We study mechanisms for the facility location problem augmented with predictions of the optimal facility location. We demonstrate that an egalitarian viewpoint which considers both the maximum distance of any agent from the facility and the minimum utility of any agent provides important new insights compared to a viewpoint that just considers the maximum distance. As in previous studies, we consider performance in terms of consistency (worst case when predictions are accurate) and robustness (worst case irrespective of the accuracy of predictions). By considering how mechanisms with predictions can perform poorly, we design new mechanisms that are more robust. Indeed, by adjusting parameters, we demonstrate how to trade robustness for consistency. We go beyond the single facility problem by designing novel strategy proof mechanisms for locating two facilities with bounded consistency and robustness that use two predictions for where to locate the two facilities.

We consider strategy proof mechanisms for facility location which maximize equitability between agents. As is common in the literature, we measure equitability with the Gini index. We first prove a simple but fundamental impossibility result that no strategy proof mechanism can bound the approximation ratio of the optimal Gini index of utilities for one or more facilities. We propose instead computing approximation ratios of the complemented Gini index of utilities, and consider how well both deterministic and randomized mechanisms approximate this. In addition, as Nash welfare is often put forwards as an equitable compromise between egalitarian and utilitarian outcomes, we consider how well mechanisms approximate the Nash welfare.

The moment that tennis fans have been waiting for is almost finally here, with the Wimbledon Championships set to kick off next week. This year's tournament will see the likes of Petra Kvitova, Novak Djokovic and Carlos Alcaraz take to the grass. But in the near future, they could face stiff competition from an unlikely new contender - a robot. Scientists from Georgia Tech have developed a new robot named ESTHER (Experimental Sport Tennis Wheelchair Robot), which can zip around the court and even return human shots. The team believes the bot could serve as a training partner for professional players in the future, removing the psychological pressure of training against another human.

In response to COVID-19, many countries have mandated social distancing and banned large group gatherings in order to slow down the spread of SARS-CoV-2. These social interventions along with vaccines remain the best way forward to reduce the spread of SARS CoV-2. In order to increase vaccine accessibility, states such as Virginia have deployed mobile vaccination centers to distribute vaccines across the state. When choosing where to place these sites, there are two important factors to take into account: accessibility and equity. We formulate a combinatorial problem that captures these factors and then develop efficient algorithms with theoretical guarantees on both of these aspects. Furthermore, we study the inherent hardness of the problem, and demonstrate strong impossibility results. Finally, we run computational experiments on real-world data to show the efficacy of our methods.

It is not possible to estimate the distance (depth) of a point object'P' from the camera using a single camera'O'. This is because'P' lying anywhere on the projective line will map to point'p' in the image. Stereo vision is a technique that can estimate the distance (depth) of a point object'P' from the camera using two cameras. The foundation of stereo vision is similar to 3D perception in human vision and is based on the triangulation of rays from multiple viewpoints. In this tutorial, we'll be using the Parallel stereo camera system for depth estimation.

Convolutional Neural Network (CNN) techniques have proven to be very useful in image-based anomaly detection applications. CNN can be used as deep features extractor where other anomaly detection techniques are applied on these features. For this scenario, using transfer learning is common since pretrained models provide deep feature representations that are useful for anomaly detection tasks. Consequentially, anomaly can be detected by applying similarly measure between extracted features and a defined model of normality. A key factor in such approaches is the decision threshold used for detecting anomaly. While most of the proposed methods focus on the approach itself, slight attention has been paid to address decision threshold settings. In this paper, we tackle this problem and propose a welldefined method to set the working-point decision threshold that improves detection accuracy. We introduce a transfer learning framework for anomaly detection based on similarity measure with a Model of Normality (MoN) and show that with the proposed threshold settings, a significant performance improvement can be achieved. Moreover, the framework has low complexity with relaxed computational requirements.

An important feature of many real world facility location problems are capacity limits on the facilities. We show here how capacity constraints make it harder to design strategy proof mechanisms for facility location, but counter-intuitively can improve the guarantees on how well we can approximate the optimal solution.

Facility location problems often permit facilities to be located at any position. But what if this is not the case in practice? What if facilities can only be located at particular locations like a highway exit or close to a bus stop? We consider here the impact of such constraints on the location of facilities on the performance of strategy proof mechanisms for locating facilities.We study four different performance objectives: the total distance agents must travel to their closest facility, the maximum distance any agent must travel to their closest facility, and the utilitarian and egalitarian welfare.We show that constraining facilities to a limited set of locations makes all four objectives harder to approximate in general.