Software simulation of MC68000

The introduction of the Motorola MC68000 family of microprocessors ushered in a new era of microprocessors. These are single-chip microprocessors designed to function as the central processing units of sophisticated computer systems. The prime objective of this thesis work is to develop a simulator for the MC68000 microprocessor mainly for educational purposes. The simulator would help in any test or research work utilizing 68000 assembly programs in the future. Most of the instructions in the 68000 family are implemented. Both the user mode and supervisory mode programs can be written and run against the simulator. Besides supporting most of the MC68000 features the simulator also has additional features to help debugging

DIAGNOSE: Avoiding Out-of-distribution Data using Submodular Information Measures

Avoiding out-of-distribution (OOD) data is critical for training supervised machine learning models in the medical imaging domain. Furthermore, obtaining labeled medical data is difficult and expensive since it requires expert annotators like doctors, radiologists, etc. Active learning (AL) is a well-known method to mitigate labeling costs by selecting the most diverse or uncertain samples. However, current AL methods do not work well in the medical imaging domain with OOD data. We propose Diagnose (avoiDing out-of-dIstribution dAta usinG submodular iNfOrmation meaSurEs), a novel active learning framework that can jointly model similarity and dissimilarity, which is crucial in mining in-distribution data and avoiding OOD data at the same time. Particularly, we use a small number of data points as exemplars that represent a query set of in-distribution data points and a private set of OOD data points. We illustrate the generalizability of our framework by evaluating it on a wide variety of real-world OOD scenarios. Our experiments verify the superiority of Diagnose over the state-of-the-art AL methods across multiple domains of medical imaging.Comment: Accepted to MICCAI 2022 MILLanD Worksho

Homogeneous Relaxation at Strong Coupling from Gravity

Homogeneous relaxation is a ubiquitous phenomenon in semiclassical kinetic theories where the quasiparticles are distributed uniformly in space, and the equilibration involves only their velocity distribution. For such solutions, the hydrodynamic variables remain constant. We construct asymptotically AdS solutions of Einstein's gravity dual to such processes at strong coupling, perturbatively in the amplitude expansion, where the expansion parameter is the ratio of the amplitude of the non-hydrodynamic shear-stress tensor to the pressure. At each order, we sum over all time derivatives through exact recursion relations. We argue that the metric has a regular future horizon, order by order in the amplitude expansion, provided the shear-stress tensor follows an equation of motion. At the linear order, this equation of motion implies that the metric perturbations are composed of zero wavelength quasinormal modes. Our method allows us to calculate the non-linear corrections to this equation perturbatively in the amplitude expansion. We thus derive a special case of our previous conjecture on the regularity condition on the boundary stress tensor that endows the bulk metric with a regular future horizon, and also refine it further. We also propose a new outlook for heavy-ion phenomenology at RHIC and ALICE.Comment: 60 pages, a section titled "Outlook for RHIC and ALICE" has been added, accepted for publication in Physical Review

CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification

Training deep learning models on medical datasets that perform well for all classes is a challenging task. It is often the case that a suboptimal performance is obtained on some classes due to the natural class imbalance issue that comes with medical data. An effective way to tackle this problem is by using targeted active learning, where we iteratively add data points to the training data that belong to the rare classes. However, existing active learning methods are ineffective in targeting rare classes in medical datasets. In this work, we propose Clinical (targeted aCtive Learning for ImbalaNced medICal imAge cLassification) a framework that uses submodular mutual information functions as acquisition functions to mine critical data points from rare classes. We apply our framework to a wide-array of medical imaging datasets on a variety of real-world class imbalance scenarios - namely, binary imbalance and long-tail imbalance. We show that Clinical outperforms the state-of-the-art active learning methods by acquiring a diverse set of data points that belong to the rare classes.Comment: Accepted to MICCAI 2022 MILLanD Worksho

Chat alors !

Une vérité universellement reconnue est que la traductrice pourvue d’informations hétéroclites glanées au gré des textes qu’elle traite doit être une redoutable joueuse de Trivial Pursuit. Quant à celle qui baigne dans le Luxe et ses affluents (la Fine Gastronomie et la Haute Couture, pour n’en citer que deux), elle brillera également dans les dîners en ville. Quelle assemblée de catophiles chics ne serait pas ravie d’apprendre, par exemple, cette information capitale tirée d’un reportage sur..

SFT, côté édition : une affaire d’équipe

Il fut un temps, il y a longtemps, où un syndicat professionnel décida de publier sa propre revue. Le syndicat fut créé en 1947. Sa revue vit le jour le 15 juillet 1952. Cette grande dame, cheveux gris aux reflets d’argent, promène toujours son « regard pluriel » sur le monde de la traduction. Dès le début, la réalisation de Traduire a été un travail d’équipe (la composition du comité de rédaction actuel se trouve en deuxième de couverture de chaque numéro). La parution était irrégulière jus..