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The traditional view on the cerebellum is that it controls motor behavior. Although recent work has revealed that the cerebellum supports also nonmotor functions such as cognition and affect, only during the last 5 years it has become evident that the cerebellum also plays an important social role. This role is evident in social cognition based on interpreting goal-directed actions through the movements of individuals (social “mirroring”) which is very close to its original role in motor learning, as well as in social understanding of other individuals’ mental state, such as their intentions, beliefs, past behaviors, future aspirations, and personality traits (social “mentalizing”). Most of this mentalizing role is supported by the posterior cerebellum (e.g., Crus I and II). The most dominant hypothesis is that the cerebellum assists in learning and understanding social action sequences, and so facilitates social cognition by supporting optimal predictions about imminent or future social interaction and cooperation. This consensus paper brings together experts from different fields to discuss recent efforts in understanding the role of the cerebellum in social cognition, and the understanding of social behaviors and mental states by others, its effect on clinical impairments such as cerebellar ataxia and autism spectrum disorder, and how the cerebellum can become a potential target for noninvasive brain stimulation as a therapeutic intervention. We report on the most recent empirical findings and techniques for understanding and manipulating cerebellar circuits in humans. Cerebellar circuitry appears now as a key structure to elucidate social interactions.

The traditional view on the cerebellum is that it controls motor behavior. Although recent work has revealed that the cerebellum supports also nonmotor functions such as cognition and affect, only during the last 5 years it has become evident that the cerebellum also plays an important social role. This role is evident in social cognition based on interpreting goal-directed actions through the movements of individuals (social “mirroring”) which is very close to its original role in motor learning, as well as in social understanding of other individuals’ mental state, such as their intentions, beliefs, past behaviors, future aspirations, and personality traits (social “mentalizing”). Most of this mentalizing role is supported by the posterior cerebellum (e.g., Crus I and II). The most dominant hypothesis is that the cerebellum assists in learning and understanding social action sequences, and so facilitates social cognition by supporting optimal predictions about imminent or future social interaction and cooperation. This consensus paper brings together experts from different fields to discuss recent efforts in understanding the role of the cerebellum in social cognition, and the understanding of social behaviors and mental states by others, its effect on clinical impairments such as cerebellar ataxia and autism spectrum disorder, and how the cerebellum can become a potential target for noninvasive brain stimulation as a therapeutic intervention. We report on the most recent empirical findings and techniques for understanding and manipulating cerebellar circuits in humans. Cerebellar circuitry appears now as a key structure to elucidate social interactions.

Cerebellar reserve refers to the capacity of the cerebellum to compensate for tissue damage or loss of function resulting from many different etiologies. When the inciting event produces acute focal damage (e.g., stroke, trauma), impaired cerebellar function may be compensated for by other cerebellar areas or by extracerebellar structures (i.e., structural cerebellar reserve). In contrast, when pathological changes compromise cerebellar neuronal integrity gradually leading to cell death (e.g., metabolic and immune-mediated cerebellar ataxias, neurodegenerative ataxias), it is possible that the affected area itself can compensate for the slowly evolving cerebellar lesion (i.e., functional cerebellar reserve). Here, we examine cerebellar reserve from the perspective of the three cornerstones of clinical ataxiology: control of ocular movements, coordination of voluntary axial and appendicular movements, and cognitive functions. Current evidence indicates that cerebellar reserve is potentiated by environmental enrichment through the mechanisms of autophagy and synaptogenesis, suggesting that cerebellar reserve is not rigid or fixed, but exhibits plasticity potentiated by experience. These conclusions have therapeutic implications. During the period when cerebellar reserve is preserved, treatments should be directed at stopping disease progression and/or limiting the pathological process. Simultaneously, cerebellar reserve may be potentiated using multiple approaches. Potentiation of cerebellar reserve may lead to compensation and restoration of function in the setting of cerebellar diseases, and also in disorders primarily of the cerebral hemispheres by enhancing cerebellar mechanisms of action. It therefore appears that cerebellar reserve, and the underlying plasticity of cerebellar microcircuitry that enables it, may be of critical neurobiological importance to a wide range of neurological/neuropsychiatric conditions.

Travel behaviour has been under the spotlight of transportation research for almost 50 years. The focus on disaggregate data to model travel demand, initially with trip-based and tour-based models in the early 70s and 80s, quickly gave birth to a growing research stream on activity-based models. These models integrate travel related decisions of people for at least 1 day and, although the exact construction of the travel pattern may vary, most frameworks include the representation of the number, purposes (activities), timing, location and travel mode of traveller’s tours and stops. Activity-based models were proposed to increase the accuracy of alternative travel behaviour models, with higher spatial and temporal resolutions and a sounder and more realistic representation of key individual behaviours affecting travel. Their effectiveness prevails the alternative travel demand models when used for policy appraisal. Rule-based and utility-based frameworks have now reached the state-of-the-art and practice of activity-based models with several deployment efforts reported by metropolitan planning agencies, players in the transportation consultancy and software development fields. These efforts showed some of the key benefits of activity-based models, but also revealed more evidence for further research efforts by the research community. Further insights on the integration of activity-based models with dynamic network assignment models, on day-to-day dynamics, on non-observed or overlapping choice alternatives or decision dimensions, on more advanced behavioural formulations, such as the inclusion of social or household interactions, and on the development of computationally efficient deployments have caught the attention of the research community. This special issue presents the most recent achievements in some of these emerging research topics, including

Let M be a cancellative and commutative (additive) monoid. The monoid M is atomic if every non-invertible element can be written as a sum of irreducible elements, which are also called atoms. Also, M satisfies the ascending chain condition on principal ideals (ACCP) if every increasing sequence of principal ideals (under inclusion) becomes constant from one point on. In the first part of this paper, we characterize torsion-free monoids that satisfy the ACCP as those torsion-free monoids whose submonoids are all atomic. A submonoid of the nonnegative cone of a totally ordered abelian group is often called a positive monoid. Every positive monoid is clearly torsion-free. In the second part of this paper, we study the atomic structure of certain classes of positive monoids.

Coordination is one of the keys for the success of open source software (OSS) communities because geographically distributed members need to collaborate on their work using communication tools (e.g., mailing lists, bulletin board systems, bug tracking systems, and so on). In this paper, we investigated the informal social structure among developers and users by analyzing two mailing lists of developers and users in the Apache community based on betweenness centrality, one centrality measure proposed by Freeman. From the analysis results, we found that (1) participants with high betweenness coordinated activities between developers and users and (2) some participants have been functioning as coordinators in the community for a long time., OSS 2008 : Open Source Development, Communities and Quality, September 7-10, 2008, Milano, Italy

This paper, originally printed 50 years ago, was the first to provide a detailed discussion of the possible design of an interferometric gravitational-wave detector. In particular, unlike any previous paper based on a similar idea, it provides a detailed analysis of the noise sources and their mitigation. Since the work eventually led to opening up the whole field of gravitational-wave astronomy, as described in the accompanying editorial note, with many exciting results already, and brought the author a share in a Nobel Prize, it is one of the most significant papers in the Golden Oldies series.

In a 1983 paper, the author has established a (decategorified) Satake equivalence for affine Hecke algebras. In this paper, we give new proofs for some results of that paper, one based on the theory of J-rings and one based on the known character formula for rational representations of a reductive group in positive, large characteristic. We also give an extension of that formula to disconnected groups.

The majority of geographical profiling research focuses on the relationship between offender and location, which works particularly well when a burial site is known. In real-world investigations, however, burial or dump sites are often not known. The aim of the current paper is to outline a relatively under-used method of geographic profiling: Winthropping. While the method has been around for several decades, few studies have provided any research findings using it. There are two likely reasons for Winthropping being under-used: first, it has not been clearly, theoretically explained; second, given its relative novelty, it may not be immediately clear how to use it in research and real-world scenarios. The current paper outlines several key psychological (e.g., satisficing and affordances) and criminological (e.g., rational choice theory and crime geometry) theories that may explain why Winthropping works. Case studies are provided, and a methodological approach (matrix forecasting) is then provided to show how it could work in research practice and real-world applications. Overall, Winthropping is deemed to be highly useful, and it is hoped that experts in the field will begin developing this tool for wider, applied use.

The majority of geographical profiling research focuses on the relationship between offender and location, which works particularly well when a burial site is known. In real-world investigations, however, burial or dump sites are often not known. The aim of the current paper is to outline a relatively under-used method of geographic profiling: Winthropping. While the method has been around for several decades, few studies have provided any research findings using it. There are two likely reasons for Winthropping being under-used: first, it has not been clearly, theoretically explained; second, given its relative novelty, it may not be immediately clear how to use it in research and real-world scenarios. The current paper outlines several key psychological (e.g., satisficing and affordances) and criminological (e.g., rational choice theory and crime geometry) theories that may explain why Winthropping works. Case studies are provided, and a methodological approach (matrix forecasting) is then provided to show how it could work in research practice and real-world applications. Overall, Winthropping is deemed to be highly useful, and it is hoped that experts in the field will begin developing this tool for wider, applied use.

This paper studies the problem of deterministic rank-one matrix completion. It is known that the simplest semidefinite programming relaxation, involving minimization of the nuclear norm, does not in general return the solution for this problem. In this paper, we show that in every instance where the problem has a unique solution, one can provably recover the original matrix through the level 2 Lasserre relaxation with minimization of the trace norm. We further show that the solution of the proposed semidefinite program is Lipschitz stable with respect to perturbations of the observed entries, unlike more basic algorithms such as nonlinear propagation or ridge regression. Our proof is based on recursively building a certificate of optimality corresponding to a dual sum-of-squares (SoS) polynomial. This SoS polynomial is built from the polynomial ideal generated by the completion constraints and the monomials provided by the minimization of the trace. The proposed relaxation fits in the framework of the Lasserre hierarchy, albeit with the key addition of the trace objective function. Finally, we show how to represent and manipulate the moment tensor in favorable complexity by means of a hierarchical low-rank factorization.

The soaring development of Web technologies and mobile devices has blurred time-space boundaries of people’s daily activities. Such development together with the life-long learning requirement give birth to a new learning style, micro learning. Micro learning aims to effectively utilize learners’ fragmented time to carry out personalized learning activities through online education resources. The whole workflow of a micro learning system can be separated into three processing stages: micro learning material generation, learning materials annotation and personalized learning materials delivery. Our micro learning framework is firstly introduced in this paper from a higher perspective. Then we will review representative segmentation and annotation strategies in the e-learning domain. As the core part of the micro learning service, we further investigate several the state-of-the-art recommendation strategies, such as soft computing, transfer learning, reinforcement learning, and context-aware techniques. From a research contribution perspective, this paper serves as a basis to depict and understand the challenges in the data sources and data mining for the research of micro learning.

This study analyses the relational dimension and the knowledge transfer mechanisms in an innovation ecosystems (IEs), assuming that the bottom-up creation of synergies and cooperative mechanisms between local actors are the drivers of a regional smart growth. More specifically, the study explores the configuration of the network structure and the variety of inter-organizational relationships in a case of a smart IE by capturing the heterogeneous nature of IE demography, whether most studies limit their analyses to inter-firm relationships and at the node-level. Secondly, the paper provides insights into the network portfolio composition, which has been underexplored in IE literature, allowing for the identification of those relationships considered more fruitful to enhance innovation processes from a local perspective. To capture both aspects of IE’s relational dimension (i.e. network structure and network portfolio of relationships) our paper adopts an explorative approach, by taking evidence from the empirical study of the biopharma IE in greater Boston area, which has been exemplified as a successful case. Our empirical study combines two methods, namely social network analysis and expert interviews. Firstly, we conduct a social network analysis to gain insights about the optimal network structure and secondly, we conduct a round of semi-structured interviews with key stakeholders in the ecosystem to explore the characteristics of the desirable network portfolio. Our findings show that a smart IE presents an open network structure with structural holes, a high level of modularity and a portfolio of relationships that privileges informal and non-redundant ties within small communities focused on specific themes.

Often marketers face the challenge of how to communicate best with the customers who have the right responsibilities, influence or purchasing power, especially in business-to-business (B2B) settings. For example, B2B marketers selling software and IT need to identify IT decision-makers (ITDMs) within organizations. The modern digital environment in theory allows marketers to target individuals in organizations through specifically designed third-party audience segments based on deterministic prospect lists or probabilistic inference. However, in this paper we show that in our context, such ‘off-the-shelf’ segments perform no better at reaching the right person than random prospecting. We present evidence that even deterministic attribute information is flawed for ITDM identification, and that the poor campaign results can be partly linked to incorrect assignment of established prospect profiles to online identifiers. We then use access to our publisher network data to investigate what would happen if the advertiser had used first-party data that are less susceptible to the identified issues. We demonstrate that first-party demographics or contextual information allows advertisers and publishers to outperform both third-party ITDM audience segments and random prospecting. Our findings have implications for understanding the shift in digital advertising away from third-party cookie tracking, and how to execute digital marketing in the context of broad privacy regulation.

This paper addresses the noise-minimal trajectory optimization problem for a specific type of aircraft: rotorcraft. It relies on a realistic noise footprint computation software provided by industry that is black-box. Locally optimal trajectories are computed through a tailored solution approach based on the Mesh-Adaptive Direct Search algorithm. We propose multiple surrogates defined according to our knowledge of the problem, including a surrogate relying on the physics of the problem (approximating the rotorcraft noise model), and another based on a machine learning (neural network) method. The proposed solution approach is further enhanced by the computation of an appropriate starting guess through a path planning algorithm tailored to the problem, and by the reduction of the variable space domain. The performance of the proposed methodology both in terms of quality of the solutions (trajectories exhibiting significant noise reduction compared to those currently flown in practice) and computing time is illustrated through numerical experiments on real-world case studies.

In order to provide adaptive and user-friendly solutions to robotic manipulation, it is important that the agent can learn to accomplish tasks even if they are only provided with very sparse instruction signals. To address the issues reinforcement learning algorithms face when task rewards are sparse, this paper proposes an intrinsic motivation approach that can be easily integrated into any standard reinforcement learning algorithm and can allow robotic manipulators to learn useful manipulation skills with only sparse extrinsic rewards. Through integrating and balancing empowerment and curiosity, this approach shows superior performance compared to other state-of-the-art intrinsic exploration approaches during extensive empirical testing. When combined with other strategies for tackling the exploration challenge, e.g. curriculum learning, our approach is able to further improve the exploration efficiency and task success rate. Qualitative analysis also shows that when combined with diversity-driven intrinsic motivations, this approach can help manipulators learn a set of diverse skills which could potentially be applied to other more complicated manipulation tasks and accelerate their learning process.

Incentive-based public transport demand management (PTDM) can effectively mitigate overcrowding issues in crowded urban rail systems. Analyzing passengers’ behavioral responses to the incentive can guide the design, implementation, and update of PTDM strategies. Though several studies reported passengers’ responses to fare incentives, they focused on passengers’ short-term behavioral responses. Limited studies explore passengers’ longitudinal behavioral responses for different types of adopters, which is important for policy assessment and adjustment. This paper explores and models passengers’ longitudinal behavior response to a pre-peak fare discount incentive using 18 months of smartcard data in public transport in Hong Kong. We classified adopters into six types based on their temporal travel pattern changes before and after the promotion. The longitudinal analysis reveals that among all adopters, 19% of users change their departure times to take advantage of fare discounts but do not contribute to the goal of reducing peak-hour travel. However, these adopters are more likely to sustain their changed behavior in a long term which is not desired by the incentive program. The spatial analysis shows that the origin station distribution of late adopters is relatively more diverse than the early adopters with more trips starting from distant areas. The diffusion modeling shows that the majority adopters are innovators and the word-of-mouth diffusion effect (imitators) is marginal. The discrete choice model results highlight the heterogeneous impact of factors on different types of adopters and their values of time changes. The significant factors common to adopters are: departure time flexibility, the expected money savings, the required departure time changes, and work locations. The findings are useful for public transport planners and policymakers for informed incentive design and management.

Why, when, and how do stereotypes change? This paper develops a computational account based on the principles of structure learning: stereotypes are governed by probabilistic beliefs about the assignment of individuals to groups. Two aspects of this account are particularly important. First, groups are flexibly constructed based on the distribution of traits across individuals; groups are not fixed, nor are they assumed to map on to categories we have to provide to the model. This allows the model to explain the phenomena of group discovery and subtyping, whereby deviant individuals are segregated from a group, thus protecting the group’s stereotype. Second, groups are hierarchically structured, such that groups can be nested. This allows the model to explain the phenomenon of subgrouping, whereby a collection of deviant individuals is organized into a refinement of the superordinate group. The structure learning account also sheds light on several factors that determine stereotype change, including perceived group variability, individual typicality, cognitive load, and sample size.

Consider an agent traversing a graph of “gadgets”, where each gadget has local state that changes with each traversal by the agent according to specified rules. Prior work has studied the computational complexity of deciding whether the agent can reach a specified location, a problem we call reachability. This paper introduces new goals for the agent, aiming to characterize when the computational complexity of these problems is the same or differs from that of reachability. First we characterize the complexity of universal traversal—where the goal is to traverse every gadget at least once—for DAG gadgets (partially), one-state gadgets, and reversible deterministic gadgets. Then we study the complexity of reconfiguration—where the goal is to bring the system of gadgets to a specified state. We prove many cases PSPACE-complete, and show in some cases that reconfiguration is strictly harder than reachability, while in other cases, reachability is strictly harder than reconfiguration.

Portrait viewpoint and illumination editing is an important problem with several applications in VR/AR, movies, and photography. Comprehensive knowledge of geometry and illumination is critical for obtaining photorealistic results. Current methods are unable to explicitly model in 3D while handling both viewpoint and illumination editing from a single image. In this paper, we propose VoRF, a novel approach that can take even a single portrait image as input and relight human heads under novel illuminations that can be viewed from arbitrary viewpoints. VoRF represents a human head as a continuous volumetric field and learns a prior model of human heads using a coordinate-based MLP with individual latent spaces for identity and illumination. The prior model is learned in an auto-decoder manner over a diverse class of head shapes and appearances, allowing VoRF to generalize to novel test identities from a single input image. Additionally, VoRF has a reflectance MLP that uses the intermediate features of the prior model for rendering One-Light-at-A-Time (OLAT) images under novel views. We synthesize novel illuminations by combining these OLAT images with target environment maps. Qualitative and quantitative evaluations demonstrate the effectiveness of VoRF for relighting and novel view synthesis, even when applied to unseen subjects under uncontrolled illumination. This work is an extension of Rao et al. (VoRF: Volumetric Relightable Faces 2022). We provide extensive evaluation and ablative studies of our model and also provide an application, where any face can be relighted using textual input.