In this work, we compare different neural topic modeling methods in learning the topical propensities of different psychiatric conditions from the psychotherapy session transcripts parsed from speech recordings. We also incorporate temporal modeling to put this additional interpretability to action by parsing out topic similarities as a time series in a turn-level resolution. We believe this topic modeling framework can offer interpretable insights for the therapist to optimally decide his or her strategy and improve psychotherapy effectiveness.

与传统的时间序列不同，人类决策的动作序列通常涉及许多认知过程，如信仰，欲望，意图和心理理论，即其他人在思考。这使得预测人类决策使得妥善治疗依据潜在的心理机制。我们建议基于长期短期内存网络（LSTM）使用经常性神经网络架构，以预测人类受试者在其决策中的每一步中采取的行动的时间序列，这是在本研究中的第一次应用这些方法领域。在这项研究中，我们将迭代囚犯困境的8个发表文献中的人类数据整理，包括168,386个个别决定，并将它们的后处理到8,257个行为轨迹，每个球员都有9个动作。同样，我们从10种不同公开的IOWA赌博任务实验与健康人类受试者进行了617个行动的轨迹。我们培训我们的预测网络，从这些出版的人类决策心理实验的行为数据上，并展示了在最先进的方法中展示了预测人类决策在诸如爱荷华州的单一代理场景中的人工决策轨迹赌博任务和多代理场景，如迭代囚犯的困境。在预测中，我们观察到，顶部表演者的权重倾向于具有更广泛的分布，并且LSTM网络中的更大偏差，这表明可能对每个组采用的策略分配的可能解释。

作为一个重要的心理和社会实验，迭代的囚犯困境（IPD）将合作或缺陷作为原子行动视为选择。我们建议研究迭代的囚犯困境（IPD）游戏中在线学习算法的行为，在那里我们研究了整个强化学习剂：多臂匪徒，上下文的强盗和钢筋学习。我们根据迭代囚犯的困境的比赛进行评估，其中多个特工可以以顺序竞争。这使我们能够分析由多个自私的独立奖励驱动的代理所学到的政策的动态，还使我们研究了这些算法适合人类行为的能力。结果表明，考虑当前的情况做出决定是这种社会困境游戏中最糟糕的情况。陈述了有关在线学习行为和临床验证的倍数，以此作为将人工智能算法与人类行为及其在神经精神病疾病中的异常状态联系起来的努力。

通常基于其一致行为和性能来评估人工行为代理，以便在环境中采取连续行动，以最大限度地提高累计奖励的一些概念。然而，现实生活中的人为决策通常涉及不同的策略和行为轨迹，这导致了同样的经验结果。通过各种神经系统和精神病疾病的临床文献激励，我们在此提出了一种更通用和灵活的参数框架，用于连续决策，涉及双流奖励处理机制。我们证明，该框架是灵活性的并且统一足以融合跨越多武装匪徒（MAB），上下文匪徒（CB）和加强学习（RL）的问题，该问题分解了不同级别的顺序决策过程。灵感来自于已知的奖励处理许多精神障碍的异常，我们的临床启发代理商在特定奖励分配的模拟任务中表现出有趣的行为轨迹和比较性能，这是一个捕获赌博任务中的人为决策的现实世界数据集，以及Pacman游戏在终身学习环境中跨越不同的奖励保单。

Optimal Power Flow (OPF) is a very traditional research area within the power systems field that seeks for the optimal operation point of electric power plants, and which needs to be solved every few minutes in real-world scenarios. However, due to the nonconvexities that arise in power generation systems, there is not yet a fast, robust solution technique for the full Alternating Current Optimal Power Flow (ACOPF). In the last decades, power grids have evolved into a typical dynamic, non-linear and large-scale control system, known as the power system, so searching for better and faster ACOPF solutions is becoming crucial. Appearance of Graph Neural Networks (GNN) has allowed the natural use of Machine Learning (ML) algorithms on graph data, such as power networks. On the other hand, Deep Reinforcement Learning (DRL) is known for its powerful capability to solve complex decision-making problems. Although solutions that use these two methods separately are beginning to appear in the literature, none has yet combined the advantages of both. We propose a novel architecture based on the Proximal Policy Optimization algorithm with Graph Neural Networks to solve the Optimal Power Flow. The objective is to design an architecture that learns how to solve the optimization problem and that is at the same time able to generalize to unseen scenarios. We compare our solution with the DCOPF in terms of cost after having trained our DRL agent on IEEE 30 bus system and then computing the OPF on that base network with topology changes

Event cameras are novel bio-inspired sensors that offer advantages over traditional cameras (low latency, high dynamic range, low power, etc.). Optical flow estimation methods that work on packets of events trade off speed for accuracy, while event-by-event (incremental) methods have strong assumptions and have not been tested on common benchmarks that quantify progress in the field. Towards applications on resource-constrained devices, it is important to develop optical flow algorithms that are fast, light-weight and accurate. This work leverages insights from neuroscience, and proposes a novel optical flow estimation scheme based on triplet matching. The experiments on publicly available benchmarks demonstrate its capability to handle complex scenes with comparable results as prior packet-based algorithms. In addition, the proposed method achieves the fastest execution time (> 10 kHz) on standard CPUs as it requires only three events in estimation. We hope that our research opens the door to real-time, incremental motion estimation methods and applications in real-world scenarios.

Neural Radiance Fields (NeRFs) are emerging as a ubiquitous scene representation that allows for novel view synthesis. Increasingly, NeRFs will be shareable with other people. Before sharing a NeRF, though, it might be desirable to remove personal information or unsightly objects. Such removal is not easily achieved with the current NeRF editing frameworks. We propose a framework to remove objects from a NeRF representation created from an RGB-D sequence. Our NeRF inpainting method leverages recent work in 2D image inpainting and is guided by a user-provided mask. Our algorithm is underpinned by a confidence based view selection procedure. It chooses which of the individual 2D inpainted images to use in the creation of the NeRF, so that the resulting inpainted NeRF is 3D consistent. We show that our method for NeRF editing is effective for synthesizing plausible inpaintings in a multi-view coherent manner. We validate our approach using a new and still-challenging dataset for the task of NeRF inpainting.

Wearable sensors for measuring head kinematics can be noisy due to imperfect interfaces with the body. Mouthguards are used to measure head kinematics during impacts in traumatic brain injury (TBI) studies, but deviations from reference kinematics can still occur due to potential looseness. In this study, deep learning is used to compensate for the imperfect interface and improve measurement accuracy. A set of one-dimensional convolutional neural network (1D-CNN) models was developed to denoise mouthguard kinematics measurements along three spatial axes of linear acceleration and angular velocity. The denoised kinematics had significantly reduced errors compared to reference kinematics, and reduced errors in brain injury criteria and tissue strain and strain rate calculated via finite element modeling. The 1D-CNN models were also tested on an on-field dataset of college football impacts and a post-mortem human subject dataset, with similar denoising effects observed. The models can be used to improve detection of head impacts and TBI risk evaluation, and potentially extended to other sensors measuring kinematics.

Event cameras are emerging vision sensors and their advantages are suitable for various applications such as autonomous robots. Contrast maximization (CMax), which provides state-of-the-art accuracy on motion estimation using events, may suffer from an overfitting problem called event collapse. Prior works are computationally expensive or cannot alleviate the overfitting, which undermines the benefits of the CMax framework. We propose a novel, computationally efficient regularizer based on geometric principles to mitigate event collapse. The experiments show that the proposed regularizer achieves state-of-the-art accuracy results, while its reduced computational complexity makes it two to four times faster than previous approaches. To the best of our knowledge, our regularizer is the only effective solution for event collapse without trading off runtime. We hope our work opens the door for future applications that unlocks the advantages of event cameras.

Deep Reinforcement Learning (RL) agents are susceptible to adversarial noise in their observations that can mislead their policies and decrease their performance. However, an adversary may be interested not only in decreasing the reward, but also in modifying specific temporal logic properties of the policy. This paper presents a metric that measures the exact impact of adversarial attacks against such properties. We use this metric to craft optimal adversarial attacks. Furthermore, we introduce a model checking method that allows us to verify the robustness of RL policies against adversarial attacks. Our empirical analysis confirms (1) the quality of our metric to craft adversarial attacks against temporal logic properties, and (2) that we are able to concisely assess a system's robustness against attacks.