目的：基于知识的计划（KBP）通常涉及培训端到端深度学习模型以预测剂量分布。但是，由于经常使用的医疗数据集规模有限，端到端方法可能与实际限制有关。为了解决这些局限性，我们提出了一种基于内容的图像检索（CBIR）方法，用于根据解剖学相似性检索先前计划的患者的剂量分布。方法：我们提出的CBIR方法训练一种代表模型，该模型可产生患者解剖信息的潜在空间嵌入。然后将新患者的潜在空间嵌入与数据库中以前患者的潜在空间嵌入，以检索剂量分布的图像。该项目的所有源代码均可在GitHub上获得。主要结果：在由我们机构的公开计划和临床计划组成的数据集上评估了各种CBIR方法的检索性能。这项研究比较了各种编码方法，从简单的自动编码器到Simsiam等最新的暹罗网络，并且在Multipask Siamese网络中观察到了最佳性能。意义：应用CBIR告知后续的治疗计划可能会解决与端到端KBP相关的许多限制。我们目前的结果表明，可以通过对先前开发的暹罗网络进行轻微更改来获得出色的图像检索性能。我们希望通过Metaplanner框架等方法将CBIR集成到未来工作中的自动化计划工作流程中。

The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.

Predictive simulations of the shock-to-detonation transition (SDT) in heterogeneous energetic materials (EM) are vital to the design and control of their energy release and sensitivity. Due to the complexity of the thermo-mechanics of EM during the SDT, both macro-scale response and sub-grid mesoscale energy localization must be captured accurately. This work proposes an efficient and accurate multiscale framework for SDT simulations of EM. We employ deep learning to model the mesoscale energy localization of shock-initiated EM microstructures upon which prediction results are used to supply reaction progress rate information to the macroscale SDT simulation. The proposed multiscale modeling framework is divided into two stages. First, a physics-aware recurrent convolutional neural network (PARC) is used to model the mesoscale energy localization of shock-initiated heterogeneous EM microstructures. PARC is trained using direct numerical simulations (DNS) of hotspot ignition and growth within microstructures of pressed HMX material subjected to different input shock strengths. After training, PARC is employed to supply hotspot ignition and growth rates for macroscale SDT simulations. We show that PARC can play the role of a surrogate model in a multiscale simulation framework, while drastically reducing the computation cost and providing improved representations of the sub-grid physics. The proposed multiscale modeling approach will provide a new tool for material scientists in designing high-performance and safer energetic materials.

近年来，深度学习（DL）方法的流行程度急剧增加，并且在生物医学科学中的监督学习问题中的应用显着增长。但是，现代生物医学数据集中缺失数据的较高流行率和复杂性对DL方法提出了重大挑战。在这里，我们在深入学习的广义线性模型的背景下，对缺失数据进行了正式处理，这是一种监督的DL架构，用于回归和分类问题。我们提出了一种新的体系结构，即\ textit {dlglm}，这是第一个能够在训练时在输入功能和响应中灵活地说明忽略和不可忽视的缺失模式之一。我们通过统计模拟证明，我们的方法在没有随机（MNAR）缺失的情况下胜过现有的监督学习任务方法。我们从UCI机器学习存储库中对银行营销数据集进行了案例研究，在该数据集中我们预测客户是否基于电话调查数据订阅了产品。

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。

我们介绍了具有磁隧道结（MTJ）突触的神经形态网络的第一个实验证明，其通过矢量矩阵乘法进行图像识别。我们还模拟了执行Mnist手写数字识别的大型MTJ网络，展示MTJ交叉栏可以匹配映射器精度，同时提供更高的精度，稳定性和耐久性。

背景：精确诊断颅底肿瘤对于提供个性化的手术治疗策略至关重要。由于肿瘤多样性和缺乏术中病理资源，术中诊断可能具有挑战性。目的：开发独立且平行的术中病理学工作流程，可以使用无标签的光学成像和人工智能提供快速准确的颅底肿瘤诊断。方法：我们使用了基于光纤激光，无标签，非消费性，高分辨率显微镜方法（$ <$ <$ <$ <$ 60秒，每1 $ \ times $ 1 mm $ $^\ text {2} $），称为刺激的拉曼组织学（SRH），以对颅底肿瘤患者的连续多中心队列进行成像。然后，使用三种表示学习策略：跨渗透性，自我监督的对比度学习和监督对比度学习，使用SRH图像来训练卷积神经网络（CNN）模型。我们训练有素的CNN模型在持有的多中心SRH数据集上进行了测试。结果：SRH能够成像良性和恶性颅底肿瘤的诊断特征。在三种表示策略中，有监督的对比度学习最有效地学习了每种颅底肿瘤类型的独特和诊断SRH图像特征。在我们的多中心测试集中，跨渗透性达到了91.5％的总体诊断准确性，自我监督的对比度学习为83.9％，并且有监督的对比度学习为96.6％。我们训练有素的模型能够鉴定出肿瘤正常的边缘，并检测整个SRH图像中微观肿瘤浸润的区域。结论：具有训练有素的人工智能模型的SRH可以对颅底肿瘤标本进行快速准确的术中分析，以告知手术决策。

近年来，深度学习（DL）方法的流行程度大大增加。尽管在图像数据的分类和操纵中证明了其最初的成功，但DL方法应用于生物医学科学中的问题的应用已显着增长。但是，生物医学数据集中缺失数据的较高流行率和复杂性对DL方法提出了重大挑战。在这里，我们在变化自动编码器（VAE）的背景下提供了对缺失数据的正式处理，这是一种普遍用于缩小尺寸，插补和学习复杂数据的潜在表示的流行无监督的DL体系结构。我们提出了一种新的VAE架构Nimiwae，这是第一个在训练时在输入功能中灵活解释可忽视和不可忽视的缺失模式之一。训练后，可以从缺失数据的后验分布中得出样本，可用于多个插补，从而促进高维不完整数据集的下游分析。我们通过统计模拟证明，我们的方法优于无监督的学习任务和插定精度的现有方法。我们以与12,000名ICU患者有关的EHR数据集的案例研究结束，该数据集具有大量诊断测量和临床结果，其中仅观察到许多特征。

As various city agencies and mobility operators navigate toward innovative mobility solutions, there is a need for strategic flexibility in well-timed investment decisions in the design and timing of mobility service regions, i.e. cast as "real options" (RO). This problem becomes increasingly challenging with multiple interacting RO in such investments. We propose a scalable machine learning based RO framework for multi-period sequential service region design & timing problem for mobility-on-demand services, framed as a Markov decision process with non-stationary stochastic variables. A value function approximation policy from literature uses multi-option least squares Monte Carlo simulation to get a policy value for a set of interdependent investment decisions as deferral options (CR policy). The goal is to determine the optimal selection and timing of a set of zones to include in a service region. However, prior work required explicit enumeration of all possible sequences of investments. To address the combinatorial complexity of such enumeration, we propose a new variant "deep" RO policy using an efficient recurrent neural network (RNN) based ML method (CR-RNN policy) to sample sequences to forego the need for enumeration, making network design & timing policy tractable for large scale implementation. Experiments on multiple service region scenarios in New York City (NYC) shows the proposed policy substantially reduces the overall computational cost (time reduction for RO evaluation of > 90% of total investment sequences is achieved), with zero to near-zero gap compared to the benchmark. A case study of sequential service region design for expansion of MoD services in Brooklyn, NYC show that using the CR-RNN policy to determine optimal RO investment strategy yields a similar performance (0.5% within CR policy value) with significantly reduced computation time (about 5.4 times faster).

The combination of conduct, emotion, motivation, and thinking is referred to as personality. To shortlist candidates more effectively, many organizations rely on personality predictions. The firm can hire or pick the best candidate for the desired job description by grouping applicants based on the necessary personality preferences. A model is created to identify applicants' personality types so that employers may find qualified candidates by examining a person's facial expression, speech intonation, and resume. Additionally, the paper emphasises detecting the changes in employee behaviour. Employee attitudes and behaviour towards each set of questions are being examined and analysed. Here, the K-Modes clustering method is used to predict employee well-being, including job pressure, the working environment, and relationships with peers, utilizing the OCEAN Model and the CNN algorithm in the AVI-AI administrative system. Findings imply that AVIs can be used for efficient candidate screening with an AI decision agent. The study of the specific field is beyond the current explorations and needed to be expanded with deeper models and new configurations that can patch extremely complex operations.