与痴呆症相关的认知障碍（CI）在全球范围内影响超过5500万人，并且每3秒钟以一个新病例的速度迅速增长。随着临床试验反复出现的失败，早期诊断至关重要，但是在低水平和中等收入国家中，全球75％的痴呆症病例未被诊断为90％。众所周知，当前的诊断方法是复杂的，涉及对医学笔记，大量认知测试，昂贵的脑部扫描或脊柱液体测试的手动审查。与CI相关的信息经常在电子健康记录（EHR）中找到，并且可以为早期诊断提供重要线索，但是专家的手动审查是繁琐的，并且容易发生。该项目开发了一种新型的最新自动筛选管道，用于可扩展和高速发现EHR中的CI。为了了解EHR中复杂语言结构的语言环境，构建了一个8,656个序列的数据库，以训练基于注意力的深度学习自然语言处理模型以对序列进行分类。使用序列级别分类器开发了基于逻辑回归的患者级别预测模型。深度学习系统的精度达到了93％，AUC = 0.98，以识别其EHR中没有较早诊断，与痴呆有关的诊断代码或与痴呆有关的药物的患者。否则，这些患者将未被发现或检测到太晚。 EHR筛选管道已部署在Neurahealthnlp中，这是一种用于自动化和实时CI筛选的Web应用程序，只需将EHR上传到浏览器中即可。 Neurahealthnlp更便宜，更快，更容易获得，并且胜过当前的临床方法，包括基于文本的分析和机器学习方法。它使得早期诊断可在稀缺的医疗服务中可行，但可访问的互联网或蜂窝服务。

痴呆症是一种神经退行性疾病，导致认知下降，并影响全世界超过5000万人。痴呆症是由医疗保健专业人士诊断的 - 只有患有痴呆症的四个人中只有一名诊断出来。即使制造诊断，也可能无法作为患者图表中的疾病（ICD）诊断码的结构化国际分类。与认知障碍（CI）有关的信息通常在电子健康记录（EHR）中发现，但专家临床医生票据的手工审查既耗时，往往容易出错。本票据的自动化挖掘为在EHR数据中标记有认知障碍患者的机会。我们开发了自然语言处理（NLP）工具，以识别具有认知障碍的患者，并证明语言背景提高了认知障碍分类任务的性能。我们微调我们的注意力深入学习模型，可以从复杂的语言结构中学习，并且相对于基线NLP模型的精度（0.93）大大提高（0.84）。此外，我们表明深度学习NLP可以成功识别没有痴呆相关的ICD代码或药物的痴呆症患者。

在自动车辆（AVS）中，预警系统依赖于碰撞预测，以确保乘员安全。然而，使用深度卷积网络的最先进的方法在建模冲突中失败或者太昂贵/慢，使它们不太适合在AV边缘硬件上部署。为了解决这些限制，我们提出了SG2VEC，一种使用图形神经网络（GNN）和长短期内存（LSTM）层的时空场景图嵌入方法，以通过视觉场景感知来预测未来的碰撞。我们展示SG2VEC预测碰撞8.11％，比综合数据集的最新方法提前39.07％，在挑战现实世界碰撞数据集中更准确地进行29.47％。我们还表明，SG2VEC在将知识从合成数据集转移到现实世界驾驶数据集时更好。最后，我们证明SG2VEC更快地执行推论9.3X，较小的型号为88.0％，功率少32.4％，而且能量少于行业标准的NVIDIA驱动PX 2平台，制作它更适合在边缘实施。

最近，已经证明了与图形学习技术结合使用的道路场景图表示，在包括动作分类，风险评估和碰撞预测的任务中优于最先进的深度学习技术。为了使Road场景图形表示的应用探索，我们介绍了RoadScene2VEC：一个开源工具，用于提取和嵌入公路场景图。 RoadScene2VEC的目标是通过提供用于生成场景图的工具，为生成时空场景图嵌入的工具以及用于可视化和分析场景图的工具来实现Road场景图的应用程序和能力基于方法。 RoadScene2VEC的功能包括（i）来自Carla Simulator的视频剪辑或数据的自定义场景图，（ii）多种可配置的时空图嵌入模型和基于基于基于CNN的模型，（iii）内置功能使用图形和序列嵌入用于风险评估和碰撞预测应用，（iv）用于评估转移学习的工具，以及（v）用于可视化场景图的实用程序，并分析图形学习模型的解释性。我们展示了道路展示的效用，用于这些用例，具有实验结果和基于CNN的模型的实验结果和定性评估。 Rodscene2vec可在https://github.com/aicps/roadscene2vec提供。

Accurate and smooth global navigation satellite system (GNSS) positioning for pedestrians in urban canyons is still a challenge due to the multipath effects and the non-light-of-sight (NLOS) receptions caused by the reflections from surrounding buildings. The recently developed factor graph optimization (FGO) based GNSS positioning method opened a new window for improving urban GNSS positioning by effectively exploiting the measurement redundancy from the historical information to resist the outlier measurements. Unfortunately, the FGO-based GNSS standalone positioning is still challenged in highly urbanized areas. As an extension of the previous FGO-based GNSS positioning method, this paper exploits the potential of the pedestrian dead reckoning (PDR) model in FGO to improve the GNSS standalone positioning performance in urban canyons. Specifically, the relative motion of the pedestrian is estimated based on the raw acceleration measurements from the onboard smartphone inertial measurement unit (IMU) via the PDR algorithm. Then the raw GNSS pseudorange, Doppler measurements, and relative motion from PDR are integrated using the FGO. Given the context of pedestrian navigation with a small acceleration most of the time, a novel soft motion model is proposed to smooth the states involved in the factor graph model. The effectiveness of the proposed method is verified step-by-step through two datasets collected in dense urban canyons of Hong Kong using smartphone-level GNSS receivers. The comparison between the conventional extended Kalman filter, several existing methods, and FGO-based integration is presented. The results reveal that the existing FGO-based GNSS standalone positioning is highly complementary to the PDR's relative motion estimation. Both improved positioning accuracy and trajectory smoothness are obtained with the help of the proposed method.

The demand of high-resolution video contents has grown over the years. However, the delivery of high-resolution video is constrained by either computational resources required for rendering or network bandwidth for remote transmission. To remedy this limitation, we leverage the eye trackers found alongside existing augmented and virtual reality headsets. We propose the application of video super-resolution (VSR) technique to fuse low-resolution context with regional high-resolution context for resource-constrained consumption of high-resolution content without perceivable drop in quality. Eye trackers provide us the gaze direction of a user, aiding us in the extraction of the regional high-resolution context. As only pixels that falls within the gaze region can be resolved by the human eye, a large amount of the delivered content is redundant as we can't perceive the difference in quality of the region beyond the observed region. To generate a visually pleasing frame from the fusion of high-resolution region and low-resolution region, we study the capability of a deep neural network of transferring the context of the observed region to other regions (low-resolution) of the current and future frames. We label this task a Foveated Video Super-Resolution (FVSR), as we need to super-resolve the low-resolution regions of current and future frames through the fusion of pixels from the gaze region. We propose Cross-Resolution Flow Propagation (CRFP) for FVSR. We train and evaluate CRFP on REDS dataset on the task of 8x FVSR, i.e. a combination of 8x VSR and the fusion of foveated region. Departing from the conventional evaluation of per frame quality using SSIM or PSNR, we propose the evaluation of past foveated region, measuring the capability of a model to leverage the noise present in eye trackers during FVSR. Code is made available at https://github.com/eugenelet/CRFP.

To reproduce the success of text-to-image (T2I) generation, recent works in text-to-video (T2V) generation employ large-scale text-video dataset for fine-tuning. However, such paradigm is computationally expensive. Humans have the amazing ability to learn new visual concepts from just one single exemplar. We hereby study a new T2V generation problem$\unicode{x2014}$One-Shot Video Generation, where only a single text-video pair is presented for training an open-domain T2V generator. Intuitively, we propose to adapt the T2I diffusion model pretrained on massive image data for T2V generation. We make two key observations: 1) T2I models are able to generate images that align well with the verb terms; 2) extending T2I models to generate multiple images concurrently exhibits surprisingly good content consistency. To further learn continuous motion, we propose Tune-A-Video with a tailored Sparse-Causal Attention, which generates videos from text prompts via an efficient one-shot tuning of pretrained T2I diffusion models. Tune-A-Video is capable of producing temporally-coherent videos over various applications such as change of subject or background, attribute editing, style transfer, demonstrating the versatility and effectiveness of our method.

Prior works on improving speech quality with visual input typically study each type of auditory distortion separately (e.g., separation, inpainting, video-to-speech) and present tailored algorithms. This paper proposes to unify these subjects and study Generalized Speech Enhancement, where the goal is not to reconstruct the exact reference clean signal, but to focus on improving certain aspects of speech. In particular, this paper concerns intelligibility, quality, and video synchronization. We cast the problem as audio-visual speech resynthesis, which is composed of two steps: pseudo audio-visual speech recognition (P-AVSR) and pseudo text-to-speech synthesis (P-TTS). P-AVSR and P-TTS are connected by discrete units derived from a self-supervised speech model. Moreover, we utilize self-supervised audio-visual speech model to initialize P-AVSR. The proposed model is coined ReVISE. ReVISE is the first high-quality model for in-the-wild video-to-speech synthesis and achieves superior performance on all LRS3 audio-visual enhancement tasks with a single model. To demonstrates its applicability in the real world, ReVISE is also evaluated on EasyCom, an audio-visual benchmark collected under challenging acoustic conditions with only 1.6 hours of training data. Similarly, ReVISE greatly suppresses noise and improves quality. Project page: https://wnhsu.github.io/ReVISE.

Relation Extraction (RE) has been extended to cross-document scenarios because many relations are not simply described in a single document. This inevitably brings the challenge of efficient open-space evidence retrieval to support the inference of cross-document relations, along with the challenge of multi-hop reasoning on top of entities and evidence scattered in an open set of documents. To combat these challenges, we propose Mr.CoD, a multi-hop evidence retrieval method based on evidence path mining and ranking with adapted dense retrievers. We explore multiple variants of retrievers to show evidence retrieval is an essential part in cross-document RE. Experiments on CodRED show that evidence retrieval with Mr.Cod effectively acquires cross-document evidence that essentially supports open-setting cross-document RE. Additionally, we show that Mr.CoD facilitates evidence retrieval and boosts end-to-end RE performance with effective multi-hop reasoning in both closed and open settings of RE.

Nowadays, the need for user editing in a 3D scene has rapidly increased due to the development of AR and VR technology. However, the existing 3D scene completion task (and datasets) cannot suit the need because the missing regions in scenes are generated by the sensor limitation or object occlusion. Thus, we present a novel task named free-form 3D scene inpainting. Unlike scenes in previous 3D completion datasets preserving most of the main structures and hints of detailed shapes around missing regions, the proposed inpainting dataset, FF-Matterport, contains large and diverse missing regions formed by our free-form 3D mask generation algorithm that can mimic human drawing trajectories in 3D space. Moreover, prior 3D completion methods cannot perform well on this challenging yet practical task, simply interpolating nearby geometry and color context. Thus, a tailored dual-stream GAN method is proposed. First, our dual-stream generator, fusing both geometry and color information, produces distinct semantic boundaries and solves the interpolation issue. To further enhance the details, our lightweight dual-stream discriminator regularizes the geometry and color edges of the predicted scenes to be realistic and sharp. We conducted experiments with the proposed FF-Matterport dataset. Qualitative and quantitative results validate the superiority of our approach over existing scene completion methods and the efficacy of all proposed components.