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Findings of this study indicate that preservice teachers engaged in student teaching tend to become less humanistic and more authoritarian in their relations with students as the student teaching experience progresses. In addition, inner-city student teachers tend to be more custodial before they begin student teaching than are suburban student teachers after they complete student teaching. The sample for the study consisted of fifty-eight secondary school student teachers in suburban schools and twenty-seven secondary student teachers in inner-city schools. Subjects were administered the Pupil Control Ideology Inventory Questionnaire during prestudent teaching orientation and again during the last week of student teaching. Survey results indicate that student teachers tend to enter student teaching with an idealistic and sometimes erroneous concept of what is involved regarding discipline maintenance in the classroom, thus undergoing a marked change toward more dominant classroom control forms as the experience progresses. The more stringent screening process for student teachers in inner-city schools appears to account in part for the finding that these student teachers are more custodial before they begin student teaching than are suburban student teachers after they complete student teaching. (MB)

Ignoring their own well-publicized calls to regulate AI development and to pause implementation of its applications, major technology companies such as Google, Microsoft, and Meta are racing to fend off regulation and integrate artificial intelligence (AI) into their platforms. The weight of the available evidence suggests that the current wholesale adoption of unregulated AI applications in schools poses a grave danger to democratic civil society and to individual freedom and liberty. Years of warnings and precedents have highlighted the risks posed by the widespread use of pre-AI digital technologies in education, which have obscured decision-making and enabled student data exploitation. Without effective public oversight, the introduction of opaque and unproven AI systems and applications will likely exacerbate these problems. This policy brief explores the harms likely if lawmakers and others do not step in with carefully considered measures to prevent these extensive risks. The authors urge school leaders to pause the adoption of AI applications until policymakers have had sufficient time to thoroughly educate themselves and develop legislation and policies ensuring effective public oversight and control of school applications.

This report investigates the progression of students from GCSE to A level in the same (or related) subject. That is, for a range of subjects, the proportion of GCSE students who continue to study the subject at A level is calculated. To understand possible sources of variation progression rates are broken down by GCSE grade and student gender. Finally, the report considers relationships between GCSE and A level grades for those students who do progress. To evaluate whether there have been any changes in progression over time, references are made to previous versions of this analysis.

The next generation of radio astronomy telescopes are challenging existing data analysis paradigms, as they have an order of magnitude larger collecting area and bandwidth. The two primary problems encountered when processing this data are the need for storage and that processing is primarily I/O limited. An example of this is the data deluge expected from the SKA-Low Telescope of about 300 PB per year. To remedy these issues, we have demonstrated lossy and lossless compression of data on an existing precursor telescope, the Australian Square Kilometre Array Pathfinder (ASKAP), using MGARD and ADIOS2 libraries. We find data processing is faster by a factor of 7 and give compression ratios from a factor of 7 (lossless) up to 37 (lossy with an absolute error bound of 1e-3). We discuss the effectiveness of lossy MGARD compression and its adherence to the designated error bounds, the trade-off between these error bounds and the corresponding compression ratios, as well as the potential consequences of these I/O and storage improvements on the science quality of the data products., Comment: 9 pages, 10 figures. Included in the conference proceedings of Cray User Group Conference 2024

Quantum computation must be performed in a fault-tolerant manner to be realizable in practice. Recent progress has uncovered quantum error-correcting codes with sparse connectivity requirements and constant qubit overhead. Existing schemes for fault-tolerant logical measurement do not always achieve low qubit overhead. Here we present a low-overhead method to implement fault-tolerant logical measurement in a quantum error-correcting code by treating the logical operator as a symmetry and gauging it. The gauging measurement procedure introduces a high degree of flexibility that can be leveraged to achieve a qubit overhead that is linear in the weight of the operator being measured up to a polylogarithmic factor. This flexibility also allows the procedure to be adapted to arbitrary quantum codes. Our results provide a new, more efficient, approach to performing fault-tolerant quantum computation, making it more tractable for near-term implementation., Comment: 5+12 pages, 4 figures

Voice Assistants (VAs) are popular for simple tasks, but users are often hesitant to use them for complex activities like online shopping. We explored whether the vocal characteristics like the VA's vocal tone, can make VAs perceived as more attractive and trustworthy to users for complex tasks. Our findings show that the tone of the VA voice significantly impacts its perceived attractiveness and trustworthiness. Participants in our experiment were more likely to be attracted to VAs with positive or neutral tones and ultimately trusted the VAs they found more attractive. We conclude that VA's perceived trustworthiness can be enhanced through thoughtful voice design, incorporating a variety of vocal tones., Comment: Extended Abstract

Many commonly studied species now have more than one chromosome-scale genome assembly, revealing a large amount of genetic diversity previously missed by approaches that map short reads to a single reference. However, many species still lack multiple reference genomes and correctly aligning references to build pangenomes is challenging, limiting our ability to study this missing genomic variation in population genetics. Here, we argue that $k$-mers are a crucial stepping stone to bridging the reference-focused paradigms of population genetics with the reference-free paradigms of pangenomics. We review current literature on the uses of $k$-mers for performing three core components of most population genetics analyses: identifying, measuring, and explaining patterns of genetic variation. We also demonstrate how different $k$-mer-based measures of genetic variation behave in population genetic simulations according to the choice of $k$, depth of sequencing coverage, and degree of data compression. Overall, we find that $k$-mer-based measures of genetic diversity scale consistently with pairwise nucleotide diversity ($\pi$) up to values of about $\pi = 0.025$ ($R^2 = 0.97$) for neutrally evolving populations. For populations with even more variation, using shorter $k$-mers will maintain the scalability up to at least $\pi = 0.1$. Furthermore, in our simulated populations, $k$-mer dissimilarity values can be reliably approximated from counting bloom filters, highlighting a potential avenue to decreasing the memory burden of $k$-mer based genomic dissimilarity analyses. For future studies, there is a great opportunity to further develop methods to identifying selected loci using $k$-mers., Comment: 6 figures, 34 pages

We show that the conventional Jarzynski equality does not hold for a system prepared in a microcanonical ensemble. We derive a modified equality that connects microcanonical work fluctuations to entropy production, in an analogous way to the Jarzynski equality, but with reference to an inverse temperature that depends on the path of the work protocol. Our result is a special case of a general expression for the microcanonical moment-generating function for any extensive quantity, which enables calculation of the breakdown of ensemble equivalence for thermodynamic fluctuations. We demonstrate our microcanonical Jarzynski equality in an ensemble of driven two-level systems., Comment: 4 pages + refs, 1 figure

Artificial Intelligence (AI) has paved the way for revolutionary decision-making processes, which if harnessed appropriately, can contribute to advancements in various sectors, from healthcare to economics. However, its black box nature presents significant ethical challenges related to bias and transparency. AI applications are hugely impacted by biases, presenting inconsistent and unreliable findings, leading to significant costs and consequences, highlighting and perpetuating inequalities and unequal access to resources. Hence, developing safe, reliable, ethical, and Trustworthy AI systems is essential. Our team of researchers working with Trustworthy and Responsible AI, part of the Transdisciplinary Scholarship Initiative within the University of Calgary, conducts research on Trustworthy and Responsible AI, including fairness, bias mitigation, reproducibility, generalization, interpretability, and authenticity. In this paper, we review and discuss the intricacies of AI biases, definitions, methods of detection and mitigation, and metrics for evaluating bias. We also discuss open challenges with regard to the trustworthiness and widespread application of AI across diverse domains of human-centric decision making, as well as guidelines to foster Responsible and Trustworthy AI models., Comment: 44 pages, 2 figures

Accurately detecting and classifying damage in analogue media such as paintings, photographs, textiles, mosaics, and frescoes is essential for cultural heritage preservation. While machine learning models excel in correcting global degradation if the damage operator is known a priori, we show that they fail to predict where the damage is even after supervised training; thus, reliable damage detection remains a challenge. We introduce DamBench, a dataset for damage detection in diverse analogue media, with over 11,000 annotations covering 15 damage types across various subjects and media. We evaluate CNN, Transformer, and text-guided diffusion segmentation models, revealing their limitations in generalising across media types.

Integrating Electronic Health Records (EHR) and the application of machine learning present opportunities for enhancing the accuracy and accessibility of data-driven diabetes prediction. In particular, developing data-driven machine learning models can provide early identification of patients with high risk for diabetes, potentially leading to more effective therapeutic strategies and reduced healthcare costs. However, regulation restrictions create barriers to developing centralized predictive models. This paper addresses the challenges by introducing a federated learning approach, which amalgamates predictive models without centralized data storage and processing, thus avoiding privacy issues. This marks the first application of federated learning to predict diabetes using real clinical datasets in Canada extracted from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) without crossprovince patient data sharing. We address class-imbalance issues through downsampling techniques and compare federated learning performance against province-based and centralized models. Experimental results show that the federated MLP model presents a similar or higher performance compared to the model trained with the centralized approach. However, the federated logistic regression model showed inferior performance compared to its centralized peer., Comment: 10 pages

Equivariant neural networks are neural networks with symmetry. Motivated by the theory of group representations, we decompose the layers of an equivariant neural network into simple representations. The nonlinear activation functions lead to interesting nonlinear equivariant maps between simple representations. For example, the rectified linear unit (ReLU) gives rise to piecewise linear maps. We show that these considerations lead to a filtration of equivariant neural networks, generalizing Fourier series. This observation might provide a useful tool for interpreting equivariant neural networks., Comment: 24 pages, many figures, comments welcome

There is a lot of interest in providing detailed reports to schools indicating which skills pupils have mastered and which still need development -- and, more broadly, the knowledge, skills and understanding that pupils have acquired and not yet acquired. Cognitive diagnostic assessment is an approach designed to provide this kind of insight. Cognitive diagnostic assessment requires a detailed theory or model of the knowledge and skills to be measured, and how they relate to items. This theory then guides the assessment design, which may follow a formalised approach such as Evidence Centred Design (Mislevy et al., 2003) to ensure sufficiently robust validity. Cognitive diagnostic assessment then requires specific technical approaches to modelling and interpreting test-takers' responses. Cognitive Diagnostic Models (CDMs) -- also known as Diagnostic Classification Models (DCMs) -- are an important mainstream formal approach to this modelling. CDMs are a subset of multidimensional latent variable models, designed to "diagnose" students according to their mastery of the various skills or attributes being measured in a particular domain. The aim of this report is to give an up-to-date view on cognitive diagnostic models and what they might offer. In particular, it considers how assessment organisations could use CDMs to provide something of meaningful value to schools, and what would be required to make this work.

Machine Learning research, as most of Statistics, heavily relies on the concept of a data-generating probability distribution. The standard presumption is that since data points are `sampled from' such a distribution, one can learn from observed data about this distribution and, thus, predict future data points which, it is presumed, are also drawn from it. Drawing on scholarship across disciplines, we here argue that this framework is not always a good model. Not only do such true probability distributions not exist; the framework can also be misleading and obscure both the choices made and the goals pursued in machine learning practice. We suggest an alternative framework that focuses on finite populations rather than abstract distributions; while classical learning theory can be left almost unchanged, it opens new opportunities, especially to model sampling. We compile these considerations into five reasons for modelling machine learning -- in some settings -- with finite populations rather than generative distributions, both to be more faithful to practice and to provide novel theoretical insights., Comment: Presented at the Humans, Algorithmic Decision-Making and Society Workshop at ICML 2024

The most common approach to causal modelling is the potential outcomes framework due to Neyman and Rubin. In this framework, outcomes of counterfactual treatments are assumed to be well-defined. This metaphysical assumption is often thought to be problematic yet indispensable. The conventional approach relies not only on counterfactuals but also on abstract notions of distributions and assumptions of independence that are not directly testable. In this paper, we construe causal inference as treatment-wise predictions for finite populations where all assumptions are testable; this means that one can not only test predictions themselves (without any fundamental problem) but also investigate sources of error when they fail. The new framework highlights the model-dependence of causal claims as well as the difference between statistical and scientific inference., Comment: Presented at the Humans, Algorithmic Decision-Making and Society Workshop at ICML 2024

Adults who are minimally verbal with autism spectrum disorder (mvASD) have pronounced speech difficulties linked to impaired motor skills. Existing research and clinical assessments primarily use indirect methods such as standardized tests, video-based facial features, and handwriting tasks, which may not directly target speech-related motor skills. In this study, we measure activity from eight facial muscles associated with speech using surface electromyography (sEMG), during carefully designed tasks. The findings reveal a higher power in the sEMG signals and a significantly greater correlation between the sEMG channels in mvASD adults (N=12) compared to age and gender-matched neurotypical controls (N=14). This suggests stronger muscle activation and greater synchrony in the discharge patterns of motor units. Further, eigenvalues derived from correlation matrices indicate lower complexity in muscle coordination in mvASD, implying fewer degrees of freedom in motor control.

Neural surfaces (e.g., neural map encoding, deep implicits and neural radiance fields) have recently gained popularity because of their generic structure (e.g., multi-layer perceptron) and easy integration with modern learning-based setups. Traditionally, we have a rich toolbox of geometry processing algorithms designed for polygonal meshes to analyze and operate on surface geometry. However, neural representations are typically discretized and converted into a mesh, before applying any geometry processing algorithm. This is unsatisfactory and, as we demonstrate, unnecessary. In this work, we propose a spherical neural surface representation (a spherical parametrization) for genus-0 surfaces and demonstrate how to compute core geometric operators directly on this representation. Namely, we show how to construct the normals and the first and second fundamental forms of the surface, and how to compute the surface gradient, surface divergence and Laplace Beltrami operator on scalar/vector fields defined on the surface. These operators, in turn, enable us to create geometry processing tools that act directly on the neural representations without any unnecessary meshing. We demonstrate illustrative applications in (neural) spectral analysis, heat flow and mean curvature flow, and our method shows robustness to isometric shape variations. We both propose theoretical formulations and validate their numerical estimates. By systematically linking neural surface representations with classical geometry processing algorithms, we believe this work can become a key ingredient in enabling neural geometry processing., Comment: 10 pages, 12 figures

We study the predictability of online speech on social media, and whether predictability improves with information outside a user's own posts. Recent work suggests that the predictive information contained in posts written by a user's peers can surpass that of the user's own posts. Motivated by the success of large language models, we empirically test this hypothesis. We define unpredictability as a measure of the model's uncertainty, i.e., its negative log-likelihood on future tokens given context. As the basis of our study, we collect a corpus of 6.25M posts from more than five thousand X (previously Twitter) users and their peers. Across three large language models ranging in size from 1 billion to 70 billion parameters, we find that predicting a user's posts from their peers' posts performs poorly. Moreover, the value of the user's own posts for prediction is consistently higher than that of their peers'. Across the board, we find that the predictability of social media posts remains low, comparable to predicting financial news without context. We extend our investigation with a detailed analysis about the causes of unpredictability and the robustness of our findings. Specifically, we observe that a significant amount of predictive uncertainty comes from hashtags and @-mentions. Moreover, our results replicate if instead of prompting the model with additional context, we finetune on additional context.

Gradient normalization and soft clipping are two popular techniques for tackling instability issues and improving convergence of stochastic gradient descent (SGD) with momentum. In this article, we study these types of methods through the lens of dissipative Hamiltonian systems. Gradient normalization and certain types of soft clipping algorithms can be seen as (stochastic) implicit-explicit Euler discretizations of dissipative Hamiltonian systems, where the kinetic energy function determines the type of clipping that is applied. We make use of unified theory from dynamical systems to show that all of these schemes converge almost surely to stationary points of the objective function.

Choosing the optimization algorithm that performs best on a given machine learning problem is often delicate, and there is no guarantee that current state-of-the-art algorithms will perform well across all tasks. Consequently, the more reliable methods that one has at hand, the larger the likelihood of a good end result. To this end, we introduce and analyze a large class of stochastic so-called soft-clipping schemes with a broad range of applications. Despite the wide adoption of clipping techniques in practice, soft-clipping methods have not been analyzed to a large extent in the literature. In particular, a rigorous mathematical analysis is lacking in the general, nonlinear case. Our analysis lays a theoretical foundation for a large class of such schemes, and motivates their usage. In particular, under standard assumptions such as Lipschitz continuous gradients of the objective function, we give rigorous proofs of convergence in expectation. These include rates in both the convex and the non-convex case, as well as almost sure convergence to a stationary point in the non-convex case. The computational cost of the analyzed schemes is essentially the same as that of stochastic gradient descent.

Spatial transcriptomics (ST) enables interrogating the molecular composition of tissue with ever-increasing resolution, depth, and sensitivity. However, costs, rapidly evolving technology, and lack of standards have constrained computational methods in ST to narrow tasks and small cohorts. In addition, the underlying tissue morphology as reflected by H&E-stained whole slide images (WSIs) encodes rich information often overlooked in ST studies. Here, we introduce HEST-1k, a collection of 1,108 spatial transcriptomic profiles, each linked to a WSI and metadata. HEST-1k was assembled using HEST-Library from 131 public and internal cohorts encompassing 25 organs, two species (Homo Sapiens and Mus Musculus), and 320 cancer samples from 25 cancer types. HEST-1k processing enabled the identification of 1.5 million expression--morphology pairs and 60 million nuclei. HEST-1k is tested on three use cases: (1) benchmarking foundation models for histopathology (HEST-Benchmark), (2) biomarker identification, and (3) multimodal representation learning. HEST-1k, HEST-Library, and HEST-Benchmark can be freely accessed via https://github.com/mahmoodlab/hest., Comment: Under review

We show how work can be extracted from number-state coherence in a two-mode Bose-Einstein condensate. With careful tuning of parameters, a sequence of thermodynamically reversible steps transforms a Glauber coherent state into a thermal state with the same energy probability distribution. The work extracted during this process arises entirely from the removal of quantum coherence. More generally, we characterise quantum (from coherence) and classical (remaining) contributions to work output, and find that in this system the quantum contribution can be dominant over a broad range of parameters. The proportion of quantum work output can be further enhanced by squeezing the initial coherent state. Due to the many-body nature of the system, the work from coherence can equivalently be understood as work from entanglement., Comment: 9 pages + refs, 3 figures

Supervised learning has gone beyond the expected risk minimization framework. Central to most of these developments is the introduction of more general aggregation functions for losses incurred by the learner. In this paper, we turn towards online learning under expert advice. Via easily justified assumptions we characterize a set of reasonable loss aggregation functions as quasi-sums. Based upon this insight, we suggest a variant of the Aggregating Algorithm tailored to these more general aggregation functions. This variant inherits most of the nice theoretical properties of the AA, such as recovery of Bayes' updating and a time-independent bound on quasi-sum regret. Finally, we argue that generalized aggregations express the attitude of the learner towards losses., Comment: 31 pages

Motivated by big data and the vast parameter spaces in modern machine learning models, optimisation approaches to Bayesian inference have seen a surge in popularity in recent years. In this paper, we address the connection between the popular new methods termed generalised Bayesian inference and Bayes linear methods. We propose a further generalisation to Bayesian inference that unifies these and other recent approaches by considering the Bayesian inference problem as one of finding the closest point in a particular solution space to a data generating process, where these notions differ depending on user-specified geometries and foundational belief systems. Motivated by this framework, we propose a generalisation to Bayes linear approaches that enables fast and principled inferences that obey the coherence requirements implied by domain restrictions on random quantities. We demonstrate the efficacy of generalised Bayes linear inference on a number of examples, including monotonic regression and inference for spatial counts. This paper is accompanied by an R package available at github.com/astfalckl/bayeslinear., Comment: Submitted to the Journal of the Royal Statistical Society: Series B

Representation learning of pathology whole-slide images (WSIs) has been has primarily relied on weak supervision with Multiple Instance Learning (MIL). However, the slide representations resulting from this approach are highly tailored to specific clinical tasks, which limits their expressivity and generalization, particularly in scenarios with limited data. Instead, we hypothesize that morphological redundancy in tissue can be leveraged to build a task-agnostic slide representation in an unsupervised fashion. To this end, we introduce PANTHER, a prototype-based approach rooted in the Gaussian mixture model that summarizes the set of WSI patches into a much smaller set of morphological prototypes. Specifically, each patch is assumed to have been generated from a mixture distribution, where each mixture component represents a morphological exemplar. Utilizing the estimated mixture parameters, we then construct a compact slide representation that can be readily used for a wide range of downstream tasks. By performing an extensive evaluation of PANTHER on subtyping and survival tasks using 13 datasets, we show that 1) PANTHER outperforms or is on par with supervised MIL baselines and 2) the analysis of morphological prototypes brings new qualitative and quantitative insights into model interpretability., Comment: CVPR 2024

Self-supervised learning (SSL) has been successful in building patch embeddings of small histology images (e.g., 224x224 pixels), but scaling these models to learn slide embeddings from the entirety of giga-pixel whole-slide images (WSIs) remains challenging. Here, we leverage complementary information from gene expression profiles to guide slide representation learning using multimodal pre-training. Expression profiles constitute highly detailed molecular descriptions of a tissue that we hypothesize offer a strong task-agnostic training signal for learning slide embeddings. Our slide and expression (S+E) pre-training strategy, called Tangle, employs modality-specific encoders, the outputs of which are aligned via contrastive learning. Tangle was pre-trained on samples from three different organs: liver (n=6,597 S+E pairs), breast (n=1,020), and lung (n=1,012) from two different species (Homo sapiens and Rattus norvegicus). Across three independent test datasets consisting of 1,265 breast WSIs, 1,946 lung WSIs, and 4,584 liver WSIs, Tangle shows significantly better few-shot performance compared to supervised and SSL baselines. When assessed using prototype-based classification and slide retrieval, Tangle also shows a substantial performance improvement over all baselines. Code available at https://github.com/mahmoodlab/TANGLE., Comment: CVPR'24, Oral

We present a simulation-based cosmological analysis using a combination of Gaussian and non-Gaussian statistics of the weak lensing mass (convergence) maps from the first three years (Y3) of the Dark Energy Survey (DES). We implement: 1) second and third moments; 2) wavelet phase harmonics; 3) the scattering transform. Our analysis is fully based on simulations, spans a space of seven $\nu w$CDM cosmological parameters, and forward models the most relevant sources of systematics inherent in the data: masks, noise variations, clustering of the sources, intrinsic alignments, and shear and redshift calibration. We implement a neural network compression of the summary statistics, and we estimate the parameter posteriors using a simulation-based inference approach. Including and combining different non-Gaussian statistics is a powerful tool that strongly improves constraints over Gaussian statistics (in our case, the second moments); in particular, the Figure of Merit $\textrm{FoM}(S_8, \Omega_{\textrm{m}})$ is improved by 70 percent ($\Lambda$CDM) and 90 percent ($w$CDM). When all the summary statistics are combined, we achieve a 2 percent constraint on the amplitude of fluctuations parameter $S_8 \equiv \sigma_8 (\Omega_{\textrm{m}}/0.3)^{0.5}$, obtaining $S_8 = 0.794 \pm 0.017$ ($\Lambda$CDM) and $S_8 = 0.817 \pm 0.021$ ($w$CDM). The constraints from different statistics are shown to be internally consistent (with a $p$-value>0.1 for all combinations of statistics examined). We compare our results to other weak lensing results from the DES Y3 data, finding good consistency; we also compare with results from external datasets, such as \planck{} constraints from the Cosmic Microwave Background, finding statistical agreement, with discrepancies no greater than $<2.2\sigma$., Comment: 24 pages, 13 figures, to be submitted to PRD. Comments welcome!

As the significance of Software Engineering (SE) professionals continues to grow in the industry, the adoption of gamification techniques for training purposes has gained traction due to its potential to enhance class appeal through game-derived elements. This paper presents a tertiary study investigating the application of gamification in Software Engineering (SE) education. The study was conducted in response to recent systematic literature reviews and mappings on the topic. The findings reveal that the areas of SE most frequently gamified are Software Testing and Software Quality, with competition and cooperation being the most commonly utilized gamification elements. Additionally, the majority of studies focus on structural gamification, where game elements are employed to modify the learning environment without altering the content. The results demonstrate the potential of gamification to improve students' engagement and motivation throughout the SE learning process, while also impacting other aspects such as performance improvement, skill development, and fostering good SE practices. However, caution is advised as unplanned and incorrectly applied gamification measures may lead to significant declines in performance and motivation. (English Version of the paper in Portuguese available here: HTTP://doi.org/10.1145/3613372.3614193

Voice Assistants (VAs) can assist users in various everyday tasks, but many users are reluctant to rely on VAs for intricate tasks like online shopping. This study aims to examine whether the vocal characteristics of VAs can serve as an effective tool to persuade users and increase user engagement with VAs in online shopping. Prior studies have demonstrated that the perceived tone, age, and gender of a voice influence the perceived persuasiveness of the speaker in interpersonal interactions. Furthermore, persuasion in product communication has been shown to affect purchase decisions in online shopping. We investigate whether variations in a VA voice's perceived tone, age, and gender characteristics can persuade users and ultimately affect their purchase decisions. Our experimental study showed that participants were more persuaded to make purchase decisions by VA voices having positive or neutral tones as well as middle-aged male or younger female voices. Our results suggest that VA designers should offer users the ability to easily customize VA voices with a range of tones, ages, and genders. This customization can enhance user comfort and enjoyment, potentially leading to higher engagement with VAs. Additionally, we discuss the boundaries of ethical persuasion, emphasizing the importance of safeguarding users' interests against unwarranted manipulation., Comment: ACM Conversational User Interface 2024

With the emergence of Artificial Intelligence (AI)-based decision-making, explanations help increase new technology adoption through enhanced trust and reliability. However, our experimental study challenges the notion that every user universally values explanations. We argue that the agreement with AI suggestions, whether accompanied by explanations or not, is influenced by individual differences in personality traits and the users' comfort with technology. We found that people with higher neuroticism and lower technological comfort showed more agreement with the recommendations without explanations. As more users become exposed to eXplainable AI (XAI) and AI-based systems, we argue that the XAI design should not provide explanations for users with high neuroticism and low technology comfort. Prioritizing user personalities in XAI systems will help users become better collaborators of AI systems., Comment: ACM CHI 2024 Workshop on Human-Centered Explainable AI (HCXAI), 5 pages

Inclusive electron scattering at carefully chosen kinematics can isolate scattering from short-range correlations (SRCs), produced through hard, short-distance interactions of nucleons in the nucleus. Because the two-nucleon (2N) SRCs arise from the same N-N interaction in all nuclei, the cross section in the SRC-dominated regime is identical up to an overall scaling factor, and the A/2H cross section ratio is constant in this region. This scaling behavior has been used to identify SRC dominance and to map out the contribution of SRCs for a wide range of nuclei. We examine this scaling behavior at lower momentum transfers using new data on $^2$H, $^3$H, and $^3$He which show that the scaling region is larger than in heavy nuclei. Based on the improved scaling, especially for $^3$H/$^3$He, we examine the ratios at kinematics where three-nucleon SRCs may play an important role. The data for the largest initial nucleon momenta are consistent with isolation of scattering from 3N-SRCs, and suggest that the very-highest momentum nucleons in $^3$He have a nearly isospin-independent momentum configuration, or a small enhancement of the proton distribution.

In the easy-plane phase, a ferromagnetic spin-1 Bose-Einstein condensate is magnetized in a plane transverse to the applied Zeeman field. This phase supports polar-core spin vortices (PCVs), which consist of phase windings of transverse magnetization. Here we show that spin-changing collisions cause a PCV to accelerate down density gradients in an inhomogeneous condensate. The dynamics is well-described by a simplified model adapted from scalar systems, which predicts the dependence of the dynamics on trap tightness and quadratic Zeeman energy. In a harmonic trap, a PCV accelerates radially to the condensate boundary, in stark contrast to the azimuthal motion of vortices in a scalar condensate. In a trap that has a local potential maximum at the centre, the PCV exhibits oscillations around the trap centre, which persist for a remarkably long time. The oscillations coincide with the emission and reabsorption of axial spin waves, which reflect off the condensate boundary., Comment: 13 pages + refs, 6 figures

Motivated by recently emerging problems in machine learning and statistics, we propose data models which relax the familiar i.i.d. assumption. In essence, we seek to understand what it means for data to come from a set of probability measures. We show that our frequentist data models, parameterized by such sets, manifest two aspects of imprecision. We characterize the intricate interplay of these manifestations, aggregate (ir)regularity and local (ir)regularity, where a much richer set of behaviours compared to an i.i.d. model is possible. In doing so we shed new light on the relationship between non-stationary, locally precise and stationary, locally imprecise data models. We discuss possible applications of these data models in machine learning and how the set of probabilities can be estimated. For the estimation of aggregate irregularity, we provide a negative result but argue that it does not warrant pessimism. Understanding these frequentist aspects of imprecise probabilities paves the way for deriving generalization of proper scoring rules and calibration to the imprecise case, which can then contribute to tackling practical problems.

An easy-plane ferromagnetic spin-1 Bose gas undergoes two Berezinskii-Kosterlitz-Thouless (BKT) transitions, associated with mass and spin superfluidity respectively. We study the effect of axial magnetization on the superfluid properties of this system. We find that nonzero axial magnetization couples mass and spin superflow, via a mechanism analogous to the Andreev-Bashkin effect present in two-component superfluids. With sufficiently large axial magnetization mass and spin superfluidity arise simultaneously. The cross-over to this phase provides a finite-temperature generalization of the zero-temperature broken-axisymmetric to easy-axis transition. We present analytic relations connecting mass and spin superfluidity with experimentally observable coherence of the three spinor components and local magnetization., Comment: 9 pages, 6 figures

Our main result, Theorem 3.3, uses Friedman's Jump Free Theorem, Theorem 2.7, which he has shown to be independent of ZFC, the usual axioms of set theory. We conjecture that Theorem 3.3, a straight forward translation of the statement of Theorem 2.7 into sets and functions, is also independent of ZFC as is its immediate Corollary 3.4. It is easy to show that a proof that P=NP will also prove Corollary 3.4. If Corollary 3.4 is in fact independent of ZFC then a ZFC proof of P=NP is impossible, perhaps because it is false., Comment: 4 pages

In this paper, we approach the problem of uncertainty quantification in deep learning through a predictive framework, which captures uncertainty in model parameters by specifying our assumptions about the predictive distribution of unseen future data. Under this view, we show that deep ensembling (Lakshminarayanan et al., 2017) is a fundamentally mis-specified model class, since it assumes that future data are supported on existing observations only -- a situation rarely encountered in practice. To address this limitation, we propose MixupMP, a method that constructs a more realistic predictive distribution using popular data augmentation techniques. MixupMP operates as a drop-in replacement for deep ensembles, where each ensemble member is trained on a random simulation from this predictive distribution. Grounded in the recently-proposed framework of Martingale posteriors (Fong et al., 2023), MixupMP returns samples from an implicitly defined Bayesian posterior. Our empirical analysis showcases that MixupMP achieves superior predictive performance and uncertainty quantification on various image classification datasets, when compared with existing Bayesian and non-Bayesian approaches.

Topological quantum matter exhibits a range of exotic phenomena when enriched by subdimensional symmetries. This includes new features beyond those that appear in the conventional setting of global symmetry enrichment. A recently discovered example is a type of subsystem symmetry fractionalization that occurs through a different mechanism to global symmetry fractionalization. In this work we extend the study of subsystem symmetry fractionalization through new examples derived from the general principle of embedding subsystem symmetry into higher-form symmetry. This leads to new types of symmetry fractionalization that are described by foliation dependent higher-form symmetries. This leads to field theories and lattice models that support previously unseen anomalous subsystem symmetry fractionalization. Our work expands the range of exotic topological physics that is enabled by subsystem symmetry in field theory and on the lattice., Comment: 39 + 9 pages, 15 figures

The Ion Composition Analyzer (ICA) on the Rosetta spacecraft observed both the solar wind and the cometary ionosphere around comet 67P/Churyumov-Gerasimenko for nearly two years. However, observations of low energy cometary ions were affected by a highly negative spacecraft potential, and the ICA ion density estimates were often much lower than plasma densities found by other instruments. Since the low energy cometary ions are often the highest density population in the plasma environment, it is nonetheless desirable to understand their properties. To do so, we select ICA data with densities comparable to those of Rosetta's Langmuir Probe (LAP)/Mutual Impedance Probe throughout the mission. We then correct the cometary ion energy distribution of each energy-angle scan for spacecraft potential and fit a drifting Maxwell-Boltzmann distribution, which gives an estimate of the drift energy and temperature for 3521 scans. The resulting drift energy is generally between 11--18 eV and the temperature between 0.5--1 eV. The drift energy shows good agreement with published ion flow speeds from LAP during the same time period and is much higher than the cometary neutral speed. We see additional higher energy cometary ions in the spectra closest to perihelion, which can either be a second Maxwellian or a kappa distribution. The energy and temperature are negatively correlated with heliocentric distance, but the slope of the change is small. It cannot be quantitatively determined whether this trend is primarily due to heliocentric distance or spacecraft distance to the comet, which increased with decreasing heliocentric distance., Comment: 9 pages, 10 figures

Many-body quantum games provide a natural perspective on phases of matter in quantum hardware, crisply relating the quantum correlations inherent in phases of matter to the securing of quantum advantage at a device-oriented task. In this paper we introduce a family of multiplayer quantum games for which topologically ordered phases of matter are a resource yielding quantum advantage. Unlike previous examples, quantum advantage persists away from the exactly solvable point and is robust to arbitrary local perturbations, irrespective of system size. We demonstrate this robustness experimentally on Quantinuum's H1-1 quantum computer by playing the game with a continuous family of randomly deformed toric code states that can be created with constant-depth circuits leveraging mid-circuit measurements and unitary feedback. We are thus able to tune through a topological phase transition - witnessed by the loss of robust quantum advantage - on currently available quantum hardware. This behavior is contrasted with an analogous family of deformed GHZ states, for which arbitrarily weak local perturbations destroy quantum advantage in the thermodynamic limit. Finally, we discuss a topological interpretation of the game, which leads to a natural generalization involving an arbitrary number of players., Comment: 4.5 pages, 3 figures

We present simulation-based cosmological $w$CDM inference using Dark Energy Survey Year 3 weak-lensing maps, via neural data compression of weak-lensing map summary statistics: power spectra, peak counts, and direct map-level compression/inference with convolutional neural networks (CNN). Using simulation-based inference, also known as likelihood-free or implicit inference, we use forward-modelled mock data to estimate posterior probability distributions of unknown parameters. This approach allows all statistical assumptions and uncertainties to be propagated through the forward-modelled mock data; these include sky masks, non-Gaussian shape noise, shape measurement bias, source galaxy clustering, photometric redshift uncertainty, intrinsic galaxy alignments, non-Gaussian density fields, neutrinos, and non-linear summary statistics. We include a series of tests to validate our inference results. This paper also describes the Gower Street simulation suite: 791 full-sky PKDGRAV dark matter simulations, with cosmological model parameters sampled with a mixed active-learning strategy, from which we construct over 3000 mock DES lensing data sets. For $w$CDM inference, for which we allow $-1

We introduce a notion of distance between supervised learning problems, which we call the Risk distance. This optimal-transport-inspired distance facilitates stability results; one can quantify how seriously issues like sampling bias, noise, limited data, and approximations might change a given problem by bounding how much these modifications can move the problem under the Risk distance. With the distance established, we explore the geometry of the resulting space of supervised learning problems, providing explicit geodesics and proving that the set of classification problems is dense in a larger class of problems. We also provide two variants of the Risk distance: one that incorporates specified weights on a problem's predictors, and one that is more sensitive to the contours of a problem's risk landscape., Comment: 87 pages

In 2018, the E12-17-003 experiment was conducted at the Thomas Jefferson National Accelerator Facility (JLab) to explore the possible existence of an nnLambda state in the reconstructed missing mass distribution from a tritium gas target [K. N. Suzuki et al., Prog. Theor. Exp. Phys. 2022, 013D01 (2022), B. Pandey et al., Phys. Rev. C 105, L051001 (2022)]. As part of this investigation, data was also collected using a gaseous hydrogen target, not only for a precise absolute mass scale calibration but also for the study of Lambda/Sigma^0 electroproduction. This dataset was acquired at Q^2~0.5 (GeV/c)^2, W=2.14 GeV, and theta_{gamma K}^{c.m.}~8 deg. It covers forward angles where photoproduction data is scarce and a low-Q^2 region that is of interest for hypernuclear experiments. On the other hand, this kinematic region is at a slightly higher Q^2 than previous hypernuclear experiments, thus providing crucial information for understanding the Q^2 dependence of the differential cross sections for Lambda/Sigma^0 hyperon electroproduction. This paper reports on the Q^2 dependence of the differential cross section for the e + p -> e' + K^+ + Lambda/Sigma^0 reaction in the 0.2-0.8 (GeV/c)^2, and provides comparisons with the currently available theoretical models., Comment: 11 pages, 15 figures