Abstract: Producing a motion planning policy for an Autonomous Driving Machine (ADM) may include producing a search tree, including a root node representing a current condition of the ADM and derivative nodes linked thereto, representing predicted conditions of the ADM, following application of an action on the ADM. The nodes may be interlinked by actions and associated quality factors. A neural network (NN) may select a plurality of quality factors. The search tree may be expended to add interlinked derivative nodes according to the NN's selection, until a terminating condition is met. Backward propagating and updating one or more quality factors along trajectories of the expanded tree may occur. The NN may be trained according to the current condition of the ADM and the updated quality factors to select an optimal action. The selected optimal action may be applied on at least one physical element of the ADM.

Abstract: Producing a motion planning policy for an Autonomous Driving Machine (ADM) may include producing a search tree, including a root node representing a current condition of the ADM and derivative nodes linked thereto, representing predicted conditions of the ADM, following application of an action on the ADM. The nodes may be interlinked by actions and associated quality factors. A neural network (NN) may select a plurality of quality factors. The search tree may be expended to add interlinked derivative nodes according to the NN's selection, until a terminating condition is met. Backward propagating and updating one or more quality factors along trajectories of the expanded tree may occur. The NN may be trained according to the current condition of the ADM and the updated quality factors to select an optimal action. The selected optimal action may be applied on at least one physical element of the ADM.

Abstract: A method and a system for training a machine-learning model to identify real-world elements using a simulated environment (SE) may include (a) receiving at least one set of appearance parameters, corresponding to appearance of real-world element; (b) generating one or more realistic elements, each corresponding to a variant of at least one real-world element; (c) generating one or more abstract-elements; (d) placing the elements within the SE; (e) producing at least one synthetic image from the SE; (f) providing the at least one synthetic image to a machine-learning model; and (g) training the machine-learning model to identify at least one real-world element from the at least one synthetic image, that corresponds to at least one realistic element in the SE.

Abstract: Systems and methods of training an image classifier, including receiving, by a processor, at least two images, each of the at least two images being pre-classified to at least one category, randomly assigning a weight to each of the at least two images, calculating a weighted value for each pixel in each of the at least two images, creating, by the processor, a combined image based on a sum of the weighted values of each pixel, assigning the combined image the classification of the image assigned the highest weight, and transferring the combined image to the image classifier.

Abstract: A wet ink texture engine and associated method. The wet ink texture engine may run in the context of any application on any device, service, or general endpoint capable of receiving ink input. For example, the wet ink texture engine may be used in the context of a note application that receives input in the form of writing or drawing. The wet ink texture engine reduces, minimizes, or eliminates lag between receiving the input and displaying the input to improve inking experience for the user.

Abstract: A wet ink texture engine and associated method. The wet ink texture engine may run in the context of any application on any device, service, or general endpoint capable of receiving ink input. For example, the wet ink texture engine may be used in the context of a note application that receives input in the form of writing or drawing. The wet ink texture engine reduces, minimizes, or eliminates lag between receiving the input and displaying the input to improve inking experience for the user.

Abstract: A wet ink texture engine and associated method. The wet ink texture engine may run in the context of any application on any device, service, or general endpoint capable of receiving ink input. For example, the wet ink texture engine may be used in the context of a note application that receives input in the form of writing or drawing. The wet ink texture engine reduces, minimizes, or eliminates lag between receiving the input and displaying the input to improve inking experience for the user.

Abstract: A wet ink texture engine and associated method. The wet ink texture engine may run in the context of any application on any device, service, or general endpoint capable of receiving ink input. For example, the wet ink texture engine may be used in the context of a note application that receives input in the form of writing or drawing. The wet ink texture engine reduces, minimizes, or eliminates lag between receiving the input and displaying the input to improve inking experience for the user.

Abstract: A wet ink texture engine and associated method. The wet ink texture engine may run in the context of any application on any device, service, or general endpoint capable of receiving ink input. For example, the wet ink texture engine may be used in the context of a note application that receives input in the form of writing or drawing. The wet ink texture engine reduces, minimizes, or eliminates lag between receiving the input and displaying the input to improve inking experience for the user.

Abstract: A wet ink texture engine and associated method. The wet ink texture engine may run in the context of any application on any device, service, or general endpoint capable of receiving ink input. For example, the wet ink texture engine may be used in the context of a note application that receives input in the form of writing or drawing. The wet ink texture engine reduces, minimizes, or eliminates lag between receiving the input and displaying the input to improve inking experience for the user.

Abstract: A system, amplifier and method are provided for amplifying an optical signal in an optical communications system where spans between amplifiers may vary. The system includes a Raman amplifier variable gain portion and an EDFA gain portion. The amount of Raman amplifier gain is chosen to trade off accumulation of noise with accumulation of multi-path interference. This variable Raman gain is used to equalize the loss of each span so that the amount of optical power supplied at the input of the EDFA gain portion is substantially constant throughout the system.

Abstract: A method of compensating for chromatic dispersion in an optical signal transmitted on a long-haul terrestrial optical communication system including a plurality of spans, including: allowing chromatic dispersion to accumulate over at least one of the spans to a first predetermined level; and compensating for the first pre-determined level of dispersion using a dispersion compensating fiber causing accumulation of dispersion to a second predetermined level. There is also provided a hybrid Raman/EDFA amplifier including a Raman portion and an EDFA portion with a dispersion compensating fiber disposed therebetween. An optical communication system and a method of communicating an optical signal using such a Raman/EDFA amplifier are also provided.

Abstract: A method of compensating for chromatic dispersion in an optical signal transmitted on a long-haul terrestrial optical communication system including a plurality of spans, including: allowing chromatic dispersion to accumulate over at least one of the spans to a first predetermined level; and compensating for the first pre-determined level of dispersion using a dispersion compensating fiber causing accumulation of dispersion to a second predetermined level. There is also provided a hybrid Raman/EDFA amplifier including a Raman portion and an EDFA portion with a dispersion compensating fiber disposed therebetween. An optical communication system and a method of communicating an optical signal using such a Raman/EDFA amplifier are also provided.

Abstract: A system, amplifier and method are provided for amplifying an optical signal in an optical communications system where spans between amplifiers may vary. The system includes a Raman amplifier variable gain portion and an EDFA gain portion. The amount of Raman amplifier gain is chosen to trade off accumulation of noise with accumulation of multi-path interference. This variable Raman gain is used to equalize the loss of each span so that the amount of optical power supplied at the input of the EDFA gain portion is substantially constant throughout the system.