Abstract: Methods, systems, and devices for wireless communications are described. In some systems, a network entity may map on-off keying (OOK) symbols of an OOK sequence to an orthogonal frequency division multiplexing (OFDM) waveform including at least one OFDM symbol with a cyclic prefix. Each OOK symbol of the OOK sequence may be mapped to at least one segment of an integer quantity of segments of the OFDM waveform based on an OOK data rate. A duration of each segment of the integer quantity of segments of the OFDM waveform may be based on the OOK data rate and a ratio between a duration of the cyclic prefix and a duration of a remainder of the OFDM symbol. The network entity may transmit the OFDM waveform via a set of frequency resources. A user equipment (UE) may receive and decode the OFDM waveform to obtain the OOK sequence.

Abstract: A resistor network with reduced area and/or improved voltage resolution and methods of designing and operating the same are provided. Generally, the resistor network includes a resistor ladder with a first number (n) of integrated resistors coupled in series between a top and a bottom contact, with one or more contacts coupled between adjacent resistors. A second number of integrated resistors is coupled in parallel between the top and bottom contacts, and a third number of integrated resistors is coupled in series between the second integrated resistors and either the top or the bottom contact. Each of the integrated resistors has a resistance of R, and a voltage developed across each resistor in the resistor ladder is equal to a voltage applied between the top and bottom contacts divided by n. Where the second number is n-1, and the third number is 1, the total number of resistors is 2n.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a configuration of a sleep mode, the configuration indicating a coding scheme used for a wake-up signal (WUS). The UE may receive a WUS message, the WUS message being encoded with product coding, wherein a coding stage of the product coding comprises locally-decodable coding. The UE may receive a communication based at least in part on the WUS message. Numerous other aspects are described.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, an apparatus may receive a wake-up signal (WUS) indicating that the user equipment (UE) is to perform a wake-up from a sleep mode. The apparatus may determine a set of parameters for a reference sequence to be used in association with performing a synchronization after the wake-up. The apparatus may receive the reference sequence based at least in part on the set of parameters. Numerous other aspects are described.

Abstract: In an example, sets of event information associated with events may be identified. The events may include intentional click events, accidental click events and/or skip events. Accidental click probabilities associated with the accidental click events and/or the skip events may be determined. Machine learning model training may be performed, using the sets of event information associated with the events and labels associated with the events, to generate a first machine learning model. The labels may include second labels associated with the intentional click events and/or third labels associated with the accidental click events and/or the skip events. The second labels may correspond to an intentional click classification. The third labels may be based upon the accidental click probabilities. Click probabilities associated with content items may be determined using the first machine learning model. A content item may be selected for presentation via a client device based upon the click probabilities.

Abstract: A resistor network with reduced area and/or improved voltage resolution and methods of designing and operating the same are provided. Generally, the resistor network includes a resistor ladder with a first number (n) of integrated resistors coupled in series between a top and a bottom contact, with one or more contacts coupled between adjacent resistors. A second number of integrated resistors is coupled in parallel between the top and bottom contacts, and a third number of integrated resistors is coupled in series between the second integrated resistors and either the top or the bottom contact. Each of the integrated resistors has a resistance of R, and a voltage developed across each resistor in the resistor ladder is equal to a voltage applied between the top and bottom contacts divided by n. Where the second number is n?1, and the third number is 1, the total number of resistors is 2n.

Abstract: Briefly, embodiments disclosed herein may relate to digital content selection, and more particularly to weighted pseudo-random digital content selection for use in and/or with online digital content delivery, such as online advertising, for example.

Abstract: One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with client devices and entities. A network profile associated with the client devices and the entities may be generated based upon the event information. A similarity profile associated with the client devices may be generated based upon the network profile. The similarity profile may be indicative of one or more similarity scores associated with a first client device and one or more client devices. A user profile associated with the first client device may be modified, based upon the similarity profile and/or one or more user profiles associated with the one or more client devices, to generate a modified user profile. Content may be selected for presentation via the first client device based upon the modified user profile.

Abstract: The present teaching relates to generating an updated model related to advertisement selection. In one example, a request is obtained for updating a model to be utilized for selecting an advertisement. A plurality of copies of the model is generated. The model is pre-selected based on a performance metric related to advertisement selection. Based on each of the plurality of copies, a candidate model is created by modifying one or more parameters of the copy of the model to create a plurality of candidate models. One of the plurality of candidate models is selected based on the performance metric. The steps of generating, creating, and selecting are repeated until a predetermined condition is met. The model is updated with the latest selected candidate model when the predetermined condition is met.

Abstract: The present teaching relates to displaying ads. An explore/exploit layer (EEL) is provided at frontend ad serving engine for storing combination distributions with respect to multiple ads. Each ad has multiple attributes. Each attribute can be instantiated using one of multiple assets. The frontend ad serving engine requests a recommended ad for bidding an ad display opportunity in a slot of a webpage viewed by a user on a user device. The recommended ad is one of the multiple ads. When the auction is successful, a combination of assets for the ad is drawn from the combination distributions in EEL and each of the assets instantiates a corresponding attribute of the ad. The combination is transmitted to the user device to render the ad.

Abstract: The present teaching relates to generating combination distributions for ads. A prediction model is obtained via machine learning with respect to a criterion. Training data are associated with multiple ads each having multiple attributes, and include combinations with recorded performance for each ad. Each combination has multiple assets representing respective attributes of an ad. Using the prediction model, performance of each combination of each ad can be predicted and used for generating combination distributions for the ads. Such generated combination distributions are then sent to an explore/exploit layer (EEL) at a frontend ad serving engine so that it can draw a combination associated with an auction winning ad for rendering on a webpage viewed by a user on a user device.

Abstract: The present teaching relates to generating combination distributions for ads. Features are computed based on training data associated with ads, each of which has a plurality of attributes. The training data include asset combinations with past performance thereof for each of the ads. Each combination includes multiple assets representing respective attributes of an ad. The features are used in machine learning to obtain an auxiliary model, which is used to generate combination distributions for each ad based on predicted performance for each combination associated with the ad. Such generated combination distributions are sent to an explore/exploit layer (EEL) for a frontend ad serving engine to draw a combination therefrom for an auction winning ad for rendering on a webpage viewed by a user on a user device.

Abstract: A voltage-glitch detection and protection circuit and method are provided. Generally, circuit includes a voltage-glitch-detection-block (GDB) and a system-reset-block coupled to the GDB to generate a reset-signal to cause devices in a chip including the circuit to be reset when a voltage-glitch in a supply voltage (VDD) is detected. The GDB includes a voltage-glitch-detector coupled to a latch. The voltage-glitch-detector detects the voltage-glitch and generates a PULSE to the system-reset-block and latch. The latch receives the PULSE and generates a PULSE_LATCHED signal to the system-reset-block to ensure the reset-signal is generated no matter a width of the PULSE. In one embodiment, the latch includes a filter and a sample and hold circuit to power the latch, and ensure the PULSE_LATCHED signal is coupled to the system-reset-block when a voltage to the GDB or to the latch drops below a minimum voltage due to the voltage-glitch.

Abstract: In an example, sets of event information associated with events may be identified. The events may include intentional click events, accidental click events and/or skip events. Accidental click probabilities associated with the accidental click events and/or the skip events may be determined. Machine learning model training may be performed, using the sets of event information associated with the events and labels associated with the events, to generate a first machine learning model. The labels may include second labels associated with the intentional click events and/or third labels associated with the accidental click events and/or the skip events. The second labels may correspond to an intentional click classification. The third labels may be based upon the accidental click probabilities. Click probabilities associated with content items may be determined using the first machine learning model. A content item may be selected for presentation via a client device based upon the click probabilities.

Abstract: One or more computing devices, systems, and/or methods for selecting content items for transmission to client devices are provided. A request for content associated with a client device may be received. Bid values and/or click probabilities associated with content items may be determined. A probability of receiving a negative signal associated with a content item of the content items from the client device responsive to presenting the content item via the client device may be determined based upon a user profile associated with the client device. A content item score, of content item scores associated with the content items, may be generated based upon the probability, a click probability and/or a bid value associated with the content item. The content item may be selected from the content items for presentation via the client device based upon the content item scores. The content item may be transmitted to the client device.

Abstract: Memory devices and methods for operating the same are provided. Generally, the device includes an array of multibit-memory-cells, each operable to store multiple bits in separate locations of a charge-trapping layer, and control-circuitry coupled to the array. The control-circuitry is operable read 1st and 2nd bit values of each cell individually based on generated first and second sensed currents, where the first and second sensed currents correspond to charges trapped in first and second bit locations. The control-circuitry executes an algorithm based on the first and second sensed currents and determines a logic state of the cell. In one embodiment, the control-circuitry averages the sensed currents, and compares this to a reference current to determine the logic state. In another, the 2nd bit value is a complement of the 1st, and the control-circuitry compares the currents to determine the logic state without use of a reference current.

Abstract: One or more computing devices, systems, and/or methods are provided. In an example, a sequence of actions performed using a first interface on a first client device may be identified. A first negative signal probability may be determined based upon the sequence of actions. The first negative signal probability may correspond to a probability of receiving a negative signal associated with a first content item from the first client device responsive to presenting the first content item via the first interface on the first client device. The first interface on the first client device may be controlled based upon the first negative signal probability.

Abstract: One or more computing devices, systems, and/or methods are provided. Event information associated with a plurality of events may be identified. The plurality of events may be associated with client devices and entities. A network profile associated with the client devices and the entities may be generated based upon the event information. A similarity profile associated with the client devices may be generated based upon the network profile. The similarity profile may be indicative of one or more similarity scores associated with a first client device and one or more client devices. A user profile associated with the first client device may be modified, based upon the similarity profile and/or one or more user profiles associated with the one or more client devices, to generate a modified user profile. Content may be selected for presentation via the first client device based upon the modified user profile.

Abstract: In an example, sets of event information associated with events may be identified. The events may include intentional click events, accidental click events and/or skip events. Accidental click probabilities associated with the accidental click events and/or the skip events may be determined. Machine learning model training may be performed, using the sets of event information associated with the events and labels associated with the events, to generate a first machine learning model. The labels may include second labels associated with the intentional click events and/or third labels associated with the accidental click events and/or the skip events. The second labels may correspond to an intentional click classification. The third labels may be based upon the accidental click probabilities. Click probabilities associated with content items may be determined using the first machine learning model. A content item may be selected for presentation via a client device based upon the click probabilities.

Abstract: One or more computing devices, systems, and/or methods for selecting content items for transmission to client devices are provided. A request for content associated with a client device may be received. Bid values and/or click probabilities associated with content items may be determined. A probability of receiving a negative signal associated with a content item of the content items from the client device responsive to presenting the content item via the client device may be determined based upon a user profile associated with the client device. A content item score, of content item scores associated with the content items, may be generated based upon the probability, a click probability and/or a bid value associated with the content item. The content item may be selected from the content items for presentation via the client device based upon the content item scores. The content item may be transmitted to the client device.