OpenRaiser/Intern-Atlas huggingface dataset:
Memory-based Cross-modal Semantic Alignment Network for Radiology Report Generation
Venue: arXiv cs.CV 2024
Year: 2024

Generating radiology reports automatically reduces the workload of radiologists and helps the diagnoses of specific diseases. Many existing methods take this task as modality transfer process. However, since the key information related to disease accounts for a small proportion in both image and report, it is hard for the model to learn the latent relation between the radiology image and its report, thus failing to generate fluent and accurate radiology reports. To tackle this problem, we propose a memory-based cross-modal semantic alignment model (MCSAM) following an encoder-decoder paradigm. MCSAM includes a well initialized long-term clinical memory bank to learn disease-related representations as well as prior knowledge for different modalities to retrieve and use the retrieved memory to perform feature consolidation. To ensure the semantic consistency of the retrieved cross modal prior knowledge, a cross-modal semantic alignment module (SAM) is proposed. SAM is also able to generate semantic visual feature embeddings which can be added to the decoder and benefits report generation. More importantly, to memorize the state and additional information while generating reports with the decoder, we use learnable memory tokens which can be seen as prompts. Extensive experiments demonstrate the promising performance of our proposed method which generates state-of-the-art performance on the MIMIC-CXR dataset.
Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining
Venue: arXiv cs.CV 2024
Year: 2024

In copy-move tampering operations, perpetrators often employ techniques, such as blurring, to conceal tampering traces, posing significant challenges to the detection of object-level targets with intact structures. Focus on these challenges, this paper proposes an Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining (IMNet). To obtain complete object-level targets, we customize prototypes for both the source and tampered regions and dynamically update them. Additionally, we extract inconsistent regions between coarse similar regions obtained through self-correlation calculations and regions composed of prototypes. The detected inconsistent regions are used as supplements to coarse similar regions to refine pixel-level detection. We operate experiments on three public datasets which validate the effectiveness and the robustness of the proposed IMNet.
Learning to Plan for Language Modeling from Unlabeled Data
Venue: arXiv cs.CL 2024
Year: 2024

By training to predict the next token in an unlabeled corpus, large language models learn to perform many tasks without any labeled data. However, their next-token-prediction objective arguably limits their performance in scenarios that require planning, such as writing a coherent article. In this paper, we train a module for planning the future writing process via a self-supervised learning objective. Given the textual context, this planning module learns to predict future abstract writing actions, which correspond to centroids in a clustered text embedding space. By conditioning on these actions, our model extends the successful language model formula to more abstract planning in an unsupervised way. Empirically, we demonstrate that our method improves language modeling performance in general, particularly with respect to the text structure. Because our framework uses a planner module that is unsupervised and external to the language model, new planner modules can be trained at large scale and easily be shared with the community.
DMSSN: Distilled Mixed Spectral-Spatial Network for Hyperspectral Salient Object Detection
Venue: arXiv cs.CV 2024
Year: 2024

Hyperspectral salient object detection (HSOD) has exhibited remarkable promise across various applications, particularly in intricate scenarios where conventional RGB-based approaches fall short. Despite the considerable progress in HSOD method advancements, two critical challenges require immediate attention. Firstly, existing hyperspectral data dimension reduction techniques incur a loss of spectral information, which adversely affects detection accuracy. Secondly, previous methods insufficiently harness the inherent distinctive attributes of hyperspectral images (HSIs) during the feature extraction process. To address these challenges, we propose a novel approach termed the Distilled Mixed Spectral-Spatial Network (DMSSN), comprising a Distilled Spectral Encoding process and a Mixed Spectral-Spatial Transformer (MSST) feature extraction network. The encoding process utilizes knowledge distillation to construct a lightweight autoencoder for dimension reduction, striking a balance between robust encoding capabilities and low computational costs. The MSST extracts spectral-spatial features through multiple attention head groups, collaboratively enhancing its resistance to intricate scenarios. Moreover, we have created a large-scale HSOD dataset, HSOD-BIT, to tackle the issue of data scarcity in this field and meet the fundamental data requirements of deep network training. Extensive experiments demonstrate that our proposed DMSSN achieves state-of-the-art performance on multiple datasets. We will soon make the code and dataset publicly available on https://github.com/anonymous0519/HSOD-BIT.
Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignment
Venue: arXiv cs.CV 2024
Year: 2024

Conventional unsupervised anomaly detection (UAD) methods build separate models for each object category. Recent studies have proposed to train a unified model for multiple classes, namely model-unified UAD. However, such methods still implement the unified model separately on each class during inference with respective anomaly decision thresholds, which hinders their application when the image categories are entirely unavailable. In this work, we present a simple yet powerful method to address multi-class anomaly detection without any class information, namely \textit{absolute-unified} UAD. We target the crux of prior works in this challenging setting: different objects have mismatched anomaly score distributions. We propose Class-Agnostic Distribution Alignment (CADA) to align the mismatched score distribution of each implicit class without knowing class information, which enables unified anomaly detection for all classes and samples. The essence of CADA is to predict each class's score distribution of normal samples given any image, normal or anomalous, of this class. As a general component, CADA can activate the potential of nearly all UAD methods under absolute-unified setting. Our approach is extensively evaluated under the proposed setting on two popular UAD benchmark datasets, MVTec AD and VisA, where we exceed previous state-of-the-art by a large margin.
An Abundance of Katherines: The Game Theory of Baby Naming
Venue: arXiv cs.GT 2024
Year: 2024

In this paper, we study the highly competitive arena of baby naming. Through making several Extremely Reasonable Assumptions (namely, that parents are myopic, perfectly knowledgeable agents who pick a name based solely on its uniqueness), we create a model which is not only tractable and clean, but also perfectly captures the real world. We then extend our investigation with numerical experiments, as well as analysis of large language model tools. We conclude by discussing avenues for future research.
Can Language Models Recognize Convincing Arguments?
Venue: arXiv cs.CL 2024
Year: 2024

The capabilities of large language models (LLMs) have raised concerns about their potential to create and propagate convincing narratives. Here, we study their performance in detecting convincing arguments to gain insights into LLMs' persuasive capabilities without directly engaging in experimentation with humans. We extend a dataset by Durmus and Cardie (2018) with debates, votes, and user traits and propose tasks measuring LLMs' ability to (1) distinguish between strong and weak arguments, (2) predict stances based on beliefs and demographic characteristics, and (3) determine the appeal of an argument to an individual based on their traits. We show that LLMs perform on par with humans in these tasks and that combining predictions from different LLMs yields significant performance gains, surpassing human performance. The data and code released with this paper contribute to the crucial effort of continuously evaluating and monitoring LLMs' capabilities and potential impact. (https://go.epfl.ch/persuasion-llm)
Enchanting Program Specification Synthesis by Large Language Models using Static Analysis and Program Verification
Venue: arXiv cs.SE 2024
Year: 2024

Formal verification provides a rigorous and systematic approach to ensure the correctness and reliability of software systems. Yet, constructing specifications for the full proof relies on domain expertise and non-trivial manpower. In view of such needs, an automated approach for specification synthesis is desired. While existing automated approaches are limited in their versatility, i.e., they either focus only on synthesizing loop invariants for numerical programs, or are tailored for specific types of programs or invariants. Programs involving multiple complicated data types (e.g., arrays, pointers) and code structures (e.g., nested loops, function calls) are often beyond their capabilities. To help bridge this gap, we present AutoSpec, an automated approach to synthesize specifications for automated program verification. It overcomes the shortcomings of existing work in specification versatility, synthesizing satisfiable and adequate specifications for full proof. It is driven by static analysis and program verification, and is empowered by large language models (LLMs). AutoSpec addresses the practical challenges in three ways: (1) driving \name by static analysis and program verification, LLMs serve as generators to generate candidate specifications, (2) programs are decomposed to direct the attention of LLMs, and (3) candidate specifications are validated in each round to avoid error accumulation during the interaction with LLMs. In this way, AutoSpec can incrementally and iteratively generate satisfiable and adequate specifications. The evaluation shows its effectiveness and usefulness, as it outperforms existing works by successfully verifying 79% of programs through automatic specification synthesis, a significant improvement of 1.592x. It can also be successfully applied to verify the programs in a real-world X509-parser project.
GAMA-IR: Global Additive Multidimensional Averaging for Fast Image Restoration
Venue: arXiv cs.CV 2024
Year: 2024

Deep learning-based methods have shown remarkable success for various image restoration tasks such as denoising and deblurring. The current state-of-the-art networks are relatively deep and utilize (variants of) self attention mechanisms. Those networks are significantly slower than shallow convolutional networks, which however perform worse. In this paper, we introduce an image restoration network that is both fast and yields excellent image quality. The network is designed to minimize the latency and memory consumption when executed on a standard GPU, while maintaining state-of-the-art performance. The network is a simple shallow network with an efficient block that implements global additive multidimensional averaging operations. This block can capture global information and enable a large receptive field even when used in shallow networks with minimal computational overhead. Through extensive experiments and evaluations on diverse tasks, we demonstrate that our network achieves comparable or even superior results to existing state-of-the-art image restoration networks with less latency. For instance, we exceed the state-of-the-art result on real-world SIDD denoising by 0.11dB, while being 2 to 10 times faster.
HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs
Venue: arXiv cs.LG 2024
Year: 2024

Heterogeneous graphs are ubiquitous in real-world applications because they can represent various relationships between different types of entities. Therefore, learning embeddings in such graphs is a critical problem in graph machine learning. However, existing solutions for this problem fail to scale to large heterogeneous graphs due to their high computational complexity. To address this issue, we propose a Multi-Level Embedding framework of nodes on a heterogeneous graph (HeteroMILE) - a generic methodology that allows contemporary graph embedding methods to scale to large graphs. HeteroMILE repeatedly coarsens the large sized graph into a smaller size while preserving the backbone structure of the graph before embedding it, effectively reducing the computational cost by avoiding time-consuming processing operations. It then refines the coarsened embedding to the original graph using a heterogeneous graph convolution neural network. We evaluate our approach using several popular heterogeneous graph datasets. The experimental results show that HeteroMILE can substantially reduce computational time (approximately 20x speedup) and generate an embedding of better quality for link prediction and node classification.
Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigm
Venue: arXiv cs.IT 2024
Year: 2024

In some applications, edge learning is experiencing a shift in focusing from conventional learning from scratch to new two-stage learning unifying pre-training and task-specific fine-tuning. This paper considers the problem of joint communication and computation resource management in a two-stage edge learning system. In this system, model pre-training is first conducted at an edge server via centralized learning on local pre-stored general data, and then task-specific fine-tuning is performed at edge devices based on the pre-trained model via federated edge learning. For the two-stage learning model, we first analyze the convergence behavior (in terms of the average squared gradient norm bound), which characterizes the impacts of various system parameters such as the number of learning rounds and batch sizes in the two stages on the convergence rate. Based on our analytical results, we then propose a joint communication and computation resource management design to minimize an average squared gradient norm bound, subject to constraints on the transmit power, overall system energy consumption, and training delay. The decision variables include the number of learning rounds, batch sizes, clock frequencies, and transmit power control for both pre-training and fine-tuning stages. Finally, numerical results are provided to evaluate the effectiveness of our proposed design. It is shown that the proposed joint resource management over the pre-training and fine-tuning stages well balances the system performance trade-off among the training accuracy, delay, and energy consumption. The proposed design is also shown to effectively leverage the inherent trade-off between pre-training and fine-tuning, which arises from the differences in data distribution between pre-stored general data versus real-time task-specific data, thus efficiently optimizing overall system performance.
Transfer Learning with Point Transformers
Venue: arXiv cs.CV 2024
Year: 2024

Point Transformers are near state-of-the-art models for classification, segmentation, and detection tasks on Point Cloud data. They utilize a self attention based mechanism to model large range spatial dependencies between multiple point sets. In this project we explore two things: classification performance of these attention based networks on ModelNet10 dataset and then, we use the trained model to classify 3D MNIST dataset after finetuning. We also train the model from scratch on 3D MNIST dataset to compare the performance of finetuned and from-scratch model on the MNIST dataset. We observe that since the two datasets have a large difference in the degree of the distributions, transfer learned models do not outperform the from-scratch models in this case. Although we do expect transfer learned models to converge faster since they already know the lower level edges, corners, etc features from the ModelNet10 dataset.
Ensemble Learning for Vietnamese Scene Text Spotting in Urban Environments
Venue: arXiv cs.CV 2024
Year: 2024

This paper presents a simple yet efficient ensemble learning framework for Vietnamese scene text spotting. Leveraging the power of ensemble learning, which combines multiple models to yield more accurate predictions, our approach aims to significantly enhance the performance of scene text spotting in challenging urban settings. Through experimental evaluations on the VinText dataset, our proposed method achieves a significant improvement in accuracy compared to existing methods with an impressive accuracy of 5%. These results unequivocally demonstrate the efficacy of ensemble learning in the context of Vietnamese scene text spotting in urban environments, highlighting its potential for real world applications, such as text detection and recognition in urban signage, advertisements, and various text-rich urban scenes.
Performance Evaluation of RIS-Assisted Spatial Modulation for Downlink Transmission
Venue: arXiv cs.IT 2024
Year: 2024

This paper explores the performance of reconfigurable intelligent surface (RIS) assisted spatial modulation (SM) downlink communication systems, focusing on the average bit error probability (ABEP). Notably, in scenarios with a large number of reflecting units, the composite channel can be approximated by a Gaussian distribution using the central limit theorem. The receiver utilizes a maximum likelihood detector to recover information in both spatial and symbol domains. In the proposed RIS-SM system, we analytically derive a closed-form expression for the union tight upper bound of ABEP, employing the Gaussian-Chebyshev quadrature method. The validity of these results is rigorously confirmed through exhaustive Monte Carlo simulations.
Development of Musculoskeletal Legs with Planar Interskeletal Structures to Realize Human Comparable Moving Function
Venue: arXiv cs.RO 2024
Year: 2024

Musculoskeletal humanoids have been developed by imitating humans and expected to perform natural and dynamic motions as well as humans. To achieve desired motions stably in current musculoskeletal humanoids is not easy because they cannot maintain the sufficient moment arm of muscles in various postures. In this research, we discuss planar structures that spread across joint structures such as ligament and planar muscles and the application of planar interskeletal structures to humanoid robots. Next, we develop MusashiOLegs, a musculoskeletal legs which has planar interskeletal structures and conducts several experiments to verify the importance of planar interskeletal structures.
Machine Learning Robustness: A Primer
Venue: arXiv cs.LG 2024
Year: 2024

This chapter explores the foundational concept of robustness in Machine Learning (ML) and its integral role in establishing trustworthiness in Artificial Intelligence (AI) systems. The discussion begins with a detailed definition of robustness, portraying it as the ability of ML models to maintain stable performance across varied and unexpected environmental conditions. ML robustness is dissected through several lenses: its complementarity with generalizability; its status as a requirement for trustworthy AI; its adversarial vs non-adversarial aspects; its quantitative metrics; and its indicators such as reproducibility and explainability. The chapter delves into the factors that impede robustness, such as data bias, model complexity, and the pitfalls of underspecified ML pipelines. It surveys key techniques for robustness assessment from a broad perspective, including adversarial attacks, encompassing both digital and physical realms. It covers non-adversarial data shifts and nuances of Deep Learning (DL) software testing methodologies. The discussion progresses to explore amelioration strategies for bolstering robustness, starting with data-centric approaches like debiasing and augmentation. Further examination includes a variety of model-centric methods such as transfer learning, adversarial training, and randomized smoothing. Lastly, post-training methods are discussed, including ensemble techniques, pruning, and model repairs, emerging as cost-effective strategies to make models more resilient against the unpredictable. This chapter underscores the ongoing challenges and limitations in estimating and achieving ML robustness by existing approaches. It offers insights and directions for future research on this crucial concept, as a prerequisite for trustworthy AI systems.
Data Analytics for Improving Energy Efficiency in Short Sea Shipping
Venue: arXiv cs.CE 2024
Year: 2024

To meet the urgent requirements for the climate change mitigation, several proactive measures of energy efficiency have been implemented in maritime industry. Many of these practices depend highly on the onboard data of vessel's operation and environmental conditions. In this paper, a high resolution onboard data from passenger vessels in short-sea shipping (SSS) have been collected and preprocessed. We first investigated the available data to deploy it effectively to model the physics of the vessel, and hence the vessel performance. Since in SSS, the weather measurements and forecasts might have not been in temporal and spatial resolutions that accurately representing the actual environmental conditions. Then, We proposed a data-driven modeling approach for vessel energy efficiency. This approach addresses the challenges of data representation and energy modeling by combining and aggregating data from multiple sources and seamlessly integrates explainable artificial intelligence (XAI) to attain clear insights about the energy efficiency for a vessel in SSS. After that, the developed model of energy efficiency has been utilized in developing a framework for optimizing the vessel voyage to minimize the fuel consumption and meeting the constraint of arrival time. Moreover, we developed a spatial clustering approach for labeling the vessel paths to detect the paths for vessels with operating routes of repeatable and semi-repeatable paths.
A Survey on Multilingual Large Language Models: Corpora, Alignment, and Bias
Venue: arXiv cs.CL 2024
Year: 2024

Based on the foundation of Large Language Models (LLMs), Multilingual LLMs (MLLMs) have been developed to address the challenges faced in multilingual natural language processing, hoping to achieve knowledge transfer from high-resource languages to low-resource languages. However, significant limitations and challenges still exist, such as language imbalance, multilingual alignment, and inherent bias. In this paper, we aim to provide a comprehensive analysis of MLLMs, delving deeply into discussions surrounding these critical issues. First of all, we start by presenting an overview of MLLMs, covering their evolutions, key techniques, and multilingual capacities. Secondly, we explore the multilingual training corpora of MLLMs and the multilingual datasets oriented for downstream tasks that are crucial to enhance the cross-lingual capability of MLLMs. Thirdly, we survey the state-of-the-art studies of multilingual representations and investigate whether the current MLLMs can learn a universal language representation. Fourthly, we discuss bias on MLLMs, including its categories, evaluation metrics, and debiasing techniques. Finally, we discuss existing challenges and point out promising research directions of MLLMs.