The AGM paradigm of belief change studies the dynamics of belief states in light of new information. Finding, or even approximating, those beliefs that are dependent on or relevant to a change is valuable because, for example, it can narrow the set of beliefs considered during belief change operations. A strong intuition in this area is captured by Gärdenforss preservation criterion (GPC), which suggests that formulas independent of a belief change should remain intact. GPC thus allows one to build dependence relations that are linked with belief change. Such dependence relations can in turn be used as a theoretical benchmark against which to evaluate other approximate dependence or relevance relations. Fariñas and Herzig axiomatize a dependence relation with respect to a belief set, and, based on GPC, they characterize the correspondence between AGM contraction functions and dependence relations. In this paper, we introduce base dependence as a relation between formulas with respect to a belief base, and prove a more general characterization that shows the correspondence between kernel contraction and base dependence. At this level of generalization, different types of base dependence emerge, which we show to be a result of possible redundancy in the belief base. We further show that one of these relations that emerge, strong base dependence, is parallel to saturated kernel contraction. We then prove that our latter characterization is a reversible generalization of Fariñas and Herzigs characterization. That is, in the special case when the underlying belief base is deductively closed (i.e., it is a belief set), strong base dependence reduces to dependence, and so do their respective characterizations. Finally, an intriguing feature of Fariñas and Herzigs formalism is that it meets other criteria for dependence, namely, Keyness conjunction criterion for dependence (CCD) and Gärdenforss conjunction criterion for independence (CCI). We prove that our base dependence formalism also meets these criteria. Even more interestingly, we offer a more specific criterion that implies both CCD and CCI, and show our base dependence formalism also meets this new criterion.

We propose a protocol to perform generalized quantum reinforcement learning with quantum technologies. At variance with recent results on quantum reinforcement learning with superconducting circuits [L. Lamata, Sci. Rep. 7, 1609 (2017)], in our current protocol coherent feedback during the learning process is not required, enabling its implementation in a wide variety of quantum systems. We consider diverse possible scenarios for an agent, an environment, and a register that connects them, involving multiqubit and multilevel systems, as well as open-system dynamics. We finally propose possible implementations of this protocol in trapped ions and superconducting circuits. The field of quantum reinforcement learning with quantum technologies will enable enhanced quantum control, as well as more efficient machine learning calculations.

Weeks ago my Twitter timeline was full of negative stories. My timeline was so full of negative content that I started thinking Twitter was full of hate:_( That's why I decided to get some metrics to see if my intuition was right. With the help of Mª Asunción Jiménez Cordero, a PhD student specialized in Machine Learning, we developed a Spark application written in Scala to analyze real-time tweets and classify them as negative, positive or neutral using Support Vector Machine. In this post, we'll tell you how we developed this app in order to get the results you can find at https://pedrovgs.github.io/Roma/. Let's split this post in the different challenges we faced!

The city of Atlanta tested a self-driving vehicle on one of its busiest streets Thursday. The test on North Avenue in the city's bustling Midtown area meant that Atlanta has become one of the largest urban areas to test autonomous vehicles, joining Sao Paulo and Shanghai. Here's a look at some of the key aspects of the test and the issues involved: The test was aimed at showing how an autonomous vehicle would navigate in real-world traffic. On Thursday, a Tesla vehicle made multiple trips along an approximately 1-mile-long (1.6-kilometer-long) route as members of the media rode along. Tesla was not officially involved with the test.

Unless you're quite out of touch with digital trends, you'd struggle to not have heard of the phrase Machine Learning (ML). Articles are shared online daily, software vendors and service providers have begun to offer Machine Learning as-a-service, thereby making it easier to integrate ML into your existing software products (no PhD required!). But what exactly is Machine Learning? Simply put, machine learning is a type of artificial intelligence, or AI. Software applications are traditionally programmed by a human.

Automated story generation is the problem of automatically selecting a sequence of events, actions, or words that can be told as a story. We seek to develop a system that can generate stories by learning everything it needs to know from textual story corpora. To date, recurrent neural networks that learn language models at character, word, or sentence levels have had little success generating coherent stories. We explore the question of event representations that provide a mid-level of abstraction between words and sentences in order to retain the semantic information of the original data while minimizing event sparsity. We present a technique for preprocessing textual story data into event sequences. We then present a technique for automated story generation whereby we decompose the problem into the generation of successive events (event2event) and the generation of natural language sentences from events (event2sentence). We give empirical results comparing different event representations and their effects on event successor generation and the translation of events to natural language.

Did you know that in Rosario, Argentina -- the hometown of Lionel Messi -- a law has been passed preventing parents from naming their children after the Barcelonian superstar?? So, I tried a lot but couldn't find the exact data but still, I believe that there would be almost 25% to 30% of the population sharing similar words in their names. I have a small task for you to do: Check this link out and see whether your name or one of your friend's name is from https://nameberry.com/popular_names Also, you would know the trouble of finding out the correct phone number from a phone book. There are a lot of similar names in this world. If we are having such a problem.

Airbus' Perlan 2 aircraft has made history this week, setting a new world altitude record for gliding. The aircraft soared to an incredible 52,000 feet over the Patagonia region of Argentina, without the support of an engine. The flight takes Airbus one step closer to its ultimate goal of sending an engine-less aircraft to the edge of space. Airbus' Perlan 2 aircraft has made history this week, setting a new world altitude record for gliding. Because it lacks an engine, Perlan 2 can explore the edge of space without polluting the atmosphere.

The trade deficit had narrowed in the spring as exports of U.S. computer products and farm goods rose. U.S. exporters benefited from a decline in the value of the dollar, which makes American products cheaper overseas. Solid global growth also helped, as economies from Europe to Asia to Latin America are expanding simultaneously. Exports in June were the highest in 2 ½ years.

G\"odel's ontological proof has been analysed for the first-time with an unprecedent degree of detail and formality with the help of higher-order theorem provers. The following has been done (and in this order): A detailed natural deduction proof. A formalization of the axioms, definitions and theorems in the TPTP THF syntax. Automatic verification of the consistency of the axioms and definitions with Nitpick. Automatic demonstration of the theorems with the provers LEO-II and Satallax. A step-by-step formalization using the Coq proof assistant. A formalization using the Isabelle proof assistant, where the theorems (and some additional lemmata) have been automated with Sledgehammer and Metis.