The use of needles to access sites within organs is fundamental to many interventional medical procedures both for diagnosis and treatment. Safe and accurate navigation of a needle through living tissue to an intra-tissue target is currently often challenging or infeasible due to the presence of anatomical obstacles in the tissue, high levels of uncertainty, and natural tissue motion (e.g., due to breathing). Medical robots capable of automating needle-based procedures in vivo have the potential to overcome these challenges and enable an enhanced level of patient care and safety. In this paper, we show the first medical robot that autonomously navigates a needle inside living tissue around anatomical obstacles to an intra-tissue target. Our system leverages an aiming device and a laser-patterned highly flexible steerable needle, a type of needle capable of maneuvering along curvilinear trajectories to avoid obstacles. The autonomous robot accounts for anatomical obstacles and uncertainty in living tissue/needle interaction with replanning and control and accounts for respiratory motion by defining safe insertion time windows during the breathing cycle. We apply the system to lung biopsy, which is critical in the diagnosis of lung cancer, the leading cause of cancer-related death in the United States. We demonstrate successful performance of our system in multiple in vivo porcine studies and also demonstrate that our approach leveraging autonomous needle steering outperforms a standard manual clinical technique for lung nodule access.

ICECUBE是一种用于检测1 GEV和1 PEV之间大气和天体中微子的光学传感器的立方公斤阵列，该阵列已部署1.45 km至2.45 km的南极的冰盖表面以下1.45 km至2.45 km。来自ICE探测器的事件的分类和重建在ICeCube数据分析中起着核心作用。重建和分类事件是一个挑战，这是由于探测器的几何形状，不均匀的散射和冰中光的吸收，并且低于100 GEV的光，每个事件产生的信号光子数量相对较少。为了应对这一挑战，可以将ICECUBE事件表示为点云图形，并将图形神经网络（GNN）作为分类和重建方法。 GNN能够将中微子事件与宇宙射线背景区分开，对不同的中微子事件类型进行分类，并重建沉积的能量，方向和相互作用顶点。基于仿真，我们提供了1-100 GEV能量范围的比较与当前ICECUBE分析中使用的当前最新最大似然技术，包括已知系统不确定性的影响。对于中微子事件分类，与当前的IceCube方法相比，GNN以固定的假阳性速率（FPR）提高了信号效率的18％。另外，GNN在固定信号效率下将FPR的降低超过8（低于半百分比）。对于能源，方向和相互作用顶点的重建，与当前最大似然技术相比，分辨率平均提高了13％-20％。当在GPU上运行时，GNN能够以几乎是2.7 kHz的中位数ICECUBE触发速率的速率处理ICECUBE事件，这打开了在在线搜索瞬态事件中使用低能量中微子的可能性。

评估在不断增长的语料库中排名期限的重要性变化是理解词汇用法变化的强大工具。在本文中，我们分析了自由响应答案的语料库，其中要求33,993 LGBTQ一代受访者从13岁到24岁，在美国被要求自我描述他们的性取向。我们观察到某些标签，例如双性恋，pansexual和女同性恋，在各个年龄段中同样重要。其他标签的重要性，例如同性恋，死亡和异性恋，跨年龄段的发展。尽管Z世代通常被定型为均匀的观念，但在其中自我描述的性取向时，我们会明显观察到不同的标签用法。我们敦促感兴趣的各方必须经常向目标受众调查最重要的性取向标签，并刷新其材料（例如人口调查），以反映不断发展的LGBTQ社区并创造包容性的环境。

人类的生态成功依赖于我们在合作社会群体中灵活地组织的特征能力。成功的团体采用实质性专业和劳动分工。与大多数其他动物不同，人类在生活中通过反复试验学习什么角色。但是，当某些关键角色比其他角色更具吸引力，并且个人是自私的，那么就会存在社会困难：每个人都希望其他人扮演关键但无人机的角色，因此他们可能会自由自在地接受一个付费的人更好的。但是，如果所有人都采取行动，并且关键作用没有填补，就会发生灾难。在这种情况下，学习最佳角色分布可能是不可能的。因此，一个基本的问题是：劳动分裂如何在自私自利的学习个人群体中出现？在这里，我们表明，通过引入一种社会规范模型，我们将其视为分散的社会制裁模式，自私自利的个人群体可以学习涉及所有关键角色的劳动力划分。这种社会规范是通过重新分配人口中的奖励来努力使反社会角色不利的，同时激励亲社会角色，而这些角色不像本质上一样付出。

平面扩散方程描述了大型自然运输过程，例如流体流动，传热和风运输。它们还用于光流量和灌注成像计算。我们开发机器学习模型，D ^ 2-sonata，基于随机的平流扩散方程构建，其预测驱动2d / 3d图像时间串联传输的速度和扩散场。特别是，我们的拟议模型包含一种运输模式，其中包括预期正常运输行为与观察到的运输之间的异常差异。在医学背景中，这种正常异常的分解可以例如用于量化病理学。具体地，我们的模型识别来自传输时间序列的平流和扩散贡献，并同时预测异常值场，以提供分解成正常和异常的平流和扩散行为。为了实现速度和扩散 - 张力场基础的速度和扩散张传导领域的提高性能，并为异常字段估计，我们创建了一个2D / 3D异常编码的平行扩散模拟器，允许监督学习。我们通过转移学习进一步在缺血性卒中患者的脑灌注数据集中进一步应用模型。广泛的比较表明，我们的模型成功地区分了来自普通脑区的行程病变（异常），同时重建底层速度和扩散张量场。

Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks. In this scene, low image resolution and noise interference are new challenges faced in surveillance FAS. Together with the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality from three aspects: (1) An Image Quality Variable module (IQV) is introduced to recover image information associated with discrimination by combining the super-resolution network. (2) Using generated sample pairs to simulate quality variance distributions to help contrastive learning strategies obtain robust feature representation under quality variation. (3) A Separate Quality Network (SQN) is designed to learn discriminative features independent of image quality. Finally, a large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL.

Proteins are fundamental biological entities that play a key role in life activities. The amino acid sequences of proteins can be folded into stable 3D structures in the real physicochemical world, forming a special kind of sequence-structure data. With the development of Artificial Intelligence (AI) techniques, Protein Representation Learning (PRL) has recently emerged as a promising research topic for extracting informative knowledge from massive protein sequences or structures. To pave the way for AI researchers with little bioinformatics background, we present a timely and comprehensive review of PRL formulations and existing PRL methods from the perspective of model architectures, pretext tasks, and downstream applications. We first briefly introduce the motivations for protein representation learning and formulate it in a general and unified framework. Next, we divide existing PRL methods into three main categories: sequence-based, structure-based, and sequence-structure co-modeling. Finally, we discuss some technical challenges and potential directions for improving protein representation learning. The latest advances in PRL methods are summarized in a GitHub repository https://github.com/LirongWu/awesome-protein-representation-learning.

As an important variant of entity alignment (EA), multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) with multiple modalities like images. However, current MMEA algorithms all adopt KG-level modality fusion strategies but ignore modality differences among individual entities, hurting the robustness to potential noise involved in modalities (e.g., unidentifiable images and relations). In this paper we present MEAformer, a multi-modal entity alignment transformer approach for meta modality hybrid, to dynamically predict the mutual correlation coefficients among modalities for instance-level feature fusion. A modal-aware hard entity replay strategy is also proposed for addressing vague entity details. Extensive experimental results show that our model not only achieves SOTA performance on multiple training scenarios including supervised, unsupervised, iterative, and low resource, but also has limited parameters, optimistic speed, and good interpretability. Our code will be available soon.

This short paper compiles the big ideas behind some philosophical views, definitions, and examples of causality. This collection spans the realms of the four commonly adopted approaches to causality: Humes regularity, counterfactual, manipulation, and mechanisms. This short review is motivated by presenting simplified views and definitions and then supplements them with examples from various fields, including economics, education, medicine, politics, physics, and engineering. It is the hope that this short review comes in handy for new and interested readers with little knowledge of causality and causal inference.

Previous work has shown the potential of deep learning to predict renal obstruction using kidney ultrasound images. However, these image-based classifiers have been trained with the goal of single-visit inference in mind. We compare methods from video action recognition (i.e. convolutional pooling, LSTM, TSM) to adapt single-visit convolutional models to handle multiple visit inference. We demonstrate that incorporating images from a patient's past hospital visits provides only a small benefit for the prediction of obstructive hydronephrosis. Therefore, inclusion of prior ultrasounds is beneficial, but prediction based on the latest ultrasound is sufficient for patient risk stratification.