灵长类动物的视觉系统是强大感知的黄金标准。因此，人们普遍认为，模仿这些系统基础的神经表现形式将产生具有对手稳健的人工视觉系统。在这项工作中，我们开发了一种直接对灵长类动物大脑活动进行对抗性视觉攻击的方法。然后，我们利用这种方法来证明上述信念可能不是很好的基础。具体而言，我们报告说，组成灵长类动物视觉系统的生物神经元表现出对对抗性扰动的敏感性，这些扰动与现有（训练有素的）人工神经网络相当。

神经科学家和机器学习研究人员通常引用对抗的例子，作为计算模型如何从生物感官系统发散的示例。最近的工作已经提出将生物启发组件添加到视觉神经网络中，作为提高其对抗性鲁棒性的一种方式。一种令人惊讶的有效组分，用于减少对抗性脆弱性是响应随机性，例如由生物神经元呈现的响应性随机性。在这里，使用最近开发的从计算神经科学的几何技术，我们研究了对抗性扰动如何影响标准，前列培训和生物学启发的随机网络的内部表示。我们为每种类型的网络找到了不同的几何签名，揭示了实现稳健表示的不同机制。接下来，我们将这些结果概括为听觉域，表明神经插值性也使听觉模型对对抗对抗扰动更鲁棒。随机网络的几何分析揭示了清洁和离前动脉扰动刺激的表示之间的重叠，并且定量表现出随机性的竞争几何效果在对抗和清洁性能之间调解权衡。我们的结果阐明了通过对外内培训和随机网络利用的强大感知的策略，并帮助解释了随机性如何有利于机器和生物计算。

虽然一些卷积神经网络（CNNS）在对象分类中超过了人类的视觉能力，但它们通常努力识别以不同类型的常见噪声模式损坏的图像中的对象，突出了这一系列模型的主要限制。最近，已经表明，在CNNS前面模拟主视觉皮质（V1）导致对这些图像扰动的鲁棒性的小改进。在本研究中，我们从观察到v1模型的不同变体显示特定腐败类型的增益。然后，我们使用合奏技术构建一个新模型，该技术将多个单独模型与不同的V1前端变体组合。该模型集合利用每个腐败类别的鲁棒性的显着改善，平均优于38％的基础模型。最后，我们表明使用蒸馏，可以将集合模型中的知识部分压缩成具有V1前端的单个模型。虽然这里使用的合并和蒸馏技术几乎没有生物学，但是这里呈现的结果表明，通过组合V1中不同神经元电路的特定强度，可以改善CNN的鲁棒性，用于广泛的扰动。

Dense prediction tasks such as segmentation and detection of pathological entities hold crucial clinical value in the digital pathology workflow. However, obtaining dense annotations on large cohorts is usually tedious and expensive. Contrastive learning (CL) is thus often employed to leverage large volumes of unlabeled data to pre-train the backbone network. To boost CL for dense prediction, some studies have proposed variations of dense matching objectives in pre-training. However, our analysis shows that employing existing dense matching strategies on histopathology images enforces invariance among incorrect pairs of dense features and, thus, is imprecise. To address this, we propose a precise location-based matching mechanism that utilizes the overlapping information between geometric transformations to precisely match regions in two augmentations. Extensive experiments on two pretraining datasets (TCGA-BRCA, NCT-CRC-HE) and three downstream datasets (GlaS, CRAG, BCSS) highlight the superiority of our method in semantic and instance segmentation tasks. Our method outperforms previous dense matching methods by up to 7.2 % in average precision for detection and 5.6 % in average precision for instance segmentation tasks. Additionally, by using our matching mechanism in the three popular contrastive learning frameworks, MoCo-v2, VICRegL and ConCL, the average precision in detection is improved by 0.7 % to 5.2 % and the average precision in segmentation is improved by 0.7 % to 4.0 %, demonstrating its generalizability.

The proliferation of automatic faithfulness metrics for summarization has produced a need for benchmarks to evaluate them. While existing benchmarks measure the correlation with human judgements of faithfulness on model-generated summaries, they are insufficient for diagnosing whether metrics are: 1) consistent, i.e., decrease as errors are introduced into a summary, 2) effective on human-written texts, and 3) sensitive to different error types (as summaries can contain multiple errors). To address these needs, we present a benchmark of unfaithful minimal pairs (BUMP), a dataset of 889 human-written, minimally different summary pairs, where a single error (from an ontology of 7 types) is introduced to a summary from the CNN/DailyMail dataset to produce an unfaithful summary. We find BUMP complements existing benchmarks in a number of ways: 1) the summaries in BUMP are harder to discriminate and less probable under SOTA summarization models, 2) BUMP enables measuring the consistency of metrics, and reveals that the most discriminative metrics tend not to be the most consistent, 3) BUMP enables the measurement of metrics' performance on individual error types and highlights areas of weakness for future work.

Recent advances in safety-critical risk-aware control are predicated on apriori knowledge of the disturbances a system might face. This paper proposes a method to efficiently learn these disturbances online, in a risk-aware context. First, we introduce the concept of a Surface-at-Risk, a risk measure for stochastic processes that extends Value-at-Risk -- a commonly utilized risk measure in the risk-aware controls community. Second, we model the norm of the state discrepancy between the model and the true system evolution as a scalar-valued stochastic process and determine an upper bound to its Surface-at-Risk via Gaussian Process Regression. Third, we provide theoretical results on the accuracy of our fitted surface subject to mild assumptions that are verifiable with respect to the data sets collected during system operation. Finally, we experimentally verify our procedure by augmenting a drone's controller and highlight performance increases achieved via our risk-aware approach after collecting less than a minute of operating data.

Importance: Social determinants of health (SDOH) are known to be associated with increased risk of suicidal behaviors, but few studies utilized SDOH from unstructured electronic health record (EHR) notes. Objective: To investigate associations between suicide and recent SDOH, identified using structured and unstructured data. Design: Nested case-control study. Setting: EHR data from the US Veterans Health Administration (VHA). Participants: 6,122,785 Veterans who received care in the US VHA between October 1, 2010, and September 30, 2015. Exposures: Occurrence of SDOH over a maximum span of two years compared with no occurrence of SDOH. Main Outcomes and Measures: Cases of suicide deaths were matched with 4 controls on birth year, cohort entry date, sex, and duration of follow-up. We developed an NLP system to extract SDOH from unstructured notes. Structured data, NLP on unstructured data, and combining them yielded seven, eight and nine SDOH respectively. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated using conditional logistic regression. Results: In our cohort, 8,821 Veterans committed suicide during 23,725,382 person-years of follow-up (incidence rate 37.18 /100,000 person-years). Our cohort was mostly male (92.23%) and white (76.99%). Across the six common SDOH as covariates, NLP-extracted SDOH, on average, covered 84.38% of all SDOH occurrences. All SDOH, measured by structured data and NLP, were significantly associated with increased risk of suicide. The SDOH with the largest effects was legal problems (aOR=2.67, 95% CI=2.46-2.89), followed by violence (aOR=2.26, 95% CI=2.11-2.43). NLP-extracted and structured SDOH were also associated with suicide. Conclusions and Relevance: NLP-extracted SDOH were always significantly associated with increased risk of suicide among Veterans, suggesting the potential of NLP in public health studies.

Fires have destructive power when they break out and affect their surroundings on a devastatingly large scale. The best way to minimize their damage is to detect the fire as quickly as possible before it has a chance to grow. Accordingly, this work looks into the potential of AI to detect and recognize fires and reduce detection time using object detection on an image stream. Object detection has made giant leaps in speed and accuracy over the last six years, making real-time detection feasible. To our end, we collected and labeled appropriate data from several public sources, which have been used to train and evaluate several models based on the popular YOLOv4 object detector. Our focus, driven by a collaborating industrial partner, is to implement our system in an industrial warehouse setting, which is characterized by high ceilings. A drawback of traditional smoke detectors in this setup is that the smoke has to rise to a sufficient height. The AI models brought forward in this research managed to outperform these detectors by a significant amount of time, providing precious anticipation that could help to minimize the effects of fires further.

We present a method for controlling a swarm using its spectral decomposition -- that is, by describing the set of trajectories of a swarm in terms of a spatial distribution throughout the operational domain -- guaranteeing scale invariance with respect to the number of agents both for computation and for the operator tasked with controlling the swarm. We use ergodic control, decentralized across the network, for implementation. In the DARPA OFFSET program field setting, we test this interface design for the operator using the STOMP interface -- the same interface used by Raytheon BBN throughout the duration of the OFFSET program. In these tests, we demonstrate that our approach is scale-invariant -- the user specification does not depend on the number of agents; it is persistent -- the specification remains active until the user specifies a new command; and it is real-time -- the user can interact with and interrupt the swarm at any time. Moreover, we show that the spectral/ergodic specification of swarm behavior degrades gracefully as the number of agents goes down, enabling the operator to maintain the same approach as agents become disabled or are added to the network. We demonstrate the scale-invariance and dynamic response of our system in a field relevant simulator on a variety of tactical scenarios with up to 50 agents. We also demonstrate the dynamic response of our system in the field with a smaller team of agents. Lastly, we make the code for our system available.

Objective: Evictions are involved in a cascade of negative events that can lead to unemployment, homelessness, long-term poverty, and mental health problems. In this study, we developed a natural language processing system to automatically detect eviction incidences and their attributes from electronic health record (EHR) notes. Materials and Methods: We annotated eviction status in 5000 EHR notes from the Veterans Health Administration. We developed a novel model, called Knowledge Injection based on Ripple Effects of Social and Behavioral Determinants of Health (KIRESH), that has shown to substantially outperform other state-of-the-art models such as fine-tuning pre-trained language models like BioBERT and Bio_ClinicalBERT. Moreover, we designed a prompt to further improve the model performance by using the intrinsic connection between the two sub-tasks of eviction presence and period prediction. Finally, we used the Temperature Scaling-based Calibration on our KIRESH-Prompt method to avoid over-confidence issues arising from the imbalance dataset. Results: KIRESH-Prompt achieved a Macro-F1 of 0.6273 (presence) and 0.7115 (period), which was significantly higher than 0.5382 (presence) and 0.67167 (period) for just fine-tuning Bio_ClinicalBERT model. Conclusion and Future Work: KIRESH-Prompt has substantially improved eviction status classification. In future work, we will evaluate the generalizability of the model framework to other applications.