnetospheric conditions by a fortuitous conjunction of six space and ground-based observatories. The study investigates the large-scale coupling of the solar wind - magnetosphere system that precipitated the onset of the magnetotail reconnection, focusing on the processes that thinned and stretched the cross-tail current layer in the absence of significant flux loading during a two-hour-long preconditioning phase. It is demonstrated with data in the (1) upstream solar wind, (2) at the low-latitude magnetopause, (3) in the high-latitude polar cap, and (4) in the magnetotail that the typical picture of solar wind-driven current sheet thinning via flux loading does not appear relevant for this particular event. We find that the current sheet thinning was, instead, initiated by a transient solar wind pressure pulse and that the current sheet thinning continued even as the magnetotail and solar wind pressures decreased. We suggest that field line curvature induced scattering (observed by Magnetospheric Multiscale (MMS)) and precipitation (observed by Defense Meteorological Satellite Program (DMSP)) of high-energy thermal protons may have evacuated plasma sheet thermal energy, which may require a thinning of the plasma sheet to preserve pressure equilibrium with the solar wind., Comment: In press, JGR space physics, JGRA58161

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train generalist X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. More details can be found on the project website https://robotics-transformer-x.github.io., Comment: Project website: https://robotics-transformer-x.github.io

We investigate the connection between the classical Larmor formula and the quantum Unruh effect by computing the emitted power by a uniformly accelerated charged particle and its angular distribution in the coaccelerated frame. We consider a classical particle accelerated with nonzero charge only for a finite period and then take the infinite-time limit after removing the effects due to the initial charging and final discharging processes. We show that the result found for the interaction rates agrees with previous studies in which the period of acceleration with nonzero charge was taken to be infinite from the beginning. We also show that the power and angular distribution of emission, which is attributed either to the emission or absorption of a Rindler photon in the coaccelerated frame, is given by the Larmor formula, confirming that, at tree level, it is necessary to take into account the Unruh effect in order to reproduce the classical Larmor radiation formula in the coaccelerated frame., Comment: 10 pages, no figures, v2 matches the published version