On A Simple Method For Analyzing Multivariate Survival Data Using Sample Survey Methods

A simple technique is illustrated for analyzing multivariate survival data. The data situation arises when an individual records multiple survival events, or when individuals recording single survival events are grouped into clusters. Past work has focused on developing new methods to handle such data. Here, we use a connection between Poisson regression and survival modeling and a cluster sampling approach to adjust the variance estimates. The approach requires parametric assumption for the marginal hazard function, but avoids specification of a joint multivariate survival distribution. A simulation study demonstrates the proposed approach is a competing method of recent developed marginal approaches in the literature

Patch-Wise Point Cloud Generation: A Divide-and-Conquer Approach

A generative model for high-fidelity point clouds is of great importance in synthesizing 3d environments for applications such as autonomous driving and robotics. Despite the recent success of deep generative models for 2d images, it is non-trivial to generate 3d point clouds without a comprehensive understanding of both local and global geometric structures. In this paper, we devise a new 3d point cloud generation framework using a divide-and-conquer approach, where the whole generation process can be divided into a set of patch-wise generation tasks. Specifically, all patch generators are based on learnable priors, which aim to capture the information of geometry primitives. We introduce point- and patch-wise transformers to enable the interactions between points and patches. Therefore, the proposed divide-and-conquer approach contributes to a new understanding of point cloud generation from the geometry constitution of 3d shapes. Experimental results on a variety of object categories from the most popular point cloud dataset, ShapeNet, show the effectiveness of the proposed patch-wise point cloud generation, where it clearly outperforms recent state-of-the-art methods for high-fidelity point cloud generation

Growth of donor-derived dendritic cells from the bone marrow of murine liver auograft recipients in response to granulocyte/macrophage colony-stimulating factor

Allografts of the liver, which has a comparatively heavy leukocyte content compared with other vascularized organs, are accepted permanently across major histocompatibility complex barriers in many murine strain combinations without immunosuppressive therapy. It has been postulated that this inherent tolerogenicity of the liver may be a consequence of the migration and perpetuation within host lymphoid tissues of potentially tolerogenic donor-derived ("chimeric") leukocytes, in particular, the precursors of chimeric dendritic cells (DC). In this study, we have used granulocyte/macrophage colony-stimulating factor to induce the propagation of progenitors that give rise to DC (CD45+, CDllc+, 33D1+, nonlymphoid dendritic cell 145 +, major histocompatibility complex class II+, B7-1+) in li-tuid cultures of murine bone marrow cells. Using this technique, together with immunocytochemical and molecular methods, we show that, in addition to cells expressing female host (C3H) phenotype (H-2Kk+; I-E+; Y chromosome-), a minor population of male donor (B10)-derived cells (H-2Kb+; I-A+; Y chromosome+) can also be grown in 10-d DC cultures from the bone marrow of liver allograft recipients 14 d after transplant. Highly purified nonlymphoid dendritic cell 145+ DC sorted from these bone marrow-derived cell cultures were shown to comprise ~1-10% cells of donor origin (Y chromosome +) by polymerase chain reaction analysis. In addition, sorted DC stimulated naive, recipient strain T lymphocytes in primary mixed leukocyte cultures. Evidence was also obtained for the growth of donor-derived cells from the spleen but not the thymus. In contrast, donor ceils could not be propagated from the bone marrow or other lymphoid tissues of nonimmunosuppressed C3H mice rejecting cardiac allografrs from the same donor strain (B10). These findings provide a basis for the establishment and perpetuation of cell chimerism after organ transplantation. © 1995, Rockefeller University Press., All rights reserved

Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives

This paper tackles the problem of reading comprehension over long narratives where documents easily span over thousands of tokens. We propose a curriculum learning (CL) based Pointer-Generator framework for reading/sampling over large documents, enabling diverse training of the neural model based on the notion of alternating contextual difficulty. This can be interpreted as a form of domain randomization and/or generative pretraining during training. To this end, the usage of the Pointer-Generator softens the requirement of having the answer within the context, enabling us to construct diverse training samples for learning. Additionally, we propose a new Introspective Alignment Layer (IAL), which reasons over decomposed alignments using block-based self-attention. We evaluate our proposed method on the NarrativeQA reading comprehension benchmark, achieving state-of-the-art performance, improving existing baselines by $51\%$ relative improvement on BLEU-4 and $17\%$ relative improvement on Rouge-L. Extensive ablations confirm the effectiveness of our proposed IAL and CL components.Comment: Accepted to ACL 201