Abstract: Systems and methods include increasing a kill mud weight based on information from a downhole tool. A tool is deployed on a bottom hole assembly (BHA) near a surface of a well being drilled. The tool is a retrievable acoustic mud level detector configured to send and receive acoustic waves during drilling. Acoustic waves are propagated using an acoustic wave source. The acoustic waves propagate constantly through an air medium in a wellbore of the well and partially reflect waves when meeting a fluid interface in the wellbore. A signal including a reflected wave reflected from the fluid interface is received using an acoustic wave receiver. An estimate of a fluid level depth of the fluid interface is determined using the tool. A kill mud weight of mud in the wellbore is increased in response to determining the estimate and using a current mud weight of mud in the wellbore.

Abstract: A system, method, and non-transitory computer readable medium that assigns an electric vehicle to a charging station is described. The system includes a software-defined networking (SDN) controller application stored in a cloud-based computing platform, a computing device stored in the cloud platform, and a fog and cloud-based charging service application stored in the computing device. The SDN controller application is linked to a plurality of fog servers and is configured to manage network communications between the fog servers and the cloud-based computing platform, between the fog and a number S of charging stations CSs, where s=1, 2, . . . , S, and between the fog and a number I of electric vehicles EVi, where I=1, 2, . . . , I. The fog and cloud-based charging service application determines an optimal charging station CSopt for each electric vehicle and transmits a route to the optimal charging station to the EVi.

Abstract: This specification describes methods and systems for sentiment analysis. One of the methods includes: receiving a plurality of documents, each document having text data and for each of the documents: (1) representing at least part of the document's text data in a multi-dimensional vector space to produce vectorized text data; (2) applying a neural network to the vectorized text data to calculate a sentiment score, wherein the neural network has been trained using a two step process including (a) training the neural network with a non-domain specific training set; and (b) training the neural network with a domain specific training set; and (3) determining a sentiment score for an entity based at least in part on the sentiment scores for the plurality of documents.

Abstract: This specification describes methods and systems for sentiment analysis. One of the methods includes: receiving a plurality of documents, each document having text data and for each of the documents: (1) representing at least part of the document's text data in a multi-dimensional vector space to produce vectorized text data; (2) applying a neural network to the vectorized text data to calculate a sentiment score, wherein the neural network has been trained using a two step process including (a) training the neural network with a non-domain specific training set; and (b) training the neural network with a domain specific training set; and (3) determining a sentiment score for an entity based at least in part on the sentiment scores for the plurality of documents.

Abstract: A system and method for determining a margin requirement associated with a plurality of financial instruments within a portfolio is disclosed.

Abstract: A system and method for determining a margin requirement associated with a plurality of financial instruments within a portfolio is disclosed.

Abstract: A system and method for determining a margin requirement associated with a plurality of financial instruments within a portfolio is disclosed.