576 research outputs found
DHC: Dual-debiased Heterogeneous Co-training Framework for Class-imbalanced Semi-supervised Medical Image Segmentation
The volume-wise labeling of 3D medical images is expertise-demanded and
time-consuming; hence semi-supervised learning (SSL) is highly desirable for
training with limited labeled data. Imbalanced class distribution is a severe
problem that bottlenecks the real-world application of these methods but was
not addressed much. Aiming to solve this issue, we present a novel
Dual-debiased Heterogeneous Co-training (DHC) framework for semi-supervised 3D
medical image segmentation. Specifically, we propose two loss weighting
strategies, namely Distribution-aware Debiased Weighting (DistDW) and
Difficulty-aware Debiased Weighting (DiffDW), which leverage the pseudo labels
dynamically to guide the model to solve data and learning biases. The framework
improves significantly by co-training these two diverse and accurate
sub-models. We also introduce more representative benchmarks for
class-imbalanced semi-supervised medical image segmentation, which can fully
demonstrate the efficacy of the class-imbalance designs. Experiments show that
our proposed framework brings significant improvements by using pseudo labels
for debiasing and alleviating the class imbalance problem. More importantly,
our method outperforms the state-of-the-art SSL methods, demonstrating the
potential of our framework for the more challenging SSL setting. Code and
models are available at: https://github.com/xmed-lab/DHC.Comment: Accepted at MICCAI202
Topological phase transition from periodic edge states in moir\'e superlattices
Topological mosaic pattern (TMP) can be formed in two-dimensional (2D)
moir\'e superlattices, a set of periodic and spatially separated domains with
distinct topologies give rise to periodic edge states on the domain walls. In
this study, we demonstrate that these periodic edge states play a crucial role
in determining global topological properties. By developing a continuum model
for periodic edge states with C6z and C3z rotational symmetry, we predict that
a global topological phase transition at the charge neutrality point (CNP) can
be driven by the size of domain walls and moir\'e periodicity. The Wannier
representation analysis reveals that these periodic edge states are
fundamentally chiral px +- ipy orbitals. The interplay between on-site chiral
orbital rotation and neighboring hopping among chiral orbitals leads to band
inversion and a topological phase transition. Our work establishes a general
model for tuning local and global topological phases, paving the way for future
research on strongly correlated topological flat minibands within topological
mosaic pattern
MoEController: Instruction-based Arbitrary Image Manipulation with Mixture-of-Expert Controllers
Diffusion-model-based text-guided image generation has recently made
astounding progress, producing fascinating results in open-domain image
manipulation tasks. Few models, however, currently have complete zero-shot
capabilities for both global and local image editing due to the complexity and
diversity of image manipulation tasks. In this work, we propose a method with a
mixture-of-expert (MOE) controllers to align the text-guided capacity of
diffusion models with different kinds of human instructions, enabling our model
to handle various open-domain image manipulation tasks with natural language
instructions. First, we use large language models (ChatGPT) and conditional
image synthesis models (ControlNet) to generate a large number of global image
transfer dataset in addition to the instruction-based local image editing
dataset. Then, using an MOE technique and task-specific adaptation training on
a large-scale dataset, our conditional diffusion model can edit images globally
and locally. Extensive experiments demonstrate that our approach performs
surprisingly well on various image manipulation tasks when dealing with
open-domain images and arbitrary human instructions. Please refer to our
project page: [https://oppo-mente-lab.github.io/moe_controller/]Comment: 5 pages,6 figure
Collaborative Optimization of Car-flow Organization for Freight Trains Based on Adjacent Technical Stations
This paper proposes a collaborative optimization model of car-flow organization for freight trains based on adjacent technical stations to minimize the average dwell time of train cars in a yard. To solve the car-flow organization problems, a priority-based hump sequence, which depends on the cars available in two adjacent technical stations, is adopted. Furthermore, a meta-heuristic algorithm based on the genetic algorithm and the taboo search algorithm is adopted to solve the model, and the introduction of the active scheduling method improves the efficiency of the algorithm. Finally, the model is applied to the car-flow organization problem of two adjacent technical stations, and the results are compared with those from a single technical station without collaboration. The results demonstrate that collaborative car-flow organization between technical stations significantly reduces the average dwell time at the stations, thereby improving the utilization rate of railroad equipment. In addition, the results indicate that the hybrid genetic algorithm can rapidly determine the train hump and marshalling schemes
Dynamic Data Augmentation via MCTS for Prostate MRI Segmentation
Medical image data are often limited due to the expensive acquisition and
annotation process. Hence, training a deep-learning model with only raw data
can easily lead to overfitting. One solution to this problem is to augment the
raw data with various transformations, improving the model's ability to
generalize to new data. However, manually configuring a generic augmentation
combination and parameters for different datasets is non-trivial due to
inconsistent acquisition approaches and data distributions. Therefore,
automatic data augmentation is proposed to learn favorable augmentation
strategies for different datasets while incurring large GPU overhead. To this
end, we present a novel method, called Dynamic Data Augmentation (DDAug), which
is efficient and has negligible computation cost. Our DDAug develops a
hierarchical tree structure to represent various augmentations and utilizes an
efficient Monte-Carlo tree searching algorithm to update, prune, and sample the
tree. As a result, the augmentation pipeline can be optimized for each dataset
automatically. Experiments on multiple Prostate MRI datasets show that our
method outperforms the current state-of-the-art data augmentation strategies
Large Language Models Only Pass Primary School Exams in Indonesia: A Comprehensive Test on IndoMMLU
Although large language models (LLMs) are often pre-trained on large-scale
multilingual texts, their reasoning abilities and real-world knowledge are
mainly evaluated based on English datasets. Assessing LLM capabilities beyond
English is increasingly vital but hindered due to the lack of suitable
datasets. In this work, we introduce IndoMMLU, the first multi-task language
understanding benchmark for Indonesian culture and languages, which consists of
questions from primary school to university entrance exams in Indonesia. By
employing professional teachers, we obtain 14,981 questions across 64 tasks and
education levels, with 46% of the questions focusing on assessing proficiency
in the Indonesian language and knowledge of nine local languages and cultures
in Indonesia. Our empirical evaluations show that GPT-3.5 only manages to pass
the Indonesian primary school level, with limited knowledge of local Indonesian
languages and culture. Other smaller models such as BLOOMZ and Falcon perform
at even lower levels.Comment: Accepted at EMNLP 202
Spatial Multiplexing for MIMO/Massive MIMO
In this chapter, we will discuss how to achieve spatial multiplexing in multiple-input multiple-output (MIMO) communications through precoding design, for both traditional small-scale MIMO systems and massive MIMO systems. The mathematical description for MIMO communications will first be introduced, based on which we discuss both block-level precoding and the emerging symbol-level precoding techniques. We begin with simple and closed-form block-level precoders such as maximum ratio transmission (MRT), zero-forcing (ZF), and regularized ZF (RZF), followed by the classic symbol-level precoding schemes such as Tomlinson-Harashima precoder (THP) and vector perturbation (VP) precoder. Subsequently, we introduce optimization-based precoding solutions, including power minimization, SINR balancing, symbol-level interference exploitation, etc. We extend our discussion to massive MIMO systems and particularly focus on precoding designs for hardware-efficient massive MIMO systems, such as hybrid analog-digital precoding, low-bit precoding, nonlinearity-aware precoding, etc
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