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Electromagnetic whistler-mode waves play a crucial role in the acceleration and precipitation of radiation belt electrons. Statistical surveys of wave characteristics suggest that these waves should preferentially scatter and precipitate relativistic electrons on the day side. However, the night-side region is expected to be primarily associated with electron acceleration. The recent low-altitude observations reveal relativistic electron precipitation in the night-side region. In this paper, we present statistical surveys of night-side relativistic electron losses due to intense precipitation bursts. We demonstrate that such bursts are associated with storm time substorm injections and are likely related to relativistic electron scattering by ducted whistler-mode waves. We also speculate on the role of injections in creating conditions favorable for relativistic electron precipitation.

This book is about conformal prediction and related inferential techniques that build on permutation tests and exchangeability. These techniques are useful in a diverse array of tasks, including hypothesis testing and providing uncertainty quantification guarantees for machine learning systems. Much of the current interest in conformal prediction is due to its ability to integrate into complex machine learning workflows, solving the problem of forming prediction sets without any assumptions on the form of the data generating distribution. Since contemporary machine learning algorithms have generally proven difficult to analyze directly, conformal prediction's main appeal is its ability to provide formal, finite-sample guarantees when paired with such methods. The goal of this book is to teach the reader about the fundamental technical arguments that arise when researching conformal prediction and related questions in distribution-free inference. Many of these proof strategies, especially the more recent ones, are scattered among research papers, making it difficult for researchers to understand where to look, which results are important, and how exactly the proofs work. We hope to bridge this gap by curating what we believe to be some of the most important results in the literature and presenting their proofs in a unified language, with illustrations, and with an eye towards pedagogy., Comment: This material will be published by Cambridge University Press as Theoretical Foundations of Conformal Prediction by Anastasios N. Angelopoulos, Rina Foygel Barber and Stephen Bates. This prepublication version is free to view/download for personal use only. Not for redistribution/resale/use in derivative works. Copyright Anastasios N. Angelopoulos, Rina Foygel Barber and Stephen Bates, 2024

Model selection is a key task in statistics, playing a critical role across various scientific disciplines. While no model can fully capture the complexities of a real-world data-generating process, identifying the model that best approximates it can provide valuable insights. Bayesian statistics offers a flexible framework for model selection by updating prior beliefs as new data becomes available, allowing for ongoing refinement of candidate models. This is typically achieved by calculating posterior probabilities, which quantify the support for each model given the observed data. However, in cases where likelihood functions are intractable, exact computation of these posterior probabilities becomes infeasible. Approximate Bayesian Computation (ABC) has emerged as a likelihood-free method and it is traditionally used with summary statistics to reduce data dimensionality, however this often results in information loss difficult to quantify, particularly in model selection contexts. Recent advancements propose the use of full data approaches based on statistical distances, offering a promising alternative that bypasses the need for summary statistics and potentially allows recovery of the exact posterior distribution. Despite these developments, full data ABC approaches have not yet been widely applied to model selection problems. This paper seeks to address this gap by investigating the performance of ABC with statistical distances in model selection. Through simulation studies and an application to toad movement models, this work explores whether full data approaches can overcome the limitations of summary statistic-based ABC for model choice.

Earth's magnetotail, and the plasma sheet embedded in it, is a highly dynamic region that is coupled to both the solar wind and to the inner magnetosphere. As a consequence of this coupling, the plasma sheet undergoes explosive energy releases in the form of substorms. One consequence of this energy release is heating of thermal electrons and acceleration of energetic (non-thermal) electrons. The upper-energy limit as well as the spatial scale size of the electron acceleration regions remain mysteries in magnetotail physics because current missions can effectively only offer us a single-point glimpse into the numerous magnetotail phenomena ranging from electron- to global-scales. These energetic electrons can provide a significant source of seed electrons for the Van Allen Radiation belts. Here we use a unique approach to study relativistic plasma sheet electron acceleration. We combine high-altitude Magnetospheric Multiscale (MMS) mission observations with low-altitude Electron Losses and Fields Investigation (ELFIN) observations, to quantify the upper-energy extent and radial scale of a burst of plasma sheet electrons that mapped to 33 Earth radii. The plasma sheet locally accelerated an intense mesoscale burst of 3 MeV electrons -- far higher and more intense than the outer Van Allen radiation belt -- and scattered them into the atmospheric loss cone. High-altitude observations Earthward of the burst at 17 Earth radii showed only the usual substorm activity signatures -- demonstrating that this burst was 1) intense, 2) localized to the far magnetotail, and 3) likely accelerated by a very efficient and rapid mechanism.

In this paper, we prove the codimension-one nonlinear asymptotic stability of the extremal Reissner-Nordstr\"om family of black holes in the spherically symmetric Einstein-Maxwell-neutral scalar field model, up to and including the event horizon. More precisely, we show that there exists a teleologically defined, codimension-one "submanifold" $\mathfrak M_\mathrm{stab}$ of the moduli space of spherically symmetric characteristic data for the Einstein-Maxwell-scalar field system lying close to the extremal Reissner-Nordstr\"om family, such that any data in $\mathfrak M_\mathrm{stab}$ evolve into a solution with the following properties as time goes to infinity: (i) the metric decays to a member of the extremal Reissner-Nordstr\"om family uniformly up to the event horizon, (ii) the scalar field decays to zero pointwise and in an appropriate energy norm, (iii) the first translation-invariant ingoing null derivative of the scalar field is approximately constant on the event horizon $\mathcal H^+$, (iv) for "generic" data, the second translation-invariant ingoing null derivative of the scalar field grows linearly along the event horizon. Due to the coupling of the scalar field to the geometry via the Einstein equations, suitable components of the Ricci tensor exhibit non-decay and growth phenomena along the event horizon. Points (i) and (ii) above reflect the "stability" of the extremal Reissner-Nordstr\"om family and points (iii) and (iv) verify the presence of the celebrated "Aretakis instability" for the linear wave equation on extremal Reissner-Nordstr\"om black holes in the full nonlinear Einstein-Maxwell-scalar field model., Comment: 73 pages + references, 12 figures

We introduce a new benchmark for reward models that quantifies their ability to produce strong language models through RLHF (Reinforcement Learning from Human Feedback). The gold-standard approach is to run a full RLHF training pipeline and directly probe downstream LLM performance. However, this process is prohibitively expensive. To address this, we build a predictive model of downstream LLM performance by evaluating the reward model on proxy tasks. These proxy tasks consist of a large-scale human preference and a verifiable correctness preference dataset, in which we measure 12 metrics across 12 domains. To investigate which reward model metrics are most correlated to gold-standard RLHF outcomes, we launch an end-to-end RLHF experiment on a large-scale crowdsourced human preference platform to view real reward model downstream performance as ground truth. Ultimately, we compile our data and findings into Preference Proxy Evaluations (PPE), the first reward model benchmark explicitly linked to post-RLHF real-world human preference performance, which we open-source for public use and further development. Our code and evaluations can be found at https://github.com/lmarena/PPE .

Using particle data from the ELFIN satellites, we present a statistical study of 284 proton isotropy boundary events on the nightside magnetosphere, characterizing their occurrence and distribution in local time, latitude (L-shell), energy, and precipitating energy flux, as a function of geomagnetic activity. For a given charged particle species and energy, its isotropy boundary (IB) is the magnetic latitude poleward of which persistently isotropic pitch-angle distributions ($J_{prec}/J_{perp}\sim 1$) are first observed to occur. This isotropization is interpreted as resulting from magnetic field-line curvature (FLC) scattering in the equatorial magnetosphere. We find that proton IBs are observed under all observed activity levels, spanning 16 to 05 MLT with $\sim$100% occurrence between 19 and 03 MLT, trending toward 60% at dawn/dusk. These results are also compared with electron IB properties observed using ELFIN, where we find similar trends across local time and activity, with the onset in $\geq$50 keV proton IB occurring on average 2 L-shells lower, and providing between 3 and 10 times as much precipitating power. Proton IBs typically span $64^\circ$-$66^\circ$ in magnetic latitude (5-6 in L-shell), corresponding to the outer edge of the ring current, tending toward lower IGRF latitudes as geomagnetic activity increases. The IBs were found to commonly occur 0.3-2.1 Re beyond the plasmapause. Proton IBs typically span $<$50 keV to $\sim$1 MeV in energy, maximizing near 22 MLT, and decreasing to a typical upper limit of 300-400 keV toward dawn and dusk, with peak observed isotropic energy increasing by $\sim$500 keV during active intervals. These results suggest that FLC in the vicinity of IBs can provide a substantial depletion mechanism for energetic protons, with the total nightside precipitating power from FLC-scattering found to be on the order of 100 MW, at times $\geq$10 GW.

Earth's magnetotail, a night-side region characterized by stretched magnetic field lines and strong plasma currents, is the primary site for the release of magnetic field energy and its transformation into plasma heating and kinetic energy plus charged particle acceleration during magnetic reconnection. In this study, we demonstrate that the efficiency of this acceleration can be sufficiently high to produce populations of relativistic and ultra-relativistic electrons, with energies up to several MeV, which exceeds all previous theoretical and simulation estimates. Using data from the low altitude ELFIN and CIRBE CubeSats, we show multiple events of relativistic electron bursts within the magnetotail, far poleward of the outer radiation belt. These bursts are characterized by power-law energy spectra and can be detected during even moderate substorms.

As robots become increasingly integrated into our daily lives, the need to make them transparent has never been more critical. Yet, despite its importance in human-robot interaction, a standardized measure of robot transparency has been missing until now. This paper addresses this gap by presenting the first comprehensive scale to measure perceived transparency in robotic systems, available in English, German, and Italian languages. Our approach conceptualizes transparency as a multidimensional construct, encompassing explainability, legibility, predictability, and meta-understanding. The proposed scale was a product of a rigorous three-stage process involving 1,223 participants. Firstly, we generated the items of our scale, secondly, we conducted an exploratory factor analysis, and thirdly, a confirmatory factor analysis served to validate the factor structure of the newly developed TOROS scale. The final scale encompasses 26 items and comprises three factors: Illegibility, Explainability, and Predictability. TOROS demonstrates high cross-linguistic reliability, inter-factor correlation, model fit, internal consistency, and convergent validity across the three cross-national samples. This empirically validated tool enables the assessment of robot transparency and contributes to the theoretical understanding of this complex construct. By offering a standardized measure, we facilitate consistent and comparable research in human-robot interaction in which TOROS can serve as a benchmark.

We initiate the study of tree structures in the context of scenario-based robust optimization. Specifically, we study Binary Search Trees (BSTs) and Huffman coding, two fundamental techniques for efficiently managing and encoding data based on a known set of frequencies of keys. Given $k$ different scenarios, each defined by a distinct frequency distribution over the keys, our objective is to compute a single tree of best-possible performance, relative to any scenario. We consider, as performance metrics, the competitive ratio, which compares multiplicatively the cost of the solution to the tree of least cost among all scenarios, as well as the regret, which induces a similar, but additive comparison. For BSTs, we show that the problem is NP-hard across both metrics. We also show how to obtain a tree of competitive ratio $\lceil \log_2(k+1) \rceil$, and we prove that this ratio is optimal. For Huffman Trees, we show that the problem is, likewise, NP-hard across both metrics; we also give an algorithm of regret $\lceil \log_2 k \rceil$, which we show is near-optimal, by proving a lower bound of $\lfloor \log_2 k \rfloor$. Last, we give a polynomial-time algorithm for computing Pareto-optimal BSTs with respect to their regret, assuming scenarios defined by uniform distributions over the keys. This setting captures, in particular, the first study of fairness in the context of data structures. We provide an experimental evaluation of all algorithms. To this end, we also provide mixed integer linear program formulation for computing optimal trees.

The study of online algorithms with machine-learned predictions has gained considerable prominence in recent years. One of the common objectives in the design and analysis of such algorithms is to attain (Pareto) optimal tradeoffs between the consistency of the algorithm, i.e., its performance assuming perfect predictions, and its robustness, i.e., the performance of the algorithm under adversarial predictions. In this work, we demonstrate that this optimization criterion can be extremely brittle, in that the performance of Pareto-optimal algorithms may degrade dramatically even in the presence of imperceptive prediction error. To remedy this drawback, we propose a new framework in which the smoothness in the performance of the algorithm is enforced by means of a user-specified profile. This allows us to regulate the performance of the algorithm as a function of the prediction error, while simultaneously maintaining the analytical notion of consistency/robustness tradeoffs, adapted to the profile setting. We apply this new approach to a well-studied online problem, namely the one-way trading problem. For this problem, we further address another limitation of the state-of-the-art Pareto-optimal algorithms, namely the fact that they are tailored to worst-case, and extremely pessimistic inputs. We propose a new Pareto-optimal algorithm that leverages any deviation from the worst-case input to its benefit, and introduce a new metric that allows us to compare any two Pareto-optimal algorithms via a dominance relation.

Science and technology have a growing need for effective mechanisms that ensure reliable, controlled performance from black-box machine learning algorithms. These performance guarantees should ideally hold conditionally on the input-that is the performance guarantees should hold, at least approximately, no matter what the input. However, beyond stylized discrete groupings such as ethnicity and gender, the right notion of conditioning can be difficult to define. For example, in problems such as image segmentation, we want the uncertainty to reflect the intrinsic difficulty of the test sample, but this may be difficult to capture via a conditioning event. Building on the recent work of Gibbs et al. [2023], we propose a methodology for achieving approximate conditional control of statistical risks-the expected value of loss functions-by adapting to the difficulty of test samples. Our framework goes beyond traditional conditional risk control based on user-provided conditioning events to the algorithmic, data-driven determination of appropriate function classes for conditioning. We apply this framework to various regression and segmentation tasks, enabling finer-grained control over model performance and demonstrating that by continuously monitoring and adjusting these parameters, we can achieve superior precision compared to conventional risk-control methods.

In space plasmas, large-amplitude Alfv\'en waves can drive compressive perturbations, accelerate ion beams, and lead to plasma heating and the excitation of ion acoustic waves at kinetic scales. This energy channelling from fluid to kinetic scales represents a complementary path to the classical turbulent cascade. Here, we present observational and computational evidence to validate this hypothesis by simultaneously resolving the fluid-scale Alfv\'en waves, kinetic-scale ion acoustic waves, and their imprints on ion velocity distributions in the Earth's magnetopause boundary layer. We show that two coexisting compressive modes, driven by the magnetic pressure gradients of Alfv\'en waves, not only accelerate the ion tail population to the Alfv\'en velocity, but also heat the ion core population near the ion acoustic velocity and generate Debye-scale ion acoustic waves. Thus, Alfv\'en-acoustic energy channeling emerges as a viable mechanism for plasma heating near plasma boundaries where large-amplitude Alfv\'en waves are present.

A global reconfiguration of the magnetotail characterizes substorms. Current sheet thinning, intensification, and magnetic field stretching are defining features of the substorm growth phase and their spatial distributions control the timing and location of substorm onset. Presently, sparse in-situ observations cannot resolve these distributions. A promising approach is to use new substorm magnetic field reconstruction methods based on data mining, termed SST19. Here we compare the SST19 reconstructions to low-altitude ELFIN measurements of energetic particle precipitations to probe the radial profile of the equatorial magnetic field curvature during a 19~August 2022 substorm. ELFIN and SST19 yield a consistent dynamical picture of the magnetotail during the growth phase and capture expected features such as the formation of a thin current sheet and its earthward motion. Furthermore, they resolve a V-like pattern of isotropic electron precipitation boundaries in the time-energy plane, consistent with earlier observations but now over a broad energy range.

The strong variations of energetic electron fluxes in the Earth's inner magnetosphere are notoriously hard to forecast. Developing accurate empirical models of electron fluxes from low to high altitudes at all latitudes is therefore useful to improve our understanding of flux variations and to assess radiation hazards for spacecraft systems. In the present work, energy- and pitch-angle-resolved precipitating, trapped, and backscattered electron fluxes measured at low altitude by Electron Loss and Fields Investigation (ELFIN) CubeSats are used to infer omnidirectional fluxes at altitudes below and above the spacecraft, from 150 km to 20,000 km, making use of adiabatic transport theory and quasi-linear diffusion theory. The inferred fluxes are fitted as a function of selected parameters using a stepwise multivariate optimization procedure, providing an analytical model of omnidirectional electron flux along each geomagnetic field line, based on measurements from only one spacecraft in low Earth orbit. The modeled electron fluxes are provided as a function of $L$-shell, altitude, energy, and two different indices of past substorm activity, computed over the preceding 4 hours or 3 days, potentially allowing to disentangle impulsive processes (such as rapid injections) from cumulative processes (such as inward radial diffusion and wave-driven energization). The model is validated through comparisons with equatorial measurements from the Van Allen Probes, demonstrating the broad applicability of the present method. The model indicates that both impulsive and time-integrated substorm activity partly control electron fluxes in the outer radiation belt and in the plasma sheet.

Contract scheduling is a widely studied framework for designing real-time systems with interruptible capabilities. Previous work has showed that a prediction on the interruption time can help improve the performance of contract-based systems, however it has relied on a single prediction that is provided by a deterministic oracle. In this work, we introduce and study more general and realistic learning-augmented settings in which the prediction is in the form of a probability distribution, or it is given as a set of multiple possible interruption times. For both prediction settings, we design and analyze schedules which perform optimally if the prediction is accurate, while simultaneously guaranteeing the best worst-case performance if the prediction is adversarial. We also provide evidence that the resulting system is robust to prediction errors in the distributional setting. Last, we present an experimental evaluation that confirms the theoretical findings, and illustrates the performance improvements that can be attained in practice., Comment: To appear in Proceedings of IJCAI 2024

People with Parkinson's Disease (PD) can slow the progression of their symptoms with physical therapy. However, clinicians lack insight into patients' motor function during daily life, preventing them from tailoring treatment protocols to patient needs. This paper introduces PD-Insighter, a system for comprehensive analysis of a person's daily movements for clinical review and decision-making. PD-Insighter provides an overview dashboard for discovering motor patterns and identifying critical deficits during activities of daily living and an immersive replay for closely studying the patient's body movements with environmental context. Developed using an iterative design study methodology in consultation with clinicians, we found that PD-Insighter's ability to aggregate and display data with respect to time, actions, and local environment enabled clinicians to assess a person's overall functioning during daily life outside the clinic. PD-Insighter's design offers future guidance for generalized multiperspective body motion analytics, which may significantly improve clinical decision-making and slow the functional decline of PD and other medical conditions., Comment: 18 pages, 11 figures, Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '24), May 11--16, 2024, Honolulu, HI, USA

Decision-making pipelines are generally characterized by tradeoffs among various risk functions. It is often desirable to manage such tradeoffs in a data-adaptive manner. As we demonstrate, if this is done naively, state-of-the art uncertainty quantification methods can lead to significant violations of putative risk guarantees. To address this issue, we develop methods that permit valid control of risk when threshold and tradeoff parameters are chosen adaptively. Our methodology supports monotone and nearly-monotone risks, but otherwise makes no distributional assumptions. To illustrate the benefits of our approach, we carry out numerical experiments on synthetic data and the large-scale vision dataset MS-COCO., Comment: 27 pages, 10 figures

The evaluation of machine learning models using human-labeled validation data can be expensive and time-consuming. AI-labeled synthetic data can be used to decrease the number of human annotations required for this purpose in a process called autoevaluation. We suggest efficient and statistically principled algorithms for this purpose that improve sample efficiency while remaining unbiased. These algorithms increase the effective human-labeled sample size by up to 50% on experiments with GPT-4., Comment: New experiments, fix fig 1

Recent study has demonstrated that electron cyclotron harmonic (ECH) waves can be excited by a low energy electron beam. Such waves propagate at moderately oblique wave normal angles (~70). The potential effects of beam-driven ECH waves on electron dynamics in Earth's plasma sheet is not known. Using two-dimensional Darwin particle-in-cell simulations with initial electron distributions that represent typical plasma conditions in the plasma sheet, we explore the excitation and saturation of such beam-driven ECH waves. Both ECH and electron acoustic waves are excited in the simulation and propagate at oblique wave normal angles. Compared with the electron acoustic waves, ECH waves grow much faster and have more intense saturation amplitudes. Cold, stationary electrons are first accelerated by ECH waves through cyclotron resonance and then accelerated in the parallel direction by both the ECH and electron acoustic waves through Landau resonance. Beam electrons, on the other hand, are decelerated in the parallel direction and scattered to larger pitch angles. The relaxation of the electron beam and the continuous heating of the cold electrons contribute to ECH wave saturation and suppress the excitation of electron acoustic waves. When the ratio of plasma to electron cyclotron frequency wpe/wce increases, the ECH wave amplitude increases while the electron acoustic wave amplitude decreases. Our work reveals the importance of ECH and electron acoustic waves in reshaping sub-thermal electron distributions and improves our understanding on the potential effects of wave-particle interactions in trapping ionospheric electron outflows and forming anisotropic (field-aligned) electron distributions in the plasma sheet.

Large Language Models (LLMs) have unlocked new capabilities and applications; however, evaluating the alignment with human preferences still poses significant challenges. To address this issue, we introduce Chatbot Arena, an open platform for evaluating LLMs based on human preferences. Our methodology employs a pairwise comparison approach and leverages input from a diverse user base through crowdsourcing. The platform has been operational for several months, amassing over 240K votes. This paper describes the platform, analyzes the data we have collected so far, and explains the tried-and-true statistical methods we are using for efficient and accurate evaluation and ranking of models. We confirm that the crowdsourced questions are sufficiently diverse and discriminating and that the crowdsourced human votes are in good agreement with those of expert raters. These analyses collectively establish a robust foundation for the credibility of Chatbot Arena. Because of its unique value and openness, Chatbot Arena has emerged as one of the most referenced LLM leaderboards, widely cited by leading LLM developers and companies. Our demo is publicly available at \url{https://chat.lmsys.org}.

The performance of an imaging system is limited by optical aberrations, which cause blurriness in the resulting image. Digital correction techniques, such as deconvolution, have limited ability to correct the blur, since some spatial frequencies in the scene are not measured adequately (i.e., 'zeros' of the system transfer function). We prove that the addition of a random mask to an imaging system removes its dependence on aberrations, reducing the likelihood of zeros in the transfer function and consequently decreasing the sensitivity to noise during deconvolution. In simulation, we show that this strategy improves image quality over a range of aberration types, aberration strengths, and signal-to-noise ratios., Comment: The first two authors contributed equally

The ion foreshock is highly dynamic, disturbing the bow shock and the magnetosphere-ionosphere system. To forecast foreshock-driven space weather effects, it is necessary to model foreshock ions as a function of upstream shock parameters. Case studies in the accompanying paper show that magnetosheath ions sometimes exhibit strong field-aligned anisotropy towards the upstream direction, which may be responsible for enhancing magnetosheath leakage and therefore foreshock ion density. To understand the conditions leading to such an anisotropy and the potential for enhanced leakage, we perform case studies and a statistical study of magnetosheath and foreshock region data surrounding ~500 THEMIS bow shock crossings. We quantify the anisotropy using the heat flux along the field-aligned direction. We show that the strong field-aligned heat flux persists across the entire magnetosheath from the magnetopause to the bow shock. Ion distribution functions reveal that the strong heat flux is caused by a secondary thermal population. We find that stronger anisotropy events exhibit heat flux preferentially towards the upstream direction near the bow shock and occur under larger IMF strength and larger solar wind dynamic pressure and/or energy flux. Additionally, we show that near the bow shock, magnetosheath leakage is a significant contributor to foreshock ions, and through enhancing the leakage the magnetosheath ion anisotropy can modulate the foreshock ion velocity and density. Our results imply that likely due to field line draping and compression against the magnetopause that leads to a directional mirror force, modeling the foreshock ions necessitates a more global accounting of downstream conditions., Comment: Under review by JGR Space Physics

We introduce a method for online conformal prediction with decaying step sizes. Like previous methods, ours possesses a retrospective guarantee of coverage for arbitrary sequences. However, unlike previous methods, we can simultaneously estimate a population quantile when it exists. Our theory and experiments indicate substantially improved practical properties: in particular, when the distribution is stable, the coverage is close to the desired level for every time point, not just on average over the observed sequence.

We introduce the study of search games between a mobile Searcher and an immobile Hider in a new setting in which the Searcher has some potentially erroneous information, i.e., a prediction on the Hider's position. The objective is to establish tight tradeoffs between the consistency of a search strategy (i.e., its worst case expected payoff assuming the prediction is correct) and its robustness (i.e., the worst case expected payoff with no assumptions on the quality of the prediction). Our study is the first to address the full power of mixed (randomized) strategies; previous work focused only on deterministic strategies, or relied on stochastic assumptions that do not guarantee worst-case robustness in adversarial situations. We give Pareto-optimal strategies for three fundamental problems, namely searching in discrete locations, searching with stochastic overlook, and searching in the infinite line. As part of our contribution, we provide a novel framework for proving optimal tradeoffs in search games which is applicable, more broadly, to any two-person zero-sum games in learning-augmented settings.

We study the classic problem of searching for a hidden target in the line and the $m$-ray star, in a setting in which the searcher has some prediction on the hider's position. We first focus on the main metric for comparing search strategies under predictions; namely, we give positive and negative results on the consistency-robustness tradeoff, where the performance of the strategy is evaluated at extreme situations in which the prediction is either error-free, or adversarially generated, respectively. For the line, we show tight bounds concerning this tradeoff, under the untrusted advice model, in which the prediction is in the form of a $k$-bit string which encodes the responses to $k$ binary queries. For the star, we give tight, and near-tight tradeoffs in the positional and the directional models, in which the prediction is related to the position of the target within the star, and to the ray on which the target hides, respectively. Last, for all three prediction models, we show how to generalize our study to a setting in which the performance of the strategy is evaluated as a function of the searcher's desired tolerance to prediction errors, both in terms of positive and inapproximability results., Comment: Conference version in MFCS 2023

We consider a supersonic flow past an airfoil in the context of the steady, isentropic and irrotational compressible Euler equations. We show that for appropriate data, either a shock forms, in which case certain derivatives of the solution blow up, or a sonic line forms, which translates to the fact that the hyperbolic character of the equations degenerates.

Resonant interactions between relativistic electrons and electromagnetic ion cyclotron (EMIC) waves provide an effective loss mechanism for this important electron population in the outer radiation belt. The diffusive regime of electron scattering and loss has been well incorporated into radiation belt models within the framework of the quasi-linear diffusion theory, whereas the nonlinear regime has been mostly studied with test particle simulations. There is also a less investigated, nonresonant regime of electron scattering by EMIC waves. All three regimes should be present, depending on the EMIC waves and ambient plasma properties, but the occurrence rates of these regimes have not been previously quantified. This study provides a statistical investigation of the most important EMIC wave-packet characteristics for the diffusive, nonlinear, and nonresonant regimes of electron scattering. We utilize 3 years of Van Allen Probe observations to derive distributions of wave amplitudes, wave-packet sizes, and rates of frequency variations within individual wave-packets. We demonstrate that EMIC waves typically propagate as wave-packets with $\sim 10$ wave periods each, and that $\sim 3-10$\% of such wave-packets can reach the regime of nonlinear resonant interaction with 2 to 6 MeV electrons. We show that EMIC frequency variations within wave-packets reach $50-100$\% of the center frequency, corresponding to a significant high-frequency tail in their wave power spectrum. We explore the consequences of these wave-packet characteristics for high and low energy electron precipitation by H-band EMIC waves and for the relative importance of quasi-linear and nonlinear regimes of wave-particle interactions.

Energetic plasma is everywhere in the Universe. The terrestrial Magnetospheric System is a key case where direct measures of plasma energization and energy transport can be made in situ at high resolution. Despite the large amount of available observations, we still do not fully understand how plasma energization and energy transport work. Key physical processes driving much plasma energization and energy transport occur where plasma on fluid scales couple to the smaller ion kinetic scales. These scales (1 RE) are strongly related to the larger mesoscales (several RE) at which large-scale plasma energization and energy transport structures form. All these scales and processes need to be resolved experimentally, however existing multi-point in situ observations do not have a sufficient number of measurement points. New multiscale observations simultaneously covering scales from mesoscales to ion kinetic scales are needed. The implementation of these observations requires a strong international collaboration in the coming years between the major space agencies. The Plasma Observatory is a mission concept tailored to resolve scale coupling in plasma energization and energy transport at fluid and ion scales. It targets the two ESA-led Medium Mission themes Magnetospheric Systems and Plasma Cross-scale Coupling of the ESA Voyage 2050 report and is currently under evaluation as a candidate for the ESA M7 mission. MagCon (Magnetospheric Constellation) is a mission concept being studied by NASA aiming at studying the flow of mass, momentum, and energy through the Earth magnetosphere at mesoscales. Coordination between Plasma Observatory and MagCon missions would allow us for the first time to simultaneously cover from mesoscales to ion kinetic scales leading to a paradigm shift in the understanding of the Earth Magnetospheric System., Comment: A White Paper submitted for the Decadal Survey for Solar and Space Physics (Heliophysics) 2024-2033

Certain forms of solar wind transients contain significant enhancements of dynamic pressure and may effectively drive magnetosphere dynamics, including substorms and storms. An integral element of such driving is the generation of a wide range of electromagnetic waves within the inner magnetosphere, either by compressionally heated plasma or by substorm plasma sheet injections. Consequently, solar wind transient impacts are traditionally associated with energetic electron scattering and losses into the atmosphere by electromagnetic waves. In this study, we show the first direct measurements of two such transient-driven precipitation events as measured by the low-altitude Electron Losses and Fields Investigation (ELFIN) CubeSats. The first event demonstrates storm-time generated electromagnetic ion cyclotron waves efficiently precipitating relativistic electrons from >300 keV to 2 MeV at the duskside. The second event demonstrates whistler-mode waves leading to scattering of electrons from 50 keV to 700 keV on the dawnside. These observations confirm the importance of solar wind transients in driving energetic electron losses and subsequent dynamics in the ionosphere.

This paper focuses on the problem of detecting and reacting to changes in the distribution of a sensorimotor controller's observables. The key idea is the design of switching policies that can take conformal quantiles as input, which we define as conformal policy learning, that allows robots to detect distribution shifts with formal statistical guarantees. We show how to design such policies by using conformal quantiles to switch between base policies with different characteristics, e.g. safety or speed, or directly augmenting a policy observation with a quantile and training it with reinforcement learning. Theoretically, we show that such policies achieve the formal convergence guarantees in finite time. In addition, we thoroughly evaluate their advantages and limitations on two compelling use cases: simulated autonomous driving and active perception with a physical quadruped. Empirical results demonstrate that our approach outperforms five baselines. It is also the simplest of the baseline strategies besides one ablation. Being easy to use, flexible, and with formal guarantees, our work demonstrates how conformal prediction can be an effective tool for sensorimotor learning under uncertainty., Comment: Conformal Policy Learning

We present PPI++: a computationally lightweight methodology for estimation and inference based on a small labeled dataset and a typically much larger dataset of machine-learning predictions. The methods automatically adapt to the quality of available predictions, yielding easy-to-compute confidence sets -- for parameters of any dimensionality -- that always improve on classical intervals using only the labeled data. PPI++ builds on prediction-powered inference (PPI), which targets the same problem setting, improving its computational and statistical efficiency. Real and synthetic experiments demonstrate the benefits of the proposed adaptations., Comment: Code available at https://github.com/aangelopoulos/ppi_py

Multiphoton microscopy (MPM) is a powerful imaging tool that has been a critical enabler for live tissue imaging. However, since most multiphoton microscopy platforms rely on point scanning, there is an inherent trade-off between acquisition time, field of view (FOV), phototoxicity, and image quality, often resulting in noisy measurements when fast, large FOV, and/or gentle imaging is needed. Deep learning could be used to denoise multiphoton microscopy measurements, but these algorithms can be prone to hallucination, which can be disastrous for medical and scientific applications. We propose a method to simultaneously denoise and predict pixel-wise uncertainty for multiphoton imaging measurements, improving algorithm trustworthiness and providing statistical guarantees for the deep learning predictions. Furthermore, we propose to leverage this learned, pixel-wise uncertainty to drive an adaptive acquisition technique that rescans only the most uncertain regions of a sample. We demonstrate our method on experimental noisy MPM measurements of human endometrium tissues, showing that we can maintain fine features and outperform other denoising methods while predicting uncertainty at each pixel. Finally, with our adaptive acquisition technique, we demonstrate a 120X reduction in acquisition time and total light dose while successfully recovering fine features in the sample. We are the first to demonstrate distribution-free uncertainty quantification for a denoising task with real experimental data and the first to propose adaptive acquisition based on reconstruction uncertainty

We introduce Conformal Decision Theory, a framework for producing safe autonomous decisions despite imperfect machine learning predictions. Examples of such decisions are ubiquitous, from robot planning algorithms that rely on pedestrian predictions, to calibrating autonomous manufacturing to exhibit high throughput and low error, to the choice of trusting a nominal policy versus switching to a safe backup policy at run-time. The decisions produced by our algorithms are safe in the sense that they come with provable statistical guarantees of having low risk without any assumptions on the world model whatsoever; the observations need not be I.I.D. and can even be adversarial. The theory extends results from conformal prediction to calibrate decisions directly, without requiring the construction of prediction sets. Experiments demonstrate the utility of our approach in robot motion planning around humans, automated stock trading, and robot manufacturing., Comment: 8 pages, 5 figures

The magnetotail current sheet's spatial configuration and stability control the onset of magnetic reconnection - the driving process for magnetospheric substorms. The near-Earth current sheet has been thoroughly investigated by numerous missions, whereas the midtail current sheet has not been adequately explored. This is especially the case for the long-term variation of its configuration in response to the solar wind. We present a statistical analysis of 1261 magnetotail current sheet crossings by the Acceleration, Reconnection, Turbulence and Electrodynamics of Moon's Interaction with the Sun (ARTEMIS) mission orbiting the moon (X~-60 RE), collected during the entirety of Solar Cycle 24. We demonstrate that the magnetotail current sheet typically remains extremely thin, with a characteristic thickness comparable to the thermal ion gyroradius, even at such large distances from Earth's dipole. We also find that a substantial fraction (~one quarter) of the observed current sheets have a partially force-free magnetic field configuration, with a negligible contribution of the thermal pressure and a significant contribution of the magnetic field shear component to the pressure balance. Further, we quantify the impact of the changing solar wind driving conditions on the properties of the midtail around the lunar orbit. During active solar wind driving conditions, we observe an increase in the occurrence rate of thin current sheets, whereas quiet solar wind driving conditions seem to favor the formation of partially force-free current sheets.

Electromagnetic Ion Cyclotron (EMIC) waves can drive radiation belt depletion and Low-Earth Orbit (LEO) satellites can detect the resulting electron and proton precipitation. The ELFIN (Electron Losses and Fields InvestigatioN) CubeSats provide an excellent opportunity to study the properties of EMIC-driven electron precipitation with much higher energy and pitch-angle resolution than previously allowed. We collect EMIC-driven electron precipitation events from ELFIN observations and use POES (Polar Orbiting Environmental Satellites) to search for 10s-100s keV proton precipitation nearby as a proxy of EMIC wave activity. Electron precipitation mainly occurs on localized radial scales (0.3 L), over 15-24 MLT and 5-8 L shells, stronger at MeV energies and weaker down to 100-200 keV. Additionally, the observed loss cone pitch-angle distribution agrees with quasilinear predictions at >250 keV (more filled loss cone with increasing energy), while additional mechanisms are needed to explain the observed low-energy precipitation., Comment: This manuscript has been accepted by Geophysical Research Letters in June 2023, currently pending publication. The version here is the accepted version

We present the results of processing the effects of the powerful Gamma Ray Burst GRB221009A captured by the charged particle detectors (electrostatic analyzers and solid-state detectors) onboard spacecraft at different points in the heliosphere on October 9, 2022. To follow the GRB221009A propagation through the heliosphere we used the electron and proton flux measurements from solar missions Solar Orbiter and STEREO-A; Earth magnetosphere and the solar wind missions THEMIS and Wind; meteorological satellites POES15, POES19, MetOp3; and MAVEN - a NASA mission orbiting Mars. GRB221009A had a structure of four bursts: less intense Pulse 1 - the triggering impulse - was detected by gamma-ray observatories at 131659 UT (near the Earth); the most intense Pulses 2 and 3 were detected on board all the spacecraft from the list, and Pulse 4 detected in more than 500 s after Pulse 1. Due to their different scientific objectives, the spacecraft, which data was used in this study, were separated by more than 1 AU (Solar Orbiter and MAVEN). This enabled tracking GRB221009A as it was propagating across the heliosphere. STEREO-A was the first to register Pulse 2 and 3 of the GRB, almost 100 seconds before their detection by spacecraft in the vicinity of Earth. MAVEN detected GRB221009A Pulses 2, 3, and 4 at the orbit of Mars about 237 seconds after their detection near Earth. By processing the time delays observed we show that the source location of the GRB221009A was at RA 288.5 degrees, Dec 18.5 degrees (J2000) with an error cone of 2 degrees

During neurosurgery, medical images of the brain are used to locate tumors and critical structures, but brain tissue shifts make pre-operative images unreliable for accurate removal of tumors. Intra-operative imaging can track these deformations but is not a substitute for pre-operative data. To address this, we use Dynamic Data-Driven Non-Rigid Registration (NRR), a complex and time-consuming image processing operation that adjusts the pre-operative image data to account for intra-operative brain shift. Our review explores a specific NRR method for registering brain MRI during image-guided neurosurgery and examines various strategies for improving the accuracy and speed of the NRR method. We demonstrate that our implementation enables NRR results to be delivered within clinical time constraints while leveraging Distributed Computing and Machine Learning to enhance registration accuracy by identifying optimal parameters for the NRR method. Additionally, we highlight challenges associated with its use in the operating room.

Electron resonant scattering by high-frequency electromagnetic whistler-mode waves has been proposed as a mechanism for solar wind electron scattering and pre-acceleration to energies that enable them to participate in shock drift acceleration around the Earth's bow shock. However, observed whistler-mode waves are often sufficiently intense to resonate with electrons nonlinearly, which prohibits the application of quasi-linear diffusion theory. This is the second of two accompanying papers devoted to developing a new theoretical approach for quantifying the electron distribution evolution subject to multiple resonant interactions with intense whistler-mode wave-packets. In the first paper, we described a probabilistic approach, applicable to systems with short wave-packets. For such systems, nonlinear resonant effects can be treated by diffusion theory, but with diffusion rates different from those of quasi-linear diffusion. In this paper we generalize this approach by merging it with a mapping technique. This technique can be used to model the electron distribution evolution in the presence of significantly non-diffusive resonant scattering by intense long wave-packets. We verify our technique by comparing its predictions with results from a numerical integration approach.

Adiabatic heating of solar wind electrons at the Earth's bow shock and its foreshock region produces transversely anisotropic hot electrons that, in turn, generate intense high-frequency whistler-mode waves. These waves are often detected by spacecraft as narrow-band, electromagnetic emissions in the frequency range of [0.1,0.5] of the local electron gyrofrequency. Resonant interactions between these waves and electrons may cause electron acceleration and pitch-angle scattering, which can be important for creating the electron population that seeds shock drift acceleration. The high intensity and coherence of the observed whistler-mode waves prohibit the use of quasi-linear theory to describe their interaction with electrons. In this paper, we aim to develop a new theoretical approach to describe this interaction, that incorporates nonlinear resonant interactions, gradients of the background density and magnetic field, and the fine structure of the waveforms that usually consist of short, intense wave-packet trains. This is the first of two accompanying papers. It outlines a probabilistic approach to describe the wave-particle interaction. We demonstrate how the wave-packet size affects electron nonlinear resonance at the bow shock and foreshock regions, and how to evaluate electron distribution dynamics in such a system that is frequented by short, intense whistler-mode wave-packets. In the second paper, this probabilistic approach is merged with a mapping technique, which allows us to model systems containing short and long wave-packets.