Abstract: The present invention relates to a method for optimization and recovery of second-generation sugar diluted stream, comprising pretreatment, enzymatic hydrolysis and mainly washing the residual solid from the enzymatic hydrolysis, which allows the increase of sugar recovery. The use of this sugar diluted stream is crucial for process integration, may have different possibilities of use, for example, can be applied in the mechanical refining step, microorganisms propagation, enzyme production, fermentation, enzymatic hydrolysis, including combination of uses.

Abstract: In various embodiments, a process for obtaining a generated dataset with a predetermined bias for evaluating algorithmic fairness of a machine learning model includes receiving an input dataset and generating an anonymized reconstructed dataset based at least on the input dataset. The process includes introducing a predetermined bias into the generated dataset, forming an evaluation dataset based at least on the generated dataset with the predetermined bias, and outputting the evaluation dataset. In various embodiments, a process for training a generative model includes configuring a generative model and receiving training data, where the training data includes a tabular dataset. The process includes using computer processor(s) and the received training data to train the generative model, where the generative model is sampled to generate a dataset with a predetermined bias.

Abstract: In an embodiment, a process for automatic model monitoring for data streams includes receiving an input dataset, using a machine learning model to determine a model score for each data record of at least a portion of the input dataset, and determining monitoring values. Each monitoring value is associated with a measure of similarity between model scores for those data records of the input dataset within a corresponding moving reference window and model scores for those data records of the input dataset within a corresponding moving target window. The process includes outputting the determined monitoring values.

Abstract: In various embodiments, a process for obtaining a generated dataset with a predetermined bias for evaluating algorithmic fairness of a machine learning model includes receiving an input dataset and generating an anonymized reconstructed dataset based at least on the input dataset. The process includes introducing a predetermined bias into the generated dataset, forming an evaluation dataset based at least on the generated dataset with the predetermined bias, and outputting the evaluation dataset. In various embodiments, a process for training a generative model includes configuring a generative model and receiving training data, where the training data includes a tabular dataset. The process includes using computer processor(s) and the received training data to train the generative model, where the generative model is sampled to generate a dataset with a predetermined bias.

Abstract: In an embodiment, a process for automatic model monitoring for data streams includes receiving an input dataset, using a machine learning model to determine a model score for each data record of at least a portion of the input dataset, and determining monitoring values. Each monitoring value is associated with a measure of similarity between model scores for those data records of the input dataset within a corresponding moving reference window and model scores for those data records of the input dataset within a corresponding moving target window. The process includes outputting the determined monitoring values.

Abstract: In an embodiment, a process for adaptive threshold estimation for streaming data includes determining initial positions for a set of percentile bins, receiving a new data item in a stream of data, and identifying one of the set of percentile bins corresponding to the new data item. The process includes incrementing a count of items in the identified percentile bin, adjusting one or more counts of data items in one or more of the percentile bins including by applying a suppression factor based on a relative ordering of items, and redistributing positions for the set of percentile bins to equalize respective count numbers of items for each percentile bin of the set of percentile bins. The process includes utilizing the redistributed positions of the set of percentile bins to determine a percentile distribution of the data stream, and calculating a threshold based at least in part on the percentiles distribution.

Abstract: In an embodiment, a process for automatic model monitoring for data streams includes receiving an input dataset, using a machine learning model to determine a model score for each data record of at least a portion of the input dataset, and determining monitoring values. Each monitoring value is associated with a measure of similarity between model scores for those data records of the input dataset within a corresponding moving reference window and model scores for those data records of the input dataset within a corresponding moving target window. The process includes outputting the determined monitoring values.

Abstract: A set of data elements is received. For each feature of a set of features, a corresponding reference distribution for the set of data elements is determined. For each feature of the set of features, one or more corresponding subset distributions for one or more subsets sampled from the set of data elements are determined. For each feature of the set of features, the corresponding reference distribution is compared with each of the one or more corresponding subset distributions to determine a corresponding distribution of divergences. At least the determined distributions of divergences for the set of features are provided for use in automated data analysis.

Abstract: One or more events of a data stream are received. For each feature of a set of features, the one or more events are used to update a corresponding distribution of data from the data stream. For each feature of the set of features, the corresponding updated distribution and a corresponding reference distribution are used to determine a corresponding divergence value. For each feature of the set of features, the corresponding determined divergence value and a corresponding distribution of divergences are used to determine a corresponding statistical value. Using the statistical values each corresponding to a different feature of the set of features, a statistical analysis is performed to determine a result associated with a likelihood of data drift detection.

Abstract: In various embodiments, a process for providing an active learning annotation system that does not require historical data includes receiving a stream of unlabeled data, identifying a portion of the unlabeled data to label without access to label information, and receiving a labeled version of the identified portion of the unlabeled data and storing the labeled version as labeled data. The process includes analyzing the labeled version and at least a portion of the received unlabeled data that has not been labeled to identify an additional portion of the unlabeled data to label and store in the labeled data including by applying at least one warm up policy.

Abstract: In an embodiment, a process for explanation reporting based on differentiation between items in different data groups includes obtaining model scores from a first machine learning model and training a second machine learning model to learn how to differentiate between two groups based on at least one of: features and the model scores obtained from the first machine learning model. The process includes applying the second machine learning model to each data record in a first group of data records to determine a corresponding ranking score for each data record in the first group, and based on the corresponding ranking scores, determining a relative contribution of each of the data records in the first group to the differentiation between the first group of data records and a second group of data records.

Abstract: In an embodiment, a process for adaptive threshold estimation for streaming data includes determining initial positions for a set of percentile bins, receiving a new data item in a stream of data, and identifying one of the set of percentile bins corresponding to the new data item. The process includes incrementing a count of items in the identified percentile bin, adjusting one or more counts of data items in one or more of the percentile bins including by applying a suppression factor based on a relative ordering of items, and redistributing positions for the set of percentile bins to equalize respective count numbers of items for each percentile bin of the set of percentile bins. The process includes utilizing the redistributed positions of the set of percentile bins to determine a percentile distribution of the data stream, and calculating a threshold based at least in part on the percentiles distribution.

Abstract: In an embodiment, a process for automatic model monitoring for data streams includes receiving an input dataset, using a machine learning model to determine a model score for each data record of at least a portion of the input dataset, and determining monitoring values. Each monitoring value is associated with a measure of similarity between model scores for those data records of the input dataset within a corresponding moving reference window and model scores for those data records of the input dataset within a corresponding moving target window. The process includes outputting the determined monitoring values.

Abstract: The present invention relates to a cold start-up system of an internal combustion engine for an engine burning gasoline, ethanol or a mixture of fuels, composed or not of a preponderant fraction of ethanol, which comprises at least one strategy of pre-injection of fuel through at least one injection nozzle, before activation of the starter motor, or during activation, in an asynchronous manner.

Abstract: A method for using polyamine analogues containing bulky hydrophobic groups against antimicrobial agents is disclosed. The antimicrobial method works by the mechanical action of disrupting the protective outer member of a bacterial cell.

Abstract: Poly(ADP-ribose) polymerase-1 (PARP-1) is a central signaling enzyme in a cell nucleus. PARP-1 is a target for the development of radio and chemo sensitizing agents in cancer treatment as well as providing protection from stroke. An SH3 domain and an SH3 ligand domain have now been discovered on the PARP-1 protein. These domains are involved in PARP-1 activation. This discovery makes possible the use of bioinformatics tools for the design of new drugs and strategies for drug target selection, specifically targeting the PARP-1 enzyme.

Abstract: A power supply system composed by inverters (105, 110, 115) connected in parallel, applicable mainly in uninterruptible power suppliers (UPS). The parallelism provides the capacity of increasing power through the connection of new inverters to the system; and reliability, once defective inverters can be removed of the system without interrupting the power supply, since the total capacity of the remaining inverters is not less than the capacity required by the loads. In a parallelism scenario, one of the inverters assumes the master role, operating as a voltage source, while the other inverters assume the slave role, operating as current sources. The master informs the reference current of each phase to the slaves through a communication bus (150) between inverters (105, 110, 115). The reference is informed as a relative value to master nominal power, allowing the use of inverters with different power in the system and a load distribution proportional to the nominal power of each inverter (105, 110, 115).

Abstract: Poly(ADP-ribose) polymerase-1 (PARP-1) is a central signaling enzyme in a cell nucleus. PARP-1 is a target for the development of radio and chemo sensitizing agents in cancer treatment as well as providing protection from stroke. An SH3 domain and an SH3 ligand domain have now been discovered on the PARP-1 protein. These domains are involved in PARP-1 activation. This discovery makes possible the use of bioinformatics tools for the design of new drugs and strategies for drug target selection, specifically targeting the PARP-1 enzyme.