This study proposes a system designed to enumerate the process of
collaborative composition among humans, using automatic music composition
technology. By integrating multiple Recurrent Neural Network (RNN) models, the
system provides an experience akin to collaborating with several composers,
thereby fostering diverse creativity. Through dynamic adaptation to the user's
creative intentions, based on feedback, the system enhances its capability to
generate melodies that align with user preferences and creative needs. The
system's effectiveness was evaluated through experiments with composers of
varying backgrounds, revealing its potential to facilitate musical creativity
and suggesting avenues for further refinement. The study underscores the
importance of interaction between the composer and AI, aiming to make music
composition more accessible and personalized. This system represents a step
towards integrating AI into the creative process, offering a new tool for
composition support and collaborative artistic exploration.

通过整合多个 RNN 模型，本研究提出了一个系统，旨在协同人类进行音乐创作，使创作过程更具多样性，通过根据反馈动态调整用户创意意图，系统增强了生成符合用户偏好和创意需求的旋律的能力，通过对具有不同背景的作曲家的实验验证了系统的有效性，进一步完善了音乐创作的可能性，突显了作曲家与人工智能的互动在使音乐创作更加个性化和易于接触方面的重要性，这个系统代表了将人工智能整合到创作过程中的一步，提供了一个新的音乐创作支持和协同艺术探索的工具。

增强音乐创作者创造力的交互式旋律生成系统

Interactive Melody Generation System for Enhancing the Creativity of  Musicians

In the realm of 5G communication systems, the accuracy of Channel State
Information (CSI) prediction is vital for optimizing performance. This letter
introduces a pioneering approach: the Spectral-Temporal Graph Neural Network
(STEM GNN), which fuses spatial relationships and temporal dynamics of the
wireless channel using the Graph Fourier Transform. We compare the STEM GNN
approach with conventional Recurrent Neural Network (RNN) and Long Short-Term
Memory (LSTM) models for CSI prediction. Our findings reveal a significant
enhancement in overall communication system performance through STEM GNNs. For
instance, in one scenario, STEM GNN achieves a sum rate of 5.009 bps/Hz which
is $11.9\%$ higher than that of LSTM and $35\%$ higher than that of RNN. The
spectral-temporal analysis capabilities of STEM GNNs capture intricate patterns
often overlooked by traditional models, offering improvements in beamforming,
interference mitigation, and ultra-reliable low-latency communication (URLLC).

在 5G 通信系统领域，准确预测信道状态信息 (CSI) 对优化性能至关重要。该论文介绍了一种开创性的方法：采用图傅里叶变换，将无线信道的空间关系和时间动态融合到谱时图神经网络 (STEM GNN) 中。我们将 STEM GNN 方法与传统的循环神经网络 (RNN) 和长短期记忆 (LSTM) 模型进行了比较，发现 STEM GNN 显著提高了整体通信系统性能。例如，在一个场景中，STEM GNN 的速率为 5.009 bps/Hz，比 LSTM 高出 11.9%，比 RNN 高出 35%。STEM GNN 的谱时分析能力捕捉到传统模型常常忽视的复杂模式，提供了波束成形、干扰抑制和超可靠低延迟通信 (URLLC) 方面的改进。

大规模 MIMO 信道状态信息预测的频谱时域图神经网络

Spectral Temporal Graph Neural Network for massive MIMO CSI Prediction

We present a differentiable stack data structure that simultaneously and
tractably encodes an exponential number of stack configurations, based on
Lang's algorithm for simulating nondeterministic pushdown automata. We call the
combination of this data structure with a recurrent neural network (RNN)
controller a Nondeterministic Stack RNN. We compare our model against existing
stack RNNs on various formal languages, demonstrating that our model converges
more reliably to algorithmic behavior on deterministic tasks, and achieves
lower cross-entropy on inherently nondeterministic tasks.

本论文提出了一种可微分的栈数据结构，它基于 Lang 算法，能够同时并行地编码指数级数量的栈配置，结合递归神经网络控制器我们称之为非确定性堆栈 RNN。我们将模型与已有的堆栈 RNN 进行比较，并在各种形式化语言上演示了模型可靠地收敛性和准确性。