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

Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs

With the rapid evolution of large language models (LLMs), new and hard-to-predict harmful capabilities are emerging. This requires developers to be able to identify risks through the evaluation of "dangerous capabilities" in order to responsibly deploy LLMs. In this work, we collect the first open-source dataset to evaluate safeguards in LLMs, and deploy safer open-source LLMs at a low cost. Our dataset is curated and filtered to consist only of instructions that responsible language models should not follow. We annotate and assess the responses of six popular LLMs to these instructions. Based on our annotation, we proceed to train several BERT-like classifiers, and find that these small classifiers can achieve results that are comparable with GPT-4 on automatic safety evaluation. Warning: this paper contains example data that may be offensive, harmful, or biased.Comment: 18 pages, 9 figures, 11 table