Multi-robot task allocation is one of the most fundamental classes of problems in robotics and is crucial for various real-world robotic applications such as search, rescue and area exploration. We consider the Single-Task robots and Multi-Robot tasks Instantaneous Assignment (ST-MR-IA) setting where each task requires at least a certain number of robots and each robot can work on at most one task and incurs an operational cost for each task. Our aim is to consider a natural computational problem of allocating robots to complete the maximum number of tasks subject to budget constraints. We consider budget constraints of three different kinds: (1) total budget, (2) task budget, and (3) robot budget. We provide a detailed complexity analysis including results on approximations as well as polynomial-time algorithms for the general setting and important restricted settings.

Neural networks represent data as projections on trained weights in a high dimensional manifold. The trained weights act as a knowledge base consisting of causal class dependencies. Inference built on features that identify these dependencies is termed as feed-forward inference. Such inference mechanisms are justified based on classical cause-to-effect inductive reasoning models. Inductive reasoning based feed-forward inference is widely used due to its mathematical simplicity and operational ease. Nevertheless, feed-forward models do not generalize well to untrained situations. To alleviate this generalization challenge, we propose using an effect-to-cause inference model that reasons abductively. Here, the features represent the change from existing weight dependencies given a certain effect. We term this change as contrast and the ensuing reasoning mechanism as contrastive reasoning. In this paper, we formalize the structure of contrastive reasoning and propose a methodology to extract a neural network's notion of contrast. We demonstrate the value of contrastive reasoning in two stages of a neural network's reasoning pipeline : in inferring and visually explaining decisions for the application of object recognition. We illustrate the value of contrastively recognizing images under distortions by reporting an improvement of 3.47%, 2.56%, and 5.48% in average accuracy under the proposed contrastive framework on CIFAR-10C, noisy STL-10, and VisDA datasets respectively.

We propose the Tough Mentions Recall (TMR) metrics to supplement traditional named entity recognition (NER) evaluation by examining recall on specific subsets of "tough" mentions: unseen mentions, those whose tokens or token/type combination were not observed in training, and type-confusable mentions, token sequences with multiple entity types in the test data. We demonstrate the usefulness of these metrics by evaluating corpora of English, Spanish, and Dutch using five recent neural architectures. We identify subtle differences between the performance of BERT and Flair on two English NER corpora and identify a weak spot in the performance of current models in Spanish. We conclude that the TMR metrics enable differentiation between otherwise similar-scoring systems and identification of patterns in performance that would go unnoticed from overall precision, recall, and F1.

The Biden organization intends to use federal funding for U.S. research and development on artificial intelligence (AI) and other cutting-edge technologies. The United States and India are logical partners in diagramming the future development of AI, which guarantees economic growth and social benefits to the two nations in key areas like healthcare, education, energy, financial technology and retail. India is an all-around nation and set to be a fundamental part of these endeavors, as the world's biggest democracy, a vital supporter for the developing world, and the home of a huge informational technology (IT) sector effectively collaborated with the United States. Recently, the Department of Science and Technology announced that the Indo-US Science and Technology Forum-IUSSTF has introduced the US India Artificial Intelligence Initiative. It will concentrate on AI implementation in the significant areas that are priorities for the two countries. India-US partnership in the field of Science and Technology is exceptionally an old collaboration.

BOSTON--Technology is upending labor markets, and governments, companies, and individuals need to look beyond aggregate numbers and consider how individual professions will be affected. The report, titled The Future of Jobs in the Era of AI, is being released today by Boston Consulting Group (BCG) and Faethm. In the report, the authors look at a variety of factors to determine how the supply and demand for individual types of jobs will change. These include shifts in the size of national workforces due to college graduation rates, retirements, and mortality, along with technology adoption rates and the impact of COVID-19 on economic growth. The result is a highly detailed analysis for all three countries across multiple scenarios.

ShareThis week, we will briefly touch upon the topic of artificial intelligence, or AI, discuss what it is, why it is so important to the American empire and national security, and how the ancient Chamorro people of the Marianas can prepare themselves to be ready for future possible job and entrepreneurial opportunities at the intersection of warfare and technology.  What is AI? Star Wars gone wild?  AI is a constellation or universe of technologies, computer hardware and software, designed to solve specific tasks that reflect and are intended to resemble human cognitive processes to include decision making, reasoning, learning, and perceiving.  AI has applications that reprogram itself to complete specific tasks called machine learning. Within the machine learning space, there are applications that take information to produce outcomes with increasing accuracy. This technology can be interpreted as an evolving manufactured ecosystem that attempts to some degree to self-develop and monitor without human intervention.  This is both potentially dangerous and beneficial stuff.  Why AI is important to the United States and our ancient Chamorro people Part of the answer is found in a report released by the congressionally established National Security Commission on Artificial Intelligence, which outlined the national importance of AI and its application to all facets of American—and by implication, American colonial—society. The commission was established to assess how AI affects imperial competitiveness and technological advantage.  AI is important because it has civilian and military applications that are used every day by every citizen, whether one resides on Saipan, Rota, in New York, Birmingham or elsewhere. If you are looking for a movie to watch or check out what time a restaurant or bar opens, or get vehicle maintenance service, or asking your iPhone a question, AI will be a part of the enabling infrastructure that will get you the answers to your questions.  AI is now found everywhere and is used knowingly or unknowingly, again posing benefits and concerns to society.  If Guam moves toward building a new hospital, AI will be an integral and ubiquitous part of how healthcare decisions are informed, how biotechnology is implemented, and how personal medical information and data are managed, distributed, protected, and delivered. AI will become more important as Guam continues to move toward technological solutions for future energy, food technology and security opportunities. Folks in the NMI will see a greater role of AI as the Commonwealth moves toward electric and eventually unmanned cars. Cyber-attacks against the United States government and other facets of society occur every day. AI was the underbelly used to enable computer networks to find vulnerabilities recently when the government of Guam experienced a cyber-attack. These attacks may come in the form of data harvesting, targeted attacks on individual citizens, or AI enabled attacks on social media, intended to influence or harm the targets.   The American national government is contemplating ways to aggressively deal with data protection, privacy, and security. Congress remains behind the curve on how to craft legislation to address ongoing technological change. Others worry that domestic data surveillance is out of control and has already compromised our privacy and protection.  Why AI is important to the governments of Guam and the CNMI Now is the time for the governments of Guam and the CNMI to consider creating a Marianas Artificial Intelligence, Security and Emerging Technologies Understanding advisory board to learn and more completely seek to comprehend the nature of AI, how it is currently used and how it presents opportunities and vulnerabilities to every aspect of Pacific island life. There is no reason why Guam and CNMI legislative committees overseeing technology cannot hold initial hearings to discuss AI.  Future educational and career opportunities: Pay attention Guam Community College is doing some work with its resources available to students in math and science tutoring and management information systems programming. The University of Guam has resources that can or will eventually be able to contribute toward providing AI jobs for young islanders who have math, engineering, nursing, education, biology, and Chamorro studies backgrounds.  Guam institutions have opportunities to further partner with the Guam Department of Education, and the CNMI Public School System on the implications of AI. Marianas educational institutions would be well served to contemplate the establishment of emerging technology certificate programs to prepare Pacific Islanders for much needed and well-paid technology jobs. All Marianas institutions can also create opportunities to seek partnerships with major American companies and entities in Silicon Valley and elsewhere, intended to create AI related jobs and training.  Lawmakers from Guam and the CNMI as well as the governors can contemplate creating national level digital annex risk management programs for all school age kids interested in future technical challenges that will provide good paying jobs while helping to secure America and its colonies. Science and technology partnerships with Taiwan and South Korea may also be something that can be operationalized for mutual benefit.  The AI race is on, security threats increase A most dangerous aspect of rapidly emerging AI enabled technology and networks is that nation state adversaries such as China and Russia may outpace the United States on this front over the next 10 to 15 years. This presents and will present fundamental risks and threats to the existing militarized resource base currently embedded and/or connected to Guam and the CNMI that may include risks tied to autonomous unmanned weapon system warfare and real future risks associated with crisis stability and human authorized employment of very dangerous nuclear weapons and networks through unmanned platforms.  AI threatens to pose real challenges to traditional process identification and validation issues related to military targeting matters, creating risks to real-time battle assessment decisions that will need to be made in the future.   Now is the time for our Chamorro Pacific Islander civilization to continue the broad conversation on issues of the military and national and colonial security to demystify and assess the importance of AI and what it means to all our families and friends in the 21st century. 

When a secretive start-up scraped the internet to build a facial-recognition tool, it tested a legal and ethical limit -- and blew the future of privacy in America wide open. In May 2019, an agent at the Department of Homeland Security received a trove of unsettling images. Found by Yahoo in a Syrian user's account, the photos seemed to document the sexual abuse of a young girl. One showed a man with his head reclined on a pillow, gazing directly at the camera. The man appeared to be white, with brown hair and a goatee, but it was hard to really make him out; the photo was grainy, the angle a bit oblique. The agent sent the man's face to child-crime investigators around the country in the hope that someone might recognize him. When an investigator in New York saw the request, she ran the face through an unusual new facial-recognition app she had just started using, called Clearview AI. The team behind it had scraped the public web -- social media, employment sites, YouTube, Venmo -- to create a database with three billion images of people, along with links to the webpages from which the photos had come. This dwarfed the databases of other such products for law enforcement, which drew only on official photography like mug shots, driver's licenses and passport pictures; with Clearview, it was effortless to go from a face to a Facebook account. The app turned up an odd hit: an Instagram photo of a heavily muscled Asian man and a female fitness model, posing on a red carpet at a bodybuilding expo in Las Vegas. The suspect was neither Asian nor a woman. But upon closer inspection, you could see a white man in the background, at the edge of the photo's frame, standing behind the counter of a booth for a workout-supplements company. On Instagram, his face would appear about half as big as your fingernail. The federal agent was astounded. The agent contacted the supplements company and obtained the booth worker's name: Andres Rafael Viola, who turned out to be an Argentine citizen living in Las Vegas.

We propose LSDAT, an image-agnostic decision-based black-box attack that exploits low-rank and sparse decomposition (LSD) to dramatically reduce the number of queries and achieve superior fooling rates compared to the state-of-the-art decision-based methods under given imperceptibility constraints. LSDAT crafts perturbations in the low-dimensional subspace formed by the sparse component of the input sample and that of an adversarial sample to obtain query-efficiency. The specific perturbation of interest is obtained by traversing the path between the input and adversarial sparse components. It is set forth that the proposed sparse perturbation is the most aligned sparse perturbation with the shortest path from the input sample to the decision boundary for some initial adversarial sample (the best sparse approximation of shortest path, likely to fool the model). Theoretical analyses are provided to justify the functionality of LSDAT. Unlike other dimensionality reduction based techniques aimed at improving query efficiency (e.g, ones based on FFT), LSD works directly in the image pixel domain to guarantee that non-$\ell_2$ constraints, such as sparsity, are satisfied. LSD offers better control over the number of queries and provides computational efficiency as it performs sparse decomposition of the input and adversarial images only once to generate all queries. We demonstrate $\ell_0$, $\ell_2$ and $\ell_\infty$ bounded attacks with LSDAT to evince its efficiency compared to baseline decision-based attacks in diverse low-query budget scenarios as outlined in the experiments.

We describe a Plug-and-Play controllable language generation framework, Plug-and-Blend, that allows a human user to input multiple control codes (topics). In the context of automated story generation, this allows a human user lose or fine grained control of the topics that will appear in the generated story, and can even allow for overlapping, blended topics. We show that our framework, working with different generation models, controls the generation towards given continuous-weighted control codes while keeping the generated sentences fluent, demonstrating strong blending capability.

Reducing the variance of the gradient estimator is known to improve the convergence rate of stochastic gradient-based optimization and sampling algorithms. One way of achieving variance reduction is to design importance sampling strategies. Recently, the problem of designing such schemes was formulated as an online learning problem with bandit feedback, and algorithms with sub-linear static regret were designed. In this work, we build on this framework and propose Avare, a simple and efficient algorithm for adaptive importance sampling for finite-sum optimization and sampling with decreasing step-sizes. Under standard technical conditions, we show that Avare achieves $\mathcal{O}(T^{2/3})$ and $\mathcal{O}(T^{5/6})$ dynamic regret for SGD and SGLD respectively when run with $\mathcal{O}(1/t)$ step sizes. We achieve this dynamic regret bound by leveraging our knowledge of the dynamics defined by the algorithm, and combining ideas from online learning and variance-reduced stochastic optimization. We validate empirically the performance of our algorithm and identify settings in which it leads to significant improvements.