需要连续监测足部溃疡愈合，以确保给定治疗的功效并避免任何恶化。脚下溃疡分割是伤口诊断的重要步骤。我们开发了一种模型，其精神与良好的编码器编码器和残留卷积神经网络相似。我们的模型包括剩余的连接以及在每个卷积块中集成的通道和空间注意力。一种基于贴剂训练，测试时间增加以及对获得预测的多数投票的简单方法，导致了卓越的性能。我们的模型没有利用任何容易获得的骨干架构，在类似的外部数据集或任何转移学习技术上进行预训练。与用于足球溃疡细分任务的可用最新模型相比，网络参数的总数约为500万，这使其成为一个显着的轻巧模型。我们的实验在斑块级和图像级别上呈现了结果。我们的模型应用于Miccai 2021的公开脚步溃疡细分（Fuseg）挑战数据集，就骰子相似性得分而言，最先进的图像级绩效为88.22％，在官方挑战排行榜中排名第二。我们还展示了一个非常简单的解决方案，可以将其与更高级的体系结构进行比较。

通常，大型数据集使深度学习模型能够以良好的准确性和可推广性能。但是，大规模的高保真仿真数据集（来自分子化学，天体物理学，计算流体动力学（CFD）等，由于维度和存储限制，策划的策划可能具有挑战性。损失的压缩算法可以帮助减轻存储的限制，只要很长时间保留了总体数据保真度。为了说明这一点，我们证明了对佩斯卡尔CFD模拟的数据进行了训练和测试的深度学习模型，对在语义细分问题中有损耗的压缩期间引入的错误是可靠的。我们的结果表明，有损压缩算法提供了一种现实的途径，可以将高保真科学数据暴露到开放源数据存储库中，以构建社区数据集。在本文中，我们概述，构建和评估建立大数据框架的要求，在https：// bastnet上证明。 github.io/，用于科学机器学习。

有限的角度X射线断层扫描重建是一个不良反问题一般。特别是当投影角度有限并且在光子限制条件下进行测量时，来自经典算法的重建，例如过滤的反光，可能导致由于缺失的问题而获取伪影。为了获得令人满意的重建结果，通常在重建算法中结合在重建算法中的令人满意的重建结果，例如总变化最小化和非局部图像相似度。在这项工作中，我们介绍了深度神经网络，以确定并应用重建过程的先前分配。我们的神经网络直接从合成训练样本中学习。因此，神经网络获得了对我们对重建感兴趣的对象类的特定的先前分配。特别是，我们使用了具有3D卷积层和3D注意图层的深生成的模型，这些层在来自DubBed电路库的3D合成集成电路（IC）数据上培训。我们证明，当投影角度和光子预算受到限制时，来自我们深度生成模型的前沿可以显着提高合成数据的IC重建质量，而与最大似然估计相比。使用电路库的合成IC数据训练深度生成模型说明了从机器学习之前学到的学习功能。我们预计，如果使用实验数据再现过程，机器学习的优势将持续存在。机器学习在有限角X射线断层扫描的优点可以进一步能够在低光子纳米级成像中实现应用。

According to the rapid development of drone technologies, drones are widely used in many applications including military domains. In this paper, a novel situation-aware DRL- based autonomous nonlinear drone mobility control algorithm in cyber-physical loitering munition applications. On the battlefield, the design of DRL-based autonomous control algorithm is not straightforward because real-world data gathering is generally not available. Therefore, the approach in this paper is that cyber-physical virtual environment is constructed with Unity environment. Based on the virtual cyber-physical battlefield scenarios, a DRL-based automated nonlinear drone mobility control algorithm can be designed, evaluated, and visualized. Moreover, many obstacles exist which is harmful for linear trajectory control in real-world battlefield scenarios. Thus, our proposed autonomous nonlinear drone mobility control algorithm utilizes situation-aware components those are implemented with a Raycast function in Unity virtual scenarios. Based on the gathered situation-aware information, the drone can autonomously and nonlinearly adjust its trajectory during flight. Therefore, this approach is obviously beneficial for avoiding obstacles in obstacle-deployed battlefields. Our visualization-based performance evaluation shows that the proposed algorithm is superior from the other linear mobility control algorithms.

Supervision for metric learning has long been given in the form of equivalence between human-labeled classes. Although this type of supervision has been a basis of metric learning for decades, we argue that it hinders further advances of the field. In this regard, we propose a new regularization method, dubbed HIER, to discover the latent semantic hierarchy of training data, and to deploy the hierarchy to provide richer and more fine-grained supervision than inter-class separability induced by common metric learning losses. HIER achieved this goal with no annotation for the semantic hierarchy but by learning hierarchical proxies in hyperbolic spaces. The hierarchical proxies are learnable parameters, and each of them is trained to serve as an ancestor of a group of data or other proxies to approximate the semantic hierarchy among them. HIER deals with the proxies along with data in hyperbolic space since geometric properties of the space are well-suited to represent their hierarchical structure. The efficacy of HIER was evaluated on four standard benchmarks, where it consistently improved performance of conventional methods when integrated with them, and consequently achieved the best records, surpassing even the existing hyperbolic metric learning technique, in almost all settings.

Steering language generation towards objectives or away from undesired content has been a long-standing goal in utilizing language models (LM). Recent work has demonstrated reinforcement learning and weighted decoding as effective approaches to achieve a higher level of language control and quality with pros and cons. In this work, we propose a novel critic decoding method for controlled language generation (CriticControl) that combines the strengths of reinforcement learning and weighted decoding. Specifically, we adopt the actor-critic framework to train an LM-steering critic from non-differentiable reward models. And similar to weighted decoding, our method freezes the language model and manipulates the output token distribution using called critic, improving training efficiency and stability. Evaluation of our method on three controlled generation tasks, namely topic control, sentiment control, and detoxification, shows that our approach generates more coherent and well-controlled texts than previous methods. In addition, CriticControl demonstrates superior generalization ability in zero-shot settings. Human evaluation studies also corroborate our findings.

Task-oriented dialogue (TOD) systems are mainly based on the slot-filling-based TOD (SF-TOD) framework, in which dialogues are broken down into smaller, controllable units (i.e., slots) to fulfill a specific task. A series of approaches based on this framework achieved remarkable success on various TOD benchmarks. However, we argue that the current TOD benchmarks are limited to surrogate real-world scenarios and that the current TOD models are still a long way from unraveling the scenarios. In this position paper, we first identify current status and limitations of SF-TOD systems. After that, we explore the WebTOD framework, the alternative direction for building a scalable TOD system when a web/mobile interface is available. In WebTOD, the dialogue system learns how to understand the web/mobile interface that the human agent interacts with, powered by a large-scale language model.

Estimating the 6D pose of objects is one of the major fields in 3D computer vision. Since the promising outcomes from instance-level pose estimation, the research trends are heading towards category-level pose estimation for more practical application scenarios. However, unlike well-established instance-level pose datasets, available category-level datasets lack annotation quality and provided pose quantity. We propose the new category level 6D pose dataset HouseCat6D featuring 1) Multi-modality of Polarimetric RGB+P and Depth, 2) Highly diverse 194 objects of 10 household object categories including 2 photometrically challenging categories, 3) High-quality pose annotation with an error range of only 1.35 mm to 1.74 mm, 4) 41 large scale scenes with extensive viewpoint coverage, 5) Checkerboard-free environment throughout the entire scene. We also provide benchmark results of state-of-the-art category-level pose estimation networks.

Recent studies have proposed a unified user modeling framework that leverages user behavior data from various applications. Most benefit from utilizing users' behavior sequences as plain texts, representing rich information in any domain or system without losing generality. Hence, a question arises: Can language modeling for user history corpus help improve recommender systems? While its versatile usability has been widely investigated in many domains, its applications to recommender systems still remain underexplored. We show that language modeling applied directly to task-specific user histories achieves excellent results on diverse recommendation tasks. Also, leveraging additional task-agnostic user histories delivers significant performance benefits. We further demonstrate that our approach can provide promising transfer learning capabilities for a broad spectrum of real-world recommender systems, even on unseen domains and services.

While witnessing the noisy intermediate-scale quantum (NISQ) era and beyond, quantum federated learning (QFL) has recently become an emerging field of study. In QFL, each quantum computer or device locally trains its quantum neural network (QNN) with trainable gates, and communicates only these gate parameters over classical channels, without costly quantum communications. Towards enabling QFL under various channel conditions, in this article we develop a depth-controllable architecture of entangled slimmable quantum neural networks (eSQNNs), and propose an entangled slimmable QFL (eSQFL) that communicates the superposition-coded parameters of eS-QNNs. Compared to the existing depth-fixed QNNs, training the depth-controllable eSQNN architecture is more challenging due to high entanglement entropy and inter-depth interference, which are mitigated by introducing entanglement controlled universal (CU) gates and an inplace fidelity distillation (IPFD) regularizer penalizing inter-depth quantum state differences, respectively. Furthermore, we optimize the superposition coding power allocation by deriving and minimizing the convergence bound of eSQFL. In an image classification task, extensive simulations corroborate the effectiveness of eSQFL in terms of prediction accuracy, fidelity, and entropy compared to Vanilla QFL as well as under different channel conditions and various data distributions.