Brain decoding is a field of computational neuroscience that uses measurable brain activity to infer mental states or internal representations of perceptual inputs. Therefore, we propose a novel approach to brain decoding that also relies on semantic and contextual similarity. We employ an fMRI dataset of natural image vision and create a deep learning decoding pipeline inspired by the existence of both bottom-up and top-down processes in human vision. We train a linear brain-to-feature model to map fMRI activity features to visual stimuli features, assuming that the brain projects visual information onto a space that is homeomorphic to the latent space represented by the last convolutional layer of a pretrained convolutional neural network, which typically collects a variety of semantic features that summarize and highlight similarities and differences between concepts. These features are then categorized in the latent space using a nearest-neighbor strategy, and the results are used to condition a generative latent diffusion model to create novel images. From fMRI data only, we produce reconstructions of visual stimuli that match the original content very well on a semantic level, surpassing the state of the art in previous literature. We evaluate our work and obtain good results using a quantitative semantic metric (the Wu-Palmer similarity metric over the WordNet lexicon, which had an average value of 0.57) and perform a human evaluation experiment that resulted in correct evaluation, according to the multiplicity of human criteria in evaluating image similarity, in over 80% of the test set.

自动算法提出的信任预测的意愿是许多领域中的关键。但是，大量的深度体系结构只能在没有相关不确定性的情况下制定预测。在本文中，我们提出了一种将标准神经网络转换为贝叶斯神经网络的方法，并通过对每个正向通行证时类似于原始网络的不同网络进行采样来估算预测的可变性。我们将方法与基于可调拒绝的方法相结合，该方法仅采用数据集的部分，该数据集的分数能够以低于用户集阈值的不确定性进行分类。我们在阿尔茨海默氏病患者的大量大脑图像中测试了我们的模型，在那里我们仅根据形态计量学图像来解决与健康对照组的歧视。我们证明了将估计的不确定性与基于拒绝的方法结合在一起如何将分类精度从0.86提高到0.95，同时保留了75％的测试集。此外，该模型可以根据过度不确定性选择建议进行手动评估的案例。我们认为，能够估计预测的不确定性，以及可以调节网络行为的工具，以使用户被告知（和舒适）可以代表用户方向的关键步骤合规性和更容易将深度学习工具集成到人类运营商当前执行的日常任务中。

在医学中，精心策划的图像数据集经常采用离散标签来描述所谓的健康状况与病理状况的连续光谱，例如阿尔茨海默氏病连续体或图像在诊断中起关键点的其他领域。我们提出了一个基于条件变异自动编码器的图像分层的体系结构。我们的框架VAESIM利用连续的潜在空间来表示疾病的连续体并在训练过程中找到簇，然后可以将其用于图像/患者分层。该方法的核心学习一组原型向量，每个向量与群集关联。首先，我们将每个数据样本的软分配给群集。然后，我们根据样品嵌入和簇的原型向量之间的相似性度量重建样品。为了更新原型嵌入，我们使用批处理大小中实际原型和样品之间最相似表示的指数移动平均值。我们在MNIST手写数字数据集和名为Pneumoniamnist的医疗基准数据集上测试了我们的方法。我们证明，我们的方法在两个数据集中针对标准VAE的分类任务（性能提高了15％）的KNN准确性优于基准，并且还以完全监督的方式培训的分类模型同等。我们还展示了我们的模型如何优于无监督分层的当前，端到端模型。

病变分割是放射线工作流程的关键步骤。手动分割需要长时间的执行时间，并且容易发生可变性，从而损害了放射线研究及其鲁棒性的实现。在这项研究中，对非小细胞肺癌患者的计算机断层扫描图像进行了深入学习的自动分割方法。还评估了手动与自动分割在生存放射模型的性能中的使用。方法总共包括899名NSCLC患者（2个专有：A和B，1个公共数据集：C）。肺部病变的自动分割是通过训练先前开发的建筑NNU-NET进行的，包括2D，3D和级联方法。用骰子系数评估自动分割的质量，以手动轮廓为参考。通过从数据集A的手动和自动轮廓中提取放射性的手工制作和深度学习特征来探索自动分割对患者生存的放射素模型对患者生存的性能的影响。评估并比较模型的精度。结果通过平均2D和3D模型的预测以及应用后处理技术来提取最大连接的组件，可以实现具有骰子= 0.78 +（0.12）的自动和手动轮廓之间的最佳一致性。当使用手动或自动轮廓，手工制作或深度特征时，在生存模型的表现中未观察到统计差异。最好的分类器显示出0.65至0.78之间的精度。结论NNU-NET在自动分割肺部病变中的有希望的作用已得到证实，从而大大降低了时必的医生的工作量，而不会损害基于放射线学的生存预测模型的准确性。

与临床上建立的疾病类别相比，缺乏大型标记的医学成像数据集以及个体间的显着可变性，在精确医学范式中利用医学成像信息方面面临重大挑战个体预测和/或将患者分为较细粒的群体，这些群体可能遵循更多均匀的轨迹，从而赋予临床试验能力。为了有效地探索以无监督的方式探索医学图像中有效的自由度可变性，在这项工作中，我们提出了一个无监督的自动编码器框架，并增加了对比度损失，以鼓励潜在空间中的高可分离性。该模型在（医学）基准数据集上进行了验证。由于群集标签是根据集群分配分配给每个示例的，因此我们将性能与监督的转移学习基线进行比较。我们的方法达到了与监督体系结构相似的性能，表明潜在空间中的分离再现了专家医学观察者分配的标签。所提出的方法可能对患者分层有益，探索较大类或病理连续性的新细分，或者由于其在变化环境中的采样能力，因此医学图像处理中的数据增强。

系统生物学和系统尤其是神经生理学，最近已成为生物医学科学中许多关键应用的强大工具。然而，这样的模型通常基于需要临时计算策略并提出极高计算需求的多尺度（可能是多物理）策略的复杂组合。深度神经网络领域的最新发展证明了与传统模型相比，具有非线性，通用近似值的可能性，以估算具有高速度和准确性优势的高度非线性和复杂问题。合成数据验证后，我们使用所谓的物理约束神经网络（PINN）同时求解生物学上合理的Hodgkin-Huxley模型，并从可变和恒定电流刺激下从真实数据中推断出其参数和隐藏的时间巡回赛，显示出极低的刺激峰值和忠实信号重建的可变性。我们获得的参数范围也与先验知识兼容。我们证明可以向神经网络提供详细的生物学知识，从而使其能够在模拟和真实数据上拟合复杂的动态。

由于其易于实施，多层感知器（MLP）在深度学习应用程序中变得无处不在。 MLP的基础图确实是多部分，即神经元的每个层仅连接到属于相邻层的神经元。在约束中，体内脑连接在单个突触的水平上表明，生物神经元网络的特征是无尺度度分布或指数截断的功率定律强度分布，这暗示了潜在的新型途径，用于开发进化衍生的神经元网络。在本文中，我们提出了“ 4ward”，这是一种方法和Python库，能够从任意复杂的定向无环形图中生成灵活有效的神经网络（NNS）。 4ward的灵感来自于从图形图纪律中绘制的分层算法以实现有效的向前传球，并在具有各种ERD \ H {O} S-R \'enyi图的计算实验中提供了显着的时间增长。 4Ward通过并行化激活的计算来克服学习矩阵方法的顺序性质，并为设计人员提供自定义权重初始化和激活函数的自由。我们的算法对于任何寻求在微观尺度的NN设计框架中利用复杂拓扑的研究者都可以帮助。

Computational units in artificial neural networks follow a simplified model of biological neurons. In the biological model, the output signal of a neuron runs down the axon, splits following the many branches at its end, and passes identically to all the downward neurons of the network. Each of the downward neurons will use their copy of this signal as one of many inputs dendrites, integrate them all and fire an output, if above some threshold. In the artificial neural network, this translates to the fact that the nonlinear filtering of the signal is performed in the upward neuron, meaning that in practice the same activation is shared between all the downward neurons that use that signal as their input. Dendrites thus play a passive role. We propose a slightly more complex model for the biological neuron, where dendrites play an active role: the activation in the output of the upward neuron becomes optional, and instead the signals going through each dendrite undergo independent nonlinear filterings, before the linear combination. We implement this new model into a ReLU computational unit and discuss its biological plausibility. We compare this new computational unit with the standard one and describe it from a geometrical point of view. We provide a Keras implementation of this unit into fully connected and convolutional layers and estimate their FLOPs and weights change. We then use these layers in ResNet architectures on CIFAR-10, CIFAR-100, Imagenette, and Imagewoof, obtaining performance improvements over standard ResNets up to 1.73%. Finally, we prove a universal representation theorem for continuous functions on compact sets and show that this new unit has more representational power than its standard counterpart.

Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose a novel grasping strategy, named 3DSGrasp, that predicts the missing geometry from the partial PCD to produce reliable grasp poses. Our proposed PCD completion network is a Transformer-based encoder-decoder network with an Offset-Attention layer. Our network is inherently invariant to the object pose and point's permutation, which generates PCDs that are geometrically consistent and completed properly. Experiments on a wide range of partial PCD show that 3DSGrasp outperforms the best state-of-the-art method on PCD completion tasks and largely improves the grasping success rate in real-world scenarios. The code and dataset will be made available upon acceptance.

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.