Abstract: Systems and methods of the present disclosure are directed to a method for machine-learned code segment prediction for optimizing software development. The method includes obtaining an incomplete segment of code. The method includes processing the incomplete segment of code with a machine-learned code prediction model to obtain a sampled set of segment completion predictions that include code that completes the incomplete segment of code. The method includes determining an aggregated segment completion prediction from the sampled set of segment completion predictions. The method includes replacing a portion of the aggregated segment completion prediction with an input field, wherein the portion of the aggregated segment completion prediction is associated with a degree of certainty less than a threshold degree of certainty.

Abstract: An exemplary compounding method of controlling a compounding device to prepare an admixture of at least two distinct material sources can include examining material source solutions for incompatibility of the ingredients and operating a first and a second pump to prevent one of the incompatible source solutions from entering a common flow path. The processing method can detect degradation of a fluid line by evaluating one or more of calibration error rate data, cumulative volumetric flow data, or cumulative pump operation data. The processing method can also selectively transfer a first group of source solutions using the first pump, receiving pump data from one or more sensors that sense actions of the pumps, applying fluid correction factors and calculating discrete pump movements, the pump movements being indicative of an amount of source solution displacement by a pump, and operating the pumps to selectively dispense the source solution amounts according to a preparation order.

Abstract: Example embodiments of aspects of the present disclosure provide an example computer-implemented method for improved prompting of a machine-learned model. The example method can include obtaining an instructive sequence descriptive of an instructive query, an instructive response, and an instructive trace of intermediate states from the instructive query to the instructive response. The example method can include inputting, to a machine-learned model, the instructive sequence and an operative query, wherein the machine-learned model is configured to process the operative query with attention over the instructive sequence. The example method can include generating, using the machine-learned model and responsive to the operative query, an operative response.

Abstract: Systems and methods of the present disclosure are directed to a method for machine-learned code segment prediction for optimizing software development. The method includes obtaining an incomplete segment of code. The method includes processing the incomplete segment of code with a machine-learned code prediction model to obtain a sampled set of segment completion predictions that include code that completes the incomplete segment of code. The method includes determining an aggregated segment completion prediction from the sampled set of segment completion predictions. The method includes replacing a portion of the aggregated segment completion prediction with an input field, wherein the portion of the aggregated segment completion prediction is associated with a degree of certainty less than a threshold degree of certainty.

Abstract: An example method for pretraining a machine-learned model is provided. The example method includes obtaining a plurality of different combinations of configuration parameters of a pretraining objective framework. The example method includes generating, using the pretraining objective framework, a plurality of corrupted training examples from one or more training examples, wherein the plurality of corrupted training examples are respectively generated according to the plurality of different combinations. The example method includes inputting the plurality of corrupted training examples into the machine-learned model, wherein the machine-learned model is configured to generate uncorrupted subportions corresponding to corrupted subportions of the corrupted training examples. The example method includes obtaining, from the machine-learned model, a plurality of outputs respectively generated by the machine-learned model based on the plurality of corrupted training examples.

Abstract: CHARGER/SYNC CABLE, THAT'S IS TYPE C TO TYPE C WITH 2 USB A TYPE ATTACHMENTS/CONNECTORS CONNECTORS/ATTACHMENTS ON BOTH ENDS THAT YOU CAN INTEGRATE TO THE BASE OTG HEAD TYPE-C AND ALTERNATES/COUNTERPARTS TYPE MICRO, TYPE-LIGHTNING. IDEA OF TYPE C TO TYPE C WITH USB A ON BOTH ENDS, MAKES ROOM FOR ITS COUNTERPARTS THAT'S FURTHER ITS SIMPLICITY OF CONNECTORS OF MICRO, LIGHTING, TYPE C TO TRANSITION HDMI AND HDMI MINI TO INTEGRATE AS WELL INTERFACE TO CABLES/SYNC DEVICES/MECHANISMS.

Abstract: The present disclosure provides a composition, e.g., a powder or liquid beverage, including L-arginine, resveratrol, curcumin, and luo han guo extract containing mogrosides. Also provided is a method of making the composition, including mixing components into a composition comprising water at certain temperatures and forming a solution. A method of improving circulation is further provided, including administering the composition to a subject, such as a human or an animal.