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。

对于新参与者 - 执行摘要：（1）任务是为语音数据开发语音匿名系统，该系统隐藏了说话者的语音身份，同时保护语言内容，副语言属性，清晰度和自然性。 （2）除3种不同的基线匿名系统，评估脚本和指标外，还提供了培训，开发和评估数据集。参与者应用其开发的匿名系统，运行评估脚本并向组织者提交客观评估结果和匿名语音数据。 （3）结果将在与Interspeech 2022结合的研讨会上展示，邀请所有参与者介绍其挑战系统并提交其他研讨会论文。对于熟悉语音挑战的读者 - 更改W.R.T. 2020年：（1）以自动扬声器验证（ASV）系统的形式进行了更强的半信息攻击模型，该系统接受了匿名（每位）语音数据的训练。 （2）互补指标包括等于误差率（EER）作为隐私指标，单词错误率（WER）作为主要实用性度量，以及音调相关性和声音独特性作为辅助效用度量标准。 （3）基于一组最小目标隐私要求的新排名策略。

本文介绍了第一个致力于2020挑战的结果和分析，重点是开发语音技术的匿名解决方案。我们提供了对提交的系统和评估结果的分析，提供了挑战设计的系统概述。特别是，我们描述了用于系统开发和评估的语音匿名任务和数据集。此外，我们呈现不同的攻击模型和相关目标和主观评估指标。我们介绍了两个匿名化的基线，并提供了由挑战参与者开发的匿名化系统的摘要描述。我们向基线和提交的系统报告客观和主观评估结果。此外，我们提出了作为评估后分析的一部分开发的替代隐私度量和攻击模型的实验结果。最后，我们总结了我们的见解和观察，这将影响下一个语音普遍挑战版的设计和未来语音匿名化研究的某些方向。

自2020年初以来，COVID-19的大流行对日常生活的许多方面产生了相当大的影响。在全球范围内已经采取了一系列不同的措施，以降低新感染的速度并管理国家卫生服务的压力。主要策略是通过优先考虑远程工作和教育来减少聚会和传播的潜力。当不可避免的聚会时，增强的手卫生和面膜的使用减少了病原体的扩散。这些特殊的措施提出了可靠的生物识别识别的挑战，例如用于面部，语音和手工生物识别技术。同时，新的挑战创造了新的机会和研究方向，例如对无约束的虹膜或眼周识别，基于无触摸的指纹和基于静脉的身份验证以及生物特征特征进行疾病检测的重新兴趣。本文概述了为解决这些挑战和新兴机会而进行的研究。

匿名化具有操纵语音信号的目标，以便降解扬声器识别的自动方法的可靠性，同时保留语音的其他方面，例如与可懂度和自然有关的那些。本文报告了一种对匿名化的方法，与其他电流方法不同，不需要培训数据，是基于众所周知的信号处理技术，并且既有效又有效。所提出的解决方案使用MCADAMS系数来转换语音信号的光谱包络。使用常见的ove voiceprivacy的结果2020数据库和协议显示随机，优化的转换可以在匿名方面优于竞争解决方案，同时只导致适度，额外的劣化，即使在半通知隐私对手的情况下也是如此。

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.

The highest grossing media franchise of all times, with over \$90 billion in total revenue, is Pokemon. The video games belong to the class of Japanese Role Playing Games (J-RPG). Developing a powerful AI agent for these games is very hard because they present big challenges to MinMax, Monte Carlo Tree Search and statistical Machine Learning, as they are vastly different from the well explored in AI literature games. An AI agent for one of these games means significant progress in AI agents for the entire class. Further, the key principles of such work can hopefully inspire approaches to several domains that require excellent teamwork under conditions of extreme uncertainty, including managing a team of doctors, robots or employees in an ever changing environment, like a pandemic stricken region or a war-zone. In this paper we first explain the mechanics of the game and we perform a game analysis. We continue by proposing unique AI algorithms based on our understanding that the two biggest challenges in the game are keeping a balanced team and dealing with three sources of uncertainty. Later on, we describe why evaluating the performance of such agents is challenging and we present the results of our approach. Our AI agent performed significantly better than all previous attempts and peaked at the 33rd place in the world, in one of the most popular battle formats, while running on only 4 single socket servers.

Unlike tabular data, features in network data are interconnected within a domain-specific graph. Examples of this setting include gene expression overlaid on a protein interaction network (PPI) and user opinions in a social network. Network data is typically high-dimensional (large number of nodes) and often contains outlier snapshot instances and noise. In addition, it is often non-trivial and time-consuming to annotate instances with global labels (e.g., disease or normal). How can we jointly select discriminative subnetworks and representative instances for network data without supervision? We address these challenges within an unsupervised framework for joint subnetwork and instance selection in network data, called UISS, via a convex self-representation objective. Given an unlabeled network dataset, UISS identifies representative instances while ignoring outliers. It outperforms state-of-the-art baselines on both discriminative subnetwork selection and representative instance selection, achieving up to 10% accuracy improvement on all real-world data sets we use for evaluation. When employed for exploratory analysis in RNA-seq network samples from multiple studies it produces interpretable and informative summaries.