Abstract: The described technology generally relates to modifying network routing and bandwidth balancing for lower latency and high quality fair traffic. For instance, based on network routing data comprising network quality data representative of a network quality applicable to a wide area network and using machine learning applied to historical network routing data, other than the network routing data, available routes are classified, the available routes being between a source node on the wide area network and a destination node on the wide area network. Further, based on the network routing data and using a result of the machine learning applied to the historical network routing data, bandwidth allocations applicable to the classified available routes can be determined. Then, based on the classified available routes and the predicted bandwidth allocations, a route is set for data transmitted from the source node to the destination node.

Abstract: Distributed queue multi-bus on multi-CPU chips is enabled. For example, a method can comprise generating, by a system comprising a processor, a reservation request for a bus between a source node and a destination node, based on the reservation request, adding, by the system, data applicable to the reservation request to a queue of a bus located between the source node and the destination node, using a defined data routing process, determining, by the system, a path between the source node and the destination node, and in response to the queue being determined to comprise no other reservation requests prior to the reservation request, sending, by the system, data from the source node to the destination node via the bus.

Abstract: Machine learning models can be trained to vend profiles with a high likelihood of matching with a user comprises: training machine learning models with feature vectors representing potential profile matches identified as input to a user match selection process, updating the trained models with feature vectors identified as further input to the selection process, in response to the updating, swapping the trained model out of and a further trained machine learning model into a foreground execution space, resulting in the untrained model being in the foreground and the trained model being in a processing background, after satisfying a defined criterion, injecting an outlier potential match entity as supplemental input to the further training with a supplemental feature vector as further input to the selection process, determining respective confidence values corresponding to candidate profiles accessible to the selection process, and rendering profile images of candidate profiles for the selection process.

Abstract: Various acoustic manipulation and microphone gain adjustment techniques are enabled. For instance, a method comprises receiving, by a voice-enabled device comprising a processor and from a microphone, an input comprising environmental audio, determining, by the voice-enabled device, a level of the environmental audio, determining, by the voice-enabled device, a gain-adjust value based on the environmental audio, and adjusting, by the voice-enabled device, a gain of the microphone by the gain-adjust value.

Abstract: Various acoustic manipulation and microphone gain adjustment techniques are enabled. For instance, a method comprises receiving, by a voice-enabled device comprising a processor and from a microphone, an input comprising environmental audio, determining, by the voice-enabled device, a level of the environmental audio, determining, by the voice-enabled device, a gain-adjust value based on the environmental audio, and adjusting, by the voice-enabled device, a gain of the microphone by the gain-adjust value.

Abstract: A method for capturing hydrocarbons from a formation is provided. After a first hydraulic fracturing of a formation to produce a first conditioned formation, and while hydrocarbons are being produced from the first conditioned formation, a predetermined wellbore characteristic is monitored for. The predetermined wellbore characteristic is based on at least a pressure within the first conditioned formation. After detecting the predetermined wellbore characteristic, a second hydraulic fracturing of the formation is effected to produce a second conditioned formation.

Abstract: Machine learning models can be trained to vend profiles with a high likelihood of matching with a user comprises: training machine learning models with feature vectors representing potential profile matches identified as input to a user match selection process, updating the trained models with feature vectors identified as further input to the selection process, in response to the updating, swapping the trained model out of and a further trained machine learning model into a foreground execution space, resulting in the untrained model being in the foreground and the trained model being in a processing background, after satisfying a defined criterion, injecting an outlier potential match entity as supplemental input to the further training with a supplemental feature vector as further input to the selection process, determining respective confidence values corresponding to candidate profiles accessible to the selection process, and rendering profile images of candidate profiles for the selection process.

Abstract: There is provided a method of producing hydrocarbon material. A first hydrocarbon material is produced from the formation, the first hydrocarbon material including a gaseous hydrocarbon material originally in place in the formation. At least a portion of the produced gaseous hydrocarbon material is injected into the formation to increase the formation pressure. A second hydrocarbon material is produced from the formation.

Abstract: There is provided a method of producing hydrocarbon material. A first hydrocarbon material is produced from the formation, the first hydrocarbon material including a gaseous hydrocarbon material originally in place in the formation. At least a portion of the produced gaseous hydrocarbon material is injected into the formation to increase the formation pressure. A second hydrocarbon material is produced from the formation.

Abstract: A method for capturing hydrocarbons from a formation is provided. After a first hydraulic fracturing of a formation to produce a first conditioned formation, and while hydrocarbons are being produced from the first conditioned formation, a predetermined wellbore characteristic is monitored for. The predetermined wellbore characteristic is based on at least a pressure within the first conditioned formation. After detecting the predetermined wellbore characteristic, a second hydraulic fracturing of the formation is effected to produce a second conditioned formation.

Abstract: An imaging reader has an illuminating light assembly for illuminating a target with illumination light, and a solid-state image sensor with a rolling shutter for sequentially exposing an array of pixels to capture an image from the illuminated target. Target illumination is controlled by controlling an exposure time and a gain of the sensor, and by controlling an output power of the illumination light. The output power is maintained at a maximum level when the exposure time and/or the gain exceed a minimum threshold, and is lowered when the exposure time and/or the gain does not exceed the minimum threshold, while concomitantly maintaining the exposure time and/or the gain constant at the minimum threshold.

Abstract: An imaging reader has an illuminating light assembly for illuminating a target with illumination light, and a solid-state image sensor with a rolling shutter for sequentially exposing an array of pixels to capture an image from the illuminated target. Target illumination is controlled by controlling an exposure time and a gain of the sensor, and by controlling an output power of the illumination light. The output power is maintained at a maximum level when the exposure time and/or the gain exceed a minimum threshold, and is lowered when the exposure time and/or the gain does not exceed the minimum threshold, while concomitantly maintaining the exposure time and/or the gain constant at the minimum threshold.

Abstract: An organic electroluminescent device comprising: a transparent substrate; a first electrode disposed over the substrate for injecting charge of a first polarity; a second electrode disposed over the first electrode for injecting charge of a second polarity opposite to said first polarity; an organic light-emitting layer disposed between the first and the second electrode, wherein the second electrode is reflective, the first electrode is transparent or semi-transparent, and one or more intermediate layers of dielectric material with a refractive index greater than 1.

Abstract: An organic electroluminescent device cfomprising: a transparent substrate; a first electrode disposed over the substrate for injecting charge of a first polarity; a second electrode disposed over the first electrode for injecting charge of a second polarity opposite to said first polarity; an organic light-emitting layer disposed between the first and the second electrode, wherein the second electrode is reflective, the first electrode is transparent or semi-transparent, and one or more intermediate layers of dielectric material with a refractive index greater than 1.

Abstract: This disclosure generally relates to improved structures for organic light emitting diodes (OLEDs), and more particularly to so-called top emitting OLEDs.

Abstract: This disclosure generally relates to improved structures for organic light emitting diodes (OLEDs), and more particularly to so-called top emitting OLEDs.

Abstract: An organic light emitting diode (OLED) device having at least one pixel, comprising a planar light coupling layer having a front surface and a back surface, said layer having a thickness T; a light emitting portion for each pixel, disposed on the back surface of the light coupling layer; and a microlens for each pixel, having a radius of curvature R, disposed on the front surface of the light coupling layer such that its center of curvature is within the light coupling layer, wherein the radius of curvature R and the thickness T are such that R=xT, where x has a value in the range from 0.2 to 0.8. The OLED device exhibits improved light emission through the use of the microlenses, formed in or attached to the light coupling layer, when so configured that the radius of curvature (R) to substrate thickness (T) ratio (R/T) is in the range from 0.2 to 0.8, preferably in the range from 0.45 to 0.6.