While Graph Neural Networks (GNNs) have been successfully leveraged for learning on graph-structured data across domains, several potential pitfalls have been described recently. Those include the inability to accurately leverage information encoded in long-range connections (over-squashing), as well as difficulties distinguishing the learned representations of nearby nodes with growing network depth (over-smoothing). An effective way to characterize both effects is discrete curvature: Long-range connections that underlie over-squashing effects have low curvature, whereas edges that contribute to over-smoothing have high curvature. This observation has given rise to rewiring techniques, which add or remove edges to mitigate over-smoothing and over-squashing. Several rewiring approaches utilizing graph characteristics, such as curvature or the spectrum of the graph Laplacian, have been proposed. However, existing methods, especially those based on curvature, often require expensive subroutines and careful hyperparameter tuning, which limits their applicability to large-scale graphs. Here we propose a rewiring technique based on Augmented Forman-Ricci curvature (AFRC), a scalable curvature notation, which can be computed in linear time. We prove that AFRC effectively characterizes over-smoothing and over-squashing effects in message-passing GNNs. We complement our theoretical results with experiments, which demonstrate that the proposed approach achieves state-of-the-art performance while significantly reducing the computational cost in comparison with other methods. Utilizing fundamental properties of discrete curvature, we propose effective heuristics for hyperparameters in curvature-based rewiring, which avoids expensive hyperparameter searches, further improving the scalability of the proposed approach.

The COVID-19 pandemic has elevated the role of technology in the workplace, and more employers are relying on artificial intelligence, machine learning and virtual reality to save money and limit in-person contact. These technologies can be effective tools for hiring, training and assessing employee performance, as well as creating meaningful interactions during a time of isolation. However, employers must ensure that their use of technology doesn't run afoul of employment and labor laws. "It's incredibly important for HR organizations and hiring managers to understand the nuances of the technology that they're using if it is making decisions on their behalf," said Marc Goldberg, chief technology officer at the Society for Human Resource Management (SHRM) in Alexandria, Va. He was speaking during a panel discussion at the American Bar Association's 14th Annual Labor and Employment Law Conference, which was held virtually.

Artificial intelligence is putting new teeth on the old saw that cheaters never prosper. New companies and new research are applying the cutting edge technology in at least three different ways to combat cheating -- on homework, on the job hunt and even on one's diet. In California, a new company called Crosschq is using machine learning and data analytics to help employers with the job reference process. The technology is meant to help companies avoid bad hires and compare how job candidates present themselves with how their references see them. In Pennsylvania, Drexel University researchers are developing an app that can predict when dieters are likely to lapse on their eating regimen, based on the time of day, the user's emotions -- even the temperature of their skin and heart rate.

Artificial intelligence is putting new teeth on the old saw that cheaters never prosper. New companies and new research are applying the cutting edge technology in at least three different ways to combat cheating -- on homework, on the job hunt and even on one's diet. In California, a new company called Crosschq is using machine learning and data analytics to help employers with the job reference process. The technology is meant to help companies avoid bad hires and compare how job candidates present themselves with how their references see them. In Pennsylvania, Drexel University researchers are developing an app that can predict when dieters are likely to lapse on their eating regimen, based on the time of day, the user's emotions -- even the temperature of their skin and heart rate.

If a woman (or non-female identifying person with a uterus and visions of starting a family) is struggling to conceive and decides to improve their reproductive odds at an IVF clinic, they'll likely interact with a doctor, a nurse, and a receptionist. They will probably never meet the army of trained embryologists working behind closed lab doors to collect eggs, fertilize them, and develop the embryos bound for implantation. One of embryologists' more time-consuming jobs is doing something called "grading" embryos--looking at their morphological features under a microscope and assigning a quality score. Round, even numbers of cells are good. They'll use that information to decide which embryos to implant first. Newer methods, like pulling off a cell to extract its DNA and test for abnormalities, something called preimplantation genetic screening, provide more information.

The quantification problem consists of determining the prevalence of a given label in a target population. However, one often has access to the labels in a sample from the training population but not in the target population. A common assumption in this situation is that of prior probability shift, that is, once the labels are known, the distribution of the features is the same in the training and target populations. In this paper, we derive a new lower bound for the risk of the quantification problem under the prior shift assumption. Complementing this lower bound, we present a new approximately minimax class of estimators, ratio estimators, which generalize several previous proposals in the literature. Using a weaker version of the prior shift assumption, which can be tested, we show that ratio estimators can be used to build confidence intervals for the quantification problem. We also extend the ratio estimator so that it can: (i) incorporate labels from the target population, when they are available and (ii) estimate how the prevalence of positive labels varies according to a function of certain covariates.

In what was perhaps one of the most memorable openings in literature in English, Charles Dickens began his immortal A Tale of Two Cities with this: "It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of light, it was the season of darkness, it was the spring of hope, it was the winter of despair, we had everything before us, we had nothing before us, we were all going direct o heaven, we were all going direct the other way--in short, the period was so far like the present period, that some of its noisiest authorities insisted on its being received, for good or for evil, in the superlative degree of comparison only." And yes, that was one long, run-on sentence….! And yes, participating in RSNA 2017, this year's edition of the annual RSNA Conference (sponsored by the Oak Brook, Ill.-based Radiological Society of North America), did bring to mind Dickens' astonishing opening to his great 1859 novel. And though I saw no one at RSNA 2017 who reminded me at all of Sydney Carton, Lucie Manette, Charles Darnay, or Madame Defarge, I did actually think a bit about France in 1775 (on the eve of the French Revolution). Here's the thing: the practice of radiology, as we've all known it, is moving into uncharted territory now, as the financial, operational, and medical practice model on which it's been based, is shifting under the feet of today's radiologists. With both Medicare and private-insurer payment under accelerating threat (let's face it, diagnostic imaging procedures are an easy target for reimbursement deficit-hawk types), and with the demands for speed of turnaround for interpretive reports also accelerating, there are literally not enough hours in the day for practicing radiologists to make up growing income shortfalls from ongoing reductions in payment from all sources.

With the future of the Affordable Care Act in question and debate about health care costs, coverage, and delivery methods continuing around the country, an increasing number of people in the United States are relying on telemedicine for their health needs. According to a report by the research firm Tractica, telemedicine use, or clinical services provided in a remote setting, is projected to increase 700 percent by 2020. Remedy, co-founded by several MIT and Princeton alumni, is trying to ease the pain of high-deductible plans by offering affordable access to doctors, augmented by artificial intelligence. "Remy," the company's automated medical companion, similar to a chatbot, can already perform tasks such as collecting and summarizing patient medical history and complaints for physicians. Remy combines chat with a structured questionnaire that ensures patients' details are captured clearly.

Classification is the task of predicting the class labels of objects based on the observation of their features. In contrast, quantification has been defined as the task of determining the prevalences of the different sorts of class labels in a target dataset. The simplest approach to quantification is Classify & Count where a classifier is optimised for classification on a training set and applied to the target dataset for the prediction of class labels. In the case of binary quantification, the number of predicted positive labels is then used as an estimate of the prevalence of the positive class in the target dataset. Since the performance of Classify & Count for quantification is known to be inferior its results typically are subject to adjustments. However, some researchers recently have suggested that Classify & Count might actually work without adjustments if it is based on a classifer that was specifically trained for quantification. We discuss the theoretical foundation for this claim and explore its potential and limitations with a numerical example based on the binormal model with equal variances. In order to identify an optimal quantifier in the binormal setting, we introduce the concept of local Bayes optimality. As a side remark, we present a complete proof of a theorem by Ye et al. (2012).