Abstract: A weather-resistant socket assembly is adapted to be used with a string light. The weather-resistant socket assembly for a string light, comprises an electrically powered housing having an opening, a chamber extending from the opening, and a ridged wall covering a portion of an internal periphery of the electrically powered housing; and a socket comprising electrical connections adapted to power the socket and a cooperating ridged wall covering a portion of an external periphery of the socket. When the socket is inserted in the chamber of the electrically powered housing, the ridged wall and the cooperating ridged wall come into contact and the contact prevents a rotation of the socket relative to the electrically powered housing. Further described is a solar string light that comprises the weather-resistant socket assembly.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for ranking alternative destination recommendations are provided. One of the methods comprises: receiving a trip request comprising an origin and an initial destination; determining one or more alternative destinations based on one or more features of the initial destination; for each of the alternative destinations, determining, based on a machine-learning classifier, a probability for the rider to select a trip option from the origin to the alternative destination, where the machine-learning classifier is trained to accept input comprising one or more attributes of the trip option and generate output comprising the probability for the rider to select the alternative destination; generating a score for each of the alternative destinations based at least on the probability; and ranking, by the computing device of the ridesharing platform, the alternative destinations based on the scores.

Abstract: A weather-resistant socket assembly is adapted to be used with a string light. The weather-resistant socket assembly for a string light, comprises an electrically powered housing having an opening, a chamber extending from the opening, and a ridged wall covering a portion of an internal periphery of the electrically powered housing; and a socket comprising electrical connections adapted to power the socket and a cooperating ridged wall covering a portion of an external periphery of the socket. When the socket is inserted in the chamber of the electrically powered housing, the ridged wall and the cooperating ridged wall come into contact and the contact prevents a rotation of the socket relative to the electrically powered housing. Further described is a solar string light that comprises the weather-resistant socket assembly.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for carpool dual-pricing in ridesharing are provided. An exemplary method comprises: obtaining a machine-learning model trained to generate a probability for a carpool request from a rider to convert to a trip based at least on a pair of price adjustment multipliers applied to the carpool request; determining one or more Key Performance Indicator (KPI) models based on the machine-learning model; constructing an optimization model based on the KPI models, the optimization model comprising a plurality of pairs of price adjustment multipliers as decision variables; and determining optimal values of the plurality of pairs of price adjustment multipliers for the plurality of pricing units by solving the optimization model based on a plurality of historical carpool requests.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for carpool dual-pricing in ridesharing are provided. An exemplary method comprises: determining an expected trip count based on a plurality of carpool requests in a pricing unit and a pair of price adjustment multipliers applied to the pricing unit; for each of the plurality of carpool requests, generating a carpool matching probability of the carpool request by a second machine learning model based on the first expected trip count; constructing one or more Key Performance Indicator (KPI) models based on the plurality of carpool matching probabilities and the pair of price adjustment multipliers; and determining optimal values of the pair of price adjustment multipliers based on an optimization model maximizing an aggregated value of the one or more KPI models.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining price multipliers in a ride-hailing platform are described. An exemplary method may comprise: obtaining a price multiplier that has been applied in a pricing unit of the ride-hailing platform during a previous period of time and a key performance indicator (KPI) value of the pricing unit during the previous period of time; constructing a hash key; updating a hash table based on the KPI value and the hash key; determining whether to perform exploration or exploitation for a current period of time; when it is determined to perform exploration, selecting a new price multiplier; and when it is determined to perform exploitation: determining the new price multiplier based on one or more entries in the hash table to apply to the pricing unit for the current period of time.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining a plurality of context-based subscription products in a ride-hailing platform are described. An exemplary method may include: determining a plurality of transportation contexts to which a plurality of context-based subscription products are applicable; obtaining a plurality of historical trip-requesting sessions between a plurality of riders and the ride-hailing platform, and a plurality of historical subscription products offered to the plurality of riders; constructing at least one key performance indicator (KPI) model that predicts a reward for the ride-hailing platform offering the plurality of context-based subscription products to the plurality of riders; constructing an optimization model to maximize a weighted sum of the at least one KPI model; and determining optimal values of the plurality of parameter vectors corresponding to the plurality of context-based subscription products.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining a plurality of context-based subscription products in a ride-hailing platform are described. An exemplary method may include: determining a plurality of transportation contexts to which a plurality of context-based subscription products are applicable; obtaining a plurality of historical trip-requesting sessions between a plurality of riders and the ride-hailing platform, and a plurality of historical subscription products offered to the plurality of riders; constructing at least one key performance indicator (KPI) model that predicts a reward for the ride-hailing platform offering the plurality of context-based subscription products to the plurality of riders; constructing an optimization model to maximize a weighted sum of the at least one KPI model; and determining optimal values of the plurality of parameter vectors corresponding to the plurality of context-based subscription products.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for joint optimization of price multipliers and coupons in a ride-hailing platform are disclosed. An exemplary method comprises: obtaining a plurality of spatial-temporal pricing units and a plurality of rider segments; constructing a plurality of key performance indicator (KPI) metrics, wherein each of the KPI metrics takes input comprising (1) the price adjustment multiplier applied to a given trip and (2) the rider coupon applied to a rider of the given trip, and generates output comprising a KPI value of the given trip; constructing an optimization problem that maximizes a weighted sum of the plurality of KPI metrics with a vector of price-adjustment-multiplier decision variables and a matrix of rider-coupon decision variables; and determining, by solving the optimization problem, a plurality of optimal price adjustment multipliers and a plurality of optimal rider coupons.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for prefiltering pending trip requests in a ride-hailing platform. An exemplary method comprises: determining a plurality of feature weight vectors, wherein each of the plurality of feature weight vectors comprises a plurality of feature weights corresponding to a plurality of features for prefiltering rider-driver pairs; simulating dispatches of a plurality of historical trips with prefiltering based on each of the plurality of feature weight vectors to obtain a plurality of scores; training a surrogate model based on the plurality of feature weight vectors in the feature weight matrix and the plurality of scores; constructing an optimization model comprising the surrogate model as an objective function; determining the optimal feature weight vector by solving the optimization model; and prefiltering pending rider-driver pairs in the ride-hailing platform based on the optimal feature weight vector.

Abstract: The present disclosure provides a breathing light adjustment method, an apparatus and an electronic device, a relation curve representing relationship between a visual brightness and an electrical signal of a breathing light is determined; the visual brightness interval is equally divided according to a brightness level limit, and an electrical signal value corresponding to each brightness level after the equally dividing is determined; a ratio between the electrical signal value corresponding to each brightness level and a maximum electrical signal value is determined; and a magnitude of the electrical signal value inputted into the breathing light is adjusted according to the ratio between the electrical signal value corresponding to each brightness level and the maximum electrical signal value, so that the brightness of the breathing light presents an effect of linear gradual change which suits human vision.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for bidding-based ridesharing are described. One exemplary method includes: obtaining a price range of a trip request for a rider; determining a plurality of trip setting candidates based on the trip request, and a plurality of price candidates based on the price range; generating a plurality of bidding bundle option candidates based on a plurality of combinations of the plurality of trip setting candidates and the plurality of price candidates; determining, based on a trained machine-learning classifier, a selection probability for the rider to select each of the plurality of bidding bundle option candidates; and ranking the plurality of bidding bundle option candidates based on corresponding probabilities; and transmitting one or more of the plurality of bidding bundle option candidates with top selection probabilities to a terminal device associated with the rider.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for bidding-based ridesharing are described. One exemplary method includes: obtaining, by a computing device of a ridesharing platform from a terminal device, a trip request for a rider, the trip request comprising an origin, a destination, and a price; identifying, by the computing device, a plurality of driver candidates to match with the trip request; determining, by the computing device, an acceptance probability for each of the plurality of driver candidates to accept the trip request; determining, by the computing device based on the plurality of determined acceptance probabilities, an estimated waiting time and an estimated matching probability for the trip request; and transmitting at least one of the estimated waiting time and the estimated matching probability to the terminal device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for bidding-based ridesharing are described. One exemplary method includes: obtaining a trip request for a rider, the trip request comprising a price; identifying a list of driver candidates to match with the trip request; determining a list of dispatch waiting times corresponding to the list of driver candidates; determining a list of acceptance probabilities corresponding to the list of driver candidates based on a machine-learning classifier; and determining an estimated waiting time or an estimated matching probability for the trip request based on the list of dispatch waiting times and the list of acceptance probabilities.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for ranking alternative destination recommendations are provided. One of the methods comprises: receiving a trip request comprising an origin and an initial destination; determining one or more alternative destinations based on one or more features of the initial destination; for each of the alternative destinations, determining, based on a machine-learning classifier, a probability for the rider to select a trip option from the origin to the alternative destination, where the machine-learning classifier is trained to accept input comprising one or more attributes of the trip option and generate output comprising the probability for the rider to select the alternative destination; generating a score for each of the alternative destinations based at least on the probability; and ranking, by the computing device of the ridesharing platform, the alternative destinations based on the scores.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for recommending one or more alternative destinations are provided. One of the methods comprises: receiving, by a computing device of a ridesharing platform from a terminal device, a trip request that comprises an origin and a destination; classifying the trip request into one of the plurality of trip purpose categories based at least on the origin and the destination of the trip request, the rider's information, and a machine-learning classifier trained to predict the one trip purpose category of the trip request; in response to the one trip purpose category belonging to a preset group of trip purpose categories, determining one or more alternative destinations for the trip request; and sending to the terminal device, the one or more alternative destinations.

Abstract: A cumulative surged ride value may be calculated for trips on a ride sharing platform. A set of segments of a trip of a vehicle on a ridesharing platform may be obtained. The trip may serve a plurality of riders. A personal surge multiplier associated with each of the plurality of riders may be obtained. A driver basic fare for each segment may be obtained. A cumulative ride value may be determined based on the driver basic fare for each segment and the personal surge multiplier associated with each of the plurality of riders.

Abstract: Dynamic carpool discounts may be determined on ridesharing platforms. Trip information of a first rider in a carpool trip on a ridesharing platform may be obtained. A matching probability of matching the carpool trip with at least one second rider may be determined. An upfront discount and a fallback discount may be determined based on a matching probability and the trip information. It may be determined whether the at least one second rider matches with the carpool trip during the carpool trip. A final price for the first rider may be determined based on the upfront discount in response to determining that the carpool trip matches with the at least one second rider. A final price for the first rider may be determined based on the fallback discount in response to determining that the carpool trip does not match with the at least one second rider.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for testing a plurality of variants among a plurality of users. One of the methods includes: determining a spatial granularity to divide an area into a plurality of grid cells; randomly splitting the plurality of grid cells into a plurality of testing groups, wherein a quantity of the plurality of testing groups is determined based on a quantity of the multiple versions of the feature to be tested; determining a temporal granularity to divide a testing period into a plurality of testing intervals; during each of the plurality of testing intervals, respectively assigning the multiple versions of the feature to the plurality of testing groups; and obtaining a treatment effect for each of the multiple versions of the feature and determining an optimal version of the feature.

Abstract: Systems and methods are disclosed herein for optimizing harvester yield. In an embodiment, a controller receives a pre-harvest image from a front-facing camera of a harvester. The controller inputs the pre-harvest image into a model, and receives as output from the model a predicted harvest yield. The controller receives, from an interior camera of the harvester, a post-harvest image including the plants as harvested. The controller inputs the post-harvest image into a second model and receives, as output, an actual harvest yield of the plants as-harvested. The controller determines that the predicted harvest yield does not match the actual harvest yield, and outputs a control signal.