Abstract: This application is directed to a speaker device having a rounded enclosure. The rounded enclosure includes two housing elements that are coupled to each other and have a substantially continuous transition. The rounded enclosure has a circular cross section defined by a first radius. A first housing element includes and extends past the circular cross section, and a second housing element has a second maximum radius that is smaller than the first radius. A speaker unit and a circuit board are arranged within the rounded enclosure. The circuit board is electrically coupled to the speaker unit. At least a portion of the first housing element includes perforations configured to enable transmission of sound generated by the speaker unit out of the speaker device. The second housing element has a power connector that is electrically coupled to the circuit board and is configured to receive power from an external power supply.

Abstract: Implementations are described herein for leveraging digital media files retrieved and/or created by users to predict/determine topics of potential relevance to the users. In various implementations, digital media file(s) created and/or retrieved by a user with a client device may be applied as input across trained machine learning model(s), which in some cases are local to the client device, to generate output that indicates object(s) detected in the digital media file(s). Data indicative of the indicated object(s) may be provided to a remote computing system without providing the digital media file(s) themselves. In some implementations, information associated with the indicated object(s) may be retrieved and proactively output to the user. In some implementations, a frequency at which objects occur across a corpus of digital media files may be considered when determining a likelihood that a detected object is potentially relevant to a user.

Abstract: Techniques for performing type-constrained operations for plug-in types are disclosed. A runtime environment encounters a request to perform a type-constrained operation that requires evaluating a type constraint associated with a particular plug-in type. The runtime environment lacks sufficient native instructions to evaluate type constraints associated with plug-in types. The runtime environment accesses a plug-in type framework to obtain a particular type descriptor instance associated with the particular plug-in type. The plug-in type framework is designated, prior to encountering any request to perform the type-constrained operation, for obtaining type descriptor instances which define constraints on plug-in types, to an extent that any such constraints exist. The particular type descriptor instance defines a particular type constraint that does not match any single built-in type.

Abstract: A module (m1) is allowed to gain private reflective access to any class in any module (C2 in m2), if and only if m2 grants to m1 deep reflective access for C2. A caller class may invoke a method to generate a second lookup object associated with a second principal class based on a first lookup object associated with a first principal class. The method is successfully executed only if (a) an access mode of the first lookup object allows for teleporting with private reflective access and (b) a module including the second principal class grants to a module including the first principal class deep reflective access for the second principal class. The second lookup object drops the access mode allowing for teleporting with private reflective access. Hence reflective access is confined within the limits imposed by module boundaries, as relaxed by access agreements between pairs of modules. The bilateral agreements are not transferable to other modules.

Abstract: Techniques for performing type-constrained operations for plug-in types are disclosed. A runtime environment encounters a request to perform a type-constrained operation that requires evaluating a type constraint associated with a particular plug-in type. The runtime environment lacks sufficient native instructions to evaluate type constraints associated with plug-in types. The runtime environment accesses a plug-in type framework to obtain a particular type descriptor instance associated with the particular plug-in type. The plug-in type framework is designated, prior to encountering any request to perform the type-constrained operation, for obtaining type descriptor instances which define constraints on plug-in types, to an extent that any such constraints exist. The particular type descriptor instance defines a particular type constraint that does not match any single built-in type.

Abstract: Implementations are described herein for leveraging digital media files retrieved and/or created by users to predict/determine topics of potential relevance to the users. In various implementations, digital media file(s) created and/or retrieved by a user with a client device may be applied as input across trained machine learning model(s), which in some cases are local to the client device, to generate output that indicates object(s) detected in the digital media file(s). Data indicative of the indicated object(s) may be provided to a remote computing system without providing the digital media file(s) themselves. In some implementations, information associated with the indicated object(s) may be retrieved and proactively output to the user. In some implementations, a frequency at which objects occur across a corpus of digital media files may be considered when determining a likelihood that a detected object is potentially relevant to a user.

Abstract: This invention relates to a crystalline form of (10R)-7-amino-12-fluoro-2,10,16-trimethyl-15-oxo-10,15,16,17-tetrahy-dro-2H-8,4-(metheno)pyrazolo[4,3-h][2,5,11]benzoxadiazacyclo-tetradecine-3-carbonitrile (lorlatinib) free base hydrate (Form 24). This invention also relates to pharmaceutical compositions comprising Form 24, and to methods of using Form 24 and such compositions in the treatment of abnormal cell growth, such as cancer, in a mammal.

Abstract: Techniques for computing a memory health metric are disclosed. A system computes a memory health metric as a function of estimated entropy in a data set. The system may determine the memory health metric based on compressed and uncompressed sizes of the data set, quantities of values in the data set, and/or patterns of structural relationships within the data set. The system determines whether the memory health metric crosses a threshold value. If the memory health metric crosses the threshold value, the system presents a warning indicating that utilization of the memory is unhealthy.

Abstract: In one approach, an import mechanism allows new hardware intrinsics to be utilized by writing or updating a library of source code, rather than specifically modifying the virtual machine for each new intrinsic. Thus, once the architecture is in place to allow the import mechanism to function, the virtual machine itself (e.g. the code which implements the virtual machine) no longer needs to be modified in order to allow new intrinsics to be utilized by end user programmers. Since source code is typically more convenient to write than the language used to implement the virtual machine and the risk of miscoding the virtual machine is minimized when introducing new intrinsics, the import mechanism described herein increases the efficiency at which new hardware intrinsics can be introduced.

Abstract: In one approach, a method comprises: a virtual machine receiving an invocation instruction from a caller that invokes a callee, wherein the caller represents a first set of instructions and the callee represents a second set of instructions, wherein the invocation instruction is associated with a first set of arguments; in response to receiving the invocation instruction and determining that the callee requires one or more additional parameters to be supplied by the virtual machine, the virtual machine causing the one or more additional parameters to be appended to the first set of arguments to create a second set of arguments; wherein the virtual machine prevents the caller from providing the one or more additional arguments that are to be supplied by the virtual machine; the virtual machine invoking the callee using the second set of arguments.

Abstract: In one approach, a method comprises: a virtual machine receiving an invocation instruction from a caller that invokes a callee, wherein the caller represents a first set of instructions and the callee represents a second set of instructions, wherein the invocation instruction is associated with a first set of arguments; in response to receiving the invocation instruction and determining that the callee requires one or more additional parameters to be supplied by the virtual machine, the virtual machine causing the one or more additional parameters to be appended to the first set of arguments to create a second set of arguments; wherein the virtual machine prevents the caller from providing the one or more additional arguments that are to be supplied by the virtual machine; the virtual machine invoking the callee using the second set of arguments.

Abstract: Techniques for performing type-constrained operations for plug-in types are disclosed. A runtime environment encounters a request to perform a type-constrained operation that requires evaluating a type constraint associated with a particular plug-in type. The runtime environment lacks sufficient native instructions to evaluate type constraints associated with plug-in types. The runtime environment accesses a plug-in type framework to obtain a particular type descriptor instance associated with the particular plug-in type. The plug-in type framework is designated, prior to encountering any request to perform the type-constrained operation, for obtaining type descriptor instances which define constraints on plug-in types, to an extent that any such constraints exist. The particular type descriptor instance defines a particular type constraint that does not match any single built-in type.

Abstract: In an approach, a virtual machine identifies, within a set of instructions, an instruction to load a constant; identifies, based on the instruction to load the constant, a first entry in a data structure that identifies a particular constant type of the one or more constant types, wherein the first entry specifies at least constant data and a first set of instructions for assembling a value or partial value from the constant data; executes the first set of instructions to assemble the value or the partial value from the constant data; and stores a particular value or a reference to the particular value onto a run-time data structure used to pass values or references between sets of instructions executing in a run-time environment, wherein the particular value is based on the value or the particular value assembled from the constant data.

Abstract: Implementations are described herein for leveraging digital media files retrieved and/or created by users to predict/determine topics of potential relevance to the users. In various implementations, digital media file(s) created and/or retrieved by a user with a client device may be applied as input across trained machine learning model(s), which in some cases are local to the client device, to generate output that indicates object(s) detected in the digital media file(s). Data indicative of the indicated object(s) may be provided to a remote computing system without providing the digital media file(s) themselves. In some implementations, information associated with the indicated object(s) may be retrieved and proactively output to the user. In some implementations, a frequency at which objects occur across a corpus of digital media files may be considered when determining a likelihood that a detected object is potentially relevant to a user.

Abstract: Techniques for computing a memory health metric are disclosed. A system computes a memory health metric as a function of estimated entropy in a data set. The system may determine the memory health metric based on compressed and uncompressed sizes of the data set, quantities of values in the data set, and/or patterns of structural relationships within the data set. The system determines whether the memory health metric crosses a threshold value. If the memory health metric crosses the threshold value, the system presents a warning indicating that utilization of the memory is unhealthy.

Abstract: This application is directed to a speaker device having a rounded enclosure. The rounded enclosure includes two housing elements that are coupled to each other and have a substantially continuous transition. The rounded enclosure has a circular cross section defined by a first radius. A first housing element includes and extends past the circular cross section, and a second housing element has a second maximum radius that is smaller than the first radius. A speaker unit and a circuit board are arranged within the rounded enclosure. The circuit board is electrically coupled to the speaker unit. At least a portion of the first housing element includes perforations configured to enable transmission of sound generated by the speaker unit out of the speaker device. The second housing element has a power connector that is electrically coupled to the circuit board and is configured to receive power from an external power supply.

Abstract: In one approach, a method comprises receiving one or more higher-level instructions specifying to assign a value of a particular value type to a particular container of a plurality of containers, wherein the plurality of containers represent a data structure for maintaining one or more variables during execution of a block of code, wherein at least two containers of the plurality of containers are different sizes; generating one or more lower-level instructions that assign the value to the particular container based on applying one or more assignment rules to the one or more higher-level instructions based on the particular value type and executing the one or more lower-level instructions.

Abstract: According to one technique, a virtual machine stores type profiling data for program code, the type profiling data indicating observed types for profiled values within the program code at specific profile points during previous executions of the program code. The virtual machine determines to optimize a particular code segment of the program code. The virtual machine generates a program representation describing a flow of data through different variables within the code segment. The virtual machine assigns speculative types to certain variables in the particular code segment by: assigning speculative types of first variables to respective observed types recorded in the type profiling data; calculating speculative types of second variables, based on propagating the speculative types of the first variables through the program representation.

Abstract: This application is directed to a compact voice-activated electronic device with speakers and electronic components and a dual waveguide/heatsink structure configured to direct sound from the speakers to the outside of the device and to dissipate to the outside of the device heat from the electronic components.

Abstract: In one approach, a method comprises receiving one or more higher-level instructions specifying to assign a value of a particular value type to a particular container of a plurality of containers, wherein the plurality of containers represent a data structure for maintaining one or more variables during execution of a block of code, wherein at least two containers of the plurality of containers are different sizes; generating one or more lower-level instructions that assign the value to the particular container based on applying one or more assignment rules to the one or more higher-level instructions based on the particular value type and executing the one or more lower-level instructions.