| 1. |
- Lévy, Romain, et al.
(författare)
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Human CARMIL2 deficiency underlies a broader immunological and clinical phenotype than CD28 deficiency.
- 2023
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Ingår i: The Journal of experimental medicine. - : Rockefeller University Press. - 1540-9538 .- 0022-1007. ; 220:2
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Tidskriftsartikel (refereegranskat)abstract
- Patients with inherited CARMIL2 or CD28 deficiency have defective T cell CD28 signaling, but their immunological and clinical phenotypes remain largely unknown. We show that only one of three CARMIL2 isoforms is produced and functional across leukocyte subsets. Tested mutant CARMIL2 alleles from 89 patients and 52 families impair canonical NF-κB but not AP-1 and NFAT activation in T cells stimulated via CD28. Like CD28-deficient patients, CARMIL2-deficient patients display recalcitrant warts and low blood counts of CD4+ and CD8+ memory T cells and CD4+ TREGs. Unlike CD28-deficient patients, they have low counts of NK cells and memory B cells, and their antibody responses are weak. CARMIL2 deficiency is fully penetrant by the age of 10 yr and is characterized by numerous infections, EBV+ smooth muscle tumors, and mucocutaneous inflammation, including inflammatory bowel disease. Patients with somatic reversions of a mutant allele in CD4+ T cells have milder phenotypes. Our study suggests that CARMIL2 governs immunological pathways beyond CD28.
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| 2. |
- Persiani, Michele, 1989-
(författare)
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Computational models for intent recognition in robotic systems
- 2020
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The ability to infer and mediate intentions has been recognized as a crucial task in recent robotics research, where it is agreed that robots are required to be equipped with intentional mechanisms in order to participate in collaborative tasks with humans.Reasoning about - or rather, perceiving - intentions enables robots to infer what other agents are doing, to communicate what are their plans, or to take proactive decisions. Intent recognition relates to several system requirements, such as the need of an enhanced collaboration mechanism in human-machine interactions, the need for adversarial technology in competitive scenarios, ambient intelligence, or predictive security systems.When attempting to describe what an intention is, agreement exists to represent it as a plan together with the goal it attempts to achieve. Being compatible with computer science concepts, this representation enables to handle intentions with methodologies based on planning, such as the Planning Domain Description Language or Hierarchical Task Networks.In this licentiate we describe how intentions can be processed using classical planning methods, with an eye also on newer technologies such as deep networks. Our goal is to study and define computational models that would allow robotic agents to infer, construct and mediate intentions. Additionally, we explore how intentions in the form of abstract plans can be grounded to sensorial data, and in particular we provide discussion on grounding over speech utterances and affordances, that correspond to the action possibilities offered by an environment.
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| 3. |
- Sutherland, Alexander, et al.
(författare)
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Tell Me Why You Feel That Way: Processing Compositional Dependency for Tree-LSTM Aspect Sentiment Triplet Extraction (TASTE)
- 2020
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Ingår i: Artificial Neural Networks and Machine Learning – ICANN 2020. - Cham : Springer International Publishing. - 9783030616090 - 9783030616083 ; , s. 660-671
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Konferensbidrag (refereegranskat)abstract
- Sentiment analysis has transitioned from classifying the sentiment of an entire sentence to providing the contextual information of what targets exist in a sentence, what sentiment the individual targets have, and what the causal words responsible for that sentiment are. However, this has led to elaborate requirements being placed on the datasets needed to train neural networks on the joint triplet task of determining an entity, its sentiment, and the causal words for that sentiment. Requiring this kind of data for training systems is problematic, as they suffer from stacking subjective annotations and domain over-fitting leading to poor model generalisation when applied in new contexts. These problems are also likely to be compounded as we attempt to jointly determine additional contextual elements in the future. To mitigate these problems, we present a hybrid neural-symbolic method utilising a Dependency Tree-LSTM’s compositional sentiment parse structure and complementary symbolic rules to correctly extract target-sentiment-cause triplets from sentences without the need for triplet training data. We show that this method has the potential to perform in line with state-of-the-art approaches while also simplifying the data required and providing a degree of interpretability through the Tree-LSTM.
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