随着真实世界量子计算的出现，参数化量子计算可以用作量子古典机器学习系统中的假设家庭的想法正在增加牵引力的增加。这种混合系统已经表现出潜力在监督和生成学习中解决现实世界任务，最近的作品已经在特殊的人工任务中建立了他们可提供的优势。然而，在加强学习的情况下，可以说是最具挑战性的，并且学习提升将是极为有价值的，在解决甚至标准的基准测试方面没有成功地取得了成功，也没有在典型算法上表达理论上的学习优势。在这项工作中，我们均达到两者。我们提出了一种使用很少的Qubits的混合量子古典强化学习模型，我们展示了可以有效地培训，以解决若干标准基准环境。此外，我们展示和正式证明，参数化量子电路解决了用于古典模型的棘手的某些学习任务的能力，包括当前最先进的深神经网络，在离散对数问题的广泛的经典硬度下。

Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology.

准确的睡眠阶段分类对于睡眠健康评估很重要。近年来，已经开发了几种基于深度学习和机器学习的睡眠阶段算法，并且在人类注释方面取得了表现。尽管性能提高，但最深入学习算法的局限性是其黑盒行为，它限制了它们在临床环境中的使用。在这里，我们提出了跨模式变压器，这是一种基于变压器的睡眠阶段分类的方法。我们的模型通过最先进的方法实现了竞争性能，并通过利用注意模块的可解释性方面消除了深度学习模型的黑盒行为。提出的跨模式变压器由一种新型的跨模式变压器编码器结构以及多尺度的一维卷积神经网络组成，用于自动表示学习。基于此设计的我们的睡眠阶段分类器能够以与最先进的方法相同或更好地达到睡眠阶段分类性能，以及可解释性，参数数量减少了四倍，并且比较培训时间减少了。到当前的最新。我们的代码可从https://github.com/jathurshan0330/cross-modal-transformer获得。

前列腺癌是美国男人的第二致致命癌症。虽然磁共振成像（MRI）越来越多地用于引导前列腺癌诊断的靶向活组织检查，但其效用仍然受到限制，因为假阳性和假否定的高率以及较低的读者协议。机器学习方法在前列腺MRI上检测和定位癌症可以帮助标准化放射科学诠释。然而，现有的机器学习方法不仅在模型架构中不等，而且还可以在用于模型培训的地面真理标签策略中。在这项研究中，我们比较不同的标记策略，即病理证实放射科标签，整个安装组织病理学图像上的病理学家标签，以及病变水平和像素级数字病理学家标签（先前验证了组织病理学图像上的深层学习算法以预测像素 - 整个安装组织病理学图像上的Gleason模式）。我们分析这些标签对训练有素的机器学习模型的性能的影响。我们的实验表明，用它们培训的（1）放射科标签和模型可能会错过癌症，或低估癌症程度，（2）与他们培训的数字病理学家标签和模型与病理学家标签有高度的一致性，而（3）用数字病理学家培训的模型标签在两种不同疾病分布的两种不同群组中达到最佳性能，而不管使用的模型建筑如何。数字病理学家标签可以减少与人类注释相关的挑战，包括劳动力，时间，和读者间变异性，并且可以通过使可靠的机器学习模型进行培训来检测和定位前列腺癌，帮助弥合前列腺放射学和病理学之间的差距在MRI。

语音神经调节物有可能为患有扰动或休闲症的人提供沟通。最近的进展已经证明了从放置在皮质表面上的电加电网的高质量文本解码和语音合成。在这里，我们研究了较少的侵入性测量模态，即立体定向脑电图（SEEG），其提供来自多个脑区的稀疏抽样，包括皮质区域。为了评估Seeg是否也可用于综合神经录音的高质量音频，我们采用了一种基于现代深度学习方法的经常性编码器 - 解码器框架。我们证明，尽管有限的训练数据，但是可以从这些微创录音来重建高质量的言论。最后，我们利用变分特征丢失来成功识别最具信息丰富的电极触点。

我们利用最先进的机器学习方法和来自CFHT的十年的档案数据来预测来自环境条件和天文台操作参数的天文台图像质量（IQ）。具体而言，我们开发了数据特征之间复杂依赖性的准确和可解释的模型，并为CFHT的宽野相机，Megacam观察到IQ。我们的贡献是几倍。首先，我们收集，整理和重新处理CFHT科学家收集的几种不同数据集。其次，我们预测IQ的概率分布函数（PDF），实现预测中位数的$ \ sim0.07'$的平均绝对误差。第三，我们探讨了2013 - 14年安装的12个圆顶“通风口”的数据驱动，以加速来自圆顶的热空气的冲洗。我们与概率的生成建模结合使用认识和炼膜的不确定性，以确定是分布（ID）的候选通风调整;对于每个ID样本的最佳配置，我们预测所需观察时间的减少以实现固定的SNR。平均而言，减少是$ \ SIM12 \％$。最后，我们通过福谢值等级来缩放输入特征，以确定每个观察的最预测变量。我们的长期目标是构建可靠和实时模型，可以预测最佳的天文台操作参数来优化IQ。然后，我们可以将这些预测送入调度协议和预测性维护例程。我们预计这些方法将成为自动化天文台运营和维护的标准，即CFHT的继承者，Maunakea光谱探险家安装在未来十年。

FIG. 1. Schematic diagram of a Variational Quantum Algorithm (VQA). The inputs to a VQA are: a cost function C(θ), with θ a set of parameters that encodes the solution to the problem, an ansatz whose parameters are trained to minimize the cost, and (possibly) a set of training data {ρ k } used during the optimization. Here, the cost can often be expressed in the form in Eq. ( 3), for some set of functions {f k }. Also, the ansatz is shown as a parameterized quantum circuit (on the left), which is analogous to a neural network (also shown schematically on the right). At each iteration of the loop one uses a quantum computer to efficiently estimate the cost (or its gradients). This information is fed into a classical computer that leverages the power of optimizers to navigate the cost landscape C(θ) and solve the optimization problem in Eq. ( 1). Once a termination condition is met, the VQA outputs an estimate of the solution to the problem. The form of the output depends on the precise task at hand. The red box indicates some of the most common types of outputs.

关于比较治疗效果的最佳证据来自临床试验，其结果在非结构化的文章中据报道。医疗专家必须手动提取文章中的信息以告知决策，这是耗时和昂贵的。在这里，我们考虑（a）从描述临床试验（实体识别）的全文物品中提取治疗和结果的端到端任务，（b）推断前者的报告结果（关系萃取）。我们为此任务介绍了新数据，并评估最近在自然语言处理中获得类似任务的最先进结果的模型。然后，我们提出了一种新的方法，激励了通常介绍了如何呈现这些纯粹数据驱动的基线的试验结果。最后，我们对该模型进行了一定的评估，并具有非营利性寻求鉴定可能重新用癌症的现有药物，显示出端到端证据提取系统的潜在效用。

New architecture GPUs like A100 are now equipped with multi-instance GPU (MIG) technology, which allows the GPU to be partitioned into multiple small, isolated instances. This technology provides more flexibility for users to support both deep learning training and inference workloads, but efficiently utilizing it can still be challenging. The vision of this paper is to provide a more comprehensive and practical benchmark study for MIG in order to eliminate the need for tedious manual benchmarking and tuning efforts. To achieve this vision, the paper presents MIGPerf, an open-source tool that streamlines the benchmark study for MIG. Using MIGPerf, the authors conduct a series of experiments, including deep learning training and inference characterization on MIG, GPU sharing characterization, and framework compatibility with MIG. The results of these experiments provide new insights and guidance for users to effectively employ MIG, and lay the foundation for further research on the orchestration of hybrid training and inference workloads on MIGs. The code and results are released on https://github.com/MLSysOps/MIGProfiler. This work is still in progress and more results will be published soon.

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/ .