1,389,774 research outputs found
Lest We Forget The Passage from Africa into the Twenty-First Century
No full textLest We Forget offers a three-dimensional, interactive look at black history in America from slavery to the Civil Rights Era, Barack Obama's presidency, and the foundation of Black Lives Matter.Cover -- Half Title -- Title -- Copyright -- TABLE OF CONTENTS -- PART ONE: LEST WE FORGET THE PASSAGE FROM AFRICA TO SLAVERY AND EMANCIPATION -- PART TWO: FREEDOM'S CHILDREN THE PASSAGE FROM EMANCIPATION TO THE GREAT MIGRATION -- PART THREE: WE SHALL NOT BE MOVED THE PASSAGE FROM THE GREAT MIGRATION INTO THE TWENTY-FIRST CENTURY -- AFTERWORD -- TRANSCRIPTIONS -- ENDNOTES -- ACKNOWLEDGMENTS -- ABOUT THE AUTHOR -- IMAGE CREDITSLest We Forget offers a three-dimensional, interactive look at black history in America from slavery to the Civil Rights Era, Barack Obama's presidency, and the foundation of Black Lives Matter.Description based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, YYYY. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
Lest We Forget Plaque
No full textRectangular wood and metal plaque featuring a metal wreath. The plaque reads, Presented to Governor Kenneth M. Curtis for untiring efforts in promoting P.O.W.\u27s & M.I.A.\u27s with deep gratitude. From Lest We Forget April 24, 1973 .https://digitalcommons.usm.maine.edu/gov_curtis/1013/thumbnail.jp
Calls for a new UK bill of rights forget the trailblazing original
No full textFirst paragraph: David Cameron is riding into the 2015 election campaign with a promise to finally fulfil one of the Conservatives’ 2010 manifesto commitments: to repeal the 1998 Human Rights Act, restore the sovereignty of Parliament against the “mission creep” of the unelected judges at the European Court, and enact a “British Bill of Rights” more properly rooted in “our values”. Access this article on The Conversation website: https://theconversation.com/calls-for-a-new-uk-bill-of-rights-forget-the-trailblazing-original-3477
Solving eigenvalue response matrix equations with nonlinear techniques
No full textThis paper presents new algorithms for use in the eigenvalue response matrix method (ERMM) for reactor eigenvalue problems. ERMM spatially decomposes a domain into independent nodes linked via boundary conditions approximated as truncated orthogonal expansions, the coefficients of which are response functions. In its simplest form, ERMM consists of a two-level eigenproblem: an outer Picard iteration updates the k-eigenvalue via balance, while the inner λ -eigenproblem imposes neutron balance between nodes. Efficient methods are developed for solving the inner λ-eigenvalue problem within the outer Picard iteration. Based on results from several diffusion and transport benchmark models, it was found that the Krylov-Schur method applied to the λ -eigenvalue problem reduces Picard solver times (excluding response generation) by a factor of 2–5. Furthermore, alternative methods, including Picard acceleration schemes, Steffensen’s method, and Newton’s method, are developed in this paper. These approaches often yield faster k-convergence and a need for fewer k-dependent response function evaluations, which is important because response generation is often the primary cost for problems using responses computed online (i.e., not from a precomputed database). Accelerated Picard iteration was found to reduce total computational times by 2–3 compared to the unaccelerated case for problems dominated by response generation. In addition, Newton’s method was found to provide nearly the same performance with improved robustness
Do not forget tuberculous meningitis
No full textTuberculous meningitis (TBM) is relatively uncommon compared with other types of meningitis and so it is easy to forget to consider it as an explanation for a patient’s presenting problem. If untreated TBM is fatal in most cases.
Who is at risk?
Children under aged 5 years,
The elderly,
HIV infected patients (in these patients TBM may be caused by an “atypical” mycobacterium especially Mycobacterium avium-intracellulare),
Alcoholics,
Diabetes mellitus,
Patients with head trauma and
Those on steroid therapy
The forget-set identification problem
No full textMachine Unlearning (MU) is the problem of removing the influence of user's unwanted evidence from a trained machine-learning model. MU is typically formulated so that the input unwanted evidence corresponds to a subset of the training set utilized to train the model upstream, which is commonly referred to as the "forget set". However, this requirement is often difficult to satisfy in real-world scenarios, as users may be unaware of the peculiarities of the training set or simply they do not have access to it. In a more realistic setting, users provide their unwanted evidence in a form that is more abstract than or anyway different from a precise subset of training data. In such cases, executing MU methods requires an essential and challenging preliminary step, which, to the best of our knowledge, has never been addressed so far: identifying the forget set based on user's unwanted evidence. In this paper, we fill this important gap in the MU literature and introduce the Forget-Set Identification (ForSId) problem: given a trained machine-learning model, an "unwanted set" of samples (evidence to unlearn), and a "wanted set" of samples (evidence to retain), identify the forget set as a subset of the training set, such that the similarity in the predictions of the original model and the model retrained on the training data remaining after the removal of the forget set is: (i) low on the unwanted set, indicating that the unwanted samples have been effectively unlearned by the model, and (ii) high on the wanted set, to ensure that the model keeps its original performance on the data to be retained. We define ForSId as an optimization problem, prove its NP-hardness, and devise an algorithm based on a theoretical connection to Red-Blue Set Cover. Our ForSId is a novel complementary problem to MU. It serves as a foundational step to be performed before executing MU methods, allowing for extending the range of applicability of MU to all those settings where user's unlearning evidence does not correspond to (or is too hard to be directly expressed in terms of) a forget set. We conduct extensive experiments based on the exact unlearning task (which is the most reliable one) on several real-world datasets and settings, involving nontrivial baselines. Results demonstrate high performance of our proposed algorithm and clear superiority over the baselines
Ocean Heat Content
No full textEstimates (OCCA2, ECCO4) of global ocean heat content (OHC) anomaly from 2004-2006 climatology. ECCO4 is a closed heat budget estimate. ECCO4 release 5 is used here that covers 1992-2019. OCCA2 was derived by 1. extending ECCO4 (r2) to 1980-2022 and 2. adding a gridded adjustment to Argo over 2004-2022. The 2004-2006 climatologies were subtracted separately before combining anomalies over 1992-2019
OCCA monthly ocean atlas
No full textThe 2004-2006 monthly OCCA atlas consists of mapped observations and derived quantities. Together they form a full representation of the ocean state and its seasonal cycle. The mapped observations are primarily altimeter data, satellite SST, and Argo profiles. GCM interpolation is used to synthesize these datasets, and the resulting atlas is a fairly close fit to each one of them. For observed quantities especially, the atlas is a practical means to evaluate free-running GCM simulations and to put field experiments into a broader context. The atlas-derived quantities include the middepth dynamic topography, as well as ocean fluxes of heat and salt–freshwater
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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