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We present an exploratory restricted latent class model where response data is for a single time point, polytomous, and differing across items, and where latent classes reflect a multi-attribute state where each attribute is ordinal. Our model extends previous work to allow for correlation of the attributes through a multivariate probit and to allow for respondent-specific covariates. We demonstrate that the model recovers parameters well in a variety of realistic scenarios, and apply the model to the analysis of a particular dataset designed to diagnose depression. The application demonstrates the utility of the model in identifying the latent structure of depression beyond single-factor approaches which have been used in the past., Comment: 34 pages, 1 figure, 7 tables

Milky Way Analogues (MWAs) provide an alternative insight into the various pathways that lead to the formation of disk galaxies with similar properties to the Milky Way. In this study, we explore different selection techniques for identifying MWAs in the SAMI Galaxy Survey. We utilise a nearest neighbours method to define MWAs using four selection parameters including stellar mass ($M_{\star}$), star formation rate ($SFR$), bulge-to-total ratio ($B/T$) and disk effective radius ($R_{\rm{e}}$). Based on 15 different selection combinations, we find that including $M_{\star}$ and SFR is essential for minimising biases in the average MWA properties as compared to the Milky Way. Furthermore, given the Milky Way's smaller-than-average size, selection combinations without $R_{\rm{e}}$ result in MWAs being too large. Lastly, we find that $B/T$ is the least important parameter out of the four tested parameters. Using all four selection criteria, we define the top 10 most Milky Way-like galaxies in the GAMA and Cluster regions of the SAMI survey. These most Milky-Way-like galaxies are typically barred spirals, with kinematically cold rotating disks and reside in a wide range of environments. Surprisingly, we find no significant differences between the MWAs selected from the GAMA and Cluster regions. Our work highlights the importance of using multiple selection criteria for finding MWAs and also demonstrates potential biases in previous MWA studies., Comment: 26 pages, 16 figures, 5 tables, accepted for publication in MNRAS

Modern applications often leverage multiple views of a subject of study. Within neuroscience, there is growing interest in large-scale simultaneous recordings across multiple brain regions. Understanding the relationship between views (e.g., the neural activity in each region recorded) can reveal fundamental principles about the characteristics of each representation and about the system. However, existing methods to characterize such relationships either lack the expressivity required to capture complex nonlinearities, describe only sources of variance that are shared between views, or discard geometric information that is crucial to interpreting the data. Here, we develop a nonlinear neural network-based method that, given paired samples of high-dimensional views, disentangles low-dimensional shared and private latent variables underlying these views while preserving intrinsic data geometry. Across multiple simulated and real datasets, we demonstrate that our method outperforms competing methods. Using simulated populations of lateral geniculate nucleus (LGN) and V1 neurons we demonstrate our model's ability to discover interpretable shared and private structure across different noise conditions. On a dataset of unrotated and corresponding but randomly rotated MNIST digits, we recover private latents for the rotated view that encode rotation angle regardless of digit class, and places the angle representation on a 1-d manifold, while shared latents encode digit class but not rotation angle. Applying our method to simultaneous Neuropixels recordings of hippocampus and prefrontal cortex while mice run on a linear track, we discover a low-dimensional shared latent space that encodes the animal's position. We propose our approach as a general-purpose method for finding succinct and interpretable descriptions of paired data sets in terms of disentangled shared and private latent variables.

Hydrogen production from water electrolysis, direct air capture (DAC), and synthetic kerosene derived from hydrogen and CO2 (`e-kerosene') are expected to play an important role in global decarbonization efforts. So far, the economics of these nascent technologies hamper their market diffusion. However, a wave of recent policy support in the United States, Europe, China, and elsewhere is anticipated to drive their commercial liftoff and bring their costs down. To this end, we evaluate the potential cost reductions driven by policy-induced scale-up of these emerging technologies through 2030 using an experience curves approach accounting for both local and global learning effects. We then analyze the consequences of projected cost declines on the competitiveness of these nascent technologies compared to conventional fossil alternatives, where applicable, and highlight some of the tradeoffs associated with their expansion. Our findings indicate that enacted policies could lead to substantial capital cost reductions for electrolyzers. Nevertheless, electrolytic hydrogen production at $1-2/kg would still require some form of policy support. Given expected costs and experience curves, it is unlikely that liquid solvent DAC (L-DAC) scale-up will bring removal costs to stated targets of $100/tCO2, though a $200/tCO2 may eventually be within reach. We also underscore the importance of tackling methane leakage for natural gas-powered L-DAC: unmitigated leaks amplify net removal costs, exacerbate the investment requirements to reach targeted costs, and cast doubt on L-DAC's role in the clean energy transition. Lastly, despite reductions in electrolysis and L-DAC costs, e-kerosene remains considerably more expensive than fossil jet fuel. The economics of e-kerosene and the resources required for production raise questions about the fuel's ultimate viability as a decarbonization tool for aviation.

Electron-initiated chemistry with chemically relevant electron energies (10-200 eV) is at the heart of several high-energy processes and phenomena. To probe these dissociation and fragmentation reactions with femtosecond resolution requires the use of femtosecond lasers to induce ionization of the polyatomic molecules via electron rescattering. Here, we combine non-commensurate fields with intensity-difference spectra using methanol as a model system. Experimentally, we find orders of magnitude enhancement in several product ions of methanol when comparing coherent vs incoherent combinations of non-commensurate fields. This approach not only mitigates multi-photon ionization and multi-cycle effects during ionization but also enhances tunnel ionization and electron rescattering energy., Comment: 21 pages 8 figures

High-dimensional variable selection has emerged as one of the prevailing statistical challenges in the big data revolution. Many variable selection methods have been adapted for identifying single nucleotide polymorphisms (SNPs) linked to phenotypic variation in genome-wide association studies. We develop a Bayesian variable selection regression model for identifying SNPs linked to phenotypic variation. We modify our Bayesian variable selection regression models to control the false discovery rate of SNPs using a knockoff variable approach. We reduce spurious associations by regressing the phenotype of interest against a set of basis functions that account for the relatedness of individuals. Using a restricted regression approach, we simultaneously estimate the SNP-level effects while removing variation in the phenotype that can be explained by population structure. We also accommodate the spatial structure among causal SNPs by modeling their inclusion probabilities jointly with a reduced rank Gaussian process. In a simulation study, we demonstrate that our spatial Bayesian variable selection regression model controls the false discovery rate and increases power when the relevant SNPs are clustered. We conclude with an analysis of Arabidopsis thaliana flowering time, a polygenic trait that is confounded with population structure, and find the discoveries of our method cluster near described flowering time genes., Comment: 32 pages and 6 figures

We investigate fractal structures in African and African diasporic music, building on hypotheses of choreographer Reggie Wilson and research on fractals in African material culture by Ron Eglash., Comment: 8 pages

Choreographer Reggie Wilson and mathematician Jesse Wolfson describe interactions of math and dance emerging from their 12+ year engagement with Black movement and music traditions as part of Wilson's research-to-performance/performance-to-research choreographic practice, with examples including fractals, braids and choreographic and mathematical notions of space, time and movement.

This paper presents a sociotechnical vision for managing personal data, including cookies, within Web browsers. We first present our vision for a future of semi-automated data governance on the Web, using policy languages to describe data terms of use, and having browsers act on behalf of users to enact policy-based controls. Then, we present an overview of the technical research required to {prove} that existing policy languages express a sufficient range of concepts for describing cookie policies on the Web today. We view this work as a stepping stone towards a future of semi-automated data governance at Web-scale, which in the long term will also be used by next-generation Web technologies such as Web agents and Solid., Comment: Submitted to "NeXt-generation Data Governance workshop 2024"; available on OpenReview at https://openreview.net/forum?id=Bhia6mPaCF

We propose a novel application of image translation networks for image classification and demonstrate its potential as a more interpretable alternative to conventional black box classification networks. We train a network to translate images between possible classes, and then quantify translation distance, i.e. the degree of alteration needed to conform an image to one class or another. These translation distances can then be examined for clusters and trends, and can be fed directly to a simple classifier (e.g. a support vector machine, SVM), providing comparable accuracy compared to a conventional end-to-end convolutional neural network classifier. In addition, visual inspection of translated images can reveal class-specific characteristics and biases in the training sets, such as visual artifacts that are more frequently observed in one class or another. We demonstrate the approach on a toy 2-class scenario, apples versus oranges, and then apply it to two medical imaging tasks: detecting melanoma from photographs of pigmented lesions and classifying 6 cell types in a bone marrow biopsy smear. This novel application of image-to-image networks shows the potential of the technology to go beyond imagining different stylistic changes and to provide greater insight into image classification and medical imaging datasets., Comment: 20 pages, 18 figures, submitted to Computational Intelligence

Language models are known to absorb biases from their training data, leading to predictions driven by statistical regularities rather than semantic relevance. We investigate the impact of these biases on answer choice preferences in the Massive Multi-Task Language Understanding (MMLU) task. Our findings reveal that differences in learned regularities across answer options are predictive of model preferences and mirror human test-taking strategies. To address this issue, we introduce two novel methods: Counterfactual Prompting with Chain of Thought (CoT) and Counterfactual Prompting with Agnostically Primed CoT (APriCoT). We demonstrate that while Counterfactual Prompting with CoT alone is insufficient to mitigate bias, our novel Primed Counterfactual Prompting with CoT approach effectively reduces the influence of base-rate probabilities while improving overall accuracy. Our results suggest that mitigating bias requires a "System-2" like process and that CoT reasoning is susceptible to confirmation bias under some prompting methodologies. Our contributions offer practical solutions for developing more robust and fair language models.

We present a numerical method for the solution of diffusion problems in unbounded planar regions with complex geometries of absorbing and reflecting bodies. Our numerical method applies the Laplace transform to the parabolic problem, yielding a modified Helmholtz equation which is solved with a boundary integral method. Returning to the time domain is achieved by quadrature of the inverse Laplace transform by deforming along the so-called Talbot contour. We demonstrate the method for various complex geometries formed by disjoint bodies of arbitrary shape on which either uniform Dirichlet or Neumann boundary conditions are applied. The use of the Laplace transform bypasses constraints with traditional time-stepping methods and allows for integration over the long equilibration timescales present in diffusion problems in unbounded domains. Using this method, we demonstrate shielding effects where the complex geometry modulates the dynamics of capture to absorbing sets. In particular, we show examples where geometry can guide diffusion processes to particular absorbing sites, obscure absorbing sites from diffusing particles, and even find the exits of confining geometries, such as mazes.

While quiescent galaxies have comparable amounts of cool gas in their outer circumgalactic medium (CGM) compared to star-forming galaxies, they have significantly less interstellar gas. However, open questions remain on the processes causing galaxies to stop forming stars and stay quiescent . Theories suggest dynamical interactions with the hot corona prevent cool gas from reaching the galaxy, therefore predicting the inner regions of quiescent galaxy CGMs are devoid of cool gas. However, there is a lack of understanding of the inner regions of CGMs due to the lack of spatial information in quasar-sightline methods. We present integral-field spectroscopy probing 10--20~kpc (2.4--4.8 R\textsubscript{e}) around a massive quiescent galaxy using a gravitationally lensed star-forming galaxy. We detect absorption from Magnesium (MgII) implying large amounts of cool atomic gas (10\textsuperscript{8.4} -- 10\textsuperscript{9.3} M\textsubscript{$\odot$} with T$\sim$10\textsuperscript{4} Kelvin), in comparable amounts to star-forming galaxies. Lens modeling of Hubble imaging also reveals a diffuse asymmetric component of significant mass consistent with the spatial extent of the MgII absorption, and offset from the galaxy light profile. This study demonstrates the power of galaxy-scale gravitational lenses to not only probe the gas around galaxies, but to also independently probe the mass of the CGM due to it's gravitational effect., Comment: accepted nature communications physics

This pictorial aims to critically consider the nature of text-to-audio and text-to-music generative tools in the context of explainable AI. As a group of experimental musicians and researchers, we are enthusiastic about the creative potential of these tools and have sought to understand and evaluate them from perspectives of prompt creation, control, usability, understandability, explainability of the AI process, and overall aesthetic effectiveness of the results. One of the challenges we have identified that is not explicitly addressed by these tools is the inherent semantic gap in using text-based tools to describe something as abstract as music. Other gaps include explainability vs. useability, and user control and input vs. the human creative process. The aim of this pictorial is to raise questions for discussion and make a few general suggestions on the kinds of improvements we would like to see in generative AI music tools., Comment: In Proceedings of Explainable AI for the Arts Workshop 2024 (XAIxArts 2024) arXiv:2406.14485

Let $L(s,\pi)=\sum_{n=1}^{\infty}\lambda_{\pi}(n)n^{-s}$ be an $L$-function that satisfies a weak form of the generalized Ramanujan conjecture. We prove that large partial sums of $\lambda_{\pi}(n)$ strongly repel the low-lying zeros of $L(s,\pi)$ away from the critical line. Our results extend and quantitatively improve preceding work of Granville and Soundararajan., Comment: 23 pages

The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge. We present Pathfinder, a machine learning framework designed to enable literature review and knowledge discovery in astronomy, focusing on semantic searching with natural language instead of syntactic searches with keywords. Utilizing state-of-the-art large language models (LLMs) and a corpus of 350,000 peer-reviewed papers from the Astrophysics Data System (ADS), Pathfinder offers an innovative approach to scientific inquiry and literature exploration. Our framework couples advanced retrieval techniques with LLM-based synthesis to search astronomical literature by semantic context as a complement to currently existing methods that use keywords or citation graphs. It addresses complexities of jargon, named entities, and temporal aspects through time-based and citation-based weighting schemes. We demonstrate the tool's versatility through case studies, showcasing its application in various research scenarios. The system's performance is evaluated using custom benchmarks, including single-paper and multi-paper tasks. Beyond literature review, Pathfinder offers unique capabilities for reformatting answers in ways that are accessible to various audiences (e.g. in a different language or as simplified text), visualizing research landscapes, and tracking the impact of observatories and methodologies. This tool represents a significant advancement in applying AI to astronomical research, aiding researchers at all career stages in navigating modern astronomy literature., Comment: 25 pages, 9 figures, submitted to AAS jorunals. Comments are welcome, and the tools mentioned are available online at https://pfdr.app

We study the Drinfeld modular curves arising from the Hecke congruence subgroups of $\mathrm{SL}_2(\mathbb{F}_q[T])$. Using a combinatorial method of Gekeler and Nonnengardt, we obtain a genus formula for these curves. In cases when the genus is one, we compute the Weierstrass equation of the corresponding curve., Comment: 26 pages

Choice constructs are an important part of the language of logic programming, yet the study of their semantics has been a challenging task. So far, only two-valued semantics have been studied, and the different proposals for such semantics have not been compared in a principled way. In this paper, an operator-based framework allow for the definition and comparison of different semantics in a principled way is proposed., Comment: Extended version of a paper accepted at KR 2024

The construction of gauge invariant states of SU(3) lattice gauge theories has garnered new interest in recent years, but implementing them is complicated by the need for SU(3) Clebsch-Gordon coefficients. In the loop-string-hadron (LSH) approach to lattice gauge theories, the elementary excitations are strictly gauge invariant, and constructing the basis requires no knowledge of Clebsch-Gordon coefficients. Originally developed for SU(2), the LSH formulation was recently generalized to SU(3), but limited to one spatial dimension. In this work, we generalize the LSH approach to constructing the basis of SU(3) gauge invariant states at a trivalent vertex - the essential building block to multidimensional space. A direct generalization from the SU(2) vertex yields a legitimate basis; however, in certain sectors of the Hilbert space, the naive LSH basis vectors so defined suffer from being nonorthogonal. The issues with orthogonality are directly related to the `missing label' or `outer multiplicity' problem associated with SU(3) tensor products, and may also be phrased in terms of Littlewood-Richardson coefficients or the need for a `seventh Casimir' operator. The states that are unaffected by the problem are orthonormalized in closed form. For the sectors that are afflicted, we discuss the nonorthogonal bases and their orthogonalization. A few candidates for seventh Casimir operators are readily constructed from the suite of LSH gauge-singlet operators. The diagonalization of a seventh Casimir represents one prescriptive solution towards obtaining a complete orthonormal basis, but a closed-form general solution remains to be found., Comment: 31 pages, 5 figures, 1 table

This is the report from the 2023 Particle Physics Project Prioritization Panel (P5) approved by High Energy Physics Advisory Panel (HEPAP) on December 8, 2023. The final version was made public on May 8, 2024 and submitted to DOE SC and NSF MPS., Comment: 2-page spread version. The online version is available at https://www.usparticlephysics.org/2023-p5-report/ and the graphics at https://usparticlephysics.org/media-assets-library

We analyze a combinatorial rule satisfied by the signs of principal minors of a real symmetric matrix. The sign patterns satisfying this rule are equivalent to uniform oriented Lagrangian matroids. We first discuss their structure and symmetries and then study their asymptotics, proving that almost all of them are not representable by real symmetric matrices. We offer several conjectures and experimental results concerning representable sign patterns and the topology of their representation spaces., Comment: 19 pages, 2 figures, 2 tables; code and data at https://mathrepo.mis.mpg.de/SymmetricPrincipalMinorSigns/

This monograph elucidates and extends many theorems and conjectures in analytic number theory and algebraic asymptotic analysis via the natural notions of degree and logexponential degree. The Riemann hypothesis, for example, is equivalent to the statement that the degree of the function $\pi(x)- \operatorname{li}(x)$ is $1/2$, where $\pi(x)$ is the prime counting function and $\operatorname{li}(x)$ is the logarithmic integral function. Part 1 of the text is a survey of analytic number theory, Part 2 introduces the notion of logexponential degree and uses it to extend results in algebraic asymptotic analysis, and Part 3 applies the results of Part 2 to the various functions that figure most prominently in analytic number theory. Central to the notion of logexponential degree are Hardy's logarithmico-exponential functions, which are real functions defined in a neighborhood of $\infty$ that can be built from $\operatorname{id}$, $\exp$, and $\log$ using the operations $+$, $\cdot$, $/$, and $\circ$. Such functions are natural benchmarks for the orders of growth of functions in analytic number theory. The main goal of Part 3 is to express the logexponential degree of various functions in analytic number theory in terms of as few "logexponential primitives" as possible. The logexponential degree of the function $e^\gamma \prod_{p\leq x}(1-1/p) -1/\log x$, for example, can be expressed in terms of that of $\pi(x)- \operatorname{li}(x)$ and vice versa (where $\gamma \approx 0.5772$ is the Euler-Mascheroni constant), despite the fact that very little is known about the logexponential degree of either function separately, even on condition of the Riemann hypothesis., Comment: 517 pages. This monograph is an unedited version of a monograph that is to be published in World Scientific's Monographs in Number Theory Series: see https://www.worldscientific.com/worldscibooks/10.1142/13521#/t=aboutBook. World Scientific has graciously agreed to allow this upload to ArXiv

Location trajectories provide valuable insights for applications from urban planning to pandemic control. However, mobility data can also reveal sensitive information about individuals, such as political opinions, religious beliefs, or sexual orientations. Existing privacy-preserving approaches for publishing this data face a significant utility-privacy trade-off. Releasing synthetic trajectory data generated through deep learning offers a promising solution. Due to the trajectories' sequential nature, most existing models are based on recurrent neural networks (RNNs). However, research in generative adversarial networks (GANs) largely employs convolutional neural networks (CNNs) for image generation. This discrepancy raises the question of whether advances in computer vision can be applied to trajectory generation. In this work, we introduce a Reversible Trajectory-to-CNN Transformation (RTCT) that adapts trajectories into a format suitable for CNN-based models. We integrated this transformation with the well-known DCGAN in a proof-of-concept (PoC) and evaluated its performance against an RNN-based trajectory GAN using four metrics across two datasets. The PoC was superior in capturing spatial distributions compared to the RNN model but had difficulty replicating sequential and temporal properties. Although the PoC's utility is not sufficient for practical applications, the results demonstrate the transformation's potential to facilitate the use of CNNs for trajectory generation, opening up avenues for future research. To support continued research, all source code has been made available under an open-source license., Comment: To appear in the proceedings of the 21st Annual International Conference on Privacy, Security & Trust (PST 2024)

The internet offers tremendous access to services, social connections, and needed products. However, to those without sufficient experience, engaging with businesses and friends across the internet can be daunting due to the ever present danger of scammers and thieves, to say nothing of the myriad of potential computer viruses. Like a forest rich with both edible and poisonous plants, those familiar with the norms inhabit it safely with ease while newcomers need a guide. However, reliance on a human digital guide can be taxing and often impractical. We propose and pilot a simple but unexplored idea: could an LLM provide the necessary support to help the elderly who are separated by the digital divide safely achieve digital autonomy?

A $\textit{neural surrogate of a program}$ is a neural network that mimics the behavior of a program. Researchers have used these neural surrogates to automatically tune program inputs, adapt programs to new settings, and accelerate computations. Researchers traditionally develop neural surrogates by training on input-output examples from a single program. Alternatively, language models trained on a large dataset including many programs can consume program text, to act as a neural surrogate. Using a language model to both generate a surrogate and act as a surrogate, however, leading to a trade-off between resource consumption and accuracy. We present $\textit{neural surrogate compilation}$, a technique for producing neural surrogates directly from program text without coupling neural surrogate generation and execution. We implement neural surrogate compilers using hypernetworks trained on a dataset of C programs and find that they produce neural surrogates that are $1.9$-$9.5\times$ as data-efficient, produce visual results that are $1.0$-$1.3\times$ more similar to ground truth, and train in $4.3$-$7.3\times$ fewer epochs than neural surrogates trained from scratch.

In recent years, Large Language Models (LLM) have demonstrated impressive capabilities in the field of natural language processing (NLP). This paper explores the application of LLMs in negotiation transcript analysis by the Vanderbilt AI Negotiation Lab. Starting in September 2022, we applied multiple strategies using LLMs from zero shot learning to fine tuning models to in-context learning). The final strategy we developed is explained, along with how to access and use the model. This study provides a sense of both the opportunities and roadblocks for the implementation of LLMs in real life applications and offers a model for how LLMs can be applied to coding in other fields.

Large-scale vision models have become integral in many applications due to their unprecedented performance and versatility across downstream tasks. However, the robustness of these foundation models has primarily been explored for a single task, namely image classification. The vulnerability of other common vision tasks, such as semantic segmentation and depth estimation, remains largely unknown. We present a comprehensive empirical evaluation of the adversarial robustness of self-supervised vision encoders across multiple downstream tasks. Our attacks operate in the encoder embedding space and at the downstream task output level. In both cases, current state-of-the-art adversarial fine-tuning techniques tested only for classification significantly degrade clean and robust performance on other tasks. Since the purpose of a foundation model is to cater to multiple applications at once, our findings reveal the need to enhance encoder robustness more broadly. Our code is available at ${github.com/layer6ai-labs/ssl-robustness}$., Comment: Accepted at the ICML 2024 Workshop on Foundation Models in the Wild

Deconvolving ("unfolding'') detector distortions is a critical step in the comparison of cross section measurements with theoretical predictions in particle and nuclear physics. However, most existing approaches require histogram binning while many theoretical predictions are at the level of statistical moments. We develop a new approach to directly unfold distribution moments as a function of another observable without having to first discretize the data. Our Moment Unfolding technique uses machine learning and is inspired by Generative Adversarial Networks (GANs). We demonstrate the performance of this approach using jet substructure measurements in collider physics. With this illustrative example, we find that our Moment Unfolding protocol is more precise than bin-based approaches and is as or more precise than completely unbinned methods., Comment: 16 pages, 6 figures, 1 table

Robots are becoming increasingly intelligent and can autonomously perform tasks such as navigating between locations. However, human oversight remains crucial. This study compared two hands-free methods for directing mobile robots: voice control and gesture control. These methods were tested with the human stationary and walking freely. We hypothesized that walking with the robot would lead to higher intuitiveness ratings and better task performance due to increased stimulus-response compatibility, assuming humans align themselves with the robot. In a 2x2 within-subject design, 218 participants guided the quadrupedal robot Spot using 90 degrees rotation and walk-forward commands. After each trial, participants rated the intuitiveness of the command mapping, while post-experiment interviews were used to gather the participants' preferences. Results showed that voice control combined with walking with Spot was the most favored and intuitive, while gesture control while standing caused confusion for left/right commands. Despite this, 29% of participants preferred gesture control, citing task engagement and visual congruence as reasons. An odometry-based analysis revealed that participants aligned behind Spot, particularly in the gesture control condition, when allowed to walk. In conclusion, voice control with walking produced the best outcomes. Improving physical ergonomics and adjusting gesture types could improve the effectiveness of gesture control.

The discovery of van der Waals superconductors in recent years has generated a lot of excitement for their potentially novel pairing mechanisms. However, their typical atomic-scale thickness and micrometer-scale lateral dimensions impose severe challenges to investigations of pairing symmetry by conventional methods. In this report we demonstrate a new technique that employs high-quality-factor superconducting resonators to measure the kinetic inductance -- up to a part per million -- and loss of a van der Waals superconductor. We analyze the equivalent circuit model to extract the kinetic inductance, superfluid stiffness, penetration depth, and ratio of imaginary and real parts of the complex conductivity. We validate the technique by measuring aluminum and finding excellent agreement in both the zero-temperature superconducting gap as well as the complex conductivity data when compared with BCS theory. We then demonstrate the utility of the technique by measuring the kinetic inductance of multi-layered niobium diselenide and discuss the limits to the accuracy of our technique when the transition temperature of the sample, NbSe$_2$ at 7.06 K, approaches our Nb probe resonator at 8.59 K. Our method will be useful for practitioners in the growing fields of superconducting physics, materials science, and quantum sensing, as a means of characterizing superconducting circuit components and studying pairing mechanisms of the novel superconducting states which arise in layered 2D materials and heterostructures., Comment: 16 pages, 4 tables, 15 figures

We present the design and implementation of a side entry laser system designed for an ultra-high vacuum scanning transmission electron microscope. This system uses a versatile probe design enclosed in a vacuum envelope such that parts can be easily aligned, modified, or exchanged without disturbing the vacuum. The system uses a mirror mounted on the sample holder such that the sample can be illuminated without being tilted. Notably the mirror can be removed and replaced with an ablation target and a higher power laser used to ablate material directly onto the sample. We argue that new capabilities hold the potential to transform the electron microscope from an analysis tool towards a more flexible synthesis system, where atomic scale fabrication and atom-by-atom experiments can be performed.

Navigating unfamiliar environments presents significant challenges for blind and low-vision (BLV) individuals. In this work, we construct a dataset of images and goals across different scenarios such as searching through kitchens or navigating outdoors. We then investigate how grounded instruction generation methods can provide contextually-relevant navigational guidance to users in these instances. Through a sighted user study, we demonstrate that large pretrained language models can produce correct and useful instructions perceived as beneficial for BLV users. We also conduct a survey and interview with 4 BLV users and observe useful insights on preferences for different instructions based on the scenario., Comment: Accepted as RO-MAN 2024 Late Breaking Report

We describe the spreading property for finite transitive permutation groups in terms of properties of their associated coherent configurations, in much the same way that separating and synchronising groups can be described via properties of their orbital graphs. We also show how the other properties in the synchronisation hierarchy naturally fit inside this framework. This combinatorial description allows for more efficient computational tools, and we deduce that every spreading permutation group of degree at most $8191$ is a $\mathbb{Q}$I-group. We also consider design-orthogonality more generally for noncommutative homogeneous coherent configurations.

The all-pairs shortest distances (APSD) with differential privacy (DP) problem takes as input an undirected, weighted graph $G = (V,E, \mathbf{w})$ and outputs a private estimate of the shortest distances in $G$ between all pairs of vertices. In this paper, we present a simple $\widetilde{O}(n^{1/3}/\varepsilon)$-accurate algorithm to solve APSD with $\varepsilon$-DP, which reduces to $\widetilde{O}(n^{1/4}/\varepsilon)$ in the $(\varepsilon, \delta)$-DP setting, where $n = |V|$. Our algorithm greatly improves upon the error of prior algorithms, namely $\widetilde{O}(n^{2/3}/\varepsilon)$ and $\widetilde{O}(\sqrt{n}/\varepsilon)$ in the two respective settings, and is the first to be optimal up to a polylogarithmic factor, based on a lower bound of $\widetilde{\Omega}(n^{1/4})$. In the case where a multiplicative approximation is allowed, we give two different constructions of algorithms with reduced additive error. Our first construction allows a multiplicative approximation of $O(k\log{\log{n}})$ and has additive error $\widetilde{O}(k\cdot n^{1/k}/\varepsilon)$ in the $\varepsilon$-DP case and $\widetilde{O}(\sqrt{k}\cdot n^{1/(2k)}/\varepsilon)$ in the $(\varepsilon, \delta)$-DP case. Our second construction allows multiplicative approximation $2k-1$ and has the same asymptotic additive error as the first construction. Both constructions significantly improve upon the currently best-known additive error of, $\widetilde{O}(k\cdot n^{1/2 + 1/(4k+2)}/\varepsilon)$ and $\widetilde{O}(k\cdot n^{1/3 + 2/(9k+3)}/\varepsilon)$, respectively. Our algorithms are straightforward and work by decomposing a graph into a set of spanning trees, and applying a key observation that we can privately release APSD in trees with $O(\text{polylog}(n))$ error., Comment: Error in Section 3: (1) Improper assumption that the topology of the shortest path trees are public. (2) Improper usage of Lemma 2.4. Error in Section 4: Improper assumption that the topology of the shortest path trees are public

Under appropriate positivity hypotheses, we prove quantitative estimates for the total $k$-th order $Q$-curvature functional near minimizing metrics on any smooth, closed $n$-dimensional Riemannian manifold for every integer $1 \leq k < \frac{n}{2}$. More precisely, we show that on a generic closed Riemannian manifold the distance to the minimizing set of metrics is controlled quadratically by the $Q$-curvature energy deficit, extending recent work by Engelstein, Neumayer and Spolaor in the case $k=1$. Next we prove, for any integer $1 \leq k< \frac{n}{2}$, the existence of an $n$-dimensional Riemannian manifold such that the $k$-th order $Q$-curvature deficit controls a higher power of the distance to the minimizing set. We believe that these degenerate examples are of independent interest and can be used for further development in the field., Comment: 43 pages. Comments welcome!

This study examines the problem of hopping robot navigation planning to achieve simultaneous goal-directed and environment exploration tasks. We consider a scenario in which the robot has mandatory goal-directed tasks defined using Linear Temporal Logic (LTL) specifications as well as optional exploration tasks represented using a reward function. Additionally, there exists uncertainty in the robot dynamics which results in motion perturbation. We first propose an abstraction of 3D hopping robot dynamics which enables high-level planning and a neural-network-based optimization for low-level control. We then introduce a Multi-task Product IMDP (MT-PIMDP) model of the system and tasks. We propose a unified control policy synthesis algorithm which enables both task-directed goal-reaching behaviors as well as task-agnostic exploration to learn perturbations and reward. We provide a formal proof of the trade-off induced by prioritizing either LTL or RL actions. We demonstrate our methods with simulation case studies in a 2D world navigation environment., Comment: 8 pages, 4 figures

The real Fourier-Mukai (RFM) transform relates calibrated graphs to so-called "deformed instantons" on Hermitian line bundles. We show that under the RFM transform, complex Lagrangian graphs in $R^{2n} \times T^{2n}$ correspond to Sp($n$)-instantons over $R^{2n} \times (T^{2n})^*$. In other words, the deformed Sp($n$)-instanton equation coincides with the usual Sp($n$)-instanton equation. Motivated by this observation, we study Sp($n$)-instantons on hyperkahler manifolds $X^{4n}$, with an emphasis on conical singularities. First, when $X = C(M)$ is a hyperkahler cone, we relate Sp($n$)-instantons on $X$ to tri-contact instantons on the 3-Sasakian link $M$ and consider various dimensional reductions. Second, when $X$ is an asymptotically conical (AC) hyperkahler manifold of rate $\nu \leq -\frac{2}{3}(2n+1)$, we prove a Lewis-type theorem to the following effect: If the set of AC Sp($n$)-instantons is non-empty, then every AC Hermitian Yang-Mills connection over $X$ with sufficiently fast decay at infinity is an Sp($n$)-instanton., Comment: 48 pages

This paper evaluates whether large language models (LLMs) exhibit cognitive fan effects, similar to those discovered by Anderson in humans, after being pre-trained on human textual data. We conduct two sets of in-context recall experiments designed to elicit fan effects. Consistent with human results, we find that LLM recall uncertainty, measured via token probability, is influenced by the fan effect. Our results show that removing uncertainty disrupts the observed effect. The experiments suggest the fan effect is consistent whether the fan value is induced in-context or in the pre-training data. Finally, these findings provide in-silico evidence that fan effects and typicality are expressions of the same phenomena.

The sudden arrest of flow by formation of a stable arch over an outlet is a unique and characteristic feature of granular materials. Previous work suggests that grains near the outlet randomly sample configurational flow microstates until a clog-causing flow microstate is reached. However, factors that lead to clogging remain elusive. Here we experimentally observe over 50,000 clogging events for a tridisperse mixture of quasi-2D circular grains, and utilize a variety of machine learning (ML) methods to search for predictive signatures of clogging microstates. This approach fares just modestly better than chance. Nevertheless, our analysis using linear Support Vector Machines (SVMs) highlights the position of potential arch cornerstones as a key factor in clogging likelihood. We verify this experimentally by varying the position of a fixed (cornerstone) grain, and show that such a grain dictates the size of feasible flow-ending arches, and thus the time and mass of each flow. Positioning this grain correctly can even increase the ejected mass by over 50%. Our findings demonstrate that interpretable ML algorithms like SVMs can uncover meaningful physics even when their predictive power is below the standards of conventional ML practice., Comment: 16 pages, 11 figures

Dynamical quantum phase transitions (DQPTs) have been established as a rigorous framework for investigating far-from-equilibrium quantum many-body criticality. Although initially thought to be trivially connected to an order parameter flipping sign, a certain kind of \textit{anomalous} DQPTs have been discovered that exhibit no direct connection to the order parameter and have been shown to arise in the presence of confinement. Here, we show in two paradigmatic models how the onset time of anomalous DQPTs is directly connected, through a power law, to the meson mass in the confined regime of a global symmetry-broken phase. This relation becomes more prominent the closer the initial parameters are to the equilibrium quantum critical point, where a relativistic quantum field theory emerges. Our findings draw a direct connection between mesons and anomalous DQPTs, highlighting the power of the latter to classify exotic far-from-equilibrium criticality., Comment: $7+2$ pages, $4+4$ figures

Dialectical frameworks are a unifying model of formal argumentation, where argumentative relations between arguments are represented by assigning acceptance conditions to atomic arguments. Their generality allow them to cover a number of different approaches with varying forms of representing the argumentation structure. Boolean regulatory networks are used to model the dynamics of complex biological processes, taking into account the interactions of biological compounds, such as proteins or genes. These models have proven highly useful for comprehending such biological processes, allowing to reproduce known behaviour and testing new hypotheses and predictions in silico, for example in the context of new medical treatments. While both these approaches stem from entirely different communities, it turns out that there are striking similarities in their appearence. In this paper, we study the relation between these two formalisms revealing their communalities as well as their differences, and introducing a correspondence that allows to establish novel results for the individual formalisms.

Efforts aimed at scaling fabrication processes to the level of single atoms, dubbed atom-by-atom fabrication or atomic fabrication, invariably encounter the obstacle of atomic scale cleanliness. When considering atomic fabrication, cleanliness of the base material and purity of the source reservoir from which atomic structures will be built are invariable constraints imposed by laws of physics and chemistry. As obvious as such statements may be, and regardless of the inevitable consequences for successful atomic fabrication, there is a poignant lack of understanding of the "dirt" (contamination/impurities). Here, we examine hydrocarbon contamination on graphene. Graphene has formed the base substrate for many e-beam-based atomic fabrication studies and many strategies for cleaning graphene have been presented in the literature. One popular method is heating to high temperatures (>500 {\deg}C). It is usually inferred that volatile hydrocarbons evaporate into the microscope vacuum system leaving behind pristine graphene. Here, we show through direct image intensity analysis that what appears to be clean graphene can be coated with a thin layer of dynamically diffusing hydrocarbons. This result holds significant implications for approaches to e-beam based atomic fabrication, updates the conceptual model of e-beam induced hydrocarbon deposition, and may extend to hot surfaces generally.

We study the cohomology of $G$-representation varieties and $G$-character stacks by means of a topological quantum field theory (TQFT). This TQFT is constructed as the composite of a so-called field theory and the 6-functor formalism of sheaves on topological stacks. We apply this framework to compute the cohomology of various $G$-representation varieties and $G$-character stacks of closed surfaces for $G = \text{SU}(2), \text{SO}(3)$ and $\text{U}(2)$. This work can be seen as a categorification of earlier work, in which such a TQFT was constructed on the level of Grothendieck groups to compute the corresponding Euler characteristics., Comment: 27 pages, comments are welcome!

Coral reefs are increasingly subjected to major disturbances threatening the health of marine ecosystems. Substantial research underway to develop intervention strategies that assist reefs in recovery from, and resistance to, inevitable future climate and weather extremes. To assess potential benefits of interventions, mechanistic understanding of coral reef recovery and resistance patterns is essential. Recent evidence suggests that more than half of the reefs surveyed across the Great Barrier Reef (GBR) exhibit deviations from standard recovery modelling assumptions when the initial coral cover is low ($\leq 10$\%). New modelling is necessary to account for these observed patterns to better inform management strategies. We consider a new model for reef recovery at the coral cover scale that accounts for biphasic recovery patterns. The model is based on a multispecies Richards' growth model that includes a change point in the recovery patterns. Bayesian inference is applied for uncertainty quantification of key parameters for assessing reef health and recovery patterns. This analysis is applied to benthic survey data from the Australian Institute of Marine Sciences (AIMS). We demonstrate agreement between model predictions and data across every recorded recovery trajectory with at least two years of observations following disturbance events occurring between 1992--2020. This new approach will enable new insights into the biological, ecological and environmental factors that contribute to the duration and severity of biphasic coral recovery patterns across the GBR. These new insights will help to inform managements and monitoring practice to mitigate the impacts of climate change on coral reefs.

We present several advancements in search-type problems for fleets of mobile agents operating in two dimensions under the wireless model. Potential hidden target locations are equidistant from a central point, forming either a disk (infinite possible locations) or regular polygons (finite possible locations). Building on the foundational disk evacuation problem, the disk priority evacuation problem with $k$ Servants, and the disk $w$-weighted search problem, we make improvements on several fronts. First we establish new upper and lower bounds for the $n$-gon priority evacuation problem with $1$ Servant for $n \leq 13$, and for $n_k$-gons with $k=2, 3, 4$ Servants, where $n_2 \leq 11$, $n_3 \leq 9$, and $n_4 \leq 10$, offering tight or nearly tight bounds. The only previous results known were a tight upper bound for $k=1$ and $n=6$ and lower bounds for $k=1$ and $n \leq 9$. Second, our work improves the best lower bound known for the disk priority evacuation problem with $k=1$ Servant from $4.46798$ to $4.64666$ and for $k=2$ Servants from $3.6307$ to $3.65332$. Third, we improve the best lower bounds known for the disk $w$-weighted group search problem, significantly reducing the gap between the best upper and lower bounds for $w$ values where the gap was largest. These improvements are based on nearly tight upper and lower bounds for the $11$-gon and $12$-gon $w$-weighted evacuation problems, while previous analyses were limited only to lower bounds and only to $7$-gons.

One approach for increasing the efficiency of randomized trials is the use of "external controls" -- individuals who received the control treatment in the trial during routine practice or in prior experimental studies. Existing external control methods, however, can have substantial bias if the populations underlying the trial and the external control data are not exchangeable. Here, we characterize a randomization-aware class of treatment effect estimators in the population underlying the trial that remain consistent and asymptotically normal when using external control data, even when exchangeability does not hold. We consider two members of this class of estimators: the well-known augmented inverse probability weighting trial-only estimator, which is the efficient estimator when only trial data are used; and a more efficient member of the class when exchangeability holds and external control data are available, which we refer to as the optimized randomization-aware estimator. To achieve robust integration of external control data in trial analyses, we then propose a combined estimator based on the efficient trial-only estimator and the optimized randomization-aware estimator. We show that the combined estimator is consistent and no less efficient than the most efficient of the two component estimators, whether the exchangeability assumption holds or not. We examine the estimators' performance in simulations and we illustrate their use with data from two trials of paliperidone extended-release for schizophrenia.

We use deep spectroscopy from the SAMI Galaxy Survey to explore the precision of the fundamental plane of early-type galaxies (FP) as a distance indicator for future single-fibre spectroscopy surveys. We study the optimal trade-off between sample size and signal-to-noise ratio (SNR), and investigate which additional observables can be used to construct hyperplanes with smaller intrinsic scatter than the FP. We add increasing levels of random noise (parametrised as effective exposure time) to the SAMI spectra to study the effect of increasing measurement uncertainties on the FP-and hyperplane-inferred distances. We find that, using direct-fit methods, the values of the FP and hyperplane best-fit coefficients depend on the spectral SNR, and reach asymptotic values for a mean SNR=40 {\AA}$^{-1}$. As additional variables for the FP we consider three stellar-population observables: light-weighted age, stellar mass-to-light ratio and a novel combination of Lick indices (I$_{\rm age}$). For a SNR=45 {\AA}$^{-1}$ (equivalent to 1-hour exposure on a 4-m telescope), all three hyperplanes outperform the FP as distance indicators. Being an empirical spectral index, I$_{\rm age}$ avoids the model-dependent uncertainties and bias underlying age and mass-to-light ratio measurements, yet yields a 10 per cent reduction of the median distance uncertainty compared to the FP. We also find that, as a by-product, the Iage hyperplane removes most of the reported environment bias of the FP. After accounting for the different signal-to-noise ratio, these conclusions also apply to a 50 times larger sample from SDSS-III. However, in this case, only age removes the environment bias., Comment: 24 pages, 18 figures, accepted for publication in MNRAS

Most reinforcement learning (RL) methods focus on learning optimal policies over low-level action spaces. While these methods can perform well in their training environments, they lack the flexibility to transfer to new tasks. Instead, RL agents that can act over useful, temporally extended skills rather than low-level actions can learn new tasks more easily. Prior work in skill-based RL either requires expert supervision to define useful skills, which is hard to scale, or learns a skill-space from offline data with heuristics that limit the adaptability of the skills, making them difficult to transfer during downstream RL. Our approach, EXTRACT, instead utilizes pre-trained vision language models to extract a discrete set of semantically meaningful skills from offline data, each of which is parameterized by continuous arguments, without human supervision. This skill parameterization allows robots to learn new tasks by only needing to learn when to select a specific skill and how to modify its arguments for the specific task. We demonstrate through experiments in sparse-reward, image-based, robot manipulation environments that EXTRACT can more quickly learn new tasks than prior works, with major gains in sample efficiency and performance over prior skill-based RL. Website at https://www.jessezhang.net/projects/extract/., Comment: 22 pages, 13 figures

The boron nitride (BN) analogue of 8-16-4 graphyne, termed SBNyne, is proposed for the first time. Its physical properties were explored using first-principles calculations and classical molecular dynamics (MD) simulations. Thermal stability assessments reveal that SBNyne maintains structural integrity up to 1000 K. We found that SBNyne exhibits a wide indirect bandgap of 4.58 eV using HSE06 and 3.20 eV using PBE. It displays strong optical absorption in the ultraviolet region while remaining transparent in the infrared and visible regions. Additionally, SBNyne exhibits significantly lower thermal conductivity compared to h-BN. Phonon spectrum analysis indicates that out-of-plane phonons predominantly contribute to the vibrational density of states only at very low frequencies, explaining its low thermal conductivity. These findings expand the knowledge of BN-based 2D materials and open new avenues for their design and advanced technological applications., Comment: We have reviewed the thermal stability calculation and found that the SBNyne structure is metastable and undergoes a transition to a new phase. We are currently investigating this new phase, and to avoid misunderstandings, we need to remove the preprint