Building a Stronger Regional Safety Net: Philanthropy's Role

Examines nonprofit organizations' capacity to serve the fast-growing low-income suburban populations in the Atlanta, Chicago, Denver, and Detroit areas and local philanthropic communities' strategies for boosting regional service capacity

Optimal discrimination of single-qubit mixed states

We consider the problem of minimum-error quantum state discrimination for single-qubit mixed states. We present a method which uses the Helstrom conditions constructively and analytically; this algebraic approach is complementary to existing geometric methods, and solves the problem for any number of arbitrary signal states with arbitrary prior probabilities.Comment: 8 pages, 1 figur

Optimal sequential measurements for bipartite state discrimination

State discrimination is a useful test problem with which to clarify the power and limitations of different classes of measurement. We consider the problem of discriminating between given states of a bipartite quantum system via sequential measurement of the subsystems, with classical feed-forward of measurement results. Our aim is to understand when sequential measurements, which are relatively easy to implement experimentally, perform as well, or almost as well, as optimal joint measurements, which are in general more technologically challenging. We construct conditions that the optimal sequential measurement must satisfy, analogous to the well-known Helstrom conditions for minimum error discrimination in the unrestricted case. We give several examples and compare the optimal probability of correctly identifying the state via global versus sequential measurement strategies

Harbour porpoises (Phocoena phocoena) and minke whales (Balaenoptera acutorostrata) observed during land-based surveys in The Minch, north-west Scotland

Peer reviewedPublisher PD

Optimal measurement strategies for the trine states with arbitrary prior probabilities

We investigate the optimal measurement strategy for state discrimination of the trine ensemble of qubit states prepared with arbitrary prior probabilities. Our approach generates the minimum achievable probability of error and also the maximum confidence strategy. Although various cases with symmetry have been considered and solution techniques put forward in the literature, to our knowledge this is only the second such closed form, analytical, arbitrary prior, example available for the minimum-error figure of merit, after the simplest and well-known two-state example

Optimal measurement strategies for the trine states with arbitrary prior probabilities

We investigate the optimal measurement strategy for state discrimination of the trine ensemble of qubit states prepared with arbitrary prior probabilities. Our approach generates the minimum achievable probability of error and also the maximum confidence strategy. Although various cases with symmetry have been considered and solution techniques put forward in the literature, to our knowledge this is only the second such closed form, analytical, arbitrary prior, example available for the minimum-error figure of merit, after the simplest and well-known two-state example

Learnersourcing Subgoal Labels for How-to Videos

Websites like YouTube host millions of how-to videos, but the interfaces are not optimized for learning. Previous research suggests that users learn more from how-to videos when the information from the video is presented in outline form, with individual steps and labels for groups of steps (subgoals) shown. We envision an alternative video player where the steps and subgoals are displayed alongside the video. To generate this information for existing videos, we propose a learnersourcing approach, where people actively learning from a video provide such information. To demonstrate this method, we created a workflow where learners contribute and refine subgoal labels for how-to videos. We deployed a live website with our workflow implemented on a set of introductory web programming videos. For the four videos with the highest participation, we found that a majority of learner-generated subgoals were comparable in quality to expert-generated ones. Learners commented that the system helped them grasp the material, suggesting that our workflow did not detract from the learning experience.Massachusetts Institute of Technology. Undergraduate Research Opportunities ProgramCisco Systems, Inc.Quanta Computer (Firm) (Qmulus Project)National Science Foundation (U.S.) (Award SOCS-1111124)Alfred P. Sloan Foundation (Sloan Research Fellowship)Samsung (Firm) (Fellowship

Deploying Artificial Intelligence to Combat Covid-19 Misinformation on Social Media: Technological and Ethical Considerations

This paper reports on research into online misinformation pertaining to the COVID-19 pandemic using artificial intelligence. This is part of our longer-term goal, i.e., the development of an artificial intelligence (machine-learning) tool to assist social media platforms, online service providers and government agencies in identifying and responding to misinformation on social media. We report herein on the predictive accuracy accomplished by applying a combination of technologies, including a custom-designed web-crawler, The Dark Crawler (TDC) and the Posit toolkit, a text-reading software solution designed by George Weir of University of Strathclyde. Overall, we found that performance of models based upon Posit-derived textual features showed high levels of correlation to the pre-determined (manual and machine-driven) data classifications. We further argue that the harms associated with COVID-19 misinformation — e.g., the social and economic damage, and the deaths and severe illnesses — outweigh the right to personal privacy and freedom of speech considerations

The making of a (dog) movie star: The effect of the portrayal of dogs in movies on breed registrations in the United States

The media is a powerful force that can affect the welfare of the domiciled dog population. Dogs have long been in human stories and their depictions can create demand for the breeds shown. While previous research has found that this effect can last for up to ten years after the release of a movie, how this phenomenon occurs is unknown. This paper examines if how a dog is portrayed in a movie is associated with a subsequent change in American Kennel Club breed registrations for that breed. Following a systematic literature review, four key themes were identified in how dogs are portrayed in the media; dogs portrayed as heroes, as anthropomorphised, as embodying the ideals of Western societies (Whiteness and heteronormativity) and as boundaries between wilderness and human society. Forty movies from between 1930 to 2004 were analysed, resulting in 95 dog characters scored, and hierarchical multiple linear regression was run. Movies with dogs portrayed as heroes were followed by significant increases in the number of American Kennel Club breed registrations for the breed shown, while anthropomorphised dogs were followed by significant decreases in the number of dogs registered for up to five years after a movie’s release. These results indicate that how dogs are portrayed may be an important driver of demand for breeds. Future work should investigate whether these portrayals may have negative welfare implications for real dogs by leading to owners having unrealistic expectations for dogs or increasing demand for dogs with in-breeding related disorders