Abstract: Disclosed herein are compositions and methods comprising a composition for light filtering. An example composition comprises a plurality of metal nanoparticles, at least a portion of the plurality of metal nanoparticles having an anisotropic shape; and a stabilizing mechanism disposed to selectively couple with at least a portion of the plurality of metal nanoparticles, wherein the composition exhibits a peak light filtering value in the range of about 600 nm to about 850 nm.

Abstract: Adaptively controlling a speaker assembly allows an acoustic characteristic of the speaker assembly to automatically change based on one or more factors. For example, when external noise is detected, the associated noise volume level is determined, and control instructions are generated based on the noise volume level. The control instructions cause the speaker assembly to adaptively increase the volume level to effectively cancel the noise volume. In another example, when a speaker assembly in a vehicle changes from playing a media file to conducting a phone conversation, the volume level associated with the phone conversation may increase. In this regard, control instructions can be generated and used to adaptively reduce the volume level of the phone conversation.

Abstract: Described are systems and methods which can be used to indicate the period of time elapsed since the occurrence of a triggering event. More particularly, this application relates to systems and methods which can be used to visually indicate the time period elapsed since an article has been removed from its packaging. The system can include an indicator disposed on or within the article and a trigger disposed within the receptacle and in contact with the indicator. The article may be enclosed within a sealable receptacle of the package. The indicator is responsive to a change in a concentration of the trigger in contact with the indicator. The removal of the article from the receptacle can induce a change in concentration of the trigger in contact with the indicator. The change in the concentration of the trigger can induce a change in color in the indicator. The color change indicates a predetermined period of time has elapsed since the article has been removed from the receptacle.

Abstract: Systems and methods for augmenting incomplete training dataset for use in a machine learning system are described herein. In an embodiment, a server computer receives a plurality of input training datasets including one or more incomplete input training datasets and one or more complete datasets which contain one or more failure training datasets, the incomplete input training datasets comprising a plurality of parameters. Using the one or more failure training datasets, the server computer generates temporal failure data describing a likelihood of failure of an item as a function of time. Using the one or more complete training datasets, the server computer generates parameter specific likelihoods of failure of an item. The server computer augments the one or more incomplete input training datasets using the temporal failure data and/or the parameter specific likelihoods of failure to create one or more augmented training datasets.

Abstract: Systems and methods for augmenting incomplete training dataset for use in a machine learning system are described herein. In an embodiment, a server computer receives a plurality of input training datasets including one or more incomplete input training datasets and one or more complete datasets which contain one or more failure training datasets, the incomplete input training datasets comprising a plurality of parameters. Using the one or more failure training datasets, the server computer generates temporal failure data describing a likelihood of failure of an item as a function of time. Using the one or more complete training datasets, the server computer generates parameter specific likelihoods of failure of an item. The server computer augments the one or more incomplete input training datasets using the temporal failure data and/or the parameter specific likelihoods of failure to create one or more augmented training datasets.