In this paper, we assess the viability of transformer models in end-to-end InfoSec settings, in which no intermediate feature representations or processing steps occur outside the model. We implement transformer models for two distinct InfoSec data formats - specifically URLs and PE files - in a novel end-to-end approach, and explore a variety of architectural designs, training regimes, and experimental settings to determine the ingredients necessary for performant detection models. We show that in contrast to conventional transformers trained on more standard NLP-related tasks, our URL transformer model requires a different training approach to reach high performance levels. Specifically, we show that 1) pre-training on a massive corpus of unlabeled URL data for an auto-regressive task does not readily transfer to binary classification of malicious or benign URLs, but 2) that using an auxiliary auto-regressive loss improves performance when training from scratch. We introduce a method for mixed objective optimization, which dynamically balances contributions from both loss terms so that neither one of them dominates. We show that this method yields quantitative evaluation metrics comparable to that of several top-performing benchmark classifiers. Unlike URLs, binary executables contain longer and more distributed sequences of information-rich bytes. To accommodate such lengthy byte sequences, we introduce additional context length into the transformer by providing its self-attention layers with an adaptive span similar to Sukhbaatar et al. We demonstrate that this approach performs comparably to well-established malware detection models on benchmark PE file datasets, but also point out the need for further exploration into model improvements in scalability and compute efficiency.

In this paper, we explore the use of metric learning to embed Windows PE files in a low-dimensional vector space for downstream use in a variety of applications, including malware detection, family classification, and malware attribute tagging. Specifically, we enrich labeling on malicious and benign PE files using computationally expensive, disassembly-based malicious capabilities. Using these capabilities, we derive several different types of metric embeddings utilizing an embedding neural network trained via contrastive loss, Spearman rank correlation, and combinations thereof. We then examine performance on a variety of transfer tasks performed on the EMBER and SOREL datasets, demonstrating that for several tasks, low-dimensional, computationally efficient metric embeddings maintain performance with little decay, which offers the potential to quickly retrain for a variety of transfer tasks at significantly reduced storage overhead. We conclude with an examination of practical considerations for the use of our proposed embedding approach, such as robustness to adversarial evasion and introduction of task-specific auxiliary objectives to improve performance on mission critical tasks.

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.

动态磁共振成像（MRI）是一种流行的医学成像技术，可生成组织和器官内部对比度材料流动的图像序列。但是，仅在少数可行性研究中证明了它在通过食道运动中的成像运动中的应用，并且相对尚未探索。在这项工作中，我们提出了一个称为力学的MRI（MRI-MEC）的计算框架，该计算框架增强了该能力，从而增加了动态MRI在诊断食管疾病中的适用性。菠萝汁用作动态MRI的吞咽对比材料，MRI图像序列被用作MRI-MECH的输入。 MRI-MECH将食道建模为柔性的一维管，弹性管壁遵循线性管定律。然后，通过一维质量和动量保护方程式，通过食道流动。这些方程是使用物理信息的神经网络（PINN）求解的。 PINN最大程度地减少了MRI测量和模型预测之间的差异，以确保始终遵循流体流量问题的物理。 MRI-Mech计算了食管转运期间的流体速度和压力，并通过计算壁刚度和主动弛豫来估计食道健康的机械健康。此外，MRI-Mech预测了在排空过程中有关下食管下括约肌的缺失信息，这证明了其适用于缺少数据或图像分辨率差的方案。除了基于食管机械健康的定量估计值来改善临床决策外，MRI-MECH还可以增强用于应用其他医学成像方式以增强其功能。

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。

我们向连续状态马尔可夫决策过程（MDP）提出了一种扩散近似方法，该方法可用于解决非结构化的越野环境中的自主导航和控制。与呈现完全已知的状态转换模型的大多数决策定理计划框架相比，我们设计了一种方法，该方法消除了这种强烈假设，这些假设通常非常难以在现实中工程师。我们首先采用价值函数的二阶泰勒扩展。然后通过部分微分方程近似贝尔曼的最优性方程，其仅依赖于转换模型的第一和第二矩。通过组合价值函数的内核表示，然后设计一种有效的策略迭代算法，其策略评估步骤可以表示为特征的方程式的线性系统，其特征是由有限组支持状态。我们首先通过大量的仿真以2D美元的$ 2D $避让和2.5d $地形导航问题进行验证。结果表明，拟议的方法在几个基线上导致了卓越的性能。然后，我们开发一个系统，该系统将我们的决策框架整合，与船上感知，并在杂乱的室内和非结构化的户外环境中进行现实世界的实验。物理系统的结果进一步展示了我们在挑战现实世界环境中的方法的适用性。

感知，规划，估算和控制的当代方法允许机器人在不确定，非结构化环境中的远程代理中稳健运行。此进度现在创造了机器人不仅在隔离，而且在我们的复杂环境中运行的机器人。意识到这个机会需要一种高效且灵活的媒介，人类可以与协作机器人沟通。自然语言提供了一种这样的媒体，通过对自然语言理解的统计方法的重大进展，现在能够解释各种自由形式命令。然而，大多数当代方法需要机器人环境的详细，现有的空间语义地图，这些环境模拟了话语可能引用的可能引用的空间。因此，当机器人部署在新的，先前未知或部分观察到的环境中时，这些方法发生故障，特别是当环境的心理模型在人类运营商和机器人之间不同时。本文提供了一种新的学习框架的全面描述，允许现场和服务机器人解释并正确执行先验未知，非结构化环境中的自然语言指令。对于我们的方法而不是我们的语言作为“传感器” - 在话语中隐含的“传感器” - 推断的空间，拓扑和语义信息，然后利用这些信息来学习在潜在环境模型上的分布。我们将此分布纳入概率，语言接地模型中，并在机器人的动作空间的象征性表示中推断出分布。我们使用模仿学习来确定对环境和行为分布的原因的信仰空间政策。我们通过各种导航和移动操纵实验评估我们的框架。

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.

We present the interpretable meta neural ordinary differential equation (iMODE) method to rapidly learn generalizable (i.e., not parameter-specific) dynamics from trajectories of multiple dynamical systems that vary in their physical parameters. The iMODE method learns meta-knowledge, the functional variations of the force field of dynamical system instances without knowing the physical parameters, by adopting a bi-level optimization framework: an outer level capturing the common force field form among studied dynamical system instances and an inner level adapting to individual system instances. A priori physical knowledge can be conveniently embedded in the neural network architecture as inductive bias, such as conservative force field and Euclidean symmetry. With the learned meta-knowledge, iMODE can model an unseen system within seconds, and inversely reveal knowledge on the physical parameters of a system, or as a Neural Gauge to "measure" the physical parameters of an unseen system with observed trajectories. We test the validity of the iMODE method on bistable, double pendulum, Van der Pol, Slinky, and reaction-diffusion systems.

While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels' activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the temporal sampling framework's assumption of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively.