This paper introduces a multimethod framework for studying spatial and social dynamics in real-world group-agent interactions with socially interactive agents. Drawing on proxemics and bonding theories, the method combines subjective self-reports and objective spatial tracking. Applied in two field studies in a museum ( N = 187) with a robot and a virtual agent, the paper addresses the challenges in aligning human perception and behavior. We focus on presenting an open source, scalable, and field-tested toolkit for future studies.

We introduce a novel molecular representation through Algebraic Data Types (ADTs) - composite data structures formed through the combination of simpler types that obey algebraic laws. By explicitly considering how the datatype of a representation constrains the operations which may be performed, we ensure meaningful inference can be performed over generative models (programs with sample} and score operations). This stands in contrast to string-based representations where string-type operations may only indirectly correspond to chemical and physical molecular properties, and at worst produce nonsensical output. The ADT presented implements the Dietz representation for molecular constitution via multigraphs and bonding systems, and uses atomic coordinate data to represent 3D information and stereochemical features. This creates a general digital molecular representation which surpasses the limitations of the string-based representations and the 2D-graph based models on which they are based. In addition, we present novel support for quantum information through representation of shells, subshells, and orbitals, greatly expanding the representational scope beyond current approaches, for instance in Molecular Orbital theory. The framework's capabilities are demonstrated through key applications: Bayesian probabilistic programming is demonstrated through integration with LazyPPL, a lazy probabilistic programming library; molecules are made instances of a group under rotation, necessary for geometric learning techniques which exploit the invariance of molecular properties under different representations; and the framework's flexibility is demonstrated through an extension to model chemical reactions. After critiquing previous representations, we provide an open-source solution in Haskell - a type-safe, purely functional programming language.

Data-driven machine learning (ML) has demonstrated tremendous potential in material property predictions. However, the scarcity of materials data with costly property labels in the vast chemical space presents a significant challenge for ML in efficiently predicting properties and uncovering structure-property relationships. Here, we propose a novel hierarchy-boosted funnel learning (HiBoFL) framework, which is successfully applied to identify semiconductors with ultralow lattice thermal conductivity ($\kappa_\mathrm{L}$). By training on only a few hundred materials targeted by unsupervised learning from a pool of hundreds of thousands, we achieve efficient and interpretable supervised predictions of ultralow $\kappa_\mathrm{L}$, thereby circumventing large-scale brute-force calculations without clear objectives. As a result, we provide a list of candidates with ultralow $\kappa_\mathrm{L}$ for potential thermoelectric applications and discover a new factor that significantly influences structural anharmonicity. This study offers a novel practical pathway for accelerating the discovery of functional materials.

Vertebrate animals benefit from a combination of rigidity for structural support and softness for adaptation. Similarly, integrating rigidity and softness can enhance the versatility of soft robotics. However, the challenges associated with creating durable bonding interfaces between soft and rigid materials have limited the development of hybrid robots. Existing solutions require specialized machinery, such as polyjet 3D printers, which are not commonly available. In response to these challenges, we have developed a 3D printing technique that can be used with almost all commercially available FDM printers. This technique leverages the common issue of underextrusion to create a strong bond between soft and rigid materials. Underextrusion generates a porous structure, similar to fibrous connective tissues, that provides a robust interface with the rigid part through layer fusion, while the porosity enables interlocking with the soft material. Our experiments demonstrated that this method outperforms conventional adhesives commonly used in soft robotics, achieving nearly 200\% of the bonding strength in both lap shear and peeling tests. Additionally, we investigated how different porosity levels affect bonding strength. We tested the technique under pressure scenarios critical to soft and hybrid robots and achieved three times more pressure than the current adhesion solution. Finally, we fabricated various hybrid robots using this technique to demonstrate the wide range of capabilities this approach and hybridity can bring to soft robotics. has context menu

Will AI replace your job? Work is a state of upheaval--and beyond shifts in where and when people work, changes are occurring in the content of work itself--literally in responsibilities, tasks and assignments. This is fueled by AI and most recently, ChatGPT. People are uncertain about whether they'll be replaced by technology--and at the same time, they're looking for greater meaning from work and more flexibility in how they go about it. But it is possible to reimagine the work experience in the new digital landscape, emphasizing what humans do best and ensuring the work experience is engaging, challenging and secure.

The responses flowed into the data bank of Billy Chat, a robot that uses artificial intelligence to text. Billy and other "chatbots" were launched at California State University campuses in 2019 to help students stay on track to graduate. But after students were sent home last spring at the onset of the COVID-19 pandemic, Billy evolved into more of a friend, blurring the line between artificial and real when the world turned away from human touch and connections.

The performance of rigid pavement is greatly affected by the properties of base/subbase as well as subgrade layer. However, the performance predicted by the AASHTOWare Pavement ME design shows low sensitivity to the properties of base and subgrade layers. To improve the sensitivity and better reflect the influence of unbound layers a new set of improved models i.e., resilient modulus (MR) and modulus of subgrade reaction (k-value) are adopted in this study. An Artificial Neural Network (ANN) model is developed to predict the modified k-value based on finite element (FE) analysis. The training and validation datasets in the ANN model consist of 27000 simulation cases with different combinations of pavement layer thickness, layer modulus and slab-base interface bond ratio. To examine the sensitivity of modified MR and k-values on pavement response, eight pavement sections data are collected from the Long-Term Pavement performance (LTPP) database and modeled by using the FE software ISLAB2000. The computational results indicate that the modified MR values have higher sensitivity to water content in base layer on critical stress and deflection response of rigid pavements compared to the results using the Pavement ME design model. It is also observed that the k-values using ANN model has the capability of predicting critical pavement response at any partially bonded conditions whereas the Pavement ME design model can only calculate at two extreme bonding conditions (i.e., fully bonding and no bonding).

The plot revolves around a potential future where a group of "synths" who look identical to humans gain consciousness and seek liberation from their subservient societal roles as owned property. In the first season, the lead synth character Anita is assigned as a nanny to a family whose human mother struggles with alcoholism. After being away from the house a number of nights in a row, the mother comes home and tells her nursery-school aged daughter she'd like to read her a bedtime story. "I want Anita to read to me." Just a mic drop of one little girl's truth--that she prefers a robot nanny to her human mom.

US President Donald Trump was greeted with laughter in September when he told the UN that he had accomplished more than "almost any administration" in his country's history. Mr Trump admitted that he "didn't expect" that reaction - but that it was "just fine". It was one example of the many reasons people laugh - and most of them are not because somebody is being particularly funny. Laughter is primarily a form of bonding; we are 30 times more likely to laugh if we are with others than if we are alone. It is an ancient, universal reaction that is not even limited to humans; it has been documented in many animal species, including apes and even rats.

Researchers have found that'dog-speak' is important for bonding with your pet and could improve your mutt's attention span. Scientists found that dogs need to hear words spoken rhythmically in a high-pitched emotional voice in order to find it relevant. Experts said that speaking to your dog using a baby-like tone can have the same benefits that occur when a parent engage sin this way with a young child. Researchers have found that'dog-speak' is important for bonding with your pet and could improve your mutt's attention span. Dr Katie Slocombe, a researcher from the University of York's Department of Psychology, said: 'A special speech register, known as infant-directed speech, is thought to aid language acquisition and improve the way a human baby bonds with an adult.