Abstract: Systems and methods are provided for collaborative decision-making in medicine. The systems can employ a distributed record-keeping and verification system to solicit suggested modifications to an initial healthcare regime from interested healthcare workers. The systems can aggregate the suggested modifications and use a consensus algorithm to determine the most appropriate modification.

Abstract: A system, method and program product for implementing a sparse sampling strategy for acquiring MRI data. A method includes: collecting and labeling a training dataset of MRI scans for a predetermined diagnostic; selecting a sampling shape and associated parameter values; sampling each MRI scan in the training data set using the sampling shape and associated parameter values to generate a set of sparse samples; training a neural network using the sparse samples and assigning an accuracy to a resulting trained neural network; and adjusting the associated parameter values, and repeating the sampling and training until optimized parameter values are established.

Abstract: Image data is run through a neural network, and the neural network produces a vector representation of the image data. Random sparse sampling masks are created. The vector representation of the image data is masked with each of the random sparse sampling masks, the masking generating corresponding sparsely sampled vectors. The sparsely sampled vectors are transmitted to nodes of a consensus network, wherein a sparsely sampled vector of the sparsely sampled vectors is transmitted to a node of the consensus network. Votes from the nodes of the consensus network are received. Whether a consensus is achieved in the votes is determined. Responsive to determining that the consensus is achieved, at least one of identification and verification of the image data may be provided.

Abstract: Systems and methods are provided for collaborative decision-making in medicine. The systems can employ a distributed record-keeping and verification system to solicit suggested modifications to an initial healthcare regime from interested healthcare workers. The systems can aggregate the suggested modifications and use a consensus algorithm to determine the most appropriate modification.

Abstract: Image data is run through a neural network, and the neural network produces a vector representation of the image data. Random sparse sampling masks are created. The vector representation of the image data is masked with each of the random sparse sampling masks, the masking generating corresponding sparsely sampled vectors. The sparsely sampled vectors are transmitted to nodes of a consensus network, wherein a sparsely sampled vector of the sparsely sampled vectors is transmitted to a node of the consensus network. Votes from the nodes of the consensus network are received. Whether a consensus is achieved in the votes is determined. Responsive to determining that the consensus is achieved, at least one of identification and verification of the image data may be provided.

Abstract: Image data is run through a neural network, and the neural network produces a vector representation of the image data. Random sparse sampling masks are created. The vector representation of the image data is masked with each of the random sparse sampling masks, the masking generating corresponding sparsely sampled vectors. The sparsely sampled vectors are transmitted to nodes of a consensus network, wherein a sparsely sampled vector of the sparsely sampled vectors is transmitted to a node of the consensus network. Votes from the nodes of the consensus network are received. Whether a consensus is achieved in the votes is determined. Responsive to determining that the consensus is achieved, at least one of identification and verification of the image data may be provided.

Abstract: Image data is run through a neural network, and the neural network produces a vector representation of the image data. Random sparse sampling masks are created. The vector representation of the image data is masked with each of the random sparse sampling masks, the masking generating corresponding sparsely sampled vectors. The sparsely sampled vectors are transmitted to nodes of a consensus network, wherein a sparsely sampled vector of the sparsely sampled vectors is transmitted to a node of the consensus network. Votes from the nodes of the consensus network are received. Whether a consensus is achieved in the votes is determined. Responsive to determining that the consensus is achieved, at least one of identification and verification of the image data may be provided.

Abstract: A system, method and program product for implementing a sparse sampling strategy for acquiring MRI data. A method includes: collecting and labeling a training dataset of MRI scans for a predetermined diagnostic; selecting a sampling shape and associated parameter values; sampling each MRI scan in the training data set using the sampling shape and associated parameter values to generate a set of sparse samples; training a neural network using the sparse samples and assigning an accuracy to a resulting trained neural network; and adjusting the associated parameter values, and repeating the sampling and training until optimized parameter values are established.

Abstract: Correcting typographical errors in electronic text may include converting a text message containing at least one phonemic spelling of a word into speech by running a text-to-speech application programming interface (API) with the text message as input. The converted speech may be input to a speech-to-text API and the speech-to-text API executed to convert the speech to text. A text file comprising the text may be generated and/or output. The text file automatically contains a corrected version of the phonemic spelling of the word in text message.

Abstract: Correcting typographical errors in electronic text may include converting a text message containing at least one phonemic spelling of a word into speech by running a text-to-speech application programming interface (API) with the text message as input. The converted speech may be input to a speech-to-text API and the speech-to-text API executed to convert the speech to text. A text file comprising the text may be generated and/or output. The text file automatically contains a corrected version of the phonemic spelling of the word in text message.

Abstract: Methods are provided for detection of a disease breakout from a mammal population. For example, the method involves continuously receiving, from two or more sensors positioned in a mammal environment, information of a population of mammals, wherein the information received from each of the two or more sensors is in a different data format; comparing the received information of the population of mammals or other animals with known disease symptom information; and in response to determining that the information of the population of mammals exceeds a pre-determined threshold alert level, generating an alert to prevent or control the disease; wherein the steps of the method are performed in accordance with a processor and a memory.

Abstract: Systems and methods are provided for collaborative decision-making in medicine. The systems can employ a distributed record-keeping and verification system to solicit suggested modifications to an initial healthcare regime from interested healthcare workers. The systems can aggregate the suggested modifications and use a consensus algorithm to determine the most appropriate modification.

Abstract: Systems and methods are provided for collaborative decision-making in medicine. The systems can employ a distributed record-keeping and verification system to solicit suggested modifications to an initial healthcare regime from interested healthcare workers. The systems can aggregate the suggested modifications and use a consensus algorithm to determine the most appropriate modification.

Abstract: A type of a waste item is identified at a waste collection location using at least a drone-based system. The drone-based system characterizes one or more properties of the waste item. Based on the identified type and the one or more properties, the drone-based system performs a risk assessment based on human health of content of the waste item. One or more actions are taken by one or more drones of the drone-based system based on the risk assessment of the content of the waste item.

Abstract: A type of a waste item is identified at a waste collection location using at least a drone-based system. The drone-based system characterizes one or more properties of the waste item. Based on the identified type and the one or more properties, the drone-based system performs a risk assessment based on human health of content of the waste item. One or more actions are taken by one or more drones of the drone-based system based on the risk assessment of the content of the waste item.

Abstract: A type of a waste item is identified at a waste collection location using at least a drone-based system. The drone-based system characterizes one or more properties of the waste item. Based on the identified type and the one or more properties, the drone-based system performs a risk assessment based on human health of content of the waste item. One or more actions are taken by one or more drones of the drone-based system based on the risk assessment of the content of the waste item.

Abstract: A type of a waste item is identified at a waste collection location using at least a drone-based system. The drone-based system characterizes one or more properties of the waste item. Based on the identified type and the one or more properties, the drone-based system performs a risk assessment based on human health of content of the waste item. One or more actions are taken by one or more drones of the drone-based system based on the risk assessment of the content of the waste item.