scholarly journals The Price of Resiliency: A Case Study on Sorting with Memory Faults

Author(s):  
Umberto Ferraro-Petrillo ◽  
Irene Finocchi ◽  
Giuseppe F. Italiano
Keyword(s):  
Algorithmica ◽  
2009 ◽  
Vol 53 (4) ◽  
pp. 597-620 ◽  
Author(s):  
Umberto Ferraro-Petrillo ◽  
Irene Finocchi ◽  
Giuseppe F. Italiano
Keyword(s):  

2021 ◽  
pp. 000276422110031
Author(s):  
E. Johanna Hartelius ◽  
Kaitlyn E. Haynal

Following the July 22, 2011, Oslo bombing and shootings at the Utøya youth camp Norway became embroiled in a conflict over commemorative ethics. The memorial initially selected in an international contest, Memory Wound by Jonas Dahlgren, drew opposition from victims’ families and local residents for its severe impact on the natural landscape. Plans for installation were cancelled in 2017. This controversy, we submit, must be contextualized in relation to the Norwegian justice system’s handling of Anders Breivik, the perpetrator whose criminal proceedings were kept relatively secluded. We demonstrate how the design of Memory Wound and the suppression of Breivik’s publicity reflect a symbolic logic traceable to a national imaginary of Norwegian exceptionalism. By interpretively aligning the use of negative space in Memory Wound with the muting of Breivik as a media event, we investigate the prescriptive force of symbols to inculcate world views. Specifically, we attend to the foreclosure of “prosthetic memory,” which through media circulation allows people to engage with memory that is not primarily theirs. We acknowledge the possibility of empathy across difference that Landsberg ascribes to prosthetic memory; however, we insist that the circumstances under which solidarity might be rejected must be considered. With a dual case study, we offer a perspective on enduring assumptions about cultural identity and the rise of rightwing extremism in Northern Europe.


2012 ◽  
Vol DMTCS Proceedings vol. AQ,... (Proceedings) ◽  
Author(s):  
Loïck Lhote ◽  
Manuel E. Lladser

International audience Consider a countable alphabet $\mathcal{A}$. A multi-modular hidden pattern is an $r$-tuple $(w_1,\ldots , w_r)$, where each $w_i$ is a word over $\mathcal{A}$ called a module. The hidden pattern is said to occur in a text $t$ when the later admits the decomposition $t = v_0 w_1v_1 \cdots v_{r−1}w_r v_r$, for arbitrary words $v_i$ over $\mathcal{A}$. Flajolet, Szpankowski and Vallée (2006) proved via the method of moments that the number of matches (or occurrences) with a multi-modular hidden pattern in a random text $X_1\cdots X_n$ is asymptotically Normal, when $(X_n)_{n\geq1}$ are independent and identically distributed $\mathcal{A}$-valued random variables. Bourdon and Vallée (2002) had conjectured however that asymptotic Normality holds more generally when $(X_n)_{n\geq1}$ is produced by an expansive dynamical source. Whereas memoryless and Markovian sequences are instances of dynamical sources with finite memory length, general dynamical sources may be non-Markovian i.e. convey an infinite memory length. The technical difficulty to count hidden patterns under sources with memory is the context-free nature of these patterns as well as the lack of logarithm-and exponential-type transformations to rewrite the product of non-commuting transfer operators. In this paper, we address a case study in which we have successfully overpassed the aforementioned difficulties and which may illuminate how to address more general cases via auto-correlation operators. Our main result shows that the number of matches with a bi-modular pattern $(w_1, w_2)$ normalized by the number of matches with the pattern $w_1$, where $w_1$ and $w_2$ are different alphabet characters, is indeed asymptotically Normal when $(X_n)_{n\geq1}$ is produced by a holomorphic probabilistic dynamical source.


ZARCH ◽  
2019 ◽  
pp. 12-33
Author(s):  
John R. Gold ◽  
Margaret M. Gold

The Olympics have a greater, more profound and more pervasive impact on the urban fabric of their host cities than any other sporting or cultural event.  This paper is concerned with issues of memory and remembering in Olympic host cities.  After a contextual introduction, it employs a case study of the Queen Elizabeth Olympic Park (QEOP), the main event space for the London 2012 Summer Games, to supply insight into how to read the urban traces of Olympic memory.  Three key themes are identified when interpreting the memories associated with the Park and its built structures, namely: treatment of the area’s displaced past, memorializing the Games, and with memory legacy.  The ensuing discussion section then adopts a historiographic slant, stressing the importance of narrative and offering wider conclusions about Olympic memory and the city.


2022 ◽  
Vol 15 ◽  
Author(s):  
Vivek Parmar ◽  
Bogdan Penkovsky ◽  
Damien Querlioz ◽  
Manan Suri

With recent advances in the field of artificial intelligence (AI) such as binarized neural networks (BNNs), a wide variety of vision applications with energy-optimized implementations have become possible at the edge. Such networks have the first layer implemented with high precision, which poses a challenge in deploying a uniform hardware mapping for the network implementation. Stochastic computing can allow conversion of such high-precision computations to a sequence of binarized operations while maintaining equivalent accuracy. In this work, we propose a fully binarized hardware-friendly computation engine based on stochastic computing as a proof of concept for vision applications involving multi-channel inputs. Stochastic sampling is performed by sampling from a non-uniform (normal) distribution based on analog hardware sources. We first validate the benefits of the proposed pipeline on the CIFAR-10 dataset. To further demonstrate its application for real-world scenarios, we present a case-study of microscopy image diagnostics for pathogen detection. We then evaluate benefits of implementing such a pipeline using OxRAM-based circuits for stochastic sampling as well as in-memory computing-based binarized multiplication. The proposed implementation is about 1,000 times more energy efficient compared to conventional floating-precision-based digital implementations, with memory savings of a factor of 45.


2020 ◽  
Author(s):  
Vahini Reddy Nareddy ◽  
Jonathan Machta ◽  
Karen C. Abbott ◽  
Shadisadat Esmaeili ◽  
Alan Hastings

AbstractLong-range synchrony from short-range interactions is a familiar pattern in biological and physical systems, many of which share a common set of “universal” properties at the point of synchronization. Common biological systems of coupled oscillators have been shown to be members of the Ising universality class, meaning that the very simple Ising model replicates certain spatial statistics of these systems at stationarity. This observation is useful because it reveals which aspects of spatial pattern arise independently of the details governing local dynamics, resulting in both deeper understanding of and a simpler baseline model for biological synchrony. However, in many situations a system’s dynamics are of greater interest than their static spatial properties. Here, we ask whether a dynamical Ising model can replicate universal and non-universal features of ecological systems, using noisy coupled metapopulation models with two-cycle dynamics as a case study. The standard Ising model makes unrealistic dynamical predictions, but the Ising model with memory corrects this by using an additional parameter to reflect the tendency for local dynamics to maintain their phase of oscillation. By fitting the two parameters of the Ising model with memory to simulated ecological dynamics, we assess the correspondence between the Ising and ecological models in several of their features (location of the critical boundary in parameter space between synchronous and asynchronous dynamics, probability of local phase changes, and ability to predict future dynamics). We find that the Ising model with memory is reasonably good at representing these properties of ecological metapopulations. The correspondence between these models creates the potential for the simple and well-known Ising class of models to become a valuable tool for understanding complex biological systems.


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