Detecting sets of relevant patterns from a given dataset is an important challenge in data mining. The relevance of a pattern, also called utility in the literature, is a subjective measure and can be actually assessed from very different points of view. Rule-based languages like Answer Set Programming (ASP) seem well suited for specifying user-provided criteria to assess pattern utility in a form of constraints; moreover, declarativity of ASP allows for a very easy switch between several criteria in order to analyze the dataset from different points of view. In this paper, we make steps toward extending the notion of High-Utility Pattern Mining; in particular, we introduce a new framework that allows for new classes of utility criteria not considered in the previous literature. We also show how recent extensions of ASP with external functions can support a fast and effective encoding and testing of the new framework. To demonstrate the potential of the proposed framework, we exploit it as a building block for the definition of an innovative method for predicting ICU admission for COVID-19 patients. Finally, an extensive experimental activity demonstrates both from a quantitative and a qualitative point of view the effectiveness of the proposed approach.

When subjected to a sudden, unanticipated threat, human groups characteristically self-organize to identify the threat, determine potential responses, and act to reduce its impact. Central to this process is the challenge of coordinating information sharing and response activity within a disrupted environment. In this paper, we consider coordination in the context of responses to the 2001 World Trade Center (WTC) disaster. Using records of communications among 17 organizational units, we examine the mechanisms driving communication dynamics, with an emphasis on the emergence of coordinating roles. We employ relational event models (REMs) to identify the mechanisms shaping communications in each unit, finding a consistent pattern of behavior across units with very different characteristics. Using a simulation-based “knock-out” study, we also probe the importance of different mechanisms for hub formation. Our results suggest that, while preferential attachment and pre-disaster role structure generally contribute to the emergence of hub structure, temporally local conversational norms play a much larger role in the WTC case. We discuss broader implications for the role of microdynamics in driving macroscopic outcomes, and for the emergence of coordination in other settings.

The main contribution of this paper is the design and development of the lower body of PANDORA (3D-Printed Autonomous humaNoid Developed for Open-source Research Applications), a new humanoid robotic platform implementing additive manufacturing techniques. The three joint configurations (hip, knee, and ankle) along with the major three structural parts (pelvis, thigh, and shin) of the lower body are discussed. The use of 3D printing and PLA+ material makes the robot an affordable solution for humanoid robotics research that gives a high power-to-weight ratio by significantly reducing the number of parts, as well as manufacturing and assembly time. The range of motion of the lower body of PANDORA has been investigated and is found to be comparable to a human lower body. Further, finite element analysis has been performed on the major parts of the lower body of PANDORA to check the structural integrity and to avoid catastrophic failures in the robot. The use of in-house developed actuators and robot electronics reduces the overall cost of the robot and makes PANDORA easily accessible to the research communities working in the field of humanoids. Overall, PANDORA has the potential for becoming popular between researchers and designers for investigating applications in the field of humanoid robotics, healthcare, and manufacturing, just to mention a few. The mechanical designs presented in this work are available open source to lower the knowledge barrier in developing and conducting research on bipedal robots.

A core part of the rehabilitation scheduling process consists of planning rehabilitation physiotherapy sessions for patients, by assigning proper operators to them in a certain time slot of a given day, taking into account several legal, medical, and ethical requirements and optimizations, for example, patient’s preferences and operator’s work balancing. Being able to efficiently solve such problem is of upmost importance, in particular after the COVID-19 pandemic that significantly increased rehabilitation’s needs. In this paper, we present a two-phase solution to rehabilitation scheduling based on Answer Set Programming, which proved to be an effective tool for solving practical scheduling problems. We first present a general encoding and then add domain-specific optimizations. Results of experiments performed on both synthetic and real benchmarks, the latter provided by ICS Maugeri, show the effectiveness of our solution as well as the impact of our domain-specific optimizations.

Werner’s set-theoretical model is one of the simplest models of CIC. It combines a functional view of predicative universes with a collapsed view of the impredicative sort “${\tt Prop}$”. However, this model of ${\tt Prop}$ is so coarse that the principle of excluded middle $P \lor \neg P$ holds. Following our previous work, we interpret ${\tt Prop}$ into a topological space (a special case of Heyting algebra) to make the model more intuitionistic without sacrificing simplicity. We improve on that work by providing a full interpretation of dependent product types, using Alexandroff spaces. We also extend our approach to inductive types by adding support for ${\mathsf{list}}$s.

Flexibility is one of the most significant advantages of legged robots in unstructured environments. However, quadruped robots cannot interact with environments to complete some manipulation tasks. One effective way is to load a manipulation arm. In this paper, we exhibit a quadruped locomotion manipulation system (LMS) named HITPhanT. This system comprises a quadruped locomotion platform and a six-degree-of-freedom manipulation arm. Besides, when the LMS moves to a designated position for operation, it is necessary to constrain the foot contact points to avoid sliding. Therefore, the foot contact point is regarded as a spherical hinge. So the locomotion platform can be considered as a parallel mechanism. A hybrid kinematics model is established by considering the serial robotic arms connecting this parallel mechanism. Besides, the trajectory planning method, which improves the system’s manipulability in evaluating the system balance, is also proposed. Finally, corresponding experiments verify the overall system’s stabilization and algorithm’s effectiveness.

The global and uneven spread of COVID-19, mirrored at the local scale, reveals stark differences along racial and ethnic lines. We respond to the pressing need to understand these divergent outcomes via neighborhood level analysis of mobility and case count information. Using data from Chicago over 2020, we leverage a metapopulation Susceptible-Exposed-Infectious-Removed model to reconstruct and simulate the spread of SARS-CoV-2 at the ZIP Code level. We demonstrate that exposures are mostly contained within one’s own ZIP Code and demographic group. Building on this observation, we illustrate that we can understand epidemic progression using a composite metric combining the volume of mobility and the risk that each trip represents, while separately these factors fail to explain the observed heterogeneity in neighborhood level outcomes. Having established this result, we next uncover how group level differences in these factors give rise to disparities in case rates along racial and ethnic lines. Following this, we ask what-if questions to quantify how segregation impacts COVID-19 case rates via altering mobility patterns. We find that segregation in the mobility network has contributed to inequality in case rates across demographic groups.