Abstract: A light emitting device array circuit capable of reducing ghost image includes: a light emitting device array, plural scan line switch circuits, and a driver circuit. The light emitting device array includes plural light emitting devices arranged in plural scan lines and plural data lines. In one frame, plural scan line switch circuits respectively electrically connect plural scan nodes in plural corresponding scan lines to a scan conduction voltage in a non-overlapping sequential order. Data line buffer circuits of the driver circuit provide predetermined dimming levels to corresponding data nodes respectively according to data operation signals. A pre-discharge control amplifier circuit of the driver circuit is coupled to the plural scan nodes and provides a pre-discharge level to at least one predetermined scan node during a predetermined pre-discharge time period according to a pre-discharge signal.

Abstract: Systems and methods for providing forward error correction for a multi-channel audio signal are described. Blocks of an audio stream are buffered into a frame. A transformation can be applied that compacts the energy of each block into a plurality of transformed channels. The energy compaction transform may compact the most energy of a block into the first transformed channel and to compact decreasing amounts of energy into each subsequent transformed channel. The transformed frame may be encoded using any suitable codec and transmitted in a packet over a network. Improved forward error correction may be provided by attaching a low bit rate encoding of the first transformed channel to a subsequent packet. To reconstruct a lost packet, the low bit rate encoding of the first channel for the lost packet may be combined with a packet loss concealment version of the other channels, constructed from a previously-received packet.

Abstract: An example redundant power supply system comprises a power supply input to receive power from a power supply; a buck-boost converter coupled to the power supply input; and a controller coupled to the buck-boost converter. The controller is to receive a power supply identification signal from the power supply. The controller is also to enable or disable the buck-boost converter based on the power supply identification signal.

Abstract: Systems and methods for providing forward error correction for a multi-channel audio signal are described. Blocks of an audio stream are buffered into a frame. A transformation can be applied that compacts the energy of each block into a plurality of transformed channels. The energy compaction transform may compact the most energy of a block into the first transformed channel and to compact decreasing amounts of energy into each subsequent transformed channel. The transformed frame may be encoded using any suitable codec and transmitted in a packet over a network. Improved forward error correction may be provided by attaching a low bit rate encoding of the first transformed channel to a subsequent packet. To reconstruct a lost packet, the low bit rate encoding of the first channel for the lost packet may be combined with a packet loss concealment version of the other channels, constructed from a previously-received packet.

Abstract: A power conversion apparatus includes: a power supply circuit, a load switch and a control circuit. The power supply circuit generates a first power. The load switch controls the power path from the power supply circuit to a load. The control circuit generates a switching control signal to control a conduction level of the load switch according to an enable signal. The control circuit controls a level of the switching control signal to soft start from a first level to a second level. During the soft start period, the switching control signal has plural waveform segments including a first waveform segment and a second waveform segment. A level variation speed of the first waveform segment is higher than a level variation speed of the second waveform segment. The first waveform segment level precedes the second waveform segment level.

Abstract: Techniques are provided for predicting talent flow to and/or from a geographical region. In one technique, multiple entity profiles are stored and analyzed to generate training data that is labeled indicating whether a corresponding entity has moved to or moved from a region. A machine-learned prediction model is generated or trained based on the training data. Using the machine-learned prediction model, a prediction is made whether, for each entity corresponding to another entity profile, that entity will move to or move from a particular geographic region. Based on multiple predictions, a number of entities that are predicted to move to or move from the particular geographic region is determined. Talent flow data that is based on the number of entities is presented on a computer display.

Abstract: A service request for communication services for communication clients is received. In response, a communication service network is set up to support the communication services. Routing metadata is generated for each of the communication clients. The routing metadata is to be used by each of the communication clients for sharing service quality information with a respective peer communication client over a light-weight peer-to-peer (P2P) network. The routing metadata is downloaded to each of the communication clients. A communication client may exchange service signaling packets or service data packets over the communication service network. When the communication client determines that there is a problematic region in a bitstream received from the communication server, the communication client can request a peer communication client for a service quality information portion related to the problematic region.

Abstract: A method and system for determining links for a linking page are provided. For each linking page of a plurality of linking pages, a similarity score between each linking page and each linked-to page is generated. Each linked-to page is associated with each linking page. Based on the similarity scores generated for each linking page, a set of linked-to pages for each linking page is identified. An importance score for the linking page is also generated. Identities of the plurality of linking pages, identities of the set of linked-to pages identified for each linking page, and the importance score for each linking page are stored as input. Based on the input, for each linking page, a subset of the set of linked-to pages identified for each linking page is assigned by a model.

Abstract: In an example embodiment, profile and/or usage data of a social networking service is leveraged to automatically generate potential career paths for users of the social networking service. Additionally, specific recommendations as to actions the users can take to increase their odds of progressing along particular career paths can be determined, and these recommendations can be shared with users. Both recommendations may be performed in a manner that is scalable for personalized service.

Abstract: This disclosure falls into the field of voice communication systems, more specifically it is related to the field of voice quality estimation in a packet based voice communication system. In particular the disclosure provides a method and device for reducing a prediction error of the voice quality estimation by considering the content of lost packets. Furthermore, this disclosure provides a method and device which uses a voice quality estimating algorithm to calculate the voice quality estimate based on an input which is switchable between a first and a second input mode.

Abstract: Methods, systems, and computer programs are presented for selecting features for a machine-learning model configured to detect anomalies in the evolution of data over time. One method includes an operation for identifying one or more key fields and value fields from the fields in a relational database. The method also includes grouping data of the value fields based on values of the one or more key fields and calculating one or more statistical values for each group of data of the value fields. The method further includes operations for monitoring an evolution of the one or more statistical values over time, and for selecting, based on the evolution of the one or more statistical values over time, features to be used by a machine-learning model to detect anomalies in content of the relational database over time. The method also includes executing the machine-learning model to detect the anomalies.

Abstract: A power conversion apparatus includes: a power supply circuit, a load switch and a control circuit. The power supply circuit generates a first power. The load switch controls the power path from the power supply circuit to a load. The control circuit generates a switching control signal to control a conduction level of the load switch according to an enable signal. The control circuit controls a level of the switching control signal to soft start from a first level to a second level. During the soft start period, the switching control signal has plural waveform segments including a first waveform segment and a second waveform segment. A level variation speed of the first waveform segment is higher than a level variation speed of the second waveform segment. The first waveform segment level precedes the second waveform segment level.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system determines output of a machine learning model, which includes a score generated by the model based on features inputted into the model and feature importance metrics representing effects of the features on the score. Next, the system maps the features to elements in a feature hierarchy that groups the features under a first level of parent features. The system also generates a ranking of the first level of parent features based on the feature importance metrics. The system then combines, based on the ranking, feature values of the mapped features with a set of insight templates to produce a list of narrative insights, wherein each narrative insight includes a natural language description of a factor that contributes to the model's output. Finally, the system outputs the list of narrative insights in a user interface.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for search result page ranking optimization. When generating a search result page, a search result optimization system generates links to other related search result pages and adds them to the generates search result page. Adding the generates links enables internet search engines to discover additional search results pages, as well as increases their search ranking. The search result optimization system generates the links based on entity values extracted from the search results page. For example, the search result optimization system extracts entity values based on filter categories available for the search results included in the search result page as well as historical search behavior indicating a number of times that users have selected to filter search results based on the identified filter categories.

Abstract: The disclosed embodiments provide a system for performing A/B testing for search engine optimization (SEO). During operation, the system divides a set of web pages into a control group, an inbound treatment group, an outbound treatment group, and a full treatment group. Next, the system generates a first set of links from the outbound treatment group to the inbound treatment group and a second set of links within the full treatment group. The system then applies an A/B test to pairs of groups selected from the control group, the inbound treatment group, the outbound treatment group, and the full treatment group. Finally, the system outputs a result of the A/B test for use in assessing an effect of the first and second sets of links on search engine results associated with the set of web pages.

Abstract: Systems and methods for providing forward error correction for a multi-channel audio signal are described. Blocks of an audio stream are buffered into a frame. A transformation can be applied that compacts the energy of each block into a plurality of transformed channels. The energy compaction transform may compact the most energy of a block into the first transformed channel and to compact decreasing amounts of energy into each subsequent transformed channel. The transformed frame may be encoded using any suitable codec and transmitted in a packet over a network. Improved forward error correction may be provided by attaching a low bit rate encoding of the first transformed channel to a subsequent packet. To reconstruct a lost packet, the low bit rate encoding of the first channel for the lost packet may be combined with a packet loss concealment version of the other channels, constructed from a previously-received packet.

Abstract: Techniques for using machine learning techniques to distinguish between different types of uses of an online service are provided. In one technique, first training data is used to train a first prediction model and second training data is used to train a second prediction model. The label of training instances in the first training data indicates whether an online action with respect to an online service of one type of action or another type of action. The label of training instances in the second training data indicates whether an entity using the online service initiated a particular action. The first prediction model is used to classify multiple actions performed by an entity relative to the online service. The second prediction model takes the classifications produced by the first prediction model to determine a likelihood that the entity will initiate the particular action.

Abstract: A service request for communication services for communication clients is received. In response, a communication service network is set up to support the communication services. Routing metadata is generated for each of the communication clients. The routing metadata is to be used by each of the communication clients for sharing service quality information with a respective peer communication client over a light-weight peer-to-peer (P2P) network. The routing metadata is downloaded to each of the communication clients. A communication client may exchange service signaling packets or service data packets over the communication service network. When the communication client determines that there is a problematic region in a bitstream received from the communication server, the communication client can request a peer communication client for a service quality information portion related to the problematic region.

Abstract: An embodiment of the disclosed technologies includes extracting, from an online connection network, digital data comprising target profile data and current profile data; where the target profile data is associated with an online submission process that has a plurality of possible outcomes and is executable via the online connection network; where the current profile data is associated with a member node of the online connection network; using an active learning process, in response to the current profile data, identifying attribute data that is in the target profile data but is not in the current profile data and is predicted to have a relationship with a positive outcome of the online submission process; outputting the attribute data for use by a downstream process or an automated digital assistant to determine a digital resource to associate with the member node through the online connection network or through an online learning system.

Abstract: Techniques for delaying the transmission of a message to one or more recipients using an alert in order to provide data protection and security with respect to data included in the message are disclosed herein. In some embodiments, a computer-implemented method comprises: receiving a request to transmit a message from a computing device of a user to a recipient, the request comprising content of the message; detecting an issue with the request using at least one classifier to classify the request as having the issue; generating an alert based on the detecting of the issue; and prior to transmitting the message to a destination associated with the recipient, causing the generated alert to be displayed on the computing device of the user, the alert indicating the issue with the message.