LLMs have demonstrated proficiency in contextualizing their outputs using
human input, often matching or beating human-level performance on a variety of
tasks. However, LLMs have not yet been used to characterize synergistic
learning in students' collaborative discourse. In this exploratory work, we
take a first step towards adopting a human-in-the-loop prompt engineering
approach with GPT-4-Turbo to summarize and categorize students' synergistic
learning during collaborative discourse. Our preliminary findings suggest
GPT-4-Turbo may be able to characterize students' synergistic learning in a
manner comparable to humans and that our approach warrants further
investigation.

采用人在循环提示工程方法与 GPT-4-Turbo 进行学生之间的协同话语的总结和分类的初步结果表明 GPT-4-Turbo 可能能够以与人类相媲美的方式表征学生的协同学习，并且我们的方法值得进一步研究。

走向人机协作讨论分析的人在循环中语言模型方法

Towards A Human-in-the-Loop LLM Approach to Collaborative Discourse  Analysis

Learning to restore multiple image degradations within a single model is
quite beneficial for real-world applications. Nevertheless, existing works
typically concentrate on regarding each degradation independently, while their
relationship has been less exploited to ensure the synergistic learning. To
this end, we revisit the diverse degradations through the lens of singular
value decomposition, with the observation that the decomposed singular vectors
and singular values naturally undertake the different types of degradation
information, dividing various restoration tasks into two groups,\ie, singular
vector dominated and singular value dominated. The above analysis renders a
more unified perspective to ascribe the diverse degradations, compared to
previous task-level independent learning. The dedicated optimization of
degraded singular vectors and singular values inherently utilizes the potential
relationship among diverse restoration tasks, attributing to the Decomposition
Ascribed Synergistic Learning (DASL). Specifically, DASL comprises two
effective operators, namely, Singular VEctor Operator (SVEO) and Singular VAlue
Operator (SVAO), to favor the decomposed optimization, which can be lightly
integrated into existing convolutional image restoration backbone. Moreover,
the congruous decomposition loss has been devised for auxiliary. Extensive
experiments on blended five image restoration tasks demonstrate the
effectiveness of our method, including image deraining, image dehazing, image
denoising, image deblurring, and low-light image enhancement.

学习如何在单个模型中恢复多个图像退化是实际应用中非常有益的；因此，本文通过奇异值分解重新审视多样的图像退化，并提出了一种基于奇异向量和奇异值优化的协同学习方法 —— 分解归因协同学习（DASL），以实现各种图像恢复任务之间的潜在关系的利用。通过将 DASL 轻松整合到现有的卷积图像恢复骨干中，我们的方法在融合了五个图像恢复任务的广泛实验中表现出了有效性。

统一图像修复的分解归因协同学习

Decomposition Ascribed Synergistic Learning for Unified Image  Restoration

Spiking neural networks (SNNs) have demonstrated excellent capabilities in
various intelligent scenarios. Most existing methods for training SNNs are
based on the concept of synaptic plasticity; however, learning in the realistic
brain also utilizes intrinsic non-synaptic mechanisms of neurons. The spike
threshold of biological neurons is a critical intrinsic neuronal feature that
exhibits rich dynamics on a millisecond timescale and has been proposed as an
underlying mechanism that facilitates neural information processing. In this
study, we develop a novel synergistic learning approach that simultaneously
trains synaptic weights and spike thresholds in SNNs. SNNs trained with
synapse-threshold synergistic learning (STL-SNNs) achieve significantly higher
accuracies on various static and neuromorphic datasets than SNNs trained with
two single-learning models of the synaptic learning (SL) and the threshold
learning (TL). During training, the synergistic learning approach optimizes
neural thresholds, providing the network with stable signal transmission via
appropriate firing rates. Further analysis indicates that STL-SNNs are robust
to noisy data and exhibit low energy consumption for deep network structures.
Additionally, the performance of STL-SNN can be further improved by introducing
a generalized joint decision framework (JDF). Overall, our findings indicate
that biologically plausible synergies between synaptic and intrinsic
non-synaptic mechanisms may provide a promising approach for developing highly
efficient SNN learning methods.

本文发展了一种新的 SNN 训练方法，即突触阈值协同学习 (Synapse-Threshold Synergistic Learning)，该方法同时训练 SNN 中的突触权重和阈值。研究结果表明，与仅使用突触学习或阈值学习的 SNN 相比，使用本文方法训练的 SNN 在各种静态和神经形态数据集上都具有明显更高的准确性，且可通过引入广义联合决策框架进一步提高性能。