With the increasing proportion of the logistics industry in the economy, the study of the vehicle routing problem has practical significance for economic development. Based on the vehicle routing problem, the customer presence probability data are introduced as an uncertain random parameter, and the VRP model of uncertain customers is established. By optimizing the robust uncertainty model, combined with a data-driven kernel density estimation method, the distribution feature set of historical data samples can then be fitted, (...) and finally, a distributed robust vehicle routing model for uncertain customers is established. The Q-learning algorithm in reinforcement learning is introduced into the high-level selection strategy using the hyper-heuristic algorithm, and a hyper-heuristic algorithm based on the Q-learning algorithm is designed to solve the problem. Compared with the certain method, the distributed robust model can effectively reduce the total cost and the robust conservatism while ensuring customer satisfaction. The improved algorithm also has good performance. (shrink)

In this paper, a data-driven superheating control strategy is developed for organic Rankine cycle processes. Due to non-Gaussian stochastic disturbances imposed on heat sources, the quantized minimum error entropy is adopted to construct the performance index of superheating control systems. Furthermore, particle swarm optimization algorithm is applied to obtain optimal control law by minimizing the performance index. The implementation procedures of the presented superheating control system in an ORC-based waste heat recovery process are presented. The simulation results testify (...) the effectiveness of the presented control algorithm. (shrink)

Our article examines how journalistic reports and online comments have made sense of computational politics. It treats the discourse around data-driven campaigns as its object of analysis and codifies four main perspectives that have structured the debates about the use of large data sets and data analytics in elections. We study American, British, and German sources on the 2016 United States presidential election, the 2017 United Kingdom general election, and the 2017 German federal election. There, groups (...) of speakers maneuvered between enthusiastic, skeptical, agnostic, or admonitory stances and so cannot be clearly mapped onto these four discursive positions. Coming along with the inconsistent accounts, public sensemaking was marked by an atmosphere of speculation about the substance and effects of computational politics. We conclude that this equivocality helped journalists and commentators to sideline prior reporting on the issue in order to repeatedly rediscover the practices they had already covered. (shrink)

A novel data-driven model-free adaptive control approach is first proposed by combining the advantages of model-free adaptive control and data-driven optimal iterative learning control, and then its stability and convergence analysis is given to prove algorithm stability and asymptotical convergence of tracking error. Besides, the parameters of presented approach are adaptively adjusted with fuzzy logic to determine the occupied proportions of MFAC and DDOILC according to their different control performances in different control stages. Lastly, the proposed (...) fuzzy DDMFAC approach is applied to the control of particle quality in drug development phase of spray fluidized-bed granulation process, and its control effect is compared with MFAC and DDOILC and their fuzzy forms, in which the parameters of MFAC and DDOILC are adaptively adjusted with fuzzy logic. The effectiveness of the presented FDDMFAC approach is verified by a series of simulations. (shrink)

Event cameras which transmit per-pixel intensity changes have emerged as a promising candidate in applications such as consumer electronics, industrial automation, and autonomous vehicles, owing to their efficiency and robustness. To maintain these inherent advantages, the trade-off between efficiency and accuracy stands as a priority in event-based algorithms. Thanks to the preponderance of deep learning techniques and the compatibility between bio-inspired spiking neural networks and event-based sensors, data-driven approaches have become a hot spot, which along with the dedicated (...) hardware and datasets constitute an emerging field named event-based data-driven technology. Focusing on data-driven technology in event-based vision, this paper first explicates the operating principle, advantages, and intrinsic nature of event cameras, as well as background knowledge in event-based vision, presenting an overview of this research field. Then, we explain why event-based data-driven technology becomes a research focus, including reasons for the rise of event-based vision and the superiority of data-driven approaches over other event-based algorithms. Current status and future trends of event-based data-driven technology are presented successively in terms of hardware, datasets, and algorithms, providing guidance for future research. Generally, this paper reveals the great prospects of event-based data-driven technology and presents a comprehensive overview of this field, aiming at a more efficient and bio-inspired visual system to extract visual features from the external environment. (shrink)

The starting point of this work is the gap between two distinct traditions in information engineering: knowledge representation and data - driven modelling. The first tradition emphasizes logic as a tool for representing beliefs held by an agent. The second tradition claims that the main source of knowledge is made of observed data, and generally does not use logic as a modelling tool. However, the emergence of fuzzy logic has blurred the boundaries between these two traditions by (...) putting forward fuzzy rules as a Janus-faced tool that may represent knowledge, as well as approximate non-linear functions representing data. This paper lays bare logical foundations of data - driven reasoning whereby a set of formulas is understood as a set of observed facts rather than a set of beliefs. Several representation frameworks are considered from this point of view: classical logic, possibility theory, belief functions, epistemic logic, fuzzy rule-based systems. Mamdani's fuzzy rules are recovered as belonging to the data - driven view. In possibility theory a third set-function, different from possibility and necessity plays a key role in the data - driven view, and corresponds to a particular modality in epistemic logic. A bi-modal logic system is presented which handles both beliefs and observations, and for which a completeness theorem is given. Lastly, our results may shed new light in deontic logic and allow for a distinction between explicit and implicit permission that standard deontic modal logics do not often emphasize. (shrink)

In this paper, we use an innovative, empirical, and–as yet–rarely applied method in bioethics, namely corpus analysis, which is commonly used in literature studies (Moretti 2013), linguistics (Bake...

Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram signals to improve the control of active prostheses with brain-computer interfaces. Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of this (...) study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group. Using these data, we trained and evaluated common BCI methods that have already been proven effective for upper limb BCI. With an average test decoding accuracy of 84% for both groups, our results show that it is possible to discriminate different lower extremity movements using EEG data with good accuracy. There are no significant differences in the decoding performance of these movements between healthy subjects and subjects with lower extremity amputation. These results show the feasibility of using BCI for lower limb prosthesis control and indicate that decoding performance is not influenced by neuroplasticity-induced differences between the two groups. (shrink)

Research in person and face perception has broadly focused on group-level consensus that individuals hold when making judgments of others. However, a growing body of research demonstrates that individual variation is larger than shared, stimulus-level variation for many social trait judgments. Despite this insight, little research to date has focused on building and explaining individual models of face perception. Studies and methodologies that have examined individual models are limited in what visualizations they can reliably produce to either noisy and blurry (...) or computer avatar representations. Methods that produce low-fidelity visual representations inhibit generalizability by being clearly computer manipulated and produced. In the present work, we introduce a novel paradigm to visualize individual models of face judgments by leveraging state-of-the-art computer vision methods. Our proposed method can produce a set of photorealistic face images that correspond to an individual's mental representation of a specific attribute across a variety of attribute intensities. We provide a proof-of-concept study which examines perceived trustworthiness/untrustworthiness and masculinity/femininity. We close with a discussion of future work to substantiate our proposed method. (shrink)

The rise of big digital data is changing the framework within which linguists, sociologists, anthropologists, and other researchers are working. Semiotics is not spared by this paradigm shift. A data-driven computational semiotics is the study with an intensive use of computational methods of patterns in human-created contents related to semiotic phenomena. One of the most promising frameworks in this research program is the Semantic Vector Space (SVS) models and their methods. The objective of this article is to (...) contribute to the exploration of the SVS for a computational semiotics by showing what types of semiotic analysis can be accomplished within this framework. The study is applied to a unique body of digitized artworks. We conducted three short experiments in which we explore three types of semiotic analysis: paradigmatic analysis, componential analysis, and topic modelling analysis. The results reported show that the SVS constitutes a powerful framework within which various types of semiotic analysis can be carried out. (shrink)

We describe a unification of old and recent ideas for formulating graphical models to explain time series data, including Granger causality, semi-automated search procedures for graphical causal models, modeling of contemporaneous influences in times series, and heuristic generalized additive model corrections to linear models. We illustrate the procedures by finding a structure of exogenous variables and mediating variables among time series of remote geospatial indices of ocean surface temperatures and pressures. The analysis agrees with known exogenous drivers of the (...) indices, not assumed in the analysis. Automated search applied to the residuals after regressing each series on its lags and the lags of its Granger causes yields a graphical model of “comtemporaneous” causal relations identical with the qualitative graphical relations from the time series. A similar analysis produces reasonable results when applied to candidate climate indices obtained by a clustering method from sea surface temperature and sea level pressure data. (shrink)

In climate science, climate models are one of the main tools for understanding phenomena. Here, we develop a framework to assess the fitness of a climate model for providing understanding. The framework is based on three dimensions: representational accuracy, representational depth, and graspability. We show that this framework does justice to the intuition that classical process-based climate models give understanding of phenomena. While simple climate models are characterized by a larger graspability, state-of-the-art models have a higher representational accuracy and representational (...) depth. We then compare the fitness-for-providing understanding of process-based to data-driven models that are built with machine learning. We show that at first glance, data-driven models seem either unnecessary or inadequate for understanding. However, a case study from atmospheric research demonstrates that this is a false dilemma. Data-driven models can be useful tools for understanding , specifically for phenomena for which scientists can argue from the coherence of the models with background knowledge to their representational accuracy and for which the model complexity can be reduced such that they are graspable to a satisfactory extent. When citing this paper, please use the full journal title Studies in History and Philosophy of Science. (shrink)

This paper proposes two formal models for understanding real-life dialogues, aimed at capturing argumentative structures performatively enacted during conversations. In the course of the investigation, two types of discourse with a high degree of well-structured argumentation were chosen: moral debate and financial communication. The research project found itself confronted by a need to analyse, structure and formally describe large volumes of textual data, where this called for the application of computational tools. It is expected that the results of the (...) proposed research will make a contribution to formal systems modelling and the evaluation of communication from the point of view of argument soundness. (shrink)

Over the past two decades urban social life has undergone a rapid and pervasive geocoding, becoming mediated, augmented and anticipated by location-sensitive technologies and services that generate and utilise big, personal, locative data. The production of these data has prompted the development of exploratory data-driven computing experiments that seek to find ways to extract value and insight from them. These projects often start from the data, rather than from a question or theory, and try to (...) imagine and identify their potential utility. In this paper, we explore the desires and mechanics of data-driven computing experiments. We demonstrate how both locative media data and computing experiments are ‘staged’ to create new values and computing techniques, which in turn are used to try and derive possible futures that are ridden with unintended consequences. We argue that using computing experiments to imagine potential urban futures produces effects that often have little to do with creating new urban practices. Instead, these experiments promote Big Data science and the prospect that data produced for one purpose can be recast for another and act as alternative mechanisms of envisioning urban futures. (shrink)

We introduce and investigate a family of consequence relations with the goal of capturing certain important patterns of data-driven inference. The inspiring idea for our framework is the fact that data may reject, possibly to some degree, and possibly by mistake, any given scientific hypothesis. There is no general agreement in science about how to do this, which motivates putting forward a logical formulation of the problem. We do so by investigating distinct definitions of ‘rejection degrees’ each (...) yielding a consequence relation. Our investigation leads to novel variations on the theme of rational consequence relations, prominent among non-monotonic logics. (shrink)

Scientific reasoning represents complex argumentation patterns that eventually lead to scientific discoveries. Social epistemology of science provides a perspective on the scientific community as a whole and on its collective knowledge acquisition. Different techniques have been employed with the goal of maximization of scientific knowledge on the group level. These techniques include formal models and computer simulations of scientific reasoning and interaction. Still, these models have tested mainly abstract hypothetical scenarios. The present thesis instead presents data-driven approaches in (...) social epistemology of science. A data-driven approach requires data collection and curation for its further usage, which can include creating empirically calibrated models and simulations of scientific inquiry, performing statistical analyses, or employing data- mining techniques and other procedures. -/- We present and analyze in detail three co-authored research projects on which the thesis’ author was engaged during her PhD. The first project sought to identify optimal team composition in high energy physics laboratories using data-mining techniques. The results of this project are published in (Perović et al. 2016), and indicate that projects with smaller numbers of teams and team members outperform bigger ones. In the second project, we attempted to determine whether there is an epistemic saturation point in experimentation in high energy physics. The initial results from this project are published in (Sikimić et al. 2018). In the thesis, we expand on this topic by using computer simulations to test for biases that could induce scientists to invest in projects beyond their epistemic saturation point. Finally, in previous examples of data-driven analyses, citations are used as a measure of epistemic efficiency of projects in high energy physics. In order to additionally justify and analyze the usage of this parameter in their data-driven research, in the third project Perović & Sikimić (2019) analyzed and compared inductive patterns in experimental physics and biology with the reliability of citation records in these fields. They conclude that while citations are a relatively reliable measure of efficiency in high energy physics research, the same does not hold for the majority of research in experimental biology. -/- Additionally, contributions of the author that are for the first time published in this theses are: (a) an empirically calibrated model of scientific interaction of research groups in biology, (b) a case study of irregular argumentation patterns in some pathogen discoveries, and (c) an introductory discussion of the benefits and limitations of data- driven approaches to the social epistemology of science. Using computer simulations of an empirically calibrated model, we demonstrate that having several levels of hierarchy and division into smaller research sub-teams is epistemically beneficial for researchers in experimental biology. We also show that argumentation analysis in biology represents a good starting point for further data-driven analyses in the field. Finally, we conclude that a data-driven approach is informative and useful for science policy, but requires careful considerations about data collection, curation, and interpretation. (shrink)

BACON.3 is a production system that discovers empirical laws. Although it does not attempt to model the human discovery process in detail, it incorporates some general heuristics that can lead to discovery in a number of domains. The main heuristics detect constancies and trends in data, and lead to the formulation of hypotheses and the definition of theoretical terms. Rather than making a hard distinction between data and hypotheses, the program represents information at varying levels of description. The (...) lowest levels correspond to direct observations, while the highest correspond to hypotheses that explain everything so for observed. To take advantage of this representation, BACON.3 has the ability to carry out and relate multiple experiments, collapse hypotheses with identical conditions, ignore differences to let similar concepts be treated as equal, and to discover and ignore irrelevant variables. BACON.3 has shown its generality by rediscovering versions of the ideal gas law, Kepler's third law of planetary motion, Coulomb's law, Ohm's law, and Galileo's laws for the pendulum and constant acceleration. (shrink)

Along with the rapid application of new information technologies, the data-driven era is coming, and online consumption platforms are booming. However, massive user data have not been fully developed for design value, and the application of data-driven methods of requirement engineering needs to be further expanded. This study proposes a data-driven expectation prediction framework based on social exchange theory, which analyzes user expectations in the consumption process, and predicts improvement plans to assist designers (...) make better design improvement. According to the classification and concept definition of social exchange resources, consumption exchange elements were divided into seven categories: money, commodity, services, information, value, emotion, and status, and based on these categories, two data-driven methods, namely, word frequency statistics and scale surveys, were combined to analyze user-generated data. Then, a mathematical expectation formula was used to expand user expectation prediction. Moreover, by calculating mathematical expectation, explicit and implicit expectations are distinguished to derive a reliable design improvement plan. To validate its feasibility and advantages, an illustrative example of CoCo Fresh Tea & Juice service system improvement design is further adopted. As an exploratory study, it is hoped that this study provides useful insights into the data mining process of consumption comment. (shrink)

Tianjaou Chu, David Danks, and Clark Glymour. Data Driven Methods for Nonlinear Granger Causality: Climate Teleconnection Mechanisms.

Materialization practices in the architecture and building industry have evolved with the advancement of manufacturing and information technologies. This evolution is evident across various design and production phases, with a pronounced impact on prototyping. Advances in design and fabrication tools have empowered prototypes, integral in any production cycle, to furnish a growing array of information and feedback for designers and manufacturers. In this context, prototypes have transformed from merely showcasing data-driven building solutions to presenting socio-environmentally conscious systems. Innovation (...) in prototyping connects the initial design and construction stages to the operational phase, creating a seamless transition throughout the project lifecycle. This chapter provides a range of definitions and prototypical case studies for smart prototyping by identifying practiced approaches in integrated design to production workflows. This chapter introduces three paradigms for smart prototyping: Digital prototyping focuses on data-driven design for mass customization, phygital prototyping involves mixed-reality-enabled design and assembly, and thirdly collaborative prototyping explores human-machine hybrid intelligence and co-production in architectural and urban contexts. The chosen case studies in this chapter and how they are categorized aim to provide a comprehensive overview of smart prototyping, covering projects conducted in both research and practice. This chapter concludes with potential future trends and the role of emerging and evolving mediums of prototyping for smart design and construction. (shrink)

We describe a unification of old and recent ideas for formulating graphical models to explain time series data, including Granger causality, semi-automated search procedures for graphical causal models, modeling of contemporaneous influences in times series, and heuristic generalized additive model corrections to linear models. We illustrate the procedures by finding a structure of exogenous variables and mediating variables among time series of remote geospatial indices of ocean surface temperatures and pressures. The analysis agrees with known exogenous drivers of the (...) indices, not assumed in the analysis. Automated search applied to the residuals after regressing each series on its lags and the lags of its Granger causes yields a graphical model of “comtemporaneous” causal relations identical with the qualitative graphical relations from the time series. A similar analysis produces reasonable results when applied to candidate climate indices obtained by a clustering method from sea surface temperature and sea level pressure data. (shrink)

Medical-legal partnerships connect legal advocates to healthcare providers and settings. Maintaining effectiveness of medical-legal partnerships and consistently identifying opportunities for innovation and adaptation takes intentionality and effort. In this paper, we discuss ways in which our use of data and quality improvement methods have facilitated advocacy at both patient (client) and population levels as we collectively pursue better, more equitable outcomes.

Multiscale modeling techniques have attracted increasing attention by philosophers of science, but the resulting discussions have almost exclusively focused on issues surrounding explanation (e.g., reduction and emergence). In this paper, I argue that besides explanation, multiscale techniques can serve important exploratory functions when scientists model systems whose organization at different scales is ill-understood. My account distinguishes explanatory and descriptive multiscale modeling based on which epistemic goal scientists aim to achieve when using multiscale techniques. In explanatory multiscale modeling, scientists use multiscale (...) techniques to select information that is relevant to explain a particular type of behavior of the target system. In descriptive multiscale modeling scientists use multiscale techniques to explore lower-scale features which could be explanatorily relevant to many different types of behavior, and to determine which features of a target system an upper-scale data pattern could refer to. Using multiscale models from data-driven neuroscience as a case study, I argue that descriptive multiscale models have an exploratory function because they are a sources of potential explanations and serve as tools to reassess our conception of the target system. (shrink)

In a congested large-scale subway network, the distribution of passenger flow in space-time dimension is very complex. Accurate estimation of passenger path choice is very important to understand the passenger flow distribution and even improve the operation service level. The availability of automated fare collection data, timetable, and network topology data opens up a new opportunity to study this topic based on multisource data. A probability model is proposed in this study to calculate the individual passenger’s path (...) choice with multisource data, in which the impact of the network time-varying state on passenger path choice is fully considered. First, according to the number and characteristics of OD candidate paths, the AFC data among special kinds of OD are selected to estimate the distribution of passengers’ walking time and waiting time of each platform. Then, based on the composition of path travel time, its real-time probability distribution is formulated with the distribution of walking time, waiting time, and in-vehicle time as parameters. Finally, a membership function is introduced to evaluate the dependence between passenger’s travel time and the real-time travel time distribution of each candidate path and take the path with the largest membership degree as passenger’s choice. Finally, a case study with Beijing Subway data is applied to verify the effectiveness of the model presented in this study. We have compared and analysed the path calculation results in which the time-varying characteristics of network state are considered or not. The results indicate that a passenger’s path choice behavior is affected by the network time-varying state, and our model can quantify the time-varying state and its impact on passenger path choice. (shrink)

There has been considerable debate around the ethical issues raised by data-driven technologies such as artificial intelligence. Ethical principles for the field have focused on the need to ensure...

This article explores the process by which intelligent transport system technologies have further advanced a data-driven culture in public transport and traffic control. Based on 12 interviews with transport engineers and fieldwork visits to three control rooms, it follows the implementation of Real-Time Passenger Information in Dublin and the various technologies on which it is dependent. It uses the concept of ‘data ratcheting’ to describe how a new data-driven rational order supplants a gradualist, conservative ethos, (...) creating technological dependencies that pressure organisations to take control of their own data and curate accessibility to outside organisations. It is argued that the implementation of Real-Time Passenger Information forms part of a changing landscape of urban technologies as cities move from a phase of opening data silos and expanded communication across departments and with citizens towards one in which new streams of digital data are recognised for their value in stabilising novel forms of city administration. (shrink)

Dans le cadre de ce travail, nous présentons une expérimentation menée auprès d’un public allophone inscrit à une formation universitaire. Ce travail a pour objectif de relever, d’une part, les spécificités dans les productions orales de ce public, et d’autre part, de démontrer l’intérêt d’un apprentissage sur corpus afin de construire un exposé structuré. Cette étude permet de s’ouvrir à d’autres perspectives didactiques en partant d’un corpus d’apprenants.

Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram signals to improve the control of active prostheses with brain-computer interfaces. Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of this (...) study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group. Using these data, we trained and evaluated common BCI methods that have already been proven effective for upper limb BCI. With an average test decoding accuracy of 84% for both groups, our results show that it is possible to discriminate different lower extremity movements using EEG data with good accuracy. There are no significant differences in the decoding performance of these movements between healthy subjects and subjects with lower extremity amputation. These results show the feasibility of using BCI for lower limb prosthesis control and indicate that decoding performance is not influenced by neuroplasticity-induced differences between the two groups. (shrink)

This paper summarizes three lines of research that are motivated by the practical problem of helping users find information from external data sources, most notably computers. The application areas include information retrieval, text categorization, and question answering. A common theme in these applications is that practical information access problems can be solved by analyzing the statistical properties of words in large volumes of real world texts. The same statistical properties constrain human performance, thus we believe that solutions to practical (...) information access problems can shed light on human knowledge representation and reasoning. (shrink)

Moral circles help define the boundaries of one’s moral consideration. One’s moral circle may provide insight into how one perceives or treats other entities. A data-driven model exploration was conducted to explore predictors and prediction models. Candidate predictors were built upon past research using moral foundations and political orientation. Moreover, we also employed additional moral psychological indicators, i.e., moral reasoning, moral identity, and empathy, based on prior research in moral development and education. We used model exploration methods, i.e., (...) Bayesian model exploration, Bayesian model averaging, and elastic-net regression. The study successfully replicated past research supporting the relationship between moral foundations, political orientation, and the moral circle. Additional moral psychological constructs, such as post-conventional moral reasoning and moral identity, significantly predicted the moral circle width. The identified components of the moral circle were conceptually related to phronesis, i.e., practical wisdom. We discussed the educational implications of the findings, particularly those related to moral education focusing on phronesis cultivation, multiculturalism and global citizenship, and climate justice. (shrink)

This paper synthesizes scholarship from several academic disciplines to identify and analyze five major ethical challenges facing data-driven policing. Because the term “data-driven policing” emcompasses a broad swath of technologies, we first outline several data-driven policing initiatives currently in use in the United States. We then lay out the five ethical challenges. Certain of these challenges have received considerable attention already, while others have been largely overlooked. In many cases, the challenges have been articulated (...) in the context of related discussions, but their distinctively ethical dimensions have not been explored in much detail. Our goal here is to articulate and clarify these ethical challenges, while also highlighting areas where these issues intersect and overlap. Ultimately, responsible data-driven policing requires collaboration between communities, academics, technology developers, police departments, and policy-makers to confront and address these challenges. And as we will see, it may also require critically reexamining the role and value of police in society. (shrink)

Large-scale data systems are increasingly envisioned as tools for justice, with big data analytics offering a key opportunity to advance health equity. Health systems face growing public pressure to collect data on patient “social factors,” and advocates and public officials seek to leverage such data sources as a means of system transformation. Despite the promise of this “data-driven” strategy, there is little empirical work that examines big data in action directly within the sites (...) of care expected to transform. In this article, I present a case study on one such initiative, focusing on a large public safety-net health system’s initiation of sexual orientation and gender identity data collection within the clinical setting. Drawing from ethnographic fieldwork and in-depth interviews with providers, staff, and administrators, I highlight three main challenges that elude big data’s grasp on inequality: provider and staff’s limited understanding of the social significance of data collection; patient perception of the cultural insensitivity of data items; and clinic need to balance data requests with competing priorities within a constrained time window. These issues reflect structural challenges within safety-net care that big data alone are unable to address in advancing social justice. I discuss these findings by considering the present data-driven strategy alongside two complementary courses of action: diversifying the health professions workforce and clinical education reform. To truly advance justice, we need more than “just data”: we need to confront the fundamental conditions of social inequality. (shrink)

This article presents a review on two methods based on dynamic mode decomposition and its multiple applications, focusing on higher order dynamic mode decomposition and spatiotemporal Koopman decomposition. These methods are purely data-driven, using either numerical or experimental data, and permit reconstructing the given data and identifying the temporal growth rates and frequencies involved in the dynamics and the spatial growth rates and wavenumbers in the case of the spatiotemporal Koopman decomposition. Thus, they may be used (...) to either identify and extrapolate the dynamics from transient behavior to permanent dynamics or construct efficient, purely data-driven reduced order models. (shrink)

This ground-breaking and timely book explores how big data, artificial intelligence and algorithms are creating new types of agency, and the impact that this is having on our lives and the rule of law. Addressing the issues in a thoughtful, cross-disciplinary manner, the authors examine the ways in which data-driven agency is transforming democratic practices and the meaning of individual choice. Leading scholars in law, philosophy, computer science and politics analyse the latest innovations in data science (...) and machine learning, assessing the actual and potential implications of these technologies. They investigate how this affects our understanding of such concepts as agency, epistemology, justice, transparency and democracy, and advocate a precautionary approach that takes the effects of data-driven agency seriously without taking it for granted. Scholars and students of law, ethics and philosophy, in particular legal, political and democratic theory, will find this book a compelling and invaluable read, as will computer scientists interested in the implications of their own work. It will also prove insightful for academics and activists working on privacy, fairness and anti-discrimination. Contributors include: J.E. Cohen, G. de Vries, S. Delacroix, P. Dumouchel, C. Ess, M. Garnett, E.H. Gerding, R. Gomer, C. Graber, M. Hildebrandt, C. Maple, K. O'Hara, P. Ohm, m.c. schraefel, D. Stevens, N. van Dijk, M. Veale. (shrink)