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Equivariant machine learning is an approach for designing deep learning models that respect the symmetries of the problem, with the aim of reducing model complexity and improving generalization. In this paper, we focus on an extension of shift equivariance, which is the basis of convolution networks on images, to general graphs. Unlike images, graphs do not have a natural notion of domain translation. Therefore, we consider the graph functional shifts as the symmetry group: the unitary operators that commute with the graph shift operator. Notably, such symmetries operate in the signal space rather than directly in the spatial space. We remark that each linear filter layer of a standard spectral graph neural network (GNN) commutes with graph functional shifts, but the activation function breaks this symmetry. Instead, we propose nonlinear spectral filters (NLSFs) that are fully equivariant to graph functional shifts and show that they have universal approximation properties. The proposed NLSFs are based on a new form of spectral domain that is transferable between graphs. We demonstrate the superior performance of NLSFs over existing spectral GNNs in node and graph classification benchmarks.

The 5$d$ series semimetallic Dirac nodal-line perovskite SrIrO$_3$ presents a promising system to study the interplay between spin-orbit coupling and electron-electron interactions in the epitaxial thin film geometry. The competition between the band splitting and the suppression of the correlation energy, from the extended d orbitals can influence the electronic and magnetic structure and lead to exotic quantum phases in such systems. This iridate member possesses a phase diagram which is acutely sensitive to both heterostructure growth conditions and geometric strain, which stabilises the orthorhombic phase over the monoclinic bulk crystal structure. The growth mode and lattice constants of this iridate perovskite have been observed to vary sharply with oxygen partial pressure and thickness during growth by pulsed laser deposition. In this study the structural and transport properties originating from two sets of growth parameters have been compared, indicating distinct differences in the electronic structure at the Fermi edge. Density functional theory calculations of the convolved density of states compared with the X-ray photoemission spectra near the Fermi edge allow the identification of the states contributing to the electronic structure in this region. The spectral differences between these case studies indicate that controlling the growth regime could enable the tuning of the electronic structure to promote enhanced concentrations of one carrier type over another at the Fermi level in this nominally semimetallic system. The control of the correlation-induced electronic transport parameters in this system will enable access to the spin-orbital entangled states and the topological crystalline properties associated with the honeycomb lattice in this Mott system.

Users can discuss a wide range of topics with large language models (LLMs), but they do not always prefer solving problems or getting information through lengthy conversations. This raises an intriguing HCI question: How does instructing LLMs to engage in longer or shorter conversations affect conversation quality? In this paper, we developed two Slack chatbots using GPT-4 with the ability to vary conversation lengths and conducted a user study. Participants asked the chatbots both highly and less conversable questions, engaging in dialogues with 0, 3, 5, and 7 conversational turns. We found that the conversation quality does not differ drastically across different conditions, while participants had mixed reactions. Our study demonstrates LLMs' ability to change conversation length and the potential benefits for users resulting from such changes, but we caution that changes in text form may not necessarily imply changes in quality or content.

Community management is critical for stakeholders to collaboratively build and sustain communities with socio-technical support. However, most of the existing research has mainly focused on the community members and the platform, with little attention given to the developers who act as intermediaries between the platform and community members and develop tools to support community management. This study focuses on third-party developers (TPDs) for the live streaming platform Twitch and explores their tool development practices. Using a mixed method with in-depth qualitative analysis, we found that TPDs maintain complex relationships with different stakeholders (streamers, viewers, platform, professional developers), and the multi-layered policy restricts their agency regarding idea innovation and tool development. We argue that HCI research should shift its focus from tool users to tool developers with regard to community management. We propose designs to support closer collaboration between TPDS and the platform and professional developers and streamline TPDs' development process with unified toolkits and policy documentation., Comment: Accepted by ACM CHI 2024

Large Language Models (LLMs) have advanced automated writing assistance, enabling complex tasks like co-writing novels and poems. However, real-world writing typically requires various support and collaboration across stages and scenarios. Existing research mainly examines how writers utilize single text generators, neglecting this broader context. This paper introduces Inspo, a web-based editor that incorporates various text generators and online crowd workers. Through a three-phase user study, we examine writers' interactions with Inspo for novel writing. Quantitative analyses of writing logs highlight changes in participants' writing progress and the influence of various text-generation models. Complementing this with qualitative insights from semi-structured interviews, we illustrate participants' perceptions of these models and the crowd. Based on the findings, we provide design recommendations for the next generation of intelligent writing tools and discuss the potential sociocultural implications of integrating AI and human input in the writing process.

Finding meaningful representations and distances of hierarchical data is important in many fields. This paper presents a new method for hierarchical data embedding and distance. Our method relies on combining diffusion geometry, a central approach to manifold learning, and hyperbolic geometry. Specifically, using diffusion geometry, we build multi-scale densities on the data, aimed to reveal their hierarchical structure, and then embed them into a product of hyperbolic spaces. We show theoretically that our embedding and distance recover the underlying hierarchical structure. In addition, we demonstrate the efficacy of the proposed method and its advantages compared to existing methods on graph embedding benchmarks and hierarchical datasets.

The proliferation of automated conversational systems such as chatbots, spoken-dialogue systems, and smart speakers, has significantly impacted modern digital life. However, these systems are primarily designed to provide answers to well-defined questions rather than to support users in exploring complex, ill-defined questions. In this paper, we aim to push the boundaries of conversational systems by examining the types of nebulous, open-ended questions that can best be answered through conversation. We first sampled 500 questions from one million open-ended requests posted on AskReddit, and then recruited online crowd workers to answer eight inquiries about these questions. We also performed open coding to categorize the questions into 27 different domains. We found that the issues people believe require conversation to resolve satisfactorily are highly social and personal. Our work provides insights into how future research could be geared to align with users' needs., Comment: To appear in CHI 2023 Late-Breaking Work

Superconducting metamaterials, which are designed and fabricated with structured fundamental circuit elements, have motivated recent developments of exploring unconventional quantum phenomena in circuit quantum electrodynamics (circuit-QED). We propose a method to engineer 1D Josephson metamaterial as a chiral waveguide by considering a programmed spatiotemporal modulation on its effective impedance. The modulation currents are in the form of traveling waves which phase velocities are much slower than the propagation speed of microwave photons. Due to the Brillouin-scattering process, non-trivial spectrum regimes where photons can propagate unidirectionally emerge. Considering superconducting qubits coupling with this metamaterial waveguide, we analyze both Markovian and non-Markovian quantum dynamics, and find that superconducting qubits can dissipate photons unidirectionally. Moreover, we show that our proposal can be extended a cascaded quantum network with multiple nodes, where chiral photon transport between remote qubits can be realized. Our work might open the possibilities to exploit SQUID metamaterials for realizing unidirectional photon transport in circuit-QED platforms., Comment: 14 pages, 6 figures

Introduction: The purpose of this study was to quantify the relationship between early motor skills, such as sitting, and the development of problem-solving skills in children with motor delays. Methods: Motor (Gross Motor Function Measure) and problem-solving (Assessment of Problem-Solving in Play) skills of 134 children 7-16 months adjusted age at baseline with motor delay were assessed up to 5 times over 12 months. Participants were divided into two groups: mild and significant motor delay. Results: Motor and problem-solving scores had large (r's = 0.53-0.67) and statistically significant (p's > 0.01) correlations at all visits. Baseline motor skills predicted baseline and change in problem solving over time. The associations between motor and problem-solving skills were moderated by level of motor delay, with children with significant motor delay generally having stronger associations compared to those with mild motor delay. Conclusions: These findings suggest that overall baseline motor skills are predictive of current and future development of problem-solving skills and that children with significant motor delay have a stronger and more stable association between motor and problem-solving skills over time. This highlights that children with motor delays are at risk for secondary delays in problem solving, and this risk increases as degree of motor delay increases. [This is the online version of an article published in "Developmental Psychobiology."]

Purpose: This study tested whether the Sitting Together and Reaching to Play (START-Play) physical therapy intervention indirectly impacts cognition through changes in perceptual-motor skills in infants with motor delays. Methods: Participants were 50 infants with motor delays randomly assigned to START-Play plus Usual Care Early Intervention (UC-EI) or UC-EI only. Infants' perceptual-motor and cognitive skills were assessed at baseline and 1.5, 3, 6, and 12 months post-baseline. Results: Short-term changes in sitting, fine motor skills, and motor-based problem-solving, but not reaching, predicted long-term changes in cognition. START-Play indirectly impacted cognition through motor-based problem-solving but not sitting, reaching, or fine motor skills. Conclusions: This study provided preliminary evidence that early physical therapy interventions that blend activities across developmental domains and are supported by an enriched social context can place infants on more optimal developmental trajectories. [This is the online version of an article published in "Pediatric Physical Therapy" (ISSN 0898-5669).]