Abstract: Devices and methods for fabrication of a multiscale porous high-temperature heat exchanger for high-temperature and high-pressure applications are disclosed. The heat exchanger can include a core with macrochannels formed in a checkerboard pattern to facilitate alternative flow of working fluid having hot and cold temperatures between adjacent macrochannels. Each macrochannel can include a two-dimensional microchannel array that further distributes flow throughout the heat exchanger to enhance heat transfer and mechanical strength without significant pressure drop penalty. The heat exchanger can further include a header integrated therewith to distribute working fluid flowing through the heat exchanger through the outlets such that it flows evenly therethrough. Methods of fabricating heat exchangers of this nature are also disclosed.

Abstract: Automatically generating sentences that a user can say to invoke a set of defined actions performed by a virtual assistant are disclosed. A sentence is received and keywords are extracted from the sentence. Based on the keywords, additional sentences are generated. A classifier model is applied to the generated sentences to determine a sentence that satisfies a threshold. In the situation a sentence satisfies the threshold, an intent associated with the classifier model can be invoked. In the situation the sentences fail to satisfy the classifier model, the virtual assistant can attempt to interpret the received sentence according to the most likely intent by invoking a sentence generation model fine-tuned for a particular domain, generate additional sentences with a high probability of having the same intent and fulfill the specific action defined by the intent.

Abstract: In one embodiment, a device in a network receives a first plurality of measurements for network metrics captured during a first time period. The device determines a first set of correlations between the network metrics using the first plurality of measurements captured during the first time period. The device receives a second plurality of measurements for the network metrics captured during a second time period. The device determines a second set of correlations between the network metrics using the second plurality of measurements captured during the second time period. The device identifies a difference between the first and second sets of correlations between the network metrics as a network anomaly.

Abstract: In one embodiment, a device in a network receives a first plurality of measurements for network metrics captured during a first time period. The device determines a first set of correlations between the network metrics using the first plurality of measurements captured during the first time period. The device receives a second plurality of measurements for the network metrics captured during a second time period. The device determines a second set of correlations between the network metrics using the second plurality of measurements captured during the second time period. The device identifies a difference between the first and second sets of correlations between the network metrics as a network anomaly.