Three-dimensional Morphological Reconstruction of Millimeter-Scale Soft Continuum Robots based on Dual-Stereo-Vision

Continuum robots can be miniaturized to just a few millimeters in diameter. Among these, notched tubular continuum robots (NTCR) show great potential in many delicate applications. Existing works in robotic modeling focus on kinematics and dynamics but still face challenges in reproducing the robot's morphology -- a significant factor that can expand the research landscape of continuum robots, especially for those with asymmetric continuum structures. This paper proposes a dual stereo vision-based method for the three-dimensional morphological reconstruction of millimeter-scale NTCRs. The method employs two oppositely located stationary binocular cameras to capture the point cloud of the NTCR, then utilizes predefined geometry as a reference for the KD tree method to relocate the capture point clouds, resulting in a morphologically correct NTCR despite the low-quality raw point cloud collection. The method has been proved feasible for an NTCR with a 3.5 mm diameter, capturing 14 out of 16 notch features, with the measurements generally centered around the standard of 1.5 mm, demonstrating the capability of revealing morphological details. Our proposed method paves the way for 3D morphological reconstruction of millimeter-scale soft robots for further self-modeling study.

Fine-Tuning Gemma-7B for Enhanced Sentiment Analysis of Financial News Headlines

In this study, we explore the application of sentiment analysis on financial news headlines to understand investor sentiment. By leveraging Natural Language Processing (NLP) and Large Language Models (LLM), we analyze sentiment from the perspective of retail investors. The FinancialPhraseBank dataset, which contains categorized sentiments of financial news headlines, serves as the basis for our analysis. We fine-tuned several models, including distilbert-base-uncased, Llama, and gemma-7b, to evaluate their effectiveness in sentiment classification. Our experiments demonstrate that the fine-tuned gemma-7b model outperforms others, achieving the highest precision, recall, and F1 score. Specifically, the gemma-7b model showed significant improvements in accuracy after fine-tuning, indicating its robustness in capturing the nuances of financial sentiment. This model can be instrumental in providing market insights, risk management, and aiding investment decisions by accurately predicting the sentiment of financial news. The results highlight the potential of advanced LLMs in transforming how we analyze and interpret financial information, offering a powerful tool for stakeholders in the financial industry.

Forgetting Fast in Recommender Systems

Users of a recommender system may want part of their data being deleted, not only from the data repository but also from the underlying machine learning model, for privacy or utility reasons. Such right-to-be-forgotten requests could be fulfilled by simply retraining the recommendation model from scratch, but that would be too slow and too expensive in practice. In this paper, we investigate fast machine unlearning techniques for recommender systems that can remove the effect of a small amount of training data from the recommendation model without incurring the full cost of retraining. A natural idea to speed this process up is to fine-tune the current recommendation model on the remaining training data instead of starting from a random initialization. This warm-start strategy indeed works for neural recommendation models using standard 1st-order neural network optimizers (like AdamW). However, we have found that even greater acceleration could be achieved by employing 2nd-order (Newton or quasi-Newton) optimization methods instead. To overcome the prohibitively high computational cost of 2nd-order optimizers, we propose a new recommendation unlearning approach AltEraser which divides the optimization problem of unlearning into many small tractable sub-problems. Extensive experiments on three real-world recommendation datasets show promising results of AltEraser in terms of consistency (forgetting thoroughness), accuracy (recommendation effectiveness), and efficiency (unlearning speed). To our knowledge, this work represents the first attempt at fast approximate machine unlearning for state-of-the-art neural recommendation models.

Obtaining Dyadic Fairness by Optimal Transport

Fairness has been taken as a critical metric on machine learning models. Many works studying how to obtain fairness for different tasks emerge. This paper considers obtaining fairness for link prediction tasks, which can be measured by dyadic fairness. We aim to propose a pre-processing methodology to obtain dyadic fairness through data repairing and optimal transport. To obtain dyadic fairness with satisfying flexibility and unambiguity requirements, we transform the dyadic repairing to the conditional distribution alignment problem based on optimal transport and obtain theoretical results on the connection between the proposed alignment and dyadic fairness. The optimal transport-based dyadic fairness algorithm is proposed for graph link prediction. Our proposed algorithm shows superior results on obtaining fairness compared with the other pre-processing methods on two benchmark graph datasets.