OpenRaiser/Intern-Atlas huggingface dataset:
Image-based adaptive domain decomposition for continuum damage models
Venue: arXiv cs.CE 2024
Year: 2024

We present a novel image-based adaptive domain decomposition FEM framework to accelerate the solution of continuum damage mechanics problems. The key idea is to use image-processing techniques in order to identify the moving interface between the healthy subdomain and unhealthy subdomain as damage propagates, and then use an iterative Schur complement approach to efficiently solve the problem. The implementation of the algorithm consists of several modular components. Following the FEM solution of a load increment, the damage detection module is activated, a step that is based on several image-processing operations including colormap manipulation and morphological convolution-based operations. Then, the damage tracking module is invoked, to identify the crack growth direction using geometrical operations and ray casting algorithm. This information is then passed into the domain decomposition module, where the domain is divided into the healthy subdomain which contains only undamaged elements, and the unhealthy subdomain which comprises both damaged and undamaged elements. Continuity between the two regions is restored using penalty constraints. The computational savings of our method stem from the Schur complement, which allows for the iterative solution of the system of equations appertaining only to the unhealthy subdomain. Through an exhaustive comparison between our approach and single domain computations, we demonstrate the accuracy, efficiency, and robustness of the framework. We ensure its compatibility against local and non-local damage laws, structured and unstructured meshes, as well as in cases where different damage paths eventually merge. Since the key novelty lies in using image processing tools to inform the decomposition, our framework can be readily extended beyond damage mechanics and model several classes of non-linear problems such as plasticity and phase-field.
Sampling-guided Heterogeneous Graph Neural Network with Temporal Smoothing for Scalable Longitudinal Data Imputation
Venue: arXiv cs.LG 2024
Year: 2024

In this paper, we propose a novel framework, the Sampling-guided Heterogeneous Graph Neural Network (SHT-GNN), to effectively tackle the challenge of missing data imputation in longitudinal studies. Unlike traditional methods, which often require extensive preprocessing to handle irregular or inconsistent missing data, our approach accommodates arbitrary missing data patterns while maintaining computational efficiency. SHT-GNN models both observations and covariates as distinct node types, connecting observation nodes at successive time points through subject-specific longitudinal subnetworks, while covariate-observation interactions are represented by attributed edges within bipartite graphs. By leveraging subject-wise mini-batch sampling and a multi-layer temporal smoothing mechanism, SHT-GNN efficiently scales to large datasets, while effectively learning node representations and imputing missing data. Extensive experiments on both synthetic and real-world datasets, including the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, demonstrate that SHT-GNN significantly outperforms existing imputation methods, even with high missing data rates. The empirical results highlight SHT-GNN's robust imputation capabilities and superior performance, particularly in the context of complex, large-scale longitudinal data.
Position Paper On Diagnostic Uncertainty Estimation from Large Language Models: Next-Word Probability Is Not Pre-test Probability
Venue: arXiv cs.AI 2024
Year: 2024

Large language models (LLMs) are being explored for diagnostic decision support, yet their ability to estimate pre-test probabilities, vital for clinical decision-making, remains limited. This study evaluates two LLMs, Mistral-7B and Llama3-70B, using structured electronic health record data on three diagnosis tasks. We examined three current methods of extracting LLM probability estimations and revealed their limitations. We aim to highlight the need for improved techniques in LLM confidence estimation.
VAIR: Visuo-Acoustic Implicit Representations for Low-Cost, Multi-Modal Transparent Surface Reconstruction in Indoor Scenes
Venue: arXiv cs.CV 2024
Year: 2024

Mobile robots operating indoors must be prepared to navigate challenging scenes that contain transparent surfaces. This paper proposes a novel method for the fusion of acoustic and visual sensing modalities through implicit neural representations to enable dense reconstruction of transparent surfaces in indoor scenes. We propose a novel model that leverages generative latent optimization to learn an implicit representation of indoor scenes consisting of transparent surfaces. We demonstrate that we can query the implicit representation to enable volumetric rendering in image space or 3D geometry reconstruction (point clouds or mesh) with transparent surface prediction. We evaluate our method's effectiveness qualitatively and quantitatively on a new dataset collected using a custom, low-cost sensing platform featuring RGB-D cameras and ultrasonic sensors. Our method exhibits significant improvement over state-of-the-art for transparent surface reconstruction.
De la Extensión a la Investigación: Como La Robótica Estimula el Interés Académico en Estudiantes de Grado
Venue: arXiv cs.CY 2024
Year: 2024

This research examines the impact of robotics groups in higher education, focusing on how these activities influence the development of transversal skills and academic motivation. While robotics goes beyond just technical knowledge, participation in these groups has been observed to significantly improve skills such as teamwork, creativity, and problem-solving. The study, conducted with the UruBots group, shows that students involved in robotics not only reinforce their theoretical knowledge but also increase their interest in research and academic commitment. These results highlight the potential of educational robotics to transform the learning experience by promoting active and collaborative learning. This work lays the groundwork for future research on how robotics can continue to enhance higher education and motivate students in their academic and professional careers
LLMs as Research Tools: A Large Scale Survey of Researchers' Usage and Perceptions
Venue: arXiv cs.CL 2024
Year: 2024

The rise of large language models (LLMs) has led many researchers to consider their usage for scientific work. Some have found benefits using LLMs to augment or automate aspects of their research pipeline, while others have urged caution due to risks and ethical concerns. Yet little work has sought to quantify and characterize how researchers use LLMs and why. We present the first large-scale survey of 816 verified research article authors to understand how the research community leverages and perceives LLMs as research tools. We examine participants' self-reported LLM usage, finding that 81% of researchers have already incorporated LLMs into different aspects of their research workflow. We also find that traditionally disadvantaged groups in academia (non-White, junior, and non-native English speaking researchers) report higher LLM usage and perceived benefits, suggesting potential for improved research equity. However, women, non-binary, and senior researchers have greater ethical concerns, potentially hindering adoption.
Towards Interpreting Language Models: A Case Study in Multi-Hop Reasoning
Venue: arXiv cs.CL 2024
Year: 2024

Answering multi-hop reasoning questions requires retrieving and synthesizing information from diverse sources. Language models (LMs) struggle to perform such reasoning consistently. We propose an approach to pinpoint and rectify multi-hop reasoning failures through targeted memory injections on LM attention heads. First, we analyze the per-layer activations of GPT-2 models in response to single- and multi-hop prompts. We then propose a mechanism that allows users to inject relevant prompt-specific information, which we refer to as "memories," at critical LM locations during inference. By thus enabling the LM to incorporate additional relevant information during inference, we enhance the quality of multi-hop prompt completions. We empirically show that a simple, efficient, and targeted memory injection into a key attention layer often increases the probability of the desired next token in multi-hop tasks, by up to 424%. We observe that small subsets of attention heads can significantly impact the model prediction during multi-hop reasoning. To more faithfully interpret these heads, we develop Attention Lens: an open source tool that translates the outputs of attention heads into vocabulary tokens via learned transformations called lenses. We demonstrate the use of lenses to reveal how a model arrives at its answer and use them to localize sources of model failures such as in the case of biased and malicious language generation.
Performance-Guided LLM Knowledge Distillation for Efficient Text Classification at Scale
Venue: arXiv cs.CL 2024
Year: 2024

Large Language Models (LLMs) face significant challenges at inference time due to their high computational demands. To address this, we present Performance-Guided Knowledge Distillation (PGKD), a cost-effective and high-throughput solution for production text classification applications. PGKD utilizes teacher-student Knowledge Distillation to distill the knowledge of LLMs into smaller, task-specific models. PGKD establishes an active learning routine between the student model and the LLM; the LLM continuously generates new training data leveraging hard-negative mining, student model validation performance, and early-stopping protocols to inform the data generation. By employing a cyclical, performance-aware approach tailored for highly multi-class, sparsely annotated datasets prevalent in industrial text classification, PGKD effectively addresses training challenges and outperforms traditional BERT-base models and other knowledge distillation methods on several multi-class classification datasets. Additionally, cost and latency benchmarking reveals that models fine-tuned with PGKD are up to 130X faster and 25X less expensive than LLMs for inference on the same classification task. While PGKD is showcased for text classification tasks, its versatile framework can be extended to any LLM distillation task, including language generation, making it a powerful tool for optimizing performance across a wide range of AI applications.
Proposal of a Contact Detection System using Micro-phones for a Chambara-based Augmented Sports
Venue: arXiv cs.HC 2024
Year: 2024

This study presents a novel contact detection system for "Parablade," a chambara-based, sword-play augmented sport. Augmented sports combine physical activities with virtual parameters (VPs) to create a balanced and equitable gaming experience, irrespective of players' physical capabilities. The proposed Parablade Microphone Unit (PMU) employs multiple micro-phones and machine learning algorithms to detect and classify hit events through sound recogni-tion. This system aims to ensure real-time updates of VPs, thereby enhancing the gameplay expe-rience. Experimental results indicate that the PMU can accurately recognize the occurrence and location of hit events with a high accuracy rate of 93.33%, with the assistance of 10kHz additional sound generated from the sword.
Haptic Reproduction of Curved Surface and Edge by Controlling the Contact Position between the Disk and the Finger Using Airborne Ultrasound
Venue: arXiv cs.HC 2024
Year: 2024

By presenting curved surfaces of various curvatures including edges to the fingertip, it is possible to reproduce the haptic sensation of object shapes that cannot be reproduced by flat surfaces alone, such as spheres and rectangular objects. In this paper, we propose a method of presenting curved surfaces by controlling the inclination of a disk in contact with the finger belly with acoustic radiation pressure of ultrasound. The user only needs to mount a lightweight device on the fingertip to experience a tactile presentation with low physical burden. In the demonstration, the user can experience the sensation of stroking an edge and different curvatures of curved surfaces.
Bridging Player Intentions: Exploring the Potential of Synchronized Haptic Controllers in Multiplayer Game
Venue: arXiv cs.HC 2024
Year: 2024

In multiplayer cooperative video games, players traditionally use individual controllers, inferring others' actions through on-screen visuals and their own movements. This indirect understanding limits truly collaborative gameplay. Research in Joint Action shows that when manipulating a single object, motor performance improves when two people operate together while sensing each other's movements. Building on this, we developed a controller allowing multiple players to operate simultaneously while sharing haptic sensations. We showcased our system at exhibitions, gathering feedback from over 150 participants on how shared sensory input affects their gaming experience. This approach could transform player interaction, enhance cooperation, and redefine multiplayer gaming experiences.
Measurement and Interpolation for Data-Driven Pressure Distribution Rendering on a Finger Pad
Venue: arXiv cs.HC 2024
Year: 2024

We propose a data-driven pressure distribution rendering method that uses the interpolation of experimentally obtained pressure values. The pressure data were collected using a pressure sensor array. The prediction was performed using linear interpolation, assuming that the pressure distribution is dependent on pushing displacement and contact angle. Leap Motion Controller was used to implement the prediction based on user input. The proposed prediction model was found to be fast and reproduce the measured data well.
Explaining Mixtures of Sources in News Articles
Venue: arXiv cs.CL 2024
Year: 2024

Human writers plan, then write. For large language models (LLMs) to play a role in longer-form article generation, we must understand the planning steps humans make before writing. We explore one kind of planning, source-selection in news, as a case-study for evaluating plans in long-form generation. We ask: why do specific stories call for specific kinds of sources? We imagine a generative process for story writing where a source-selection schema is first selected by a journalist, and then sources are chosen based on categories in that schema. Learning the article's plan means predicting the schema initially chosen by the journalist. Working with professional journalists, we adapt five existing schemata and introduce three new ones to describe journalistic plans for the inclusion of sources in documents. Then, inspired by Bayesian latent-variable modeling, we develop metrics to select the most likely plan, or schema, underlying a story, which we use to compare schemata. We find that two schemata: stance and social affiliation best explain source plans in most documents. However, other schemata like textual entailment explain source plans in factually rich topics like "Science". Finally, we find we can predict the most suitable schema given just the article's headline with reasonable accuracy. We see this as an important case-study for human planning, and provides a framework and approach for evaluating other kinds of plans. We release a corpora, NewsSources, with annotations for 4M articles.
ARLang: An Outdoor Augmented Reality Application for Portuguese Vocabulary Learning
Venue: arXiv cs.HC 2024
Year: 2024

With recent computer vision techniques and user-generated content, we can augment the physical world with metadata that describes attributes, such as names, geo-locations, and visual features of physical objects. To assess the benefits of these potentially ubiquitous labels for foreign vocabulary learning, we built a proof-of-concept system that displays bilingual text and sound labels on physical objects outdoors using augmented reality. Established tools for language learning have focused on effective content delivery methods such as books and flashcards. However, recent research and consumer learning tools have begun to focus on how learning can become more mobile, ubiquitous, and desirable. To test whether our system supports vocabulary learning, we conducted a preliminary between-subjects (N=44) study. Our results indicate that participants preferred learning with virtual labels on real-world objects outdoors over learning with flashcards. Our findings motivate further investigation into mobile AR-based learning systems in outdoor settings.
Abstract2Appendix: Academic Reviews Enhance LLM Long-Context Capabilities
Venue: arXiv cs.CL 2024
Year: 2024

Large language models (LLMs) have shown remarkable performance across various tasks, yet their ability to handle long-context reading remains challenging. This study explores the effectiveness of leveraging high-quality academic peer review data for fine-tuning LLMs to enhance their long-context capabilities. We compare the Direct Preference Optimization (DPO) method with the Supervised Fine-Tuning (SFT) method, demonstrating DPO's superiority and data efficiency. Our experiments show that the fine-tuned model achieves a 4.04-point improvement over phi-3 and a 2.6\% increase on the Qasper benchmark using only 2000 samples. Despite facing limitations in data scale and processing costs, this study underscores the potential of DPO and high-quality data in advancing LLM performance. Additionally, the zero-shot benchmark results indicate that aggregated high-quality human reviews are overwhelmingly preferred over LLM-generated responses, even for the most capable models like GPT-4o. This suggests that high-quality human reviews are extremely rich in information, reasoning, and long-context retrieval, capabilities that even the most advanced models have not fully captured. These findings highlight the high utility of leveraging human reviews to further advance the field.
On Training of Kolmogorov-Arnold Networks
Venue: arXiv cs.LG 2024
Year: 2024

Kolmogorov-Arnold Networks have recently been introduced as a flexible alternative to multi-layer Perceptron architectures. In this paper, we examine the training dynamics of different KAN architectures and compare them with corresponding MLP formulations. We train with a variety of different initialization schemes, optimizers, and learning rates, as well as utilize back propagation free approaches like the HSIC Bottleneck. We find that (when judged by test accuracy) KANs are an effective alternative to MLP architectures on high-dimensional datasets and have somewhat better parameter efficiency, but suffer from more unstable training dynamics. Finally, we provide recommendations for improving training stability of larger KAN models.
SASWISE-UE: Segmentation and Synthesis with Interpretable Scalable Ensembles for Uncertainty Estimation
Venue: arXiv cs.LG 2024
Year: 2024

This paper introduces an efficient sub-model ensemble framework aimed at enhancing the interpretability of medical deep learning models, thus increasing their clinical applicability. By generating uncertainty maps, this framework enables end-users to evaluate the reliability of model outputs. We developed a strategy to develop diverse models from a single well-trained checkpoint, facilitating the training of a model family. This involves producing multiple outputs from a single input, fusing them into a final output, and estimating uncertainty based on output disagreements. Implemented using U-Net and UNETR models for segmentation and synthesis tasks, this approach was tested on CT body segmentation and MR-CT synthesis datasets. It achieved a mean Dice coefficient of 0.814 in segmentation and a Mean Absolute Error of 88.17 HU in synthesis, improved from 89.43 HU by pruning. Additionally, the framework was evaluated under corruption and undersampling, maintaining correlation between uncertainty and error, which highlights its robustness. These results suggest that the proposed approach not only maintains the performance of well-trained models but also enhances interpretability through effective uncertainty estimation, applicable to both convolutional and transformer models in a range of imaging tasks.
Improving Multi-Domain Task-Oriented Dialogue System with Offline Reinforcement Learning
Venue: arXiv cs.CL 2024
Year: 2024

Task-oriented dialogue (TOD) system is designed to accomplish user-defined tasks through dialogues. The TOD system has progressed towards end-to-end modeling by leveraging pre-trained large language models. Fine-tuning the pre-trained language models using only supervised learning leads to the exposure bias and token loss problem and it deviates the models from completing the user's task. To address these issues, we propose a TOD system that leverages a unified pre-trained language model, GPT2, as a base model. It is optimized using supervised learning and reinforcement learning (RL). The issues in the TOD system are mitigated using a non-differentiable reward function. The reward is calculated using the weighted sum of the success rate and BLEU evaluation metrics. The success rate and BLEU metrics in reward calculation guide the language model for user task completion while ensuring a coherent and fluent response. Our model is acquired by fine-tuning a pre-trained model on the dialogue-session level which comprises user utterance, belief state, system act, and system response. Experimental results on MultiWOZ2.1 demonstrate that our model increases the inform rate by 1.60% and the success rate by 3.17% compared to the baseline.
RED: Residual Estimation Diffusion for Low-Dose PET Sinogram Reconstruction
Venue: arXiv cs.LG 2024
Year: 2024

Recent advances in diffusion models have demonstrated exceptional performance in generative tasks across vari-ous fields. In positron emission tomography (PET), the reduction in tracer dose leads to information loss in sino-grams. Using diffusion models to reconstruct missing in-formation can improve imaging quality. Traditional diffu-sion models effectively use Gaussian noise for image re-constructions. However, in low-dose PET reconstruction, Gaussian noise can worsen the already sparse data by introducing artifacts and inconsistencies. To address this issue, we propose a diffusion model named residual esti-mation diffusion (RED). From the perspective of diffusion mechanism, RED uses the residual between sinograms to replace Gaussian noise in diffusion process, respectively sets the low-dose and full-dose sinograms as the starting point and endpoint of reconstruction. This mechanism helps preserve the original information in the low-dose sinogram, thereby enhancing reconstruction reliability. From the perspective of data consistency, RED introduces a drift correction strategy to reduce accumulated prediction errors during the reverse process. Calibrating the inter-mediate results of reverse iterations helps maintain the data consistency and enhances the stability of reconstruc-tion process. Experimental results show that RED effec-tively improves the quality of low-dose sinograms as well as the reconstruction results. The code is available at: https://github.com/yqx7150/RED.