| 1. |
- Aler Tubella, Andrea, 1990-, et al.
(författare)
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ACROCPoLis : a descriptive framework for making sense of fairness
- 2023
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Ingår i: FAccT '23. - : ACM Digital Library. - 9781450372527 ; , s. 1014-1025
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Konferensbidrag (refereegranskat)abstract
- Fairness is central to the ethical and responsible development and use of AI systems, with a large number of frameworks and formal notions of algorithmic fairness being available. However, many of the fairness solutions proposed revolve around technical considerations and not the needs of and consequences for the most impacted communities. We therefore want to take the focus away from definitions and allow for the inclusion of societal and relational aspects to represent how the effects of AI systems impact and are experienced by individuals and social groups. In this paper, we do this by means of proposing the ACROCPoLis framework to represent allocation processes with a modeling emphasis on fairness aspects. The framework provides a shared vocabulary in which the factors relevant to fairness assessments for different situations and procedures are made explicit, as well as their interrelationships. This enables us to compare analogous situations, to highlight the differences in dissimilar situations, and to capture differing interpretations of the same situation by different stakeholders.
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| 2. |
- Björklund, Henrik, et al.
(författare)
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Implementing a speech-to-text pipeline on the MICO platform
- 2016
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- MICO is an open-source platform for cross-media analysis, querying, and recommendation. It is the major outcome of the European research project Media in Context, and has been contributed to by academic and industrial partners from Germany, Austria, Sweden, Italy, and the UK. A central idea is to group sets of related media objects into multimodal content items, and to process and store these as logical units. The platform is designed to be easy to extend and adapt, and this makes it a useful building block for a diverse set of multimedia applications. To promote the platform and demonstrate its potential, we describe our work on a Kaldi-based speech-recognition pipeline.
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| 3. |
- Björklund, Johanna, 1961-, et al.
(författare)
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Bridging Perception, Memory, and Inference through Semantic Relations
- 2021
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Ingår i: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. - : Association for Computational Linguistics (ACL). - 9781955917094 ; , s. 9136-9142
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Konferensbidrag (refereegranskat)abstract
- There is a growing consensus that surface form alone does not enable models to learn meaning and gain language understanding. This warrants an interest in hybrid systems that combine the strengths of neural and symbolic methods. We favour triadic systems consisting of neural networks, knowledge bases, and inference engines. The network provides perception, that is, the interface between the system and its environment. The knowledge base provides explicit memory and thus immediate access to established facts. Finally, inference capabilities are provided by the inference engine which reflects on the perception, supported by memory, to reason and discover new facts. In this work, we probe six popular language models for semantic relations and outline a future line of research to study how the constituent subsystems can be jointly realised and integrated.
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| 4. |
- Christopoulos, Arthur, et al.
(författare)
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THE CONCISE GUIDE TO PHARMACOLOGY 2021/22: G protein-coupled receptors.
- 2021
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Ingår i: British journal of pharmacology. - : Wiley. - 1476-5381 .- 0007-1188. ; 178 Suppl 1
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Forskningsöversikt (refereegranskat)abstract
- The Concise Guide to PHARMACOLOGY 2021/22 is the fifth in this series of biennial publications. The Concise Guide provides concise overviews, mostly in tabular format, of the key properties of nearly 1900 human drug targets with an emphasis on selective pharmacology (where available), plus links to the open access knowledgebase source of drug targets and their ligands (www.guidetopharmacology.org), which provides more detailed views of target and ligand properties. Although the Concise Guide constitutes over 500 pages, the material presented is substantially reduced compared to information and links presented on the website. It provides a permanent, citable, point-in-time record that will survive database updates. The full contents of this section can be found at http://onlinelibrary.wiley.com/doi/bph.15538. G protein-coupled receptors are one of the six major pharmacological targets into which the Guide is divided, with the others being: ion channels, nuclear hormone receptors, catalytic receptors, enzymes and transporters. These are presented with nomenclature guidance and summary information on the best available pharmacological tools, alongside key references and suggestions for further reading. The landscape format of the Concise Guide is designed to facilitate comparison of related targets from material contemporary to mid-2021, and supersedes data presented in the 2019/20, 2017/18, 2015/16 and 2013/14 Concise Guides and previous Guides to Receptors and Channels. It is produced in close conjunction with the Nomenclature and Standards Committee of the International Union of Basic and Clinical Pharmacology (NC-IUPHAR), therefore, providing official IUPHAR classification and nomenclature for human drug targets, where appropriate.
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| 5. |
- Dahlgren, Curt, et al.
(författare)
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Gustafsson, Göran
- 2020
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Ingår i: The SAGE Encyclopedia of the Sociology of Religion. - 2455 Teller Road, Thousand Oaks, California 91320 : SAGE Publications, Inc.. - 9781473942202 - 9781529714401 ; 1, s. 331-332
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Bokkapitel (övrigt vetenskapligt/konstnärligt)
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| 6. |
- Dahlgren Lindström, Adam, et al.
(författare)
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CLEVR-Math : A Dataset for Compositional Language, Visual and Mathematical Reasoning
- 2022
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Ingår i: Neural-Symbolic Learning and Reasoning 2022. - : Technical University of Aachen. ; , s. 155-170
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Konferensbidrag (refereegranskat)abstract
- We introduce CLEVR-Math, a multi-modal math word problems dataset consisting of simple math word problems involving addition/subtraction, represented partly by a textual description and partly by an image illustrating the scenario. The text describes actions performed on the scene that is depicted in the image. Since the question posed may not be about the scene in the image, but about the state of the scene before or after the actions are applied, the solver envision or imagine the state changes due to these actions. Solving these word problems requires a combination of language, visual and mathematical reasoning. We apply state-of-the-art neural and neuro-symbolic models for visual question answering on CLEVR-Math and empirically evaluate their performances. Our results show how neither method generalise to chains of operations. We discuss the limitations of the two in addressing the task of multi-modal word problem solving.
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| 7. |
- Dahlgren Lindström, Adam, 1993-
(författare)
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Learning, reasoning, and compositional generalisation in multimodal language models
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- We humans learn language and how to interact with the world through our different senses, grounding our language in what we can see, touch, hear, and smell. We call these streams of information different modalities, and our efficient processing and synthesis of the interactions between different modalities is a cornerstone of our intelligence. Therefore, it is important to study how we can build multimodal language models, where machine learning models learn from more than just text. This is particularly important in the era of large language models (LLMs), where their general capabilities are unclear and unreliable. This thesis investigates learning and reasoning in multimodal language models, and their capabilities to compositionally generalise in visual question answering tasks. Compositional generalisation is the process in which we produce and understand novel sentences, by systematically combining words and sentences to uncover the meaning in language, and has proven a challenge for neural networks. Previously, the literature has focused on compositional generalisation in text-only language models. One of the main contributions of this work is the extensive investigation of text-image language models. The experiments in this thesis compare three neural network-based models, and one neuro-symbolic method, and operationalise language grounding as the ability to reason with relevant functions over object affordances.In order to better understand the capabilities of multimodal models, this thesis introduces CLEVR-Math as a synthetic benchmark of visual mathematical reasoning. The CLEVR-Math dataset involve tasks such as adding and removing objects from 3D scenes based on textual instructions, such as \emph{Remove all blue cubes. How many objects are left?}, and is given as a curriculum of tasks of increasing complexity. The evaluation set of CLEVR-Math includes extensive testing of different functional and object attribute generalisations. We open up the internal representations of these models using a technique called probing, where linear classifiers are trained to recover concepts such as colours or named entities from the internal embeddings of input data. The results show that while models are fairly good at generalisation with attributes (i.e.~solving tasks involving never before seen objects), it is a big challenge to generalise over functions and to learn abstractions such as categories. The results also show that complexity in the training data is a driver of generalisation, where an extended curriculum improves the general performance across tasks and generalisation tests. Furthermore, it is shown that training from scratch versus transfer learning has significant effects on compositional generalisation in models.The results identify several aspects of how current methods can be improved in the future, and highlight general challenges in multimodal language models. A thorough investigation of compositional generalisation suggests that the pre-training of models allow models access to inductive biases that can be useful to solve new tasks. Contrastingly, models trained from scratch show much lower overall performance on the synthetic tasks at hand, but show lower relative generalisation gaps. In the conclusions and outlook, we discuss the implications of these results as well as future research directions.
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| 8. |
- Dahlgren Lindström, Adam, et al.
(författare)
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Probing Multimodal Embeddings for Linguistic Properties: the Visual-Semantic Case
- 2020
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Ingår i: Proceedings of the 28th International Conference on Computational Linguistics (COLING). - Stroudsburg, PA, USA : International Committee on Computational Linguistics. ; , s. 730-744
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Konferensbidrag (refereegranskat)abstract
- Semantic embeddings have advanced the state of the art for countless natural language processing tasks, and various extensions to multimodal domains, such as visual-semantic embeddings, have been proposed. While the power of visual-semantic embeddings comes from the distillation and enrichment of information through machine learning, their inner workings are poorly understood and there is a shortage of analysis tools. To address this problem, we generalize the notion of probing tasks to the visual-semantic case. To this end, we (i) discuss the formalization of probing tasks for embeddings of image-caption pairs, (ii) define three concrete probing tasks within our general framework, (iii) train classifiers to probe for those properties, and (iv) compare various state-of-the-art embeddings under the lens of the proposed probing tasks. Our experiments reveal an up to 12% increase in accuracy on visual-semantic embeddings compared to the corresponding unimodal embeddings, which suggest that the text and image dimensions represented in the former do complement each other
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| 9. |
- Dahlgren, Madelene W., et al.
(författare)
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Type I Interferons Promote Germinal Centers Through B Cell Intrinsic Signaling and Dendritic Cell Dependent Th1 and Tfh Cell Lineages
- 2022
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Ingår i: Frontiers in Immunology. - : Frontiers Media SA. - 1664-3224. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- Type I interferons (IFNs) are essential for antiviral immunity, appear to represent a key component of mRNA vaccine-adjuvanticity, and correlate with severity of systemic autoimmune disease. Relevant to all, type I IFNs can enhance germinal center (GC) B cell responses but underlying signaling pathways are incompletely understood. Here, we demonstrate that a succinct type I IFN response promotes GC formation and associated IgG subclass distribution primarily through signaling in cDCs and B cells. Type I IFN signaling in cDCs, distinct from cDC1, stimulates development of separable Tfh and Th1 cell subsets. However, Th cell-derived IFN-γ induces T-bet expression and IgG2c isotype switching in B cells prior to this bifurcation and has no evident effects once GCs and bona fide Tfh cells developed. This pathway acts in synergy with early B cell-intrinsic type I IFN signaling, which reinforces T-bet expression in B cells and leads to a selective amplification of the IgG2c+ GC B cell response. Despite the strong Th1 polarizing effect of type I IFNs, the Tfh cell subset develops into IL-4 producing cells that control the overall magnitude of the GCs and promote generation of IgG1+ GC B cells. Thus, type I IFNs act on B cells and cDCs to drive GC formation and to coordinate IgG subclass distribution through divergent Th1 and Tfh cell-dependent pathways.
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| 10. |
- Dahlgren, Peter, 1983, et al.
(författare)
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Reinforcing spirals at work? Mutual influences between selective news exposure and ideological leaning
- 2019
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Ingår i: European Journal of Communication. - : SAGE Publications. - 0267-3231 .- 1460-3705. ; 34:2, s. 159-174
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Tidskriftsartikel (refereegranskat)abstract
- The growth of partisan news sources has raised concerns that people will increasingly select attitude-consistent information, which might lead to increasing political polarization. Thus far, there is limited research on the long-term mutual influences between selective exposure and political attitudes. To remedy this, this study investigates the reciprocal influences between selective exposure and political attitudes over several years, using a three-wave panel survey conducted in Sweden during 2014–2016. More specifically, we analyse how ideological selective exposure to both traditional and online news media influences citizens’ ideological leaning. Findings suggest that (1) people seek-out ideologically consistent print news and online news and (2) such attitude-consistent news exposure reinforces citizens’ ideological leaning over time. In practice, however, such reinforcement effects are hampered by (3) relatively low overall ideological selective exposure and a (4) significant degree of cross-cutting news exposure online. These findings are discussed in light of selective exposure theory and the reinforcing spirals model.
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