A preference profile is single-peaked on a tree if the candidate set can be equipped with a tree structure so that the preferences of each voter are decreasing from their top candidate along all paths in the tree. This notion was introduced by Demange (1982), and subsequently Trick (1989b) described an efficient algorithm for deciding if a given profile is single-peaked on a tree. We study the complexity of multiwinner elections under several variants of the Chamberlin–Courant rule for preferences single-peaked on trees. We show that in this setting the egalitarian version of this rule admits a polynomial-time winner determination algorithm. For the utilitarian version, we prove that winner determination remains NP-hard for the Borda scoring function; indeed, this hardness results extends to a large family of scoring functions. However, a winning committee can be found in polynomial time if either the number of leaves or the number of internal vertices of the underlying tree is bounded by a constant. To benefit from these positive results, we need a procedure that can determine whether a given profile is single-peaked on a tree that has additional desirable properties (such as, e.g., a small number of leaves). To address this challenge, we develop a structural approach that enables us to compactly represent all trees with respect to which a given profile is single-peaked. We show how to use this representation to efficiently find the best tree for a given profile for use with our winner determination algorithms: Given a profile, we can efficiently find a tree with the minimum number of leaves, or a tree with the minimum number of internal vertices among trees on which the profile is single-peaked. We then explore the power and limitations of this framework: we develop polynomial-time algorithms to find trees with the smallest maximum degree, diameter, or pathwidth, but show that it is NP-hard to check whether a given profile is single-peaked on a tree that is isomorphic to a given tree, or on a regular tree.

We introduce an algorithm for computing geodesics on sampled manifolds that relies on simulation of quantum dynamics on a graph embedding of the sampled data. Our approach exploits classic results in semiclassical analysis and the quantum-classical correspondence, and forms a basis for techniques to learn the manifold from which a dataset is sampled, and subsequently for nonlinear dimensionality reduction of high-dimensional datasets. We illustrate the new algorithm with data sampled from model manifolds and also by a clustering demonstration based on COVID-19 mobility data. Finally, our method reveals interesting connections between the discretization provided by data sampling and quantization.

This paper describes the evolution of our research from video analytics to a global security system with focus on the video surveillance component. Indeed video surveillance has evolved from a commodity security tool up to the most efficient way of tracking perpetrators when terrorism hits our modern urban centers. As number of cameras soars, one could expect the system to leverage the huge amount of data carried through the video streams to provide fast access to video evidences, actionable intelligence for monitoring real-time events and enabling predictive capacities to assist operators in their surveillance tasks. This research explores a hybrid platform for video intelligence capture, automated data extraction, supervised Machine Learning for intelligently assisted urban video surveillance; Extension to other components of a global security system are discussed. Applying Knowledge Management principles in this research helps with deep problem understanding and facilitates the implementation of efficient information and experience sharing decision support systems providing assistance to people on the field as well as in operations centers. The originality of this work is also the creation of "common" human-machine and machine to machine language and a security ontology.

The rapid emergence of airborne platforms and imaging sensors are enabling new forms of aerial surveillance due to their unprecedented advantages in scale, mobility, deployment and covert observation capabilities. This paper provides a comprehensive overview of human-centric aerial surveillance tasks from a computer vision and pattern recognition perspective. It aims to provide readers with an in-depth systematic review and technical analysis of the current state of aerial surveillance tasks using drones, UAVs and other airborne platforms. The main object of interest is humans, where single or multiple subjects are to be detected, identified, tracked, re-identified and have their behavior analyzed. More specifically, for each of these four tasks, we first discuss unique challenges in performing these tasks in an aerial setting compared to a ground-based setting. We then review and analyze the aerial datasets publicly available for each task, and delve deep into the approaches in the aerial literature and investigate how they presently address the aerial challenges. We conclude the paper with discussion on the missing gaps and open research questions to inform future research avenues.

NLP researchers need more, higher-quality text datasets. Human-labeled datasets are expensive to collect, while datasets collected via automatic retrieval from the web such as WikiBio are noisy and can include undesired biases. Moreover, data sourced from the web is often included in datasets used to pretrain models, leading to inadvertent cross-contamination of training and test sets. In this work we introduce a novel method for efficient dataset curation: we use a large language model to provide seed generations to human raters, thereby changing dataset authoring from a writing task to an editing task. We use our method to curate SynthBio - a new evaluation set for WikiBio - composed of structured attribute lists describing fictional individuals, mapped to natural language biographies. We show that our dataset of fictional biographies is less noisy than WikiBio, and also more balanced with respect to gender and nationality.

PRNewswire/ — VISIMO, in partnership with the United States Military Academy at West Point, has won an Air Force Phase I Small Business …

WENDELL WALLACH: It gives me great pleasure to welcome my longtime colleague Shannon Vallor to this Artificial Intelligence & Equality podcast. Shannon and I have both expressed concerns that ethics and ethical philosophy is inadequate for addressing the issues posed by artificial intelligence (AI) and other emerging technologies, so I have been looking forward to our having a conversation about why that is the case and ideas for reenvisioning ethics and empowering it for the information age. Before we get to that conversation, let me introduce Shannon to our listeners, provide a very cursory overview of how ethical theories are understood within academic circles, and provide Shannon with the opportunity to introduce you to the research and insights for which she is best known. Again, before turning to Shannon, let me make sure that listeners have at least a cursory understanding of the field of ethics. Ethical theories are often said to all fall into two big tents, and one of those tents--the determination of what is right, good, or just--derives from following the rules or doing your duty. Often these rules are captured in high-level principles, that the rules can be the Ten Commandments or the four principles of biomedical ethics. In India they might be Yama and Niyama. Each culture has its own set of rules. Even Asimov's "Three Laws of Robotics" do count as rules meant to direct the behavior of robots. All of these theories are said to be deontological, a term going back to the Greeks, referring to duties, and it is basically saying that rules and duties define ethics--but of course there are outstanding questions about whose rules, what to do when rules conflict, and how you deal with situations when people prioritize the rules very differently. At the end of the 18th and beginning of the 19th centuries, Jeremy Bentham, a British philosopher, came up with a totally different approach to ethics, which is sometimes called utilitarianism or consequentialism.

BrainChip Holdings has added Information Systems Laboratories in its Early Access Program and will provide the latter with assistance in the development of an artificial intelligence technology meant to support the Air Force Research Laboratory's radar projects. ISL is developing the technology based on BrainChip's Akida neural networking processor, which uses a neuromorphic architecture to support edge-based AI growth, the Australian software company said Sunday. Akida is designed to mimic the human brain's spiking nature and support edge AI applications including learning, inference and on-chip training. ISL can assess the ultra-low-power processor and utilize related engineering resources as a member of BrainChip's EAP. "As part of BrainChip's EAP, we've had the opportunity to evaluate firsthand the capabilities that Akida provides to the AI ecosystem," said Jamie Bergin, senior vice president and manager for ISL's research, development and engineering solutions division.

Machine learning becomes engaging when we face various challenges and thus finding suitable datasets relevant to the use case is essential. Flexibility refers to the number of tasks that it supports. For example, Microsoft's COCO( Common Objects in Context) is used for object classification, detection, and segmentation. Add a bunch of captions for the same, and we can use it as a dataset for an image caption generator as well. Well, when we are just starting, we shall be working with some of the small and standard machine learning datasets like the CIFAR-10, MNIS, Iris, etc.

Donald Trump has won the United States presidency and Brexit has promised to take the United Kingdom out of the European Union. Both campaigns employ Cambridge Analytica, who harvest the data of millions of Facebook users to personalise electoral messaging and sway their voting intentions. Millions of people begin to ask themselves whether, in the digital era, they have lost something that they valued dearly: their privacy. Two years later, millions of email inboxes in Europe would be filling up with messages from companies, asking them for permission to continue processing their data, complying with the new General Data Protection Regulation (GDPR). Despite its imperfections, this law has served as a point of reference for laws in Brazil and Japan and began the era of data protection in earnest. Nevertheless, what was once seen as a triumph for privacy is now seen as a roadblock in Europe's quest to develop digital technologies, especially artificial intelligence.