Food rescue organizations simultaneously tackle food insecurity and waste by working with volunteers to redistribute food from donors who have excess to recipients who need it. Volunteer feedback allows food rescue organizations to identify issues early and ensure volunteer satisfaction. However, food rescue organizations monitor feedback manually, which can be cumbersome and labor-intensive, making it difficult to prioritize which issues are most important. In this work, we investigate how large language models (LLMs) assist food rescue organizers in understanding and taking action based on volunteer experiences. We work with 412 Food Rescue, a large food rescue organization based in Pittsburgh, Pennsylvania, to design RescueLens, an LLM-powered tool that automatically categorizes volunteer feedback, suggests donors and recipients to follow up with, and updates volunteer directions based on feedback. We evaluate the performance of RescueLens on an annotated dataset, and show that it can recover 96% of volunteer issues at 71% precision. Moreover, by ranking donors and recipients according to their rates of volunteer issues, RescueLens allows organizers to focus on 0.5% of donors responsible for more than 30% of volunteer issues. RescueLens is now deployed at 412 Food Rescue and through semi-structured interviews with organizers, we find that RescueLens streamlines the feedback process so organizers better allocate their time.

Reinforcement Learning is a machine learning methodology that has demonstrated strong performance across a variety of tasks. In particular, it plays a central role in the development of artificial autonomous agents. As these agents become increasingly capable, market readiness is rapidly approaching, which means those agents, for example taking the form of humanoid robots or autonomous cars, are poised to transition from laboratory prototypes to autonomous operation in real-world environments. This transition raises concerns leading to specific requirements for these systems - among them, the requirement that they are designed to behave ethically. Crucially, research directed toward building agents that fulfill the requirement to behave ethically - referred to as artificial moral agents(AMAs) - has to address a range of challenges at the intersection of computer science and philosophy. This study explores the development of reason-based artificial moral agents (RBAMAs). RBAMAs are build on an extension of the reinforcement learning architecture to enable moral decision-making based on sound normative reasoning, which is achieved by equipping the agent with the capacity to learn a reason-theory - a theory which enables it to process morally relevant propositions to derive moral obligations - through case-based feedback. They are designed such that they adapt their behavior to ensure conformance to these obligations while they pursue their designated tasks. These features contribute to the moral justifiability of the their actions, their moral robustness, and their moral trustworthiness, which proposes the extended architecture as a concrete and deployable framework for the development of AMAs that fulfills key ethical desiderata. This study presents a first implementation of an RBAMA and demonstrates the potential of RBAMAs in initial experiments.

Positives The paper is well-written and includes a through literature review. The following paper is also very relevant to the submission: Shrivastava, Ashish, et al. "Learning from simulated and unsupervised images through adversarial training." Novelty of the method over [44] is not major. Still, I believe no one has shown that computing flow on simulated data and using it for training improves over RGB only (although the improvement is quite marginal). Simulation pipeline proposed in the paper seems to be quite useful.

After reviewer discussion and rebuttal this paper received three acceptance recommendations. R1 and R2 are more positive about the paper and acknoweldge the contribution. R3 points out that the impact of using just flow and no person and camera motion is limited. Please consider the post-rebuttal portion of the review to include in a final revision. The method, approach and quality of the paper are high as acknowledged by all reviewers.

Synthetic visual data can provide practicically infinite diversity and rich labels, while avoiding ethical issues with privacy and bias. However, for many tasks, current models trained on synthetic data generalize poorly to real data. The task of 3D human pose estimation is a particularly interesting example of this sim2real problem, because learning-based approaches perform reasonably well given real training data, yet labeled 3D poses are extremely difficult to obtain in the wild, limiting scalability. In this paper, we show that standard neural-network approaches, which perform poorly when trained on synthetic RGB images, can perform well when the data is pre-processed to extract cues about the person's motion, notably as optical flow and the motion of 2D keypoints. Therefore, our results suggest that motion can be a simple way to bridge a sim2real gap when video is available. We evaluate on the 3D Poses in the Wild dataset, the most challenging modern benchmark for 3D pose estimation, where we show full 3D mesh recovery that is on par with state-of-the-art methods trained on real 3D sequences, despite training only on synthetic humans from the SURREAL dataset.

UAVs are playing an increasingly important role in the field of wilderness rescue by virtue of their flexibility. This paper proposes a fusion of UAV vision technology and satellite image analysis technology for active wildfires detection and road networks extraction of wildfire areas and real-time dynamic escape route planning for people in distress. Firstly, the fire source location and the segmentation of smoke and flames are targeted based on Sentinel 2 satellite imagery. Secondly, the road segmentation and the road condition assessment are performed by D-linkNet and NDVI values in the central area of the fire source by UAV. Finally, the dynamic optimal route planning for humans in real time is performed by the weighted A* algorithm in the road network with the dynamic fire spread model. Taking the Chongqing wildfire on August 24, 2022, as a case study, the results demonstrate that the dynamic escape route planning algorithm can provide an optimal real-time navigation path for humans in the presence of fire through the information fusion of UAVs and satellites.

Just before 3 a.m. one night this month, Scott Slumpff was awakened by the ding of a text message. "An ALERTCalifornia anomaly has been confirmed in your area of interest," the message said. Slumpff, a battalion chief with the California Department of Forestry and Fire Protection, sprang into action. The message meant the agency's new artificial intelligence system had identified signs of a wildfire with a remote mountaintop camera in San Diego County. Within minutes, crews were dispatched to the burgeoning blaze on Mount Laguna -- squelching it before it grew any larger than a 10-foot-by-10-foot spot.

Machine learning models are often provisioned as a cloud-based service where the clients send their data to the service provider to obtain the result. This setting is commonplace due to the high value of the models, but it requires the clients to forfeit the privacy that the query data may contain. Homomorphic encryption (HE) is a promising technique to address this adversity. With HE, the service provider can take encrypted data as a query and run the model without decrypting it. The result remains encrypted, and only the client can decrypt it. All these benefits come at the cost of computational cost because HE turns simple floating-point arithmetic into the computation between long (degree over 1024) polynomials. Previous work has proposed to tailor deep neural networks for efficient computation over encrypted data, but already high computational cost is again amplified by HE, hindering performance improvement. In this paper we show hyperdimensional computing can be a rescue for privacy-preserving machine learning over encrypted data. We find that the advantage of hyperdimensional computing in performance is amplified when working with HE. This observation led us to design HE-HDC, a machine-learning inference system that uses hyperdimensional computing with HE. We carefully structure the machine learning service so that the server will perform only the HE-friendly computation. Moreover, we adapt the computation and HE parameters to expedite computation while preserving accuracy and security. Our experimental result based on real measurements shows that HE-HDC outperforms existing systems by 26~3000 times with comparable classification accuracy.

Last month, the entire world was abuzz when five über wealthy explorers perished at the bottom of the Atlantic Ocean near the grave of the once "unsinkable ship." Disturbingly, during the same week, hundreds of war-torn refugees drowned in the Mediterranean with little news of their plight. The irony of machine versus nature illustrates how tiny humans are in the universe, and that every soul rich or poor is precious. It is with this attitude that many roboticists have been tackling some of the hardest problems in the galaxy from space exploration to desert mining to oceanography to search & rescue. Following the news of the implosion of the Titan submersible, I reached out to Professor F. Javier Diez of Rutgers University for his comment on the rescue mission and the role of robots.

The past few years have seen an uptick of interest in studying cooperative AI, that is, AI systems that are designed to be effective at cooperating. Indeed, a number of influential researchers recently argued that "[w]e need to build a science of cooperative AI... progress towards socially valuable AI will be stunted unless we put the problem of cooperation at the centre of our research" [6]. One type of cooperative behavior is joint behavior, that is, collaboration scenarios where the success of the joint action is dependent on all agents doing their parts; one agent deviating can cause the efforts of others to be ineffective. The notion of joint behavior has been studied (in much detail) under various names such as "acting together", "teamwork", "collaborative plans", and "shared plans", and highly influential models of it were developed (see, e.g., [2, 4, 10, 11, 15, 24]). Efforts were also made to engineer some of these theories into real-world joint planning systems [23, 20].