University of Notre Dame

In recent years, societal narratives around teens and screens have shifted significantly. First, high-profile media coverage has raised concerns over the negative impact of technology use on the mental health, well-being, and physical safety of adolescents. Consequently, this has led to restrictive approaches to implementing parental control software, age verification systems, artificial intelligence-based risk detection, and other safety mechanisms to protect teens from such harm. At the same time, recommendations regarding digital technology use have moved beyond imposing time limits to more advanced frameworks that consider active and intentional technology use. Additionally, research recommends shifting from restrictive parenting practices to include more developmentally appropriate and resilience-based approaches that empower teens as they prepare for adulthood. Yet, researchers have acknowledged that solutions towards digital inclusion cannot be “one-size-fits-all,” as individual development, family differences, and cultural norms may influence youth outcomes. Finally, we have seen more tangible efforts through human-centered design and legislative policies targeted toward making digital platforms that engage teen users more accountable for their online safety. In this chapter, we discuss these trends and raise important questions and recommendations for setting a forward-thinking agenda for future socio-technical research and practice on promoting the digital well-being and safety of teens.

This study showcases a global, heterogeneously coupled total water level system wherein salinity and temperature outputs from a coarser‐resolution (∼ ${\sim}$12 km) ocean general circulation model are used to calculate density‐driven terms within a global, higher‐resolution (∼ ${\sim}$2.5 km) depth‐averaged total water level model. We demonstrate that the inclusion of baroclinic forcing in the barotropic model requires modification of the internal wave drag term to prevent excess degradation of tidal results compared to the barotropic model. By scaling the internal tide dissipation by an easy to calculate dissipation ratio, the resulting heterogeneously coupled model has complex root mean square errors (RMSE) of 2.27 cm in the deep ocean and 12.16 cm in shallow waters for the M2 ${\mathrm{M}}_{2}$ tidal constituent. While this represents a 10%–20% deterioration as compared to the barotropic model, the improvements in total water level prediction more than offset this degradation. Global median RMSE compared to observations of total water levels, 30‐day sea levels, and non‐tidal residuals improve by 1.86 (18.5%), 2.55 (42.5%), and 0.36 (5.3%) cm respectively. The drastic improvement in model performance highlights the importance of including density‐driven effects within global hydrodynamic models and will help to improve the results of both hindcasts and forecasts in modeling extreme and nuisance flooding. With only an 11% increase in model run time compared to the fully barotropic total water level model, this approach paves the way for high resolution coastal water level and flood models to be used alongside climate models, improving operational forecasting of total water levels.

The inertia–instability paradox poses an intriguing question in depression research: How can the affective experiences of depressed individuals demonstrate both resistance to change and fluctuation? Prior studies examining this paradox have faced limitations, including small sample sizes, analytic approaches prone to biased parameter estimates, and inconsistent results. Using data from 842 adults (Mage = 54.31, SD = 13.25, age range: 18–88; 58.2% female) collected over 56 consecutive days, we applied dynamic structural equation modeling to quantify individualized indices of mean levels, variability, instability, and inertia of negative affect. When adjusting for shared variances among affect dynamic measures, depressive symptoms were uniquely associated with both higher mean levels and inertia of negative affect. However, neither variability nor instability demonstrated unique links to depressive symptoms after accounting for the mean and inertia. Findings indicate that greater predictability in day-to-day negative affect is an important dynamic feature of depression.

The effects of the intergenerational continuity of adverse childhood experiences (ACEs) on youth outcomes have been documented, particularly among mother–child dyads. Most literature has focused on the continuity of family‐level ACEs (Traditional ACEs [T‐ACEs]) and not community‐level ACEs (Expanded ACEs [E‐ACEs]) that disproportionately impact minoritized individuals. We aimed to (a) examine the effect of mothers' and fathers' T‐ACEs and E‐ACEs on youth's T‐ACEs and E‐ACEs, respectively, and on youth's depressive and anxiety symptoms; (b) examine whether youth's own ACE exposure explains the link between parental ACEs and youth depressive and anxiety symptoms; and (c) explore differential risks by mothers versus fathers. We collected cross‐sectional data from a community sample of Mexican‐origin youth ( M age , 13.5 years; 51.7% males; 93.0% US‐born), mothers ( M age , 41.4 years; 7.2% US‐born), and fathers ( M age , 44.0 years; 5.1% US‐born) from the Seguimos Avanzando project (167 youth‐mother–father triads, 177 youth‐mother/father dyads). Results showed that (a) fathers', but not mothers', T‐ACEs and E‐ACEs were associated with youth's T‐ACES and E‐ACEs, respectively, (b) youth's T‐ACEs explained the association between fathers' T‐ACEs and youth's depressive symptoms, and (c) only youth's E‐ACEs were associated with anxiety symptoms. These findings highlight the greater need to understand how fathers' childhood experiences may impact outcomes across generations and that targeting youth's ACEs can reduce the pervasive effects of intergenerational continuity of ACEs.

Slightly over a decade ago, as part of a special issue of this journal devoted to twentieth-century Italian opera, I published an article that began by asking ‘What happened to verismo ?’ ¹ The answer, somewhat in the manner of its time, involved apparitions, ghostly echoes and the uncanny magic of wireless technology. This current issue of the Cambridge Opera Journal – which, needless to say, focuses on repertoire undiscussed and largely unknown back in 2012 – provides a rather different response to the question, suggesting that, in the years around the First World War, the aggressive materiality of operatic realism instead gave way to the even more visceral and immediate pleasures of Italian operetta. As Marco Ladd and Ditlev Rindom observe in their introduction, the leading lights of the verismo movement all went on to embrace the new genre: Pietro Mascagni with Sì (1919), a work that in fact begins with a distinctly un -uncanny chorus of telegraph operators; Umberto Giordano with Giove a Pompei (1921); and above all Ruggero Leoncavallo, author of Prestami tua moglie (1916) and A chi la giarrettiera? (1919), as well as many other less-memorably titled entertainments for audiences in Italy, New York and London. The Sonzogno publishing house followed its operatic concorso of 1888, which famously introduced Cavalleria rusticana to the world, with a similarly conceived operetta contest in 1913. In this context, Giacomo Puccini’s embrace of ‘Silver Age’ conventions in La rondine (1917), a work whose generic fuzziness has long puzzled listeners, may seem less an outlier than an acknowledgement of larger shifts in taste and value.

This study reconsiders the 1967 Russell Tribunal through the lens of U.S. media. Scholars who study the Tribunal are often left apologizing for its lack of impact on public discourse. But a close look at news coverage , primary Tribunal literature, and members’ correspondence reveals many ways that it affected debates about the war in the USA. These connections made the Tribunal a transatlantic component in the Vietnam era anti-war movement as much as an experiment in international law.

Large household water storage containers are among the most productive habitats for Aedes aegypti (Linnaeus, 1762), the primary mosquito vector for dengue and other arboviral pathogens. Increasing concerns for insecticide resistance and larvicide safety are limiting the successful treatment of large household water storage containers, which are among the most productive habitats for Aedes juveniles. The recent development of species-specific RNAi-based yeast larvicides could help overcome these problems, particularly if shelf stable ready-to-use formulations with significant residual activity in water can be developed. Here we examine the hypothesis that development of a shelf-stable controlled-release RNAi yeast formulation can facilitate lasting control of A. aegypti juveniles in large water storage containers. In this study, a dried inactivated yeast was incorporated into a biodegradable matrix containing a mixture of polylactic acid, a preservative, and UV protectants. The formulation was prepared using food-grade level components to prevent toxicity to humans or other organisms. Both floating and sinking versions of the tablets were prepared for treatment of various sized water containers, including household water storage tank-sized containers. The tablets passed accelerated storage tests of shelf life stability and demonstrated up to six months residual activity in water. The yeast performed well in both small and large containers, including water barrels containing 20-1000 larvae each, and in outdoor barrel trials. Future studies will include the evaluation of the yeast larvicide in larger operational field trials that will further assess the potential for incorporating this new technology into integrated mosquito control programs worldwide.

Grazing experiments were conducted for the zooplankton Artemia franciscana on three of its most common Great Salt Lake (Utah: USA) phytoplankton species (> 80–90% of phytoplankton biovolume: a chlorophyte, Dunaliella viridis; a cyanobacterium, Euhalothece sp., and a bacillariophyte, the pennate diatom Nitzschia epithemioides). For each Artemia developmental stage (nauplii, juveniles and adults), grazing rates (same phytoplankton abundances, temperatures, and salinities) are reported along with grazing preferences for the phytoplankton species in mixes of species pairs and all three species together. Each Artemia developmental stage exhibited different preferences for the phytoplankton species. Preferences measured for each species pair were consistent with preferences when all three species were together and were correlated with the phytoplankton’s survival value for each Artemia developmental stage. Survival values were positively related to the ingestion rate for each phytoplankton species (biovolume/individual/h), likely a function of cell size, and its nutritional quality treated as a function of phytoplankton N:P relative to Artemia developmental stage N:P.

This paper considers the problem of decentralized analysis and control synthesis to verify and enforce properties like stability and dissipativity of large-scale networked systems comprised of linear subsystems interconnected in an arbitrary topology. In particular, we design systematic networked system analysis and control synthesis processes that can be executed in a decentralized manner with minimal information sharing among the subsystems. We also show that, for such decentralized processes, optimizing the used subsystem indexing scheme can substantially reduce the required inter-subsystem information-sharing. We also provide insights into our decentralization technique so that it can be quickly adopted to decentralize many other centralized control solutions. To show this, we derive a centralized solution for the dissipative dynamic distributed output feedback controller design problem and present its decentralized version. We conclude this paper by providing several simulation results demonstrating the proposed novel decentralized processes and dissipativity-based centralized and decentralized control solutions.

Large language models (LLMs), such as GPT4 and LLaMA, are creating significant advancements in natural language processing, due to their strong text encoding/decoding ability and newly found emergent capability ( e.g. , reasoning). While LLMs are mainly designed to process pure texts, there are many real-world scenarios where text data is associated with rich structure information in the form of graphs ( e.g. , academic networks, and e-commerce networks) or scenarios where graph data is paired with rich textual information ( e.g. , molecules with descriptions). Besides, although LLMs have shown their pure text-based reasoning ability, it is underexplored whether such ability can be generalized to graphs ( i.e. , graph-based reasoning). In this paper, we provide a systematic review of scenarios and techniques related to large language models on graphs. We first summarize potential scenarios of adopting LLMs on graphs into three categories, namely pure graphs, text-attributed graphs, and text-paired graphs. We then discuss detailed techniques for utilizing LLMs on graphs, including LLM as Predictor, LLM as Encoder, and LLM as Aligner, and compare the advantages and disadvantages of different schools of models. Furthermore, we discuss the real-world applications of such methods and summarize open-source codes and benchmark datasets. Finally, we conclude with potential future research directions in this fast-growing field. The related source can be found at https://github.com/PeterGriffinJin/Awesome-Language-Model-on-Graphs .

This article presents an in-depth investigation into the impact of alternating current (ac) and direct current (dc) positive bias temperature instability (PBTI) on hydrogen (H) formation in oxide semiconductor field-effect transistors (OSFETs). Utilizing highly stable co-sputtered indium–gallium–zinc–tin oxide (IGZTO) FETs, we provide a systematic and holistic analysis that reveals key differences between ac and dc PBTI effects, particularly at high temperatures ( T ). Our study uncovers several critical findings: 1) ac and dc PBTI exhibit distinct phenomena associated with the H formation at high T ; 2) ac PBTI could mitigate the H formation, with both frequency ( f ) and duty factor (DF) significantly influencing the extent of the mitigation of the H effect; 3) DF demonstrates a more substantial impact compared to f in ac PBTI; and 4) up to 99.2% of the negative threshold voltage shift ( $\Delta\textit{V}_\text{th}$ ) induced by H formation could be alleviated by replacing dc PBTI with ac PBTI (25%, 1 MHz). These findings provide significant insights into the mechanisms of the H effect under different PBTI conditions.

In this work, we have developed a large memory window (MW) ferroelectric field effect transistor (FeFET) memory for vertical NAND storage. We demonstrate that: 1) by inserting a top functional layer above the ferroelectric, gate side injection pumped by ferroelectric switching event can be enhanced, thus increasing the MW; 2) inspired by the charge trap flash, SiN _X is chosen as the charge trapping layer and the proposed structures have been experimentally demonstrated to effectively increase MW; 3) the MIFIS structure demonstrates a 6V-8V MW for 11V 1μs write pulse and 8V-12V window for 15V 1μs with a SiO _X composite functional layer; 4) interestingly, the MIFIS device shows immediate read-after-write capability, which is not observed in the baseline FeFET, suggesting minor channel side injection and relaxation.

Recommendation systems (RecSys) can effectively suggest items to a given user by predicting their preferences based on the massive amount of historical data from a collection of users. RecSys based on deep neural networks (DNNs) are widely adopted, and must handle large embedding tables (ETs) and many ET related operations. The memory size and bandwidth of conventional computing architectures usually restrict the performance of RecSys, especially for operations related to ETs. To reduce both memory and computational bottlenecks, this work proposes implementing compressed ETs on an in-memory-computing (IMC) architecture, nMARS, for accelerating DNN-based RecSys. The IMC fabric consists of random-access memories (RAMs), configurable random-access/content-addressable memories (RA/CAMs), and crossbars that can be implemented with CMOS or ferroelectric field effect transistors (FeFETs). RA/CAMs can be configured to switch between a content addressable memory mode and RAM mode to support RecSys operations. Based on this IMC fabric, we design IMC-friendly ETs and explore the integration of mixed-dimension and mixed-precision ET compression techniques, which reduce the memory requirements, through a mapping scheme specifically designed to support the compressed format. Detailed circuit-level and system-level evaluations show that nMARS designed with a CMOS 45 nm process node achieves ~32×and 14× latency improvement on the filtering and ranking stages of the MovieLens dataset compared to a 16 nm GPU-based solution. Moreover, for ET related operations on the Criteo dataset, nMARS can achieve an 80×speedup compared to the GPU-based solution. For a FeFET implementation, it achieves a 39% energy reduction compared to the previous FeFET-based in-memory RecSys solution [1] on the MovieLens dataset.

The increasing adoption of Electric Vehicle (EV) systems necessitates the development of an Energy Market structure that facilitates peer-to-peer energy sharing among multiple EVs and entities while ensuring a self-regulating pricing mechanism. Real-time State of Charge (SoC) estimation is critical to meeting the dynamic energy demands of EV systems. In this study, we propose a blockchain-based automated market maker (AMM) that utilizes constant function products to establish an effective self-regulating pricing system for EV energy market prices. Our unique State of Charge estimation system leverages blockchain-based oracles to efficiently handle requests and monitor EV-oriented energy markets. This enables precise monitoring of battery states and achieves improved SoC values through the interior point method. Experimentation on a blockchain network reveals cost-effective energy regulation within EV systems and enhanced SoC estimation predictability within Energy Markets. All contracts undergo rigorous testing and are deployed at a gas cost of $2.1913742~x 10^7$ Wei. Our approach demonstrates high efficiency, for all designed protocols, affirming the efficacy of our proposal. By implementing our blockchain-based AMM and State of Charge estimation system, we ensure transparent and self-regulated energy distribution and pricing within EV Markets, fostering the advancement of autonomous EV systems.

We report the observation of field-free spin–orbit torque (SOT) magnetization switching in a single layer of (Ga,Mn)(As,P) ferromagnetic film exhibiting perpendicular magnetic anisotropy. The SOT switching phenomenon is characterized by distinct transitions between two Hall resistance (HR) states during current scans. When subjected to an in-plane bias field, the observed switching chirality in the HR hysteresis loop consistently aligns with SOT induced by spin polarization arising from Rashba- and Dresselhaus-type spin–orbit fields within the tensile-strained crystalline structure of the (Ga,Mn)(As,P) film. Remarkably, in the present experiments, we observe SOT switching even in the absence of an external bias field, and with its chirality depending on the direction of initial magnetization. We attribute this field-free switching to symmetry breaking facilitated by an internal coupling field, the orientation of which is determined by the external field experienced as the magnetization is initialized. Further evidence supporting the presence of such a coupling field includes a shift in the field-scan HR hysteresis depending on the direction of initialized magnetization. Structural analysis reveals a surface layer enriched in Mn and O, indicating the presence of oxide-based magnetic structures that are magnetically coupled to the (Ga,Mn)(As,P) film. The temperature dependence of field-free SOT switching corroborates this explanation, as the internal coupling field disappears above 40 K, consistent with the expected magnetic transition of the Mn3O4 structure. Our discovery of field-free SOT magnetization switching in a single-layer film represents a significant advancement, offering a novel pathway for the development of simpler and more energy-efficient spintronic devices.

Background Idiopathic inflammatory myopathies (IIMs) are a group of autoimmune diseases characterised by inflammation of skeletal muscle and other organ systems. They have high morbidity and mortality but, in part because of their rarity and heterogeneity, improving understanding and outcomes remains challenging. To address these problems, numerous IIM registries exist globally, but no national registry yet exists in Australia. Aims The Australian Myositis Registry (AMR) is a national prospective cohort database designed to record clinical, laboratory and patient‐experience data of Australian IIM patients with the potential for wide‐reaching research impact. Methods The AMR was built on the Research Electronic Data Capture secure database system. An extensive set of data fields informed by a contemporary understanding of IIM pathogenesis and clinically relevant features are available to help capture the full breadth of disease phenotype and treatment. Data fields include current classification criteria, all currently available autoantibodies and the internationally accepted core set measures. After an extended period of design, collaboration and review, the AMR launched in 2023 across two sites in New South Wales and Western Australia. The AMR is seeking to expand with more sites across Australia. Results As of August 2024, 170 participants are enrolled. Conclusions The AMR is the first nationwide registry in Australia for patients with IIMs and one of the very few national registries for IIMs globally. It aims to provide valuable insight into the epidemiology and clinical experience of IIMs in Australia to help address multiple research agendas.