OpenRaiser/Intern-Atlas huggingface dataset:
A multimodal ensemble approach for clear cell renal cell carcinoma treatment outcome prediction
Venue: arXiv cs.CV 2024
Year: 2024

Purpose: A reliable cancer prognosis model for clear cell renal cell carcinoma (ccRCC) can enhance personalized treatment. We developed a multi-modal ensemble model (MMEM) that integrates pretreatment clinical data, multi-omics data, and histopathology whole slide image (WSI) data to predict overall survival (OS) and disease-free survival (DFS) for ccRCC patients. Methods: We analyzed 226 patients from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) dataset, which includes OS, DFS follow-up data, and five data modalities: clinical data, WSIs, and three multi-omics datasets (mRNA, miRNA, and DNA methylation). Separate survival models were built for OS and DFS. Cox-proportional hazards (CPH) model with forward feature selection is used for clinical and multi-omics data. Features from WSIs were extracted using ResNet and three general-purpose foundation models. A deep learning-based CPH model predicted survival using encoded WSI features. Risk scores from all models were combined based on training performance. Results: Performance was assessed using concordance index (C-index) and AUROC. The clinical feature-based CPH model received the highest weight for both OS and DFS tasks. Among WSI-based models, the general-purpose foundation model (UNI) achieved the best performance. The final MMEM model surpassed single-modality models, achieving C-indices of 0.820 (OS) and 0.833 (DFS), and AUROC values of 0.831 (3-year patient death) and 0.862 (cancer recurrence). Using predicted risk medians to stratify high- and low-risk groups, log-rank tests showed improved performance in both OS and DFS compared to single-modality models. Conclusion: MMEM is the first multi-modal model for ccRCC patients, integrating five data modalities. It outperformed single-modality models in prognostic ability and has the potential to assist in ccRCC patient management if independently validated.
Integrating MedCLIP and Cross-Modal Fusion for Automatic Radiology Report Generation
Venue: arXiv cs.CV 2024
Year: 2024

Automating radiology report generation can significantly reduce the workload of radiologists and enhance the accuracy, consistency, and efficiency of clinical documentation.We propose a novel cross-modal framework that uses MedCLIP as both a vision extractor and a retrieval mechanism to improve the process of medical report generation.By extracting retrieved report features and image features through an attention-based extract module, and integrating them with a fusion module, our method improves the coherence and clinical relevance of generated reports.Experimental results on the widely used IU-Xray dataset demonstrate the effectiveness of our approach, showing improvements over commonly used methods in both report quality and relevance.Additionally, ablation studies provide further validation of the framework, highlighting the importance of accurate report retrieval and feature integration in generating comprehensive medical reports.
MIT-10M: A Large Scale Parallel Corpus of Multilingual Image Translation
Venue: arXiv cs.CV 2024
Year: 2024

Image Translation (IT) holds immense potential across diverse domains, enabling the translation of textual content within images into various languages. However, existing datasets often suffer from limitations in scale, diversity, and quality, hindering the development and evaluation of IT models. To address this issue, we introduce MIT-10M, a large-scale parallel corpus of multilingual image translation with over 10M image-text pairs derived from real-world data, which has undergone extensive data cleaning and multilingual translation validation. It contains 840K images in three sizes, 28 categories, tasks with three levels of difficulty and 14 languages image-text pairs, which is a considerable improvement on existing datasets. We conduct extensive experiments to evaluate and train models on MIT-10M. The experimental results clearly indicate that our dataset has higher adaptability when it comes to evaluating the performance of the models in tackling challenging and complex image translation tasks in the real world. Moreover, the performance of the model fine-tuned with MIT-10M has tripled compared to the baseline model, further confirming its superiority.
Graph Neural Networks Are More Than Filters: Revisiting and Benchmarking from A Spectral Perspective
Venue: arXiv cs.LG 2024
Year: 2024

Graph Neural Networks (GNNs) have achieved remarkable success in various graph-based learning tasks. While their performance is often attributed to the powerful neighborhood aggregation mechanism, recent studies suggest that other components such as non-linear layers may also significantly affecting how GNNs process the input graph data in the spectral domain. Such evidence challenges the prevalent opinion that neighborhood aggregation mechanisms dominate the behavioral characteristics of GNNs in the spectral domain. To demystify such a conflict, this paper introduces a comprehensive benchmark to measure and evaluate GNNs' capability in capturing and leveraging the information encoded in different frequency components of the input graph data. Specifically, we first conduct an exploratory study demonstrating that GNNs can flexibly yield outputs with diverse frequency components even when certain frequencies are absent or filtered out from the input graph data. We then formulate a novel research problem of measuring and benchmarking the performance of GNNs from a spectral perspective. To take an initial step towards a comprehensive benchmark, we design an evaluation protocol supported by comprehensive theoretical analysis. Finally, we introduce a comprehensive benchmark on real-world datasets, revealing insights that challenge prevalent opinions from a spectral perspective. We believe that our findings will open new avenues for future advancements in this area. Our implementations can be found at: https://github.com/yushundong/Spectral-benchmark.
When Graph Meets Retrieval Augmented Generation for Wireless Networks: A Tutorial and Case Study
Venue: arXiv cs.NI 2024
Year: 2024

The rapid development of next-generation networking technologies underscores their transformative role in revolutionizing modern communication systems, enabling faster, more reliable, and highly interconnected solutions. However, such development has also brought challenges to network optimizations. Thanks to the emergence of Large Language Models (LLMs) in recent years, tools including Retrieval Augmented Generation (RAG) have been developed and applied in various fields including networking, and have shown their effectiveness. Taking one step further, the integration of knowledge graphs into RAG frameworks further enhanced the performance of RAG in networking applications such as Intent-Driven Networks (IDNs) and spectrum knowledge maps by providing more contextually relevant responses through more accurate retrieval of related network information. This paper introduces the RAG framework that integrates knowledge graphs in its database and explores such framework's application in networking. We begin by exploring RAG's applications in networking and the limitations of conventional RAG and present the advantages that knowledge graphs' structured knowledge representation brings to the retrieval and generation processes. Next, we propose a detailed GraphRAG-based framework for networking, including a step-by-step tutorial on its construction. Our evaluation through a case study on channel gain prediction demonstrates GraphRAG's enhanced capability in generating accurate, contextually rich responses, surpassing traditional RAG models. Finally, we discuss key future directions for applying knowledge-graphs-empowered RAG frameworks in networking, including robust updates, mitigation of hallucination, and enhanced security measures for networking applications.
Modifying AI, Enhancing Essays: How Active Engagement with Generative AI Boosts Writing Quality
Venue: arXiv cs.HC 2024
Year: 2024

Students are increasingly relying on Generative AI (GAI) to support their writing-a key pedagogical practice in education. In GAI-assisted writing, students can delegate core cognitive tasks (e.g., generating ideas and turning them into sentences) to GAI while still producing high-quality essays. This creates new challenges for teachers in assessing and supporting student learning, as they often lack insight into whether students are engaging in meaningful cognitive processes during writing or how much of the essay's quality can be attributed to those processes. This study aimed to help teachers better assess and support student learning in GAI-assisted writing by examining how different writing behaviors, especially those indicative of meaningful learning versus those that are not, impact essay quality. Using a dataset of 1,445 GAI-assisted writing sessions, we applied the cutting-edge method, X-Learner, to quantify the causal impact of three GAI-assisted writing behavioral patterns (i.e., seeking suggestions but not accepting them, seeking suggestions and accepting them as they are, and seeking suggestions and accepting them with modification) on four measures of essay quality (i.e., lexical sophistication, syntactic complexity, text cohesion, and linguistic bias). Our analysis showed that writers who frequently modified GAI-generated text-suggesting active engagement in higher-order cognitive processes-consistently improved the quality of their essays in terms of lexical sophistication, syntactic complexity, and text cohesion. In contrast, those who often accepted GAI-generated text without changes, primarily engaging in lower-order processes, saw a decrease in essay quality. Additionally, while human writers tend to introduce linguistic bias when writing independently, incorporating GAI-generated text-even without modification-can help mitigate this bias.
BrokerChain: A Blockchain Sharding Protocol by Exploiting Broker Accounts
Venue: arXiv cs.CR 2024
Year: 2024

State-of-the-art blockchain sharding solutions such as Monoxide, can cause severely imbalanced distribution of transaction (TX) workloads across all blockchain shards due to the deployment policy of their accounts. Imbalanced TX distributions then produce hot shards, in which the cross-shard TXs may experience an unlimited confirmation latency. Thus, how to address the hot-shard issue and how to reduce crossshard TXs become significant challenges of blockchain sharding. Through reviewing the related studies, we find that a crossshard TX protocol that can achieve workload balance among all shards and simultaneously reduce the quantity of crossshard TXs is still absent from the literature. To this end, we propose BrokerChain, which is a cross-shard blockchain protocol dedicated to account-based state sharding. Essentially, BrokerChain exploits fine-grained state partition and account segmentation. We also elaborate on how BrokerChain handles cross-shard TXs through broker accounts. The security issues and other properties of BrokerChain are analyzed rigorously. Finally, we conduct comprehensive evaluations using an opensource blockchain sharding prototype named BlockEmulator. The evaluation results show that BrokerChain outperforms other baselines in terms of transaction throughput, transaction confirmation latency, the queue size of the transaction pool, and workload balance.
Developing a Dataset-Adaptive, Normalized Metric for Machine Learning Model Assessment: Integrating Size, Complexity, and Class Imbalance
Venue: arXiv cs.LG 2024
Year: 2024

Traditional metrics like accuracy, F1-score, and precision are frequently used to evaluate machine learning models, however they may not be sufficient for evaluating performance on tiny, unbalanced, or high-dimensional datasets. A dataset-adaptive, normalized metric that incorporates dataset characteristics like size, feature dimensionality, class imbalance, and signal-to-noise ratio is presented in this study. Early insights into the model's performance potential in challenging circumstances are provided by the suggested metric, which offers a scalable and adaptable evaluation framework. The metric's capacity to accurately forecast model scalability and performance is demonstrated via experimental validation spanning classification, regression, and clustering tasks, guaranteeing solid assessments in settings with limited data. This method has important ramifications for effective resource allocation and model optimization in machine learning workflows.
Label-Confidence-Aware Uncertainty Estimation in Natural Language Generation
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) display formidable capabilities in generative tasks but also pose potential risks due to their tendency to generate hallucinatory responses. Uncertainty Quantification (UQ), the evaluation of model output reliability, is crucial for ensuring the safety and robustness of AI systems. Recent studies have concentrated on model uncertainty by analyzing the relationship between output entropy under various sampling conditions and the corresponding labels. However, these methods primarily focus on measuring model entropy with precision to capture response characteristics, often neglecting the uncertainties associated with greedy decoding results-the sources of model labels, which can lead to biased classification outcomes. In this paper, we explore the biases introduced by greedy decoding and propose a label-confidence-aware (LCA) uncertainty estimation based on Kullback-Leibler (KL) divergence bridging between samples and label source, thus enhancing the reliability and stability of uncertainty assessments. Our empirical evaluations across a range of popular LLMs and NLP datasets reveal that different label sources can indeed affect classification, and that our approach can effectively capture differences in sampling results and label sources, demonstrating more effective uncertainty estimation.
PTSBench: A Comprehensive Post-Training Sparsity Benchmark Towards Algorithms and Models
Venue: arXiv cs.LG 2024
Year: 2024

With the increased attention to model efficiency, post-training sparsity (PTS) has become more and more prevalent because of its effectiveness and efficiency. However, there remain questions on better practice of PTS algorithms and the sparsification ability of models, which hinders the further development of this area. Therefore, a benchmark to comprehensively investigate the issues above is urgently needed. In this paper, we propose the first comprehensive post-training sparsity benchmark called PTSBench towards algorithms and models. We benchmark 10+ PTS general-pluggable fine-grained techniques on 3 typical tasks using over 40 off-the-shelf model architectures. Through extensive experiments and analyses, we obtain valuable conclusions and provide several insights from both algorithms and model aspects. Our PTSBench can provide (1) new observations for a better understanding of the PTS algorithms, (2) in-depth and comprehensive evaluations for the sparsification ability of models, and (3) a well-structured and easy-integrate open-source framework. We hope this work will provide illuminating conclusions and advice for future studies of post-training sparsity methods and sparsification-friendly model design. The code for our PTSBench is released at \href{https://github.com/ModelTC/msbench}{https://github.com/ModelTC/msbench}.
Reconciling Human Development and Giant Panda Protection Goals: Cost-efficiency Evaluation of Farmland Reverting and Energy Substitution Programs in Wolong National Reserve
Venue: arXiv cs.CY 2024
Year: 2024

Balancing human development with conservation necessitates ecological policies that optimize outcomes within limited budgets, highlighting the importance of cost-efficiency and local impact analysis. This study employs the Socio-Econ-Ecosystem Multipurpose Simulator (SEEMS), an Agent-Based Model (ABM) designed for simulating small-scale Coupled Human and Nature Systems (CHANS), to evaluate the cost-efficiency of two major ecology conservation programs: Grain-to-Green (G2G) and Firewood-to-Electricity (F2E). Focusing on China Wolong National Reserve, a worldwide hot spot for flagship species conservation, the study evaluates the direct benefits of these programs, including reverted farmland area and firewood consumption, along with their combined indirect benefits on habitat quality, carbon emissions, and gross economic benefits. The findings are as follows: (1) The G2G program achieves optimal financial efficiency at approximately 500 CNY/Mu, with diminishing returns observed beyond 1000 CNY/Mu; (2) For the F2E program, the most fiscally cost-efficient option arises when the subsidized electricity price is at 0.4-0.5 CNY/kWh, while further reductions of the prices to below 0.1 CNY/kWh result in a diminishing cost-benefit ratio; (3) Comprehensive cost-efficiency analysis reveals no significant link between financial burden and carbon emissions, but a positive correlation with habitat quality and an inverted U-shaped relationship with total economic income; (4) Pareto analysis identifies 18 optimal dual-policy combinations for balancing carbon footprint, habitat quality, and gross economic benefits; (5) Posterior Pareto optimization further refines the selection of a specific policy scheme for a given realistic scenario. The analytical framework of this paper helps policymakers design economically viable and environmentally sustainable policies, addressing global conservation challenges.
Enhancing Relation Extraction via Supervised Rationale Verification and Feedback
Venue: arXiv cs.CL 2024
Year: 2024

Despite the rapid progress that existing automated feedback methods have made in correcting the output of large language models (LLMs), these methods cannot be well applied to the relation extraction (RE) task due to their designated feedback objectives and correction manner. To address this problem, we propose a novel automated feedback framework for RE, which presents a rationale supervisor to verify the rationale and provides re-selected demonstrations as feedback to correct the initial prediction. Specifically, we first design a causal intervention and observation method to collect biased/unbiased rationales for contrastive training the rationale supervisor. Then, we present a verification-feedback-correction procedure to iteratively enhance LLMs' capability of handling the RE task. Extensive experiments prove that our proposed framework significantly outperforms existing methods.
ConceptSearch: Towards Efficient Program Search Using LLMs for Abstraction and Reasoning Corpus (ARC)
Venue: arXiv cs.LG 2024
Year: 2024

The Abstraction and Reasoning Corpus (ARC) poses a significant challenge to artificial intelligence, demanding broad generalization and few-shot learning capabilities that remain elusive for current deep learning methods, including large language models (LLMs). While LLMs excel in program synthesis, their direct application to ARC yields limited success. To address this, we introduce ConceptSearch, a novel function-search algorithm that leverages LLMs for program generation and employs a concept-based scoring method to guide the search efficiently. Unlike simplistic pixel-based metrics like Hamming distance, ConceptSearch evaluates programs on their ability to capture the underlying transformation concept reflected in the input-output examples. We explore three scoring functions: Hamming distance, a CNN-based scoring function, and an LLM-based natural language scoring function. Experimental results demonstrate the effectiveness of ConceptSearch, achieving a significant performance improvement over direct prompting with GPT-4. Moreover, our novel concept-based scoring exhibits up to 30% greater efficiency compared to Hamming distance, measured in terms of the number of iterations required to reach the correct solution. These findings highlight the potential of LLM-driven program search when integrated with concept-based guidance for tackling challenging generalization problems like ARC.
Fusion Embedding for Pose-Guided Person Image Synthesis with Diffusion Model
Venue: arXiv cs.CV 2024
Year: 2024

Pose-Guided Person Image Synthesis (PGPIS) aims to synthesize high-quality person images corresponding to target poses while preserving the appearance of the source image. Recently, PGPIS methods that use diffusion models have achieved competitive performance. Most approaches involve extracting representations of the target pose and source image and learning their relationships in the generative model's training process. This approach makes it difficult to learn the semantic relationships between the input and target images and complicates the model structure needed to enhance generation results. To address these issues, we propose Fusion embedding for PGPIS using a Diffusion Model (FPDM). Inspired by the successful application of pre-trained CLIP models in text-to-image diffusion models, our method consists of two stages. The first stage involves training the fusion embedding of the source image and target pose to align with the target image's embedding. In the second stage, the generative model uses this fusion embedding as a condition to generate the target image. We applied the proposed method to the benchmark datasets DeepFashion and RWTH-PHOENIX-Weather 2014T, and conducted both quantitative and qualitative evaluations, demonstrating state-of-the-art (SOTA) performance. An ablation study of the model structure showed that even a model using only the second stage achieved performance close to the other PGPIS SOTA models. The code is available at https://github.com/dhlee-work/FPDM.
A Spectral Framework for Tracking Communities in Evolving Networks
Venue: arXiv cs.SI 2024
Year: 2024

Discovering and tracking communities in time-varying networks is an important task in network science, motivated by applications in fields ranging from neuroscience to sociology. In this work, we characterize the celebrated family of spectral methods for static clustering in terms of the low-rank approximation of high-dimensional node embeddings. From this perspective, it becomes natural to view the evolving community detection problem as one of subspace tracking on the Grassmann manifold. While the resulting optimization problem is nonconvex, we adopt a block majorize-minimize Riemannian optimization scheme to learn the Grassmann geodesic which best fits the data. Our framework generalizes any static spectral community detection approach and leads to algorithms achieving favorable performance on synthetic and real temporal networks, including those that are weighted, signed, directed, mixed-membership, multiview, hierarchical, cocommunity-structured, bipartite, or some combination thereof. We demonstrate how to specifically cast a wide variety of methods into our framework, and demonstrate greatly improved dynamic community detection results in all cases.
A Review of Challenges in Speech-based Conversational AI for Elderly Care
Venue: arXiv cs.CL 2024
Year: 2024

Artificially intelligent systems optimized for speech conversation are appearing at a fast pace. Such models are interesting from a healthcare perspective, as these voice-controlled assistants may support the elderly and enable remote health monitoring. The bottleneck for efficacy, however, is how well these devices work in practice and how the elderly experience them, but research on this topic is scant. We review elderly use of voice-controlled AI and highlight various user- and technology-centered issues, that need to be considered before effective speech-controlled AI for elderly care can be realized.