19,536 research outputs found
Securing the Livelihoods and Nutritional Needs of Fish-Dependent Communities
Starting in June 2012, the Rockefeller Foundation began investigating the pressing problem of the declining health of the oceans due to climate change, overfishing, pollution and habitat destruction, and the effects of this decline on poor and vulnerable people who depend on marine ecosystems for food and livelihoods. The goal was to better understand the nature of the problem and the potential impact of interventions in the fields of fisheries, aquaculture, poverty, and food security.The Foundation assembled a portfolio of learning grants that examined this problem from multiple perspectives in order to inform and assess the viability of and potential impact for future engagement on this topic. We supported four scoping studies that sought to identify populations dependent on marine fisheries, as well as review past experience with integrated approaches to fisheries management within a livelihoods and food security context. In partnership with Bloomberg Philanthropies, we also supported scoping work in four countries to assess opportunities for a coordinated strategy integrating national policy, local management, and innovative financing.We have learned a tremendous amount from the work our grantees have done, captured here by partner FSG in a summary and synthesis. We hope this information will contribute to the broader body of knowledge on this topic, as well as our own work
A Pattern Language for High-Performance Computing Resilience
High-performance computing systems (HPC) provide powerful capabilities for
modeling, simulation, and data analytics for a broad class of computational
problems. They enable extreme performance of the order of quadrillion
floating-point arithmetic calculations per second by aggregating the power of
millions of compute, memory, networking and storage components. With the
rapidly growing scale and complexity of HPC systems for achieving even greater
performance, ensuring their reliable operation in the face of system
degradations and failures is a critical challenge. System fault events often
lead the scientific applications to produce incorrect results, or may even
cause their untimely termination. The sheer number of components in modern
extreme-scale HPC systems and the complex interactions and dependencies among
the hardware and software components, the applications, and the physical
environment makes the design of practical solutions that support fault
resilience a complex undertaking. To manage this complexity, we developed a
methodology for designing HPC resilience solutions using design patterns. We
codified the well-known techniques for handling faults, errors and failures
that have been devised, applied and improved upon over the past three decades
in the form of design patterns. In this paper, we present a pattern language to
enable a structured approach to the development of HPC resilience solutions.
The pattern language reveals the relations among the resilience patterns and
provides the means to explore alternative techniques for handling a specific
fault model that may have different efficiency and complexity characteristics.
Using the pattern language enables the design and implementation of
comprehensive resilience solutions as a set of interconnected resilience
patterns that can be instantiated across layers of the system stack.Comment: Proceedings of the 22nd European Conference on Pattern Languages of
Program
i2MapReduce: Incremental MapReduce for Mining Evolving Big Data
As new data and updates are constantly arriving, the results of data mining
applications become stale and obsolete over time. Incremental processing is a
promising approach to refreshing mining results. It utilizes previously saved
states to avoid the expense of re-computation from scratch.
In this paper, we propose i2MapReduce, a novel incremental processing
extension to MapReduce, the most widely used framework for mining big data.
Compared with the state-of-the-art work on Incoop, i2MapReduce (i) performs
key-value pair level incremental processing rather than task level
re-computation, (ii) supports not only one-step computation but also more
sophisticated iterative computation, which is widely used in data mining
applications, and (iii) incorporates a set of novel techniques to reduce I/O
overhead for accessing preserved fine-grain computation states. We evaluate
i2MapReduce using a one-step algorithm and three iterative algorithms with
diverse computation characteristics. Experimental results on Amazon EC2 show
significant performance improvements of i2MapReduce compared to both plain and
iterative MapReduce performing re-computation
Capitalization, Scale, and Investment: Does Growth Equal Gain?
This study, commissioned by the William Penn Foundation, examines the state of Philadelphia's arts and culture sector.The study is divided into two major sections: trends in the greater Philadelphia ecosystem and assessing investments toward growth
kTrans: Knowledge-Aware Transformer for Binary Code Embedding
Binary Code Embedding (BCE) has important applications in various reverse
engineering tasks such as binary code similarity detection, type recovery,
control-flow recovery and data-flow analysis. Recent studies have shown that
the Transformer model can comprehend the semantics of binary code to support
downstream tasks. However, existing models overlooked the prior knowledge of
assembly language. In this paper, we propose a novel Transformer-based
approach, namely kTrans, to generate knowledge-aware binary code embedding. By
feeding explicit knowledge as additional inputs to the Transformer, and fusing
implicit knowledge with a novel pre-training task, kTrans provides a new
perspective to incorporating domain knowledge into a Transformer framework. We
inspect the generated embeddings with outlier detection and visualization, and
also apply kTrans to 3 downstream tasks: Binary Code Similarity Detection
(BCSD), Function Type Recovery (FTR) and Indirect Call Recognition (ICR).
Evaluation results show that kTrans can generate high-quality binary code
embeddings, and outperforms state-of-the-art (SOTA) approaches on downstream
tasks by 5.2%, 6.8%, and 12.6% respectively. kTrans is publicly available at:
https://github.com/Learner0x5a/kTrans-releas
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