496 research outputs found
Remembering Darrell Posey´s Contribution to Ethnobiology and Ethnoecology
No full textThe main purpose of this article is to provide an account of Darrell Posey’s academic life, from a north-American undergraduate student interested in entomology into one of the main figures and builders of Ethnobiology and major advocate of Indigenous Peoples ‘rights around the world. This short biography highlights his relations with the Kayapó indigenous people and his role in coordinating a decade-long interdisciplinary ethnobiology research project named “The Kayapó Project”. It was mostly through the development of this team effort that he contributed fundamental and long-lasting theoretical, methodological and practical advancements to the field of ethnobiology.
Recommended from our members
Improving File Management Through Provenance And Rich Metadata
No full textModern high end computing systems store hundreds of petabytes of data and have billions of files, as many files as the internet of only a few years ago. Even modern personal computers store numbers of files that would be massive for the largest mainframe computers of forty years ago. The quantities of data in modern computing have long since overwhelmed anyone's ability to manage it manually, and the forty year old tools currently in use for file finding and management are reaching the limits of scale. In an environment like this, secure, effective, and efficient search algorithms and automatic file management become a necessity, not a nicety.This dissertation addresses the question of how users can better find and manage files by taking advantage of advances in file systems. We focus on a multi-user scientific computing environment, but many of the techniques we describe are effective and advantageous at desktop scale as well. We begin with an empirical description of the problem, drawn from user studies and our statistical analysis of scientific data, in order to better understand the problem domain. We then describe a new technique for studying provenance in scientific systems, and a technique to synthesize system level provenance from existing traces. We describe our novel algorithm designed to provide importance ranking for file system search by leveraging provenance, and discuss the relationship between ranking and access prediction. And finally, we show how rich metadata can be used to improve file management by automatically generating expressive, unique file names.Modern file management must be automatic and scalable, allowing users and file systems to focus on what each does best. By exploiting richer information such as provenance and semantic metadata, file systems can offer sophisticated new capabilities to ease the burdens of users, making file systems easier to use, navigate, and understand
Recommended from our members
Embedding Security into Systems After Their Design
No full textSecurity is rarely designed into systems and architectures from the beginning. Typically, security enters into the design process only after applications are built and security issues arise. While security is often dependent on specific use cases, decades of development provide an opportunity to synthesize common security needs into a set of critical features and embed them into the core underlying systems.Please don't get this dissertation through ProQuest. Copies are freely available from the UC Santa Cruz library and the University of California's open access eScholarship initiative (see escholarship.org) and my website: capelis.d
Recommended from our members
Dynamic performance enhancement of scientific networks and systems
No full textLarge distributed storage systems such as High Performance Computing (HPC) systems used by national or international laboratories must deliver performance for demanding scientific workloads that pose challenges from scale, complexity, and dynamism. Ideally, data is placed in locations and network routes are set to optimize performance, but the aforementioned challenges of large scientific systems and networks inhibits adaptive performance tuning on a large scale.If the data is not placed or routed with the potential future access patterns, the system risks having popular data stored on a slow file system. This can cause a steep drop in performance when access demand shifts to that data. Ideally, determining when and how to move data around in anticipation of future demand spikes can prevent steep drops in performance, and we refer to these drops as performance bottlenecks. We can leverage the fact that workloads shift through time, creating access patterns that can be used to forecast demand. Past accesses can be used to reveal important information when determining future accesses.An existing solution for determining where to move the data is to use access logs to create system performance models that can predict how the system reacts to fluctuations in demand. The drawback is that the search space of how to build an effective model is massive. The more there are locations where the data can be stored to larger the search space becomes. Additionally, applying the model to a system can potentially negatively impact any performance benefits of dynamically moving the data around the system. If a model is not set up correctly, the added overhead of using the model on the system may overshadow the benefit of applying the model on a target system. Ideally, the models created by the system engineers place and route the data to optimize performance, but workloads shift can be hard to predict using traditional methods such as heuristics.This dissertation applies and enhances online learning to predict and accelerate the performance of data movements and accesses in large scale storage systems. We focus on large scientific systems and networks like the CERN EOS, Pacific Northwest National Laboratory’s (PNNL) BlueSky system and the Caltech Tier 2 system. These systems allow for us to stress test our models with real data. Additionally it allows us to collect information about how accesses are distributed and executed on real systems. From the collected information we are able to create models based on real workloads. We are also able to reduce the training overhead of our models by determining which features may not bring added information to a model during training, and removing them. We developed a methodology to select features over time that is resilient to data collection noise added by new data collected from the system.Access logs created by system engineers often track hundreds of metrics describing its daily operation, and we must sift through these metrics to discover the most relevant metrics for the modeling task at hand. To explore different feature selection techniques, we have developed WinnowML, a system of automatically determining the most relevant feature subset for a specific modeling methodology when modeling system performance over time. We use WinnowML to determine what combination of existing techniques allow us to get a stable selected subset of features which will not vary significantly over time while keeping a low prediction error. Using the created list of features, system analysts can determine what features should be used when modeling an aspect of their system over time. Using WinnowML lowered the resulting mean absolute error by 13.6\% on average compared to the closest performing approach such as L1-regularization or Principal Component Analysis (PCA).To optimize the placement of data, we developed Geomancy, a tool that models the placement of data within a distributed storage system and reacts to drops in performance. Additionally to optimize the network routing decisions, we developed Diolkos, a tool that dynamically reroutes data flow in response to drops in performance. Using a combination of machine learning techniques suitable for temporal modeling, Geomancy and Diolkos determines when and where a bottleneck may happen due to changing workloads and suggests changes in the layout and routing decisions to mitigate or prevent them. Using WinnowML to determine which features to use when training our Geomancy tool, the predicted data layouts of Geomancy offered benefits for storage systems such as avoiding potential bottlenecks and increasing overall I/O throughput from 11\% to 30\%. It managed to free up resources that other workloads running concurrently could use. We then moved on to tackle the data transfer overhead in scientific networks. There exist several techniques that reroute data within scientific networks while improving network performance, leveraging latent parallel data transfer capabilities. The issue with these techniques is that they require a central authority, which may add computational and network overhead to the system. We propose a decentralized rerouting technique that exists at the switch level. When we applied Diolkos to the Caltech Tier 2 network, we found that our most accurate model, a dense model with one hidden layer trained using all the ports, increases throughput of switches up to 49\% compared to the best performing heuristic approach, exponentially weighted moving average, and up to 28\% when using a traditional controller
Securing Distance-Vector Routing Protocols
No full textThe security requirements of distance-vector routing protocols are analyzed, their vulnerabilities identi ed, and countermeasures to these vulnerabilities are proposed. The innovation presented involves the use of mechanisms from the path- nding class of distance-vector protocols as a solution to the security problems of distance-vector protocols. The result is the rst proposal to e ectively and e ciently secures distance-vector protocols in constant space. iv Acknowledgments While any academic achievement, almost by de nition, owes much credit to others, this is especially true in the case of my Master's degree. I owe special thanks to Darrell Long whose support and encouragement over the years have more than once kept me going on what turned into quite a long road. Similarly, I owe a special thanks to Pat Mantey | an outstanding manager and mentor, he has both inspired and encouraged me in my academic career. I consider the opportunity towork with these two gentlemen to be one o
A Note on Bit-Mapped Free Sector Management
No full textThe most common methods for maintain a list of free sectors on disk are to use either a linked list or a bit map [1]. Using a linked list has the advantage that is requires no extra storage since the links are stored in the free sectors. It also provides quick allocation and deallocation, requiring only that a free sector be removed from the head of the list, or a freed sector be added to the head of the list, respectively. The main disadvantage of a linked list is that over time the list tends towards random. That is, unless the list is sorted, sectors are placed on the list in no particular order. The result is poor locality during le access, signicantly impacting performance by increasing the average seek time. By using a bit map, adjacent free sectors will always appear adjacent in the bit map. There is a small cost in terms of storage; that is, the bit map will contain one eighth as many bytes as there are sectors on the disk. There is a potentially more important concern: the average number of bits that must be scanned in order to nd a free sector. Since this technique was rst used, the size of disks has increased by approximately four orders of magnitude. If the number of bits to b
Recommended from our members
A Technique for Managing Mirrored Disks
No full textDisk mirroring is a highly effective technique for improving the fault tolerance and performance of storage systems. Mirroring requires that those disks holding the current data must be determined when recovering from total system failure. In a two disk system, this is only a minor problem since the operator can indicate which disk is current. The complexity increases when more disks are introduced, and requiring human intervention is impractical when the target environment is the consumer market. In these situations, automatic recovery must be used when possible. We describe an efficient algorithm for managing the consistency of mirrored storage. Our algorithm requires only n + log n bits of state per disk and does not require logging or quorum collection
A tradução de contos literários abordando relações entre o mundo anglófono e lusófono
No full textO presente trabalho final tem como propósito abordar a tradução de uma pequena parte contística de literatura de diáspora portuguesa nos Estados Unidos da América.
Tendo como base dois contos de dois autores lusodescendentes, nomeadamente "The Woman Who Stole the Moon", de Darrell Kastin (n. 1957), e "A Candle in the Wind", de Julian Silva (n. 1947), propõe-se uma tradução e enumeram-se as dificuldades apresentadas, identificando-se o tipo de problemas, juntamente com as estratégias para a sua resolução.
A tradução dos contos afigurou-se um processo longo e moroso, em parte graças à tentativa cuidada de reproduzir os estilos de ambos os autores, e em parte devido à complexidade de normas a que a tradução tem de atender. Um dos principais desafios prendeu-se com a tipologia de narrativa breve ficcional. Outro aspeto de relevo na investigação foi o estudo de autores literários e a tradução das obras dos mesmos, uma vez que vivem uma situação intercultural e ambivalente entre os mundos de partida e chegada.
Procurou-se traduzir contos recentes de forma a poder estabelecer um diálogo com a literatura de diáspora atual, aproveitando igualmente a oportunidade de contactar com autores vivos e esclarecer qualquer dúvida ou pedir algum parecer.
Neste trabalho, apresenta-se uma introdução ao contexto dos lusodescendentes nos E.U.A., assim como uma biografia de cada autor e caracterização dos contos, ao que se segue uma análise dos procedimentos e estratégias de tradução que possibilitaram a mesma, em conformidade com a perceção de estrutura, estilística e função de cada um dos contos.
Em anexo encontram-se os textos de partida e as traduções propostas.
Por fim, espera-se que o presente trabalho final de mestrado possa suscitar a curiosidade dos leitores sobre a literatura de diáspora portuguesa nos E.U.A., assim como dar a conhecer a escrita de Darrell Kastin e Julian Silva.This master's thesis' primary aim is to present the translation of a small part of Portuguese diasporic literature in the United States of America.
The work is based on two short stories, "The Woman who Stole the Moon", by Darrell Kastin (b. 1957), and "A Candle in the Wind", by Julian Silva (b. 1947), both of Portuguese descent. The difficulties of translating these into Portuguese will be enumerated, along with the type of problem they represent and the strategies used to solve them.
Translating the two short stories was a long and time-consuming process, in part due to the attempt to reproduce the narrative styles of both authors, and in part due to the complex array of norms interfering with the translation process. One special challenge was to deal with the short fiction form. Another important aspect was the study of literary authors and the translation of their writings, since the authors live in an intercultural and ambivalent situation between both source and target worlds.
The aim was to translate contemporary authors in order not only to establish a dialogue with diasporic literature, but with the authors themselves, who kindly answered questions and doubts and had their say in the translations.
This present study includes an introduction to the context of Portuguese descendants living in the USA, as well as a biography on both authors and a characterization of each short story. There is also an extensive analysis of all the strategies and procedures that made the translation process possible according to the structure, stylistics and function of each short story.
The annexes include the original short stories and respective translation.
Lastly, we hope that this master's thesis can help readers understand Portuguese literary diaspora in the USA better, contributing to the dissemination of the writings of both Darrell Kastin and Julian Silva
- …
