肿瘤浸润淋巴细胞（TIL）的定量已被证明是乳腺癌患者预后的独立预测因子。通常，病理学家对含有tils的基质区域的比例进行估计，以获得TILS评分。乳腺癌（Tiger）挑战中肿瘤浸润淋巴细胞旨在评估计算机生成的TILS评分的预后意义，以预测作为COX比例风险模型的一部分的存活率。在这一挑战中，作为Tiager团队，我们已经开发了一种算法，以将肿瘤与基质与基质进行第一部分，然后将肿瘤散装区域用于TILS检测。最后，我们使用这些输出来生成每种情况的TILS分数。在初步测试中，我们的方法达到了肿瘤 - 细胞瘤的加权骰子评分为0.791，而淋巴细胞检测的FROC得分为0.572。为了预测生存，我们的模型达到了0.719的C索引。这些结果在老虎挑战的初步测试排行榜中获得了第一名。

学习以上对象的场景表示对于实现复杂场景的结构理解和抽象至关重要。然而，由于目前为无监督的对象表示学习的方法建立在静止观察者假设或静态场景假设之上，它们通常是：i）遭受单视图空间歧义，或ii）从动态场景中不正确或不准确的对象表示。为了解决此问题，我们提出了动态感知的多目标网络（DYMON），这是一种扩展多视图以对象的表示学习学习到动态场景的方法的方法。我们在多视图 - 动态场景数据上训练Dymon，并显示Dymon学习 - 没有监督 - 从一系列观察序列来构建观察者动作和场景对象动态的纠缠效果，并构建适合渲染的场景对象空间表示在任意次（跨时间查询）和任意视点（查询空间）。我们还显示分解场景表示（W.R.T.对象）支持通过独立和时间通过空间和时间查询单个对象。

This article presents a novel review of Active SLAM (A-SLAM) research conducted in the last decade. We discuss the formulation, application, and methodology applied in A-SLAM for trajectory generation and control action selection using information theory based approaches. Our extensive qualitative and quantitative analysis highlights the approaches, scenarios, configurations, types of robots, sensor types, dataset usage, and path planning approaches of A-SLAM research. We conclude by presenting the limitations and proposing future research possibilities. We believe that this survey will be helpful to researchers in understanding the various methods and techniques applied to A-SLAM formulation.

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.

Accurate recognition of food items along with quality assessment is of paramount importance in the agricultural industry. Such automated systems can speed up the wheel of the food processing sector and save tons of manual labor. In this connection, the recent advancement of Deep learning-based architectures has introduced a wide variety of solutions offering remarkable performance in several classification tasks. In this work, we have exploited the concept of Densely Connected Convolutional Neural Networks (DenseNets) for fruit quality assessment. The feature propagation towards the deeper layers has enabled the network to tackle the vanishing gradient problems and ensured the reuse of features to learn meaningful insights. Evaluating on a dataset of 19,526 images containing six fruits having three quality grades for each, the proposed pipeline achieved a remarkable accuracy of 99.67%. The robustness of the model was further tested for fruit classification and quality assessment tasks where the model produced a similar performance, which makes it suitable for real-life applications.

In this paper, deep-learning-based approaches namely fine-tuning of pretrained convolutional neural networks (VGG16 and VGG19), and end-to-end training of a developed CNN model, have been used in order to classify X-Ray images into four different classes that include COVID-19, normal, opacity and pneumonia cases. A dataset containing more than 20,000 X-ray scans was retrieved from Kaggle and used in this experiment. A two-stage classification approach was implemented to be compared to the one-shot classification approach. Our hypothesis was that a two-stage model will be able to achieve better performance than a one-shot model. Our results show otherwise as VGG16 achieved 95% accuracy using one-shot approach over 5-fold of training. Future work will focus on a more robust implementation of the two-stage classification model Covid-TSC. The main improvement will be allowing data to flow from the output of stage-1 to the input of stage-2, where stage-1 and stage-2 models are VGG16 models fine-tuned on the Covid-19 dataset.

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.

数据驱动的优化和基于机器学习的无线电访问网络的性能诊断不仅需要源于基本数据源的性质，而且还归因于复杂的时空关系以及由于用户移动性和不同流量模式而引起的单元格之间的相互依赖性。我们讨论如何使用多元分析来研究这些配置和性能管理数据集以及在关键性能指标方面识别细胞之间的关系。为此，我们利用了基于规范相关分析（CCA）的新框架，这不仅是降低维度的高效方法，而且还用于分析跨不同多元数据集的关系。作为一个案例研究，我们讨论了基于商业蜂窝网络中细胞关闭的节能用例，在该案例中，我们将CCA应用于分析容量细胞关闭对同一部门覆盖电池KPI的影响。来自LTE网络的数据用于分析示例案例。我们得出的结论是，CCA是一种可行的方法，用于识别网络计划和配置数据之间的关键关系，还可以动态绩效数据，为诸如降低维度降低，绩效分析和性能诊断的根本原因分析等努力铺平道路。

作物疾病是对粮食安全的主要威胁，其快速识别对于防止产量损失很重要。由于缺乏必要的基础设施，因此很难迅速识别这些疾病。计算机视觉的最新进展和智能手机渗透的渗透为智能手机辅助疾病识别铺平了道路。大多数植物疾病在植物的叶面结构上留下了特定的文物。这项研究于2020年在巴基斯坦拉合尔工程技术大学计算机科学与工程系进行，以检查基于叶片的植物疾病识别。这项研究为叶面疾病鉴定提供了基于神经网络的深度解决方案，并纳入了图像质量评估，以选择执行识别所需质量的图像，并将其命名为农业病理学家（AGRO PATH）。新手摄影师的捕获图像可能包含噪音，缺乏结构和模糊，从而导致诊断失败或不准确。此外，Agropath模型具有99.42％的叶面疾病鉴定精度。拟议的添加对于在农业领域的叶面疾病鉴定的应用特别有用。

本文提出了Mburst，这是一种新型的多模式解决方案，用于视听语音增强功能，该解决方案考虑了有关前额叶皮层和其他大脑区域的锥体细胞的最新神经系统发现。所谓的爆发传播实现了几个标准，以更加可行的方式解决信用分配问题：通过反馈来指导可塑性的标志和大小，并线性化反馈信号。 Mburst从这种能力中受益于学习嘈杂信号和视觉刺激之间的相关性，从而通过扩增相关信息和抑制噪声来归因于语音。通过网格语料库和基于Chime3的数据集进行的实验表明，Mburst可以将类似的掩模重建基于多模态反向传播基线，同时证明了出色的能量效率管理，从而降低了神经元的发射速率，以降低价值，最高为\ textbf {$ 70 \％$}降低。这样的功能意味着更可持续的实现，适合助听器或任何其他类似的嵌入式系统。