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Data-driven parcellations are widely used for exploring the functional organization of the brain, and also for reducing the high dimensionality of fMRI data. Despite the flurry of methods proposed in the literature, functional brain parcellations are not highly reproducible at the level of individual subjects, even with very long acquisitions. Some brain areas are also more difficult to parcellate than others, with association heteromodal cortices being the most challenging. An important limitation of classical parcellations is that they are static, that is, they neglect dynamic reconfigurations of brain networks. In this paper, we proposed a new method to identify dynamic states of parcellations, which we hypothesized would improve reproducibility over static parcellation approaches. For a series of seed voxels in the brain, we applied a cluster analysis to regroup short (3 min) time windows into “states” with highly similar seed parcels. We split individual time series of the Midnight scan club sample into two independent sets of 2.5 hr (test and retest). We found that average within-state parcellations, called stability maps, were highly reproducible (over 0.9 test-retest spatial correlation in many instances) and subject specific (fingerprinting accuracy over 70% on average) between test and retest. Consistent with our hypothesis, seeds in heteromodal cortices (posterior and anterior cingulate) showed a richer repertoire of states than unimodal (visual) cortex. Taken together, our results indicate that static functional parcellations are incorrectly averaging well-defined and distinct dynamic states of brain parcellations. This work calls to revisit previous methods based on static parcellations, which includes the majority of published network analyses of fMRI data. Our method may, thus, impact how researchers model the rich interactions between brain networks in health and disease., Author Summary Functional brain parcellation has been a very active topic of investigation for the past two decades, yet there is no evidence to date of reproducible results at the individual level—see for example Figure 2 in Gordon, Laumann, Gilmore, et al. (2017), with a Dice coefficient plateauing around 0.7 using 40 min or more of data. In this paper, we show that highly reproducible brain parcels can be observed using short (3 min) time windows. Different modes—or states—of reproducible parcellations can be observed in a single brain region, and these modes have only little overlap with each other. We carefully quantified these individual dynamic states of parcellation using the Midnight Brain Scan dataset, featuring 5 hr of functional MRI per subject. Our results indicate that static functional parcellation are incorrectly averaging well-defined and distinct dynamic states. This brings important caution for any work based on static atlases, which is the dominant approach currently in so-called network analysis of fMRI data.

Ontologies of research areas are important tools for characterizing, exploring, and analyzing the research landscape. Some fields of research are comprehensively described by large-scale taxonomies, e.g., MeSH in Biology and PhySH in Physics. Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly. For instance, the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012. In this paper, we introduce the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 14K topics and 162K semantic relationships. It was created by applying the Klink-2 algorithm on a very large data set of 16M scientific articles. CSO presents two main advantages over the alternatives: i) it includes a very large number of topics that do not appear in other classifications, and ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO, we have also released the CSO Classifier, a tool for automatically classifying research papers, and the CSO Portal, a Web application that enables users to download, explore, and provide granular feedback on CSO. Users can use the portal to navigate and visualize sections of the ontology, rate topics and relationships, and suggest missing ones. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various research communities engaged with scholarly data.

This paper focuses on fine-grained, secure access to FAIR data, for which we propose ontology-based data access policies. These policies take into account both the FAIR aspects of the data relevant to access (such as provenance and licence), expressed as metadata, and additional metadata describing users. With this tripartite approach (data, associated metadata expressing FAIR information, and additional metadata about users), secure and controlled access to object data can be obtained. This yields a security dimension to the “A” (accessible) in FAIR, which is clearly needed in domains like security and intelligence. These domains need data to be shared under tight controls, with widely varying individual access rights. In this paper, we propose an approach called Ontology-Based Access Control (OBAC), which utilizes concepts and relations from a data set's domain ontology. We argue that ontology-based access policies contribute to data reusability and can be reconciled with privacy-aware data access policies. We illustrate our OBAC approach through a proof-of-concept and propose that OBAC to be adopted as a best practice for access management of FAIR data.

AMiner is a novel online academic search and mining system, and it aims to provide a systematic modeling approach to help researchers and scientists gain a deeper understanding of the large and heterogeneous networks formed by authors, papers, conferences, journals and organizations. The system is subsequently able to extract researchers’ profiles automatically from the Web and integrates them with published papers by a way of a process that first performs name disambiguation. Then a generative probabilistic model is devised to simultaneously model the different entities while providing a topic-level expertise search. In addition, AMiner offers a set of researcher-centered functions, including social influence analysis, relationship mining, collaboration recommendation, similarity analysis, and community evolution. The system has been in operation since 2006 and has been accessed from more than 8 million independent IP addresses residing in more than 200 countries and regions.

This study focuses on an essential precondition for reproducibility in computational linguistics: the willingness of authors to share relevant source code and data. Ten years after Ted Pedersen’s influential “Last Words” contribution in Computational Linguistics, we investigate to what extent researchers in computational linguistics are willing and able to share their data and code. We surveyed all 395 full papers presented at the 2011 and 2016 ACL Annual Meetings, and identified whether links to data and code were provided. If working links were not provided, authors were requested to provide this information. Although data were often available, code was shared less often. When working links to code or data were not provided in the paper, authors provided the code in about one third of cases. For a selection of ten papers, we attempted to reproduce the results using the provided data and code. We were able to reproduce the results approximately for six papers. For only a single paper did we obtain the exact same results. Our findings show that even though the situation appears to have improved comparing 2016 to 2011, empiricism in computational linguistics still largely remains a matter of faith. Nevertheless, we are somewhat optimistic about the future. Ensuring reproducibility is not only important for the field as a whole, but also seems worthwhile for individual researchers: The median citation count for studies with working links to the source code is higher.

This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference—and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate neuronal processing as belief propagation under deep generative models. Crucially, these models can entertain both discrete and continuous states, leading to distinct schemes for belief updating that play out on the same (neuronal) architecture. Technically, we use Forney (normal) factor graphs to elucidate the requisite message passing in terms of its form and scheduling. To accommodate mixed generative models (of discrete and continuous states), one also has to consider link nodes or factors that enable discrete and continuous representations to talk to each other. When mapping the implicit computational architecture onto neuronal connectivity, several interesting features emerge. For example, Bayesian model averaging and comparison, which link discrete and continuous states, may be implemented in thalamocortical loops. These and other considerations speak to a computational connectome that is inherently state dependent and self-organizing in ways that yield to a principled (variational) account. We conclude with simulations of reading that illustrate the implicit neuronal message passing, with a special focus on how discrete (semantic) representations inform, and are informed by, continuous (visual) sampling of the sensorium. Author Summary This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference—and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate neuronal processing as belief propagation under deep generative models that can entertain both discrete and continuous states. This leads to distinct schemes for belief updating that play out on the same (neuronal) architecture. Technically, we use Forney (normal) factor graphs to characterize the requisite message passing, and link this formal characterization to canonical microcircuits and extrinsic connectivity in the brain.

This paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity. This identification can be contrasted with functional connectivity methods based on symmetric correlations that are ubiquitous in resting-state functional MRI (fMRI). We use spectral dynamic causal modeling (DCM) to invert large graphs comprising dozens of nodes or regions. The ensuing graphs are directed and weighted, hence providing a neurobiologically plausible characterization of connectivity in terms of excitatory and inhibitory coupling. Furthermore, we show that the use of Bayesian model reduction to discover the most likely sparse graph (or model) from a parent (e.g., fully connected) graph eschews the arbitrary thresholding often applied to large symmetric (functional connectivity) graphs. Using empirical fMRI data, we show that spectral DCM furnishes connectivity estimates on large graphs that correlate strongly with the estimates provided by stochastic DCM. Furthermore, we increase the efficiency of model inversion using functional connectivity modes to place prior constraints on effective connectivity. In other words, we use a small number of modes to finesse the potentially redundant parameterization of large DCMs. We show that spectral DCM—with functional connectivity priors—is ideally suited for directed graph theoretic analyses of resting-state fMRI. We envision that directed graphs will prove useful in understanding the psychopathology and pathophysiology of neurodegenerative and neurodevelopmental disorders. We will demonstrate the utility of large directed graphs in clinical populations in subsequent reports, using the procedures described in this paper.

Recent work has presented intriguing results examining the knowledge contained in language models (LM) by having the LM fill in the blanks of prompts such as "Obama is a _ by profession". These prompts are usually manually created, and quite possibly sub-optimal; another prompt such as "Obama worked as a _" may result in more accurately predicting the correct profession. Because of this, given an inappropriate prompt, we might fail to retrieve facts that the LM does know, and thus any given prompt only provides a lower bound estimate of the knowledge contained in an LM. In this paper, we attempt to more accurately estimate the knowledge contained in LMs by automatically discovering better prompts to use in this querying process. Specifically, we propose mining-based and paraphrasing-based methods to automatically generate high-quality and diverse prompts, as well as ensemble methods to combine answers from different prompts. Extensive experiments on the LAMA benchmark for extracting relational knowledge from LMs demonstrate that our methods can improve accuracy from 31.1% to 39.6%, providing a tighter lower bound on what LMs know. We have released the code and the resulting LM Prompt And Query Archive (LPAQA) at https://github.com/jzbjyb/LPAQA., TACL 2020

Pre-training by language modeling has become a popular and successful approach to NLP tasks, but we have yet to understand exactly what linguistic capacities these pre-training processes confer upon models. In this paper we introduce a suite of diagnostics drawn from human language experiments, which allow us to ask targeted questions about information used by language models for generating predictions in context. As a case study, we apply these diagnostics to the popular BERT model, finding that it can generally distinguish good from bad completions involving shared category or role reversal, albeit with less sensitivity than humans, and it robustly retrieves noun hypernyms, but it struggles with challenging inference and role-based event prediction— and, in particular, it shows clear insensitivity to the contextual impacts of negation.

Target-dependent sentiment analysis (TDSA) aims to classify the sentiment of a text towards a given target. The major challenge of this task lies in modeling the semantic relatedness between a target and its context sentence. This paper proposes a novel Target-Guided Structured Attention Network (TG-SAN), which captures target-related contexts for TDSA in a fine-to-coarse manner. Given a target and its context sentence, the proposed TG-SAN first identifies multiple semantic segments from the sentence using a target-guided structured attention mechanism. It then fuses the extracted segments based on their relatedness with the target for sentiment classification. We present comprehensive comparative experiments on three benchmarks with three major findings. First, TG-SAN outperforms the state-of-the-art by up to 1.61% and 3.58% in terms of accuracy and Marco-F1, respectively. Second, it shows a strong advantage in determining the sentiment of a target when the context sentence contains multiple semantic segments. Lastly, visualization results show that the attention scores produced by TG-SAN are highly interpretable

meaning representation (AMR)-to-text generation is the challenging task of generating natural language texts from AMR graphs, where nodes represent concepts and edges denote relations. The current state-of-the-art methods use graph-to-sequence models; however, they still cannot significantly outperform the previous sequence-to-sequence models or statistical approaches. In this paper, we propose a novel graph-to-sequence model (Graph Transformer) to address this task. The model directly encodes the AMR graphs and learns the node representations. A pairwise interaction function is used for computing the semantic relations between the concepts. Moreover, attention mechanisms are used for aggregating the information from the incoming and outgoing neighbors, which help the model to capture the semantic information effectively. Our model outperforms the state-of-the-art neural approach by 1.5 BLEU points on LDC2015E86 and 4.8 BLEU points on LDC2017T10 and achieves new state-of-the-art performances.

OpenCitations is an infrastructure organization for open scholarship dedicated to the publication of open citation data as Linked Open Data using Semantic Web technologies, thereby providing a disruptive alternative to traditional proprietary citation indexes. Open citation data are valuable for bibliometric analysis, increasing the reproducibility of large-scale analyses by enabling publication of the source data. Following brief introductions to the development and benefits of open scholarship and to Semantic Web technologies, this paper describes OpenCitations and its data sets, tools, services, and activities. These include the OpenCitations Data Model; the SPAR (Semantic Publishing and Referencing) Ontologies; OpenCitations’ open software of generic applicability for searching, browsing, and providing REST APIs over resource description framework (RDF) triplestores; Open Citation Identifiers (OCIs) and the OpenCitations OCI Resolution Service; the OpenCitations Corpus (OCC), a database of open downloadable bibliographic and citation data made available in RDF under a Creative Commons public domain dedication; and the OpenCitations Indexes of open citation data, of which the first and largest is COCI, the OpenCitations Index of Crossref Open DOI-to-DOI Citations, which currently contains over 624 million bibliographic citations and is receiving considerable usage by the scholarly community.

Only scarce information is available on doctorate recipients’ career outcomes ( BuWiN, 2013 ). With the current information base, graduate students cannot make an informed decision on whether to start a doctorate or not ( Benderly, 2018 ; Blank et al., 2017 ). However, administrative labor market data, which could provide the necessary information, are incomplete in this respect. In this paper, we describe the record linkage of two data sets to close this information gap: data on doctorate recipients collected in the catalog of the German National Library (DNB), and the German labor market biographies (IEB) from the German Institute of Employment Research. We use a machine learning-based methodology, which (a) improves the record linkage of data sets without unique identifiers, and (b) evaluates the quality of the record linkage. The machine learning algorithms are trained on a synthetic training and evaluation data set. In an exemplary analysis, we compare the evolution of the employment status of female and male doctorate recipients in Germany.

The insights emerging from embodied (or 4E) cognition (Newen, et al., 2018) hold considerable promise for education, but thus far have had little impact. The widespread implementation of digital technologies in classrooms presents a timely occasion to remedy this situation. The increasing abstraction entailed in the transition from pen and paper to keyboards, and from reading in print books to reading on screens, warrants supplementing extant perspectives on learning and technologies as they are currently represented in curricula and educational policy documents. This chapter helps educators to rethink and redefine the role and meaning of technology in education broadly speaking, and describes how the use of digital technologies in the acquisition of basic skills like reading and writing specifically impacts learning from an embodied perspective. Drawing on examples from Nordic school contexts, we illustrate how 4E cognition can be pursued to benefit the learning experience in our digital age. For us, as human beings, the skills of reading and writing are not innate — meaning, there is no genetic blueprint for reading or writing (Wolf et al., 2012). Whereas children normally develop the ability to speak and communicate by means of language socialization, both reading and writing require systematic training over an extended period of time to develop. Helping children learn to read and write is one of the major tasks of basic education. A recent study using functional magnetic resonance imaging found that both reading and writing are multisensory experiences (Smith et al., 2018). Yet the ongoing digitalization poses new challenges for researchers and schools concerned with students’ literacy skills. As advances in technology in classroom applications become more mainstream, the way in which children engage in reading and writing is changing. Therefore, we argue that the theory of embodied cognition (4E) should be acknowledged when considering the strengths and weaknesses of various technologies in supporting different aspects of reading (e.g., low-level processes such as letter-sound correspondences, and high-level processes such as inference-based comprehension skills) and writing. peerReviewed

The best solution of structured prediction models in NLP is often inaccurate because of limited expressive power of the model or to non-exact parameter estimation. One way to mitigate this problem is sampling candidate solutions from the model’s solution space, reasoning that effective exploration of this space should yield high-quality solutions. Unfortunately, sampling is often computationally hard and many works hence back-off to sub-optimal strategies, such as extraction of the best scoring solutions of the model, which are not as diverse as sampled solutions. In this paper we propose a perturbation-based approach where sampling from a probabilistic model is computationally efficient. We present a learning algorithm for the variance of the perturbations, and empirically demonstrate its importance. Moreover, while finding the argmax in our model is intractable, we propose an efficient and effective approximation. We apply our framework to cross-lingual dependency parsing across 72 corpora from 42 languages and to lightly supervised dependency parsing across 13 corpora from 12 languages, and demonstrate strong results in terms of both the quality of the entire solution list and of the final solution.1

This paper presents an unsupervised framework for jointly modeling topic content and discourse behavior in microblog conversations. Concretely, we propose a neural model to discover word clusters indicating what a conversation concerns (i.e., topics) and those reflecting how participants voice their opinions (i.e., discourse). Extensive experiments show that our model can yield both coherent topics and meaningful discourse behavior. Further study shows that our topic and discourse representations can benefit the classification of microblog messages, especially when they are jointly trained with the classifier., Accepted in Transactions of the Association for Computational Linguistics

Neural dependency parsing has proven very effective, achieving state-of-the-art results on numerous domains and languages. Unfortunately, it requires large amounts of labeled data, that is costly and laborious to create. In this paper we propose a self-training algorithm that alleviates this annotation bottleneck by training a parser on its own output. Our Deep Contextualized Self-training (DCST) algorithm utilizes representation models trained on sequence labeling tasks that are derived from the parser's output when applied to unlabeled data, and integrates these models with the base parser through a gating mechanism. We conduct experiments across multiple languages, both in low resource in-domain and in cross-domain setups, and demonstrate that DCST substantially outperforms traditional self-training as well as recent semi-supervised training methods., Comment: Accepted to TACL in September 2019

This study introduces PV-RNN, a novel variational RNN inspired by the predictive-coding ideas. The model learns to extract the probabilistic structures hidden in fluctuating temporal patterns by dynamically changing the stochasticity of its latent states. Its architecture attempts to address two major concerns of variational Bayes RNNs: how can latent variables learn meaningful representations and how can the inference model transfer future observations to the latent variables. PV-RNN does both by introducing adaptive vectors mirroring the training data, whose values can then be adapted differently during evaluation. Moreover, prediction errors during backpropagation, rather than external inputs during the forward computation, are used to convey information to the network about the external data. For testing, we introduce error regression for predicting unseen sequences as inspired by predictive coding that leverages those mechanisms. The model introduces a weighting parameter, the meta-prior, to balance the optimization pressure placed on two terms of a lower bound on the marginal likelihood of the sequential data. We test the model on two datasets with probabilistic structures and show that with high values of the meta-prior the network develops deterministic chaos through which the data's randomness is imitated. For low values, the model behaves as a random process. The network performs best on intermediate values, and is able to capture the latent probabilistic structure with good generalization. Analyzing the meta-prior's impact on the network allows to precisely study the theoretical value and practical benefits of incorporating stochastic dynamics in our model. We demonstrate better prediction performance on a robot imitation task with our model using error regression compared to a standard variational Bayes model lacking such a procedure., The paper is accepted in Neural Computation

Online marketers make efforts to sell more products to their customers to increase turnover. One way to sell more products is to ensure that products belonging to the same scene are offered together. As such, it is beneficial to categorize products into different groups and tag them to particular, defined scenes. The mechanism of building relationships between products and scenes is called “scene-based knowledge system construction”. The construction of a scene-based knowledge system is usually based on business operators’ expert knowledge and past experiences, which often causes product categorization to be inaccurate. Hence, in this paper, we discuss the importance and necessity of constructing a scene-based knowledge system, analyze it from a business perspective, and discuss its challenges.

In this paper, we present the virtual knowledge graph (VKG) paradigm for data integration and access, also known in the literature as Ontology-based Data Access. Instead of structuring the integration layer as a collection of relational tables, the VKG paradigm replaces the rigid structure of tables with the flexibility of graphs that are kept virtual and embed domain knowledge. We explain the main notions of this paradigm, its tooling ecosystem and significant use cases in a wide range of applications. Finally, we discuss future research directions.

With the popularity of social media, there has been an increasing interest in user profiling and its applications nowadays. This paper presents our system named UIR-SIST for User Profiling Technology Evaluation Campaign in SMP CUP 2017. UIR-SIST aims to complete three tasks, including keywords extraction from blogs, user interests labeling and user growth value prediction. To this end, we first extract keywords from a user's blog, including the blog itself, blogs on the same topic and other blogs published by the same user. Then a unified neural network model is constructed based on a convolutional neural network (CNN) for user interests tagging. Finally, we adopt a stacking model for predicting user growth value. We eventually receive the sixth place with evaluation scores of 0.563, 0.378 and 0.751 on the three tasks, respectively.

This paper describes a neural semantic parser that maps natural language utterances onto logical forms which can be executed against a task-specific environment, such as a knowledge base or a database, to produce a response. The parser generates tree-structured logical forms with a transition-based approach which combines a generic tree-generation algorithm with domain-general operations defined by the logical language. The generation process is modeled by structured recurrent neural networks, which provide a rich encoding of the sentential context and generation history for making predictions. To tackle mismatches between natural language and logical form tokens, various attention mechanisms are explored. Finally, we consider different training settings for the neural semantic parser, including a fully supervised training where annotated logical forms are given, weakly-supervised training where denotations are provided, and distant supervision where only unlabeled sentences and a knowledge base are available. Experiments across a wide range of datasets demonstrate the effectiveness of our parser., Comment: In Journal of Computational Linguistics

Debugging a machine learning model is hard since the bug usually involves the training data and the learning process. This becomes even harder for an opaque deep learning model if we have no clue about how the model actually works. In this survey, we review papers that exploit explanations to enable humans to give feedback and debug NLP models. We call this problem explanation-based human debugging (EBHD). In particular, we categorize and discuss existing work along three dimensions of EBHD (the bug context, the workflow, and the experimental setting), compile findings on how EBHD components affect the feedback providers, and highlight open problems that could be future research directions., Comment: Accepted for publication at TACL. This version is a pre-MIT Press publication version

The last letter of the FAIR acronym stands for Reusability. Data and metadata should be made available with a clear and accessible usage license. But, what are the choices? How can researchers share data and allow reusability? Are all the licenses available for sharing content suitable for data? Data can be covered by different layers of copyright protection making the relationship between data and copyright particularly complex. Some research data can be considered as a work and therefore covered by full copyright while other data can be in the public domain due to their lack of originality. Moreover, a collection of data can be protected by special rights in Europe to acknowledge the investment in time and money in obtaining, presenting, arranging or verifying the data. The need of using a license when sharing data comes from the fact that, under current copyright laws, when rights exist, the absence of any legal notice must be understood as the default “all rights reserved” regime. Unless an exception applies, the authorisation of right holders is necessary for reuse. Right holders could use any text to state the reusability of data but it is advisable to use some of the existing licenses, and especially the ones that are suitable for data and databases. We hope that with this paper we can bring some clarity in relation to the rights involved when sharing research data.

We propose an approach to the automatic synthesis of robot control software based on the finite state machine (FSM) formalism. In our previous research, we have introduced Boolean network robotics as a novel approach to the automatic design of robot control software. In this paper, we show that it is possible to leverage automatically designed Boolean networks to synthesize FSMs for robot control. Boolean network robotics exhibits a number of interesting properties. Firstly, notwithstanding the large size of the state space of a Boolean network and its ability to display complex and rich dynamics, the automatic design is able to produce networks whose trajectories are confined in small volumes of the state space. Secondly, the automatic design produces networks in which one can identify clusters of states associated with functional behavioral units of the robots. It is our contention that the automatic design of a Boolean network controller can be a convenient intermediate step in the synthesis of a FSM, which offers the advantage of being a compact, readable, and modifiable representation. In this paper, we show that clusters of states traversed by network trajectories can be mapped to states of a FSM. We illustrate the viability of our proposal in two notable robotic tasks, namely collision avoidance and sequence recognition. The first task can be achieved by a memoryless control program, whilst in the second the robots need memory.

Social media content is changing the way people interact with each other and share information, personal messages, and opinions about situations, objects, and past experiences. Most social media texts are short online conversational posts or comments that do not contain enough information for natural language processing (NLP) tools, as they are often accompanied by non-linguistic contextual information, including meta-data (e.g., the user’s profile, the social network of the user, and their interactions with other users). Exploiting such different types of context and their interactions makes the automatic processing of social media texts a challenging research task. Indeed, simply applying traditional text mining tools is clearly sub-optimal, as, typically, these tools take into account neither the interactive dimension nor the particular nature of this data, which shares properties with both spoken and written language. This special issue contributes to a deeper understanding of the role of these interactions to process social media data from a new perspective in discourse interpretation. This introduction first provides the necessary background to understand what context is from both the linguistic and computational linguistic perspectives, then presents the most recent context-based approaches to NLP for social media. We conclude with an overview of the papers accepted in this special issue, highlighting what we believe are the future directions in processing social media texts.

This chapter discusses new science policy initiatives that involve water in terms of environmental representation: who represents, what is represented, for what purpose (equity, sustainability). One such initiative, of science and politics (and nature–society) as being distinct and distinguishable. The effect of this is that particular water “facts” (e.g., the water consumed per unit of land, or the crops produced per unit of water) appear as the (only or most) objective starting points for improving water management and governance; other “facts” and other possible ways of representing water come to be seen as less true or important. Typically modernist claims of neutrality and independenceWater Accounting, adheres, at least on paper, to a modernist treatment also make it difficult to recognize how water accounting representations, measurements, and calculations derive from specific epistemic and policy communities, whose members have specific concerns, are part of specific knowledge traditions, and pursue specific societal projects of betterment. Inspired by social studies of science, it is suggested that water accounting and other attempts to speak for water or represent the environment become more useful and honest when efforts are made to address, more explicitly, the entanglements between science and politics. This abandonment or relaxation of modernism includes embracing diversity, plurality, or multiplicity as well as acknowledging, accepting, and reconciling the existence of many different ways to engage with, relate to, and account for water—many different versions of water. It also includes replacing the aspirations of transcendence, integration (or universality, commensuration), and inclusion (consensus) with equally difficult to attain, but more modest and pragmatic, ideals of situatedness, translation (mediation), and contestation (or dissent).

This chapter discusses new science policy initiatives that involve water in terms of environmental representation: who represents, what is represented, for what purpose (equity, sustainability). One such initiative, of science and politics (and nature–society) as being distinct and distinguishable. The effect of this is that particular water “facts” (e.g., the water consumed per unit of land, or the crops produced per unit of water) appear as the (only or most) objective starting points for improving water management and governance; other “facts” and other possible ways of representing water come to be seen as less true or important. Typically modernist claims of neutrality and independenceWater Accounting, adheres, at least on paper, to a modernist treatment also make it difficult to recognize how water accounting representations, measurements, and calculations derive from specific epistemic and policy communities, whose members have specific concerns, are part of specific knowledge traditions, and pursue specific societal projects of betterment. Inspired by social studies of science, it is suggested that water accounting and other attempts to speak for water or represent the environment become more useful and honest when efforts are made to address, more explicitly, the entanglements between science and politics. This abandonment or relaxation of modernism includes embracing diversity, plurality, or multiplicity as well as acknowledging, accepting, and reconciling the existence of many different ways to engage with, relate to, and account for water—many different versions of water. It also includes replacing the aspirations of transcendence, integration (or universality, commensuration), and inclusion (consensus) with equally difficult to attain, but more modest and pragmatic, ideals of situatedness, translation (mediation), and contestation (or dissent).

The proliferation of channels through which customers can purchase makes it imperative for managers to understand the process by which customers choose which channel to use. Understanding this process can help managers design their channels more effectively, predict usage rates for each channel, and learn how to “right-channel” customers to desired channels. Despite this need, there is evidence that managers do not fully understand the customer channel decision process. We propose that one reason for this is that the decision process is a “moving target”. It evolves over the lifetime of the customer. A newly acquired customer may use a different decision process than a customer who has been with the company for over a year. A customer may make choices differently right after a major change has taken place in the channel environment, e.g., a new channel has been added, than after the “dust has settled” and the channel environment is more stable. Our paper examines four main questions: (1) Does the customer decision process evolve over time? (2) If so, how many customers evolve relatively quickly (the “learners”) versus how many evolve more slowly (the “stayers”)? (3) Among the learners, how does their “trial” decision process compare to the process to which they evolve, the “post-trial” process? (4) What are the managerial and profit implications of these findings? We conduct an empirical analysis of two datasets: one from a book retailer located in Italy; the second from an durables/apparel retailer located in the U.S. We find that (1) the process by which customers choose purchase channels does evolve, (2) a substantial segment (22-35%) evolves relatively quickly, (3) consumers are relatively responsive to marketing communications in the trial stage, but become less marketing responsive as they move to the post-trial stage. This evolution has important ramifications for managers. In the book dataset, we observe over time the channel choice behavior of newly acquired customers. We find a significant learner segment (22% of customers) that becomes less inertial with respect to channel choices and more driven by channel preferences. At first, their channel decisions are shaped significantly by the firm’s marketing communications, but later they become less responsive to marketing. Companies therefore should try to “right-channel” these customers soon after they are acquired. We find those who are more likely to be in the learner segment can be characterized in terms of directly measurable indicators such how these customers are acquired, their proclivity to provide their email address, and their age. We illustrate in an example scenario that by using these characteristics to identify the learner segment, and by using email to transition them early into the cheaper Internet channel, the company can increase its profits by about 24%. Another important managerial implication is that mature customers will be less amenable to marketing efforts aimed at getting them to try new channels. Companies should therefore focus on improving channel design to ensure mature customers are satisfied with the service experience of the channel(s) they have chosen to use.

In this paper we introduce a new model of spatial network growth in which nodes are placed at randomly selected locations in space over time, forming new connections to old nodes subject to the constraint that edges do not cross. The resulting network has a power law degree distribution, high clustering and the small world property. We argue that these characteristics are a consequence of two features of our mechanism, growth and planarity conservation. We further propose that our model can be understood as a variant of random Apollonian growth. We then investigate the robustness of our findings by relaxing the planarity. Specifically, we allow edges to cross with a defined probability. Varying this probability demonstrates a smooth transition from a power law to an exponential degree distribution.

International audience; This paper presents a new model for the development of artificial creatures from a single cell. The model aims at providing a more biologically plausible abstraction of the morphogenesis and the specialization process, which the organogenesis follows. It is built upon three main elements: a cellular physics system that simulates division and intercellular adhesion dynamics, a simplified cell cycle offering to the cells the possibility to select actions such as division, quiescence, differentiation or apoptosis and, finally, a cell specialization mechanism quantifying the ability to perform different functions. An evolved artificial gene regulatory network is employed as a cell controller. As a proof-of-concept, we present two experiments where the morphology of a multicellular organism is guided by cell weaknesses and efficiency at performing different functions under environmental stress.

The multipopulation method has been widely used to solve dynamic optimization problems (DOPs) with the aim of maintaining multiple populations on different peaks to locate and track multiple changing optima simultaneously. However, to make this approach effective for solving DOPs, two challenging issues need to be addressed. They are how to adapt the number of populations to changes and how to adaptively maintain the population diversity in a situation where changes are complicated or hard to detect or predict. Tracking the changing global optimum in dynamic environments is difficult because we cannot know when and where changes occur and what the characteristics of changes would be. Therefore, it is necessary to take these challenging issues into account in designing such adaptive algorithms. To address the issues when multipopulation methods are applied for solving DOPs, this paper proposes an adaptive multi-swarm algorithm, where the populations are enabled to be adaptive in dynamic environments without change detection. An experimental study is conducted based on the moving peaks problem to investigate the behavior of the proposed method. The performance of the proposed algorithm is also compared with a set of algorithms that are based on multipopulation methods from different research areas in the literature of evolutionary computation.

Household credit, especially for mortgages, has doubled over the past years in the new European Union member countries, raising concerns about the economic and social consequences of household indebtedness in the event of a macroeconomic crisis. Using household survey data for 2005, 2006, and 2007 for both old and new European Union members, this paper assesses the determinants of access to mortgage finance. It also examines whether mortgage holders were more likely to suffer financial distress compared with non-mortgage holders in the period before the global financial crisis. The analysis does not find any systematic evidence that mortgage holders are financially more vulnerable than renters or outright owners; in fact, the incidence of financial vulnerability generally fell between 2005 and 2007, possibly reflecting the strong income growth experienced by these countries over this period. In addition, although tenure status is more difficult to explain in the new European Union member countries, the analysis finds that many of the same drivers of tenure status in the older member countries generally drive tenure status in the newer member countries as well. Finally, there is no evidence that access to mortgage credit is based on expected income in the old or in the new European Union member countries.

Household credit, especially for mortgages, has doubled over the past years in the new European Union member countries, raising concerns about the economic and social consequences of household indebtedness in the event of a macroeconomic crisis. Using household survey data for 2005, 2006, and 2007 for both old and new European Union members, this paper assesses the determinants of access to mortgage finance. It also examines whether mortgage holders were more likely to suffer financial distress compared with non-mortgage holders in the period before the global financial crisis. The analysis does not find any systematic evidence that mortgage holders are financially more vulnerable than renters or outright owners; in fact, the incidence of financial vulnerability generally fell between 2005 and 2007, possibly reflecting the strong income growth experienced by these countries over this period. In addition, although tenure status is more difficult to explain in the new European Union member countries, the analysis finds that many of the same drivers of tenure status in the older member countries generally drive tenure status in the newer member countries as well. Finally, there is no evidence that access to mortgage credit is based on expected income in the old or in the new European Union member countries.

The paper examine the effects of household composition and socio-economic context on reproductive process in nineteenth-century Italy, comparing and contrasting a rural Tuscan community and a urban neighborhoods of Venice. The local economic context will also be accounted for by introducing proxies for short term variations in economic conditions in the community. We consider reproduction as a function of the demographic characteristics of each couple, their reproductive histories, the structure of the household in which they live, and the individual and family responses to epidemics and short term economic stress.

In remote sensing applications "ground-truth" data is often used as the basis for training pattern recognition algorithms to generate thematic maps or to detect objects of interest. In practical situations, experts may visually examine the images and provide a subjective noisy estimate of the truth. Calibrating the reliability and bias of expert labellers is a non-trivial problem. In this paper we discuss some of our recent work on this topic in the context of detecting small volcanoes in Magellan SAR images of Venus. Empirical results (using the Expectation-Maximization procedure) suggest that accounting for subjective noise can be quite significant in terms of quantifying both human and algorithm detection performance.

In this paper we study the two-dimensional compaction of integrated circuit layouts. A curvilinear representation for circuit elements, specifically chosen to make the compaction efficient, is developed. A Monte Carlo algorithm with heuristic termination criteria was applied to a variety of designs. These experiments give running times for compaction that are consistent with a conjectured average complexity of O(N^(3/2) log^2(N)) where N is the number of non-wire primitives in the cell. These experiments also produced favorable comparisons with hand-designs and with designs using iterated applications of one dimensional compactors. Several cells also were fabricated and tested to demonstrate the practicality of the representation and the compaction technique.