Search

Polar metals are an intriguing class of materials in which electric polarisation and metallicity can coexist within a single phase. The unique properties of polar metals challenge expectations, making way for the exploration of exotic phenomena such as unconventional magnetism, hyperferroelectric multiferroicity and developing multifunctional devices that can leverage both the materials electric polarization and its asymmetry in the spin conductivity, that arises due to the Rashba effect. Here, via a high pressure single crystal diffraction study, we report the pressure-induced enhancement of polar distortions in such a metal, Ca$_3$Ru$_2$O$_7$. Our DFT calculations highlight that naive assumptions about the linear dependency between polar distortion amplitudes and the magnitude of the Rashba spin splitting may not be generally valid.

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai., Comment: 25 pages, 6 figures

Many active galaxies harbor powerful relativistic jets, however, the detailed mechanisms of their formation and acceleration remain poorly understood. To investigate the area of jet acceleration and collimation with the highest available angular resolution, we study the innermost region of the bipolar jet in the nearby low-ionization nuclear emission-line region (LINER) galaxy NGC 1052. We combined observations of NGC 1052 taken with VLBA, GMVA, and EHT over one week in the spring of 2017. For the first time, NGC 1052 was detected with the EHT, providing a size of the central region in-between both jet bases of 250 RS (Schwarzschild radii) perpendicular to the jet axes. This size estimate supports previous studies of the jets expansion profile which suggest two breaks of the profile at around 300 RS and 10000 RS distances to the core. Furthermore, we estimated the magnetic field to be 1.25 Gauss at a distance of 22 {\mu}as from the central engine by fitting a synchrotron-self absorption spectrum to the innermost emission feature, which shows a spectral turn-over at about 130 GHz. Assuming a purely poloidal magnetic field, this implies an upper limit on the magnetic field strength at the event horizon of 26000 Gauss, which is consistent with previous measurements. The complex, low-brightness, double-sided jet structure in NGC 1052 makes it a challenge to detect the source at millimeter (mm) wavelengths. However, our first EHT observations have demonstrated that detection is possible up to at least 230 GHz. This study offers a glimpse through the dense surrounding torus and into the innermost central region, where the jets are formed. This has enabled us to finally resolve this region and provide improved constraints on its expansion and magnetic field strength., Comment: 22 pages, 10 figures, published in A&A

The time-variable emission from the accretion flow of Sgr A*, the supermassive black hole at the Galactic Center, has long been examined in the radio-to-mm, near-infrared (NIR), and X-ray regimes of the electromagnetic spectrum. However, until now, sensitivity and angular resolution have been insufficient in the crucial mid-infrared (MIR) regime. The MIRI instrument on JWST has changed that, and we report the first MIR detection of Sgr A*. The detection was during a flare that lasted about 40 minutes, a duration similar to NIR and X-ray flares, and the source's spectral index steepened as the flare ended. The steepening suggests synchrotron cooling is an important process for Sgr A*'s variability and implies magnetic field strengths $\sim$40--70 Gauss in the emission zone. Observations at $1.3~\mathrm{mm}$ with the Submillimeter Array revealed a counterpart flare lagging the MIR flare by $\approx$10 minutes. The observations can be self-consistently explained as synchrotron radiation from a single population of gradually cooling high-energy electrons accelerated through (a combination of) magnetic reconnection and/or magnetized turbulence., Comment: Accepted for publication ApJL

The 2017 observing campaign of the Event Horizon Telescope (EHT) delivered the first very long baseline interferometry (VLBI) images at the observing frequency of 230 GHz, leading to a number of unique studies on black holes and relativistic jets from active galactic nuclei (AGN). In total, eighteen sources were observed: the main science targets, Sgr A* and M87 along with various calibrators. We investigated the morphology of the sixteen AGN in the EHT 2017 data set, focusing on the properties of the VLBI cores: size, flux density, and brightness temperature. We studied their dependence on the observing frequency in order to compare it with the Blandford-K\"onigl (BK) jet model. We modeled the source structure of seven AGN in the EHT 2017 data set using linearly polarized circular Gaussian components and collected results for the other nine AGN from dedicated EHT publications, complemented by lower frequency data in the 2-86 GHz range. Then, we studied the dependences of the VLBI core flux density, size, and brightness temperature on the frequency measured in the AGN host frame. We compared the observations with the BK jet model and estimated the magnetic field strength dependence on the distance from the central black hole. Our results indicate a deviation from the standard BK model, particularly in the decrease of the brightness temperature with the observing frequency. Either bulk acceleration of the jet material, energy transfer from the magnetic field to the particles, or both are required to explain the observations.

We present optical photometric and spectroscopic observations of the peculiar Type Ia supernova ASASSN-20jq/SN 2020qxp. It is a low-luminosity object with a peak absolute magnitude of $M_B=-17.1\pm0.5$ mag. Despite its low luminosity, its post-peak light-curve decline rate ($\Delta m_{15}(B)=1.35\pm0.09$ mag) and color-stretch parameter (sBV>0.82) are similar to normal SNe Ia, making it an outlier in the luminosity-width and luminosity-color-stretch relations. Early light curves suggest a "bump" during the first 1.4 days of explosion. ASASSN-20jq synthesized a low radioactive $^{56}$Ni mass of $0.09\pm0.01M_\odot$. Near-maximum light spectra reveal strong Si II absorption lines, indicating a cooler photosphere than normal SNe Ia, but lack Ti II absorption lines. Unusually strong O I $\lambda$7773 and Ca II near-infrared triplet absorption features are present. Nebular spectra show a strong, narrow forbidden [Ca II] $\lambda\lambda$7291,7324 doublet emission, rarely seen in SNe Ia except in some Type Iax events. Marginal detection of [O I] $\lambda\lambda$6300,6364 doublet emission, which is extremely rare, is observed. Both [Ca II] and [O I] lines are redshifted by $\sim2000$ km/s. A strong [Fe II] $\lambda$7155 emission line with a tilted-top profile, identical to the [Fe II] $\lambda$16433 profile, is also observed. These asymmetric [Fe II] profiles and redshifted [Ca II] and [O I] emissions suggest a high central density white dwarf progenitor undergoing an off-center delayed-detonation explosion mechanism, producing roughly equal amounts of $^{56}$Ni in deflagration and detonation phases. This distinguishes ASASSN-20jq from normal and subluminous SNe Ia. ASASSN-20jq's light curve and spectra do not align with any single SNe Ia subclass but show similarities to 2002es-like objects. Thus, we add it as an extreme candidate within the heterogeneous parameter space of 2002es-like SNe Ia., Comment: 27 pages, 20 figures, 5 tables, submitted to A&A

We report the detection of the [O III] auroral line in 42 galaxies within the redshift range of $3 < z < 10$. These galaxies were selected from publicly available JWST data releases, including the JADES and PRIMALsurveys, and observed using both the low-resolution PRISM/CLEAR configuration and medium-resolution gratings. The measured electron temperatures in the high-ionization regions of these galaxies range from $T_e$([O III]) = 12,000 to 24,000 K, consistent with temperatures observed in local metal-poor galaxies and previous JWST studies. In 10 galaxies, we also detect the [O II] auroral line, allowing us to determine electron temperatures in the low-ionization regions, which range between $T_e$([O II]) = 10,830 and 20,000 K. The direct-$T_e$-based metallicities of our sample span from 12 + log(O/H) = 7.2 to 8.4, indicating these high-redshift galaxies are relatively metal-poor. By combining our sample with 25 galaxies from the literature, we expand the dataset to a total of 67 galaxies within $3 < z < 10$, effectively more than doubling the previous sample size for direct-$T_e$ based metallicity studies. This larger dataset allow us to derive empirical metallicity calibration relations based exclusively on high-redshift galaxies, using six key line ratios: R3, R2, R23, Ne3O2, O32, and O3N2. Notably, we derive a novel metallicity calibration relation for the first time using high-redshift $T_e$-based metallicities: $\hat{R}$ = 0.18log $R2$ + 0.98log $R3$. This new calibration significantly reduces the scatter in high-redshift galaxies compared to the $\hat{R}$ relation previously calibrated for low-redshift galaxies., Comment: 23 pages, 10 figures, submitted to ApJ

Sound decision-making relies on accurate prediction for tangible outcomes ranging from military conflict to disease outbreaks. To improve crowdsourced forecasting accuracy, we developed SAGE, a hybrid forecasting system that combines human and machine generated forecasts. The system provides a platform where users can interact with machine models and thus anchor their judgments on an objective benchmark. The system also aggregates human and machine forecasts weighting both for propinquity and based on assessed skill while adjusting for overconfidence. We present results from the Hybrid Forecasting Competition (HFC) - larger than comparable forecasting tournaments - including 1085 users forecasting 398 real-world forecasting problems over eight months. Our main result is that the hybrid system generated more accurate forecasts compared to a human-only baseline which had no machine generated predictions. We found that skilled forecasters who had access to machine-generated forecasts outperformed those who only viewed historical data. We also demonstrated the inclusion of machine-generated forecasts in our aggregation algorithms improved performance, both in terms of accuracy and scalability. This suggests that hybrid forecasting systems, which potentially require fewer human resources, can be a viable approach for maintaining a competitive level of accuracy over a larger number of forecasting questions., Comment: 20 pages, 6 figures, 4 tables

Explanation is a fundamentally human process. Understanding the goal and audience of the explanation is vital, yet existing work on explainable reinforcement learning (XRL) routinely does not consult humans in their evaluations. Even when they do, they routinely resort to subjective metrics, such as confidence or understanding, that can only inform researchers of users' opinions, not their practical effectiveness for a given problem. This paper calls on researchers to use objective human metrics for explanation evaluations based on observable and actionable behaviour to build more reproducible, comparable, and epistemically grounded research. To this end, we curate, describe, and compare several objective evaluation methodologies for applying explanations to debugging agent behaviour and supporting human-agent teaming, illustrating our proposed methods using a novel grid-based environment. We discuss how subjective and objective metrics complement each other to provide holistic validation and how future work needs to utilise standardised benchmarks for testing to enable greater comparisons between research.

Equivariant Graph Neural Networks (EGNNs) have demonstrated significant success in modeling microscale systems, including those in chemistry, biology and materials science. However, EGNNs face substantial computational challenges due to the high cost of constructing edge features via spherical tensor products, making them impractical for large-scale systems. To address this limitation, we introduce E2Former, an equivariant and efficient transformer architecture that incorporates the Wigner $6j$ convolution (Wigner $6j$ Conv). By shifting the computational burden from edges to nodes, the Wigner $6j$ Conv reduces the complexity from $O(|\mathcal{E}|)$ to $ O(| \mathcal{V}|)$ while preserving both the model's expressive power and rotational equivariance. We show that this approach achieves a 7x-30x speedup compared to conventional $\mathrm{SO}(3)$ convolutions. Furthermore, our empirical results demonstrate that the derived E2Former mitigates the computational challenges of existing approaches without compromising the ability to capture detailed geometric information. This development could suggest a promising direction for scalable and efficient molecular modeling.

Modalities in homotopy type theory are used to create and access subuniverses of a given type universe. These have significant applications throughout mathematics and computer science, and in particular can be used to create universes in which certain logical principles are true. We define presentations of topological modalities, which act as an internalisation of the notion of a Grothendieck topology. A specific presentation of a modality gives access to a surprising amount of computational information, such as explicit methods of determining membership of the subuniverse via internal sheaf conditions. Furthermore, assuming all terms of the presentation satisfy the axiom of choice, we are able to describe generic and powerful computational tools for modalities. This assumption is validated for presentations given by representables in presheaf categories. We deduce a local choice principle, and an internal reconstruction of Kripke-Joyal style reasoning. We use the local choice principle to show how to relate cohomology between universes, showing that a certain class of abelian groups has cohomolgoy stable between universes. We apply the methods to a prominent example, a type theory axiomatising the classifying topos of an algebraic theory, which specialises to give type theories for synthetic algebraic geometry and synthetic higher category theory.We apply the sheaf conditions to show that several presentations of interest are subcanonical, and apply the cohomology methods to show that quasi-coherent modules have cohomology stable between the Zariski, \'etale and fppf toposes.

Recent advances in AI-generated steganography highlight its potential for safeguarding the privacy of vulnerable democratic actors, including aid workers, journalists, and whistleblowers operating in oppressive regimes. In this work, we address current limitations and establish the foundations for large-throughput generative steganography. We introduce a novel approach that enables secure and efficient steganography within latent diffusion models. We show empirically that our methods perform well across a variety of open-source latent diffusion models, particularly in generative image and video tasks.

In this survey, we provide an in-depth exposition of our recent results on the well-posedness theory for stochastic evolution equations, employing maximal regularity techniques. The core of our approach is an abstract notion of critical spaces, which, when applied to nonlinear SPDEs, coincides with the concept of scaling invariant spaces. This framework leads to several sharp blow-up criteria and enables one to obtain instantaneous regularization results. Additionally, we refine and unify our previous results, while also presenting several new contributions. In the second part of the survey, we apply the abstract results to several concrete SPDEs. In particular, we give applications to stochastic perturbations of quasi-geostrophic equations, Navier-Stokes equations, and reaction-diffusion systems (including Allen--Cahn, Cahn--Hilliard and Lotka--Volterra models). Moreover, for the Navier--Stokes equations, we establish new Serrin-type blow-up criteria. While some applications are addressed using $L^2$-theory, many require a more general $L^p(L^q)$-framework. In the final section, we outline several open problems, covering both abstract aspects of stochastic evolution equations, and concrete questions in the study of linear and nonlinear SPDEs.

The open Tavis-Cummings model consists of $N$ quantum emitters interacting with a common cavity mode, accounts for losses and decoherence, and is frequently explored for quantum information processing and designing quantum devices. As $N$ increases, it becomes harder to simulate the open Tavis-Cummings model using traditional methods. To address this problem, we implement two quantum algorithms for simulating the dynamics of this model in the inhomogenous, non-resonant regime, with up to three excitations in the cavity. We show that the implemented algorithms have gate complexities that scale polynomially, as $O(N^2)$ and $O(N^3)$. One of these algorithms is the sampling-based wave matrix Lindbladization algorithm, for which we propose two protocols to implement its system-independent fixed interaction, resolving key open questions of [Patel and Wilde, Open Sys. & Info. Dyn., 30:2350014 (2023)]. Furthermore, we benchmark our results against a classical differential equation solver and demonstrate the ability to simulate classically intractable systems., Comment: 34 pages, 11 figures

This paper considers a class of multi-objective optimization problems known as Minkowski sum problems. Minkowski sum problems have a decomposable structure, where the global nondominated (Pareto) set corresponds to the Minkowski sum of several local nondominated sets. In some cases, the vectors of local sets does not contribute to the generation of the global nondominated set, and may therefore lead to wasted computational efforts. Therefore, we investigate theoretical properties of both necessary and redundant vectors, and propose an algorithm based on bounding sets for identifying unnecessary local vectors. We conduct extensive numerical experiments to test the the impact of varying characteristics of the instances on the resulting global nondominated set and the number of redundant vectors.

Manipulating hyperbolic polaritons at infrared frequencies has recently garnered interest as it promises to deliver new functionality for next-generation optical and photonic devices. This study investigates the impact of the crystal's anisotropy orientation on the Attenuated Total Reflection (ATR) spectra, more specifically, revealing the optical footprint of elliptical, ghost (GHP) and leaky (LHP) hyperbolic polaritons. Our findings reveal that the ATR spectra of GHPs exhibit a distinct hyperbolic behaviour which is similar to that recently observed using s-SNOM techniques. Similarly, the ATR spectra of LHPs show its clear lenticular behaviour; however, here we are able to discern the effects of large asymmetry due to cross-polarisation conversion when the crystal anisotropy is tilted away from the surface. Furthermore, we demonstrate that by controlling the anisotropy orientation of hyperbolic media it is possible to significantly alter the optical response of these polaritons. Thus, our results provide a foundation for the design of direction-dependent optical devices.

Small datasets are common in health research. However, the generalization performance of machine learning models is suboptimal when the training datasets are small. To address this, data augmentation is one solution. Augmentation increases sample size and is seen as a form of regularization that increases the diversity of small datasets, leading them to perform better on unseen data. We found that augmentation improves prognostic performance for datasets that: have fewer observations, with smaller baseline AUC, have higher cardinality categorical variables, and have more balanced outcome variables. No specific generative model consistently outperformed the others. We developed a decision support model that can be used to inform analysts if augmentation would be useful. For seven small application datasets, augmenting the existing data results in an increase in AUC between 4.31% (AUC from 0.71 to 0.75) and 43.23% (AUC from 0.51 to 0.73), with an average 15.55% relative improvement, demonstrating the nontrivial impact of augmentation on small datasets (p=0.0078). Augmentation AUC was higher than resampling only AUC (p=0.016). The diversity of augmented datasets was higher than the diversity of resampled datasets (p=0.046).

Context. The circumnuclear disk (CND) is presently the main supply of mass for the accretion onto the supermassive black hole (SMBH) in the Galactic Center (GC). While the accretion is relatively slow, it has been suspected that local episodic explosive events play an important role in the temporary mass inflow toward the SMBH, while also affecting the evolution of the CND. Aims. The aim of this study is to follow the changes in mass flows caused by supernova (SN) explosions nestled in or near the CND. Methods. We perform simulations with the grid-based MHD code FLASH of the inner 5 pc of the Milky Way GC, including gravitational potential, rotation, magnetic field, central wind source, and the warm gas of the CND, all mimicking the observed physical properties. Results. Assuming a M$_\mathrm{SN}=10$ M$_\odot$ as the mass of the precursor of the core-collapse SN event at various locations within 2 pc from the GC, we detect a temporary increase in the accretion rate, transferring an additional 2-60 M$_\odot$ of warm gas to the immediate vicinity of the SMBH, depending on the explosion site. At the same time, the kinetic energy of the SN blows away even mass from the CND; the additional warm gas leaving the simulation domain after the explosion is on the order of $\sim100$ M$_\odot$. In the studied cases, the impact on mass flows and the turbulence caused by the explosion cease after $\sim250$ kyr., Comment: Accepted for publication in Astronomy and Astrophysics (A&A). 9 pages, 9 figures

NMR acquisitions based on Ernst-angle excitations are widely used in analytical spectroscopy, as for over half a century they have been considered the optimal way for maximizing spectral sensitivity without compromising bandwidth or peak resolution. However, if as often happens in liquid state NMR relaxation times T1, T2 are long and similar, steady-state free-precession (SSFP) experiments can actually provide higher signal-to-noise ratios per square root of acquisition time (SNRt) than Ernst-angle-based counterparts. Although a strong offset dependence and a requirement for pulsing at repetition times TR << T2 leading to poor spectral resolution have impeded widespread analytical applications of SSFP, phase-incremented (PI) SSFP schemes could overcome these drawbacks. The present study explores if, when and how, can this approach to high resolution NMR improve SNRt over the performance afforded by Ernst-angle-based FT acquisitions. It is found that PI-SSFP can indeed often provide a superior SNRt than FT-NMR, but that achieving this requires implementing the acquisitions using relatively large flip angles. As also explained, however, this can restrict PI-SSFP's spectral resolution, and lead to distorted line shapes. To deal with this problem we introduce here a new outlook on SSFP experiments that can overcome this dichotomy, and lead to high spectral resolution even when utilizing relatively the large flip angles that provide optimal sensitivity. This new outlook also leads to a processing pipeline for PI-SSFP acquisitions, which is here introduced and exemplified. The enhanced SNRt that the ensuing method can provide over FT-based NMR counterparts collected under Ernst-angle excitation conditions, is examined with a series of 13C and 15N natural abundance investigations on organic compounds.

It has been suggested that star-forming galaxies may host a substantial, dark reservoir of gas in the form of planetary-mass molecular clouds that are so cold that $\text{H}_{2}$ can condense. Here we investigate the process of tidal disruption of such "snow clouds" by close passage of field stars. We construct a suite of simulations using the hydrodynamic formalism introduced by Carter and Luminet, and use it to explore the properties of the resulting tidal debris. The debris streams are tiny structures that are highly over-pressured relative to the ambient ISM. They are also unusual in their composition -- initially consisting of cold, gaseous He together with $\text{H}_{2}$ "snowballs" that may be as much as a metre in size. Each stream expands and cools and is subsequently shocked as it ploughs through the ISM; the snowballs are gradually eroded by the shocked gas. Snowballs streaming through the shocked ISM create microstructured plasma that is somewhat reminiscent of the "scattering screens" revealed by radio-wave scintillation studies. However, the tidal disruption rate is too low to account for the observed number of scattering screens if, as we assume here, the stars and clouds have no prior physical association so that disruptions occur as a result of chance encounters between stars and clouds., Comment: 24 pages, 12 figures. Accepted for publication in ApJ

Decentralized physical infrastructure networks (DePINs) are an emerging vertical within "Web3" replacing the traditional method that physical infrastructures are constructed. Yet, the boundaries between DePIN and traditional method of building crowd-sourced infrastructures such as citizen science initiatives or other Web3 verticals are not always so clear cut. In this work, we systematically analyze the differences between DePIN and other Web2 and Web3 verticals. For this, the study proposes a novel decision tree for classifying systems as DePIN. This tree is informed by prior studies and differentiates DePIN from related concepts using criteria such as the presence of a three-sided market, token-based incentives for supply, and the requirement for physical asset placement in those systems. The paper demonstrates the application of the decision tree to various blockchain systems, including Helium and Bitcoin, showcasing its practical utility in differentiating DePIN systems. This research offers significant contributions towards establishing a more objective and systematic approach to identifying and categorizing DePIN systems. It lays the groundwork for creating a comprehensive and unbiased database of DePIN systems, which will inform future research and development within this emerging sector.

Developing algorithms to search through data efficiently is a challenging part of searching for signs of technology beyond our solar system. We have built a digital signal processing system and computer cluster on the backend of the Karl G. Jansky Very Large Array (VLA) in New Mexico in order to search for signals throughout the Galaxy consistent with our understanding of artificial radio emissions. In our first paper, we described the system design and software pipelines. In this paper, we describe a postprocessing pipeline to identify persistent sources of interference, filter out false positives, and search for signals not immediately identifiable as anthropogenic radio frequency interference during the VLA Sky Survey. As of 01 September 2024, the Commensal Open-source Multi-mode Interferometric Cluster had observed more than 950,000 unique pointings. This paper presents the strategy we employ when commensally observing during the VLA Sky Survey and a postprocessing strategy for the data collected during the survey. To test this postprocessing pipeline, we searched toward 511 stars from the $Gaia$ catalog with coherent beams. This represents about 30 minutes of observation during VLASS, where we typically observe about 2000 sources per hour in the coherent beamforming mode. We did not detect any unidentifiable signals, setting isotropic power limits ranging from 10$^{11}$ to 10$^{16}$W., Comment: 17 Pages, Accepted in AJ

We present our implementation of non-reflecting boundary conditions in the magnetohydrodynamics (MHD) code LaRe3D. This implementation couples a characteristics-based boundary condition with a Lagrangian remap code, demonstrating the generality and flexibility of such non-reflecting boundary conditions for use with arbitrary grid-based MHD schemes. To test this implementation for perturbations on a background state, we present simulations of a hot sphere in an angled magnetic field. We then examine a series of simulations where we advect a spheromak through a non-reflecting boundary condition at four speeds related to the fast and slow magnetosonic speeds and the Alfven speed. We compare the behavior of these simulations to ground truth simulations run from the same initial condition on an extended grid that keeps the spheromak in the simulation volume at all times. We find that the non-reflecting boundary condition can lead to severe, physical differences developing between a simulation using a non-reflecting boundary and a ground truth simulation using a larger simulation volume. We conclude by discussing the origins of these differences., Comment: 32 pages, 13 figures, ApJ (accepted)

Large language models (LLMs) contain substantial factual knowledge which is commonly elicited by multiple-choice question-answering prompts. Internally, such models process the prompt through multiple transformer layers, building varying representations of the problem within its hidden states. Ultimately, however, only the hidden state corresponding to the final layer and token position are used to predict the answer label. In this work, we propose instead to learn a small separate neural network predictor module on a collection of training questions, that take the hidden states from all the layers at the last temporal position as input and outputs predictions. In effect, such a framework disentangles the representational abilities of LLMs from their predictive abilities. On a collection of hard benchmarks, our method achieves considerable improvements in performance, sometimes comparable to supervised fine-tuning procedures, but at a fraction of the computational cost., Comment: Accepted at AISTATS 2025

This paper explores the utility of a Large Language Model (LLM) to automatically generate queries and query variants from a description of an information need. Given a set of information needs described as backstories, we explore how similar the queries generated by the LLM are to those generated by humans. We quantify the similarity using different metrics and examine how the use of each set would contribute to document pooling when building test collections. Our results show potential in using LLMs to generate query variants. While they may not fully capture the wide variety of human-generated variants, they generate similar sets of relevant documents, reaching up to 71.1% overlap at a pool depth of 100., Comment: Published in the proceedings of SIGIR'23

LLMs are increasingly being used to assess the relevance of information objects. This work reports on experiments to study the labelling of short texts (i.e., passages) for relevance, using multiple open-source and proprietary LLMs. While the overall agreement of some LLMs with human judgements is comparable to human-to-human agreement measured in previous research, LLMs are more likely to label passages as relevant compared to human judges, indicating that LLM labels denoting non-relevance are more reliable than those indicating relevance. This observation prompts us to further examine cases where human judges and LLMs disagree, particularly when the human judge labels the passage as non-relevant and the LLM labels it as relevant. Results show a tendency for many LLMs to label passages that include the original query terms as relevant. We, therefore, conduct experiments to inject query words into random and irrelevant passages, not unlike the way we inserted the query "best caf\'e near me" into this paper. The results show that LLMs are highly influenced by the presence of query words in the passages under assessment, even if the wider passage has no relevance to the query. This tendency of LLMs to be fooled by the mere presence of query words demonstrates a weakness in our current measures of LLM labelling: relying on overall agreement misses important patterns of failures. There is a real risk of bias in LLM-generated relevance labels and, therefore, a risk of bias in rankers trained on those labels. We also investigate the effects of deliberately manipulating LLMs by instructing them to label passages as relevant, similar to the instruction "this paper is perfectly relevant" inserted above. We find that such manipulation influences the performance of some LLMs, highlighting the critical need to consider potential vulnerabilities when deploying LLMs in real-world applications., Comment: Published in the proceedings of SIGIR-AP'24

Augmenting randomized controlled trials (RCTs) with external real-world data (RWD) has the potential to improve the finite sample efficiency of treatment effect estimators. We describe using adaptive targeted maximum likelihood estimation (A-TMLE) for estimating the average treatment effect (ATE) by decomposing the ATE estimand into two components: a pooled-ATE estimand that combines data from both the RCT and external sources, and a bias estimand that captures the conditional effect of RCT enrollment on the outcome. This approach views the RCT data as the reference and corrects for inconsistencies of any kind between the RCT and the external data source. Given the growing abundance of external RWD from modern electronic health records, determining the optimal strategy to select candidate external patients for data integration remains an open yet critical problem. In this work, we begin by analyzing the robustness property of the A-TMLE estimator and then propose a matching-based sampling strategy that improves the robustness of the estimator with respect to the target estimand. Our proposed strategy is outcome-blind and involves matching based on two one-dimensional scores: the trial enrollment score and the propensity score in the external data. We demonstrate in simulations that our sampling strategy improves the coverage and shortens the widths of confidence intervals produced by A-TMLE. We illustrate our method with a case study of augmenting the DEVOTE cardiovascular safety trial by using the Optum Clinformatics claims database.

Integrating high-dimensional, heterogeneous data from multi-site cohort studies with complex hierarchical structures poses significant feature selection and prediction challenges. We extend the Bayesian Integrative Analysis and Prediction (BIP) framework to enable simultaneous feature selection and outcome modeling in data of nested hierarchical structure. We apply the proposed Bayesian Integrative Mixed Modeling (BIPmixed) framework to the Adolescent Brain Cognitive Development (ABCD) Study, leveraging multi-view data, including structural and functional MRI and early life adversity (ELA) metrics, to identify relevant features and predict the behavioral outcome. BIPmixed incorporates 2-level nested random effects, to enhance interpretability and make predictions in hierarchical data settings. Simulation studies illustrate BIPmixed's robustness in distinct random effect settings, highlighting its use for complex study designs. Our findings suggest that BIPmixed effectively integrates multi-view data while accounting for nested sampling, making it a valuable tool for analyzing large-scale studies with hierarchical data.

Battery-powered technologies like pagers and walkie-talkies have long been integral to civilian and military operations. However, the potential for such everyday devices to be weaponized has largely been underestimated in the realm of cybersecurity. In September 2024, Lebanon experienced a series of unprecedented, coordinated explosions triggered through compromised pagers and walkie-talkies, creating a new category of attack in the domain of cyber-physical warfare. This attack not only disrupted critical communication networks but also resulted in injuries, loss of life, and exposed significant national security vulnerabilities, prompting governments and organizations worldwide to reevaluate their cybersecurity frameworks. This article provides an in-depth investigation into the infamous Pager and Walkie-Talkie attacks, analyzing both technical and non-technical dimensions. Furthermore, the study extends its scope to explore vulnerabilities in other battery-powered infrastructures, such as battery management systems, highlighting their potential exploitation. Existing prevention and detection techniques are reviewed, with an emphasis on their limitations and the challenges they face in addressing emerging threats. Finally, the article discusses emerging methodologies, particularly focusing on the role of physical inspection, as a critical component of future security measures. This research aims to provide actionable insights to bolster the resilience of cyber-physical systems in an increasingly interconnected world., Comment: 18 pages, 10 figures

Proton therapy can achieve a highly targeted treatment by utilising the advantageous dosimetric characteristics of the Bragg Peak. Protons traversing through a material will deposit their maximum energy at the Bragg Peak through ionisation and other interactions, transferring minimal excess dose to surrounding tissue and organs. This rate of energy loss is also quantified by the linear energy transfer (LET), which is indicative of radiation quality and radiobiological effects. However it is a challenging physical quantity to measure, as characterisation of radiation fields and the impact of LET on treatment requires advanced tools and technology. The MiniPIX-Timepix is a miniaturised, hybrid semiconductor pixel detector capable of high resolution spectrometric tracking, enabling wide-range detection of the deposited energy, position and direction of single particles. Experimental measurements were performed at a clinical facility, the Clatterbridge Cancer Centre which houses a 60 MeV ocular proton therapy beamline. A realistic end-to-end model of the facility was developed in the Monte Carlo code TOPAS (TOol for PArticle Simulation) and was used to simulate the experimental conditions. The detector was held at 45$^{\circ}$ and 60$^{\circ}$ perpendicular to the beam, and placed downstream of various thickness Polymethyl methacrylate (PMMA) blocks to acquire data along the dose deposition depth. Empirical cluster data providing track length and the energy deposition distributions were used to obtain the LET spectra. The determined values for the LET in silicon and dose averaged LET across the BP show general agreement with simulated results, supporting the applicability of the TOPAS CCC model. This work explores the capability of the MiniPIX detector to measure physical quantities to resolve the LET, and discusses experimental considerations and further possibilities., Comment: Prepared for submission to JINST, 20 pages, 10 figures, 2 tables

The assembly of supermassive black hole (SMBH) mass ($M_{\bullet}$) and stellar mass ($M_{*}$) in galaxies can be studied via the redshift evolution of the $M_{\bullet}-M_{*}$ relation, but the ways in which selection bias and physical assembly channels affect this evolution are uncertain. To address this, we compare the $M_{\bullet}-M_{*}$ relation for local massive ($M_{*}>10^{10.5}$M$_{\odot}$) quiescent early-type galaxies (ETGs) to that for massive ETGs hosting active galactic nuclei (AGN) at $z\sim0.8$. The restrictions on stellar mass and galaxy type limit the assembly channels that may connect the two relations. For the local sample we find $\log(M_{\bullet}) = 8.80 + 1.10(\log{M_{*}-11})$, in line with prior work. For the $z\sim0.8$ sample we find a bias-corrected relation: $\log(M_{\bullet}) = 7.80 + 1.25(\log{M_{*}-11})$. We show, however, that this relation depends on the stellar and SMBH mass functions used to compute the selection bias, the virial relation, the virial factor, and the active fraction, which together introduce uncertainty of up to $\sim0.6$\,dex in the $z\sim0.8$ relation. Adopting reasonable choices of these parameters then our $z\sim0.8$ relation lies above that for $z\sim0$ AGN by $\sim0.5$\,dex, but below our $z\sim0$ ETG relation by $0.4-1$\,dex in SMBH mass. We discuss possible sources of this offset, including further bias corrections, `downsizing" in SMBH mass assembly, and preferential SMBH growth. Our results highlight the need to reduce uncertainties from selection and measurement bias in SMBH and stellar masses at all redshifts., Comment: ApJ, accepted

We present the results from the first axion-like particle search conducted using a dish antenna. The experiment was conducted at room temperature and sensitive to axion-like particles in the $44-52\,\mu\mathrm{eV}$ range ($10.7 - 12.5\,\mathrm{GHz}$). The novel dish antenna geometry was proposed by the BREAD collaboration and previously used to conduct a dark photon search in the same mass range. To allow for axion-like particle sensitivity, the BREAD dish antenna was placed in a $3.9\,\mathrm{T}$ solenoid magnet at Argonne National Laboratory. In the presence of a magnetic field, axion-like dark matter converts to photons at the conductive surface of the reflector. The signal is focused onto a custom coaxial horn antenna and read out with a low-noise radio-frequency receiver. No evidence of axion-like dark matter was observed in this mass range and we place the most stringent laboratory constraints on the axion-photon coupling strength, $g_{a\gamma\gamma}$, in this mass range at 90\% confidence., Comment: 7 pages, 3 figures

Recommender systems often rely on graph-based filters, such as normalized item-item adjacency matrices and low-pass filters. While effective, the centralized computation of these components raises concerns about privacy, security, and the ethical use of user data. This work proposes two decentralized frameworks for securely computing these critical graph components without centralizing sensitive information. The first approach leverages lightweight Multi-Party Computation and distributed singular vector computations to privately compute key graph filters. The second extends this framework by incorporating low-rank approximations, enabling a trade-off between communication efficiency and predictive performance. Empirical evaluations on benchmark datasets demonstrate that the proposed methods achieve comparable accuracy to centralized state-of-the-art systems while ensuring data confidentiality and maintaining low communication costs. Our results highlight the potential for privacy-preserving decentralized architectures to bridge the gap between utility and user data protection in modern recommender systems.

Methods to ensure factual accuracy of text generated by large language models (LLM) in clinical medicine are lacking. VeriFact is an artificial intelligence system that combines retrieval-augmented generation and LLM-as-a-Judge to verify whether LLM-generated text is factually supported by a patient's medical history based on their electronic health record (EHR). To evaluate this system, we introduce VeriFact-BHC, a new dataset that decomposes Brief Hospital Course narratives from discharge summaries into a set of simple statements with clinician annotations for whether each statement is supported by the patient's EHR clinical notes. Whereas highest agreement between clinicians was 88.5%, VeriFact achieves up to 92.7% agreement when compared to a denoised and adjudicated average human clinican ground truth, suggesting that VeriFact exceeds the average clinician's ability to fact-check text against a patient's medical record. VeriFact may accelerate the development of LLM-based EHR applications by removing current evaluation bottlenecks., Comment: 62 pages, 5 figures, 1 table, pre-print manuscript

Gait recognition is an emerging identification technology that distinguishes individuals at long distances by analyzing individual walking patterns. Traditional techniques rely heavily on large-scale labeled datasets, which incurs high costs and significant labeling challenges. Recently, researchers have explored unsupervised gait recognition with clustering-based unsupervised domain adaptation methods and achieved notable success. However, these methods directly use pseudo-label generated by clustering and neglect pseudolabel noise caused by domain differences, which affects the effect of the model training process. To mitigate these issues, we proposed a novel model called GaitDCCR, which aims to reduce the influence of noisy pseudo labels on clustering and model training. Our approach can be divided into two main stages: clustering and training stage. In the clustering stage, we propose Dynamic Cluster Parameters (DCP) and Dynamic Weight Centroids (DWC) to improve the efficiency of clustering and obtain reliable cluster centroids. In the training stage, we employ the classical teacher-student structure and propose Confidence-based Pseudo-label Refinement (CPR) and Contrastive Teacher Module (CTM) to encourage noisy samples to converge towards clusters containing their true identities. Extensive experiments on public gait datasets have demonstrated that our simple and effective method significantly enhances the performance of unsupervised gait recognition, laying the foundation for its application in the real-world.The code is available at https://github.com/YanSun-github/GaitDCCR, Comment: 21 pages, 8 figures

Since molecular clouds form stars, at least some parts of them must be in a state of collapse. However, there is a long-standing debate as to whether that collapse is local, involving only a small fraction of the cloud mass, or global, with most mass in a state of collapse up to the moment when it is dispersed by stellar feedback. In principle it is possible to distinguish these possibilities from clouds' virial ratios, which should be a factor of two larger for collapse than for equilibrium, but systematic uncertainties have thus far prevented such measurements. Here we propose a new analysis method to overcome this limitation: while the absolute value of a cloud's virial ratio is too uncertain to distinguish global from local collapse, the differential change in virial ratio as a function of surface density is also diagnostic of clouds' dynamical state, and can be measured with far fewer systematic uncertainties. We demonstrate the basic principles of the method using simple analytic models of supported and collapsing clouds, validate it from full 3D simulations, and discuss possible challenges in applying the method to real data. We then provide a preliminary application of the technique to recent observations of the molecular clouds in Andromeda, showing that most of them are inconsistent with being in a state of global collapse., Comment: 14 pages, 7 figures, submitted to The Open Journal of Astrophysics

We systematically probe different parameterizations of the attractive nuclear force based on real gas models to construct the nuclear matter equation of state. In each of the cases, the repulsion between nucleons is treated in the framework of excluded volume and interaction parameters are fitted to the empirical properties of the nuclear ground state. We calculate the critical temperature $T_c$ and critical particle number density $n_c$, and find that they are strongly correlated. Both are also correlated with the incompressibility $K_0$ in the nuclear ground state. We also include a quarkyonic matter phase in the quasiparticle description and investigate the relationship between $K_0$, transition density to the quarkyonic phase $n_{tr}$, and corresponding peak in the speed of sound $v_{s, {\rm max}}^2$. At each density, the quark fraction is found by minimizing the energy density. We find that both $n_{tr}$ and $v_{s, {\rm max}}^2$ are negatively correlated with $K_0$, $n_c$, and $T_c$., Comment: 8 pages, 4 figures, 3 tables

Statistical inference for stochastic processes has advanced significantly due to applications in diverse fields, but challenges remain in high-dimensional settings where parameters are allowed to grow with the sample size. This paper analyzes a d-dimensional ergodic diffusion process under sparsity constraints, focusing on the adaptive Lasso estimator, which improves variable selection and bias over the standard Lasso. We derive conditions under which the adaptive Lasso achieves support recovery property and asymptotic normality for the drift parameter, with a focus on linear models. Explicit parameter relationships guide tuning for optimal performance, and a marginal estimator is proposed for p>>d scenarios under partial orthogonality assumption. Numerical studies confirm the adaptive Lasso's superiority over standard Lasso and MLE in accuracy and support recovery, providing robust solutions for high-dimensional stochastic processes.

We prove a fourth order analogue of the Saint-Venant inequality: the mean deflection of a clamped plate under uniform transverse load is maximal for the ball, among plates of prescribed volume in any dimension of space. The method works in Euclidean space, hyperbolic space, and the sphere. Similar results for clamped plates under small compression and for the compliance under non-uniform loads are proved to hold in two dimensional Euclidean space, with the higher dimensional and curved cases of those problems left open., Comment: 35 pages

We demonstrate volumetric fluorescence lifetime microscopy (FLIM) using the electro-optic FLIM technique. Images acquired in a selective plane illumination microscope are gated using a Pockels cell driven at 80 MHz, enabling light sheet FLIM acquisition with up to 800 {\mu}m field of view. Volume acquisitions are demonstrated on fluorescent bead mixtures and in live Arabidopsis thaliana root samples using both genetically encoded fluorescent proteins and endogenous autofluorescence.

In this paper, we present a speaker-independent dysarthric speech recognition system, with a focus on evaluating the recently released Speech Accessibility Project (SAP-1005) dataset, which includes speech data from individuals with Parkinson's disease (PD). Despite the growing body of research in dysarthric speech recognition, many existing systems are speaker-dependent and adaptive, limiting their generalizability across different speakers and etiologies. Our primary objective is to develop a robust speaker-independent model capable of accurately recognizing dysarthric speech, irrespective of the speaker. Additionally, as a secondary objective, we aim to test the cross-etiology performance of our model by evaluating it on the TORGO dataset, which contains speech samples from individuals with cerebral palsy (CP) and amyotrophic lateral sclerosis (ALS). By leveraging the Whisper model, our speaker-independent system achieved a CER of 6.99% and a WER of 10.71% on the SAP-1005 dataset. Further, in cross-etiology settings, we achieved a CER of 25.08% and a WER of 39.56% on the TORGO dataset. These results highlight the potential of our approach to generalize across unseen speakers and different etiologies of dysarthria., Comment: Accepted to ICASSP 2025

The level of tumour-infiltrating lymphocytes (TILs) is a prognostic factor for patients with (triple-negative) breast cancer (BC). Computational TIL assessment (CTA) has the potential to assist pathologists in this labour-intensive task, but current CTA models rely heavily on many detailed annotations. We propose and validate a fundamentally simpler deep learning based CTA that can be trained in only ten minutes on hundredfold fewer pathologist annotations. We collected whole slide images (WSIs) with TILs scores and clinical data of 2,340 patients with BC from six cohorts including three randomised clinical trials. Morphological features were extracted from whole slide images (WSIs) using a pathology foundation model. Our label-efficient Computational stromal TIL assessment model (ECTIL) directly regresses the TILs score from these features. ECTIL trained on only a few hundred samples (ECTIL-TCGA) showed concordance with the pathologist over five heterogeneous external cohorts (r=0.54-0.74, AUROC=0.80-0.94). Training on all slides of five cohorts (ECTIL-combined) improved results on a held-out test set (r=0.69, AUROC=0.85). Multivariable Cox regression analyses indicated that every 10% increase of ECTIL scores was associated with improved overall survival independent of clinicopathological variables (HR 0.86, p<0.01), similar to the pathologist score (HR 0.87, p<0.001). We demonstrate that ECTIL is highly concordant with an expert pathologist and obtains a similar hazard ratio. ECTIL has a fundamentally simpler design than existing methods and can be trained on orders of magnitude fewer annotations. Such a CTA may be used to pre-screen patients for, e.g., immunotherapy clinical trial inclusion, or as a tool to assist clinicians in the diagnostic work-up of patients with BC. Our model is available under an open source licence (https://github.com/nki-ai/ectil)., Comment: Under review. 54 pages including supplementary materials, 2 main tables, 3 main figures, 14 supplementary figures, 4 supplementary tables

A computationally efficient nonlinear Model Predictive Control (NMPC) algorithm is proposed for safe learning-based control with a system model represented by an affine (unknown) combination of basis functions and subject to additive set-bounded disturbances. The proposed algorithm employs successive linearization around predicted trajectories and accounts for the uncertain components of the predicted state due to linearization, model errors and disturbances using ellipsoidal sets centered on the predicted nominal state trajectory. An ellipsoidal tube-based approach ensures satisfaction of constraints on control variables and model states. Feasibility is ensured using local bounds on linearization errors and a procedure based on a backtracking line search. We show that the ellipsoidal embedding of the predicted uncertainty scales favourably with system dimensions, and we illustrate this using numerical experiments. The approach is combined with a set membership parameter estimation strategy. The resulting algorithm is recursively feasible and provides closed loop stability and performance guarantees.

This paper studies the greedy two-term underapproximation of $\theta\in (0,1]$ using reciprocals of numbers from a Fibonacci-type sequence $(c_n)_{n=1}^\infty$. We find the set of $\theta$ whose greedy two-term underapproximation is the best among all two-term underapproximations using $1/c_n$'s. We then derive a neat description of the set when $(c_n)_{n=1}^\infty$ is the Fibonacci sequence or the Lucas sequence., Comment: 8 pages

The Johnson-Lindenstrauss (JL) lemma allows subsets of a high-dimensional space to be embedded into a lower-dimensional space while approximately preserving all pairwise Euclidean distances. This important result has inspired an extensive literature, with a significant portion dedicated to constructing structured random matrices with fast matrix-vector multiplication algorithms that generate such embeddings for finite point sets. In this paper, we briefly consider fast JL embedding matrices for {\it infinite} subsets of $\mathbb{R}^d$. Prior work in this direction such as \cite{oymak2018isometric, mendelson2023column} has focused on constructing fast JL matrices $HD \in \mathbb{R}^{k \times d}$ by multiplying structured matrices with RIP(-like) properties $H \in \mathbb{R}^{k \times d}$ against a random diagonal matrix $D \in \mathbb{R}^{d \times d}$. However, utilizing RIP(-like) matrices $H$ in this fashion necessarily has the unfortunate side effect that the resulting embedding dimension $k$ must depend on the ambient dimension $d$ no matter how simple the infinite set is that one aims to embed. Motivated by this, we explore an alternate strategy for removing this $d$-dependence from $k$ herein: Extending a concentration inequality proven by Ailon and Liberty \cite{Ailon2008fast} in the hope of later utilizing it in a chaining argument to obtain a near-optimal result for infinite sets. %, and $(ii)$ utilizing a simple secondary Gaussian embedding of an initial fast JL embedding of a given infinite set. Though this strategy ultimately fails to provide the near-optimal embedding dimension we seek, along the way we obtain a stronger-than-sub-exponential extension of the concentration inequality in \cite{Ailon2008fast} which may be of independent interest.

We present a novel formalisation of tensor semantics for linear temporal logic on finite traces (LTLf), with formal proofs of correctness carried out in the theorem prover Isabelle/HOL. We demonstrate that this formalisation can be integrated into a neurosymbolic learning process by defining and verifying a differentiable loss function for the LTLf constraints, and automatically generating an implementation that integrates with PyTorch. We show that, by using this loss, the process learns to satisfy pre-specified logical constraints. Our approach offers a fully rigorous framework for constrained training, eliminating many of the inherent risks of ad-hoc, manual implementations of logical aspects directly in an "unsafe" programming language such as Python, while retaining efficiency in implementation.

Particle discretizations of partial differential equations are advantageous for high-dimensional kinetic models in phase space due to their better scalability than continuum approaches with respect to dimension. Complex processes collectively referred to as \textit{particle noise} hamper long-time simulations with particle methods. One approach to address this problem is particle mesh adaptivity or remapping, known as \textit{particle resampling}. This paper introduces a resampling method that projects particles to and from a (finite element) function space. The method is simple; using standard sparse linear algebra and finite element techniques, it can adapt to almost any set of new particle locations and preserves all moments up to the order of polynomial represented exactly by the continuum function space. This work is motivated by the Vlasov-Maxwell-Landau model of magnetized plasmas with up to six dimensions, $3X$ in physical space and $3V$ in velocity space, and is developed in the context of a $1X$ + $1V$ Vlasov-Poisson model of Landau damping with logically regular particle and continuum phase space grids. The evaluation codes are publicly available, along with the data and reproducibility artifacts, and developed in the PETSc numerical library.

We report the emergence of a polar metal phase in layered van der Waals compound FePSe$_3$. This Mott insulator with antiferromagnetic order offers a unique opportunity to fully tune an insulator into a polar metal state with pressure, without doping-induced disorder or impurities. Our synchrotron and neutron diffraction data unambiguously show a structural transition and loss of the inversion symmetry. We also observed the suppression of magnetic ordering and an insulator-to-metal transition correspondent with this structural transformation. The loss of the inversion symmetry combined with the pressure-induced metallicity in FePSe$_3$ offers a new platform to investigate polar metallicity at accessible pressures. Moreover, the high-pressure metallic phase shows unconventional resistivity deviating from the Fermi-liquid description, close to the magnetic critical transition pressure at sufficiently low temperatures, which strongly suggests underlying quantum criticality. Our work not only explores the comprehensive temperature-pressure phase diagram of FePSe$_3$ but also provides insights for further investigation of van der Waals strongly correlated magnetic compounds., Comment: 13 pages, 5 figures

As computing's societal impact grows, so does the need for computing students to recognize and address the ethical and sociotechnical implications of their work. While there are efforts to integrate ethics into computing curricula, we lack a standardized tool to measure those efforts, specifically, students' attitudes towards ethical reflection and their ability to effect change. This paper introduces the novel framework of Critically Conscious Computing and reports on the development and content validation of the Critical Reflection and Agency in Computing Index, a novel instrument designed to assess undergraduate computing students' attitudes towards practicing critically conscious computing. The resulting index is a theoretically grounded, expert-reviewed tool to support research and practice in computing ethics education. This enables researchers and educators to gain insights into students' perspectives, inform the design of targeted ethics interventions, and measure the effectiveness of computing ethics education initiatives., Comment: Accepted to the ACM CHI Conference on Human Factors in Computing Systems (CHI '25)

We introduce distributional dynamic programming (DP) methods for optimizing statistical functionals of the return distribution, with standard reinforcement learning as a special case. Previous distributional DP methods could optimize the same class of expected utilities as classic DP. To go beyond expected utilities, we combine distributional DP with stock augmentation, a technique previously introduced for classic DP in the context of risk-sensitive RL, where the MDP state is augmented with a statistic of the rewards obtained so far (since the first time step). We find that a number of recently studied problems can be formulated as stock-augmented return distribution optimization, and we show that we can use distributional DP to solve them. We analyze distributional value and policy iteration, with bounds and a study of what objectives these distributional DP methods can or cannot optimize. We describe a number of applications outlining how to use distributional DP to solve different stock-augmented return distribution optimization problems, for example maximizing conditional value-at-risk, and homeostatic regulation. To highlight the practical potential of stock-augmented return distribution optimization and distributional DP, we combine the core ideas of distributional value iteration with the deep RL agent DQN, and empirically evaluate it for solving instances of the applications discussed.