Training artificial intelligence (AI) systems requires vast quantities of data, and AI developers face a variety of barriers to accessing the information they need. Synthetic data has captured researchers’ and industry’s imagination as a potential solution to this problem. While some of the enthusiasm for synthetic data may be warranted, in this short paper we offer critical counterweight to simplistic narratives that position synthetic data as a cost-free solution to every data-access challenge—provocations (...) highlighting ethical, political, and governance issues the use of synthetic data can create. We question the idea that synthetic data, by its nature, is exempt from privacy and related ethical concerns. We caution that framing synthetic data in binary opposition to “real” measurement data could subtly shift the normative standards to which data collectors and processors are held. And we argue that by promising to divorce data from its constituents—the people it represents and impacts—synthetic data could create new obstacles to democratic data governance. (shrink)

What is the relationship between ideas of sameness and difference for machine learning and AI? Algorithms are often understood to participate in the continual displacement of the different and heterogeneous in society in favour of sameness, of that which is socio-politically similar and proximate. In contrast to this prevalent emphasis on sameness, however, this paper argues that there is a nascent heterophilic logic underpinning the intersection of synthetic data and machine learning, a move towards actively generating differences and (...) heterogeneous data attributes to train, fine-tune, and optimize algorithms. Yet, these synthetic attribute data are nonetheless always machine compatible, devoid of their socio-cultural dynamics and tensions. As such, through a critical examination of three core dimensions of this emergent politics of difference of synthetic data – disentanglement, compositionality, and normativity – the paper argues that this has the potential to ultimately undercut a politics of intervention that seeks to foreground the systemic unfairness and violence of machine learning. (shrink)

The generation of synthetic data is becoming a fundamental task in the daily life of any organization due to the new protection data laws that are emerging. Because of the rise in the use of Artificial Intelligence, one of the most recent proposals to address this problem is the use of Generative Adversarial Networks. These types of networks have demonstrated a great capacity to create synthetic data with very good performance. The goal of synthetic (...) data generation is to create data that will perform similarly to the original dataset for many analysis tasks, such as classification. The problem of GANs is that in a classification problem, GANs do not take class labels into account when generating new data, it is treated as any other attribute. This research work has focused on the creation of new synthetic data from datasets with different characteristics with a Conditional Generative Adversarial Network. CGANs are an extension of GANs where the class label is taken into account when the new data is generated. The performance of our results has been measured in two different ways: firstly, by comparing the results obtained with classification algorithms, both in the original datasets and in the data generated; secondly, by checking that the correlation between the original data and those generated is minimal. (shrink)

Cho and Martinez-Martin (2023) provide a compelling critique of the profound influence that data sourcing for artificial intelligence (AI) has on the healthcare sector. They emphasize the need for...

Researchers and practitioners are increasingly using machine‐generated synthetic data as a tool for advancing health science and practice, by expanding access to health data while—potentially—mitigating privacy and related ethical concerns around data sharing. While using synthetic data in this way holds promise, we argue that it also raises significant ethical, legal, and policy concerns, including persistent privacy and security problems, accuracy and reliability issues, worries about fairness and bias, and new regulatory challenges. The virtue (...) of synthetic data is often understood to be its detachment from the data subjects whose measurement data is used to generate it. However, we argue that addressing the ethical issues synthetic data raises might require bringing data subjects back into the picture, finding ways that researchers and data subjects can be more meaningfully engaged in the construction and evaluation of datasets and in the creation of institutional safeguards that promote responsible use. (shrink)

Researchers and practitioners are increasingly using machine-generated synthetic data as a tool for advancing health science and practice, by expanding access to health data while—potentially—mitigating privacy and related ethical concerns around data sharing. While using synthetic data in this way holds promise, we argue that it also raises significant ethical, legal, and policy concerns, including persistent privacy and security problems, accuracy and reliability issues, worries about fairness and bias, and new regulatory challenges. The virtue (...) of synthetic data is often understood to be its detachment from the data subjects whose measurement data is used to generate it. However, we argue that addressing the ethical issues synthetic data raises might require bringing data subjects back into the picture, finding ways that researchers and data subjects can be more meaningfully engaged in the construction and evaluation of datasets and in the creation of institutional safeguards that promote responsible use. (shrink)

Synthetic biology, as an engineering approach to biological systems, has the potential to disruptively innovate the development of vaccines, therapeutics, and diagnostics. Data accessibility and differences in data-usage capabilities are important factors in shaping this innovation landscape. In this paper, the data that underpin synthetic biology responses to the COVID-19 pandemic are analyzed as positional information goods—goods whose value depends on exclusivity. The positionality of biological data impacts the ability to guide innovations toward societally (...) preferred goals. From both an ethical and economic point of view, positionality can lead to suboptimal as well as beneficial situations. When aiming for responsible innovation (i.e. embedding societal deliberation in the innovation process), it is important to consider hurdles and facilitators in data access and use. Central governance and knowledge commons provide routes to mitigate the negative effects of data positionality. (shrink)

Synthetic biology may be an important source of progress as well as societal and political conflict. Against this backdrop, several technology assessment organizations have been seeking to contribute to timely societal and political opinion-making on synthetic biology. The Rathenau Instituut, based in the Netherlands, is one of these organizations. In 2011, the institute organized a ‘Meeting of Young Minds’: a young people’s debate between ‘future synthetic biologists’ and ‘future politicians’. The former were represented by participants in the (...) international Genetically Engineered Machines competition, the latter by political youth organizations linked to Dutch political parties. The Rathenau Instituut found seven PYOs—including right wing, left wing, Green and Christian groups—willing to commit to an intensive process aimed at formulating a tentative partisan view on synthetic biology and discussing it with fellow PYOs and iGEM participants. Given the minimal amount of available data on how political parties understand synthetic biology, mapping the debate may provide valuable insights. In this article, I aim to provide such a mapping exercise and also to reflect on how and why the Rathenau Instituut organized the event. (shrink)

What happens when there is not enough data to train machine learning algorithms? In recent years, so-called ‘synthetic data’ have been increasingly used to add to or supplement the training regimes of various machine learning algorithms. Seeking to read the notion of supplementarity differently through an engagement with the work of Jacques Derrida, I propose that the nascent emergence of synthetic data embodies what I call the logic of the synthetic supplement in algorithmic societies. (...) I argue, on the one hand, that the synthetic supplement promises and claims to resolve the ethico-political tensions, frictions, and intractabilities of machine learning. On the other hand, it always falls short of these promises because it necessarily intervenes in that which it claims to merely augment. Ultimately, this means that the gaps and frictions of machine learning cannot be completely filled, supplemented, or resolved. (shrink)

Archival data processing consists of cleaning and formatting data between the moment a dataset is deposited and its publication on the archive’s website. In this article, I approach data processing by combining scholarship on invisible labor in knowledge infrastructures with a Marxian framework and show the relevance of considering data processing as factory labor. Using this perspective to analyze ethnographic data collected during a six-month participatory observation at a U.S. data archive, I generate a (...) taxonomy of the forms of alienation that data processing generates, but also the types of resistance that processors develop, across four categories: routine, speed, skill, and meaning. This synthetic approach demonstrates, first, that data processing reproduces typical forms of factory worker’s alienation: processors are asked to work along a strict standardized pipeline, at a fast pace, without acquiring substantive skills or having a meaningful involvement in their work. It reveals, second, how data processors resist the alienating nature of this workflow by developing multiple tactics along the same four categories. Seen through this dual lens, data processors are therefore not only invisible workers, but also factory workers who follow and subvert a workflow organized as an assembly line. I conclude by proposing a four-step framework to better value the social contribution of data workers beyond the archive. (shrink)

In this paper we propose an non-machine learning artificial intelligence (AI) based approach for telecom data analysis, with a special focus on clique detection. Clique detection can be used to identify households, which is a major challenge in telecom data analysis and predictive analytics. Our approach does not use any form of machine learning, but another type of algorithm: satisfiability for propositional logic. This is a neglected approach in modern AI, and we aim to demonstrate that for certain (...) tasks, it may be a good alternative to machine learning-based approaches. We have used a simple DPLL satisfiability solver over an artificially generated telecom dataset (due to GDPR regulations), but our approach can be implemented on any telecom data by following the SAT encoding we have developed, and the DPLL solver can be substituted by a more advanced alternative such as CDCL. This paper extends the method presented in [1] for banking logs to data containing caller information, and proposes a more efficient encoding. -/- . (shrink)

Synthetic biology is designing and creating biological tools and systems for useful purposes. It uses knowledge from biology, such as biotechnology, molecular biology, biophysics, biochemistry, bioinformatics, and other disciplines, such as engineering, mathematics, computer science, and electrical engineering. It is recognized as both a branch of science and technology. The scope of synthetic biology ranges from modifying existing organisms to gain new properties to creating a living organism from non-living components. Synthetic biology has many applications in important (...) fields such as energy, chemistry, medicine, environment, agriculture, national security, and nanotechnology. The development of synthetic biology also raises ethical and social debates. This article aims to identify the place of ethics in synthetic biology. In this context, the theoretical ethical debates on synthetic biology from the 2000s to 2020, when the development of synthetic biology was relatively faster, were analyzed using the systematic review method. Based on the results of the analysis, the main ethical problems related to the field, problems that are likely to arise, and suggestions for solutions to these problems are included. The data collection phase of the study included a literature review conducted according to protocols, including planning, screening, selection and evaluation. The analysis and synthesis process was carried out in the next stage, and the main themes related to synthetic biology and ethics were identified. Searches were conducted in Web of Science, Scopus, PhilPapers and MEDLINE databases. Theoretical research articles and reviews published in peer-reviewed journals until the end of 2020 were included in the study. The language of publications was English. According to preliminary data, 1,453 publications were retrieved from the four databases. Considering the inclusion and exclusion criteria, 58 publications were analyzed in the study. Ethical debates on synthetic biology have been conducted on various issues. In this context, the ethical debates in this article were examined under five themes: the moral status of synthetic biology products, synthetic biology and the meaning of life, synthetic biology and metaphors, synthetic biology and knowledge, and expectations, concerns, and problem solving: risk versus caution. (shrink)

Combining the communicative language competence model and the perspective of multimodal research, this research proposes a research framework for oral communicative competence under the multimodal perspective. This not only truly reflects the language communicative competence but also fully embodies the various contents required for assessment in the basic attributes of spoken language. Aiming at the feature sparseness of the user evaluation matrix, this paper proposes a feature weight assignment algorithm based on the English spoken category keyword dictionary and user search (...) records. The algorithm is mainly based on the self-built English oral category classification dictionary and converts the user’s query vector into a user-English-speaking type vector. Through the calculation rules proposed in this paper, the target user’s preference score for a specific type of spoken English is obtained, and this score is assigned to the unrated item of the original user’s feature matrix as the initial starting score. At the same time, in order to solve the problem of insufficient user similarity calculation accuracy, a user similarity calculation algorithm based on “Synonyms Cilin Extended Edition” and search records is proposed. The algorithm introduces “Synonyms Cilin” to calculate the correlation between the semantic items, vocabulary, and query vector in the user query record to obtain the similarity between users and finally gives a user similarity calculation that integrates user ratings and query vectors method. For the task of Chinese grammar error correction, this article uses two methods of predicting the relationship between words in the corpus, Word2Vec and GloVe, to train the word vectors of different dimensions and use the word vectors to represent the text features of the experimental samples, avoiding sentences brought by word segmentation. On the basis of word vectors, the advantages and disadvantages of CNN, LSTM, and SVM models in this shared task are analyzed through experimental data. The comparative experiment shows that the method in this paper has achieved relatively good results. (shrink)

Model-data symbiosis is the view that there is an interdependent and mutually beneficial relationship between data and models, whereby models are not only data-laden, but data are also model-laden or model filtered. In this paper I elaborate and defend the second, more controversial, component of the symbiosis view. In particular, I construct a preliminary taxonomy of the different ways in which theoretical and simulation models are used in the production of data sets. These include (...) class='Hi'>data conversion, data correction, data interpolation, data scaling, data fusion, data assimilation, and synthetic data. Each is defined and briefly illustrated with an example from the geosciences. I argue that model-filtered data are typically more accurate and reliable than the so-called raw data, and hence beneficially serve the epistemic aims of science. By illuminating the methods by which raw data are turned into scientifically useful data sets, this taxonomy provides a foundation for developing a more adequate philosophy of data. (shrink)

In synthetic aperture radar image segmentation field, regional algorithms have shown great potential for image segmentation. The SAR images have a multiplicity of complex texture, which are difficult to be divided as a whole. Existing algorithm may cause mixed super-pixels with different labels due to speckle noise. This study presents the technique based on organization evolution algorithm to improve ISODATA in pixels. This approach effectively filters out the useless local information and successfully introduces the effective information. To verify the (...) accuracy of OEA-ISO data algorithm, the segmentation effect of this algorithm is tested on SAR image and compared with other techniques. The results demonstrate that the OEA-ISO data algorithm is 10.16% more accurate than the WIPFCM algorithm, 23% more accurate than the K-means algorithm, and 27.14% more accurate than the fuzzy C-means algorithm in the light-colored farmland category. It can be seen that the OEA-ISO data algorithm introduces the pixel block strategy, which successfully reduces the noise interference in the image, and the effect is more obvious when the image background is complex. (shrink)

Personal data is highly vulnerable to security exploits, spurring moves to lock it down through encryption, to cryptographically ‘cloud’ it. But personal data is also highly valuable to corporations and states, triggering moves to unlock its insights by relocating it in the cloud. We characterise this twinned condition as ‘clouded data’. Clouded data constructs a political and technological notion of privacy that operates through the intersection of corporate power, computational resources and the ability to obfuscate, gain (...) insights from and valorise a dependency between public and private. First, we survey prominent clouded data approaches, suggesting their particular affordances produce distinctive versions of privacy. Next, we perform two notional code-based experiments using synthetic datasets. In the field of health, we submit a patient’s blood pressure to a notional cloud-based diagnostics service; in education, we construct a student survey that enables aggregate reporting without individual identification. We argue that these technical affordances legitimate new political claims to capture and commodify personal data. The final section broadens the discussion to consider the political force of clouded data and its reconstitution of traditional notions such as the public and the private. (shrink)

Big data biology—bioinformatics, computational biology, systems biology (including ‘omics’), and synthetic biology—raises a number of issues for the philosophy of science. This article deals with several such: Is data-intensive biology a new kind of science, presumably post-reductionistic? To what extent is big data biology data-driven? Can data ‘speak for themselves?’ I discuss these issues by way of a reflection on Carl Woese’s worry that “a society that permits biology to become an engineering discipline, that (...) allows that science to slip into the role of changing the living world without trying to understand it, is a danger to itself.” And I argue that scientific perspectivism, a philosophical stance represented prominently by Giere, Van Fraassen, and Wimsatt, according to which science cannot as a matter of principle transcend our human perspective, provides the best resources currently at our disposal to tackle many of the philosophical issues implied in the modeling of complex, multilevel/multiscale phenomena. (shrink)

One of the most active areas of research in semi-supervised learning has been to study methods for constructing good ensembles of classifiers. Ensemble systems are techniques that create multiple models and then combine them to produce improved results. These systems usually produce more accurate solutions than a single model would. Specially, multi-view ensemble systems improve the accuracy of text classification because they optimize the functions to exploit different views of the same input data. However, despite being more promising than (...) the single-view approaches, document datasets often have no natural multiple views available. This study proposes an algorithm to generate a synthetic view from a standard text dataset. The model generates a new view from the standard bag-of-words approach using an algorithm based on hidden Markov models. To show the effectiveness of the proposed HMM-based synthetic view generation method, it has been integrated in a co-training ensemble system and tested with four text corpora: Reuters, 20 Newsgroup, TREC Genomics and OHSUMED. The results obtained are promising, showing a significant increase in the efficiency of the ensemble system compared to a single-view approach. (shrink)

W.V.O. tried to prove that no statement is necessarily true. In this work, Quine's argument is stated, analyzed, and shown to be a broken argument for a false conclusion. It is shown that necessary truths are as important as empirical truths to the empirical sciences, the reason being that, without necessary truths, there is no way to organize or interpret data. It comes to light that, in addition to being false, every form of extreme empiricism (e.g. Dewey's pragmatism, Wittgenstein's (...) verificationism, Comte's positivism) is so incoherent that it cannot be clearly stated without thereby being refuted. (shrink)

In some applications, researchers using the synthetic control method (SCM) to evaluate the effect of a policy may struggle to determine whether they have identified a “good match” between the control group and treated group. In this paper, we demonstrate the utility of the mean and maximum Absolute Standardized Mean Difference (ASMD) as a test of balance between a synthetic control unit and treated unit, and provide guidance on what constitutes a poor fit when using a synthetic (...) control. We explore and compare other potential metrics using a simulation study. We provide an application of our proposed balance metric to the 2013 Los Angeles (LA) Firearm Study [9]. Using Uniform Crime Report data, we apply the SCM to obtain a counterfactual for the LA firearm-related crime rate based on a weighted combination of control units in a donor pool of cities. We use this counterfactual to estimate the effect of the LA Firearm Study intervention and explore the impact of changing the donor pool and pre-intervention duration period on resulting matches and estimated effects. We demonstrate how decision-making about the quality of a synthetic control can be improved by using ASMD. The mean and max ASMD clearly differentiate between poor matches and good matches. Researchers need better guidance on what is a meaningful imbalance between synthetic control and treated groups. In addition to the use of gap plots, the proposed balance metric can provide an objective way of determining fit. (shrink)

Herbert Spencer, Victorian philosopher, biologist, sociologist and political theorist, one of the founders of Social Darwinism and author of the phrase 'survival of the fittest', was nominated for the Nobel Prize for Literature in 1902, losing out to Theodor Mommsen. Spencer left his post at The Economist in 1857 to focus on writing his ten-volume System of Synthetic Philosophy, a work that offers an ethics-based guide to human conduct to replace that provided by conventional religious belief. Published in 1879, (...) this volume seeks to demonstrate that social evolution tends towards greater individualism, altruism and co-operation. Spencer argues that it is possible to establish rules of right conduct on a scientific basis, and declares that this work is the culmination of his life's study. He was anxious to publish it outside the planned order of the System, because he feared that his death would prevent its completion. (shrink)

Presence is a palpable sense of space, things and others that overlaps with matters of meaning, yet is not reducible to it: it is a dimension of things that hides in plain sight. This paper is motivated by observations that (1) presence is under-appreciated in questions of modern and nascent human-synthetic agent interaction, and (2) that presence matters because it affects and moves us. The paper’s goal is to articulate a multi-faceted understanding of presence, and why it matters, so (...) the importance of presence may be readily understood by those who regulate media, digital and artificial intelligence (AI) industries. Novel forms of presence raise all sorts of questions of what it means to live with new forms of presences. Some of these are highly positive and others are resistant to simplistic moral diagnosis, a point explored through extended consideration of ‘thanatechnology’ and ‘ghostbots’. To clarify and foreground presence, this paper draws on continental philosophy and technologist ideas about presence to understand the significance and parameters of presence. It then puts these to work by considering a range of existing and emerging human-synthetic agent interactions, arguing that that presence is an underappreciated yet crucial factor in human-synthetic interactions, particularly involving AI and ghostbots. The paper concludes with points of focus for organisations charged with media, data protection and AI governance regarding facets of presence-based characteristics for emergent human-synthetic interaction. Foremost is consideration of open standards for a presence-based afterlife and suggestion of a temporal firewall, or a break of time before resurrection, to prevent harm to those who have recently lost someone. (shrink)

A commitment to ‘making’—creating or producing things—can shape scientific and technological fields in important ways. This article demonstrates this by exploring synthetic biology, a field committed to making use of advanced techniques from molecular biology in order to make with living matter. I describe and analyse how this field’s ‘drive to make’ shapes its organisational, methodological, epistemological, and ontological character. Synthetic biologists’ ambition to make helps determine how their field demarcates itself, sets appropriate methods and practices, construes the (...) purpose and character of knowledge, and views the things of the living world. Using empirical data from extensive ethnographic and interview-based research, I discuss the importance of seemingly simple and unimportant commitments—in this case, a focus on the making of things rather than the production of knowledge claims. I conclude by examining the ramifications of this line of research for studies of science and technology. (shrink)

The emergence of new techniques in both microbial biotechnology and artificial intelligence (AI) is opening up a completely new field for monitoring and sometimes even controlling the evolution of pathogens. However, the now famous generative AI extracts and reorganizes prior knowledge from large datasets, making it poorly suited to making predictions in an unreliable future. In contrast, an unfamiliar perspective can help us identify key issues related to the emergence of new technologies, such as those arising from synthetic biology, (...) whilst revisiting old views of AI or including generative AI as a generator of abduction as a resource. This could enable us to identify dangerous situations that are bound to emerge in the not-too-distant future, and prepare ourselves to anticipate when and where they will occur. Here, we emphasize the fact that amongst the many causes of pathogen outbreaks, often driven by the explosion of the human population, laboratory accidents are a major cause of epidemics. This review, limited to animal pathogens, concludes with a discussion of potential epidemic origins based on unusual organisms or associations of organisms that have rarely been highlighted or studied. (shrink)

Many philosophers take the distinguishing mark of their subject to be its a priori status. In their view, where empirical science is based on the data of experience, philosophy is founded on a priori intuitions. In this paper I shall argue that there is no good sense in which philosophical knowledge is informed by a priori intuitions. Philosophical results have just the same a posteriori status as scientific theories. My strategy will be to pose a familiar dilemma for the (...) friends of a priori philosophical intuitions. I shall ask them whether their intuitions are supposed to be analytic or synthetic. And then I shall argue that, if the intuitions are analytic, they may be a priori, but will be philosophically uninteresting; while, if they are synthetic, they may be philosophically interesting, but will not merit being treated as a priori in the context of philosophical debate. At the end of the paper I explain that this does not mean that philosophy should abandon ‘armchair’ methods. We still need to uncover and examine the often unrecognized intuitions that drive our thinking. However this is not because they provide a distinctive source of a priori knowledge, but on the contrary because they may be leading us astray. (shrink)

Virtual screening is the most critical process in drug discovery, and it relies on machine learning to facilitate the screening process. It enables the discovery of molecules that bind to a specific protein to form a drug. Despite its benefits, virtual screening generates enormous data and suffers from drawbacks such as high dimensions and imbalance. This paper tackles data imbalance and aims to improve virtual screening accuracy, especially for a minority dataset. For a dataset identified without considering the (...) data’s imbalanced nature, most classification methods tend to have high predictive accuracy for the majority category. However, the accuracy was significantly poor for the minority category. The paper proposes a K-mean algorithm coupled with Synthetic Minority Oversampling Technique to overcome the problem of imbalanced datasets. The proposed algorithm is named as KSMOTE. Using KSMOTE, minority data can be identified at high accuracy and can be detected at high precision. A large set of experiments were implemented on Apache Spark using numeric PaDEL and fingerprint descriptors. The proposed solution was compared to both no-sampling method and SMOTE on the same datasets. Experimental results showed that the proposed solution outperformed other methods. (shrink)

. Many large companies transact from multiple branches. It results in generating multiple databases, since local transactions are stored locally. The number of multi-branch companies as well as the number of branches of a multi-branch company is increasing over time. Thus, it is important to study data mining on multiple databases. Global exceptional patterns describe interesting individuality of few branches. Therefore, it is interesting to identify such patterns. In this paper, we propose type I and type II global exceptional (...) frequent itemsets in multiple databases by extending the notion of global exceptional frequent itemset. Also, we propose the notion of exceptional sources for a type II global exceptional frequent itemset. We propose type I and type II global exceptional association rules in multiple databases by extending the notion of global exceptional association rule. We propose an algorithm for synthesizing type II global exceptional frequent itemsets. Experimental results are presented on both real and synthetic databases. We compare the proposed algorithm with the existing algorithm theoretically as well as experimentally. The experimental results show that the proposed algorithm is effective and promising. (shrink)

The pace of technological change continues to outstrip the evolution of democratic institutions, creating an urgent need for innovative approaches to democratic reform. However, the experimentation bottleneck - characterized by slow speed, high costs, limited scalability, and ethical risks - has long hindered progress in democracy research. This paper proposes a novel solution: employing generative artificial intelligence (GenAI) to create synthetic data through the simulation of digital homunculi, GenAI-powered entities designed to mimic human behavior in social contexts. By (...) enabling rapid, low-risk experimentation with alternative institutional designs, this approach could significantly accelerate democratic innovation. I examine the potential of GenAI-assisted research to mitigate current limitations in democratic experimentation, including the ability to simulate large-scale societal interactions and test complex institutional mechanisms. While acknowledging potential risks such as algorithmic bias, reproducibility challenges, and AI alignment issues, I argue that the benefits of synthetic data are likely to outweigh their drawbacks if implemented with proper caution. To address existing challenges, I propose a range of technical, methodological, and institutional adaptations. The paper concludes with a call for interdisciplinary collaboration in the development and implementation of GenAI-assisted methods in democracy research, highlighting their potential to bridge the gap between democratic theory and practice in an era of rapid technological change. (shrink)

In this article we discuss the aspects of designing facial expressions for virtual humans (VHs) with a specific culture. First we explore the notion of cultures and its relevance for applications with a VH. Then we give a general scheme of designing emotional facial expressions, and identify the stages where a human is involved, either as a real person with some specific role, or as a VH displaying facial expressions. We discuss how the display and the emotional meaning of facial (...) expressions may be measured in objective ways, and how the culture of displayers and the judges may influence the process of analyzing human facial expressions and evaluating synthesized ones. We review psychological experiments on cross-cultural perception of emotional facial expressions. By identifying the culturally critical issues of data collection and interpretation with both real and VHs, we aim at providing a methodological reference and inspiration for further research. (shrink)

At least since W. V. O. Quine's famous critique of the analytic/synthetic distinction, philosophers have been deeply divided over whether there are any analytic truths. One line of thought suggests that the simple fact that people have ' intuitions of analyticity' might provide an independent argument for analyticities. If defenders of analyticity can explain these intuitions and opponents cannot, then perhaps there are analyticities after all. We argue that opponents of analyticity have some unexpected resources for explaining these intuitions (...) and that, accordingly, the argument from intuition fails. (shrink)

Diffusions in Architecture: Artificial Intelligence and Image Generators delves into the impact of Diffusion AI algorithms and generative image models on architecture design and aesthetics. The book presents an in-depth analysis of how these new technologies are revolutionizing the field of architecture. The architects presented in the book focus on the application of specific AI techniques and tools used in generative design, such as Diffusion models, Dall-E2, Stable Diffusion, and MidJourney. It discusses how these techniques can generate synthetic images (...) that are both realistic and imaginative, creating new possibilities for architectural design and aesthetics. This comprehensive overview of the impact of Diffusion AI algorithms and generative image models on the field of architecture shows how they are changing the way architects approach their work. With a foreword by Lev Manovich—artist, writer and one of the world's most influential digital culture theorists. Sharing the work and insights of a team of designers and theorists from a range of backgrounds, offering disciplinary depth, and covering the full impact of these tools and models on architecture, Diffusions in Architecture includes information on: Fresh perspectives on design and aesthetics, with designs, images, and commentary from 22 leading architects, theorists, and designersThe potential of prompt engineering and text-to-image models applicable to a broad interdisciplinary audienceWhether image generators, or AI in general, will be an aid for human design, or potentially displace humans from the market fundamentals of a larger debate in the architectural discourse (and practice) on the use of image generatorsCollecting the different ideas that have emerged in the field, Diffusions in Architecture provides architects, designers, concept artists, and theorists with an overview of this cornucopia of architectural imaginations produced in a common effort between humans and artificial intelligence. (shrink)

It is commonplace to encounter nonstationary or heterogeneous data, of which the underlying generating process changes over time or across data sets. Such a distribution shift feature presents both challenges and opportunities for causal discovery. In this paper we develop a principled framework for causal discovery from such data, called Constraint-based causal Discovery from Nonstationary/heterogeneous Data, which addresses two important questions. First, we propose an enhanced constraint-based procedure to detect variables whose local mechanisms change and recover (...) the skeleton of the causal structure over observed variables. Second, we present a way to determine causal orientations by making use of independence changes in the data distribution implied by the underlying causal model, benefiting from information carried by changing distributions. Experimental results on various synthetic and real-world data sets are presented to demonstrate the efficacy of our methods. (shrink)

To better reflect the precise clustering results of the data samples with different shapes and densities for affinity propagation clustering algorithm, an improved integrated clustering learning strategy based on three-stage affinity propagation algorithm with density peak optimization theory was proposed in this paper. DPKT-AP combined the ideology of integrated clustering with the AP algorithm, by introducing the density peak theory and k-means algorithm to carry on the three-stage clustering process. In the first stage, the clustering center point was selected (...) by density peak clustering. Because the clustering center was surrounded by the nearest neighbor point with lower local density and had a relatively large distance from other points with higher density, it could help the k-means algorithm in the second stage avoiding the local optimal situation. In the second stage, the k-means algorithm was used to cluster the data samples to form several relatively small spherical subgroups, and each of subgroups had a local density maximum point, which is called the center point of the subgroup. In the third stage, DPKT-AP used the AP algorithm to merge and cluster the spherical subgroups. Experiments on UCI data sets and synthetic data sets showed that DPKT-AP improved the clustering performance and accuracy for the algorithm. (shrink)

In this paper, we study the path based continuous spatial keyword queries, which find the answer set continuously when the query point moves on a given path. Under this setting, we explore two primitive spatial keyword queries, namely k nearest neighbor query and range query. The technical challenges lie in that: retrieving qualified vertices in large road networks efficiently, and issuing the query continuously for points on the path, which turns out to be inapplicable. To overcome the above challenges, we (...) first propose a backbone road network index structure, which supports the distance computation efficiently and offers a global insights of the whole road network. Motivated by the safe zone technique, we then transform our queries to the issue of finding event points, which capture the changes of answer set. By this transformation, our queries are to be simple and feasible. To answer the queries, we propose a Two-Phase Progressive computing framework, which first computes the answer sets for some crucial vertices on the path, and then identifies the event points by the retrieved answer sets. Extensive experiments on both real and synthetic data sets are conducted to evaluate the performance of our proposed algorithms, and the results show that our algorithms outperform competitors by several orders of magnitude. (shrink)

In this article, I shall argue that AI’s likely developments and possible challenges are best understood if we interpret AI not as a marriage between some biological-like intelligence and engineered artefacts, but as a divorce between agency and intelligence, that is, the ability to solve problems successfully and the necessity of being intelligent in doing so. I shall then look at five developments: (1) the growing shift from logic to statistics, (2) the progressive adaptation of the environment to AI rather (...) than of AI to the environment, (3) the increasing translation of difficult problems into complex problems, (4) the tension between regulative and constitutive rules underpinning areas of AI application, and (5) the push for synthetic data. (shrink)

For years now, phase-amplitude cross frequency coupling (CFC) has been observed across multiple brain regions under different physiological and pathological conditions. It has been suggested that CFC serves as a mechanism that facilitates communication and information transfer between local and spatially separated neuronal populations. In non-invasive brain computer interfaces (BCI), CFC has not been thoroughly explored. In this work, we propose a CFC estimation method based on Linear Parameter Varying Autoregressive (LPV-AR) models and we assess its performance using both (...) class='Hi'>synthetic data and electroencephalographic (EEG) data recorded during attempted arm/hand movements of spinal cord injured (SCI) participants. Our results corroborate the potentiality of CFC as a feature for movement attempt decoding and provide evidence of the superiority of our proposed CFC estimation approach compared to other commonly used techniques. (shrink)

The estimation of causal effects is fundamental in situations where the underlying system will be subject to active interventions. Part of building a causal inference engine is defining how variables relate to each other, that is, defining the functional relationship between variables entailed by the graph conditional dependencies. In this article, we deviate from the common assumption of linear relationships in causal models by making use of neural autoregressive density estimators and use them to estimate causal effects within Pearl’s do-calculus (...) framework. Using synthetic data, we show that the approach can retrieve causal effects from non-linear systems without explicitly modeling the interactions between the variables and include confidence bands using the non-parametric bootstrap. We also explore scenarios that deviate from the ideal causal effect estimation setting such as poor data support or unobserved confounders. (shrink)

Deliberative democracy depends on carefully designed institutional frameworks-such as participant selection, facilitation methods, and decision-making mechanisms-that shape how deliberation occurs. However, determining which institutional design best suits a given context often proves difficult when relying solely on real-world observations or laboratory experiments, which can be resource-intensive and hard to replicate. To address these challenges, this paper explores Digital Twin (DT) technology as a regulatory sandbox for deliberative democracy. DTs enable researchers and policymakers to run "what-if" scenarios on varied deliberative designs (...) in a controlled virtual environment by creating dynamic, computer-based models that mirror real or synthetic data. This makes systematic analysis of the institutional design possible without the practical constraints of real-world or lab-based settings. The paper also discusses the limitations of this approach and outlines key considerations for future research. (shrink)

We describe a new approach for developing and validating cognitive process models. We develop graphical models (specifically, hidden Markov models) both from human empirical data on a task, as well as from synthetic data traces generated by a cognitive process model of human behavior on the task. We show that considering differences between the two graphical models can unveil substantive and nuanced imperfections of cognitive process models that can then be addressed to increase their fidelity to empirical (...) data. (shrink)

Impressions, energy radiated by phenomena in the momentary environmental scene, enter sensory neurons, creating in afferent nerves a data stream. Following Kant, by our inner sense the mind perceives its own thoughts as it ties together sense data into an internalized scene. The mind, residing in the brain, logically a Language Machine, processes and stores items as coded grammatical entities. Kantian synthetic unity in the linguistic brain is able to deliver our experience of the scene as we (...) appear to see it. Uniquely, the brain records its own history, synthesizing a Movie-in-the-Brain, called the Noumenal Cosmos. Attempting thereby to represent the actual Universe, this makes for a sovereign brain that governs itself. The brain is domicile of an Ego, with its selfhood at stake at all times. Yet, it can know itself only by its actions, in which it appears as an actor in its own movie. Phenomena enter garbled, as confused apparitions, and must be put in good form using top–down feedback control by Ego, so that each movie frame makes rational sense within the overall context of the Noumenal Cosmos. A stack of frames is processed typically in 40 Hz rhythm with 300 ms process time each, for about 12 in the stack at any time. Successive neural centers are processing the stack in the brain assembly line, based on data from increasingly global receptive fields. Ego stitches together the movie frames, but only the top frame is in consciousness for 25 ms. The top frame contains the whole scene where the Ego makes an appearance as the actor that imposes Kantian synthetic unity on the scene, merely an assembly of grammatical texts, in a system-internal coded process language, fitting the scene into the Noumenal Cosmos. But Ego observes Ego only to the extent permitted by the objectivity rule, only what it does and thinks, not its true face. From the Noumenal Cosmos, the Ego receives grammatical messages in the internal sense code. They are integrated into a whole in the reaction of the Ego to the momentary scene. The voluntary nature of Ego’s decisions is explained, based on its ability to code in advance its own actions sequentially in time, as it sees fit with a view to an orderly Noumenal Cosmos, records of code being arranged spatially in neural structures. (shrink)

Often associated with themes in political philosophy and aesthetics, the work of Jean-François Lyotard is most known for his infamous definition of the postmodern in his best-known book, La condition postmoderne, as incredulity towards metanarratives. The claim of this article is that this famous claim of Lyotard is actually embedded in a philosophy of technology, one that is, moreover, still relevant for understanding present technoscience. The first part of the article therefore sketches Lyotard’s philosophy of technology, mainly by correcting three (...) common misconceptions: that La condition postmoderne would only be about metanarratives, whereas in fact, it is mainly about what replaces them, namely performativity; that performativity would be shorthand for capitalism, whereas in reality, capitalism is the latest instance of a longer history of performativity; and that Lyotard’s reflections on science and technology would be restricted to this book alone, whereas in reality, a well-articulated philosophy of technology, centered around the concept of technoscience, is found in his later work. The second part of the article then aims to highlight the contemporary relevance of this philosophy of technoscience through a brief examination of two contemporary technosciences: synthetic biology and data science. (shrink)

The first promising results from “streamlined,” minimal genomes tend to support the notion that these are a useful tool in biological systems engineering. However, compared with the speed with which genomic microbial sequencing has provided us with a wealth of data to study biological functions, it is a slow process. So far only a few projects have emerged whose synthetic ambition even remotely matches our analytic capabilities. Here, we survey current technologies converging into a future ability to engineer (...) large‐scale biological systems. We argue that the underlying synthetic technology, de novo DNA synthesis, is already rather mature – in particular relative to the scope of our current synthetic ambitions. Furthermore, technologies towards rationalizing the design of the newly synthesized DNA fragment are emerging. These include techniques to implement complex regulatory circuits, suites of parts on a DNA and RNA level to fine tune gene expression, and supporting computational tools. As such DNA fragments will, in most cases, be destined for operating in a cellular context, attention has to be paid to the potential interactions of the host with the functions encoded on the engineered DNA fragment. Here, the need of biological systems engineering to deal with a robust and predictable bacterial host coincides with current scientific efforts to theoretically and experimentally explore minimal bacterial genomes. (shrink)

Evolution and Story: Beyond Neo-Darwinism and Intelligent Design This article analyses evolution from a philosophical perspective, contrasting the problems raised by both Neo-Darwinism and Intelligent Design theory. First, I discuss some of the problems that Synthetic theory presents from a scientific point of view, as well as the philosophical problems involved in the Neo-Darwinism of Dawkins, Dennett, Monod and Ruse. I argue that Neo-Darwinism, although presented as a scientific doctrine, is in fact fundamentally philosophical, and presents some important problems (...) that make it difficult to believe. On the other hand, I discuss the scope of intelligent design as a philosophical stance on the evolution of living beings, showing its problems. Finally, I propose a credible philosophical account of evolution that seeks to go beyond a scientific reading of the data of experience. (shrink)

The application of synthetic aperture radar (SAR) for ship and iceberg monitoring is important to carry out marine activities safely. The task of differentiating the two target classes, i.e. ship and iceberg, presents a challenge for operational scenarios. The dataset comprising SAR images of ship and iceberg poses a major challenge, as we are provided with a small number of labeled samples in the training set compared to a large number of unlabeled test samples. This paper proposes a semisupervised (...) learning approach known as pseudolabeling to deal with the insufficient amount of training data. By adopting this approach, we make use of both labeled data (supervised learning) and unlabeled data (unsupervised learning) to build a robust convolutional neural network model that results in a superior binary classification performance of the proposed method. (shrink)

A science is an intellectual activity defined by its mechanisms that prevent its scientists from always reaching the conclusions that they set out to reach. Such mechanisms are needed because, if scientists are given full control over what hypotheses they select, what data they discard, and what results they publish, they can communicate any conclusion that they desire. Synthesis, by setting a grand challenge, forces scientists across uncharted territory where they encounter and solve unscripted problems. When theory is inadequate, (...) the synthesis fails, and fails in a way that cannot be ignored. Therefore, synthesis drives discovery and paradigm change in ways that simple hypothesis testing cannot. Here, we describe the discoveries that emerged when synthetic biologists were challenged to create an alternative genetic system that has different molecular structures than DNA and RNA. In pursuit of this particular “grand challenge,” synthesis forced the recognition that the community did not know as much about the double helix as it had constructively assumed. In general, a field of science having access to synthesis as a research strategy can create knowledge even if its practitioners do not fit the ideal of a dispassionate, advocacy-free scientist. (shrink)

Paradigms that combine synthetic (one-word) and periphrastic forms in complementary distribution have loomed large in discussions of morphological blocking (McCloskey and Hale 1983, Poser 1986, Andrews 1990). Such composite paradigms potentially challenge the lexicalist claim that words and sentences are organized by distinct subsystems of grammar. They are of course grist for the mill of Distributed Morphology, a theory which revels in every kind of interpenetration of morphology and syntax. But they have prompted even Paradigm Function Morphologists to introduce (...) syntactic constructions into their morphology. I shall argue, instead, for a lexicalist treatment, which is based on the idea that blocking is a filtering device that applies to the output of the generative system, rather than operating directly on its derivations (Wunderlich 1996). I present this approach to blocking in section 1, and show in section 2 how it deals with the intricate composite verb paradigm of Latin, where the periphrastic perfect passive supplies the missing pieces of an otherwise synthetic inflectional system. This part of Latin verb morphology has recently been treated from the perspective of Distributed Morphology and Paradigm Function Morphology. I compare my solution to these treatments and argue that it is superior in two respects: it predicts the complementarity of the synthetic and periphrastic formations and yet allows their respective morphological and syntactic properties to be captured, and it readily covers some basic data that the other analyses get wrong. (shrink)

The philosophy of nature has become virtually an oxymoron for the prevailing philosophical consensus. Reason, we are told, is powerless to conceive what nature is in itself but must instead hand over all understanding of physical reality to empirical science. Philosophy may reflect upon how natural science models its data, scrutinizing the consistency of scientific theories and the way research projects are framed, but reason must never go beyond its frail limits to provide a priori ampliative, synthetic knowledge (...) of what holds universally and necessarily of nature. Insofar as the problems of knowing nature a priori apply to any extension of a priori knowledge beyond reason’s knowledge of itself, philosophy should .. (shrink)

Randomized controlled trials are often considered the gold standard for estimating causal effect, but they may lack external validity when the population eligible to the RCT is substantially different from the target population. Having at hand a sample of the target population of interest allows us to generalize the causal effect. Identifying the treatment effect in the target population requires covariates to capture all treatment effect modifiers that are shifted between the two sets. Standard estimators then use either weighting, outcome (...) modeling, or combine the two in doubly robust approaches. However, such covariates are often not available in both sets. In this article, after proving L 1 {L}^{1} -consistency of these three estimators, we compute the expected bias induced by a missing covariate, assuming a Gaussian distribution, a continuous outcome, and a semi-parametric model. Under this setting, we perform a sensitivity analysis for each missing covariate pattern and compute the sign of the expected bias. We also show that there is no gain in linearly imputing a partially unobserved covariate. Finally, we study the substitution of a missing covariate by a proxy. We illustrate all these results on simulations, as well as semi-synthetic benchmarks using data from the Tennessee student/teacher achievement ratio, and a real-world example from critical care medicine. (shrink)