In this paper, we propose a novel technique, namely INVALIDATOR, to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning. INVALIDATOR reasons about program semantic via program invariants while it also captures program syntax via language semantic learned from large code corpus using the pre-trained language model. Given a buggy program and the developer-patched program, INVALIDATOR infers likely invariants on both programs. Then, INVALIDATOR determines that a APR-generated patch overfits if: (1) it violates correct specifications or (2) maintains errors behaviors of the original buggy program. In case our approach fails to determine an overfitting patch based on invariants, INVALIDATOR utilizes a trained model from labeled patches to assess patch correctness based on program syntax. The benefit of INVALIDATOR is three-fold. First, INVALIDATOR is able to leverage both semantic and syntactic reasoning to enhance its discriminant capability. Second, INVALIDATOR does not require new test cases to be generated but instead only relies on the current test suite and uses invariant inference to generalize the behaviors of a program. Third, INVALIDATOR is fully automated. We have conducted our experiments on a dataset of 885 patches generated on real-world programs in Defects4J. Experiment results show that INVALIDATOR correctly classified 79% overfitting patches, accounting for 23% more overfitting patches being detected by the best baseline. INVALIDATOR also substantially outperforms the best baselines by 14% and 19% in terms of Accuracy and F-Measure, respectively.

在光场压缩中，基于图的编码功能强大，可以利用沿着不规则形状的信号冗余并获得良好的能量压实。然而，除了高度复杂性到处理高维图外，它们的图形构造方法对观点之间的差异信息的准确性非常敏感。在计算机软件生成的现实世界光场或合成光场中，由于渐晕效果和两种类型的光场视图之间的视图之间的巨大差异，将视差信息用于超射线投影可能会遭受不准确性。本文介绍了两种新型投影方案，导致差异信息的错误较小，其中一个投影方案还可以显着降低编码器和解码器的时间计算。实验结果表明，与原始投影方案和基于HEVC或基于JPEG PLENO的编码方法相比，使用这些建议可以大大增强超级像素的投影质量，以及率延伸性能。

我们考虑如何在从流环境中学习贝叶斯模型时有效地使用先验知识，其中数据无限依次出现。这个问题在数据爆炸时代非常重要，富有培训的模型，本体，维基百科等珍贵外部知识的富裕来源非常重要。我们表明一些现有的方法可以忘记任何知识。然后，我们提出了一种新颖的框架，使能够将不同形式的先验知识纳入基础贝叶斯模型的数据流。我们的框架载有一些现有的时序/动态数据的流行模型。广泛的实验表明，我们的框架优于具有大边距的现有方法。特别是，我们的框架可以帮助贝叶斯模型在极短的文本上概括，而其他方法过度装备。我们的框架的实施是在https://github.com/bachtranxuan/tps.git上获得的。

从数据流学习隐藏的主题已经成为绝对必要的，但构成了挑战性问题，如概念漂移以及短而嘈杂的数据。使用先验知识来丰富主题模型是应对这些挑战的潜在解决方案之一。先前知识，其来自人类知识（例如Wordnet）或预先训练的模型（例如Word2Vec）是非常有价值的，并且有助于帮助主题模型更好地工作。然而，在数据到达不断且无限的流动环境中，现有研究仅限于有效利用这些资源。特别是，忽略了包含有意义的词关系的知识图形。在本文中，为了有效利用知识图，我们提出了一种新颖的图形卷积主题模型（GCTM），它将图形卷积网络（GCN）集成到一个主题模型和学习方法，它同时学习网络和主题模型数据流。在每个小纤维中，我们的方法不仅可以利用外部知识图，还可以平衡外部和旧知识，以便在新数据上表现良好。我们进行广泛的实验来评估我们的方法，以评估我们的知识图（WordNet）和由预先接受训练的Word Embeddings（Word2VEC）构建的图形的图表。实验结果表明，在概率预测措施和主题连贯性方面，我们的方法比最先进的基线达到更好的表现。特别是，在处理短文本以及概念漂移时，我们的方法可以很好地工作。 GCTM的实现可在\ URL {https://github.com/bachtranxuan/gctm.git}。

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

本文研究了通过机器学习模型估计特征对特定实例预测的贡献的问题，以及功能对模型的总体贡献。特征（变量）对预测结果的因果效应反映了该特征对预测的贡献。一个挑战是，如果没有已知的因果图，就无法从数据中估算大多数现有的因果效应。在本文中，我们根据假设的理想实验定义了解释性因果效应。该定义给不可知论的解释带来了一些好处。首先，解释是透明的，具有因果关系。其次，解释性因果效应估计可以数据驱动。第三，因果效应既提供了特定预测的局部解释，又提供了一个全局解释，显示了一个特征在预测模型中的总体重要性。我们进一步提出了一种基于解释性因果效应来解释的方法和组合变量的方法。我们显示了对某些现实世界数据集的实验的定义和方法。

基于硬件的加速度是促进许多计算密集型数学操作的广泛尝试。本文提出了一个基于FPGA的体系结构来加速卷积操作 - 在许多卷积神经网络模型中出现的复杂且昂贵的计算步骤。我们将设计定为标准卷积操作，打算以边缘-AI解决方案启动产品。该项目的目的是产生一个可以一次处理卷积层的FPGA IP核心。系统开发人员可以使用Verilog HDL作为体系结构的主要设计语言来部署IP核心。实验结果表明，我们在简单的边缘计算FPGA板上合成的单个计算核心可以提供0.224 GOPS。当董事会充分利用时，可以实现4.48 GOP。

随着近期智能手机或平板电脑的移动设备的爆炸性增长，保证了所有环境的一致网页外观已成为一个重大问题。这只是因为很难跟踪不同大小和渲染网页的设备类型的网络外观。因此，修复网页的不一致外观可能是困难的，并且所产生的成本可能是巨大的，例如，由于它的用户体验和财务损失差。最近，已经提出了自动化的Web修复技术来自动解决不一致的网页外观，专注于提高可用性。然而，生成的补丁倾向于破坏网页的布局，使修复的网页呈现美学令人难以释放，例如扭曲的图像或组件的未对准。在本文中，我们提出了一种基于Meta-heuristic算法的网页自动修复方法，可以保证可用性和美学。赋予我们方法的关键新颖性是一种新颖的健身功能，使我们能够乐观地发展错误的网页，以查找同时优化可用性和美学的最佳解决方案。实证评估表明，我们的方法能够在94％的评估科目中成功解决移动友好问题，在可用性和美学方面显着优于最先进的基线技术。

Modern deep neural networks have achieved superhuman performance in tasks from image classification to game play. Surprisingly, these various complex systems with massive amounts of parameters exhibit the same remarkable structural properties in their last-layer features and classifiers across canonical datasets. This phenomenon is known as "Neural Collapse," and it was discovered empirically by Papyan et al. \cite{Papyan20}. Recent papers have theoretically shown the global solutions to the training network problem under a simplified "unconstrained feature model" exhibiting this phenomenon. We take a step further and prove the Neural Collapse occurrence for deep linear network for the popular mean squared error (MSE) and cross entropy (CE) loss. Furthermore, we extend our research to imbalanced data for MSE loss and present the first geometric analysis for Neural Collapse under this setting.

We present a Machine Learning (ML) study case to illustrate the challenges of clinical translation for a real-time AI-empowered echocardiography system with data of ICU patients in LMICs. Such ML case study includes data preparation, curation and labelling from 2D Ultrasound videos of 31 ICU patients in LMICs and model selection, validation and deployment of three thinner neural networks to classify apical four-chamber view. Results of the ML heuristics showed the promising implementation, validation and application of thinner networks to classify 4CV with limited datasets. We conclude this work mentioning the need for (a) datasets to improve diversity of demographics, diseases, and (b) the need of further investigations of thinner models to be run and implemented in low-cost hardware to be clinically translated in the ICU in LMICs. The code and other resources to reproduce this work are available at https://github.com/vital-ultrasound/ai-assisted-echocardiography-for-low-resource-countries.