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Variable importance is one of the most widely used measures for interpreting machine learning with significant interest from both statistics and machine learning communities. Recently, increasing attention has been directed toward uncertainty quantification in these metrics. Current approaches largely rely on one-step procedures, which, while asymptotically efficient, can present higher sensitivity and instability in finite sample settings. To address these limitations, we propose a novel method by employing the targeted learning (TL) framework, designed to enhance robustness in inference for variable importance metrics. Our approach is particularly suited for conditional permutation variable importance. We show that it (i) retains the asymptotic efficiency of traditional methods, (ii) maintains comparable computational complexity, and (iii) delivers improved accuracy, especially in finite sample contexts. We further support these findings with numerical experiments that illustrate the practical advantages of our method and validate the theoretical results.

Observations of the Jovian upper atmosphere at high latitudes in the UV, IR and mm/sub-mm all indicate that the chemical distributions and thermal structure are broadly influenced by auroral particle precipitations. Mid-IR and UV observations have shown that several light hydrocarbons (up to 6 carbon atoms) have altered abundances near Jupiter's main auroral ovals. Ion-neutral reactions influence the hydrocarbon chemistry, with light hydrocarbons produced in the upper stratosphere, and heavier hydrocarbons as well as aerosols produced in the lower stratosphere. One consequence of the magnetosphere-ionosphere coupling is the existence of ionospheric jets that propagate into the neutral middle stratosphere, likely acting as a dynamical barrier to the aurora-produced species. As the ionospheric jets and the background atmosphere do not co-rotate at the same rate, this creates a complex system where chemistry and dynamics are intertwined. The ion-neutral reactions produce species with a spatial distribution following the SIII longitude system in the upper stratosphere. As these species sediment down to the lower stratosphere, and because of the progressive dynamical decoupling between the ionospheric flows and the background atmosphere, the spatial distribution of the auroral-related species progressively follows a zonal distribution with increasing pressures that ultimately produces a system of polar and subpolar hazes that extends down to the bottom of the stratosphere. This paper reviews the most recent work addressing different aspects of this environment., Comment: Accepted for publication in Space Science Reviews. 65 pages, 20 figures (including appendix)

We introduce a new, open-source, Python module for the acquisition and processing of archival data from many X-ray telescopes - Democratising Archival X-ray Astronomy (hereafter referred to as DAXA). Our software is built to increase access to, and use of, large archives of X-ray astronomy data; providing a unified, easy-to-use, Python interface to the disparate archives and processing tools. We provide this interface for the majority of X-ray telescopes launched within the last 30 years. This module enables much greater access to X-ray data for non-specialists, while preserving low-level control of processing for X-ray experts. It is useful for identifying relevant observations of a single object of interest but it excels at creating multi-mission datasets for serendipitous or targeted studies of large samples of X-ray emitting objects. The management and organization of datasets is also made easier; DAXA archives can be version controlled and updated if new data become available. Once relevant observations are identified, the raw data can be downloaded (and optionally processed) through DAXA, or pre-processed event lists, images, and exposure maps can be downloaded if they are available. X-ray observations are perfectly suited to serendipitous discoveries and archival analyses, and with a decade-long `X-ray desert' potentially on the horizon archival data will take on even greater importance; enhanced access to those archives will be vital to the continuation of X-ray astronomy., Comment: 5 pages, 1 figure, submitted to JOSS; GitHub repository - https://github.com/DavidT3/DAXA; Documentation - https://daxa.readthedocs.io/

Large Language Models (LLMs) have demonstrated remarkable capabilities in language understanding and generation. Advanced utilization of the knowledge embedded in LLMs for automated annotation has consistently been explored. This study proposed to develop an emotion lexicon for Cantonese, a low-resource language, through collaborative efforts between LLM and human annotators. By integrating emotion labels provided by LLM and human annotators, the study leveraged existing linguistic resources including lexicons in other languages and local forums to construct a Cantonese emotion lexicon enriched with colloquial expressions. The consistency of the proposed emotion lexicon in emotion extraction was assessed through modification and utilization of three distinct emotion text datasets. This study not only validates the efficacy of the constructed lexicon but also emphasizes that collaborative annotation between human and artificial intelligence can significantly enhance the quality of emotion labels, highlighting the potential of such partnerships in facilitating natural language processing tasks for low-resource languages., Comment: 13 pages

A quantum critical point (QCP) represents a continuous phase transition at absolute zero. At the QCP of an unconventional superconductor, enhanced superconducting transition temperature and magnetic fluctuations strength are often observed together, indicating magnetism-mediated superconductivity. This raises the question of whether quantum fluctuations in other degrees of freedom, such as charge, could similarly boost superconductivity. However, because charge is frequently intertwined with magnetism, isolating and understanding its specific role in Cooper pair formation poses a significant challenge. Here, we report persistent charge density fluctuations (CDF) down to 15 K in the non-magnetic superconductor Sr$_{0.77}$Ba$_{0.23}$Ni$_{2}$As$_{2}$, which lies near a proposed nematic QCP associated with a six-fold enhancement of superconductivity. Our results show that the quasi-elastic CDF does not condense into resolution-limited Bragg peaks and displays non-saturated strength. The CDF completely softens at 25 K, with its critical behavior described by the same mathematical framework as the antiferromagnetic Fermi liquid model, yielding a fitted Curie-Weiss temperature of $\theta \approx 0$ K. Additionally, we find that the nematic fluctuations are not lattice-driven, as evidenced by the absence of softening in nematic-coupled in-plane transverse acoustic phonons. Our discovery positions Sr$_{x}$Ba$_{1-x}$Ni$_{2}$As$_{2}$ as a promising candidate for charge-fluctuation-driven nematicity and superconductivity., Comment: 7pages, 4 figures

Optical phonon engineering through nonlinear effects has been utilized in ultrafast control of material properties. However, nonlinear optical phonons typically exhibit rapid decay due to strong mode-mode couplings, limiting their effectiveness in temperature or frequency sensitive applications. In this study, we report the observation of long-lived nonlinear optical phonons through the spontaneous formation of phonon frequency combs in the van der Waals material CrXTe$_3$ (X=Ge, Si) using high-resolution Raman scattering. Unlike conventional optical phonons, the highest $A_g$ mode in CrGeTe$_3$ splits into equidistant, sharp peaks forming a frequency comb that persists for hundreds of oscillations and survives up to 100K before decaying. These modes correspond to localized oscillations of Ge$_2$Te$_6$ clusters, isolated from Cr hexagons, behaving as independent quantum oscillators. Introducing a cubic nonlinear term to the harmonic oscillator model, we simulate the phonon time evolution and successfully replicate the observed comb structure. Similar frequency comb behavior is observed in CrSiTe$_3$, demonstrating the generalizability of this phenomenon. Our findings reveal that Raman scattering effectively probes high-frequency nonlinear phonon modes, providing new insight into generating long-lived, tunable phonon frequency combs with applications in ultrafast material control and phonon-based technologies., Comment: 22 pages, 10 figures

This study investigates the learnability of Recurrent Neural Networks (RNNs) in classifying structured formal languages, focusing on counter and Dyck languages. Traditionally, both first-order (LSTM) and second-order (O2RNN) RNNs have been considered effective for such tasks, primarily based on their theoretical expressiveness within the Chomsky hierarchy. However, our research challenges this notion by demonstrating that RNNs primarily operate as state machines, where their linguistic capabilities are heavily influenced by the precision of their embeddings and the strategies used for sampling negative examples. Our experiments revealed that performance declines significantly as the structural similarity between positive and negative examples increases. Remarkably, even a basic single-layer classifier using RNN embeddings performed better than chance. To evaluate generalization, we trained models on strings up to a length of 40 and tested them on strings from lengths 41 to 500, using 10 unique seeds to ensure statistical robustness. Stability comparisons between LSTM and O2RNN models showed that O2RNNs generally offer greater stability across various scenarios. We further explore the impact of different initialization strategies revealing that our hypothesis is consistent with various RNNs. Overall, this research questions established beliefs about RNNs' computational capabilities, highlighting the importance of data structure and sampling techniques in assessing neural networks' potential for language classification tasks. It emphasizes that stronger constraints on expressivity are crucial for understanding true learnability, as mere expressivity does not capture the essence of learning., Comment: 21 pages, 5 figures, 5 tables

The prompt spectra of gamma-ray bursts are known to follow broadband continuum behavior over decades in energy. GRB 221009A, given the moniker the brightest of all time (BOAT), is the brightest gamma-ray burst identified in half a century of observations, and was first identified by the Fermi Gamma-ray Burst Monitor (GBM). On behalf of the Fermi-GBM Team, Lesage et al. (2023) described the initial GBM analysis. Ravasio et al. (2024) report the identification of a spectral line in part of the prompt emission of this burst, which they describe as evolving over 80 s from $\sim$12 MeV to 6 MeV. We report a GBM Team analysis on the Ravasio Line: 1) We cannot identify an instrumental effect that could have produced this signal, and 2) our method of calculating the statistical significance of the line shows it easily exceeds the 5$\sigma$ discovery threshold. We additionally comment on the claim of the line beginning at earlier time intervals, up to 37 MeV, as reported in Zhang et al. (2024). We find that it is reasonable to utilize these measurements for characterization of the line evolution, with caution. We encourage theoretical studies exploring this newly discovered gamma-ray burst spectral feature, unless any rigorous alternative explanation unrelated to the emission from GRB 221009A is identified.

While there has been progress towards aligning Large Language Models (LLMs) with human values and ensuring safe behaviour at inference time, safety-aligned LLMs are known to be vulnerable to training-time attacks such as supervised fine-tuning (SFT) on harmful datasets. In this paper, we ask if LLMs are vulnerable to adversarial reinforcement learning. Motivated by this goal, we propose Reverse Preference Attacks (RPA), a class of attacks to make LLMs learn harmful behavior using adversarial reward during reinforcement learning from human feedback (RLHF). RPAs expose a critical safety gap of safety-aligned LLMs in RL settings: they easily explore the harmful text generation policies to optimize adversarial reward. To protect against RPAs, we explore a host of mitigation strategies. Leveraging Constrained Markov-Decision Processes, we adapt a number of mechanisms to defend against harmful fine-tuning attacks into the RL setting. Our experiments show that ``online" defenses that are based on the idea of minimizing the negative log likelihood of refusals -- with the defender having control of the loss function -- can effectively protect LLMs against RPAs. However, trying to defend model weights using ``offline" defenses that operate under the assumption that the defender has no control over the loss function are less effective in the face of RPAs. These findings show that attacks done using RL can be used to successfully undo safety alignment in open-weight LLMs and use them for malicious purposes.

We present Nebula, a system for differential private histogram estimation of data distributed among clients. Nebula enables clients to locally subsample and encode their data such that an untrusted server learns only data values that meet an aggregation threshold to satisfy differential privacy guarantees. Compared with other private histogram estimation systems, Nebula uniquely achieves all of the following: \textit{i)} a strict upper bound on privacy leakage; \textit{ii)} client privacy under realistic trust assumptions; \textit{iii)} significantly better utility compared to standard local differential privacy systems; and \textit{iv)} avoiding trusted third-parties, multi-party computation, or trusted hardware. We provide both a formal evaluation of Nebula's privacy, utility and efficiency guarantees, along with an empirical evaluation on three real-world datasets. We demonstrate that clients can encode and upload their data efficiently (only 0.0058 seconds running time and 0.0027 MB data communication) and privately (strong differential privacy guarantees $\varepsilon=1$). On the United States Census dataset, the Nebula's untrusted aggregation server estimates histograms with above 88\% better utility than the existing local deployment of differential privacy. Additionally, we describe a variant that allows clients to submit multi-dimensional data, with similar privacy, utility, and performance. Finally, we provide an open source implementation of Nebula.

We present a complete analysis of Fermi Large Area Telescope (LAT) data of GRB 221009A, the brightest Gamma-Ray Burst (GRB) ever detected. The burst emission above 30 MeV detected by the LAT preceded by 1 s the low-energy (< 10 MeV) pulse that triggered the Fermi Gamma-Ray Burst Monitor (GBM), as has been observed in other GRBs. The prompt phase of GRB 221009A lasted a few hundred seconds. It was so bright that we identify a Bad Time Interval (BTI) of 64 seconds caused by the extremely high flux of hard X-rays and soft gamma rays, during which the event reconstruction efficiency was poor and the dead time fraction quite high. The late-time emission decayed as a power law, but the extrapolation of the late-time emission during the first 450 seconds suggests that the afterglow started during the prompt emission. We also found that high-energy events observed by the LAT are incompatible with synchrotron origin, and, during the prompt emission, are more likely related to an extra component identified as synchrotron self-Compton (SSC). A remarkable 400 GeV photon, detected by the LAT 33 ks after the GBM trigger and directionally consistent with the location of GRB 221009A, is hard to explain as a product of SSC or TeV electromagnetic cascades, and the process responsible for its origin is uncertain. Because of its proximity and energetic nature, GRB 221009A is an extremely rare event., Comment: 60 pages, 38 figures, 9 tables

We use polarization data from SOFIA HAWC+ to investigate the interplay between magnetic fields and stellar feedback in altering gas dynamics within the high-mass star-forming region RCW 36, located in Vela C. This region is of particular interest as it has a bipolar HII region powered by a massive star cluster which may be impacting the surrounding magnetic field. To determine if this is the case, we apply the Histogram of Relative Orientations (HRO) method to quantify the relative alignment between the inferred magnetic field and elongated structures observed in several datasets such as dust emission, column density, temperature, and spectral line intensity maps. The HRO results indicate a bimodal alignment trend, where structures observed with dense gas tracers show a statistically significant preference for perpendicular alignment relative to the magnetic field, while structures probed by photo-dissociation region (PDR) tracers tend to align preferentially parallel relative to the magnetic field. Moreover, the dense gas and PDR associated structures are found to be kinematically distinct such that a bimodal alignment trend is also observed as a function of line-of-sight velocity. This suggests that the magnetic field may have been dynamically important and set a preferred direction of gas flow at the time that RCW 36 formed, resulting in a dense ridge developing perpendicular to the magnetic field. However on filament-scales near the PDR region, feedback may be energetically dominating the magnetic field, warping its geometry and the associated flux-frozen gas structures, causing the observed the preference for parallel relative alignment., Comment: 40 pages (25 pages main paper, 15 pages appendix), 24 figures, 5 tables, accepted for publication in ApJ

Curbs -- the edge of a raised sidewalk at the point where it meets a street -- crucial in urban environments where they help delineate safe pedestrian zones, from dangerous vehicular lanes. However, curbs themselves are significant navigation hazards, particularly for people who are blind or have low vision (pBLV). The challenges faced by pBLV in detecting and properly orientating themselves for these abrupt elevation changes can lead to falls and serious injuries. Despite recent advancements in assistive technologies, the detection and early warning of curbs remains a largely unsolved challenge. This paper aims to tackle this gap by introducing a novel, multi-faceted sensory substitution approach hosted on a smart wearable; the platform leverages an RGB camera and an embedded system to capture and segment curbs in real time and provide early warning and orientation information. The system utilizes YOLO (You Only Look Once) v8 segmentation model, trained on our custom curb dataset for the camera input. The output of the system consists of adaptive auditory beeps, abstract sonification, and speech, conveying information about the relative distance and orientation of curbs. Through human-subjects experimentation, we demonstrate the effectiveness of the system as compared to the white cane. Results show that our system can provide advanced warning through a larger safety window than the cane, while offering nearly identical curb orientation information.

People with blindness and low vision (pBLV) face challenges in navigating. Mobility aids are crucial for enhancing independence and safety. This paper presents an electronic travel aid that leverages a haptic-based, higher-order sensory substitution approach called Virtual Whiskers, designed to help pBLV negotiate obstacles effectively, efficiently, and safely. Virtual Whiskers is equipped with a plurality of modular vibration units that operate independently to deliver haptic feedback to users. Virtual Whiskers features two navigation modes: open path mode and depth mode, each addressing obstacle negotiation from different perspectives. The open path mode detects and delineate a traversable area within an analyzed field of view. Then, it guides the user through to the traversable direction adaptive vibratory feedback. The depth mode assists users in negotiating obstacles by highlighting spatial areas with prominent obstacles via haptic feedback. We recruited 10 participants with blindness or low vision to participate in user testing for Virtual Whiskers. Results show that the device significantly reduces idle periods and decreases the number of cane contacts. Virtual Whiskers is a promising obstacle negotiation strategy that demonstrating great potential to assist with pBLV navigation.

Data-driven surrogate models have shown immense potential as quick, inexpensive approximations to complex numerical and experimental modelling tasks. However, most surrogate models of physical systems do not quantify their uncertainty, rendering their predictions unreliable, requiring further validation. Though Bayesian approximations offer some solace in estimating the error associated with these models, they cannot provide guarantees, and the quality of their inferences depends on the availability of prior information and good approximations to posteriors for complex problems. This is particularly pertinent to multi-variable or spatio-temporal problems. Our work constructs and formalises a conformal prediction framework that satisfies marginal coverage for spatio-temporal predictions in a model-agnostic manner, requiring near-zero computational costs. We provide an extensive empirical study of the application of the framework to ascertain valid error bars that provide guaranteed coverage across the surrogate model's domain of operation. The application scope of our work extends across a large range of spatio-temporal models, from solving partial differential equations to weather forecasting. Through the applications, the paper looks at providing statistically valid error bars for deterministic models, as well as crafting guarantees to the error bars of probabilistic models. Our conformal prediction formalisation provides guaranteed coverage of the surrogate model, regardless of model architecture, and its training regime and is unbothered by the curse of dimensionality.

Resolving the dichotomy between the human-like yet constrained reasoning processes of Cognitive Architectures and the broad but often noisy inference behavior of Large Language Models (LLMs) remains a challenging but exciting pursuit, for enabling reliable machine reasoning capabilities in production systems. Because Cognitive Architectures are famously developed for the purpose of modeling the internal mechanisms of human cognitive decision-making at a computational level, new investigations consider the goal of informing LLMs with the knowledge necessary for replicating such processes, e.g., guided perception, memory, goal-setting, and action. Previous approaches that use LLMs for grounded decision-making struggle with complex reasoning tasks that require slower, deliberate cognition over fast and intuitive inference -- reporting issues related to the lack of sufficient grounding, as in hallucination. To resolve these challenges, we introduce LLM-ACTR, a novel neuro-symbolic architecture that provides human-aligned and versatile decision-making by integrating the ACT-R Cognitive Architecture with LLMs. Our framework extracts and embeds knowledge of ACT-R's internal decision-making process as latent neural representations, injects this information into trainable LLM adapter layers, and fine-tunes the LLMs for downstream prediction. Our experiments on novel Design for Manufacturing tasks show both improved task performance as well as improved grounded decision-making capability of our approach, compared to LLM-only baselines that leverage chain-of-thought reasoning strategies., Comment: 20 pages, 8 figures, 2 tables

We report observations of deuterated acetylene (C$_2$HD) at 19.3 $\mu$m (519 cm$^{-1}$) with the Texas Echelon Cross Echelle Spectrograph on the NASA Infrared Telescope Facility in July 2017. Six individual lines from the Q-branch of the $\nu_4$ band were clearly detected with a S/N ratio up to 10. Spectral intervals around 8.0 $\mu$m (745 cm$^{-1}$) and 13.4 $\mu$m (1247 cm$^{-1}$) containing acetylene (C$_2$H$_2$) and methane (CH$_4$) lines respectively, were observed during the same run to constrain the disk-averaged C$_2$H$_2$ abundance profile and temperature profile. Cassini observations with the Composite Infrared Spectrometer (CIRS) were used to improve the flux calibration and help to constrain the atmospheric model. The measured D/H ratio in acetylene, derived from the C$_2$HD/C$_2$H$_2$ abundance ratio, is (1.22$^{+0.27}_{-0.21})$ $\times$ 10$^{-4}$, consistent with that in methane obtained in previous studies. Possible sources of fractionation at different steps of the acetylene photochemistry are investigated.

Employing inelastic X-ray scattering and neutron scattering techniques, we observed nematic and magnetic phase transitions with distinct characters in K$_{5}$Fe$_{4}$Ag$_{6}$Te$_{10}$. Upon cooling, the nematic order undergoes a strongly first-order phase transition followed by a second-order magnetic transition at $T_{\textrm{N}}$ $\approx$ 34.6 K. The temperature difference between these two phase transitions is $\sim$ 1 K. The observed phenomenon can be attributed to a distinctive first-order preemptive Ising-nematic transition, a characteristic unique to a quasi-two-dimensional scenario marked by strong out-of-plane spatial anisotropy due to weak coupling. Our studies establish K$_{5}$Fe$_{4}$Ag$_{6}$Te$_{10}$ as the first material in the family of iron pnictides and chalcogenides that possesses a nematic tricritical point preceding the magnetic one upon decreasing nematic coupling., Comment: 7 pages, 5 figures

The polarization spectrum, or wavelength dependence of the polarization fraction, of interstellar dust emission provides important insights into the grain alignment mechanism of interstellar dust grains. We investigate the far-infrared polarization spectrum of a realistic simulated high-mass star forming cloud under various models of grain alignment and emission. We find that neither a homogeneous grain alignment model nor a grain alignment model that includes collisional dealignment is able to produce the falling spectrum seen in observations. On the other hand, we find that a grain alignment model with grain alignment efficiency dependent on local temperature is capable of producing a falling spectrum that is in qualitative agreement with observations of OMC-1. For the model most in agreement with OMC-1, we find no correlation between temperature and the slope of the polarization spectrum. However, we do find a positive correlation between column density and the slope of the polarization spectrum. We suggest this latter correlation to be the result of wavelength-dependent polarization by absorption., Comment: 14 pages, 6 figures, 2 tables; Accepted to ApJ

Relaxor-ferroelectrics display exceptional dielectric properties resulting from the underlying random dipolar fields induced by strong chemical inhomogeneity. An unusual structural aspect of relaxors is a skin-effect where the near-surface region in single crystals exhibit structures and critical phenomena that differ from the bulk. Relaxors are unique in that this skin effect extends over a macroscopic lengthscale of $\sim$ 100$\mu$m whereas usual surface layers only extend over a few unit cells (or $\sim$ nm). We present a muon spectroscopy study of Pb(Fe$_{1/2}$Nb$_{1/2}$)O$_{3}$ (PFN) which displays ferroelectric order, including many relaxor-like dielectric properties such as a frequency broadened dielectric response, and antiferromagnetism with spatially short-range polar correlations and hence can be termed a multiferroic. In terms of the magnetic behavior determined by the Fe$^{3+}$ ($S=5/2$, $L\approx0$) ions, PFN has been characterized as a unique example of a "cluster spin-glass". We use variable momentum muon spectroscopy to study the depth dependence of the slow magnetic relaxations in a large 1 cm$^{3}$ crystal of PFN. Zero-field positive muon spin relaxation is parameterized using a stretched exponential, indicative of a distribution of relaxation rates of the Fe$^{3+}$ spins. This bandwidth of frequencies changes as a function of muon momentum, indicative of a change in the Fe$^{3+}$ relaxation rates as a function of muon implantation depth in our single crystal. Using negative muon elemental analysis, we find small-to-no measurable change in the Fe$^{3+}$/Nb$^{5+}$ concentration with depth implying that chemical concentration alone cannot account for the change in the relaxational dynamics. PFN displays an analogous magnetic skin effect reported to exist in the structural properties of relaxor-ferroelectrics., Comment: 32 pages, 13 figures

Automatic differentiation (AD) has driven recent advances in machine learning, including deep neural networks and Hamiltonian Markov Chain Monte Carlo methods. Partially observed nonlinear stochastic dynamical systems have proved resistant to AD techniques because widely used particle filter algorithms yield an estimated likelihood function that is discontinuous as a function of the model parameters. We show how to embed two existing AD particle filter methods in a theoretical framework that provides an extension to a new class of algorithms. This new class permits a bias/variance tradeoff and hence a mean squared error substantially lower than the existing algorithms. We develop likelihood maximization algorithms suited to the Monte Carlo properties of the AD gradient estimate. Our algorithms require only a differentiable simulator for the latent dynamic system; by contrast, most previous approaches to AD likelihood maximization for particle filters require access to the system's transition probabilities. Numerical results indicate that a hybrid algorithm that uses AD to refine a coarse solution from an iterated filtering algorithm show substantial improvement on current state-of-the-art methods for a challenging scientific benchmark problem.

In functional data analysis, replicate observations of a smooth functional process and its derivatives offer a unique opportunity to flexibly estimate continuous-time ordinary differential equation models. Ramsay (1996) first proposed to estimate a linear ordinary differential equation from functional data in a technique called Principal Differential Analysis, by formulating a functional regression in which the highest-order derivative of a function is modelled as a time-varying linear combination of its lower-order derivatives. Principal Differential Analysis was introduced as a technique for data reduction and representation, using solutions of the estimated differential equation as a basis to represent the functional data. In this work, we re-formulate PDA as a generative statistical model in which functional observations arise as solutions of a deterministic ODE that is forced by a smooth random error process. This viewpoint defines a flexible class of functional models based on differential equations and leads to an improved understanding and characterisation of the sources of variability in Principal Differential Analysis. It does, however, result in parameter estimates that can be heavily biased under the standard estimation approach of PDA. Therefore, we introduce an iterative bias-reduction algorithm that can be applied to improve parameter estimates. We also examine the utility of our approach when the form of the deterministic part of the differential equation is unknown and possibly non-linear, where Principal Differential Analysis is treated as an approximate model based on time-varying linearisation. We demonstrate our approach on simulated data from linear and non-linear differential equations and on real data from human movement biomechanics. Supplementary R code for this manuscript is available at \url{https://github.com/edwardgunning/UnderstandingOfPDAManuscript}., Comment: Main text: 34 pages, 16 figures. Appendix: 34 pages, 18 figures. References: 3 pages

We demonstrate a free-space amplitude modulator for mid-infrared radiation (lambda=9.6 um) that operates at room temperature up to at least 20 GHz (above the -3dB cutoff frequency measured at 8.2 GHz). The device relies on the ultra-fast transition between weak and strong-coupling regimes induced by the variation of the applied bias voltage. Such transition induces a modulation of the device reflectivity. It is made of a semiconductor heterostructure enclosed in a judiciously designed array of metal-metal optical resonators, that - all-together - behave as an electrically tunable surface. At negative bias, it operates in the weak light-matter coupling regime. Upon application of an appropriate positive bias, the quantum wells populate with electrons and the device transitions to the strong-coupling regime. The modulator transmission keeps linear with input RF power in the 0dBm - 9dBm range. The increase of optical powers up to 25 mW exhibit a weak beginning saturation a little bit below.

Neural weather models have shown immense potential as inexpensive and accurate alternatives to physics-based models. However, most models trained to perform weather forecasting do not quantify the uncertainty associated with their forecasts. This limits the trust in the model and the usefulness of the forecasts. In this work we construct and formalise a conformal prediction framework as a post-processing method for estimating this uncertainty. The method is model-agnostic and gives calibrated error bounds for all variables, lead times and spatial locations. No modifications are required to the model and the computational cost is negligible compared to model training. We demonstrate the usefulness of the conformal prediction framework on a limited area neural weather model for the Nordic region. We further explore the advantages of the framework for deterministic and probabilistic models.

The differentiable programming paradigm is a cornerstone of modern scientific computing. It refers to numerical methods for computing the gradient of a numerical model's output. Many scientific models are based on differential equations, where differentiable programming plays a crucial role in calculating model sensitivities, inverting model parameters, and training hybrid models that combine differential equations with data-driven approaches. Furthermore, recognizing the strong synergies between inverse methods and machine learning offers the opportunity to establish a coherent framework applicable to both fields. Differentiating functions based on the numerical solution of differential equations is non-trivial. Numerous methods based on a wide variety of paradigms have been proposed in the literature, each with pros and cons specific to the type of problem investigated. Here, we provide a comprehensive review of existing techniques to compute derivatives of numerical solutions of differential equations. We first discuss the importance of gradients of solutions of differential equations in a variety of scientific domains. Second, we lay out the mathematical foundations of the various approaches and compare them with each other. Third, we cover the computational considerations and explore the solutions available in modern scientific software. Last but not least, we provide best-practices and recommendations for practitioners. We hope that this work accelerates the fusion of scientific models and data, and fosters a modern approach to scientific modelling.

The under-representation of cloud formation is a long-standing bias associated with climate simulations. Parameterisation schemes are required to capture cloud processes within current climate models but have known biases. We overcome these biases by embedding a Multi-Output Gaussian Process (MOGP) trained on high resolution Unified Model simulations to represent the variability of temperature and specific humidity within a climate model. A trained MOGP model is coupled in-situ with a simplified Atmospheric General Circulation Model named SPEEDY. The temperature and specific humidity profiles of SPEEDY are perturbed at fixed intervals according to the variability predicted from the MOGP. Ten-year predictions are generated for both control and ML-hybrid models. The hybrid model reduces the global precipitation bias by 18\% and over the tropics by 22\%. To further understand the drivers of these improvements, physical quantities of interest are explored, such as the distribution of lifted index values and the alteration of the Hadley cell. The control and hybrid set-ups are also run in a plus 4K sea-surface temperature experiment to explore the effects of the approach on patterns relating to cloud cover and precipitation in a warmed climate setting.

Curbs serve as vital borders that delineate safe pedestrian zones from potential vehicular traffic hazards. Curbs also represent a primary spatial hazard during dynamic navigation with significant stumbling potential. Such vulnerabilities are particularly exacerbated for persons with blindness and low vision (PBLV). Accurate visual-based discrimination of curbs is paramount for assistive technologies that aid PBLV with safe navigation in urban environments. Herein, we investigate the efficacy of curb segmentation for foundation models. We introduce the largest curb segmentation dataset to-date to benchmark leading foundation models. Our results show that state-of-the-art foundation models face significant challenges in curb segmentation. This is due to their high false-positive rates (up to 95%) with poor performance distinguishing curbs from curb-like objects or non-curb areas, such as sidewalks. In addition, the best-performing model averaged a 3.70-second inference time, underscoring problems in providing real-time assistance. In response, we propose solutions including filtered bounding box selections to achieve more accurate curb segmentation. Overall, despite the immediate flexibility of foundation models, their application for practical assistive technology applications still requires refinement. This research highlights the critical need for specialized datasets and tailored model training to address navigation challenges for PBLV and underscores implicit weaknesses in foundation models., Comment: 21 pages, 8 figures, submitted to Assistive Technology

Pseudocode in a scholarly paper provides a concise way to express the algorithms implemented therein. Pseudocode can also be thought of as an intermediary representation that helps bridge the gap between programming languages and natural languages. Having access to a large collection of pseudocode can provide various benefits ranging from enhancing algorithmic understanding, facilitating further algorithmic design, to empowering NLP or computer vision based models for tasks such as automated code generation and optical character recognition (OCR). We have created a large pseudocode collection by extracting nearly 320,000 pseudocode examples from arXiv papers. This process involved scanning over $2.2$ million scholarly papers, with 1,000 of them being manually inspected and labeled. Our approach encompasses an extraction mechanism tailored to optimize the coverage and a validation mechanism based on random sampling to check its accuracy and reliability, given the inherent heterogeneity of the collection. In addition, we offer insights into common pseudocode structures, supported by clustering and statistical analyses. Notably, these analyses indicate an exponential-like growth in the usage of pseudocodes, highlighting their increasing significance.

Empirical evidence for a gap between the computational powers of classical and quantum computers has been provided by experiments that sample the output distributions of two-dimensional quantum circuits. Many attempts to close this gap have utilized classical simulations based on tensor network techniques, and their limitations shed light on the improvements to quantum hardware required to frustrate classical simulability. In particular, quantum computers having in excess of $\sim 50$ qubits are primarily vulnerable to classical simulation due to restrictions on their gate fidelity and their connectivity, the latter determining how many gates are required (and therefore how much infidelity is suffered) in generating highly-entangled states. Here, we describe recent hardware upgrades to Quantinuum's H2 quantum computer enabling it to operate on up to $56$ qubits with arbitrary connectivity and $99.843(5)\%$ two-qubit gate fidelity. Utilizing the flexible connectivity of H2, we present data from random circuit sampling in highly connected geometries, doing so at unprecedented fidelities and a scale that appears to be beyond the capabilities of state-of-the-art classical algorithms. The considerable difficulty of classically simulating H2 is likely limited only by qubit number, demonstrating the promise and scalability of the QCCD architecture as continued progress is made towards building larger machines., Comment: Includes minor updates to the text and an updated author list to include researchers who made technical contributions in upgrading the machine to 56 qubits but were left off the original version by mistake

Between 2001 and 2023, we obtained high spectral resolution mid-infrared observations of Io using the TEXES instrument at NASA's Infrared Telescope Facility. These observations were centered at 529.8 cm-1 (18.88 {\mu}m) and include several SO2 absorption lines. By modeling the shapes and strengths of these absorption lines, we are able to determine how Io's SO2 atmospheric density varies over the 22-year time period, covering nearly two Jovian years. Previous analysis has shown that the density of Io's atmosphere on the anti-Jovian hemisphere exhibits clear seasonal temporal variability, which can be modeled as the sum of a seasonally-varying frost sublimation component and a constant component, assumed to be volcanic. The new data show that the seasonal pattern repeats during the second Jovian year, confirming the importance of sublimation support. The considerable longitudinal variability in Io's atmospheric density found in previous work is also stable over the second Jovian year with the SO2 column density on the Jupiter-facing hemisphere being 5--8 times lower than the anti-Jovian hemisphere. For the first time, we detect seasonal variability on the Jupiter-facing hemisphere as well. This can also be modeled as a combination of sublimation and a small constant source. The lower atmospheric density on the Jupiter-facing hemisphere can plausibly be explained by the daily Jupiter eclipses, which decrease the surface temperature and therefore reduce the sublimation-driven component of the atmosphere, combined with a lower level of volcanic activity directly emitting SO2 into the atmosphere., Comment: Accepted in Icarus

We present a pair of seismometers capable of measurement in all six axes of rigid motion. The vacuum-compatible devices implement compact interferometric displacement sensors to surpass the sensitivity of typical electrical readout schemes. Together with the capability to subtract the sensitivity-limiting coupling of ground tilt into horizontal motion, our seismometers can widen the sensing band towards mHz frequencies. This has notable applications across a range of fields requiring access to low-frequency signals, such as seismology and climate research. We particularly highlight their potential application in gravitational-wave observatories (LIGO) for observation of intermediate-mass black holes ($\sim 1000\,M_\odot$). The sensors are based on a near-monolithic fused-silica design consisting of a fused-silica mass and fibre, showing improved stability and robustness to tilt drifts, alignment, and control compared to all-metal or mixed metal-silica designs. We demonstrate tilt sensitivity that surpasses the best commercial alternatives in a significantly reduced footprint compared to our previous iterations of these sensors., Comment: 11 pages, 3 figures

Prompting techniques have significantly enhanced the capabilities of Large Language Models (LLMs) across various complex tasks, including reasoning, planning, and solving math word problems. However, most research has predominantly focused on language-based reasoning and word problems, often overlooking the potential of LLMs in handling symbol-based calculations and reasoning. This study aims to bridge this gap by rigorously evaluating LLMs on a series of symbolic tasks, such as addition, multiplication, modulus arithmetic, numerical precision, and symbolic counting. Our analysis encompasses eight LLMs, including four enterprise-grade and four open-source models, of which three have been pre-trained on mathematical tasks. The assessment framework is anchored in Chomsky's Hierarchy, providing a robust measure of the computational abilities of these models. The evaluation employs minimally explained prompts alongside the zero-shot Chain of Thoughts technique, allowing models to navigate the solution process autonomously. The findings reveal a significant decline in LLMs' performance on context-free and context-sensitive symbolic tasks as the complexity, represented by the number of symbols, increases. Notably, even the fine-tuned GPT3.5 exhibits only marginal improvements, mirroring the performance trends observed in other models. Across the board, all models demonstrated a limited generalization ability on these symbol-intensive tasks. This research underscores LLMs' challenges with increasing symbolic complexity and highlights the need for specialized training, memory and architectural adjustments to enhance their proficiency in symbol-based reasoning tasks.

Dayside magnetic reconnection allows for the transfer of the solar wind's energy into Earth's magnetosphere. This process takes place in electron diffusion regions (EDRs) embedded in ion diffusion regions (IDRs), which form in the magnetopause boundary's current sheet. A significant out-of-plane parallel current contribution in the diffusion regions was reported in Beedle et al. 2023. In order to understand the underlying structure of this parallel current, we compared EDR statistical results with a 2.5D Particle-In Cell (PIC) simulation. From this comparison, we identified out-of-plane parallel current signatures as defining features of the outer EDR and IDR. This significant out-of-plane parallel current indicates the interaction of the IDR and EDR systems, and provides implications for not only understanding energy dissipation in the diffusion regions, but also determining the location of the outer EDR.

GW230529 is the first compact binary coalescence detected by the LIGO-Virgo-KAGRA collaboration with at least one component mass confidently in the lower mass-gap, corresponding to the range 3-5$M_{\odot}$. If interpreted as a neutron star-black hole merger, this event has the most symmetric mass ratio detected so far and therefore has a relatively high probability of producing electromagnetic (EM) emission. However, no EM counterpart has been reported. At the merger time $t_0$, Swift-BAT and Fermi-GBM together covered 100$\%$ of the sky. Performing a targeted search in a time window $[t_0-20 \text{s},t_0+20 \text{s}]$, we report no detection by the Swift-BAT and the Fermi-GBM instruments. Combining the position-dependent $\gamma-$ray flux upper limits and the gravitational-wave posterior distribution of luminosity distance, sky localization and inclination angle of the binary, we derive constraints on the characteristic luminosity and structure of the jet possibly launched during the merger. Assuming a top-hat jet structure, we exclude at 90$\%$ credibility the presence of a jet which has at the same time an on-axis isotropic luminosity $\gtrsim 10^{48}$ erg s$^{-1}$, in the bolometric band 1 keV-10 MeV, and a jet opening angle $\gtrsim 15$ deg. Similar constraints are derived testing other assumptions about the jet structure profile. Excluding GRB 170817A, the luminosity upper limits derived here are below the luminosity of any GRB observed so far., Comment: 18 pages, 1 table, 11 figures

Magnetic fields likely play an important role in the formation of young protostars. Multiscale and multiwavelength dust polarization observations can reveal the inferred magnetic field from scales of the cloud to core to protostar. We present continuum polarization observations of the young protostellar triple system IRAS 16293-2422 at 89 $\mu$m using HAWC+ on SOFIA. The inferred magnetic field is very uniform with an average field angle of 89$^\circ\pm$23$^\circ$ (E of N), which is different from the $\sim$170$^\circ$ field morphology seen at 850 $\mu$m at larger scales (> 2000 au) with JCMT POL-2 and at 1.3 mm on smaller scales (< 300 au) with ALMA. The HAWC+ magnetic field direction is aligned with the known E-W outflow. This alignment difference suggests that the shorter wavelength HAWC+ data is tracing the magnetic field associated with warmer dust likely from the outflow cavity, whereas the longer wavelength data are tracing the bulk magnetic field from cooler dust. Also, we show in this source the dust emission peak is strongly affected by the observing wavelength. The dust continuum peaks closer to source B (northern source) at shorter wavelengths and progressively moves toward the southern A source with increasing wavelength (from 22 $\mu$m to 850 $\mu$m)., Comment: 10 pages, 5 figures. Accepted to ApJ

In this note, we present spectral fits of the well-documented sloshing cool-core cluster Zwicky 3146 ($z=0.291$), to test the existence of the highly speculated 3.5 keV line. We report excesses at $>3\sigma$ significance at $E=3.575$ keV, yielding a flux $F = 8.73_{-2.22}^{+2.17}$ $\times 10^{-6}$ photons cm$^{-2}$ s$^{-1}$, in \textit{XMM-Newton}, and $E=3.55$ keV, with a flux $F = 10.0_{-2.96}^{+3.05}$ $\times 10^{-6}$ photons cm$^{-2}$ s$^{-1}$ in \textit{Chandra}. We explore the possibility that the 3.5 keV excess is correlated to the presence of cold gas within the cluster, based on optical and sub-mm literature analyses. Following the launch of the X-ray Imaging and Spectroscopy Mission (XRISM), high resolution spectroscopy ($\leq 7$ eV) will reveal in unprecedented detail, the origin of this unidentified feature, for which Zwicky 3146 should be considered a viable target, due to the strength of the feature in two independent X-ray telescopes, opening a new window into plasma or charge exchange studies in galaxy clusters., Comment: 3 pages, 1 figure. Accepted for publication in the Research Notes of the AAS

The adoption of artificial intelligence (AI) across industries has led to the widespread use of complex black-box models and interpretation tools for decision making. This paper proposes an adversarial framework to uncover the vulnerability of permutation-based interpretation methods for machine learning tasks, with a particular focus on partial dependence (PD) plots. This adversarial framework modifies the original black box model to manipulate its predictions for instances in the extrapolation domain. As a result, it produces deceptive PD plots that can conceal discriminatory behaviors while preserving most of the original model's predictions. This framework can produce multiple fooled PD plots via a single model. By using real-world datasets including an auto insurance claims dataset and COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) dataset, our results show that it is possible to intentionally hide the discriminatory behavior of a predictor and make the black-box model appear neutral through interpretation tools like PD plots while retaining almost all the predictions of the original black-box model. Managerial insights for regulators and practitioners are provided based on the findings.

Significance: Pulsatile blood Oxygen Saturation (SpO2 ) via pulse oximetry is a valuable clinical metric for assessing oxygen delivery. Individual anatomical features, including skin tone, may affect current optical pulse oximetry methods. Aim: Develop an optical pulse oximetry method based on Dual-Ratio (DR) measurements to suppress individual anatomical features on SpO2. Approach: Design a DR-based finger pulse oximeter, hypothesizing that DR would suppress confounds from optical coupling and superficial tissue-absorption. This method is tested using Monte Carlo (MC) simulations and in vivo experiments. Results: Different melanosome volume fraction in the epidermis, a surrogate for skin tone, cause changes in the recovered SpO2 on the order of 1%. Different heterogeneous pulsatile hemodynamics cause greater changes on the order of 10%. SpO2 recovered with DR measurements showed less variability than the traditional Single-Distance (SD) transmission method. Conclusions: For the models and methods considered here, SpO2 measurements are more strongly impacted by heterogeneous pulsatile hemodynamics than by melanosome volume fraction. This is consistent with previous reports that, the skin tone bias is smaller than the observed variation in recovered SpO 2 across individual people. The partial suppression of variability in the SpO2 recovered by DR suggests promise of DR for pulse oximetry.

Data assimilation is a core component of numerical weather prediction systems. The large quantity of data processed during assimilation requires the computation to be distributed across increasingly many compute nodes, yet existing approaches suffer from synchronisation overhead in this setting. In this paper, we exploit the formulation of data assimilation as a Bayesian inference problem and apply a message-passing algorithm to solve the spatial inference problem. Since message passing is inherently based on local computations, this approach lends itself to parallel and distributed computation. In combination with a GPU-accelerated implementation, we can scale the algorithm to very large grid sizes while retaining good accuracy and compute and memory requirements.

Preceding a core-collapse supernova, various processes produce an increasing amount of neutrinos of all flavors characterized by mounting energies from the interior of massive stars. Among them, the electron antineutrinos are potentially detectable by terrestrial neutrino experiments such as KamLAND and Super-Kamiokande via inverse beta decay interactions. Once these pre-supernova neutrinos are observed, an early warning of the upcoming core-collapse supernova can be provided. In light of this, KamLAND and Super-Kamiokande, both located in the Kamioka mine in Japan, have been monitoring pre-supernova neutrinos since 2015 and 2021, respectively. Recently, we performed a joint study between KamLAND and Super-Kamiokande on pre-supernova neutrino detection. A pre-supernova alert system combining the KamLAND detector and the Super-Kamiokande detector was developed and put into operation, which can provide a supernova alert to the astrophysics community. Fully leveraging the complementary properties of these two detectors, the combined alert is expected to resolve a pre-supernova neutrino signal from a 15 M$_{\odot}$ star within 510 pc of the Earth, at a significance level corresponding to a false alarm rate of no more than 1 per century. For a Betelgeuse-like model with optimistic parameters, it can provide early warnings up to 12 hours in advance., Comment: Resubmitted to ApJ. 22 pages, 16 figures, for more information about the combined pre-supernova alert system, see https://www.lowbg.org/presnalarm/

The world has recently witnessed an unprecedented acceleration in demands for Machine Learning and Artificial Intelligence applications. This spike in demand has imposed tremendous strain on the underlying technology stack in supply chain, GPU-accelerated hardware, software, datacenter power density, and energy consumption. If left on the current technological trajectory, future demands show insurmountable spending trends, further limiting market players, stifling innovation, and widening the technology gap. To address these challenges, we propose a fundamental change in the AI training infrastructure throughout the technology ecosystem. The changes require advancements in supercomputing and novel AI training approaches, from high-end software to low-level hardware, microprocessor, and chip design, while advancing the energy efficiency required by a sustainable infrastructure. This paper presents the analytical framework that quantitatively highlights the challenges and points to the opportunities to reduce the barriers to entry for training large language models.

Crafting effective captions for figures is important. Readers heavily depend on these captions to grasp the figure's message. However, despite a well-developed set of AI technologies for figures and captions, these have rarely been tested for usefulness in aiding caption writing. This paper introduces SciCapenter, an interactive system that puts together cutting-edge AI technologies for scientific figure captions to aid caption composition. SciCapenter generates a variety of captions for each figure in a scholarly article, providing scores and a comprehensive checklist to assess caption quality across multiple critical aspects, such as helpfulness, OCR mention, key takeaways, and visual properties reference. Users can directly edit captions in SciCapenter, resubmit for revised evaluations, and iteratively refine them. A user study with Ph.D. students indicates that SciCapenter significantly lowers the cognitive load of caption writing. Participants' feedback further offers valuable design insights for future systems aiming to enhance caption writing., Comment: CHI EA '24: Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems

Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI. Accomplishing this goal requires learning to ground language in perception and embodied actions, in order to accomplish complex tasks. The Scalable, Instructable, Multiworld Agent (SIMA) project tackles this by training agents to follow free-form instructions across a diverse range of virtual 3D environments, including curated research environments as well as open-ended, commercial video games. Our goal is to develop an instructable agent that can accomplish anything a human can do in any simulated 3D environment. Our approach focuses on language-driven generality while imposing minimal assumptions. Our agents interact with environments in real-time using a generic, human-like interface: the inputs are image observations and language instructions and the outputs are keyboard-and-mouse actions. This general approach is challenging, but it allows agents to ground language across many visually complex and semantically rich environments while also allowing us to readily run agents in new environments. In this paper we describe our motivation and goal, the initial progress we have made, and promising preliminary results on several diverse research environments and a variety of commercial video games.

We describe features of the X-ray: Generate and Analyse (XGA) open-source software package that have been developed to facilitate automated hydrostatic mass ($M_{\rm hydro}$) measurements from XMM X-ray observations of clusters of galaxies. This includes describing how XGA measures global, and radial, X-ray properties of galaxy clusters. We then demonstrate the reliability of XGA by comparing simple X-ray properties, namely the X-ray temperature and gas mass, with published values presented by the XMM Cluster Survey (XCS), the Ultimate XMM eXtragaLactic survey project (XXL), and the Local Cluster Substructure Survey (LoCuSS). XGA measured values for temperature are, on average, within 1% of the values reported in the literature for each sample. XGA gas masses for XXL clusters are shown to be ${\sim}$10% lower than previous measurements (though the difference is only significant at the $\sim$1.8$\sigma$ level), LoCuSS $R_{2500}$ and $R_{500}$ gas mass re-measurements are 3% and 7% lower respectively (representing a 1.5$\sigma$ and 3.5$\sigma$ difference). Like-for-like comparisons of hydrostatic mass are made to LoCuSS results, which show that our measurements are $10{\pm}3%$ ($19{\pm}7%$) higher for $R_{2500}$ ($R_{500}$). The comparison between $R_{500}$ masses shows significant scatter. Finally, we present new $M_{\rm hydro}$ measurements for 104 clusters from the SDSS DR8 redMaPPer XCS sample (SDSSRM-XCS). Our SDSSRM-XCS hydrostatic mass measurements are in good agreement with multiple literature estimates, and represent one of the largest samples of consistently measured hydrostatic masses. We have demonstrated that XGA is a powerful tool for X-ray analysis of clusters; it will render complex-to-measure X-ray properties accessible to non-specialists., Comment: 24 pages (18 + 6 appendices), 15 figures, submitted to MNRAS; see https://github.com/DavidT3/XCS-Mass-Paper-I-Analysis for the code and samples

We investigate the large-scale behaviour of the Self-Repelling Brownian Polymer (SRBP) in the critical dimension $d=2$. The SRBP is a model of self-repelling motion, which is formally given by the solution a stochastic differential equation driven by a standard Brownian motion and with a drift given by the negative gradient of its own local time. As with its discrete counterpart, the "true" self-avoiding walk (TSAW) of [D.J. Amit, G. Parisi, & L. Peliti, Asymptotic behaviour of the "true" self-avoiding walk, Phys. Rev. B, 1983], it is conjectured to be logarithmically superdiffusive, i.e. to be such that its mean-square displacement grows as $t(\log t)^\beta$ for $t$ large and some currently unknown $\beta\in(0,1)$. The main result of the paper is an invariance principle for the SRBP under the weak coupling scaling, which corresponds to scaling the SRBP diffusively and simultaneously tuning down the strength of the self-interaction in a scale-dependent way. The diffusivity for the limiting Brownian motion is explicit and its expression provides compelling evidence that the $\beta$ above should be $1/2$. Further, we derive the scaling limit of the so-called environment seen by the particle process, which formally solves a non-linear singular stochastic PDE of transport-type, and prove this is given by the solution of a stochastic linear transport equation with enhanced diffusivity.