145 research outputs found
Efficient Algorithms for a Mesh-Connected Computer with Additional Global Bandwidth
This thesis shows that adding additional global bandwidths to a mesh-connected computer can greatly improve the performance. The goal of this project is to design algorithms for mesh-connected computers augmented with limited global bandwidth, so that we can further enhance our understanding of the parallel/serial nature of the problems on evolving parallel architectures. We do this by first solving several problems associated with fundamental data movement, then summarize ways to resolve different situations one may observe in data movement in parallel computing. This can help us to understand whether the problem is easily parallelizable on different parallel models. We give efficient algorithms to solve several fundamental problems, which include sorting, counting, fast Fourier transform, finding a minimum spanning tree, finding a convex hull, etc. We show that adding a small amount of global bandwidth makes a practical design that combines aspects of mesh and fully connected models to achieve the benefits of each. Most of the algorithms are optimal. For future work, we believe that algorithms with peak-power constrains can make our model well adapted to the recent architectures in high performance computing.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/150001/1/anyujie_1.pd
Temporal Action Localization with Enhanced Instant Discriminability
Temporal action detection (TAD) aims to detect all action boundaries and
their corresponding categories in an untrimmed video. The unclear boundaries of
actions in videos often result in imprecise predictions of action boundaries by
existing methods. To resolve this issue, we propose a one-stage framework named
TriDet. First, we propose a Trident-head to model the action boundary via an
estimated relative probability distribution around the boundary. Then, we
analyze the rank-loss problem (i.e. instant discriminability deterioration) in
transformer-based methods and propose an efficient scalable-granularity
perception (SGP) layer to mitigate this issue. To further push the limit of
instant discriminability in the video backbone, we leverage the strong
representation capability of pretrained large models and investigate their
performance on TAD. Last, considering the adequate spatial-temporal context for
classification, we design a decoupled feature pyramid network with separate
feature pyramids to incorporate rich spatial context from the large model for
localization. Experimental results demonstrate the robustness of TriDet and its
state-of-the-art performance on multiple TAD datasets, including hierarchical
(multilabel) TAD datasets.Comment: An extended version of the CVPR paper arXiv:2303.07347, submitted to
IJC
Visualize Before You Write: Imagination-Guided Open-Ended Text Generation
Recent advances in text-to-image synthesis make it possible to visualize
machine imaginations for a given context. On the other hand, when generating
text, human writers are gifted at creative visualization, which enhances their
writings by forming imaginations as blueprints before putting down the stories
in words. Inspired by such a cognitive process, we ask the natural question of
whether we can endow machines with the same ability to utilize visual
information and construct a general picture of the context to guide text
generation. In this work, we propose iNLG that uses machine-generated images to
guide language models (LM) in open-ended text generation. The experiments and
analyses demonstrate the effectiveness of iNLG on open-ended text generation
tasks, including text completion, story generation, and concept-to-text
generation in few-shot scenarios. Both automatic metrics and human evaluations
verify that the text snippets generated by our iNLG are coherent and
informative while displaying minor degeneration
GPT-4V(ision) as a Generalist Evaluator for Vision-Language Tasks
Automatically evaluating vision-language tasks is challenging, especially
when it comes to reflecting human judgments due to limitations in accounting
for fine-grained details. Although GPT-4V has shown promising results in
various multi-modal tasks, leveraging GPT-4V as a generalist evaluator for
these tasks has not yet been systematically explored. We comprehensively
validate GPT-4V's capabilities for evaluation purposes, addressing tasks
ranging from foundational image-to-text and text-to-image synthesis to
high-level image-to-image translations and multi-images to text alignment. We
employ two evaluation methods, single-answer grading and pairwise comparison,
using GPT-4V. Notably, GPT-4V shows promising agreement with humans across
various tasks and evaluation methods, demonstrating immense potential for
multi-modal LLMs as evaluators. Despite limitations like restricted visual
clarity grading and real-world complex reasoning, its ability to provide
human-aligned scores enriched with detailed explanations is promising for
universal automatic evaluator
Metabolic syndrome and metastatic prostate cancer correlation study, a real-world study in a prostate cancer clinical research center, Xinjiang, China
ObjectiveThe aim of this study was to investigate the relevance of metabolic syndrome (MetS) and metabolic scores to the occurrence, progression and prognosis of metastatic prostate cancer (mPCA), assessing the definition of the variables of metabolic syndrome, and the potential mechanisms of MetS and mPCA.MethodsData were obtained from the database of prostate cancer follow-up at the Urology Centre of the First Affiliated Hospital of Xinjiang Medical University (N=1303). After screening by inclusion and exclusion criteria, clinical data of 190 patients diagnosed with mPCA by pathology and imaging from January 2010 to August 2021 were finally included, including 111 cases in the MetS group and 79 cases in the Non-MetS group.ResultsThe MetS group was higher than the Non-MetS group: T stage, Gleasson score, initial PSA, tumor load, PSA after 7 months of ADT (P<0.05),with a shorter time to progression to CRPC stage(P<0.05)[where the time to progression to CRPC was relatively shorter in the high metabolic score subgroup of the MetS group than in the low subgroup (P<0.05)].Median survival time was significantly shorter in the MetS group than in the Non-MetS group (P<0.05),and there was a correlation with metabolic score, with the higher metabolic score subgroup having a lower survival time than the lower metabolic score subgroup (P<0.05).ConclusionThose with mPCA combined with MetS had lower PSA remission rates, more aggressive tumors, shorter time to progression to CRPC and shorter median survival times than those with mPCA without MetS.Tumour progression and metabolic score showed a positive correlation, predicting that MetS may promote the progression of mPCA, suggesting that MetS may be a risk factor affecting the prognosis of mPCA
The trans-ancestral genomic architecture of glycemic traits
Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 x 10(-8)), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution. A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.Peer reviewe
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