During the deployment of deep neural networks (DNNs) on edge devices, many research efforts are devoted to the limited hardware resource. However, little attention is paid to the influence of dynamic power management. As edge devices typically only have a budget of energy with batteries (rather than almost unlimited energy support on servers or workstations), their dynamic power management often changes the execution frequency as in the widely-used dynamic voltage and frequency scaling (DVFS) technique. This leads to highly unstable inference speed performance, especially for computation-intensive DNN models, which can harm user experience and waste hardware resources. We firstly identify this problem and then propose All-in-One, a highly representative pruning framework to work with dynamic power management using DVFS. The framework can use only one set of model weights and soft masks (together with other auxiliary parameters of negligible storage) to represent multiple models of various pruning ratios. By re-configuring the model to the corresponding pruning ratio for a specific execution frequency (and voltage), we are able to achieve stable inference speed, i.e., keeping the difference in speed performance under various execution frequencies as small as possible. Our experiments demonstrate that our method not only achieves high accuracy for multiple models of different pruning ratios, but also reduces their variance of inference latency for various frequencies, with minimal memory consumption of only one model and one soft mask.

Objective: We aim to develop an open-source natural language processing (NLP) package, SODA (i.e., SOcial DeterminAnts), with pre-trained transformer models to extract social determinants of health (SDoH) for cancer patients, examine the generalizability of SODA to a new disease domain (i.e., opioid use), and evaluate the extraction rate of SDoH using cancer populations. Methods: We identified SDoH categories and attributes and developed an SDoH corpus using clinical notes from a general cancer cohort. We compared four transformer-based NLP models to extract SDoH, examined the generalizability of NLP models to a cohort of patients prescribed with opioids, and explored customization strategies to improve performance. We applied the best NLP model to extract 19 categories of SDoH from the breast (n=7,971), lung (n=11,804), and colorectal cancer (n=6,240) cohorts. Results and Conclusion: We developed a corpus of 629 cancer patients notes with annotations of 13,193 SDoH concepts/attributes from 19 categories of SDoH. The Bidirectional Encoder Representations from Transformers (BERT) model achieved the best strict/lenient F1 scores of 0.9216 and 0.9441 for SDoH concept extraction, 0.9617 and 0.9626 for linking attributes to SDoH concepts. Fine-tuning the NLP models using new annotations from opioid use patients improved the strict/lenient F1 scores from 0.8172/0.8502 to 0.8312/0.8679. The extraction rates among 19 categories of SDoH varied greatly, where 10 SDoH could be extracted from >70% of cancer patients, but 9 SDoH had a low extraction rate (<70% of cancer patients). The SODA package with pre-trained transformer models is publicly available at https://github.com/uf-hobiinformatics-lab/SDoH_SODA.

Online forms are widely used to collect data from human and have a multi-billion market. Many software products provide online services for creating semi-structured forms where questions and descriptions are organized by pre-defined structures. However, the design and creation process of forms is still tedious and requires expert knowledge. To assist form designers, in this work we present FormLM to model online forms (by enhancing pre-trained language model with form structural information) and recommend form creation ideas (including question / options recommendations and block type suggestion). For model training and evaluation, we collect the first public online form dataset with 62K online forms. Experiment results show that FormLM significantly outperforms general-purpose language models on all tasks, with an improvement by 4.71 on Question Recommendation and 10.6 on Block Type Suggestion in terms of ROUGE-1 and Macro-F1, respectively.

This study investigates clustered federated learning (FL), one of the formulations of FL with non-i.i.d. data, where the devices are partitioned into clusters and each cluster optimally fits its data with a localized model. We propose a novel clustered FL framework, which applies a nonconvex penalty to pairwise differences of parameters. This framework can automatically identify clusters without a priori knowledge of the number of clusters and the set of devices in each cluster. To implement the proposed framework, we develop a novel clustered FL method called FPFC. Advancing from the standard ADMM, our method is implemented in parallel, updates only a subset of devices at each communication round, and allows each participating device to perform a variable amount of work. This greatly reduces the communication cost while simultaneously preserving privacy, making it practical for FL. We also propose a new warmup strategy for hyperparameter tuning under FL settings and consider the asynchronous variant of FPFC (asyncFPFC). Theoretically, we provide convergence guarantees of FPFC for general nonconvex losses and establish the statistical convergence rate under a linear model with squared loss. Our extensive experiments demonstrate the advantages of FPFC over existing methods.

数据爆炸和模型尺寸的增加推动了大规模机器学习的显着进步，但也使模型训练时间耗时和模型存储变得困难。为了解决具有较高计算效率和设备限制的分布式模型培训设置中的上述问题，仍然存在两个主要困难。一方面，交换信息的沟通成本，例如，不同工人之间的随机梯度是分布式培训效率的关键瓶颈。另一方面，较少的参数模型容易用于存储和通信，但是损坏模型性能的风险。为了同时平衡通信成本，模型容量和模型性能，我们提出了量化的复合镜下降自适应亚基（QCMD Adagrad），并量化正规化双平均平均自适应亚级别（QRDA ADAGRAD）进行分布式培训。具体来说，我们探讨了梯度量化和稀疏模型的组合，以降低分布式培训中每次迭代的通信成本。构建了基于量化梯度的自适应学习率矩阵，以在沟通成本，准确性和模型稀疏性之间达到平衡。此外，从理论上讲，我们发现大量化误差会引起额外的噪声，从而影响模型的收敛性和稀疏性。因此，在QCMD Adagrad和QRDA Adagrad中采用了具有相对较小误差的阈值量化策略，以提高信噪比并保留模型的稀疏性。理论分析和经验结果都证明了所提出的算法的功效和效率。

基于深度学习的超分辨率（SR）近年来由于其高图像质量性能和广泛的应用方案而获得了极大的知名度。但是，先前的方法通常会遭受大量计算和巨大的功耗，这会导致实时推断的困难，尤其是在资源有限的平台（例如移动设备）上。为了减轻这种情况，我们建议使用自适应SR块进行深度搜索和每层宽度搜索，以进行深度搜索和每层宽度搜索。推理速度与SR损失一起直接将其带入具有高图像质量的SR模型，同​​时满足实时推理需求。借用了与编译器优化的速度模型在搜索过程中每次迭代中的移动设备上的速度，以预测具有各种宽度配置的SR块的推理潜伏期，以更快地收敛。通过提出的框架，我们在移动平台的GPU/DSP上实现了实时SR推断，以实现具有竞争性SR性能的720p分辨率（三星Galaxy S21）。

面部影响分析仍然是一项艰巨的任务，其设置从实验室控制到野外情况。在本文中，我们提出了新的框架，以应对第四次情感行为分析（ABAW）竞争的两个挑战：i）多任务学习（MTL）挑战和II）从合成数据（LSD）中学习挑战。对于MTL挑战，我们采用SMM-EmotionNet具有更好的特征向量策略。对于LSD挑战，我们建议采用各自的方法来应对单个标签，不平衡分布，微调限制和模型体系结构的选择。竞争的官方验证集的实验结果表明，我们提出的方法的表现优于基线。该代码可在https://github.com/sylyoung/abaw4-hust-ant上找到。

本文介绍了Pytsk，这是一种用于开发Takagi-Sugeno-Kang（TSK）模糊系统的Python工具箱。基于Scikit-Learn和Pytorch，PYTSK允许用户使用基于模糊的聚类或基于迷你批处理梯度下降（MBGD）算法优化TSK模糊系统。工具箱中实现了几种基于MBGD的最先进的优化算法，这可以改善TSK模糊系统的概括性能，尤其是对于大数据应用程序。PYTSK也可以轻松扩展和定制，以用于更复杂的算法，例如修改TSK模糊系统的结构，开发更复杂的训练算法，并将TSK模糊系统与神经网络相结合。可以在https://github.com/yuqicui/pytsk上找到PYTSK的代码。

尽管神经形态计算的快速进展，但尖刺神经网络（SNNS）的能力不足和不足的表现力严重限制了其在实践中的应用范围。剩余学习和捷径被证明是培训深层神经网络的重要方法，但以前的工作评估了他们对基于尖峰的通信和时空动力学的特征的适用性。在本文中，我们首先确定这种疏忽导致受阻信息流程和伴随以前的残留SNN中的降解问题。然后，我们提出了一种新型的SNN定向的残余块MS-Reset，能够显着地扩展直接训练的SNN的深度，例如，在ImageNet上最多可在CiFar-10和104层上完成482层，而不会观察到任何轻微的降级问题。我们验证了基于帧和神经形态数据集的MS-Reset的有效性，并且MS-Resnet104在直接训练的SNN的域中的第一次实现了在ImageNet上的76.02％精度的优越结果。还观察到巨大的能量效率，平均仅需要每根神经元的一穗来分类输入样本。我们相信我们强大且可扩展的型号将为进一步探索SNN提供强大的支持。

汽车之后（CF）建模，模拟人类CF行为的重要组成部分，在过去几十年中吸引了越来越多的研究兴趣。本文通过提出一种新型生成混合CF模型推动了现有技术，这在表征动态人类CF行为方面实现了高精度，并且能够为任何特定的人观察甚至不观察到的驾驶风格产生现实的人类CF行为。具体地，通过使用时变参数设计和校准智能驱动程序模型（IDM）来确保精确捕获人CF行为的能力。后面的原因是这种时变参数可以表达驱动器间异质性，即不同驱动器的不同驱动方式，以及驱动器内异质性，即改变同一驱动器的驱动样式。通过应用基于神经过程（NP）的模型来实现产生任何给定观察样式的现实人类CF行为的能力。通过探索校准的时变IDM参数与NP中间变量之间的关系来支持推断出不观察到的驱动风格的CF行为的能力。为了展示我们提出的模型的有效性，我们进行了广泛的实验和比较，包括CF模型参数校准，CF行为预测和不同驾驶风格的轨迹模拟。