Atomic clauses are fundamental text units for understanding complex sentences. Identifying the atomic sentences within complex sentences is important for applications such as summarization, argument mining, discourse analysis, discourse parsing, and question answering. Previous work mainly relies on rule-based methods dependent on parsing. We propose a new task to decompose each complex sentence into simple sentences derived from the tensed clauses in the source, and a novel problem formulation as a graph edit task. Our neural model learns to Accept, Break, Copy or Drop elements of a graph that combines word adjacency and grammatical dependencies. The full processing pipeline includes modules for graph construction, graph editing, and sentence generation from the output graph. We introduce DeSSE, a new dataset designed to train and evaluate complex sentence decomposition, and MinWiki, a subset of MinWikiSplit. ABCD achieves comparable performance as two parsing baselines on MinWiki. On DeSSE, which has a more even balance of complex sentence types, our model achieves higher accuracy on the number of atomic sentences than an encoder-decoder baseline. Results include a detailed error analysis.

The paper proposes a supervised machine learning algorithm to uncover treatment effect heterogeneity in classical regression discontinuity (RD) designs. Extending Athey and Imbens (2016), I develop a criterion for building an honest ``regression discontinuity tree'', where each leaf of the tree contains the RD estimate of a treatment (assigned by a common cutoff rule) conditional on the values of some pre-treatment covariates. It is a priori unknown which covariates are relevant for capturing treatment effect heterogeneity, and it is the task of the algorithm to discover them, without invalidating inference. I study the performance of the method through Monte Carlo simulations and apply it to the data set compiled by Pop-Eleches and Urquiola (2013) to uncover various sources of heterogeneity in the impact of attending a better secondary school in Romania.

June 21, 2021--Researchers in the Center for Research on Entertainment and Learning (CREL) at the University of California San Diego have developed a system to analyze and track eye movements to enhance teaching in tomorrow's virtual classrooms – and perhaps future virtual concert halls. UC San Diego music and computer science professor Shlomo Dubnov, an expert in computer music who directs the Qualcomm Institute-based CREL, began developing the new tool to deal with a downside of teaching music over Zoom during the COVID-19 pandemic. "In a music classroom, non-verbal communication such as facial affect and body gestures is critical to keep students on task, coordinate musical flow and communicate improvisational ideas," said Dubnov. "Unfortunately, this non-verbal aspect of teaching and learning is dramatically hampered in the virtual classroom where you don't inhabit the same physical space." To overcome the problem, Dubnov and Ph.D. student Ross Greer recently published a conference paper on a system that uses eye tracking and machine learning to allow an educator to make'eye contact' with individual students or performers in disparate locations – and lets each student know when he or she is the focus of the teacher's attention.

AI, or artificial intelligence, has become huge in recent years and has affected many aspects of our society. We've seen it in our restaurants, our hospitals, and even our schools. AI is even impacting the latest casino bonuses. Since many people like to gamble online, online casinos have implemented AI to help you beat probabilities and earn you more bonuses. However, you sometimes have to wonder whether AI is affecting your life as well.

We have encountered units and elementary dimensional analysis in our high school science classes. For instance, the mass of an object is expressed in kilograms (kg). Likewise, length is expressed using meters (m) and time in seconds (s). Other physical quantities such as acceleration has dimensions m s-2 (derived from its definition), whereas force has dimensions kg m s-2. The latter arises from Newton's second law that states that force (F) is equal to the mass (m) times the acceleration (a).

Of all the perils he faced during World War II, Winston Churchill said German submarine wolfpacks were his greatest concern, because their attacks on merchant ship convoys threatened to choke Britain's economic lifelines. Today, it seems there is another emerging undersea threat, one that has the potential to disrupt the global economy by severing fiber-optic lines of communication that run along the world's various seabeds. There are nearly 400 undersea cables that stretch for almost three-quarters of a million miles, the densest concentrations of them being in the North Atlantic and the North Sea, the Mediterranean, and in Southeast Asia and around Japan. They carry virtually all (97%) international communications, and their exact locations are reasonably well-known. They are also increasingly vulnerable to being tapped or even cut by advanced submarine craft of a range of types, from manned mini-subs to remotely operated undersea drones, and even fully autonomous "U-bots."

Priya is the Founder CEO of CENTURY Tech, an award-winning Artificial Intelligence (AI) educational technology company that produces intelligent …

My code blocks zero-day exploits on hundreds of millions of computers. Always hoping to make the world a better place. What if we trained AI to complete equations instead of images of cats? Can AI help solve the Unified Theory? Remember that shock of seeing some breakthrough for the first time?

It is important to note that there are several functional and operational models that enterprises are adapting in regard to CoE. The change management model focuses on emphasizing the prospective innovation that artificial intelligence can provide for business stakeholders in the organization. Central to this model is education and training of executives and business units. In addition to change management, the Sandbox approach is another central model, in which the CoE acts as the company's hub of innovation and R&D. This model emphasizes proofs of concepts and different emerging technologies.

If you want to learn data science from scratch, the first thing you need to do is learn how to code. Pick a programming language (either Python or R), and start learning. I suggest starting out with Python because it is more widely used than R. It is also more general and highly flexible, and you will be able to make the transition to different domains (data analytics, web development) if you have Python knowledge. This DataCamp course will take you through exercises and teach you how to code in Python. What will you learn in this course?