Abstract: Disclosed herein are microelectronic assemblies, as well as related apparatuses and methods. In some embodiments, a microelectronic assembly may include a first die having a first surface, an opposing second surface, and a first footprint; a second die electrically coupled to the second surface of the first die and having a second footprint, wherein the second footprint is smaller than the first footprint; and a mold material on the second surface of the first die and surrounding the second die, the mold material including a trench.

Abstract: An apparatus includes a memory and a processor. The memory stores descriptions of known vulnerabilities and information generated by a monitoring subsystem. Each description of a known vulnerability identifies software components that are associated with the known vulnerability. The monitoring subsystem monitors software programs that are installed within a computer system. The information includes descriptions of issues that are associated with the software programs. The processor generates a set of mappings, based on a comparison between the text describing the known software vulnerabilities and the text describing the issues. Each mapping associates a software program that is associated with an issue with a known software vulnerability. The processor also uses a machine learning algorithm to predict that a given software program is associated with a particular software vulnerability.

Abstract: Attachment systems for attaching a wearable electronic device to a user including a sizing band and a retaining band are disclosed. The retaining band can include a plug assembly (e.g., actuatable button, retractable arms, etc.) configured to be inserted in a selected eyelet of the sizing band. Upon insertion into the eyelet, the plug assembly can resist unintended separation of the sizing and retaining band by increasing its outer dimension. Release can be achieved by allowing the user to decrease the outer dimension of the plug assembly so that removal from the selected eyelet is easily achieved.

Abstract: An apparatus includes a memory and a processor. The memory stores descriptions of known vulnerabilities and information generated by a monitoring subsystem. Each description of a known vulnerability identifies software components that are associated with the known vulnerability. The monitoring subsystem monitors software programs that are installed within a computer system. The information includes descriptions of issues that are associated with the software programs. The processor generates a set of mappings, based on a comparison between the text describing the known software vulnerabilities and the text describing the issues. Each mapping associates a software program that is associated with an issue with a known software vulnerability. The processor also uses a machine learning algorithm to predict that a given software program is associated with a particular software vulnerability.

Abstract: A method and apparatus are provided for viewing video images. The method includes the steps of an input of a video server receiving indicia identifying an image source providing a sequence of video images of interest and requesting that notification be sent to a user of a wireless electronic device, pushing a notification of the identified source through a persistent connection to the user of a wireless electronic device in response to the request, a user input of the wireless electronic device receiving input accepting the notification and the wireless electronic device automatically launching a viewing application installed on the wireless electronic device in response to the acceptance, the viewing application downloading video images from the image source to the wireless electronic device for viewing.