Abstract: A solid electrolytic capacitor comprising a capacitor element, anode lead extending from a surface of the capacitor element, an anode termination that is in electrical connection with the anode lead, a cathode termination that is in electrical connection with the solid electrolyte, and a casing material that encapsulates the capacitor element and anode lead is provided. A barrier coating is disposed on at least a portion of the capacitor element and is in contact with the casing material. The coating contains a polymeric material that includes a fluorinated component and a non-fluorinated component. The polymeric material has a glass transition temperature of from about 10° C. to about 120° C. and a thermal decomposition temperature of about 200° C. to about 300° C.

Abstract: A computer-implemented method, a computer program product, and a system for reducing labeled sample quantities required to update test sets. The computer-implemented method includes inputting a portion of unlabeled production data into a base model and generating labeled output relating to the unlabeled production data. The computer-implemented method also includes inputting the labeled output into a performance predictor. The performance predictor is a meta model of the base model that is trained with another portion of the unlabeled production data, a training set used to train the base model, and a test set portioned from the training set. The computer-implemented method further includes outputting, by the performance predictor, a performance metric relating to the labeled output produced by the trained base model. The performance metric can be any metric capable of measuring the output performance of the base model.

Abstract: A computer-implemented method, a computer program product, and a computer system for diagnosing anomalies detected by a black-box machine learning model. A computer determines a local variance of a test sample in a test dataset, where the local variance represents uncertainty of a prediction by the black-box machine learning model. The computer initializes optimal compensations for the test sample, where the optimal compensations are optimal perturbations to test sample values of respective components of a multivariate input variable. The computer determines local gradients for the test sample. Based on the local variance and the local gradients, the computer updates the optimal compensations until convergences of the optimal compensations are reached. Using the optimal compensations, the computer diagnoses the anomalies detected by the black-box machine learning model.

Abstract: A solid electrolytic capacitor comprising a capacitor element, anode lead extending from a surface of the capacitor element, an anode termination that is in electrical connection with the anode lead, a cathode termination that is in electrical connection with the solid electrolyte, and a casing material that encapsulates the capacitor element and anode lead is provided. A barrier coating is disposed on at least a portion of the anode termination and/or cathode termination and is in contact with the casing material. The coating contains a hydrophobic resinous material that includes an olefin polymer having a glass transition temperature of from about 20° C. to about 160° C.

Abstract: A solid electrolytic capacitor comprising a capacitor element, anode lead extending from a surface of the capacitor element, an anode termination that is in electrical connection with the anode lead, a cathode termination that is in electrical connection with the solid electrolyte, and a casing material that encapsulates the capacitor element and anode lead is provided. A barrier coating is disposed on at least a portion of the capacitor element and is in contact with the casing material. The coating contains a polymeric material that includes a fluorinated component and a non-fluorinated component. The polymeric material has a glass transition temperature of from about 10° C. to about 120° C. and a thermal decomposition temperature of about 200° C. to about 300° C.

Abstract: A computer implemented method of performing large-scale machine learning experiments includes expanding on one or more input datasets by systematically generating several data set drift splits. A set of experimental jobs corresponding to the generated data set drift splits are executed to generate experimental results. The experimental results are processed, consolidated, and clustered according to the generated data set drift splits.

Abstract: A solid electrolytic capacitor is provided that contains a casing material that encapsulates the capacitor element. The casing material is formed from a curable resinous matrix that has a coefficient of thermal expansion of about 42 ppm/° C. or less at a temperature above the glass transition temperature of the resinous matrix. Further, the capacitor exhibits an initial equivalence series resistance of about 200 mohms or less as determined at an operating frequency of 100 kHz and temperature of 23° C., and the ratio of the equivalence series resistance of the capacitor after being exposed to a temperature of 125° C. for 560 hours to the initial equivalence series resistance of the capacitor is about 2.0 or less.

Abstract: A computer-implemented method, a computer program product, and a computer system for diagnosing anomalies detected by a black-box machine learning model. A computer determines a local variance of a test sample in a test dataset, where the local variance represents uncertainty of a prediction by the black-box machine learning model. The computer initializes optimal compensations for the test sample, where the optimal compensations are optimal perturbations to test sample values of respective components of a multivariate input variable. The computer determines local gradients for the test sample. Based on the local variance and the local gradients, the computer updates the optimal compensations until convergences of the optimal compensations are reached. Using the optimal compensations, the computer diagnoses the anomalies detected by the black-box machine learning model.

Abstract: A computer-implemented method, a computer program product, and a system for reducing labeled sample quantities required to update test sets. The computer-implemented method includes inputting a portion of unlabeled production data into a base model and generating labeled output relating to the unlabeled production data. The computer-implemented method also includes inputting the labeled output into a performance predictor. The performance predictor is a meta model of the base model that is trained with another portion of the unlabeled production data, a training set used to train the base model, and a test set portioned from the training set. The computer-implemented method further includes outputting, by the performance predictor, a performance metric relating to the labeled output produced by the trained base model. The performance metric can be any metric capable of measuring the output performance of the base model.

Abstract: A technique for generating a performance prediction of a machine learning model with uncertainty intervals includes obtaining a first model configured to perform a task and a production dataset. At least one metric predicting a performance of the first model at performing the task on the production dataset is generated using a second model. The second model is a meta-model associated with the first model. At least one value predicting an uncertainty of the at least one metric predicting the performance of the first model at performing the task on the production dataset is generated using a third model. The third model is a meta-meta-model associated with the second model. An indication of the at least one metric and the at least one value is provided.

Abstract: A module of capacitor assemblies is provided. To increase the capacitance and efficiency of the module, capacitor assemblies may be stacked. A variety of aspects of the module are controlled in the present invention, including the number of capacitor assemblies, the manner in which the capacitor assemblies are arranged and incorporated into the module, and the manner in which the capacitor assemblies are formed. For example, the anode terminations of each capacitor assembly are electrically connected and the cathode terminations of each capacitor assembly are electrically connected. The capacitance and efficiency of the module can be improved while maintaining the footprint of a single capacitor assembly.

Abstract: A solid electrolytic capacitor comprising a capacitor element, anode lead extending from a surface of the capacitor element, an anode termination that is in electrical connection with the anode lead, a cathode termination that is in electrical connection with the solid electrolyte, and a casing material that encapsulates the capacitor element and anode lead is provided. A barrier coating is disposed on at least a portion of the anode termination and/or cathode termination and is in contact with the casing material. The coating contains a hydrophobic resinous material that includes an olefin polymer having a glass transition temperature of from about 20° C. to about 160° C.

Abstract: A method is provided that includes generating answer-passage pairs, each associated with a respective one of multiple feature vectors. The method includes, for each answer in the pairs, merging the feature vectors associated with any of the pairs that include the answer to obtain a merged answer vector. The method includes, for each passage in the pairs, (i) merging the feature vectors associated with any of the pairs that includes the passage to obtain a merged passage vector, (ii) merging the feature vectors from the merged answer vector for each answer that is associated with the passage via at least one of the pairs to obtain a merged passage-answer vector, and (iii) concatenating the merged passage vector and the merged passage-answer vector to obtain a concatenated passage vector. The method includes ranking the concatenated passage vector for each passage to obtain a ranked list of passages with associated confidence scores.

Abstract: According to one embodiment, a method, computer system, and computer program product for continuously ranking components in a live information is provided. The present embodiment may include receiving search feedback derived from interactions between users and information retrieval systems; assigning weights to each of the ranking components; adjusting the assigned weights based on search feedback; modifying the current set of ranking components based on the search feedback by adding new ranking components and deleting old ranking components; transmitting a query from the users to the current set of ranking components; aggregating ranking results from the transmitted query into a single ranking based on the weights; and transmitting the single ranking to the users.

Abstract: According to one embodiment, a method, computer system, and computer program product for continuously ranking components in a live information is provided. The present embodiment may include receiving search feedback derived from interactions between users and information retrieval systems; assigning weights to each of the ranking components; adjusting the assigned weights based on search feedback; modifying the current set of ranking components based on the search feedback by adding new ranking components and deleting old ranking components; transmitting a query from the users to the current set of ranking components; aggregating ranking results from the transmitted query into a single ranking based on the weights; and transmitting the single ranking to the users.

Abstract: A solid electrolytic capacitor comprising a capacitor element, anode lead extending from a surface of the capacitor element, an anode termination that is in electrical connection with the anode lead, a cathode termination that is in electrical connection with the solid electrolyte, and a casing material that encapsulates the capacitor element and anode lead is provided. An interfacial coating is disposed on at least a portion of the anode termination and/or cathode termination and is in contact with the casing material. The coating contains a hydrophobic resinous material and the adhesion strength of the casing material is about 5 newtons per square millimeter or more as determined at a temperature of about 23° C. and relative humidity of about 30%.

Abstract: A solid electrolytic capacitor is provided that contains a casing material that encapsulates the capacitor element. The casing material is formed from a curable resinous matrix that has a coefficient of thermal expansion of about 42 ppm/° C. or less at a temperature above the glass transition temperature of the resinous matrix. Further, the capacitor exhibits an initial equivalence series resistance of about 200 mohms or less as determined at an operating frequency of 100 kHz and temperature of 23° C., and the ratio of the equivalence series resistance of the capacitor after being exposed to a temperature of 125° C. for 560 hours to the initial equivalence series resistance of the capacitor is about 2.0 or less.

Abstract: A capacitor element for use in high voltage environments is provided. More particularly, the capacitor element contains an anode that includes a solid electrolyte that overlies an anode. The anode includes a sintered porous pellet and a dielectric layer having a reduced degree of crystallinity formed on a surface of the pellet and within its pores.

Abstract: A computer querying an application programming interface with each of multiple synthetic samples, each of the synthetic samples representing a separate sample assigned an original class from among multiple classes classified by a particular machine learning model and distorted to induce the particular machine learning model to misclassify the separate sample as a different class from among the classes. The computer accumulating, by the computer, a score of a number of results returned by the application programming interface that match an expected class label assignment of the different class for each of the synthetic samples. The computer, in response to the score exceeding a threshold, verifying, by the computer, that a service provided by the application programming interface is running the particular machine learning model.

Abstract: A computer accesses a machine learning model and multiple samples, each classified with a separate original class of multiple classes within training data used to train the machine learning model to identify each of the classes. The computer generates multiple synthetic samples, each comprising a sample distorted to induce the machine learning model to misclassify the sample to a different class from the original class. The computer creates a synthetic sample signature, for use in verifying the identity of the machine learning model at runtime, from the synthetic samples and a matrix of returned class labels each identifying one of the classes, determined from actual classifications of each the synthetic samples in response to running the synthetic samples on the machine learning model.