AdaER: An Adaptive Experience Replay Approach for Continual Lifelong Learning

Continual lifelong learning is an machine learning framework inspired by human learning, where learners are trained to continuously acquire new knowledge in a sequential manner. However, the non-stationary nature of streaming training data poses a significant challenge known as catastrophic forgetting, which refers to the rapid forgetting of previously learned knowledge when new tasks are introduced. While some approaches, such as experience replay (ER), have been proposed to mitigate this issue, their performance remains limited, particularly in the class-incremental scenario which is considered natural and highly challenging. In this paper, we present a novel algorithm, called adaptive-experience replay (AdaER), to address the challenge of continual lifelong learning. AdaER consists of two stages: memory replay and memory update. In the memory replay stage, AdaER introduces a contextually-cued memory recall (C-CMR) strategy, which selectively replays memories that are most conflicting with the current input data in terms of both data and task. Additionally, AdaER incorporates an entropy-balanced reservoir sampling (E-BRS) strategy to enhance the performance of the memory buffer by maximizing information entropy. To evaluate the effectiveness of AdaER, we conduct experiments on established supervised continual lifelong learning benchmarks, specifically focusing on class-incremental learning scenarios. The results demonstrate that AdaER outperforms existing continual lifelong learning baselines, highlighting its efficacy in mitigating catastrophic forgetting and improving learning performance.Comment: 18 pages, 26 figure

Information recovery in the Hayden-Preskill protocol

We revisit information retrieval from evaporating black holes in the Hayden-Preskill protocol, treating the black hole dynamics as Haar-random. We compute, down to the first exponentially suppressed terms, all integer-indexed R\'enyi mutual informations between a black hole, its radiation, and a reference that catalogues Alice's diaries. We find that dropping a diary into a young black hole effectively delays the Page time. We also compute the radiation : diary reflected R\'enyi entropies, and identify a technical reason why they cannot be continued to the reflected entropy by the replica trick.Comment: 24 pages plus appendice

Reducing Sensitivity on Speaker Names for Text Generation from Dialogues

Changing speaker names consistently throughout a dialogue should not affect its meaning and corresponding outputs for text generation from dialogues. However, pre-trained language models, serving as the backbone for dialogue-processing tasks, have shown to be sensitive to nuances. This may result in unfairness in real-world applications. No comprehensive analysis of this problem has been done in the past. In this work, we propose to quantitatively measure a model's sensitivity on speaker names, and comprehensively evaluate a number of known methods for reducing speaker name sensitivity, including a novel approach of our own. Extensive experiments on multiple datasets provide a benchmark for this problem and show the favorable performance of our approach in sensitivity reduction and quality of generation.Comment: findings of ACL'2

Knowledge Represent and Reconstruction by “Fundamentals of Materials Science” Classroom Teaching Mode Reform

AbstractClassroom teaching is the main form of teaching organization and activity way, and is also the main base on the classroom teaching mode reform. This article by “Fundamentals of Materials Science” as an example, generalizing the knowledge representation of three types and advantages in the classroom teaching, points out that the teacher's role in this progresss. We analyze that the feasibility and the ideal effect on rebuilding the students of materials science knowledge by the inquiry learning new knowledge, hierarchical practice and the freedom of assignments. The teachers can link of knowledge and new knowledge from participating in the generation of new knowledge; The teachers help students from standing in “the shoulders of giants” and not on “beach” by the careful design “training”; The teachers ensure that all students get interesting on learning “Fundamentals of Materials Science” by flexible free homework

In-sample Curriculum Learning by Sequence Completion for Natural Language Generation

Curriculum learning has shown promising improvements in multiple domains by training machine learning models from easy samples to hard ones. Previous works which either design rules or train models for scoring the difficulty highly rely on task-specific expertise, and cannot generalize. Inspired by the ``easy-to-hard'' intuition, we propose to do in-sample curriculum learning for natural language generation tasks. Our learning strategy starts training the model to generate the last few words, i.e., do sequence completion, and gradually extends to generate the whole output sequence. Comprehensive experiments show that it generalizes well to different tasks and achieves significant improvements over strong baselines

Experimental investigation on plugging performance of nanospheres in low-permeability reservoir with bottom water

The oil production rate decreases rapidly after a short period of high yield from acidizing or fracturing in low-permeability reservoirs. In this paper, nanospheres are applied before the fracturing step, which possess the ability to absorb water and expand in the water layer, reducing the flow capacity of bottom water and finally enhancing the oil recovery. The plugging performance is investigated by nanosphere displacement  experiments in cores and sand-packs, which explores the plugging effect in the oil layer, the oil-water transition zones, the water layer and the fracturing zones. In addition, a nuclear magnetic resonance experiment is conducted to study the flow mechanism of nanospheres and determine the plugging rates, which can characterize the plugging performance of nanospheres in porous media. The results show that the plugging rate is 85.84% and 78.65% on the water layer and oil-water transition zone, respectively, and 94.36% in the fracturing zone. Meanwhile, the nanospheres cannot plug the oil layer. The formation pressure has a less considerable effect on the plugging performance of nanospheres. The nanospheres have good injectivity, and the intensity variations in small, medium and large pores account for 34.46%, 13.22% and 52.32%, respectively. Overall, this paper explores the feasibility of applying nanospheres for water plugging and enhanced oil recovery.Cited as: Tang, M., Wang, C., Deng, X., Yang, H., Lu, J., Yu, H. Experimental investigation on plugging performance of nanospheres in low-permeability reservoir with bottom water. Advances in Geo-Energy Research, 2022, 6(2): 95-103. https://doi.org/10.46690/ager.2022.02.0