医学图像分割可以为临床分析提供详细信息，这对于发现的详细位置很重要的情况可能是有用的。了解疾病的位置可以在治疗和决策中发挥重要作用。基于卷积神经网络（CNN）的编码器 - 解码器技术具有自动化医学图像分割系统的性能。几种基于CNN的方法利用了诸如空间和渠道的技术来提高性能。近年来引起关注的另一种技术是残留致密块（RDB）。密集连接块中的连续卷积层能够用不同的接收领域提取各种特征，从而提高性能。然而，连续堆积的卷积运营商可能不一定生成有助于识别目标结构的功能。在本文中，我们提出了一种逐步的交替注意网络（PAANET）。我们开发逐步交替注意密度（Paad）块，其在密集块中的每个卷积层中使用来自所有尺度的特征构建指导注意力图（GAM）。 GAM允许密集块中的以下层集中在与目标区域相关的空间位置。每个备用Paad块都反转GAM以生成反向注意地图，指导后面的图层，以提取边界和边缘相关信息，精炼分割过程。我们对三种不同的生物医学图像分割数据集的实验表明，与其他最先进的方法相比，我们的Paanet达到了有利的性能。

Despite their widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans. In this research, we examine user utterances as causes and generated responses as effects, recognizing that changes in a cause should produce a different effect. To further explore this concept, we have compiled and expanded upon a new dataset called CausalDialogue through crowd-sourcing. This dataset includes multiple cause-effect pairs within a directed acyclic graph (DAG) structure. Our analysis reveals that traditional loss functions can struggle to effectively incorporate the DAG structure, leading us to propose a causality-enhanced method called Exponential Maximum Average Treatment Effect (ExMATE) to enhance the impact of causality at the utterance level in training neural conversation models. To evaluate the effectiveness of this approach, we have built a comprehensive benchmark using the CausalDialogue dataset leveraging large-scale pre-trained language models, and have assessed the results through both human and automatic evaluation metrics for coherence, diversity, and agility. Our findings show that current techniques are still unable to effectively address conversational DAGs, and that the ExMATE method can improve the diversity and agility of conventional loss functions while maintaining coherence.

In recent years, generative models have undergone significant advancement due to the success of diffusion models. The success of these models is often attributed to their use of guidance techniques, such as classifier and classifier-free methods, which provides effective mechanisms to trade-off between fidelity and diversity. However, these methods are not capable of guiding a generated image to be aware of its geometric configuration, e.g., depth, which hinders the application of diffusion models to areas that require a certain level of depth awareness. To address this limitation, we propose a novel guidance approach for diffusion models that uses estimated depth information derived from the rich intermediate representations of diffusion models. To do this, we first present a label-efficient depth estimation framework using the internal representations of diffusion models. At the sampling phase, we utilize two guidance techniques to self-condition the generated image using the estimated depth map, the first of which uses pseudo-labeling, and the subsequent one uses a depth-domain diffusion prior. Experiments and extensive ablation studies demonstrate the effectiveness of our method in guiding the diffusion models toward geometrically plausible image generation. Project page is available at https://ku-cvlab.github.io/DAG/.

Estimating the structure of directed acyclic graphs (DAGs) of features (variables) plays a vital role in revealing the latent data generation process and providing causal insights in various applications. Although there have been many studies on structure learning with various types of data, the structure learning on the dynamic graph has not been explored yet, and thus we study the learning problem of node feature generation mechanism on such ubiquitous dynamic graph data. In a dynamic graph, we propose to simultaneously estimate contemporaneous relationships and time-lagged interaction relationships between the node features. These two kinds of relationships form a DAG, which could effectively characterize the feature generation process in a concise way. To learn such a DAG, we cast the learning problem as a continuous score-based optimization problem, which consists of a differentiable score function to measure the validity of the learned DAGs and a smooth acyclicity constraint to ensure the acyclicity of the learned DAGs. These two components are translated into an unconstraint augmented Lagrangian objective which could be minimized by mature continuous optimization techniques. The resulting algorithm, named GraphNOTEARS, outperforms baselines on simulated data across a wide range of settings that may encounter in real-world applications. We also apply the proposed approach on two dynamic graphs constructed from the real-world Yelp dataset, demonstrating our method could learn the connections between node features, which conforms with the domain knowledge.

Front-door adjustment is a classic technique to estimate causal effects from a specified directed acyclic graph (DAG) and observed data. The advantage of this approach is that it uses observed mediators to identify causal effects, which is possible even in the presence of unobserved confounding. While the statistical properties of the front-door estimation are quite well understood, its algorithmic aspects remained unexplored for a long time. Recently, Jeong, Tian, and Barenboim [NeurIPS 2022] have presented the first polynomial-time algorithm for finding sets satisfying the front-door criterion in a given DAG, with an $O(n^3(n+m))$ run time, where $n$ denotes the number of variables and $m$ the number of edges of the graph. In our work, we give the first linear-time, i.e. $O(n+m)$, algorithm for this task, which thus reaches the asymptotically optimal time complexity, as the size of the input is $\Omega(n+m)$. We also provide an algorithm to enumerate all front-door adjustment sets in a given DAG with delay $O(n(n + m))$. These results improve the algorithms by Jeong et al. [2022] for the two tasks by a factor of $n^3$, respectively.

Predicting the future motion of road agents is a critical task in an autonomous driving pipeline. In this work, we address the problem of generating a set of scene-level, or joint, future trajectory predictions in multi-agent driving scenarios. To this end, we propose FJMP, a Factorized Joint Motion Prediction framework for multi-agent interactive driving scenarios. FJMP models the future scene interaction dynamics as a sparse directed interaction graph, where edges denote explicit interactions between agents. We then prune the graph into a directed acyclic graph (DAG) and decompose the joint prediction task into a sequence of marginal and conditional predictions according to the partial ordering of the DAG, where joint future trajectories are decoded using a directed acyclic graph neural network (DAGNN). We conduct experiments on the INTERACTION and Argoverse 2 datasets and demonstrate that FJMP produces more accurate and scene-consistent joint trajectory predictions than non-factorized approaches, especially on the most interactive and kinematically interesting agents. FJMP ranks 1st on the multi-agent test leaderboard of the INTERACTION dataset.

Causal structure learning from observational data remains a non-trivial task due to various factors such as finite sampling, unobserved confounding factors, and measurement errors. Constraint-based and score-based methods tend to suffer from high computational complexity due to the combinatorial nature of estimating the directed acyclic graph (DAG). Motivated by the `Cause-Effect Pair' NIPS 2013 Workshop on Causality Challenge, in this paper, we take a different approach and generate a probability distribution over all possible graphs informed by the cause-effect pair features proposed in response to the workshop challenge. The goal of the paper is to propose new methods based on this probabilistic information and compare their performance with traditional and state-of-the-art approaches. Our experiments, on both synthetic and real datasets, show that our proposed methods not only have statistically similar or better performances than some traditional approaches but also are computationally faster.

Causal discovery, the inference of causal relations from data, is a core task of fundamental importance in all scientific domains, and several new machine learning methods for addressing the causal discovery problem have been proposed recently. However, existing machine learning methods for causal discovery typically require that the data used for inference is pooled and available in a centralized location. In many domains of high practical importance, such as in healthcare, data is only available at local data-generating entities (e.g. hospitals in the healthcare context), and cannot be shared across entities due to, among others, privacy and regulatory reasons. In this work, we address the problem of inferring causal structure - in the form of a directed acyclic graph (DAG) - from a distributed data set that contains both observational and interventional data in a privacy-preserving manner by exchanging updates instead of samples. To this end, we introduce a new federated framework, FED-CD, that enables the discovery of global causal structures both when the set of intervened covariates is the same across decentralized entities, and when the set of intervened covariates are potentially disjoint. We perform a comprehensive experimental evaluation on synthetic data that demonstrates that FED-CD enables effective aggregation of decentralized data for causal discovery without direct sample sharing, even when the contributing distributed data sets cover disjoint sets of interventions. Effective methods for causal discovery in distributed data sets could significantly advance scientific discovery and knowledge sharing in important settings, for instance, healthcare, in which sharing of data across local sites is difficult or prohibited.

自然语言和生物学序列之间的明显相似之处已导致最新的深层语言模型（LMS）在抗体和其他生物学序列分析中的应用激增。但是，缺乏对生物序列语言的严格语言形式化，这些语言将定义基本组成部分，例如词典（即语言的离散单元）和语法（即，将序列序列良好的规则，结构和结构和结构和结构和结构链接的规则链接在一起含义）导致了LMS的主要域无规定应用，这些应用未考虑研究的生物序列的基础结构。另一方面，语言形式化为LM应用建立了语言信息，因此适应域的组件。它将有助于更好地理解自然语言和生物序列之间的差异和相似性如何影响LMS的质量，这对于具有可解释的模型具有可解释的模型至关重要。解密抗体特异性规则对于加速有理和硅生物治疗药物设计至关重要。在这里，我们将抗体语言的特性形式化，因此不仅建立了语言工具在适应性免疫受体分析中应用的基础，而且还为免疫受体特异性的系统免疫语言学研究提供了基础。

通过组合术语代表一个证明树，该术语还原为树，使树内的重复形式微妙形式化为组合术语的重复子。在组合术语的DAG表示中，这些直接因素将其分为共享子图。为了搜索证据，可以列举组合术语，例如clausal tableaux，与与枚举结构的节点相关的公式的统一交织。为了限制搜索空间，枚举可以基于定义为参数化组合术语的证明模式。我们在这里介绍这种“组合术语为证明结构”的方法，用于自动化一阶证明，提出实现和首次实验结果。该方法建立在基于凝结脱离和连接方法的证明结构的术语视图上。它实现了从连接结构计算已知的功能，到目前为止尚未实现。