Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI. Accomplishing this goal requires learning to ground language in perception and embodied actions, in order to accomplish complex tasks. The Scalable, Instructable, Multiworld Agent (SIMA) project tackles this by training agents to follow free-form instructions across a diverse range of virtual 3D environments, including curated research environments as well as open-ended, commercial video games. Our goal is to develop an instructable agent that can accomplish anything a human can do in any simulated 3D environment. Our approach focuses on language-driven generality while imposing minimal assumptions. Our agents interact with environments in real-time using a generic, human-like interface: the inputs are image observations and language instructions and the outputs are keyboard-and-mouse actions. This general approach is challenging, but it allows agents to ground language across many visually complex and semantically rich environments while also allowing us to readily run agents in new environments. In this paper we describe our motivation and goal, the initial progress we have made, and promising preliminary results on several diverse research environments and a variety of commercial video games.

Intratumoral heterogeneity arises as a result of genetically distinct subclones emerging during tumor progression. These subclones are characterized by various types of somatic genomic aberrations, with single nucleotide variants (SNVs) and copy number aberrations (CNAs) being the most prominent. While single-cell sequencing provides powerful data for studying tumor progression, most existing and newly generated sequencing datasets are obtained through conventional bulk sequencing. Most of the available methods for studying tumor progression from multi-sample bulk sequencing data are either based on the use of SNVs from genomic loci not impacted by CNAs or designed to handle a small number of SNVs via enumerating their possible copy number trees. In this paper, we introduce DETOPT, a combinatorial optimization method for accurate tumor progression tree inference that places SNVs impacted by CNAs on trees of tumor progression with minimal distortion on their variant allele frequencies observed across available samples of a tumor. We show that on simulated data DETOPT provides more accurate tree placement of SNVs impacted by CNAs than the available alternatives. When applied to a set of multi-sample bulk exome-sequenced tumor metastases from a treatment-refractory, triple-positive metastatic breast cancer, DETOPT reports biologically plausible trees of tumor progression, identifying the tree placement of copy number state gains and losses impacting SNVs, including those in clinically significant genes.

The approach of evaluating the final scores of multi-criteria decision-making (MCDM) methods according to the strength of association with real-life rankings is interesting for comparing MCDM methods. This approach has recently been applied mostly to financial data. In these studies, where it is emphasized that some methods show more stable success, it would be useful to see the results that will emerge by testing the approach on different data structures more comprehensively. Moreover, not only the final MCDM results but also the performance of normalization techniques and data types (fuzzy or crisp), which are components of MCDM, can be compared using the same approach. These components also have the potential to affect MCDM results directly. In this direction, in our study, the economic performances of G-20 (Group of 20) countries, which have different data structures, were calculated over ten different periodic decision matrices. Ten different crisp-based MCDM methods (COPRAS, CODAS, MOORA, TOPSIS, MABAC, VIKOR (S, R, Q), FUCA, and ELECTRE III) with different capabilities were used to better visualize the big picture. The relationships between two different real-life reference anchors and MCDM methods were used as a basis for comparison. The CODAS method develops a high correlation with both anchors in most periods. The most appropriate normalization technique for CODAS was identified using these two anchors. Interestingly, the maximum normalization technique was the most successful among the alternatives (max, min–max, vector, sum, and alternative ranking-based). Moreover, we compared the two main data types by comparing the correlation results of crisp-based and fuzzy-based CODAS. The results were very consistent, and the “Maximum normalization-based fuzzy integrated CODAS procedure” was proposed to decision-makers to measure the economic performance of the countries.

Posterior reversible encephalopathy syndrome (PRES) and reversible cerebral vasoconstriction syndrome (RCVS) may cause ischaemic stroke and intracranial haemorrhage. The aim of our study was to assess the frequency of the afore‐mentioned outcomes.

<p><strong>Introduction.</strong> Digital literacy includes things like being able to use information systems and supporting infrastructure. With the increasing use of technology in healthcare, it is important for healthcare staff to be digitally literate. The aim of the paper is to determine the attitudes of primary and secondary health care workers towards the use of computers in health care and to examine the influence of sociodemographic factors on the information literacy of health care workers.&nbsp;<br /><strong>Methods.</strong> The research was conducted according to the principle of a cross-sectional study. The research included 80 respondents, healthcare workers. Data analysis included methods of descriptive and inferential statistics. The data will be presented in the form of a table.<br /><strong>Results.</strong> The results showed that certain socio-demographic factors influenced the attitude of health workers towards the use of computers. The most significant factors were the level of education and previous IT education, but the time the respondents sopent working on the computer and whether they used the computer exclusively at work or at home also had an impact.<br /><strong>Conclusion. </strong>Healthcare workers showed a positive attitude towards the use of computers in healthcare. The most significant socio-demographic factors influencing knowledge of computer work are the level of education of the respondents and whether and where they received their education in information technology.</p>

A polygenic risk score (PRS) combines the associations of multiple genetic variants that could be due to direct causal effects, indirect genetic effects, or other sources of familial confounding. We have developed new approaches to assess evidence for and against causation by using family data for pairs of relatives (Inference about Causation from Examination of FAmiliaL CONfounding [ICE FALCON]) or measures of family history (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLyses [ICE CRISTAL]). Inference is made from the changes in regression coefficients of relatives' PRSs or PRS and family history before and after adjusting for each other. We applied these approaches to two breast cancer PRSs and multiple studies and found that (a) for breast cancer diagnosed at a young age, for example, <50 years, there was no evidence that the PRSs were causal, while (b) for breast cancer diagnosed at later ages, there was consistent evidence for causation explaining increasing amounts of the PRS‐disease association. The genetic variants in the PRS might be in linkage disequilibrium with truly causal variants and not causal themselves. These PRSs cause minimal heritability of breast cancer at younger ages. There is also evidence for nongenetic factors shared by first‐degree relatives that explain breast cancer familial aggregation. Familial associations are not necessarily due to genes, and genetic associations are not necessarily causal.

Cellular manufacturing represents a production system arrangement in which machines, tools, workers, and devices are grouped to produce a single product or a group of products with similar production requirements. By implementing cellular manufacturing, it is possible to significantly influence the elimination of the seven types of Lean waste: transport, inventory, unnecessary motion, waiting, overproduction, overprocessing, and defects. There are various models of a cellular manufacturing organization, and some of the most widely used and studied include the Toyota Sewing System, Bucket Brigades, Working Balance, and Rabbit Chase. This paper aims to present different types of lean cellular manufacturing organizations. By reviewing the literature, the advantages and disadvantages of individual types of cellular manufacturing will be systematized. In the practical part of the paper, the theoretical assumptions will be confirmed, and the impact and robustness of individual types of cellular manufacturing will be explored in different situations. Simulations were performed in real conditions on real products to demonstrate the efficiency of individual types of cellular manufacturing organizations depending on the duration of technological operations. The goal was also to examine the robustness of individual types of cellular organization in case of the absence of certain operators or insufficiently trained operators. The criteria used to compare different cellular models were productivity, non-conformance, WIP inventory, time to deliver the first correct piece, and flow time. Simulations were performed for the Toyota Sewing System, Bucket Brigade, Working Balance, and Rabbit Chase cellular manufacturing concepts. The simulation results indicate significant differences in the performance of individual concepts, where the difference in some criteria can reach up to 100%.  

Participatory robot design projects with older adults often use multiple sessions to encourage design feedback and active participation from users. Prior projects have, however, not analyzed the learning outcomes for older adults across co-design sessions and how they support constructive design feedback and meaningful participation. To bridge this gap, we examined the learning outcomes within a "longitudinal panel." This panel comprised seven co-design sessions with 11 older adults of varying cognitive abilities over six months, aimed at designing a robot to guide a photograph-based conversational activity. Using Nelson and Stolterman’s framework of the hierarchy of design-learning, we demonstrate how older adult panelists achieved multiple design-learning outcomes – capacity, confidence, capability, competence, courage, and connection – which allowed them to provide actionable design suggestions. We provide guidelines for conducting longitudinal panels that can enhance user design-learning and participation in robot design.CCS CONCEPTS• Human-centered computing → Participatory design; User centered design; • Computer systems organization → Robotics; • General and reference → Design.

Robot designers commonly conceptualize robot sociality as a collection of features and capabilities. In contrast, sociologists define sociality as continuously constructed through interpersonal interactions. Based on the latter perspective, we trace how robots are incorporated into emerging social interaction ritual chains by robot companies and their staff and by robot owners across diverse contexts: homes, cafes, robot stores, user-organized meetups, and company events for robot users. Our empirical findings from ethnographic field work in Japan relating to three robots – aibo, RoboHon, and LOVOT – show how companies create positive interactions between people and robots by incorporating familiar design patterns into robots, modeling successful interactions in person and online, and bringing owners together in events that establish common values of acceptance of social robots as artifacts to live with and nurture. Owners, for their part, develop interaction rituals that include robots in their daily activities, make interpersonal connections, and experience emotionally resonant interactions, around robots in public meetups and events. Through these emerging interaction ritual chains, companies and owners construct the notion of robots as social agents to live with as a meaningful component of their emotional experiences and broader social relationships. Our work suggests that social robot design should consider this broader framing of sociality and create affordances for establishing interaction ritual chains more explicitly.CCS CONCEPTS • Human-centered computing → Empirical studies in HCI; Field studies; Empirical studies in interaction design; Interaction design theory, concepts and paradigms.

Though teens are a population with growing agency and use of smart technologies, their concerns surrounding privacy with AI and robots are under-represented in research. Using focus group discussions and a mixed methods analysis, we present teens’ comfort levels with robotic information collection and sharing during three hypothetical scenarios involving a child interacting with the Haru social robot in the home. We find participant concerns align with an access-based definition of privacy which prioritizes being in control of their information and of when the robot behaves autonomously. Responses also indicate that teens conceptualize Haru not just as an intelligent device, but also as a social entity. Their shifts in comfort and discussions reflect an engagement in social relationship management with robots in the home in cases where the robot mediates a user’s responsibilities and relationships with others.CCS CONCEPTS• Security and privacy → Social aspects of security and privacy; • Human-centered computing → User studies; • Social and professional topics → Adolescents.

As robots become increasingly active around human environments, they must navigate both physical and social realms, necessitating awareness of their surroundings and inhabitants, including the potential collection of sensitive data. To be accepted in human spaces, they need to be trusted to handle personal information adequately, not only adhering to security and data protection standards, but also aligning with contextual norms, individual expectations, and domain-specific requirements. Challenges intensify when robots engage with multiple humans across varied contexts over extended periods. Drawing from psychology, sociology, ethics, and law, and the experience of participants, we seek to outline dimensions and prerequisites for privacy-awareness in HRI. In this workshop we want to discuss methodologies, user interfaces, and personalizing options, and AI reasoning to design privacy-aware robot behavior in the human-robot interaction community.

One of the questions human-robot interaction (HRI) research needs to address prior to in-home robot deployment is when optimal moments for everyday interaction might occur. These can vary based on robot users' existing routines and personal preferences. As part of a larger project to design a conversational robot that can assist older adults in recognizing and maintaining their ikigai (sense of meaning and purpose in life), we explored the question "when might be good times for the robot to engage older adults in activities?". 11 older adults who were familiar with our prototype robot from prior participation in our research took part in a two week-long "diary study" to identify their habits and preferred times of engagement with the robot. The diary was performed by sending text messages to the older adults twice daily, asking what they were doing at the moment and whether this was a suitable time for interacting with the robot. The findings of the study allowed us to determine optimal times for interaction with the robot' - commonly before and after lunch and before sleep. Insights from this approach contribute to designing robots that can be integrated into the daily lives of older adults.