Abstract: The present disclosure is directed to methods and apparatus for validating and authenticating use of machine learning models. For example, various techniques are described herein to limit the vulnerability of machine learning models to attack and/or exploitation of the model for malicious use, and for detecting when such attack/exploitation has occurred. Additionally, various embodiments described herein promote the protection of sensitive and/or valuable data, for example by ensuring only licensed use is permissible. Moreover, techniques are described for version tracking, usage tracking, permission tracking, and evolution of machine learning models.

Abstract: The present disclosure is directed to methods and apparatus for validating and authenticating use of machine learning models. For example, various techniques are described herein to limit the vulnerability of machine learning models to attack and/or exploitation of the model for malicious use, and for detecting when such attack/exploitation has occurred. Additionally, various embodiments described herein promote the protection of sensitive and/or valuable data, for example by ensuring only licensed use is permissible. Moreover, techniques are described for version tracking, usage tracking, permission tracking, and evolution of machine learning models.

Abstract: A controller (122, 910/920) for interventional procedure optimization includes a processor (12210, 910) and a memory (12220, 920) that stores instructions. When executed by the processor, the instructions cause the controller (12210, 910) to implement a process that includes identifying (S210) anatomical characteristics from pre-interventional imagery of anatomy for each of multiple candidate types of an interventional procedure for the anatomy and comparing (S220) the anatomical characteristics with tool characteristics of candidate tools to use in each of the candidate types. The process also includes generating (S240) a feasibility report for each of the candidate types based on the identifying and the comparing. Each feasibility report includes a feasibility grade for each of the candidate types. The process also includes selecting (S260), based on the feasibility reports, an optimal interventional procedure type among the candidate types.

Abstract: The present disclosure is directed to methods and apparatus for validating and authenticating use of machine learning models. For example, various techniques are described herein to limit the vulnerability of machine learning models to attack and/or exploitation of the model for malicious use, and for detecting when such attack/exploitation has occurred. Additionally, various embodiments described herein promote the protection of sensitive and/or valuable data, for example by ensuring only licensed use is permissible. Moreover, techniques are described for version tracking, usage tracking, permission tracking, and evolution of machine learning models.

Abstract: The present disclosure is directed to methods and apparatus for validating and authenticating use of machine learning models. For example, various techniques are described herein to limit the vulnerability of machine learning models to attack and/or exploitation of the model for malicious use, and for detecting when such attack/exploitation has occurred. Additionally, various embodiments described herein promote the protection of sensitive and/or valuable data, for example by ensuring only licensed use is permissible. Moreover, techniques are described for version tracking, usage tracking, permission tracking, and evolution of machine learning models.

Abstract: The present disclosure is directed to methods and apparatus for validating and authenticating use of machine learning models. For example, various techniques are described herein to limit the vulnerability of machine learning models to attack and/or exploitation of the model for malicious use, and for detecting when such attack/exploitation has occurred. Additionally, various embodiments described herein promote the protection of sensitive and/or valuable data, for example by ensuring only licensed use is permissible. Moreover, techniques are described for version tracking, usage tracking, permission tracking, and evolution of machine learning models.

Abstract: The present disclosure is directed to methods and apparatus for validating and authenticating use of machine learning models. For example, various techniques are described herein to limit the vulnerability of machine learning models to attack and/or exploitation of the model for malicious use, and for detecting when such attack/exploitation has occurred. Additionally, various embodiments described herein promote the protection of sensitive and/or valuable data, for example by ensuring only licensed use is permissible. Moreover, techniques are described for version tracking, usage tracking, permission tracking, and evolution of machine learning models.

Abstract: A ladder stabilizer having a base plate that engages with a lower portion of the ladder and having a tire receiving portion at an end portion of the base plate. The weight of a vehicle tire on the tire receiving portion provides increased stability to the base plate of the ladder stabilizer. The tire receiving portion can be moved to either side of the base plate and can have a base plate-engaging mechanism on its bottom surface to couple with a mating surface of the base plate. The ladder can be retained by a leg retainer, rung retainer, or other retaining member that is coupled to a ladder retainer attachment portion of the base plate. Preferably, the ladder retainer mechanism includes some type of fastener or tensioning mechanism to secure the lower portion of the ladder to the base plate.

Abstract: A slip-on eave brace and a system incorporating the same for providing fall arrest on a sloped roof is disclosed. The slip-on eave brace has a soffit load distributing member, a vertical member coupled to the soffit load distributing member at one end portion thereof, and a sloped roof engaging member coupled to an opposite end portion of the vertical member. A safety line attachment member is fixed to an opposite end portion of sloped roof engaging member to the coupling with the vertical member. In a fall arrest event, fall forces are transferred via the safety line into the slip-on eave brace and into the eave of a sloped roof. A sloped roof fall arrest system can use one or more slip-on cave braces to provide fall protection to one or more workers on a sloped roof.

Abstract: A slip-on eave brace and a system incorporating the same for providing fall arrest on a sloped roof is disclosed. The slip-on eave brace has a soffit load distributing member, a vertical member coupled to the soffit load distributing member at one end portion thereof, and a sloped roof engaging member coupled to an opposite end portion of the vertical member. A safety line attachment member is fixed to an opposite end portion of sloped roof engaging member to the coupling with the vertical member. In a fall arrest event, fall forces are transferred via the safety line into the slip-on eave brace and into the eave of a sloped roof. A sloped roof fall arrest system can use one or more slip-on eave braces to provide fall protection to one or more workers on a sloped roof.

Abstract: A ladder stabilizer having a base plate that engages with a lower portion of the ladder and having a tire receiving portion at an end portion of the base plate. The weight of a vehicle tire on the tire receiving portion provides increased stability to the base plate of the ladder stabilizer. The tire receiving portion can be moved to either side of the base plate and can have a base plate-engaging mechanism on its bottom surface to couple with a mating surface of the base plate. The ladder can be retained by a leg retainer, rung retainer, or other retaining member that is coupled to a ladder retainer attachment portion of the base plate. Preferably, the ladder retainer mechanism includes some type of fastener or tensioning mechanism to secure the lower portion of the ladder to the base plate.