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Caring for a child with cancer involves navigating through complex medical information, often delivered through lengthy handbooks and consultations with healthcare providers. Overnight, parents are expected to become an expert on a domain which they knew noth- ing about. Conversational UIs, powered by Large Language Models (LLMs) and validated information sources, could play a key role in supporting caregivers. In this paper, we investigate the usability, acceptance, and perceived utility of an LLM-based conversational AI tool for pediatric cancer caregiving, grounded in the Children’s Oncology Group Family Handbook–the leading resource in pe- diatric oncology care. We employed a mixed-methods approach, interviewing and surveying 12 caregivers as they engaged with a functional prototype. We offer insights into caregiver’s needs and expectations from AI-driven tools, and design guidelines for devel- oping safer, more personalized, and supportive AI interventions for pediatric cancer care. 

Meals are a central (and messy) part of family life. Previous design framings for mealtime technologies have focused on supporting dietary needs or social and celebratory interactions at the dinner table; however, family meals involve the coordination of many activities and complicated family dynamics. In this paper, we report on findings from interviews and design sessions with 18 families from the Midwestern United States (including both partners/parents and children) to uncover important family differences and tensions that arise around domestic meal experiences. Drawing on feminist theory, we unpack the work of feeding a family as a form of care, drawing attention to the social and emotional complexity of family meals. Critically situating our data within current design narratives, we propose the sensitizing concepts of generative and systemic discontents as a productive way towards troubling the design space of family-food interaction to contend with the struggles that are a part of everyday family meal experiences. 

When a child is admitted to the hospital with a critical illness, their family must adapt and manage care and stress. CSCW researchers have shown the potential for collaborative technologies to support and augment care collaboration between patients and caregivers. However, as a field CSCW lacks a holistic, theory-driven understanding of how collaborative technologies might best augment and support the family caregiving circle as a socio-technical system. In this paper, we report findings from interviews with 14 parents of children with cancer admitted for extended hospitalizations. We use the resilience-based Family Adaptive Systems framework from family therapy as a lens to characterize their challenges and practices across four key subsystems: Emotion, Control, Meaning, and Maintenance. Then, we introduce a fifth system-the Information system-and draw on our empirical findings to suggest theory-driven opportunities for designing future collaborative technology to augment collaborative caregiving and enhance family resilience. 

Sunlike stars can transmute into comparable mass black holes by steadily accumulating heavy nonannihilating dark matter particles over the course of their lives. If such stars form in binary systems, they could give rise to quasi-monochromatic, persistent gravitational waves, commonly known as continuous gravitational waves, as they inspiral toward one another. We demonstrate that next-generation space-based detectors, e.g., Laser Interferometer Space Antenna (LISA) and Big Bang Observer (BBO), can provide novel constraints on dark matter parameters (dark matter mass and its interaction cross-section with the nucleons) by probing gravitational waves from transmuted sunlike stars that are in close binaries. Our projected constraints depend on several astrophysical uncertainties and nevertheless are competitive with the existing constraints obtained from cosmological measurements as well as terrestrial direct searches, demonstrating a notable science case for these space-based gravitational wave detectors as probes of particle dark matter. Published by the American Physical Society2024 

When a child is hospitalized with a serious illness such as cancer, parents and other close family often take on new roles as caregivers. Previous qualitative studies indicate that caregiving coordination work changes systematically across illness and treatment phases, but less is known about individuals’ technology preferences and how technology needs might change over time. In this study, we employed Q-methodology, a sorting technique for quantitatively analyzing subjective opinion. We interviewed 20 caregivers of children with cancer, who sorted 25 statements about potential design solutions. We describe four distinct caregiving coordination technology archetypes at diagnosis, and show how caregivers’ preferences change over time, eventually converging on one set of priorities during extended hospitalization. 

TEE-based smart contracts are an emerging blockchain architecture, offering fully programmable privacy with better performance than alternatives like secure multiparty computation. They can also support compatibility with existing smart contract languages, such that existing (plaintext) applications can be readily ported, picking up privacy enhancements automatically. While previous analysis of TEE-based smart contracts have focused on failures of TEE itself, we asked whether other aspects might be understudied. We focused on state consistency, a concern area highlighted by Li et al., as well as new concerns including access pattern leakage and software upgrade mechanisms. We carried out a code review of a cohort of four TEE-based smart contract platforms. These include Secret Network, the first to market with in-use applications, as well as Oasis, Phala, and Obscuro, which have at least released public test networks.The first and most broadly applicable result is that access pattern leakage occurs when handling persistent contract storage. On Secret Network, its fine-grained access pattern is catastrophic for the transaction privacy of SNIP-20 tokens. If ERC-20 tokens were naively ported to Oasis they would be similarly vulnerable; the others in the cohort leak coarse-grained information at approximately the page level (4 kilobytes). Improving and characterizing this will require adopting techniques from ORAMs or encrypted databases.Second, the importance of state consistency has been underappreciated, in part because exploiting such vulnerabilities is thought to be impractical. We show they are fully practical by building a proof-of-concept tool that breaks all advertised privacy properties of SNIP-20 tokens, able to query the balance of individual accounts and the token amount of each transfer. We additionally demonstrate MEV attacks against the Sienna Swap application. As a final consequence of lacking state consistency, the developers have inadvertently introduced a decryption backdoor through their software upgrade process. We have helped the Secret developers mitigate this through a coordinated vulnerability disclosure, after which their state consistency should be roughly on par with the rest. 

Abstract Gravitational wave (GW) interferometers are able to detect a change in distance of ~1/10 000th the size of a proton. Such sensitivity leads to large rates of non-gaussian, transient bursts of noise, also known as glitches, which hinder the detection and parameter estimation of short- and long-lived GW signals in the main detector strain. Glitches, come in a wide range of frequency-amplitude-time morphologies and may be caused by environmental or instrumental processes, so a key step towards their mitigation is to understand their population. Current approaches for their identification use supervised models to learn their morphology in the main strain with a fixed set of classes, but do not consider relevant information provided by auxiliary channels that monitor the state of the interferometers. In this work, we present an unsupervised algorithm to find anomalous glitches. Firstly, we encode a subset of auxiliary channels from Laser Interferometer Gravitational-Wave Observatory Livingston in the fractal dimension (FD), which measures the complexity of the signal. For this aim, we speed up the fractal dimension calculation to encode $1$h of data in $11$s. Secondly, we learn the underlying distribution of the data using an autoencoder with cyclic periodic convolutions. In this way, we learn the underlying distribution of glitches and we uncover unknown glitch morphologies, and overlaps in time between different glitches and misclassifications. This led to the discovery of $6.6\mathrm{\%}$anomalies in the input data. The results of this investigation stress the learnable structure of auxiliary channels encoded in FD and provide a flexible framework for glitch discovery. 

When a child is hospitalized with a serious illness, their family members must process emotional stress, quickly absorb complicated clinical information, and take on new caregiving tasks. They also have to coordinate with each other and with other family caregivers without abandoning existing work and home life responsibilities. Previous CSCW and HCI research has shown how the patient’s experience changes across the illness journey, but less is known about the effect of this journey on family caregivers and their coordination work. CSCW technologies could support and augment family care coordination work across the journey, reducing stress levels and improving families’ ability to stay connected and informed. In this paper, we report findings from an interview study we conducted with 14 parents of children undergoing extended hospitalization for cancer treatment. We propose the concept of caregiving coordination journeys and describe caregivers’ current communication and coordination practices across different phases of the hospitalization journey, from diagnosis and early hospitalization to extended hospitalization and beyond. We characterize families’ caregiving coordination routines across different time scales, and describe the current role of communication technologies in families’ coordination practices. We then propose design opportunities for social computing technologies to support and augment families’ communication and caregiving work during the hospitalization journey of their child.