64 research outputs found

### Massively Parallel Algorithms for the Stochastic Block Model

Learning the community structure of a large-scale graph is a fundamental
problem in machine learning, computer science and statistics. We study the
problem of exactly recovering the communities in a graph generated from the
Stochastic Block Model (SBM) in the Massively Parallel Computation (MPC) model.
Specifically, given $kn$ vertices that are partitioned into $k$ equal-sized
clusters (i.e., each has size $n$), a graph on these $kn$ vertices is randomly
generated such that each pair of vertices is connected with probability~$p$ if
they are in the same cluster and with probability $q$ if not, where $p > q >
0$. We give MPC algorithms for the SBM in the (very general) \emph{$s$-space
MPC model}, where each machine has memory $s=\Omega(\log n)$. Under the
condition that $\frac{p-q}{\sqrt{p}}\geq
\tilde{\Omega}(k^{\frac12}n^{-\frac12+\frac{1}{2(r-1)}})$ for any integer $r\in
[3,O(\log n)]$, our first algorithm exactly recovers all the $k$ clusters in
$O(kr\log_s n)$ rounds using $\tilde{O}(m)$ total space, or in $O(r\log_s n)$
rounds using $\tilde{O}(km)$ total space. If $\frac{p-q}{\sqrt{p}}\geq
\tilde{\Omega}(k^{\frac34}n^{-\frac14})$, our second algorithm achieves
$O(\log_s n)$ rounds and $\tilde{O}(m)$ total space complexity. Both algorithms
significantly improve upon a recent result of Cohen-Addad et al. [PODC'22], who
gave algorithms that only work in the \emph{sublinear space MPC model}, where
each machine has local memory~$s=O(n^{\delta})$ for some constant $\delta>0$,
with a much stronger condition on $p,q,k$.
Our algorithms are based on collecting the $r$-step neighborhood of each
vertex and comparing the difference of some statistical information generated
from the local neighborhoods for each pair of vertices. To implement the
clustering algorithms in parallel, we present efficient approaches for
implementing some basic graph operations in the $s$-space MPC model

### Massively Parallel Algorithms for the Stochastic Block Model

Learning the community structure of a large-scale graph is a fundamental problem in machine learning, computer science and statistics. Among others, the Stochastic Block Model (SBM) serves a canonical model for community detection and clustering, and the Massively Parallel Computation (MPC) model is a mathematical abstraction of real-world parallel computing systems, which provides a powerful computational framework for handling large-scale datasets. We study the problem of exactly recovering the communities in a graph generated from the SBM in the MPC model. Specifically, given kn vertices that are partitioned into k equal-sized clusters (i.e., each has size n), a graph on these kn vertices is randomly generated such that each pair of vertices is connected with probability p if they are in the same cluster and with probability q if not, where p > q > 0.
We give MPC algorithms for the SBM in the (very general) s-space MPC model, where each machine is guaranteed to have memory s = ?(log n). Under the condition that (p-q)/?p ? ??(k^{1/2} n^{-1/2+1/(2(r-1))}) for any integer r ? [3,O(log n)], our first algorithm exactly recovers all the k clusters in O(kr log_s n) rounds using O?(m) total space, or in O(rlog_s n) rounds using O?(km) total space. If (p-q)/?p ? ??(k^{3/4} n^{-1/4}), our second algorithm achieves O(log_s n) rounds and O?(m) total space complexity. Both algorithms significantly improve upon a recent result of Cohen-Addad et al. [PODC\u2722], who gave algorithms that only work in the sublinear space MPC model, where each machine has local memory s = O(n^?) for some constant ? > 0, with a much stronger condition on p,q,k. Our algorithms are based on collecting the r-step neighborhood of each vertex and comparing the difference of some statistical information generated from the local neighborhoods for each pair of vertices. To implement the clustering algorithms in parallel, we present efficient approaches for implementing some basic graph operations in the s-space MPC model

### SAM-PARSER: Fine-tuning SAM Efficiently by Parameter Space Reconstruction

Segment Anything Model (SAM) has received remarkable attention as it offers a
powerful and versatile solution for object segmentation in images. However,
fine-tuning SAM for downstream segmentation tasks under different scenarios
remains a challenge, as the varied characteristics of different scenarios
naturally requires diverse model parameter spaces. Most existing fine-tuning
methods attempt to bridge the gaps among different scenarios by introducing a
set of new parameters to modify SAM's original parameter space. Unlike these
works, in this paper, we propose fine-tuning SAM efficiently by parameter space
reconstruction (SAM-PARSER), which introduce nearly zero trainable parameters
during fine-tuning. In SAM-PARSER, we assume that SAM's original parameter
space is relatively complete, so that its bases are able to reconstruct the
parameter space of a new scenario. We obtain the bases by matrix decomposition,
and fine-tuning the coefficients to reconstruct the parameter space tailored to
the new scenario by an optimal linear combination of the bases. Experimental
results show that SAM-PARSER exhibits superior segmentation performance across
various scenarios, while reducing the number of trainable parameters by
$\approx 290$ times compared with current parameter-efficient fine-tuning
methods

### USAGE: A Unified Seed Area Generation Paradigm for Weakly Supervised Semantic Segmentation

Seed area generation is usually the starting point of weakly supervised
semantic segmentation (WSSS). Computing the Class Activation Map (CAM) from a
multi-label classification network is the de facto paradigm for seed area
generation, but CAMs generated from Convolutional Neural Networks (CNNs) and
Transformers are prone to be under- and over-activated, respectively, which
makes the strategies to refine CAMs for CNNs usually inappropriate for
Transformers, and vice versa. In this paper, we propose a Unified optimization
paradigm for Seed Area GEneration (USAGE) for both types of networks, in which
the objective function to be optimized consists of two terms: One is a
generation loss, which controls the shape of seed areas by a temperature
parameter following a deterministic principle for different types of networks;
The other is a regularization loss, which ensures the consistency between the
seed areas that are generated by self-adaptive network adjustment from
different views, to overturn false activation in seed areas. Experimental
results show that USAGE consistently improves seed area generation for both
CNNs and Transformers by large margins, e.g., outperforming state-of-the-art
methods by a mIoU of 4.1% on PASCAL VOC. Moreover, based on the USAGE-generated
seed areas on Transformers, we achieve state-of-the-art WSSS results on both
PASCAL VOC and MS COCO

### Absolute Wrong Makes Better: Boosting Weakly Supervised Object Detection via Negative Deterministic Information

Weakly supervised object detection (WSOD) is a challenging task, in which
image-level labels (e.g., categories of the instances in the whole image) are
used to train an object detector. Many existing methods follow the standard
multiple instance learning (MIL) paradigm and have achieved promising
performance. However, the lack of deterministic information leads to part
domination and missing instances. To address these issues, this paper focuses
on identifying and fully exploiting the deterministic information in WSOD. We
discover that negative instances (i.e. absolutely wrong instances), ignored in
most of the previous studies, normally contain valuable deterministic
information. Based on this observation, we here propose a negative
deterministic information (NDI) based method for improving WSOD, namely
NDI-WSOD. Specifically, our method consists of two stages: NDI collecting and
exploiting. In the collecting stage, we design several processes to identify
and distill the NDI from negative instances online. In the exploiting stage, we
utilize the extracted NDI to construct a novel negative contrastive learning
mechanism and a negative guided instance selection strategy for dealing with
the issues of part domination and missing instances, respectively. Experimental
results on several public benchmarks including VOC 2007, VOC 2012 and MS COCO
show that our method achieves satisfactory performance.Comment: 7 pages, 5 figure

### A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction

The rapid development of deep learning has made a great progress in image
segmentation, one of the fundamental tasks of computer vision. However, the
current segmentation algorithms mostly rely on the availability of pixel-level
annotations, which are often expensive, tedious, and laborious. To alleviate
this burden, the past years have witnessed an increasing attention in building
label-efficient, deep-learning-based image segmentation algorithms. This paper
offers a comprehensive review on label-efficient image segmentation methods. To
this end, we first develop a taxonomy to organize these methods according to
the supervision provided by different types of weak labels (including no
supervision, inexact supervision, incomplete supervision and inaccurate
supervision) and supplemented by the types of segmentation problems (including
semantic segmentation, instance segmentation and panoptic segmentation). Next,
we summarize the existing label-efficient image segmentation methods from a
unified perspective that discusses an important question: how to bridge the gap
between weak supervision and dense prediction -- the current methods are mostly
based on heuristic priors, such as cross-pixel similarity, cross-label
constraint, cross-view consistency, and cross-image relation. Finally, we share
our opinions about the future research directions for label-efficient deep
image segmentation.Comment: Accepted to IEEE TPAM

### Effects of deformation temperature on edge crack characteristics and mechanical properties of as-cast aluminum alloy

In this study, the rolling technique of aluminum alloy was investigated, and the effects of deformation temperature on the edge cracks and mechanical properties of aluminum alloy were studied through a hot compression experiment on high magnesium aluminum alloy. Based on the test, DEFORM-3D software was introduced to optimize the selection of the influence conditions of the experiment. The research results suggested that the crack length of the as-cast aluminum alloy samples decreased with the increase of temperature when the deformation temperature was between 300 °C and 450 °C; the tensile strength and elongation after fracture increased with the increase of temperature when the deformation temperature was between 300 °C and 500 °C. Therefore it is concluded that the cracks of high magnesium aluminum alloy can be reduced through controlling deformation temperature, which provides an idea for the optimization of aluminium alloy

### Phenolic compounds weaken the impact of drought on soil enzyme activity in global wetlands

Soil enzymes play a central role in carbon and nutrient cycling, and their activities can be affected by drought-induced oxygen exposure. However, a systematic global estimate of enzyme sensitivity to drought in wetlands is still lacking. Through a meta-analysis of 55 studies comprising 761 paired observations, this study found that phosphorus-related enzyme activity increased by 38% as result of drought in wetlands, while the majority of other soil enzyme activities remained stable. The expansion of vascular plants under long-term drought significantly promoted the accumulation of phenolic compounds. Using a 2-week incubation experiment with phenol supplementation, we found that phosphorus-related enzyme could tolerate higher biotoxicity of phenolic compounds than other enzymes. Moreover, a long-term (35 years) drainage experiment in a northern peatland in China confirmed that the increased phenolic concentration in surface layer resulting from a shift in vegetation composition inhibited the increase in enzyme activities caused by rising oxygen availability, except for phosphorus-related enzyme. Overall, these results demonstrate the complex and resilient nature of wetland ecosystems, with soil enzymes showing a high degree of adaptation to drought conditions. These new insights could help evaluate the impact of drought on future wetland ecosystem services and provide a theoretical foundation for the remediation of degraded wetlands

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