Cultura y calidad

Hasta mediados del siglo xx,cuando se comparaban obras de arte y productos industriales, no hacía falta explicar de qué lado estaba la calidad. Se suponía que la industria era el mundo de la prisa, los grandes volúmenes, la preocupación por los costos, las utilidades, la eficiencia, frente a la cultura artística, literaria, intelectual, científica, donde pesaban las preocupaciones contrarias: el amor al oficio, la excelencia sin límites, el rigor y la perfección hasta en el último detalle

Restoring T cell homeostasis after allogeneic stem cell transplantation; principal limitations and future challenges

For several leukemia patients, allogeneic stem cell transplantation (allogeneic-SCT) is the unique therapeutic modality that could potentially cure their disease. Despite significant progress made in clinical management of allogeneic-SCT, acute graft-versushost disease (aGVHD) and infectious complications remain the second and third cause of death after disease recurrence. Clinical options to restore immunocompetence after allogeneic-SCT are very limited as studies have raised awareness about the safety with regards to graft-versus-host disease (GVHD). Preclinical works are now focusing on strategies to improve thymic functions and to restore the peripheral niche that have been damaged by alloreactive T cells. In this mini review, we will provide a brief overview about the adverse effects of GVHD on the thymus and the peripheral niche and the resulting negative outcome on peripheral T cell homeostasis. Finally, we will discuss the potential relevance of coordinating our studies on thymic rejuvenation and improvement of the peripheral lymphoid niche to achieve optimal T cell regeneration in GVHD patients

Understanding Methodology for Efficient CNN Architectures in Profiling Attacks

The use of deep learning in side-channel analysis has been more and more prominent recently. In particular, Convolution Neural Networks (CNN) are very efficient tools to extract the secret information from side-channel traces. Previous work regarding the use of CNN in side-channel has been mostly proposed through practical results. Zaid et al. have proposed a theoretical methodology in order to better understand the convolutional part of CNN and to understand how to construct an efficient CNN in the side-channel context [ZBHV19]. The proposal of Zaid et al. has been recently questioned by [WAGP20]. However this revisit is based on wrong assumptions and misinterpretations. Hence, many of the claims of [WAGP20] are unfounded regarding [ZBHV19]. In this paper, we clear out the potential misunderstandings brought by [WAGP20] and explain more thoroughly the contributions of [ZBHV19]

Methodology for Efficient CNN Architectures in Profiling Attacks -- Extended Version

The side-channel community recently investigated a new approach, based on deep learning, to significantly improve profiled attacks against embedded systems. Previous works have shown the benefit of using convolutional neural networks (CNN) to limit the effect of some countermeasures such as desynchronization. Compared with template attacks, deep learning techniques can deal with trace misalignment and the high dimensionality of the data. Pre-processing is no longer mandatory. However, the performance of attacks depends to a great extent on the choice of each hyperparameter used to configure a CNN architecture. Hence, we cannot perfectly harness the potential of deep neural networks without a clear understanding of the network’s inner-workings. To reduce this gap, we propose to clearly explain the role of each hyperparameters during the feature selection phase using some specific visualization techniques including Weight Visualization, Gradient Visualization and Heatmaps. By highlighting which features are retained by filters, heatmaps come in handy when a security evaluator tries to interpret and understand the efficiency of CNN. We propose a methodology for building efficient CNN architectures in terms of attack efficiency and network complexity, even in the presence of desynchronization. We evaluate our methodology using public datasets with and without desynchronization. In each case, our methodology outperforms the previous state-of-the-art CNN models while significantly reducing network complexity. Our networks are up to 25 times more efficient than previous state-of-the-art while their complexity is up to 31810 times smaller. Our results show that CNN networks do not need to be very complex to perform well in the side-channel context

Online Performance Evaluation of Deep Learning Networks for Side-Channel Analysis

Deep learning based side-channel analysis has seen a rise in popularity over the last few years. A lot of work is done to understand the inner workings of the neural networks used to perform the attacks and a lot is still left to do. However, finding a metric suitable for evaluating the capacity of the neural networks is an open problem that is discussed in many articles. We propose an answer to this problem by introducing an online evaluation metric dedicated to the context of side-channel analysis and use it to perform early stopping on existing convolutional neural networks found in the literature. This metric compares the performance of a network on the training set and on the validation set to detect underfitting and overfitting. Consequently, we improve the performance of the networks by finding their best training epoch and thus reduce the number of traces used by 30%. The training time is also reduced for most of the networks considered

Does the book have a future?

Advances in technology have produced a range of devices on which a book can be read, from an e‐book reader to a tablet or phone. E‐books have the advantages that a reader can take a sizeable selection when traveling, read backlit text, and enlarge the type size to suit. The book exists in both printed and digital form, as p‐book and e‐book. Digital technology has also revolutionized the production of printed books. Digital printing, as opposed to traditional offset printing, enables genuine print on demand as well as short runs. This facility has less relevance to the world of mass‐market paperbacks, where large print runs mean that the benefits of offset printing still apply, but it is of great interest to most publishers and those who want to self‐publish. In fact, more books are published than ever before, and there has been a boom in self‐publishing

Integrated genomic characterization of oesophageal carcinoma

Oesophageal cancers are prominent worldwide; however, there are few targeted therapies and survival rates for these cancers remain dismal. Here we performed a comprehensive molecular analysis of 164 carcinomas of the oesophagus derived from Western and Eastern populations. Beyond known histopathological and epidemiologic distinctions, molecular features differentiated oesophageal squamous cell carcinomas from oesophageal adenocarcinomas. Oesophageal squamous cell carcinomas resembled squamous carcinomas of other organs more than they did oesophageal adenocarcinomas. Our analyses identified three molecular subclasses of oesophageal squamous cell carcinomas, but none showed evidence for an aetiological role of human papillomavirus. Squamous cell carcinomas showed frequent genomic amplifications of CCND1 and SOX2 and/or TP63, whereas ERBB2, VEGFA and GATA4 and GATA6 were more commonly amplified in adenocarcinomas. Oesophageal adenocarcinomas strongly resembled the chromosomally unstable variant of gastric adenocarcinoma, suggesting that these cancers could be considered a single disease entity. However, some molecular features, including DNA hypermethylation, occurred disproportionally in oesophageal adenocarcinomas. These data provide a framework to facilitate more rational categorization of these tumours and a foundation for new therapies