Abstract: A method for generating a musical composition based on user input is described. A first set of musical characteristics from a first input musical piece is received as an input vector. The first set of musical characteristics is perturbed to create a perturbed input vector as input in a first set of nodes in a first visible layer of an unsupervised neural net. The unsupervised neural net comprised of a plurality of computing layers, each computing layer composed of a respective set of nodes. The unsupervised neural net is operated to calculate an output vector from a higher level hidden layer in the unsupervised neural net. The output vector is used to create an output musical piece.

Abstract: A method for generating a musical composition based on user input is described. A first set of musical characteristics from a first input musical piece is received as an input vector. The first set of musical characteristics is perturbed to create a perturbed input vector as input in a first set of nodes in a first visible layer of an unsupervised neural net. The unsupervised neural net comprised of a plurality of computing layers, each computing layer composed of a respective set of nodes. The unsupervised neural net is operated to calculate an output vector from a higher level hidden layer in the unsupervised neural net. The output vector is used to create an output musical piece.

Abstract: A method for generating a musical composition based on user input is described. A first set of musical characteristics from a first input musical piece is received as an input vector. The first set of musical characteristics is perturbed to create a perturbed input vector as input in a first set of nodes in a first visible layer of an unsupervised neural net. The unsupervised neural net comprised of a plurality of computing layers, each computing layer composed of a respective set of nodes. The unsupervised neural net is operated to calculate an output vector from a higher level hidden layer in the unsupervised neural net. The output vector is used to create an output musical piece.

Abstract: A computer-implemented method includes receiving user data from a plurality of data sources, where the user data describes a user. A plurality of events are determined within the user data by applying anomaly detection to the user data. A request to authenticate the user is received. A key event is selected from among the plurality of events, responsive to the request to authenticate the user. For the user, a challenge is generated, by a computer processor, based on the key event and responsive to the request to authenticate the user. A response to the challenge is received. It is decided whether to authenticate the user based on the response to the challenge.

Abstract: Examples of techniques for a built-in self-test (BIST) for embedded spin-transfer torque magnetic random access memory (STT-MRAM) are disclosed. In one example implementation according to aspects of the present disclosure, a computer-implemented method may include: initiating, by a processor, a BIST for the STT-MRAM; performing, by the processor, an error-correcting code (ECC) test for a portion of the STT-MRAM; responsive to the ECC test not being passed, determining whether a maximum signal is reached; responsive to the maximum signal not being reached, increasing the signal and performing the ECC test again; and responsive to the maximum signal being reached, determining that the portion of the STT-MRAM is bad.

Abstract: An approach is provided in which an information handling system configures a reinforcement learning model based inspiration selections received from a user. The information handling system performs training iterations using the configured reinforcement learning model, which generates multiple actions and multiple rewards corresponding to multiple actions. The information handling system determines that the multiple rewards reach an empirical threshold and, in turn, generates a musical composition based on the multiple actions.

Abstract: An approach is provided that executes on a mobile device with a touch-enabled display screen. The approach receives, from a user of the mobile device, a gesture entered on the touch-enabled display screen with the gesture being entered using an input instrument, such as a finger or thumb, operated by the user. In response to receiving the gesture, the approach identifies the application that is currently being displayed, predicts an intent of the user based on past user actions received during previous executions of the application. The predicted intent predicts the element displayed on the screen that is desired by the user. The approach also creates a cloned copy of the current application display and provides a virtual target window by displaying the cloned copy on the touch-enabled display screen in a position that makes the predicted element accessible to the input instrument operated by the user.

Abstract: An approach is provided for receiving, from a user of the mobile device, a directional gesture entered on a touch-enabled display screen. The directional gesture was entered using an input instrument, such as a finger or thumb, operated by the user. In response to receiving the directional gesture various actions are performed. A desired element that is currently displayed on the touch-enabled display screen is predicted based on the direction of the directional gesture. The current application display is cloned to create a cloned copy that is provided to the user in a virtual target window in a position that makes the desired element accessible to the user's input instrument.

Abstract: A computer-implemented method includes receiving user data from a plurality of data sources, where the user data describes a user. A plurality of events are determined within the user data by applying anomaly detection to the user data. A request to authenticate the user is received. A key event is selected from among the plurality of events, responsive to the request to authenticate the user. For the user, a challenge is generated, by a computer processor, based on the key event and responsive to the request to authenticate the user. A response to the challenge is received. It is decided whether to authenticate the user based on the response to the challenge.

Abstract: Examples of techniques for a built-in self-test (BIST) for embedded spin-transfer torque magnetic random access memory (STT-MRAM) are disclosed. In one example implementation according to aspects of the present disclosure, a computer-implemented method may include: initiating, by a processor, a BIST for the STT-MRAM; performing, by the processor, an error-correcting code (ECC) test for a portion of the STT-MRAM; responsive to the ECC test not being passed, determining whether a maximum signal is reached; responsive to the maximum signal not being reached, increasing the signal and performing the ECC test again; and responsive to the maximum signal being reached, determining that the portion of the STT-MRAM is bad.

Abstract: An approach is provided in which an information handling system configures a reinforcement learning model based inspiration selections received from a user. The information handling system performs training iterations using the configured reinforcement learning model, which generates multiple actions and multiple rewards corresponding to multiple actions. The information handling system determines that the multiple rewards reach an empirical threshold and, in turn, generates a musical composition based on the multiple actions.

Abstract: An approach is provided in which an information handling system configures a reinforcement learning model based inspiration selections received from a user. The information handling system performs training iterations using the configured reinforcement learning model, which generates multiple actions and multiple rewards corresponding to multiple actions. The information handling system determines that the multiple rewards reach an empirical threshold and, in turn, generates a musical composition based on the multiple actions.

Abstract: An approach is provided that executes on a mobile device with a touch-enabled display screen. The approach receives, from a user of the mobile device, a gesture entered on the touch-enabled display screen with the gesture being entered using an input instrument, such as a finger or thumb, operated by the user. In response to receiving the gesture, the approach identifies the application that is currently being displayed, predicts an intent of the user based on past user actions received during previous executions of the application. The predicted intent predicts the element displayed on the screen that is desired by the user. The approach also creates a cloned copy of the current application display and provides a virtual target window by displaying the cloned copy on the touch-enabled display screen in a position that makes the predicted element accessible to the input instrument operated by the user.

Abstract: An approach is provided for receiving, from a user of the mobile device, a directional gesture entered on a touch-enabled display screen. The directional gesture was entered using an input instrument, such as a finger or thumb, operated by the user. In response to receiving the directional gesture various actions are performed. A desired element that is currently displayed on the touch-enabled display screen is predicted based on the direction of the directional gesture. The current application display is cloned to create a cloned copy that is provided to the user in a virtual target window in a position that makes the desired element accessible to the user's input instrument.

Abstract: A method for generating a musical composition based on user input is described. A first set of musical characteristics is extracted from a first input musical piece. The first set of music characteristics is prepared as an input vector into an unsupervised neural net comprised of a plurality of computing layers by perturbing the first set of musical characteristics according to a user intent expressed in the user input to create a perturbed vector. The perturbed vector is input into the first set of nodes of the unsupervised neural net. The unsupervised neural net is operated to calculate an output vector from a highest set of nodes. The output vector is used to create an output musical piece.

Abstract: A method for generating a musical composition based on user input is described. A first set of musical characteristics from a first input musical piece is received as an input vector. The first set of musical characteristics is perturbed to create a perturbed input vector as input in a first set of nodes in a first visible layer of an unsupervised neural net. The unsupervised neural net comprised of a plurality of computing layers, each computing layer composed of a respective set of nodes. The unsupervised neural net is operated to calculate an output vector from a higher level hidden layer in the unsupervised neural net. The output vector is used to create an output musical piece.

Abstract: A method for generating a musical composition based on user input is described. A first set of musical characteristics is extracted from a first input musical piece. The first set of music characteristics is prepared as an input vector into an unsupervised neural net comprised of a plurality of computing layers by perturbing the first set of musical characteristics according to a user intent expressed in the user input to create a perturbed vector. The perturbed vector is input into the first set of nodes of the unsupervised neural net. The unsupervised neural net is operated to calculate an output vector from a highest set of nodes. The output vector is used to create an output musical piece.

Abstract: Mechanisms are provided, in a non-volatile memory device comprising a non-volatile memory and a memory controller, for controlling an operation of the non-volatile memory device. The non-volatile memory device receives a single combined memory command for accessing the non-volatile memory. The non-volatile memory device decodes the row address and the column address for the word-line to be accessed by the single combined memory command. The non-volatile memory device accesses the word-line such that at least a most significant bit (MSB) page and a least significant bit (LSB) page are accessed simultaneously.

Abstract: Mechanisms are provided, in a non-volatile memory device comprising a non-volatile memory and a memory controller, for controlling an operation of the non-volatile memory device. The non-volatile memory device receives a single combined memory command for accessing the non-volatile memory. The non-volatile memory device decodes the row address and the column address for the word-line to be accessed by the single combined memory command. The non-volatile memory device accesses the word-line such that at least a most significant bit (MSB) page and a least significant bit (LSB) page are accessed simultaneously.

Abstract: A memory is provided that comprises a bank of non-volatile memory cells configured into a plurality of banklets. Each banklet in the plurality of banklets can be enabled separately and independently of the other banklets in the bank of non-volatile memory cells. The memory further comprises peripheral banklet circuitry, coupled to the bank of a non-volatile memory array, that is configured to enable selected subsets of bit lines within a selected banklet within the plurality of banklets. Moreover, the memory comprises banklet select circuitry, coupled to the peripheral banklet circuitry, that is configured to select data associated with a selected banklet for reading out from the banklet or writing to the banklet.