Abstract: In one embodiment, a device obtains input features for a neural network-based model. The device pre-defines a set of neurons of the model to represent known behaviors associated with the input features. The device constrains weights for a plurality of outputs of the model. The device trains the neural network-based model using the constrained weights for the plurality of outputs of the model and by excluding the pre-defined set of neurons from updates during the training.

Abstract: A solid organic material comprising at least one amphiphilic block copolymer of the methacrylic type, the block copolymer being dispersed in a polymer matrix, the block copolymer having a number-average molar mass (Mn) greater than or equal to 20 000 g/mol; a method for preparing said solid organic material; use of said solid organic material for antibacterial, antimicrobial, antiviral, anti-inflammatory and/or antifungal applications; a coating comprising said solid organic material.

Abstract: A liquid hand dishwashing detergent composition, includes from about 5% to about 50% by weight of the total composition of a surfactant system wherein: the surfactant system comprises: an anionic surfactant, and the composition further comprises from about 0.1% to about 15% by weight of the composition of at least one triamine of formula (I) wherein: X is N or each a is independently selected from about 1 to about 6; and each R is independently selected from: hydrogen, C1-C4 alkyl, or alkenyl or alkoxy.

Abstract: In one embodiment, a device obtains input features for a neural network-based model. The device pre-defines a set of neurons of the model to represent known behaviors associated with the input features. The device constrains weights for a plurality of outputs of the model. The device trains the neural network-based model using the constrained weights for the plurality of outputs of the model and by excluding the pre-defined set of neurons from updates during the training.

Abstract: A Special Purpose Incorporation Company (SPIC) is configured to incorporate a company seeking capital. The SPIC is configured to hold investment funds and incorporate a company seeking capital. A first portion of the investment funds is used to acquire the company seeking capital and place it in a special purpose vehicle company that holds substantially all assets of the company seeking capital. A second portion of the investment funds is invested in real estate.

Abstract: A solid organic material comprising at least one amphiphilic block copolymer of the methacrylic type, the block copolymer being dispersed in a polymer matrix, the block copolymer having a number-average molar mass (Mn) greater than or equal to 20 000 g/mol; a method for preparing said solid organic material; use of said solid organic material for antibacterial, antimicrobial, antiviral, anti-inflammatory and/or antifungal applications; a medical device comprising said solid organic material.

Abstract: Aspects of the subject technology relate to systems, methods, and computer-readable media for validating a test associated with operation of an autonomous vehicle (AV). Road data generated in association with operation of an AV in a real-world environment is accessed. A simulation environment for running a software stack associated with controlling the AV is generated based on the road data. A portion of the software stack is modified and the modified software stack is run as part of a test using sensor data included as part of the road data. The test of the modified software stack is validated based on the running of the modified software stack in the simulation environment in relation to operation of the AV in the real-world environment.

Abstract: In one embodiment, a device obtains input features for a neural network-based model. The device pre-defines a set of neurons of the model to represent known behaviors associated with the input features. The device constrains weights for a plurality of outputs of the model. The device trains the neural network-based model using the constrained weights for the plurality of outputs of the model and by excluding the pre-defined set of neurons from updates during the training.

Abstract: Provided herein are systems and methods for automatically limiting video surveillance collection to authorized uses and authorized users. To achieve this control, the authorization system can be configured to manage and secure a plurality of crypto keys associated with encrypting a plurality of corresponding video footages and release a crypto key for a video footage at approved times to limit user access to the video footage. In particular, the surveillance system can generate a video collection including a copy of portions of a received video footage that include one or more approved events from a watchlist of a user. Thereafter, the video footage can be encrypted by a first key managed by the authorization system and prevents the user from accessing video content of the video footage once encrypted. Accordingly, the user may be limited to accessing the video collection and not all the portions in the encrypted video footage.

Abstract: The need for a liquid detergent composition for washing dishes which provides effective removal of both crystalline and polymerised grease, while requiring less time and effort to clean and rinse the dishes, is met when the composition is formulated with a surfactant system having an anionic surfactant, a co-surfactant, and an esteramine.

Abstract: The need for a liquid detergent composition for washing dishes which provides effective removal of both crystalline and polymerised grease, while requiring less time and effort to clean and rinse the dishes, is met when the composition is formulated with a surfactant system having an anionic surfactant, a co-surfactant, and a non-alkoxylated esteramine.

Abstract: The present disclosure relates to pouches made from a combination of chemically different water-soluble films and containing household care compositions that are at least partially enclosed by the water-soluble films in at least one compartment.

Abstract: In one embodiment, a device obtains input features for a neural network-based model. The device pre-defines a set of neurons of the model to represent known behaviors associated with the input features. The device constrains weights for a plurality of outputs of the model. The device trains the neural network-based model using the constrained weights for the plurality of outputs of the model and by excluding the pre-defined set of neurons from updates during the training.