为了在多个机器人系统中有效完成任务，必须解决的问题是同时定位和映射（SLAM）。激光雷达（光检测和范围）由于其出色的精度而用于许多SLAM解决方案，但其性能在无特征环境（如隧道或长走廊）中降低。集中式大满贯解决了云服务器的问题，云服务器需要大量的计算资源，并且缺乏针对中央节点故障的鲁棒性。为了解决这些问题，我们提出了一个分布式的SLAM解决方案，以使用超宽带（UWB）范围和探测测量值估算一组机器人的轨迹。所提出的方法在机器人团队之间分配了处理，并显着减轻了从集中式大满贯出现的计算问题。我们的解决方案通过最大程度地减少在机器人处于近距离接近时在不同位置进行的UWB范围测量方法来确定两个机器人之间的相对姿势（也称为环闭合）。 UWB在视线条件下提供了良好的距离度量，但是由于机器人的噪声和不可预测的路径，检索精确的姿势估计仍然是一个挑战。为了处理可疑的循环封闭，我们使用成对的一致性最大化（PCM）来检查循环封闭质量并执行异常拒绝。然后，在分布式姿势图优化（DPGO）模块中将过滤的环闭合与探光仪融合，以恢复机器人团队的完整轨迹。进行了广泛的实验以验证所提出的方法的有效性。

机器人的本地化对于导航和路径计划至关重要，例如需要环境地图的情况。多年来，由于引入低成本UWB模块提供了厘米级的准确性，多年来，用于室内位置系统的Ultra Wideband（UWB）一直在越来越受欢迎。但是，在环境中存在障碍的情况下，UWB的非视线（NLOS）测量将产生不准确的结果。由于低成本UWB设备不提供渠道信息，因此我们提出了一种方法来决定测量是否在视线（LOS）之内（NN）模型。该模型的结果是测量值是LOS的概率，该测量是通过加权最高方（WLS）方法定位的。我们的方法在大厅测试数据中将本地化精度提高了16.93％，使用从办公室培训数据中提取的所有输入的NN模型，在走廊测试数据上，将本地化精度提高了16.93％。

在室内运行的自主机器人和GPS拒绝的环境可以使用LIDAR进行大满贯。但是，由于循环闭合检测和计算负载以执行扫描匹配的挑战，在几何衰减的环境中，LIDAR的表现不佳。现有的WiFi基础架构可以用低硬件和计算成本来进行本地化和映射。然而，使用WiFi进行准确的姿势估计是具有挑战性的，因为由于信号传播的不可预测性，可以在同一位置测量不同的信号值。因此，我们介绍了WiFi指纹序列的使用量估计（即循环闭合）。这种方法利用移动机器人移动时获得的位置指纹的空间连贯性。这具有更好的校正探针流漂移的能力。该方法还结合了激光扫描，从而提高了大型和几何衰减环境的计算效率，同时保持LIDAR SLAM的准确性。我们在室内环境中进行了实验，以说明该方法的有效性。基于根平方误差（RMSE）评估结果，并在测试环境中达到了88m的精度。

Diabetic Retinopathy (DR) is considered one of the primary concerns due to its effect on vision loss among most people with diabetes globally. The severity of DR is mostly comprehended manually by ophthalmologists from fundus photography-based retina images. This paper deals with an automated understanding of the severity stages of DR. In the literature, researchers have focused on this automation using traditional machine learning-based algorithms and convolutional architectures. However, the past works hardly focused on essential parts of the retinal image to improve the model performance. In this paper, we adopt transformer-based learning models to capture the crucial features of retinal images to understand DR severity better. We work with ensembling image transformers, where we adopt four models, namely ViT (Vision Transformer), BEiT (Bidirectional Encoder representation for image Transformer), CaiT (Class-Attention in Image Transformers), and DeiT (Data efficient image Transformers), to infer the degree of DR severity from fundus photographs. For experiments, we used the publicly available APTOS-2019 blindness detection dataset, where the performances of the transformer-based models were quite encouraging.

This paper presents our solutions for the MediaEval 2022 task on DisasterMM. The task is composed of two subtasks, namely (i) Relevance Classification of Twitter Posts (RCTP), and (ii) Location Extraction from Twitter Texts (LETT). The RCTP subtask aims at differentiating flood-related and non-relevant social posts while LETT is a Named Entity Recognition (NER) task and aims at the extraction of location information from the text. For RCTP, we proposed four different solutions based on BERT, RoBERTa, Distil BERT, and ALBERT obtaining an F1-score of 0.7934, 0.7970, 0.7613, and 0.7924, respectively. For LETT, we used three models namely BERT, RoBERTa, and Distil BERTA obtaining an F1-score of 0.6256, 0.6744, and 0.6723, respectively.

Objective: Despite numerous studies proposed for audio restoration in the literature, most of them focus on an isolated restoration problem such as denoising or dereverberation, ignoring other artifacts. Moreover, assuming a noisy or reverberant environment with limited number of fixed signal-to-distortion ratio (SDR) levels is a common practice. However, real-world audio is often corrupted by a blend of artifacts such as reverberation, sensor noise, and background audio mixture with varying types, severities, and duration. In this study, we propose a novel approach for blind restoration of real-world audio signals by Operational Generative Adversarial Networks (Op-GANs) with temporal and spectral objective metrics to enhance the quality of restored audio signal regardless of the type and severity of each artifact corrupting it. Methods: 1D Operational-GANs are used with generative neuron model optimized for blind restoration of any corrupted audio signal. Results: The proposed approach has been evaluated extensively over the benchmark TIMIT-RAR (speech) and GTZAN-RAR (non-speech) datasets corrupted with a random blend of artifacts each with a random severity to mimic real-world audio signals. Average SDR improvements of over 7.2 dB and 4.9 dB are achieved, respectively, which are substantial when compared with the baseline methods. Significance: This is a pioneer study in blind audio restoration with the unique capability of direct (time-domain) restoration of real-world audio whilst achieving an unprecedented level of performance for a wide SDR range and artifact types. Conclusion: 1D Op-GANs can achieve robust and computationally effective real-world audio restoration with significantly improved performance. The source codes and the generated real-world audio datasets are shared publicly with the research community in a dedicated GitHub repository1.

Uncertainty quantification is crucial to inverse problems, as it could provide decision-makers with valuable information about the inversion results. For example, seismic inversion is a notoriously ill-posed inverse problem due to the band-limited and noisy nature of seismic data. It is therefore of paramount importance to quantify the uncertainties associated to the inversion process to ease the subsequent interpretation and decision making processes. Within this framework of reference, sampling from a target posterior provides a fundamental approach to quantifying the uncertainty in seismic inversion. However, selecting appropriate prior information in a probabilistic inversion is crucial, yet non-trivial, as it influences the ability of a sampling-based inference in providing geological realism in the posterior samples. To overcome such limitations, we present a regularized variational inference framework that performs posterior inference by implicitly regularizing the Kullback-Leibler divergence loss with a CNN-based denoiser by means of the Plug-and-Play methods. We call this new algorithm Plug-and-Play Stein Variational Gradient Descent (PnP-SVGD) and demonstrate its ability in producing high-resolution, trustworthy samples representative of the subsurface structures, which we argue could be used for post-inference tasks such as reservoir modelling and history matching. To validate the proposed method, numerical tests are performed on both synthetic and field post-stack seismic data.

In recent years distributional reinforcement learning has produced many state of the art results. Increasingly sample efficient Distributional algorithms for the discrete action domain have been developed over time that vary primarily in the way they parameterize their approximations of value distributions, and how they quantify the differences between those distributions. In this work we transfer three of the most well-known and successful of those algorithms (QR-DQN, IQN and FQF) to the continuous action domain by extending two powerful actor-critic algorithms (TD3 and SAC) with distributional critics. We investigate whether the relative performance of the methods for the discrete action space translates to the continuous case. To that end we compare them empirically on the pybullet implementations of a set of continuous control tasks. Our results indicate qualitative invariance regarding the number and placement of distributional atoms in the deterministic, continuous action setting.

Automated synthesis of histology images has several potential applications in computational pathology. However, no existing method can generate realistic tissue images with a bespoke cellular layout or user-defined histology parameters. In this work, we propose a novel framework called SynCLay (Synthesis from Cellular Layouts) that can construct realistic and high-quality histology images from user-defined cellular layouts along with annotated cellular boundaries. Tissue image generation based on bespoke cellular layouts through the proposed framework allows users to generate different histological patterns from arbitrary topological arrangement of different types of cells. SynCLay generated synthetic images can be helpful in studying the role of different types of cells present in the tumor microenvironmet. Additionally, they can assist in balancing the distribution of cellular counts in tissue images for designing accurate cellular composition predictors by minimizing the effects of data imbalance. We train SynCLay in an adversarial manner and integrate a nuclear segmentation and classification model in its training to refine nuclear structures and generate nuclear masks in conjunction with synthetic images. During inference, we combine the model with another parametric model for generating colon images and associated cellular counts as annotations given the grade of differentiation and cell densities of different cells. We assess the generated images quantitatively and report on feedback from trained pathologists who assigned realism scores to a set of images generated by the framework. The average realism score across all pathologists for synthetic images was as high as that for the real images. We also show that augmenting limited real data with the synthetic data generated by our framework can significantly boost prediction performance of the cellular composition prediction task.

Autonomous mobile agents such as unmanned aerial vehicles (UAVs) and mobile robots have shown huge potential for improving human productivity. These mobile agents require low power/energy consumption to have a long lifespan since they are usually powered by batteries. These agents also need to adapt to changing/dynamic environments, especially when deployed in far or dangerous locations, thus requiring efficient online learning capabilities. These requirements can be fulfilled by employing Spiking Neural Networks (SNNs) since SNNs offer low power/energy consumption due to sparse computations and efficient online learning due to bio-inspired learning mechanisms. However, a methodology is still required to employ appropriate SNN models on autonomous mobile agents. Towards this, we propose a Mantis methodology to systematically employ SNNs on autonomous mobile agents to enable energy-efficient processing and adaptive capabilities in dynamic environments. The key ideas of our Mantis include the optimization of SNN operations, the employment of a bio-plausible online learning mechanism, and the SNN model selection. The experimental results demonstrate that our methodology maintains high accuracy with a significantly smaller memory footprint and energy consumption (i.e., 3.32x memory reduction and 2.9x energy saving for an SNN model with 8-bit weights) compared to the baseline network with 32-bit weights. In this manner, our Mantis enables the employment of SNNs for resource- and energy-constrained mobile agents.