230 research outputs found
Exploring Communities in Large Profiled Graphs
Given a graph and a vertex , the community search (CS) problem
aims to efficiently find a subgraph of whose vertices are closely related
to . Communities are prevalent in social and biological networks, and can be
used in product advertisement and social event recommendation. In this paper,
we study profiled community search (PCS), where CS is performed on a profiled
graph. This is a graph in which each vertex has labels arranged in a
hierarchical manner. Extensive experiments show that PCS can identify
communities with themes that are common to their vertices, and is more
effective than existing CS approaches. As a naive solution for PCS is highly
expensive, we have also developed a tree index, which facilitate efficient and
online solutions for PCS
Model-free screening procedure for ultrahigh-dimensional survival data based on Hilbert-Schmidt independence criterion
How to select the active variables which have significant impact on the event
of interest is a very important and meaningful problem in the statistical
analysis of ultrahigh-dimensional data. Sure independent screening procedure
has been demonstrated to be an effective method to reduce the dimensionality of
data from a large scale to a relatively moderate scale. For censored survival
data, the existing screening methods mainly adopt the Kaplan--Meier estimator
to handle censoring, which may not perform well for scenarios which have heavy
censoring rate. In this article, we propose a model-free screening procedure
based on the Hilbert-Schmidt independence criterion (HSIC). The proposed method
avoids the complication to specify an actual model from a large number of
covariates. Compared with existing screening procedures, this new approach has
several advantages. First, it does not involve the Kaplan--Meier estimator,
thus its performance is much more robust for the cases with a heavy censoring
rate. Second, the empirical estimate of HSIC is very simple as it just depends
on the trace of a product of Gram matrices. In addition, the proposed procedure
does not require any complicated numerical optimization, so the corresponding
calculation is very simple and fast. Finally, the proposed procedure which
employs the kernel method is substantially more resistant to outliers.
Extensive simulation studies demonstrate that the proposed method has favorable
exhibition over the existing methods. As an illustration, we apply the proposed
method to analyze the diffuse large-B-cell lymphoma (DLBCL) data and the
ovarian cancer data
An Augmented Index-based Efficient Community Search for Large Directed Graphs
Given a graph G and a query vertex q, the topic of community search (CS),
aiming to retrieve a dense subgraph of G containing q, has gained much
attention. Most existing works focus on undirected graphs which overlooks the
rich information carried by the edge directions. Recently, the problem of
community search over directed graphs (or CSD problem) has been studied; it
finds a connected subgraph containing q, where the in-degree and out-degree of
each vertex within the subgraph are at least k and l, respectively. However,
existing solutions are inefficient, especially on large graphs. To tackle this
issue, in this paper, we propose a novel index called D-Forest, which allows a
CSD query to be completed within the optimal time cost. We further propose
efficient index construction methods. Extensive experiments on six real large
graphs show that our index-based query algorithm is up to two orders of
magnitude faster than existing solutions.Comment: Full version of our IJCAI20 pape
Automatic Label Sequence Generation for Prompting Sequence-to-sequence Models
Prompting, which casts downstream applications as language modeling tasks,
has shown to be sample efficient compared to standard fine-tuning with
pre-trained models. However, one pitfall of prompting is the need of
manually-designed patterns, whose outcome can be unintuitive and requires large
validation sets to tune. To tackle the challenge, we propose AutoSeq, a fully
automatic prompting method: (1) We adopt natural language prompts on
sequence-to-sequence models, enabling free-form generation and larger label
search space; (2) We propose label sequences -- phrases with indefinite lengths
to verbalize the labels -- which eliminate the need of manual templates and are
more expressive than single label words; (3) We use beam search to
automatically generate a large amount of label sequence candidates and propose
contrastive re-ranking to get the best combinations. AutoSeq significantly
outperforms other no-manual-design methods, such as soft prompt tuning, adapter
tuning, and automatic search on single label words; the generated label
sequences are even better than curated manual ones on a variety of tasks. Our
method reveals the potential of sequence-to-sequence models in few-shot
learning and sheds light on a path to generic and automatic prompting. The
source code of this paper can be obtained from
https://github.com/thunlp/Seq2Seq-Prompt.Comment: Accepted to COLING 202
Impact of Enteromorpha Blooms on Aquaculture Research Off Qianliyan Island, Yellow Sea, China
Between 2008 and 2016, there were mass summer blooms of Enteromorpha in the Yellow Sea, China. They covered an area of thousands of square kilometers annually, lasting an average of 90Â days. Remote sensing data, model predictions, and marine environment ecological data measured by ships before, during, and after the Enteromorpha blooms were used in this study of the Qianliyan Island area. Underwater robots survey trepang, wrinkles abalone, and submarine ecological status. We found that the time taken by Enteromorpha to cover the Qianliyan Island area was relevant, as were changes in sea surface temperature (SST). The Enteromorpha made a rise in inorganic nitrogen, reactive phosphate, and heavy metals content in upper, middle, and bottom layers of sea water, dissolved oxygen (DO) and pH were reduced; and there were changes in the dominant animal and plant population. Enteromorpha sedimentation during outbreaks was measured by benthos sampling. Considerable growth in starfish number was obtained by underwater robot observation. All of this directly influenced the regional ecological environment. Numbers of trepang and wrinkles abalone were declined over the years. Global warming and SST anomalies are the two main reasons for frequent marine disasters that take place. National aquatic germ plasm resources of Qianliyan should be protected from the blooms
Variator: Accelerating Pre-trained Models with Plug-and-Play Compression Modules
Pre-trained language models (PLMs) have achieved remarkable results on NLP
tasks but at the expense of huge parameter sizes and the consequent
computational costs. In this paper, we propose Variator, a parameter-efficient
acceleration method that enhances computational efficiency through
plug-and-play compression plugins. Compression plugins are designed to reduce
the sequence length via compressing multiple hidden vectors into one and
trained with original PLMs frozen. Different from traditional model
acceleration methods, which compress PLMs to smaller sizes, Variator offers two
distinct advantages: (1) In real-world applications, the plug-and-play nature
of our compression plugins enables dynamic selection of different compression
plugins with varying acceleration ratios based on the current workload. (2) The
compression plugin comprises a few compact neural network layers with minimal
parameters, significantly saving storage and memory overhead, particularly in
scenarios with a growing number of tasks. We validate the effectiveness of
Variator on seven datasets. Experimental results show that Variator can save
53% computational costs using only 0.9% additional parameters with a
performance drop of less than 2%. Moreover, when the model scales to billions
of parameters, Variator matches the strong performance of uncompressed PLMs.Comment: Accepted by Findings of EMNL
An acetyl-L-carnitine switch on mitochondrial dysfunction and rescue in the metabolomics study on aluminum oxide nanoparticles
Characteristics of multicystic biliary hamartoma: A case report
IntroductionMulticystic biliary hamartoma (MCBH) is a very rare hepatic benign neoplasm that manifests as a localized cystic-solid mass. Only 17 cases have been described in the literature to date. MCBH diagnosis is currently dependent on imaging and pathology following surgical resection and no precise standards are in place.Case PresentationThis case study involves a middle-aged male patient with a history of drinking but no other liver diseases. A routine ultrasound examination showed a 6.0 × 5.5 cm inhomogeneous echo mass in the right lobe of the liver. The patient experienced no discomfort or other symptoms, and blood tests were normal. Imaging revealed a localized cystic-solid neoplasm in segment 6 of the liver that did not have the features of a malignant tumor. Surgical resection was performed. Based on imaging, macroscopic examination, and histological results, a final diagnosis of MCBH was made.ConclusionThe imaging and pathological features of MCBH were summarized based on the published case reports to date. As a non-invasive examination, the imaging features will aid in the diagnosis of MCBH. Furthermore, these features, along with tumor size and patient symptoms, will facilitate clinicians in selecting surgical resection or follow-up for individual patients
Emergent Modularity in Pre-trained Transformers
This work examines the presence of modularity in pre-trained Transformers, a
feature commonly found in human brains and thought to be vital for general
intelligence. In analogy to human brains, we consider two main characteristics
of modularity: (1) functional specialization of neurons: we evaluate whether
each neuron is mainly specialized in a certain function, and find that the
answer is yes. (2) function-based neuron grouping: we explore finding a
structure that groups neurons into modules by function, and each module works
for its corresponding function. Given the enormous amount of possible
structures, we focus on Mixture-of-Experts as a promising candidate, which
partitions neurons into experts and usually activates different experts for
different inputs. Experimental results show that there are functional experts,
where clustered are the neurons specialized in a certain function. Moreover,
perturbing the activations of functional experts significantly affects the
corresponding function. Finally, we study how modularity emerges during
pre-training, and find that the modular structure is stabilized at the early
stage, which is faster than neuron stabilization. It suggests that Transformers
first construct the modular structure and then learn fine-grained neuron
functions. Our code and data are available at
https://github.com/THUNLP/modularity-analysis.Comment: Findings of ACL 202
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