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.

车辆到达时间预测已被广泛研究。随着物联网设备和深度学习技术的出现，估计的到达时间（ETA）已成为智能运输系统中的关键组成部分。尽管ETA存在许多工具，但由于特殊车辆的交通数据有限，ETA的特殊车辆（例如救护车，消防车等）仍然具有挑战性。现有作品使用一种模型用于所有类型的车辆，这可能会导致精确度较低。为了解决这个问题，作为该领域的第一个，我们为驾驶时间预测提出了一个深度转移学习框架TLETA。 TLETA构建了细胞时空知识网格，用于提取驾驶模式，并结合道路网络结构嵌入以构建ETA的深神经网络。 Tleta包含可转移的层，以支持不同类别的车辆之间的知识转移。重要的是，我们的转移模型仅训练最后一层以绘制转移的知识，从而大大减少了训练时间。实验研究表明，我们的模型以高精度预测旅行时间，并胜过许多最先进的方法。

虽然深度神经网络在分类任务方面取得了很大的表现，但最近的研究表明，训练有素的网络可以通过添加微妙的噪音来欺骗。本文介绍了一种新方法，通过将恢复过程应用于自然训练的分类器的顶部来提高神经网络鲁棒性。在这种方法中，图像将被一些重要操作员故意破坏，然后在通过分类器之前恢复。Sargan - 生成对抗网络（GaN）的延伸能够去噪雷达信号。本文将显示Sargan还可以通过去除对抗效应来恢复损坏的图像。我们的结果表明，这种方法确实提高了自然培训的网络的性能。

在过去的几十年中，由于其在广泛的应用中，现场文本认可从学术界和实际用户获得了全世界的关注。尽管在光学字符识别方面取得了成就，但由于诸如扭曲或不规则布局等固有问题，现场文本识别仍然具有挑战性。大多数现有方法主要利用基于复发或卷积的神经网络。然而，虽然经常性的神经网络（RNN）通常由于顺序计算而遭受慢的训练速度，并且遇到消失的梯度或瓶颈，但CNN在复杂性和性能之间衡量折衷。在本文中，我们介绍了SAFL，一种基于自我关注的神经网络模型，具有场景文本识别的焦点损失，克服现有方法的限制。使用焦损而不是负值对数似然有助于模型更多地关注低频样本训练。此外，为应对扭曲和不规则文本，我们在传递到识别网络之前，我们利用空间变换（STN）来纠正文本。我们执行实验以比较拟议模型的性能与七个基准。数值结果表明，我们的模型实现了最佳性能。

寻找合适的工作和狩猎符合条件的候选人对求职和人力资源机构来说很重要。通过关于职位描述的广泛信息，员工和雇主需要帮助，以根据职位描述文本自动检测职位标题。在本文中，我们提出了用于预测作业描述文本的相关职位标题的多标签分类方法，并实现具有不同预先训练的语言模型的BI-GRU-LSTM-CNN来申请作业标题预测问题。具有多语言预先训练模型的伯特获得了开发和测试集的F1分数的最高结果，该组在开发集中为62.20％，测试集47.44％。

高级深度学习（DL）算法可以预测患者基于乳房成像报告和数据系统（BI-RAD）和密度标准的患者发育乳腺癌的风险。最近的研究表明，多视图分析的结合改善了整体乳房考试分类。在本文中，我们提出了一种新的多视图DL方法，用于乳房X线照片的Bi-RAD和密度评估。所提出的方法首先部署深度卷积网络，用于分别对每个视图进行特征提取。然后将提取的特征堆叠并馈入光梯度升压机（LightGBM）分类器中以预测Bi-RAD和密度分数。我们对内部乳房数据集和公共数据集数字数据库进行广泛的实验，用于筛选乳房X线摄影（DDSM）。实验结果表明，所提出的方法在两个基准数据集中突出了巨大的边距（内部数据集5％，DDSM数据集10％）优于两个基准分类方法。这些结果突出了组合多视图信息来改善乳腺癌风险预测性能的重要作用。

在线游戏论坛对大多数游戏玩家都很受欢迎。他们用它来沟通和讨论游戏的策略，甚至结交朋友。然而，游戏论坛还包含滥用和骚扰演讲，令人不安和威胁的球员。因此，有必要自动检测和删除网络欺凌评论，以保持游戏论坛清洁和友好。我们使用从魔兽世界（WOW）和联盟（LOL）论坛（LOL）论坛和火车分类模型中收集的网络欺凌数据集，以自动检测玩家的评论是否是滥用的。结果获得了LOL论坛的82.69％的宏F1分数，并通过网络伯文数据集的毒性BERT模型为哇论坛的83.86％的宏F1分数。

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.

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.