Complex problem-solving and creative work in the real world are rarely individual endeavors and typically unfold within teams and group settings. While advancements in generative artificial intelligence (GenAI) have shown promise in augmenting creativity and productivity, these tools are primarily designed for individual use and overlook group dynamics and the collaborative aspects of teamwork. This workshop will provide a platform for researchers and practitioners to explore the design of future human-AI groups across four key themes: (1) the role of GenAI in group settings, (2) collaborative and multimodal interactions with GenAI, (3) evaluating GenAI’s influence within groups and designing for appropriate reliance, and (4) evolving group practices in the presence of GenAI. We hope to build a community and construct alignment across participants around how to pursue research that understands how GenAI can augment, undermine, or bring new practices to collaborative settings and groupwork.

Control systems are critical in ensuring the safety of cyber-physical systems (CPS) across domains like airplanes and missiles. Safeguarding CPS necessitates runtime methodologies that continuously monitor safety-critical conditions and respond in a verifiably safe manner. Many real-time safety approaches require predicting the future behavior of systems. However, achieving this requires accurate models that can operate in real time. Inspired by DeepONets, we propose a novel approach that combines B-splines' inductive bias with data-driven neural networks (NNs). Our hybrid B-spline neural operator serves as a universal approximator, validated on a 6DOF quadrotor.

This article introduces the versatile software-defined cluster, a novel framework that integrates high-performance computing (HPC) and cloud technologies offering a service-oriented approach for computing resources instead of a hardware-focused one, maintaining infrastructure independence and avoiding vendor lock-in. It addresses the challenges of rigidity and lack of customizability in conventional HPC systems, facilitating a more efficient use of shared infrastructures. The core concept revolves around a three-tiered structure—infrastructure, service management, and software as a service—ensuring immutable and consistent service deployment for the diverse scientific communities. This integration enhances scientific workflows’ adaptability and efficiency, enabling science as a service with cost-effective solutions for shared digital research infrastructure with diverse usage patterns, such as batch, interactive, urgent computing, or machine learning workflows.

Supercomputers have been driving innovations for performance and scaling benefiting several scientific applications for the past few decades. Yet their ecosystems remain virtually unchanged when it comes to integrating distributed data-driven workflows, primarily due to rather rigid access methods and restricted configuration management options. X-as-a-Service model of cloud has introduced, among other features, a developer-centric DevOps approach empowering developers of infrastructure, platform to software artefacts, which, unfortunately contemporary supercomputers still lack. We introduce vClusters (versatile software-defined clusters), which is based on Infrastructure-as-code (IaC) technology. vClusters approach is a unique fusion of HPC and cloud technologies resulting in a software-defined, multi-tenant cluster on a supercomputing ecosystem, that, together with software-defined storage, enable DevOps for complex, data-driven workflows like grid middleware, alongside a classic HPC platform. IaC has been a commonplace in cloud computing, however, it lacked adoption within multi-Petascale ecosystems due to concerns related to performance and interoperability with classic HPC data centres' ecosystems. We present an overview of the Swiss National Supercomputing Centre's flagship Alps ecosystem as an implementation target for vClusters for HPC and data-driven workflows. Alps is based on the Cray-HPE Shasta EX supercomputing platform that includes an IaC compliant, microservices architecture (MSA) management system, which we leverage for demonstrating vClusters usage for our diverse operational workflows. We provide implementation details of two operational vClusters platforms: a classic HPC platform that is used predominantly by hundreds of users running thousands of large-scale numerical simulations batch jobs; and a widely used, data-intensive, Grid computing middleware platform used for CERN Worldwide LHC Computing Grid (WLCG) operations. The resulting solution showcases reuse and reduction of common configuration recipes across vCluster implementations, minimising operational change management overheads while introducing flexibility for managing artefacts for DevOps required by diverse workflows.

Identifying the best ideas from the vast volumes generated by open innovation engagements is costly and often time-consuming. One approach is to engage crowds in filtering the ideas, not just generating them. Klein and Garcia, 2015 proposed a “BOL” approach that is better (in terms of accuracy and speed) at idea filtering than other filtering methods such as a conventional Likert approach. The idea behind this approach (BOL) is that it asks the crowd to distribute a fixed budget of tokens that eliminate bad ideas rather than select good ones. In this paper, we explain why BOL works better than other filtering methods using empirical experiments (with n = 850 subjects). Also, we present the effect of the token budget size on idea-filtering engagement and found, among others, that the accuracy of a filter depends on the token budget size.

This case study examines an online deliberation experiment in which a group of supporters of a large political party were invited to propose ways to reform a national electoral law. Researchers compared a traditional comment forum with the Deliberatorium, an online collaborative platform where users build "argument maps" to capture the various proposals and their associated arguments for and against. The aim of the study was to assess the capability of this tool to support large-scale deliberation in a real-world case, comparing the argument-map approach to a traditional discussion forum. By comparing users' experience across several metrics related to usability, activity levels, and quality of collaboration, we found that while the argument-map platform was perceived as less intuitive and fluid, users nevertheless maintained their engagement at a similar rate to the forum condition and ended up producing more interactions, fewer self-referential arguments, and a more respectful tone.

Many of humanity’s most pressing and challenging problems - such as environmental degradation, physical and economic security, and public health - are inherently complex (involve many different interacting components) as well as widely impactful (effect many diverse stakeholders). Solving such problems requires crowd-scale deliberation in order to cover all the types of disciplinary expertise needed, as well as to take into account the many impacts the decision will have. Current approaches to group decision-making, however, fail at scale, producing outcomes that are needlessly sub-optimal for all the parties involved. This chapter will investigate why group decision-making fails in this way, explaining the problems of achieving Pareto optimality and noting the tendency to miss win-win solutions that are not the “dream choices” of any participant. It will go on to describe how recent advances in social computing technology can address these failings, for example through the use of deliberation maps, idea filtering, and crowd-scale complex negotiation.

The association of protein kinase CK2 (formerly casein kinase II or 2) with cell growth and proliferation in cells was apparent at early stages of its investigation. A cancer-specific role for CK2 remained unclear until it was determined that CK2 was also a potent suppressor of cell death (apoptosis); the latter characteristic differentiated its function in normal versus malignant cells because dysregulation of both cell growth and cell death is a universal feature of cancer cells. Over time, it became evident that CK2 exerts its influence on a diverse range of cell functions in normal as well as in transformed cells. As such, CK2 and its substrates are localized in various compartments of the cell. The dysregulation of CK2 is documented in a wide range of malignancies; notably, by increased CK2 protein and activity levels with relatively moderate change in its RNA abundance. High levels of CK2 are associated with poor prognosis in multiple cancer types, and CK2 is a target for active research and testing for cancer therapy. Aspects of CK2 cellular roles and targeting in cancer are discussed in the present review, with focus on nuclear and mitochondrial functions and prostate, breast and head and neck malignancies.

Background Oropharyngeal squamous cell carcinoma (OPSCC) incidence is rising worldwide, especially human papillomavirus (HPV)-associated disease. Historically, high levels of protein kinase CK2 were linked with poor outcomes in head and neck squamous cell carcinoma (HNSCC), without consideration of HPV status. This retrospective study examined tumor CK2α protein expression levels and related clinical outcomes in a cohort of Veteran OPSCC patient tumors which were determined to be predominantly HPV(+). Methods Patients at the Minneapolis VA Health Care System with newly diagnosed primary OPSCC from January 2005 to December 2015 were identified. A total of 119 OPSCC patient tumors were stained for CK2α, p16 and Ki-67 proteins and E6/E7 RNA. CK2α protein levels in tumors and correlations with HPV status and Ki-67 index were assessed. Overall survival (OS) analysis was performed stratified by CK2α protein score and separately by HPV status, followed by Cox regression controlling for smoking status. To strengthen the limited HPV(−) data, survival analysis for HPV(−) HNSCC patients in the publicly available The Cancer Genome Atlas (TCGA) PanCancer RNA-seq dataset was determined for CSNK2A1. Results The patients in the study population were all male and had a predominant history of tobacco and alcohol use. This cohort comprised 84 HPV(+) and 35 HPV(−) tumors. CK2α levels were higher in HPV(+) tumors compared to HPV(−) tumors. Higher CK2α scores positively correlated with higher Ki-67 index. OS improved with increasing CK2α score and separately OS was significantly better for those with HPV(+) as opposed to HPV(−) OPSCC. Both remained significant after controlling for smoking status. High CSNK2A1 mRNA levels from TCGA data associated with worse patient survival in HPV(−) HNSCC. Conclusions High CK2α protein levels are detected in HPV(+) OPSCC tumors and demonstrate an unexpected association with improved survival in a strongly HPV(+) OPSCC cohort. Worse survival outcomes for high CSNK2A1 mRNA levels in HPV(−) HNSCC are consistent with historical data. Given these surprising findings and the rising incidence of HPV(+) OPSCC, further study is needed to understand the biological roles of CK2 in HPV(+) and HPV(−) HNSCC and the potential utility for therapeutic targeting of CK2 in these two disease states.

The global outbreak of the coronavirus pneumonia (COVID-19) showed how epidemics today can spread very rapidly, with potentially ruinous impact on economies and societies. Whereas medical research is crucial to define effective treatment protocols, technology innovation and social research can contribute by defining effective approaches to emergency management, especially to optimize the complex dynamics arising within actors and systems during the outbreak. The purpose of this article is to define a framework for modeling activities, actors and resources coordination in the epidemic management scenario, and to reflect on its use to enhance response practices and actions. We identify 25 types of resources and 8 activities involved in the management of epidemic, and study 29 "flow", "fit", and "share" dependencies among those resources and activities, along with purposeful management criteria. Next, we use a coordination framework to conceptualize an emergency management system encompassing practices and response actions. This study has the potential to impact a broad audience, and can opens avenues for follow up works at the intersection between technology and innovation management and societal challenges. The outcomes can have immediate applicability to an ongoing societal problem, as well as be generalized for application in future (possible although undesired) events.

Head and neck squamous cell carcinoma (HNSCC) can be categorized into human papillomavirus (HPV) positive or negative disease. Elevated protein kinase CK2 level and activity have been historically observed in HNSCC cells. Previous studies on CK2 in HNSCC did not generally include consideration of HPV(+) and HPV(−) status. Here, we investigated the response of HPV(+) and HPV(−) HNSCC cells to CK2 targeting using CX-4945 or siRNA downregulation combined with cisplatin treatment. HNSCC cell lines were examined for CK2 expression levels and activity and response to CX-4945, with and without cisplatin. CK2 levels and NFκB p65-related activity were high in HPV(+) HNSCC cells relative to HPV(−) HNSCC cells. Treatment with CX-4945 decreased viability and cisplatin IC50 in all cell lines. Targeting of CK2 increased tumor suppressor protein levels for p21 and PDCD4 in most instances. Further study is needed to understand the role of CK2 in HPV(+) and HPV(−) HNSCC and to determine how incorporation of the CK2-targeted inhibitor CX-4945 could improve cisplatin response in HNSCC.

As supercomputing systems gradually become an integral part of data driven workflows such as ML and AI or tightly-coupled pre- and post-processing pipelines, users need programmable access to shared resources to avoid moving large volume of data to dedicated systems or to public cloud providers. Public clouds or the private ones using technologies like OpenStack, multi-tenancy on shared hardware has been a commonplace over a decade, offering users programmable and privileged access to resources like compute, network and storage. Such access is unavailable to users on batch-scheduled, multi-Petascale supercomputing systems, which are designed for achieving close-to-metal performance for scientific applications at unprecedented scales. In this paper, we focus on multi-tenancy within hardware and software stacks of Cray-HPE EX Shasta supercomputing systems for creating high performance and cloud clusters for HPC and AI/ML workloads respectively. Using orchestration examples for zero downtime upgrades of virtual clusters, we demonstrate benefits of multi-tenant machines for achieving close-to-metal performance, as well as elasticity and customization of resources without interruption to operational services.

Complex applications and workflows needs are often exclusively expressed in terms of computational resources on HPC systems. In many cases, other resources like storage or network are not allocatable and are shared across the entire HPC system. By looking at the storage resources in particular, any workflow or application should be able to select both its preferred data manager and its required storage capability or capacity. To achieve such a goal, new mechanisms should be introduced. In this work, we present such a tool that dynamically provisions a data management system on top of storage devices. We propose a proof-of-concept that is able to deploy, on-demand, a parallel file-system across intermediate storage nodes on a Cray XC50 system. We show how this mechanism can be easily extended to support more data managers and any type of intermediate storage. Finally, we evaluate the performance of the provisioned storage system with a set of benchmarks.

Large scale experimental facilities such as the Swiss Light Source and the free-electron X-ray laser SwissFEL at the Paul Scherrer Institute, and the particle accelerators and detectors at CERN are experiencing unprecedented data generation growth rates. Consequently, management, processing and storage requirements of data are increasing rapidly. Historically, online and on-demand processing of data generated by the instruments used to be tightly-coupled with a dedicated, domains-specific, site-local IT infrastructure. Cost and performance scaling of these facilities not only pose technical but also planning and scheduling challenges. Supercomputing ecosystems optimize cost and scaling for computing and storage resources but typically exploit a shared batch access model, which is optimized for high utilization of compute resources. In comparison, in public clouds, on-demand service delivery models address the concept of elasticity while maintaining isolation with performance trade-offs. Furthermore, these on-demand access models allow for different degrees of privileges to users for managing IT infrastructure services, in contrast with shared, bare-metal supercomputing ecosystems. This paper outlines an approach for enabling interactive, on-demand supercomputing for experimental data-driven workflows, which are characterised by a managed but bursty data and computing requirements. We present a delegated batch reservation model, controlled by the customer and provisioned by the supercomputing site, that allows scientists at the experimental facility to couple generation of data to the allocation of compute, data and network resources at the supercomputing centre. Scientists are then able to manage resources both at the experimental and supercomputing facilities interactively for managing their scientific workflows. Prototype implementation demonstrates that this rather simple co-designed extension to a supercomputing classic batch scheduling system with a controlled degree of privilege can be easily incorporated to the experimental facilities existing IT resource management and scheduling pipelines.

Complex applications and workflows needs are often exclusively expressed in terms of computational resources on HPC systems. In many cases, other resources like storage or network are not allocatable and are shared across the entire HPC system. By looking at the storage resource in particular, any workflow or application should be able to select both its preferred data manager and its required storage capability or capacity. To achieve such a goal, new mechanisms should be introduced. In this work, we introduce such a mechanism for dynamically provision a data management system on top of storage devices. We particularly focus our effort on deploying a BeeGFS instance across multiple DataWarp nodes on a Cray XC50 system. However, we also demonstrate that the same mechanism can be used to deploy BeeGFS on non-Cray system.

Nowadays society is more and more dependent on critical infrastructures. Critical network infrastructures (CNI) are communication networks whose disruption can create a severe impact. In this paper we propose REACT, a distributed framework for reactive network resilience, which allows networks to reconfigure themselves in the event of a security incidents so that the risk of further damage is mitigated. Our framework takes advantage of a risk model based on multilayer networks, as well as a graph-coloring problem conversion, to identify new, more resilient configurations for networks in the event of an attack. We propose two different solution approaches, and evaluate them from two different perspectives, with a number of centralized optimization techniques. Experiments show that our approaches outperform the reference approaches in terms of risk mitigation and performance.

This paper presents an approach, building on techniques from computational geometry, for compiling nonmonotonic utility functions into a form that substantially speeds utility calculation. We demonstrate speedups of up to 70x in our experimental evaluations.

Self-adaptive systems depend on models of themselves and their environment to decide whether and how to adapt, but these models are often affected by uncertainty. While current adaptation decision approaches are able to model and reason about this uncertainty, they do not consider ways to reduce it. This presents an opportunity for improving decision-making in self-adaptive systems, because reducing uncertainty results in a better characterization of the current and future states of the system and the environment (at some cost), which in turn supports making better adaptation decisions. We propose uncertainty reduction as the natural next step in uncertainty management in the field of self-adaptive systems. This requires both an approach to decide when to reduce uncertainty, and a catalog of tactics to reduce different kinds of uncertainty. We present an example of such a decision, examples of uncertainty reduction tactics, and describe how uncertainty reduction requires changes to the different activities in the typical self-adaptation loop.