Abstract: A visual guidance-based mobile game system (001) comprises a terminal (100) and a gamepad (200) connected with the terminal (100). The terminal (100) comprises a user interface module configured to receive an eye movement image and output a mobile game response. The mobile game system (001) further comprises a visual intelligent guidance module (210) and a data processing module (220) that are connected with the user interface module (120). The eye tracker converts the eye movement image into the eye tracking information. The visual intelligent guidance module (210) is configured to transmit the eye tracking information to the data processing module (220). The data processing module (220) is configured to determine a responsive instruction corresponding to the eye tracking information according to the eye tracking information, so as to guide a control point on a screen of the terminal according to the responsive instruction and complete the mobile game response.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. A portfolio return computation request configured to specify simulated market scenarios generated using neural networks and a set of filters is obtained. Constituent portfolio securities of a portfolio are determined. The simulated market scenarios are filtered based on the set of filters. Expected returns for the constituent portfolio securities are retrieved.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. A portfolio return computation request configured to specify simulated market scenarios generated using multi-variate mixture datastructures and a set of filters is obtained. Constituent portfolio securities of a portfolio are determined. The simulated market scenarios are filtered based on the set of filters. Expected returns for the constituent portfolio securities are retrieved.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. User selection of simulated market scenarios generated using neural networks is obtained. A range of unfiltered simulated market factor values for each market factor is determined. Customized market factors are updated based on a user modification. A range of allowable values for each customized market factor is determined.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. User selection of simulated market scenarios generated using multi-variate mixture datastructures is obtained. A range of unfiltered simulated market factor values for each market factor is determined. Customized market factors are updated based on a user modification. A range of allowable values for each customized market factor is determined.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. An asset return metrics calculation request datastructure is obtained. The number of sessions to utilize for calculating asset return metrics data is determined.

Abstract: The Machine Learning Portfolio Simulating and Optimizing Apparatuses, Methods and Systems (“MLPO”) transforms machine learning simulation request, decision tree ensembles training request, expected returns calculation request, portfolio construction request, predefined scenario construction request, portfolio returns visualization request inputs via MLPO components into machine learning simulation response, decision tree ensembles training response, expected returns calculation response, portfolio construction response, predefined scenario construction response, portfolio returns visualization response outputs. A portfolio construction request configured to include a set of optimization parameters is obtained. A set of simulated market scenarios is generated using neural networks. A set of expected returns for securities in the universe of securities for the set of simulated market scenarios is retrieved.

Abstract: In one embodiment, an optical subassembly includes a housing, a ball lens, a constraining insert, and a ball lens constraint. The housing includes a fiber receptacle formed in a first end of the housing and a second receptacle formed in a second end of the housing opposite the first end. The fiber receptacle and second receptacle define a cavity through the housing from the first end to the second end of the housing. The ball lens and the constraining insert are disposed within the cavity. The ball lens constraint is configured to cooperate with the constraining insert to constrain the ball lens in three dimensions within the cavity.

Abstract: In one embodiment, an optical subassembly includes a housing, a ball lens, a constraining insert, and a ball lens constraint. The housing includes a fiber receptacle formed in a first end of the housing and a second receptacle formed in a second end of the housing opposite the first end. The fiber receptacle and second receptacle define a cavity through the housing from the first end to the second end of the housing. The ball lens and the constraining insert are disposed within the cavity. The ball lens constraint is configured to cooperate with the constraining insert to constrain the ball lens in three dimensions within the cavity.

Abstract: In one embodiment, an optical subassembly includes a housing, a ball lens, a constraining insert, and a ball lens constraint. The housing includes a fiber receptacle formed in a first end of the housing and a second receptacle formed in a second end of the housing opposite the first end. The fiber receptacle and second receptacle define a cavity through the housing from the first end to the second end of the housing. The ball lens and the constraining insert are disposed within the cavity. The ball lens constraint is configured to cooperate with the constraining insert to constrain the ball lens in three dimensions within the cavity.

Abstract: In one embodiment, an optical subassembly includes a housing, a ball lens, a constraining insert, and a ball lens constraint. The housing includes a fiber receptacle formed in a first end of the housing and a second receptacle formed in a second end of the housing opposite the first end. The fiber receptacle and second receptacle define a cavity through the housing from the first end to the second end of the housing. The ball lens and the constraining insert are disposed within the cavity. The ball lens constraint is configured to cooperate with the constraining insert to constrain the ball lens in three dimensions within the cavity.

Abstract: In one example, an optical device includes a body having a first surface and a second surface. At least a portion of the body is formed from a material that is transmissible to light. The body is configured to be positioned in an optical sub-assembly along an axis defined between an optoelectronic transducer and a port configured to receive an optical fiber. The axis is defined between a point on an optically active portion of the optoelectronic transducer and a point on a surface of the optical fiber. The first surface of the body is positioned at a first angle relative to a plane that is perpendicular to the axis. The second surface of the body is positioned at a second angle relative to the plane. The first surface and the second surface are positioned at respective opposing ends of the body.

Abstract: In at least one example, an optical component includes a central optical surface proximate an optical axis, a peripheral portion extending radially from the central optical surface, and a stepped portion between the central optical surface and the peripheral portion. The stepped portion may be formed to raise the central optical surface above the peripheral portion.

Abstract: In at least one example, an optical component includes a central optical surface proximate an optical axis, a peripheral portion extending radially from the central optical surface, and a stepped portion between the central optical surface and the peripheral portion. The stepped portion may be formed to raise the central optical surface above the peripheral portion.

Abstract: In one example, an optical device includes a body having a first surface and a second surface. At least a portion of the body is formed from a material that is transmissible to light. The body is configured to be positioned in an optical sub-assembly along an axis defined between an optoelectronic transducer and a port configured to receive an optical fiber. The axis is defined between a point on an optically active portion of the optoelectronic transducer and a point on a surface of the optical fiber. The first surface of the body is positioned at a first angle relative to a plane that is perpendicular to the axis. The second surface of the body is positioned at a second angle relative to the plane. The first surface and the second surface are positioned at respective opposing ends of the body.