Abstract: Presented herein are systems and methods for generative design of custom biologics. In particular, in certain embodiments, generative biologic design technologies of the present disclosure utilize a machine learning models to create custom (e.g., de-novo) peptide backbones that, among other things, can be tailored to exhibit desired properties and/or bind to specified target molecules, such as other proteins (e.g., receptors). Generative machine learning models described herein may be trained on, and accordingly leverage, a vast landscape of existing protein and peptide structures. Once trained, however, these generative models may create wholly new (de-novo) custom peptide backbones that are expressly tailored to particular targets. These generated custom peptide backbones can, e.g., subsequently, be populated with amino acid sequences to generate final custom biologics providing enhanced performance for binding to desired targets.

Abstract: Presented herein are systems and methods for generative design of custom biologics. In particular, in certain embodiments, generative biologic design technologies of the present disclosure utilize a machine learning models to create custom (e.g., de-novo) peptide backbones that, among other things, can be tailored to exhibit desired properties and/or bind to specified target molecules, such as other proteins (e.g., receptors). Generative machine learning models described herein may be trained on, and accordingly leverage, a vast landscape of existing protein and peptide structures. Once trained, however, these generative models may create wholly new (de-novo) custom peptide backbones that are expressly tailored to particular targets. These generated custom peptide backbones can, e.g., subsequently, be populated with amino acid sequences to generate final custom biologics providing enhanced performance for binding to desired targets.

Abstract: Presented herein are systems and methods for generative design of custom biologics. In particular, in certain embodiments, generative biologic design technologies of the present disclosure utilize a machine learning models to create custom (e.g., de-novo) peptide backbones that, among other things, can be tailored to exhibit desired properties and/or bind to specified target molecules, such as other proteins (e.g., receptors). Generative machine learning models described herein may be trained on, and accordingly leverage, a vast landscape of existing protein and peptide structures. Once trained, however, these generative models may create wholly new (de-novo) custom peptide backbones that are expressly tailored to particular targets. These generated custom peptide backbones can, e.g., subsequently, be populated with amino acid sequences to generate final custom biologics providing enhanced performance for binding to desired targets.

Abstract: The embodiments herein provide a system and method for controlling an operation of the components in the two/three wheeled vehicle through a smartphone. The system comprises a vehicle module, a smartphone and a cloud computing system. The vehicle module reads the vehicle parameters with a vehicle parameter reader and sensors. The read vehicle parameter are forwarded to the microprocessor for transmission to the smartphone through a transceiver using a wired or wireless network. The microprocessor sends the vehicle parameters data to the smartphone for generating the commands/events for controlling an operation of the parts/components of the vehicle through the vehicle parameter controller. The data is uploaded to a cloud computing system and analyzed using an artificial intelligence module, a machine learning module and a pattern recognition module for providing insights/predictions to the user for controlling the vehicle.

Abstract: The embodiments herein provide a system and method for controlling an operation of the components in the two/three wheeled vehicle through a smartphone. The system comprises a vehicle module, a smartphone and a cloud computing system. The vehicle module reads the vehicle parameters with a vehicle parameter reader and sensors. The read vehicle parameter are forwarded to the microprocessor for transmission to the smartphone through a transceiver using a wired or wireless network. The microprocessor sends the vehicle parameters data to the smartphone for generating the commands/events for controlling an operation of the parts/components of the vehicle through the vehicle parameter controller. The data is uploaded to a cloud computing system and analyzed using an artificial intelligence module, a machine learning module and a pattern recognition module for providing insights/predictions to the user for controlling the vehicle.

Abstract: Methods and systems for searching logical patterns in voluminous multi sensor data from the industrial internet is provided. The method retrieves instances of patterns in time-series data where patterns are specified logically, using a sequence of symbols. The logical symbols used are a subset of the qualitative abstractions specifically, the concepts of steady, increasing, decreasing. Patterns can include symbol-sequences for multiple sensors, approximate duration as well as slope values for each symbol. To facilitate efficient querying, each sensor time-series is pre-processed into a sequence of logical symbols. Each position in the resulting compressed sequence is registered across a TRIE-based index structure corresponding to the multiple logical patterns it may belong to. Logical multi-sensor patterns are efficiently retrieved and ranked using such a structure. This method of indexing and searching provides an efficient mechanism for exploratory analysis of voluminous multi-sensor data.

Abstract: Frame-shaped anchored elements are described. In one or more embodiments, anchored text elements are identified for primary text that is located in a non-rectangular frame (e.g., a circular frame, a rounded rectangle frame, and so on) and that references the anchored text elements. The anchored text elements may be footnotes or endnotes that are identified for primary text located in a non-rectangular text box, for example. Once identified, the anchored text elements may be fit within and at a bottom of the non-rectangular frame. The anchored text elements are considered to fit “within” the non-rectangular frame insofar as the anchored text elements do not extend outside the boundaries of the non-rectangular frame.

Abstract: Methods and systems for searching logical patterns in voluminous multi sensor data from the industrial internet is provided. The method retrieves instances of patterns in time-series data where patterns are specified logically, using a sequence of symbols. The logical symbols used are a subset of the qualitative abstractions specifically, the concepts of steady, increasing, decreasing. Patterns can include symbol-sequences for multiple sensors, approximate duration as well as slope values for each symbol. To facilitate efficient querying, each sensor time-series is pre-processed into a sequence of logical symbols. Each position in the resulting compressed sequence is registered across a TRIE-based index structure corresponding to the multiple logical patterns it may belong to. Logical multi-sensor patterns are efficiently retrieved and ranked using such a structure. This method of indexing and searching provides an efficient mechanism for exploratory analysis of voluminous multi-sensor data.

Abstract: Frame-shaped anchored elements are described. In one or more embodiments, anchored text elements are identified for primary text that is located in a non-rectangular frame (e.g., a circular frame, a rounded rectangle frame, and so on) and that references the anchored text elements. The anchored text elements may be footnotes or endnotes that are identified for primary text located in a non-rectangular text box, for example. Once identified, the anchored text elements may be fit within and at a bottom of the non-rectangular frame. The anchored text elements are considered to fit “within” the non-rectangular frame insofar as the anchored text elements do not extend outside the boundaries of the non-rectangular frame.