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Concept

Memory segmentation

Related publications (29)

Lausanne Historical Censuses Dataset HTR 35k

Rémi Guillaume Petitpierre, Lucas Arnaud André Rappo, Marion Kramer

This training dataset includes a total of 34,913 manually transcribed text segments. It is dedicated to the handwritten text recognition (HTR) of historical sources, typically tabular records, such as censuses. This dataset is based on a sample of 83 pages ...

Zenodo2023

1805-1898 Census Records of Lausanne : a Long Digital Dataset for Demographic History

Isabella Di Lenardo, Rémi Guillaume Petitpierre, Lucas Arnaud André Rappo, Marion Kramer

This historical dataset stems from the project of automatic extraction of 72 census records of Lausanne, Switzerland. The complete dataset covers a century of historical demography in Lausanne (1805-1898), which corresponds to 18,831 pages, and nearly 6 mi ...

Zenodo2023

An end-to-end pipeline for historical censuses processing

Rémi Guillaume Petitpierre, Lucas Arnaud André Rappo, Marion Kramer

Censuses are structured documents of great value for social and demographic history, which became widespread from the nineteenth century on. However, the plurality of formats and the natural variability of historical data make their extraction arduous and ...

2023

HPCache: memory-efficient OLAP through proportional caching revisited

Anastasia Ailamaki, Periklis Chrysogelos, Hamish Mcniece Hill Nicholson

Analytical engines rely on in-memory data caching to avoid storage accesses and provide timely responses by keeping the most frequently accessed data in memory. Purely frequency- and time-based caching decisions, however, are a proxy of the expected query ...

Springer2023

Rebooting Virtual Memory with Midgard

Siddharth Gupta

Virtual Memory (VM) is a critical programming abstraction that is widely used in various modern computing platforms. With the rise of datacenter computing and birth of planet-scale online services, the semantic and capacity requirements from memory have ev ...

EPFL2023

impresso Text Reuse at Scale. An interface for the exploration of text reuse data in semantically enriched historical newspapers

Maud Ehrmann, Matteo Romanello

Text Reuse reveals meaningful reiterations of text in large corpora. Humanities researchers use text reuse to study, e.g., the posterior reception of influential texts or to reveal evolving publication practices of historical media. This research is often ...

2023

Method for reading and writing unreliable memories and a corresponding memory controller device and memory

Andreas Peter Burg, Reza Ghanaatian Jahromi

A method of accessing a memory space of a memory device with a decoder, the memory space having faults, including the steps of performing a memory access operation by an electronic device to a access a logical memory space of the memory device, and randomi ...

2022

Object Priors for Volumetric Image Segmentation

Pamuditha Udaranga Wickramasinghe

Large training datasets have played a vital role in the success of modern deep learning methods in computer vision. But, obtaining sufficient amount of training data is challenging, specially when annotating volumetric images. This is because fully annotat ...

EPFL2022

Dynamic Probabilistic Pruning: A General Framework for Hardware-Constrained Pruning at Different Granularities

Alexandre Schmid

Unstructured neural network pruning algorithms have achieved impressive compression ratios. However, the resulting-typically irregular-sparse matrices hamper efficient hardware implementations, leading to additional memory usage and complex control logic t ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

HPCache: Memory-Efficient OLAP Through Proportional Caching

Anastasia Ailamaki, Hamish Mcniece Hill Nicholson, Periklis Chrysogelos

Analytical engines rely on in-memory caching to avoid disk accesses and provide timely responses by keeping the most frequently accessed data in memory. Purely frequency- & time-based caching decisions, however, are a proxy of the expected query execution ...

ACM2022

Data transformer apparatus

Christoph Koch, Babak Falsafi, Siddharth Gupta, Mark Johnathon Sutherland, Mario Paulo Drumond Lages De Oliveira, Arash Pourhabibi Zarandi, Zilu Tian, Hussein Kassir

Data transformer apparatus comprising at least a dispatcher module (D), a reader module (R), a converter module (C) and a writer module (W). The dispatcher module (D) is configured to: receive a data transformation request (DTR) including: a first informat ...

2021

Preventing Use-After-Free Attacks with Fast Forward Allocation

Sanidhya Kashyap

Memory-unsafe languages are widely used to implement critical systems like kernels and browsers, leading to thousands of memory safety issues every year. A use-after-free bug is a temporal memory error where the program accidentally visits a freed memory l ...

USENIX ASSOC2021

Networks of Coupled VO2 Oscillators for Neuromorphic Computing

Elisabetta Corti

Neuromorphic computing is a wide research field aimed to the realization of brain-inspired hardware, apt to tackle computation of unstructured data more efficiently than currently done with standard computational units. Oscillatory neural networks are know ...

EPFL2021

Access pattern based optimization of memory access

Anastasia Ailamaki, Georgios Psaropoulos

A method for optimizing memory access for database operations is provided. The method may include identifying an access pattern associated with a database operation. The access pattern may include data required to perform the database operation. One or mor ...

2020

SoK: Shining Light on Shadow Stacks

Mathias Josef Payer

Control-Flow Hijacking attacks are the dominant attack vector against C/C++ programs. Control-Flow Integrity (CFI) solutions mitigate these attacks on the forward edge, i.e., indirect calls through function pointers and virtual calls. Protecting the backwa ...

IEEE COMPUTER SOC2019

A Product Engine for Energy-Efficient Execution of Binary Neural Networks Using Resistive Memories

David Atienza Alonso, Pierre-Emmanuel Julien Marc Gaillardon, Marina Zapater Sancho, Xifan Tang, Yasir Mahmood Qureshi, Edouard Giacomin

The need for running complex Machine Learning (ML) algorithms, such as Convolutional Neural Networks (CNNs), in edge devices, which are highly constrained in terms of computing power and energy, makes it important to execute such applications efficiently. ...

2019

Timely and cost-efficient data exploration through adaptive tuning

Matthaios Alexandros Olma

Modern applications accumulate data at an exponentially increasing rate and traditional database systems struggle to keep up. Decision support systems used in industry, rely heavily on data analysis, and require real-time responses irrespective of data siz ...

EPFL2019

CoreNEURON : An Optimized Compute Engine for the NEURON Simulator

Felix Schürmann, James Gonzalo King, Pramod Shivaji Kumbhar, Fabien Jonathan Delalondre, Aleksandr Ovcharenko, Jérémy Pierre Benoit Fouriaux

The NEURON simulator has been developed over the past three decades and is widely used by neuroscientists to model the electrical activity of neuronal networks. Large network simulation projects using NEURON have supercomputer allocations that individually ...

2019

Probabilistic Atlases to Enforce Topological Constraints

Pascal Fua, Graham Knott, Pamuditha Udaranga Wickramasinghe

Probabilistic atlases (PAs) have long been used in standard segmentation approaches and, more recently, in conjunction with Convolutional Neural Networks (CNNs). However, their use has been restricted to relatively standardized structures such as the brain ...

2019

Automated CT bone segmentation using statistical shape modelling and local template matching

Alexandre Terrier, Alain Farron, Philippe Büchler

Accurate CT bone segmentation is essential to develop chair-side manufacturing of implants based on additive manufacturing. We herewith present an automated method able to accurately segment challenging bone regions, while simultaneously providing anatomic ...

2019