La ricerca post-qualitativa e gli interventi formativi della CHAT: Validità epistemologica e superamento dei problemi di metodo

Cultural-historical activity theory (CHAT) appears to match the tenets of post-qualitative inquiry. However, post-qualitative inquiry is credited with being averse to method and to adopt post-modernist stances that are not consistent with CHAT’s structured reading of social reality. Notwithstanding this, it is possible to propose an interpretation of post-qualitative inquiry that overcomes such conceptual challenges. This article tackles the issue both theoretically and with reference to the way current CHAT research is undertaken. First, we propose post-qualitative research should be understood as compliant with post-Gettier epistemological standpoints. Second, we show that CHAT-inspired formative interventions are both educational in nature and, given their approach to learning processes, display the core features of post-qualitative research. Given CHAT’s distinction between immanent aspects of social reality and methods tailored to tackle local issues, post-qualitative inquiry is justified in retaining its flexible—almost anarchic—methodology while, at the same time, enjoying epistemological soundness.La teoria dell’attività storico-culturale (CHAT) mostra di corrispondere ai principi della ricerca post-qualitativa. Tuttavia, la ricerca post-qualitativa si oppone al metodo e adotta posizioni post-moderniste che non paiono coerenti con la visione strutturata della realtà sociale promossa dalla CHAT. Ciononostante, proponiamo un’interpretazione della ricerca post-qualitativa che supera tali difficoltà concettuali. Questo articolo affronta la questione sia teoricamente che con riferimento al modo in cui si svolge l’attuale ricerca in seno alla CHAT. Per prima cosa, proponiamo di collocare la ricerca post-qualitativa entro il quadro dell’epistemologia post-Gettier. Successivamente, mostriamo che gli interventi formativi ispirati alla CHAT hanno sia carattere educativo che investigativo in virtù del loro approccio ai processi di apprendimento. Poiché la CHAT distingue tra aspetti immanenti della realtà sociale e metodi costruiti su misura per affrontare problemi locali, la ricerca post-qualitativa risulta giustificata nel preservare una metodologia flessibile—quasi anarchica—e, parimenti, godere di validità epistemologica

Synchronous Robots vs Asynchronous Lights-Enhanced Robots on Graphs

AbstractIn this paper, we consider the distributed setting of computational mobile entities, called robots, that have to perform tasks without global coordination. Depending on the environment as well as on the robots' capabilities, tasks might be accomplished or not.In particular, we focus on the well-known scenario where the robots reside on the nodes of a graph and operate in Look-Compute-Move cycles. In one cycle, a robot perceives the current configuration in terms of robots positions (Look), decides whether to move toward some edge of the graph (Compute), and in the positive case it performs an instantaneous move along the computed edge (Move).We then compare two basic models: in the first model robots are fully synchronous, while in the second one robots are asynchronous and lights-enhanced, that is, each robot is equipped with a constant number of lights visible to all other robots. The question whether one model is more powerful than the other in terms of computable tasks has been considered in [Das et al., Int.'l Conf. on Distributed Computing Systems, 2012] but for robots moving on the Euclidean plane rather than on a graph.We provide two different tasks, and show that on graphs one task can be solved in the fully synchronous model but not in the asynchronous lights-enhanced model, while for the other task the converse holds. Hence we can assert that the fully synchronous model and the asynchronous lights-enhanced model are incomparable on graphs. This opens challenging directions in order to understand which peculiarities make the models so different

Comparing Adversarial and Supervised Learning for Organs at Risk Segmentation in CT images

Organ at Risk (OAR) segmentation from CT scans is a key component of the radiotherapy treatment workflow. In recent years, deep learning techniques have shown remarkable potential in automating this process. In this paper, we investigate the performance of Generative Adversarial Networks (GANs) compared to supervised learning approaches for segmenting OARs from CT images. We propose three GAN-based models with identical generator architectures but different discriminator networks. These models are compared with well-established CNN models, such as SE-ResUnet and DeepLabV3, using the StructSeg dataset, which consists of 50 annotated CT scans containing contours of six OARs. Our work aims to provide insight into the advantages and disadvantages of adversarial training in the context of OAR segmentation. The results are very promising and show that the proposed GAN-based approaches are similar or superior to their CNN-based counterparts, particularly when segmenting more challenging target organs

Identification of Sparse Reciprocal Graphical Models

In this paper we propose an identification procedure of a sparse graphical model associated to a Gaussian stationary stochastic process. The identification paradigm exploits the approximation of autoregressive processes through reciprocal processes in order to improve the robustness of the identification algorithm, especially when the order of the autoregressive process becomes large. We show that the proposed paradigm leads to a regularized, circulant matrix completion problem whose solution only requires computations of the eigenvalues of matrices of dimension equal to the dimension of the process

Link Prediction: A Graphical Model Approach

We consider the problem of link prediction in networks whose edge structure may vary (sufficiently slowly) over time. This problem, with applications in many important areas including social networks, has two main variants: the first, known as positive link prediction or PLP consists in estimating the appearance of a link in the network. The second, known as negative link prediction or NLP consists in estimating the disappearance of a link in the network. We propose a data-driven approach to estimate the appearance/disappearance of edges. Our solution is based on a regularized optimization problem for which we prove existence and uniqueness of the optimal solution