随着面部生物识别技术的广泛采用，在自动面部识别（FR）应用中区分相同的双胞胎和非双胞胎外观相似的问题变得越来越重要。由于同卵双胞胎和外观相似的面部相似性很高，因此这些面对对面部识别工具表示最困难的病例。这项工作介绍了迄今为止汇编的最大的双胞胎数据集之一，以应对两个挑战：1）确定相同双胞胎和2）的面部相似性的基线度量和2）应用此相似性措施来确定多ppelgangers的影响或外观 - Alikes，关于大面部数据集的FR性能。面部相似性度量是通过深度卷积神经网络确定的。该网络经过量身定制的验证任务进行培训，旨在鼓励网络在嵌入空间中将高度相似的面对对组合在一起，并达到0.9799的测试AUC。所提出的网络为任何两个给定的面提供了定量相似性评分，并已应用于大规模面部数据集以识别相似的面对对。还执行了一个附加分析，该分析还将面部识别工具返回的比较分数以及提议网络返回的相似性分数。

我们提出了一种质量感知的多模式识别框架，其将来自多个生物特征的表示与不同的质量和样本数量相结合，以通过基于样本的质量提取互补识别信息来实现增加的识别准确性。我们通过使用以弱监督时尚估计的质量分数加权，为融合输入方式的质量意识框架，以融合输入方式的融合。此框架利用两个融合块，每个融合块由一组质量感知和聚合网络表示。除了架构修改外，我们还提出了两种特定于任务特定的损耗功能：多模式可分离性损失和多模式紧凑性损失。第一个损失确保了类的模态的表示具有可比的大小来提供更好的质量估计，而不同类别的多式数代表分布以实现嵌入空间中的最大判别。第二次丢失，被认为是正规化网络权重，通过规范框架来提高泛化性能。我们通过考虑由面部，虹膜和指纹方式组成的三个多模式数据集来评估性能。通过与最先进的算法进行比较来证明框架的功效。特别是，我们的框架优于BioMdata的模式的级别和得分级别融合超过30％以获得$ 10 ^ { - 4} $ 10 ^ { - 4} $的真正验收率。

在本文中，我们考虑了面部变形攻击的挑战，这大大破坏了面部识别系统的完整性，例如在边境保护机构中采用的那些。变形检测可以制定为提取细粒度的表示，其中利用局部鉴别特征来学习假设。为了在不同的粒度和去耦的光谱信息中获取辨别特征，我们利用小波域分析来深入了解变形面的空间频率含量。这样，而不是使用RGB域中的图像，我们使用2D小波分解将每个图像分解为其小波子频带，并且采用深度监督特征选择方案来查找输入图像的最辨别的小波子带。为此，我们使用变形和真绒图像的分解小波子带训练深度神经网络（DNN）变形探测器。在训练阶段，我们的结构群稀疏受约束的DNN从所有子带中选择了最多的鉴别性小波子带，我们恢复了我们的DNN，导致在探测器上实现了推理时的变形图像的精确检测图片。通过结构化组套索增强的深形变形探测器的功效通过三个面部变形图像数据库，即Visapp17，LMA和摩根进行了验证了通过实验验证。

变形是为了创建一个包含无论是个人的特点一个新的身份在图像中两个或多个学科相结合的过程。演变图像可以骗过面部识别系统（FRS）为虚假接受多人，导致国家安全故障。由于演变图像合成变得更容易，至关重要的是要研究界的可用数据扩展，以协助打击这种困境。在本文中，我们探索的两种方法音素变形图像生成，这些几何变换（翘曲和混合以产生变形的图像）和光度扰动组合。我们利用这两种方法来从FERET，FRGC和FRLL数据集高品质adversarially扰动变种。最终图像保留高相似两个输入受试者从而在视觉域最小伪像一段时间。图像通过融合来自两个外观类似主题小波子带合成，然后adversarially扰乱创建高度说服力的形象欺骗人类和深变形探测器。

Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by the robotic limb. However, the method for controlling multiple degrees of freedom via user-generated signals remains challenging. To address this issue, various machine learning controllers have been developed to better predict movement intent. As these controllers become more intelligent and take on more autonomy in the system, the traditional approach of representing the human-machine interface as a human controlling a tool becomes limiting. One possible approach to improve the understanding of these interfaces is to model them as collaborative, multi-agent systems through the lens of joint action. The field of joint action has been commonly applied to two human partners who are trying to work jointly together to achieve a task, such as singing or moving a table together, by effecting coordinated change in their shared environment. In this work, we compare different prosthesis controllers (proportional electromyography with sequential switching, pattern recognition, and adaptive switching) in terms of how they present the hallmarks of joint action. The results of the comparison lead to a new perspective for understanding how existing myoelectric systems relate to each other, along with recommendations for how to improve these systems by increasing the collaborative communication between each partner.

Hyperspectral Imaging (HSI) provides detailed spectral information and has been utilised in many real-world applications. This work introduces an HSI dataset of building facades in a light industry environment with the aim of classifying different building materials in a scene. The dataset is called the Light Industrial Building HSI (LIB-HSI) dataset. This dataset consists of nine categories and 44 classes. In this study, we investigated deep learning based semantic segmentation algorithms on RGB and hyperspectral images to classify various building materials, such as timber, brick and concrete.

Strategic test allocation plays a major role in the control of both emerging and existing pandemics (e.g., COVID-19, HIV). Widespread testing supports effective epidemic control by (1) reducing transmission via identifying cases, and (2) tracking outbreak dynamics to inform targeted interventions. However, infectious disease surveillance presents unique statistical challenges. For instance, the true outcome of interest - one's positive infectious status, is often a latent variable. In addition, presence of both network and temporal dependence reduces the data to a single observation. As testing entire populations regularly is neither efficient nor feasible, standard approaches to testing recommend simple rule-based testing strategies (e.g., symptom based, contact tracing), without taking into account individual risk. In this work, we study an adaptive sequential design involving n individuals over a period of {\tau} time-steps, which allows for unspecified dependence among individuals and across time. Our causal target parameter is the mean latent outcome we would have obtained after one time-step, if, starting at time t given the observed past, we had carried out a stochastic intervention that maximizes the outcome under a resource constraint. We propose an Online Super Learner for adaptive sequential surveillance that learns the optimal choice of tests strategies over time while adapting to the current state of the outbreak. Relying on a series of working models, the proposed method learns across samples, through time, or both: based on the underlying (unknown) structure in the data. We present an identification result for the latent outcome in terms of the observed data, and demonstrate the superior performance of the proposed strategy in a simulation modeling a residential university environment during the COVID-19 pandemic.

It is crucial to choose the appropriate scale in order to build an effective and informational representation of a complex system. Scientists carefully choose the scales for their experiments to extract the variables that describe the causalities in the system. They found that the coarse scale(macro) is sometimes more causal and informative than the numerous-parameter observations(micro). The phenomenon that the causality emerges by coarse-graining is called Causal Emergence(CE). Based on information theory, a number of recent works quantitatively showed that CE indeed happens while coarse-graining a micro model to the macro. However, the existing works have not discussed the question of why and when the CE happens. We quantitatively analyze the redistribution of uncertainties for coarse-graining and suggest that the redistribution of uncertainties is the cause of causal emergence. We further analyze the thresholds that determine if CE happens or not. From the regularity of the transition probability matrix(TPM) of discrete systems, the mathematical expressions of the model properties are derived. The values of thresholds for different operations are computed. The results provide the critical and specific conditions of CE as helpful suggestions for choosing the proper coarse-graining operation. The results also provided a new way to better understand the nature of causality and causal emergence.

Artificial intelligence (AI) has enormous potential to improve Air Force pilot training by providing actionable feedback to pilot trainees on the quality of their maneuvers and enabling instructor-less flying familiarization for early-stage trainees in low-cost simulators. Historically, AI challenges consisting of data, problem descriptions, and example code have been critical to fueling AI breakthroughs. The Department of the Air Force-Massachusetts Institute of Technology AI Accelerator (DAF-MIT AI Accelerator) developed such an AI challenge using real-world Air Force flight simulator data. The Maneuver ID challenge assembled thousands of virtual reality simulator flight recordings collected by actual Air Force student pilots at Pilot Training Next (PTN). This dataset has been publicly released at Maneuver-ID.mit.edu and represents the first of its kind public release of USAF flight training data. Using this dataset, we have applied a variety of AI methods to separate "good" vs "bad" simulator data and categorize and characterize maneuvers. These data, algorithms, and software are being released as baselines of model performance for others to build upon to enable the AI ecosystem for flight simulator training.

Real-time air pollution monitoring is a valuable tool for public health and environmental surveillance. In recent years, there has been a dramatic increase in air pollution forecasting and monitoring research using artificial neural networks (ANNs). Most of the prior work relied on modeling pollutant concentrations collected from ground-based monitors and meteorological data for long-term forecasting of outdoor ozone, oxides of nitrogen, and PM2.5. Given that traditional, highly sophisticated air quality monitors are expensive and are not universally available, these models cannot adequately serve those not living near pollutant monitoring sites. Furthermore, because prior models were built on physical measurement data collected from sensors, they may not be suitable for predicting public health effects experienced from pollution exposure. This study aims to develop and validate models to nowcast the observed pollution levels using Web search data, which is publicly available in near real-time from major search engines. We developed novel machine learning-based models using both traditional supervised classification methods and state-of-the-art deep learning methods to detect elevated air pollution levels at the US city level, by using generally available meteorological data and aggregate Web-based search volume data derived from Google Trends. We validated the performance of these methods by predicting three critical air pollutants (ozone (O3), nitrogen dioxide (NO2), and fine particulate matter (PM2.5)), across ten major U.S. metropolitan statistical areas (MSAs) in 2017 and 2018.