开发有效的自动分类器将真实来源与工件分开，对于宽场光学调查的瞬时随访至关重要。在图像差异过程之后，从减法伪像的瞬态检测鉴定是此类分类器的关键步骤，称为真实 - 博格斯分类问题。我们将自我监督的机器学习模型，深入的自组织地图（DESOM）应用于这个“真实的模拟”分类问题。 DESOM结合了自动编码器和一个自组织图以执行聚类，以根据其维度降低的表示形式来区分真实和虚假的检测。我们使用32x32归一化检测缩略图作为底部的输入。我们展示了不同的模型训练方法，并发现我们的最佳DESOM分类器显示出6.6％的检测率，假阳性率为1.5％。 Desom提供了一种更细微的方法来微调决策边界，以确定与其他类型的分类器（例如在神经网络或决策树上构建的）结合使用时可能进行的实际检测。我们还讨论了DESOM及其局限性的其他潜在用法。

We explore the generation of visualisations of audio latent spaces using an audio-to-image generation pipeline. We believe this can help with the interpretability of audio latent spaces. We demonstrate a variety of results on the NSynth dataset. A web demo is available.

In this work, we estimate the depth in which domestic waste are located in space from a mobile robot in outdoor scenarios. As we are doing this calculus on a broad range of space (0.3 - 6.0 m), we use RGB-D camera and LiDAR fusion. With this aim and range, we compare several methods such as average, nearest, median and center point, applied to those which are inside a reduced or non-reduced Bounding Box (BB). These BB are obtained from segmentation and detection methods which are representative of these techniques like Yolact, SOLO, You Only Look Once (YOLO)v5, YOLOv6 and YOLOv7. Results shown that, applying a detection method with the average technique and a reduction of BB of 40%, returns the same output as segmenting the object and applying the average method. Indeed, the detection method is faster and lighter in comparison with the segmentation one. The committed median error in the conducted experiments was 0.0298 ${\pm}$ 0.0544 m.

Despite the huge advancement in knowledge discovery and data mining techniques, the X-ray diffraction (XRD) analysis process has mostly remained untouched and still involves manual investigation, comparison, and verification. Due to the large volume of XRD samples from high-throughput XRD experiments, it has become impossible for domain scientists to process them manually. Recently, they have started leveraging standard clustering techniques, to reduce the XRD pattern representations requiring manual efforts for labeling and verification. Nevertheless, these standard clustering techniques do not handle problem-specific aspects such as peak shifting, adjacent peaks, background noise, and mixed phases; hence, resulting in incorrect composition-phase diagrams that complicate further steps. Here, we leverage data mining techniques along with domain expertise to handle these issues. In this paper, we introduce an incremental phase mapping approach based on binary peak representations using a new threshold based fuzzy dissimilarity measure. The proposed approach first applies an incremental phase computation algorithm on discrete binary peak representation of XRD samples, followed by hierarchical clustering or manual merging of similar pure phases to obtain the final composition-phase diagram. We evaluate our method on the composition space of two ternary alloy systems- Co-Ni-Ta and Co-Ti-Ta. Our results are verified by domain scientists and closely resembles the manually computed ground-truth composition-phase diagrams. The proposed approach takes us closer towards achieving the goal of complete end-to-end automated XRD analysis.

Solar activity is usually caused by the evolution of solar magnetic fields. Magnetic field parameters derived from photospheric vector magnetograms of solar active regions have been used to analyze and forecast eruptive events such as solar flares and coronal mass ejections. Unfortunately, the most recent solar cycle 24 was relatively weak with few large flares, though it is the only solar cycle in which consistent time-sequence vector magnetograms have been available through the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) since its launch in 2010. In this paper, we look into another major instrument, namely the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SOHO) from 1996 to 2010. The data archive of SOHO/MDI covers more active solar cycle 23 with many large flares. However, SOHO/MDI data only has line-of-sight (LOS) magnetograms. We propose a new deep learning method, named MagNet, to learn from combined LOS magnetograms, Bx and By taken by SDO/HMI along with H-alpha observations collected by the Big Bear Solar Observatory (BBSO), and to generate vector components Bx' and By', which would form vector magnetograms with observed LOS data. In this way, we can expand the availability of vector magnetograms to the period from 1996 to present. Experimental results demonstrate the good performance of the proposed method. To our knowledge, this is the first time that deep learning has been used to generate photospheric vector magnetograms of solar active regions for SOHO/MDI using SDO/HMI and H-alpha data.

To analyze this characteristic of vulnerability, we developed an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images. A total of 8,403 IVOCT image frames from 85 lesions and 37 normal segments were analyzed. Manual annotation was done using a dedicated software (OCTOPUS) previously developed by our group. Data augmentation in the polar (r,{\theta}) domain was applied to raw IVOCT images to ensure that microvessels appear at all possible angles. Pre-processing methods included guidewire/shadow detection, lumen segmentation, pixel shifting, and noise reduction. DeepLab v3+ was used to segment microvessel candidates. A bounding box on each candidate was classified as either microvessel or non-microvessel using a shallow convolutional neural network. For better classification, we used data augmentation (i.e., angle rotation) on bounding boxes with a microvessel during network training. Data augmentation and pre-processing steps improved microvessel segmentation performance significantly, yielding a method with Dice of 0.71+/-0.10 and pixel-wise sensitivity/specificity of 87.7+/-6.6%/99.8+/-0.1%. The network for classifying microvessels from candidates performed exceptionally well, with sensitivity of 99.5+/-0.3%, specificity of 98.8+/-1.0%, and accuracy of 99.1+/-0.5%. The classification step eliminated the majority of residual false positives, and the Dice coefficient increased from 0.71 to 0.73. In addition, our method produced 698 image frames with microvessels present, compared to 730 from manual analysis, representing a 4.4% difference. When compared to the manual method, the automated method improved microvessel continuity, implying improved segmentation performance. The method will be useful for research purposes as well as potential future treatment planning.

由于其高质量的重建以及将现有迭代求解器结合起来的易于性，因此最近将扩散模型作为强大的生成反问题解决器研究。但是，大多数工作都专注于在无噪声设置中解决简单的线性逆问题，这显着不足以使实际问题的复杂性不足。在这项工作中，我们将扩散求解器扩展求解器，以通过后采样的拉普拉斯近似有效地处理一般噪声（非）线性反问题。有趣的是，所得的后验采样方案是扩散采样的混合版本，具有歧管约束梯度，而没有严格的测量一致性投影步骤，与先前的研究相比，在嘈杂的设置中产生了更可取的生成路径。我们的方法表明，扩散模型可以结合各种测量噪声统计量，例如高斯和泊松，并且还有效处理嘈杂的非线性反问题，例如傅立叶相检索和不均匀的脱毛。

太阳耀斑，尤其是M级和X级耀斑，通常与冠状质量弹出（CMES）有关。它们是太空天气影响的最重要来源，可能会严重影响近地环境。因此，必须预测耀斑（尤其是X级），以减轻其破坏性和危险后果。在这里，我们介绍了几种统计和机器学习方法，以预测AR的耀斑指数（FI），这些方法通过考虑到一定时间间隔内的不同类耀斑的数量来量化AR的耀斑生产力。具体而言，我们的样本包括2010年5月至2017年12月在太阳能磁盘上出现的563个AR。25个磁性参数，由空中震动和磁性成像器（HMI）的太空天气HMI活性区域（Sharp）提供的太阳能动力学观测值（HMI）。 （SDO），表征了代理中存储在ARS中的冠状磁能，并用作预测因子。我们研究了这些尖锐的参数与ARS的FI与机器学习算法（样条回归）和重采样方法（合成少数群体过度采样技术，用于使用高斯噪声回归的合成少数群体过度采样技术，smogn简短）。基于既定关系，我们能够在接下来的1天内预测给定AR的FIS值。与其他4种流行的机器学习算法相比，我们的方法提高了FI预测的准确性，尤其是对于大型FI。此外，我们根据Borda Count方法从由9种不同的机器学习方法渲染的等级计算出尖锐参数的重要性。

人类机器人相互作用（HRI）对于在日常生活中广泛使用机器人至关重要。机器人最终将能够通过有效的社会互动来履行人类文明的各种职责。创建直接且易于理解的界面，以与机器人开始在个人工作区中扩散时与机器人互动至关重要。通常，与模拟机器人的交互显示在屏幕上。虚拟现实（VR）是一个更具吸引力的替代方法，它为视觉提示提供了更像现实世界中看到的线索。在这项研究中，我们介绍了Jubileo，这是一种机器人的动画面孔，并使用人类机器人社会互动领域的各种研究和应用开发工具。Jubileo Project不仅提供功能齐全的开源物理机器人。它还提供了一个全面的框架，可以通过VR接口进行操作，从而为HRI应用程序测试带来沉浸式环境，并明显更好地部署速度。

机器学习潜力是分子模拟的重要工具，但是由于缺乏高质量数据集来训练它们的发展，它们的开发阻碍了它们。我们描述了Spice数据集，这是一种新的量子化学数据集，用于训练与模拟与蛋白质相互作用的药物样的小分子相关的潜在。它包含超过110万个小分子，二聚体，二肽和溶剂化氨基酸的构象。它包括15个元素，带电和未充电的分子以及广泛的共价和非共价相互作用。它提供了在{\ omega} b97m-d3（bj）/def2-tzVPPD理论水平以及其他有用的数量（例如多极矩和键阶）上计算出的力和能量。我们在其上训练一组机器学习潜力，并证明它们可以在化学空间的广泛区域中实现化学精度。它可以作为创建可转移的，准备使用潜在功能用于分子模拟的宝贵资源。