Abstract: A modified polyester staple fiber and a preparation method of the modified polyester staple fiber are provided. The modified polyester staple fiber contains a modified nanocomposite material including an organic compound, the organic compound including one of tea polyphenol, naringin, and emodin. The organic compound is in a weight percentage range of approximately 0.1%-5% based on a total weight of the modified polyester staple fiber.

Abstract: Aspects of the disclosure provide for a computer program product comprising a computer readable medium having program instructions embodied therewith, the program instructions executable by a processor to generate a set of scenarios corresponding to a test data set and depending on a selected data analysis model, determine a value for each point in time over a defined time interval and an exposure profile that is a continuous time representation of each value determined for each point in time, determine a risk envelope desired for the scenarios, determine a test statistic defining a fraction of the defined time interval that the exposure profile is outside the risk envelope, determine a cumulative distribution of the test statistic, the cumulative distribution having a critical value corresponding to a defined probability of accuracy of the data analysis model, and validate the data analysis model based on the critical value and the test statistic.

Abstract: Aspects of the disclosure provide for a computer program product comprising a computer readable medium having program instructions embodied therewith, the program instructions executable by a processor to generate a set of scenarios corresponding to a test data set and depending on a selected data analysis model, determine a value for each point in time over a defined time interval and an exposure profile that is a continuous time representation of each value determined for each point in time, determine a risk envelope desired for the scenarios, determine a test statistic defining a fraction of the defined time interval that the exposure profile is outside the risk envelope, determine a cumulative distribution of the test statistic, the cumulative distribution having a critical value corresponding to a defined probability of accuracy of the data analysis model, and validate the data analysis model based on the critical value and the test statistic.

Abstract: Disclosed herein are technologies for automatically updating object model and associated code for software applications, such as web pages, by invoking an automatic componentization engine client to determine changes in the component code of web pages, receiving the changes in the component code, and compiling and verifying the code. Generated or revised and updated code may then be provided to a user.

Abstract: Test results can be analyzed and linked using an automated system. In a first embodiment, an error log can be received including log data associated with an error. The log data can be parsed to transform it into a standardized format to allow for easier searching and comparison. Once standardized, an automatic comparison can be made between the parsed log data and previous errors obtained from error logs. If a match is found between the parsed log data and a previous error, then the parsed log data can be linked to the previous error so as to generate a relationship between the two.

Abstract: Disclosed herein are technologies for automatically updating object model and associated code for software applications, such as web pages, by invoking an automatic componentization engine client to determine changes in the component code of web pages, receiving the changes in the component code, and compiling and verifying the code. Generated or revised and updated code may then be provided to a user.

Abstract: Test results can be analyzed and linked using an automated system. In a first embodiment, an error log can be received including log data associated with an error. The log data can be parsed to transform it into a standardized format to allow for easier searching and comparison. Once standardized, an automatic comparison can be made between the parsed log data and previous errors obtained from error logs. If a match is found between the parsed log data and a previous error, then the parsed log data can be linked to the previous error so as to generate a relationship between the two.

Abstract: Systems and methods for generating an integrated market and credit loss distribution for the purpose of calculating one or more risk measures associated with a portfolio of instruments are disclosed. In at least one embodiment, compound risk factor sampling is performed that comprises conditionally generating multiple systemic credit driver samples for each market risk factor sample generated per time step of a simulation. There are also disclosed systems and methods for determining an optimal number of sample values for each of the market risk factors, systemic credit drivers, and optionally, idiosyncratic credit risk factors that would be required in order to obtain an acceptable amount of variability in the calculated risk estimates and/or to satisfy an available computational budget.

Abstract: Systems and methods for generating an integrated market and credit loss distribution for the purpose of calculating one or more risk measures associated with a portfolio of instruments are disclosed. In at least one embodiment, compound risk factor sampling is performed that comprises conditionally generating multiple systemic credit driver samples for each market risk factor sample generated per time step of a simulation. There are also disclosed systems and methods for determining an optimal number of sample values for each of the market risk factors, systemic credit drivers, and optionally, idiosyncratic credit risk factors that would be required in order to obtain an acceptable amount of variability in the calculated risk estimates and/or to satisfy an available computational budget.

Abstract: Systems and methods for generating an integrated market and credit loss distribution for the purpose of calculating one or more risk measures associated with a portfolio of instruments are disclosed. In at least one embodiment, compound risk factor sampling is performed that comprises conditionally generating multiple systemic credit driver samples for each market risk factor sample generated per time step of a simulation. There are also disclosed systems and methods for determining an optimal number of sample values for each of the market risk factors, systemic credit drivers, and optionally, idiosyncratic risk factors that would be required in order to obtain an acceptable amount of variability in the calculated risk estimates and/or to satisfy an available computational budget.

Abstract: Systems and methods for generating an integrated market and credit loss distribution for the purpose of calculating one or more risk measures associated with a portfolio of instruments are disclosed. In at least one embodiment, compound risk factor sampling is performed that comprises conditionally generating multiple systemic credit driver samples for each market risk factor sample generated per time step of a simulation. There are also disclosed systems and methods for determining an optimal number of sample values for each of the market risk factors, systemic credit drivers, and optionally, idiosyncratic risk factors that would be required in order to obtain an acceptable amount of variability in the calculated risk estimates and/or to satisfy an available computational budget.

Abstract: Systems and methods for generating an integrated market and credit loss distribution for the purpose of calculating one or more risk measures associated with a portfolio of instruments are disclosed. In at least one embodiment, compound risk factor sampling is performed that comprises conditionally generating multiple systemic credit driver samples for each market risk factor sample generated per time step of a simulation. There are also disclosed systems and methods for determining an optimal number of sample values for each of the market risk factors, systemic credit drivers, and optionally, idiosyncratic risk factors that would be required in order to obtain an acceptable amount of variability in the calculated risk estimates and/or to satisfy an available computational budget.

Abstract: Systems and methods for generating an integrated market and credit loss distribution for the purpose of calculating one or more risk measures associated with a portfolio of instruments are disclosed. In at least one embodiment, compound risk factor sampling is performed that comprises conditionally generating multiple systemic credit driver samples for each market risk factor sample generated per time step of a simulation. There are also disclosed systems and methods for determining an optimal number of sample values for each of the market risk factors, systemic credit drivers, and optionally, idiosyncratic risk factors that would be required in order to obtain an acceptable amount of variability in the calculated risk estimates and/or to satisfy an available computational budget.