83 research outputs found
Graph coarsening: From scientific computing to machine learning
The general method of graph coarsening or graph reduction has been a
remarkably useful and ubiquitous tool in scientific computing and it is now
just starting to have a similar impact in machine learning. The goal of this
paper is to take a broad look into coarsening techniques that have been
successfully deployed in scientific computing and see how similar principles
are finding their way in more recent applications related to machine learning.
In scientific computing, coarsening plays a central role in algebraic multigrid
methods as well as the related class of multilevel incomplete LU
factorizations. In machine learning, graph coarsening goes under various names,
e.g., graph downsampling or graph reduction. Its goal in most cases is to
replace some original graph by one which has fewer nodes, but whose structure
and characteristics are similar to those of the original graph. As will be
seen, a common strategy in these methods is to rely on spectral properties to
define the coarse graph
Linear Latent World Models in Simple Transformers: A Case Study on Othello-GPT
Foundation models exhibit significant capabilities in decision-making and
logical deductions. Nonetheless, a continuing discourse persists regarding
their genuine understanding of the world as opposed to mere stochastic mimicry.
This paper meticulously examines a simple transformer trained for Othello,
extending prior research to enhance comprehension of the emergent world model
of Othello-GPT. The investigation reveals that Othello-GPT encapsulates a
linear representation of opposing pieces, a factor that causally steers its
decision-making process. This paper further elucidates the interplay between
the linear world representation and causal decision-making, and their
dependence on layer depth and model complexity. We have made the code public
Show, Recall, and Tell: Image Captioning with Recall Mechanism
Generating natural and accurate descriptions in image cap-tioning has always
been a challenge. In this paper, we pro-pose a novel recall mechanism to
imitate the way human con-duct captioning. There are three parts in our recall
mecha-nism : recall unit, semantic guide (SG) and recalled-wordslot (RWS).
Recall unit is a text-retrieval module designedto retrieve recalled words for
images. SG and RWS are de-signed for the best use of recalled words. SG branch
cangenerate a recalled context, which can guide the process ofgenerating
caption. RWS branch is responsible for copyingrecalled words to the caption.
Inspired by pointing mecha-nism in text summarization, we adopt a soft switch
to balancethe generated-word probabilities between SG and RWS. Inthe CIDEr
optimization step, we also introduce an individualrecalled-word reward (WR) to
boost training. Our proposedmethods (SG+RWS+WR) achieve BLEU-4 / CIDEr /
SPICEscores of 36.6 / 116.9 / 21.3 with cross-entropy loss and 38.7 /129.1 /
22.4 with CIDEr optimization on MSCOCO Karpathytest split, which surpass the
results of other state-of-the-artmethods.Comment: Published in AAAI 202
Research on the Application of Cross-Specialty Education and Situational Simulation Teaching in Operation Nursing Practice Teaching
Objective To examine the practical effect of inter-professional education and situational simulation teaching implemented in surgical nursing practice teaching. Methods On the whole, 100 undergraduate nursing students in the operating room of the hospital of the authors from May 2019 to August 2020 were selected. These students fell to two groups with the random number table method. The control received the regular teaching, and the research group were given the interprofessional education and context. The Simulation teaching was conducted to compare the theoretical knowledge, skill level, various abilities of the two groups of students, as well as the satisfaction of the operating room doctors to the nursing cooperation of the interns. Results The research group achieved higher theoretical knowledge and a higher skill level than the control (p < 0.05); the various abilities of the research group were higher than those of the control (p < 0.05); the operating room doctors of the research group were more satisfied with the nursing cooperation of interns, as compared with those of the control (p < 0.05). Conclusion In the surgical nursing practice teaching, the inter-professional education and the situational simulation teaching have significant effects and are worth clinical applications
A Journey into the City. Migrant Workers' Relation with the Urban Space and Struggle for Existence in Xu Zechen's Early Jingpiao Fiction
In contemporary China, rural-urban migrants constitute a new urban subject with entirely new identity-related issues. This study aims at demonstrating how literature can be a valid field in investigating such evolving subjectivities, through an analysis of Xu Zechen’s early novellas depicting migrants’ vicissitudes in Beijing. Combining a close reading of the texts and a review of the main social problems characterising rural-urban migration in China, this paper focuses on the representation of the identity crisis within the migrant self in Xu’s stories, taking into account the network of meanings employed by the writer to signify the objective and subjective tension between the city and the countryside
Object-Centric Multiple Object Tracking
Unsupervised object-centric learning methods allow the partitioning of scenes
into entities without additional localization information and are excellent
candidates for reducing the annotation burden of multiple-object tracking (MOT)
pipelines. Unfortunately, they lack two key properties: objects are often split
into parts and are not consistently tracked over time. In fact,
state-of-the-art models achieve pixel-level accuracy and temporal consistency
by relying on supervised object detection with additional ID labels for the
association through time. This paper proposes a video object-centric model for
MOT. It consists of an index-merge module that adapts the object-centric slots
into detection outputs and an object memory module that builds complete object
prototypes to handle occlusions. Benefited from object-centric learning, we
only require sparse detection labels (0%-6.25%) for object localization and
feature binding. Relying on our self-supervised
Expectation-Maximization-inspired loss for object association, our approach
requires no ID labels. Our experiments significantly narrow the gap between the
existing object-centric model and the fully supervised state-of-the-art and
outperform several unsupervised trackers.Comment: ICCV 2023 camera-ready versio
Single-cell atlas reveals different immune environments between stable and vulnerable atherosclerotic plaques
IntroductionRegardless of the degree of stenosis, vulnerable plaque is an important cause of ischemic stroke and thrombotic complications. The changes of the immune microenvironment within plaques seem to be an important factor affecting the characteristics of the plaque. However, the differences of immune microenvironment between stable and vulnerable plaques were remained unknown.MethodsIn this study, RNA-sequencing was performed on superficial temporal arteries from 5 traumatic patients and plaques from 3 atherosclerotic patients to preliminary identify the key immune response processes in plaques. Mass cytometry (CyTOF) technology was used to explore differences in immune composition between 9 vulnerable plaques and 12 stable plaques. Finally, immunofluorescence technique was used to validate our findings in the previous analysis.ResultsOur results showed that more CD86+CD68+ M1 pro-inflammatory macrophages were found in vulnerable plaques, while CD4+T memory cells were mainly found in stable plaques. In addition, a CD11c+ subset of CD4+T cells with higher IFN-r secretion was found within the vulnerable plaque. In two subsets of B cells, CD19+CD20-B cells in vulnerable plaques secreted more TNF-a and IL-6, while CD19-CD20+B cells expressed more PD-1 molecules.ConclusionIn conclusion, our study suggested that M1-like macrophages are the major cell subset affecting plaque stability, while functional B cells may also contribute to plaque stability
A murine preclinical syngeneic transplantation model for breast cancer precision medicine
We previously demonstrated that altered activity of lysophosphatidic acid in murine mammary glands promotes tumorigenesis. We have now established and characterized a heterogeneous collection of mouse-derived syngeneic transplants (MDSTs) as preclinical platforms for the assessment of personalized pharmacological therapies. Detailed molecular and phenotypic analyses revealed that MDSTs are the most heterogeneous group of genetically engineered mouse models (GEMMs) of breast cancer yet observed. Response of MDSTs to trametinib, a mitogen-activated protein kinase (MAPK) kinase inhibitor, correlated with RAS/MAPK signaling activity, as expected from studies in xenografts and clinical trials providing validation of the utility of the model. Sensitivity of MDSTs to talazoparib, a poly(adenosine 5′-diphosphate–ribose) polymerase (PARP) inhibitor, was predicted by PARP1 protein levels and by a new PARP sensitivity predictor (PSP) score developed from integrated analysis of drug sensitivity data of human cell lines. PSP score–based classification of The Cancer Genome Atlas breast cancer suggested that a subset of patients with limited therapeutic options would be expected to benefit from PARP-targeted drugs. These results indicate that MDSTs are useful models for studies of targeted therapies, and propose novel potential biomarkers for identification of breast cancer patients likely to benefit from personalized pharmacological treatments
An integrated map of structural variation in 2,504 human genomes
© 2015 Macmillan Publishers Limited. All rights reserved. Structural variants are implicated in numerous diseases and make up the majority of varying nucleotides among human genomes. Here we describe an integrated set of eight structural variant classes comprising both balanced and unbalanced variants, which we constructed using short-read DNA sequencing data and statistically phased onto haplotype blocks in 26 human populations. Analysing this set, we identify numerous gene-intersecting structural variants exhibiting population stratification and describe naturally occurring homozygous gene knockouts that suggest the dispensability of a variety of human genes. We demonstrate that structural variants are enriched on haplotypes identified by genome-wide association studies and exhibit enrichment for expression quantitative trait loci. Additionally, we uncover appreciable levels of structural variant complexity at different scales, including genic loci subject to clusters of repeated rearrangement and complex structural variants with multiple breakpoints likely to have formed through individual mutational events. Our catalogue will enhance future studies into structural variant demography, functional impact and disease association
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