Abstract: A method may include determining, with a queue availability module, that an entry is available in a queue, asserting a bit in a register based on determining that an entry is available in the queue, determining, with a processor, that the bit is asserted, and processing, with the processor, the entry in the queue based on determining that the bit is asserted. The method may further include storing the register in a tightly coupled memory associated with the processor. The method may further include storing the queue in the tightly coupled memory. The method may further include determining, with the queue availability module, that an entry is available in a second queue, and asserting a second bit in the register based on determining that an entry is available in the second queue. The method may further include finding the first bit in the register using a find first instruction.

Abstract: This application discloses a method for implementing a software-defined network (SDN), including: receiving, by an SDN controller, virtual network information sent by a cloud computing platform, where the virtual network information includes a correspondence between information about a virtual machine (VM) and information about a server running the VM. The method also includes acquiring, by the SDN controller, physical network information, where the physical network information includes a correspondence between information about a top-of-rack (TOR) switch and the information about the server. Additionally, the method includes acquiring, by the SDN controller, a correspondence between the information about the VM and the information about the TOR switch according to the virtual network information and the physical network information.

Abstract: A material box storage and sorting system includes a material box storage module, a batch-of-orders sorting module, an order box goods collection cache module, an order box allocation module and a warehouse management module. The material box storage module is configured to store material boxes, and the material boxes are configured to store goods. The batch-of-orders sorting module is configured to perform parallel sorting of a batch of orders, so as to pick up goods from the material boxes into order boxes. The order box goods collection cache module is configured to cache and transfer the order boxes. The order box allocation module is configured to receive the order boxes cached and transferred by the order box goods collection cache module and to allocate the order boxes according to information of the user orders, so as to complete goods sorting for the batch of orders.

Abstract: The invention provides a method of producing graphene. The method comprising: A) mixing graphite powders with a silk fibroin nanofiber solution, performing mechanical stirring to exfoliate graphite to form graphene flakes; wherein the silk fibroin nanofibers in the silk fibroin nanofiber solution have a crystallinity of 40% or above; the silk fibroin nanofibers have a diameter of 10 to 30 nm; the silk fibroin nanofibers have a length of 100 nm to 3 ?m; the mechanical stirring has a shearing speed of 1,000 to 50,000 rpm; and the duration of the mechanical stirring is 10 min to 6 h; B) centrifuging the solution obtained in step A) after exfoliation to remove unexfoliated graphite; and C) centrifuging the centrifuged solution obtained in step B), and separating graphene from the silk fibroin nanofibers to obtain the graphene.

Abstract: A method may include determining, with a queue availability module, that an entry is available in a queue, asserting a bit in a register based on determining that an entry is available in the queue, determining, with a processor, that the bit is asserted, and processing, with the processor, the entry in the queue based on determining that the bit is asserted. The method may further include storing the register in a tightly coupled memory associated with the processor. The method may further include storing the queue in the tightly coupled memory. The method may further include determining, with the queue availability module, that an entry is available in a second queue, and asserting a second bit in the register based on determining that an entry is available in the second queue. The method may further include finding the first bit in the register using a find first instruction.

Abstract: In an example embodiment, position bias and other types of bias may be compensated for by using two-phase training of a machine-learned model. In a first phase, the machine-learned model is trained using non-randomized training data. Since certain types of machine-learned models, such as those involving deep learning (e.g., neural networks) require a lot of training data, this allows the bulk of the training to be devoted to training using non-randomized training data. However, since this non-randomized training data may be biased, a second training phase is then used to revise the machine-learned model based on randomized training data to remove the bias from the machine-learned model. Since this randomized training data may be less plentiful, this allows the deep learning machine-learned model to be trained to operate in an unbiased manner without the need to generate additional randomized training data.

Abstract: Representative embodiments set forth techniques for optimizing user interfaces on a client device. A method may include receiving a spatial difficulty map associated with the user interface. The method also includes identifying one or more user interface elements using an element detection model and generating a user interface layout based on at least the spatial difficulty map. The method also includes generating an updated user interface by editing the one or more user interface elements using the user interface layout and rendering, on a display of the client device, the updated user interface.

Abstract: Calibration of eye tracking is improved by collecting additional calibration pairs when user is using apps with eye tracking. A user input component is presented on a display of an electronic device, detecting a dwelling action for user input component, and in response to detecting the dwelling action, obtaining a calibration pair comprising an uncalibrated gaze point and a screen location of the user input component, wherein the uncalibrated gaze point is determined based on an eye pose during the dwelling action. A screen gaze estimation is determine based on the uncalibrated gaze point, and in response to determining that the calibration pair is a valid calibration pair, training a calibration model using the calibration pair.

Abstract: Methods, systems, and apparatuses are described causing light to be emitted, causing a frequency at which the light is emitted to vary, receiving, based on the frequency variation, a user input, determining a critical flicker frequency (CFF) corresponding to the user input, and determining, based on the CFF, a disease state.

Abstract: A honeycomb system includes at least one modular storage unit. The modular storage unit comprises a set of dual-goods-location shelves at least one lifting machine, at least one shuttle vehicle and at least one power station. The set of dual-goods-location shelves are parallel to each other to form a roadway where the shuttle vehicle operates. The power station is arranged at two ends or in a middle portion of the dual-goods-location shelves and is integrated with a transmission mechanism and a support frame. The transmission mechanism is connected to the shuttle vehicle and the lifting machine to perform goods transfer therebetween.

Abstract: In an example embodiment, position bias and other types of bias may be compensated for by using two-phase training of a machine-learned model. In a first phase, the machine-learned model is trained using non-randomized training data. Since certain types of machine-learned models, such as those involving deep learning (e.g., neural networks) require a lot of training data, this allows the bulk of the training to be devoted to training using non-randomized training data. However, since this non-randomized training data may be biased, a second training phase is then used to revise the machine-learned model based on randomized training data to remove the bias from the machine-learned model. Since this randomized training data may be less plentiful, this allows the deep learning machine-learned model to be trained to operate in an unbiased manner without the need to generate additional randomized training data.

Abstract: This application discloses a method for implementing a software-defined network (SDN), including: receiving, by an SDN controller, virtual network information sent by a cloud computing platform, where the virtual network information includes a correspondence between information about a virtual machine (VM) and information about a server running the VM. The method also includes acquiring, by the SDN controller, physical network information, where the physical network information includes a correspondence between information about a top-of-rack (TOR) switch and the information about the server. Additionally, the method includes acquiring, by the SDN controller, a correspondence between the information about the VM and the information about the TOR switch according to the virtual network information and the physical network information.

Abstract: A method for implementing a software-defined network (SDN) includes receiving, by an SDN controller, virtual network information sent by a cloud computing platform, where the virtual network information includes a correspondence between information about a virtual machine (VM) and information about a server running the VM. The method also includes acquiring, by the SDN controller, physical network information, where the physical network information includes a correspondence between information about a top-of-rack (TOR) switch and the information about the server. Additionally, the method includes acquiring, by the SDN controller, a correspondence between the information about the VM and the information about the TOR switch according to the virtual network information and the physical network information.

Abstract: The invention provides a method of producing graphene. The method comprising: A) mixing graphite powders with a silk fibroin nanofiber solution, performing mechanical stirring to exfoliate graphite to form graphene flakes; wherein the silk fibroin nanofibers in the silk fibroin nanofiber solution have a crystallinity of 40% or above; the silk fibroin nanofibers have a diameter of 10 to 30 nm; the silk fibroin nanofibers have a length of 100 nm to 3 ?m; the mechanical stirring has a shearing speed of 1,000 to 50,000 rpm; and the duration of the mechanical stirring is 10 min to 6 h; B) centrifuging the solution obtained in step A) after exfoliation to remove unexfoliated graphite; and C) centrifuging the centrifuged solution obtained in step B), and separating graphene from the silk fibroin nanofibers to obtain the graphene.

Abstract: In one embodiment, a method includes accessing, from a data store of the communication network, user information associated with a first user of the communication network, identifying one or more entities associated with the communication network that are relevant to the first user based on the user information, and retrieving, for each identified entity, one or more content frames associated with the entity. The method includes ranking the one or more content frames based on the user information. The method also includes sending, to a client device of the first user, one or more of the content frames for display to the first user in ranked order, wherein each content frame is selectable by the first user to display the selected content frame in association with a particular content item for a specified period of time.

Abstract: Techniques for text entry using gestures are disclosed. As disclosed, a camera may capture a frame and the face of the user can be detected therein. Landmarks can be aligned to the face in the captured frame. A left eye image and a right eye image may be extracted from the captured frame. The left eye image and the right image each may be resized and compared to a calibration template. A direction of eye gaze may be determined based upon the comparison. A character or word may be predicted based upon the determination of the direction of eye gaze and a known configuration of an eye gaze board (e.g., an E-tran board). The predicted character or word can be included as a part of a text-based message.

Abstract: Techniques for text entry using gestures are disclosed. As disclosed, a camera may capture a frame and the face of the user can be detected therein. Landmarks can be aligned to the face in the captured frame. A left eye image and a right eye image may be extracted from the captured frame. The left eye image and the right image each may be resized and compared to a calibration template. A direction of eye gaze may be determined based upon the comparison. A character or word may be predicted based upon the determination of the direction of eye gaze and a known configuration of an eye gaze board (e.g., an E-tran board). The predicted character or word can be included as a part of a text-based message.

Abstract: In one embodiment, a method includes accessing, from a data store of the communication network, user information associated with a first user of the communication network, identifying one or more entities associated with the communication network that are relevant to the first user based on the user information, and retrieving, for each identified entity, one or more content frames associated with the entity. The method includes ranking the one or more content frames based on the user information. The method also includes sending, to a client device of the first user, one or more of the content frames for display to the first user in ranked order, wherein each content frame is selectable by the first user to display the selected content frame in association with a particular content item for a specified period of time.