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.

在本文中，我们介绍了RISP，这是一种减少的指令尖峰处理器。虽然大多数尖峰神经处理器都是基于大脑或大脑的概念，但我们为简化而不是复杂的尖峰处理器提供了案例。因此，它具有离散的集成周期，可配置的泄漏等等。我们介绍了RISP的计算模型，并突出了其简单性的好处。我们展示了它如何帮助开发用于简单计算任务的手部神经网络，并详细介绍如何使用它来简化使用更复杂的机器学习技术构建的神经网络，并演示其与其他尖峰神经过程相似的性能。

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

对于大型小分子的大型库，在考虑一系列疾病模型，测定条件和剂量范围时，详尽的组合化学筛选变得不可行。深度学习模型已实现了硅的最终技术，以预测协同得分。但是，药物组合的数据库对协同剂有偏见，这些结果不一定会概括分布不足。我们采用了使用深度学习模型的顺序模型优化搜索来快速发现与癌细胞系相比的协同药物组合，而与详尽的评估相比，筛查要少得多。在仅3轮ML引导的体外实验（包括校准圆圈）之后，我们发现，对高度协同组合进行了查询的一组药物对。进行了另外两轮ML引导实验，以确保趋势的可重复性。值得注意的是，我们重新发现药物组合后来证实将在临床试验中研究。此外，我们发现仅使用结构信息生成的药物嵌入开始反映作用机理。

虽然已知存在强烈相关的抗病毒发动机的组，但目前有限地了解如何或为什么这些相关性所在的理解。使用代表杀毒扫描数据十年的2500万致毒素报告的语料库，我们挑战普遍的智慧，即这些相关性主要来自“一阶”互动，例如杀毒供应商复制领先供应商标签。我们介绍时间秩-1相似性矩阵分解（R1SM-T），以研究这些相关性的起源，并模拟杀毒发动机之间的共识如何随时间变化。我们揭示了一流的相互作用，并不像以前认为杀毒相关的那么多的行为，并且杀毒发动机之间的关系具有高度挥发性。我们提出了根据我们的研究结果需要未来学习和考虑的项目的建议。

恶意软件家庭分类是具有公共安全的重要问题，并通过专家标签的高成本受到阻碍的重要问题。绝大多数公司使用嘈杂的标签方法，阻碍了结果的定量量化和更深的相互作用。为了提供进一步前进所需的数据，我们创建了恶意软件开源威胁情报族（图案）数据集。 MOTIF包含来自454个家庭的3,095个恶意软件样本，使其成为最大，最多样化的公共恶意软件数据集，迄今为止，比以前的Windows恶意软件语料库大于任何先前的专家标记的语料库，近3倍。 MOTIF还附带了从恶意软件样本到威胁报告的映射，以信誉良好的行业来源发布，这两者都验证了标签，并打开了将不透明的恶意软件样本连接到人类可读描述的新的研究机会。这使得重要的评估通常是不可行的，由于行业的非标准化报告。例如，我们提供用于描述相同恶意软件系列的不同名称的别名，允许我们在从不同源获得名称时，为您的第一次准确性进行基准测试。使用MOTIF数据集获得的评估结果表明现有任务具有重要的改进空间，抗病毒多数投票的准确性仅以62.10％和众所周知的高度精度测量。我们的调查结果表明，由于在所考虑的样品中可能无法清楚的类别，因此，恶意软件家庭分类与大多数ML文献中的研究不同的标记噪声遭受任何类型的标记噪声。

Coronary Computed Tomography Angiography (CCTA) provides information on the presence, extent, and severity of obstructive coronary artery disease. Large-scale clinical studies analyzing CCTA-derived metrics typically require ground-truth validation in the form of high-fidelity 3D intravascular imaging. However, manual rigid alignment of intravascular images to corresponding CCTA images is both time consuming and user-dependent. Moreover, intravascular modalities suffer from several non-rigid motion-induced distortions arising from distortions in the imaging catheter path. To address these issues, we here present a semi-automatic segmentation-based framework for both rigid and non-rigid matching of intravascular images to CCTA images. We formulate the problem in terms of finding the optimal \emph{virtual catheter path} that samples the CCTA data to recapitulate the coronary artery morphology found in the intravascular image. We validate our co-registration framework on a cohort of $n=40$ patients using bifurcation landmarks as ground truth for longitudinal and rotational registration. Our results indicate that our non-rigid registration significantly outperforms other co-registration approaches for luminal bifurcation alignment in both longitudinal (mean mismatch: 3.3 frames) and rotational directions (mean mismatch: 28.6 degrees). By providing a differentiable framework for automatic multi-modal intravascular data fusion, our developed co-registration modules significantly reduces the manual effort required to conduct large-scale multi-modal clinical studies while also providing a solid foundation for the development of machine learning-based co-registration approaches.

Due to the environmental impacts caused by the construction industry, repurposing existing buildings and making them more energy-efficient has become a high-priority issue. However, a legitimate concern of land developers is associated with the buildings' state of conservation. For that reason, infrared thermography has been used as a powerful tool to characterize these buildings' state of conservation by detecting pathologies, such as cracks and humidity. Thermal cameras detect the radiation emitted by any material and translate it into temperature-color-coded images. Abnormal temperature changes may indicate the presence of pathologies, however, reading thermal images might not be quite simple. This research project aims to combine infrared thermography and machine learning (ML) to help stakeholders determine the viability of reusing existing buildings by identifying their pathologies and defects more efficiently and accurately. In this particular phase of this research project, we've used an image classification machine learning model of Convolutional Neural Networks (DCNN) to differentiate three levels of cracks in one particular building. The model's accuracy was compared between the MSX and thermal images acquired from two distinct thermal cameras and fused images (formed through multisource information) to test the influence of the input data and network on the detection results.

Rapid advancements in collection and dissemination of multi-platform molecular and genomics data has resulted in enormous opportunities to aggregate such data in order to understand, prevent, and treat human diseases. While significant improvements have been made in multi-omic data integration methods to discover biological markers and mechanisms underlying both prognosis and treatment, the precise cellular functions governing these complex mechanisms still need detailed and data-driven de-novo evaluations. We propose a framework called Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Genomic Data (fiBAG), that allows simultaneous identification of upstream functional evidence of proteogenomic biomarkers and the incorporation of such knowledge in Bayesian variable selection models to improve signal detection. fiBAG employs a conflation of Gaussian process models to quantify (possibly non-linear) functional evidence via Bayes factors, which are then mapped to a novel calibrated spike-and-slab prior, thus guiding selection and providing functional relevance to the associations with patient outcomes. Using simulations, we illustrate how integrative methods with functional calibration have higher power to detect disease related markers than non-integrative approaches. We demonstrate the profitability of fiBAG via a pan-cancer analysis of 14 cancer types to identify and assess the cellular mechanisms of proteogenomic markers associated with cancer stemness and patient survival.

Recent increases in the computational demands of deep neural networks (DNNs) have sparked interest in efficient deep learning mechanisms, e.g., quantization or pruning. These mechanisms enable the construction of a small, efficient version of commercial-scale models with comparable accuracy, accelerating their deployment to resource-constrained devices. In this paper, we study the security considerations of publishing on-device variants of large-scale models. We first show that an adversary can exploit on-device models to make attacking the large models easier. In evaluations across 19 DNNs, by exploiting the published on-device models as a transfer prior, the adversarial vulnerability of the original commercial-scale models increases by up to 100x. We then show that the vulnerability increases as the similarity between a full-scale and its efficient model increase. Based on the insights, we propose a defense, $similarity$-$unpairing$, that fine-tunes on-device models with the objective of reducing the similarity. We evaluated our defense on all the 19 DNNs and found that it reduces the transferability up to 90% and the number of queries required by a factor of 10-100x. Our results suggest that further research is needed on the security (or even privacy) threats caused by publishing those efficient siblings.