与常规的闭合设定识别相反，开放式识别（OSR）假设存在未知类别，在训练过程中未被视为模型。 OSR中的一种主要方法是度量学习，其中对模型进行了训练以分离已知类别数据的类间表示。 OSR中的许多作品报告说，即使模型仅通过已知类别的数据进行培训，模型也会意识到未知数，并学会将未知类表征与已知类别表示分开。本文通过观察雅各布的代表规范来分析这种新兴现象。从理论上讲，我们表明已知集中的阶层内距离最小化会减少已知类表征的雅各布式规范，同时最大化已知集合中的阶层间距离会增加未知类别的雅各布式规范。因此，封闭式度量学习通过迫使其雅各布规范值有所不同，从而将未知的未知数与已知分开。我们通过使用标准OSR数据集的大量证据来验证我们的理论框架。此外，在我们的理论框架下，我们解释了标准的深度学习技术如何有助于OSR并将框架作为指导原则来开发有效的OSR模型。

基于深卷积神经网络（CNN）的面部识别表现出归因于提取的高判别特征的卓越精度性能。然而，经常忽略了深度学习模型（深度特征）提取的功能的安全性和隐私。本文提出了从深度功能中重建面部图像，而无需访问CNN网络配置作为约束优化问题。这种优化可最大程度地减少从原始面部图像中提取的特征与重建的面部图像之间的距离。我们没有直接解决图像空间中的优化问题，而是通过寻找GAN发电机的潜在向量来重新重新制定问题，然后使用它来生成面部图像。 GAN发电机在这个新颖的框架中起着双重作用，即优化目标和面部发电机的面部分布约束。除了新颖的优化任务之外，我们还提出了一条攻击管道，以基于生成的面部图像模拟目标用户。我们的结果表明，生成的面部图像可以达到最先进的攻击率在LFW上的最先进的攻击率在I型攻击下为0.1 \％。我们的工作阐明了生物识别部署，以符合隐私和安全政策。

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.

我们使用实际用户设备上的联合学习训练了一个关键字发现模型，并在部署模型以推断电话时观察到了重大改进。为了补偿在设备培训缓存中缺少的数据域，我们采用了联合联邦中心化培训。为了在没有策划标签的设备上学习，我们根据用户反馈信号制定了置信度过滤策略，用于联合蒸馏。这些技术创建了模型，可在实时A/B实验中显着改善离线评估和用户体验指标的质量指标。

经过审计的语言模型（PTLMS）通常是通过大型静态语料库学习的，并针对各种下游任务进行了微调。但是，当部署在现实世界中时，基于PTLM的模型必须处理偏离PTLM最初培训的数据分布。在本文中，我们研究了一个终身语言模型预处理挑战，其中不断更新PTLM以适应新兴数据。在域内收入的研究纸流和按时间顺序排序的推文流上，我们从具有不同持续学习算法的PTLM逐渐预处理PTLM，并跟踪下游任务性能（经过微调之后）。我们评估了PTLM在保留早期语料库中学习知识的同时适应新语料库的能力。我们的实验表明，基于蒸馏的方法最有效地在早期域中保持下游性能。该算法还可以改善知识传递，从而使模型能够比最新数据实现更好的下游性能，并在由于时间而在培训和评估之间存在分配差距时改善时间概括。我们认为，我们的问题制定，方法和分析将激发未来的研究朝着语言模型的持续预处理。

Advancements in neural machinery have led to a wide range of algorithmic solutions for molecular property prediction. Two classes of models in particular have yielded promising results: neural networks applied to computed molecular fingerprints or expert-crafted descriptors, and graph convolutional neural networks that construct a learned molecular representation by operating on the graph structure of the molecule.However, recent literature has yet to clearly determine which of these two methods is superior when generalizing to new chemical space. Furthermore, prior research has

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.

A Digital Twin (DT) is a simulation of a physical system that provides information to make decisions that add economic, social or commercial value. The behaviour of a physical system changes over time, a DT must therefore be continually updated with data from the physical systems to reflect its changing behaviour. For resource-constrained systems, updating a DT is non-trivial because of challenges such as on-board learning and the off-board data transfer. This paper presents a framework for updating data-driven DTs of resource-constrained systems geared towards system health monitoring. The proposed solution consists of: (1) an on-board system running a light-weight DT allowing the prioritisation and parsimonious transfer of data generated by the physical system; and (2) off-board robust updating of the DT and detection of anomalous behaviours. Two case studies are considered using a production gas turbine engine system to demonstrate the digital representation accuracy for real-world, time-varying physical systems.

Optical coherence tomography (OCT) captures cross-sectional data and is used for the screening, monitoring, and treatment planning of retinal diseases. Technological developments to increase the speed of acquisition often results in systems with a narrower spectral bandwidth, and hence a lower axial resolution. Traditionally, image-processing-based techniques have been utilized to reconstruct subsampled OCT data and more recently, deep-learning-based methods have been explored. In this study, we simulate reduced axial scan (A-scan) resolution by Gaussian windowing in the spectral domain and investigate the use of a learning-based approach for image feature reconstruction. In anticipation of the reduced resolution that accompanies wide-field OCT systems, we build upon super-resolution techniques to explore methods to better aid clinicians in their decision-making to improve patient outcomes, by reconstructing lost features using a pixel-to-pixel approach with an altered super-resolution generative adversarial network (SRGAN) architecture.