这项研究提出了一种新的数据库和方法，以检测由于酒精，药物消耗和昏昏欲睡而导致的警报条件的减少，而近亲（NIR）眼球周围眼部图像。该研究的重点是确定外部因素对中枢神经系统（CNS）的影响。目的是分析这如何影响虹膜和学生运动行为，以及是否可以用标准的IRIS NIR捕获装置对这些更改进行分类。本文提出了修改的MobileNetV2，以对来自酒精/药物/嗜睡影响的受试者拍摄的虹膜NIR图像进行分类。结果表明，基于MobileNETV2的分类器可以在耐心等方面从饮酒和药物消耗后捕获的虹膜样品的不合适性条件，分别检测精度分别为91.3％和99.1％。嗜睡状况是最具挑战性的72.4％。对于属于FIT/UNFIT类的两类分组图像，该模型的准确度分别为94.0％和84.0％，使用的参数数量较小，而不是标准的深度学习网络算法。这项工作是开发自动系统以对“适合值班”进行分类并防止因酒精/吸毒和嗜睡而导致事故的生物识别应用程序迈出的一步。

这项研究提出了一种检测近距离红外（NIR）眼周眼图像的酒精消耗的方法。该研究的重点是确定外部因素（例如酒精对中枢神经系统（CNS））的影响。目的是分析这如何影响虹膜和学生运动，以及是否可以使用标准的Iris NIR相机捕获这些更改。本文提出了一个新型的融合胶囊网络（F-CAPSNET），以对饮酒受试者拍摄的虹膜NIR图像进行分类。结果表明，使用一半参数作为标准胶囊网络算法，F-CAPSNET算法可以检测IRIS NIR图像中的酒精消耗，精度为92.3％。这项工作是开发自动系统以估计“适合值班”并防止因饮酒而导致事故的一步。

在本文中，我们利用了最近的物理信息神经网络（PINN）的进步，并开发了一种基于通用的Pinn的框架，以评估多状态系统（MSS）的可靠性。提议的方法包括两个主要步骤。在第一步中，我们将MS的可靠性评估作为使用Pinn框架的机器学习问题。构建具有两个单独损耗组的前馈神经网络以编码由MS中的常微分方程（ODES）管理的初始条件和状态转换。接下来，从多任务学习的角度来看，我们解决了Pinn中的背部传播梯度大小的高不平衡问题。特别是，我们将损失函数中的每个元素视为个别任务，采用名为Projecting冲突渐变（PCGRAD）的梯度手术方法，其中任务的渐变将投影到具有冲突梯度的任何其他任务的常规平面上。梯度投影操作显着降低了训练销时梯度干扰引起的有害影响，从而将PINN的收敛速度加速到高精度解决方案到MSS可靠性评估。通过提出的基于Pinn的框架，我们在几乎不受时间或依赖状态转换和系统尺度从小到介质时，研究其对MSS可靠性评估的应用程序的应用。结果表明，基于Pinn的框架在MSS可靠性评估中显示了通用和显着性能，并且Pinn中的PCGrad掺入了溶液质量和收敛速度的大量提高。

In this paper, we propose an end-to-end framework that jointly learns keypoint detection, descriptor representation and cross-frame matching for the task of image-based 3D localization. Prior art has tackled each of these components individually, purportedly aiming to alleviate difficulties in effectively train a holistic network. We design a self-supervised image warping correspondence loss for both feature detection and matching, a weakly-supervised epipolar constraints loss on relative camera pose learning, and a directional matching scheme that detects key-point features in a source image and performs coarse-to-fine correspondence search on the target image. We leverage this framework to enforce cycle consistency in our matching module. In addition, we propose a new loss to robustly handle both definite inlier/outlier matches and less-certain matches. The integration of these learning mechanisms enables end-to-end training of a single network performing all three localization components. Bench-marking our approach on public data-sets, exemplifies how such an end-to-end framework is able to yield more accurate localization that out-performs both traditional methods as well as state-of-the-art weakly supervised methods.

In constrained reinforcement learning (C-RL), an agent seeks to learn from the environment a policy that maximizes the expected cumulative reward while satisfying minimum requirements in secondary cumulative reward constraints. Several algorithms rooted in sampled-based primal-dual methods have been recently proposed to solve this problem in policy space. However, such methods are based on stochastic gradient descent ascent algorithms whose trajectories are connected to the optimal policy only after a mixing output stage that depends on the algorithm's history. As a result, there is a mismatch between the behavioral policy and the optimal one. In this work, we propose a novel algorithm for constrained RL that does not suffer from these limitations. Leveraging recent results on regularized saddle-flow dynamics, we develop a novel stochastic gradient descent-ascent algorithm whose trajectories converge to the optimal policy almost surely.

We propose a structure-preserving model-reduction methodology for large-scale dynamic networks with tightly-connected components. First, the coherent groups are identified by a spectral clustering algorithm on the graph Laplacian matrix that models the network feedback. Then, a reduced network is built, where each node represents the aggregate dynamics of each coherent group, and the reduced network captures the dynamic coupling between the groups. We provide an upper bound on the approximation error when the network graph is randomly generated from a weight stochastic block model. Finally, numerical experiments align with and validate our theoretical findings.

In this work we propose a novel token-based training strategy that improves Transformer-Transducer (T-T) based speaker change detection (SCD) performance. The conventional T-T based SCD model loss optimizes all output tokens equally. Due to the sparsity of the speaker changes in the training data, the conventional T-T based SCD model loss leads to sub-optimal detection accuracy. To mitigate this issue, we use a customized edit-distance algorithm to estimate the token-level SCD false accept (FA) and false reject (FR) rates during training and optimize model parameters to minimize a weighted combination of the FA and FR, focusing the model on accurately predicting speaker changes. We also propose a set of evaluation metrics that align better with commercial use cases. Experiments on a group of challenging real-world datasets show that the proposed training method can significantly improve the overall performance of the SCD model with the same number of parameters.

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.

The demonstrated success of transfer learning has popularized approaches that involve pretraining models from massive data sources and subsequent finetuning towards a specific task. While such approaches have become the norm in fields such as natural language processing, implementation and evaluation of transfer learning approaches for chemistry are in the early stages. In this work, we demonstrate finetuning for downstream tasks on a graph neural network (GNN) trained over a molecular database containing 2.7 million water clusters. The use of Graphcore IPUs as an AI accelerator for training molecular GNNs reduces training time from a reported 2.7 days on 0.5M clusters to 1.2 hours on 2.7M clusters. Finetuning the pretrained model for downstream tasks of molecular dynamics and transfer to a different potential energy surface took only 8.3 hours and 28 minutes, respectively, on a single GPU.

Latent variable models such as the Variational Auto-Encoder (VAE) have become a go-to tool for analyzing biological data, especially in the field of single-cell genomics. One remaining challenge is the interpretability of latent variables as biological processes that define a cell's identity. Outside of biological applications, this problem is commonly referred to as learning disentangled representations. Although several disentanglement-promoting variants of the VAE were introduced, and applied to single-cell genomics data, this task has been shown to be infeasible from independent and identically distributed measurements, without additional structure. Instead, recent methods propose to leverage non-stationary data, as well as the sparse mechanism shift assumption in order to learn disentangled representations with a causal semantic. Here, we extend the application of these methodological advances to the analysis of single-cell genomics data with genetic or chemical perturbations. More precisely, we propose a deep generative model of single-cell gene expression data for which each perturbation is treated as a stochastic intervention targeting an unknown, but sparse, subset of latent variables. We benchmark these methods on simulated single-cell data to evaluate their performance at latent units recovery, causal target identification and out-of-domain generalization. Finally, we apply those approaches to two real-world large-scale gene perturbation data sets and find that models that exploit the sparse mechanism shift hypothesis surpass contemporary methods on a transfer learning task. We implement our new model and benchmarks using the scvi-tools library, and release it as open-source software at \url{https://github.com/Genentech/sVAE}.