Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: generating a feature vector for a user based, at least in part, on historical data pertaining to the user's previous transactions; generating, using a quantity prediction model of a machine learning architecture, a respective item quantity prediction for each of one or more items included in a predicted basket based, at least in part, on the feature vector for the user; and populating a respective quantity selection option for each of the one or more items included in the predicted basket based on the respective item quantity prediction generated for each of the one or more items. Other embodiments are disclosed herein.

Abstract: Systems and methods for random vehicle movement to prevent theft are disclosed. The vehicle system may automatically reposition to make it seem like the user is home and to reduce battery drain and flat spots on the tire. The system may randomize the start time based on the preferences of the user. At the randomly decided time, the vehicle may start up on its own and reposition itself. In addition, the system may learn a driving behavior pattern of the vehicle via machine learning based on driving behavior associated with the vehicle, such that the vehicle may perform vehicle movement based on the learned driving behavior pattern.

Abstract: Training query intents are allocated for multiple training entities into training time intervals in a time series based on a corresponding query intent time for each training query intent. Training performance results for the multiple training entities are allocated into the training time intervals in the time series based on a corresponding performance time of each training performance result. A machine learning model for a training milestone of the time series is trained based on the training query intents allocated to a training time interval prior to the training milestone and the training performance results allocated to a training time interval after the training milestone. Target performance for the target entity for an interval after a target milestone in the time series is predicted by inputting to the trained machine learning model target query intents allocated to the target entity in a target time interval before the target milestone.

Abstract: The present invention is based on the finding that subanesthetic doses of N-methyl-D-aspartate (NMDA) receptor antagonists alleviate social interaction deficits associated with behavioral disorders, such as Smith-Magenis Syndrome (SMS). The invention therefore provides methods and compositions for treating SMS. Also provided are methods of selecting NMDA receptor agonists useful in alleviating social interaction deficits associated with such behavioral disorders.

Abstract: Various example embodiments relate to an automatic drain system for use with a fluid water separator. The automatic drain system includes a liquid-in-fuel sensor. The liquid in-fuel sensor is configured to detect a liquid level in a water sump. The solenoid has an open state and a closed state. A control unit is configured to activate the solenoid in response to a signal from the liquid-in-fuel sensor. Activation of the solenoid causes the solenoid to go from the closed state to the open state. The automatic drain system is placed in a condition for allowing fluid flow through the automatic drain system from the water sump.

Abstract: The disclosed embodiments relate to nanotechnology and to nano-electronics and molecular electronic sensors. In an exemplary embodiment, a nano-sensor having a nanoparticle complex attached at each end to a respective nano-electrode. An exemplary nanoparticle complex includes a biomolecule coupled at each end to a metallic nanoparticle to form a dumbbell-shaped molecular bridge.

Abstract: The disclosed computer-implemented method may include a process for monitoring and improving end-to-end video quality based on scaled and/or interpolated perceptual quality scores across various video views. The method may also include a process for improving search experience for user expectations. Additionally, the method may include a process for providing hardware virtualization and simulation for server hosting. Furthermore, the method may include a process for filtering network traffic in a hosting environment. The method may additionally include a process for testing applications in a hosting environment. The method may further include a process for supporting multi-touch applications. The method may also include a process for optimized graphics rendering. Various other related methods and systems are also disclosed.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: generating a feature vector for a user based, at least in part, on historical data pertaining to the user's previous transactions; generating, using a quantity prediction model of a machine learning architecture, a respective item quantity prediction for each of one or more items included in a predicted basket based, at least in part, on the feature vector for the user; and populating a respective quantity selection option for each of the one or more items included in the predicted basket based on the respective item quantity prediction generated for each of the one or more items. Other embodiments are disclosed herein.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform functions comprising: generating one or more feature vectors for a user, the one or more feature vectors at least comprising transaction-based features and slot-based features; generating, using a machine learning architecture, a repurchase prediction for the user based, at least in part, on the one or more feature vectors; generating, using the machine learning architecture, a time slot prediction for the user based, at least in part, on the one or more feature vectors, the time slot prediction predicting a time slot desired by the user for an upcoming transaction; and executing a reservation function that facilitates reserving of the time slot for the user. Other embodiments are disclosed herein.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: generating a feature vector for a user based, at least in part, on historical data pertaining to the user's previous transactions; generating, using a quantity prediction model of a machine learning architecture, a respective item quantity prediction for each of one or more items included in a predicted basket based, at least in part, on the feature vector for the user; and populating a respective quantity selection option for each of the one or more items included in the predicted basket based on the respective item quantity prediction generated for each of the one or more items. Other embodiments are disclosed herein.

Abstract: A hardware acceleration device can include a switch communicatively linked to a host central processing unit (CPU), an adapter coupled to the switch via a control bus, wherein the control bus is configured to convey addresses of descriptors from the host central CPU to the adapter, and a random-access memory (RAM) coupled to the switch through a data bus. The RAM is configured to store descriptors received from the host CPU via the data bus. The hardware acceleration device can include a compute unit coupled to the adapter and configured to perform operations specified by the descriptors. The adapter may be configured to retrieve the descriptors from the RAM via the data bus, provide arguments from the descriptors to the compute unit, and provide control signals to the compute unit to initiate the operations using the arguments.

Abstract: The disclosure relates to stent detection and shadow detection in the context of intravascular data sets obtained using a probe such as, for example, and optical coherence tomography probe or an intravascular ultrasound probe.

Abstract: Operations may include obtaining a buggy code snippet from a question included in a post of a discussion forum, the buggy code snippet including an error and a plurality of lines of code. The operations may further include obtaining text from an answer to the question and identifying keywords from the text. In addition, the operations may include marking a particular line from the plurality of lines as being related to the error based on one or more of the keywords corresponding to one or more elements of the particular line. Moreover, the operations may include performing one or more software program repair operations based on the marking of the particular line.

Abstract: Training query intents are allocated for multiple training entities into training time intervals in a time series based on a corresponding query intent time for each training query intent. Training performance results for the multiple training entities are allocated into the training time intervals in the time series based on a corresponding performance time of each training performance result. A machine learning model for a training milestone of the time series is trained based on the training query intents allocated to a training time interval prior to the training milestone and the training performance results allocated to a training time interval after the training milestone. Target performance for the target entity for an interval after a target milestone in the time series is predicted by inputting to the trained machine learning model target query intents allocated to the target entity in a target time interval before the target milestone.

Abstract: Aspects of the application relate to methods and compositions for delivering therapeutic nucleic acids to neural cells or tissue in a subject. Additional aspects of the application relate to therapeutic nucleic acids, for example therapeutic ribozymes, that are useful for inhibiting viral reactivation in a subject.

Abstract: Provided herein are methods and compositions for producing cannabinoids and other metabolites in a host cell.

Abstract: A faulted circuit indicator includes: a power source; a sensor system configured to measure a voltage at the power source and a temperature at the power source; a controller coupled to the sensor system. The controller is configured to determine a health status of the power source based on the measured voltage at the power source and the measured temperature at the power source. The faulted circuit indicator also includes an indicator module coupled to the controller. The indicator is configured to provide an indication of the health status of the power source.

Abstract: An affinity reagent, having: (a) a retaining component such as a structured nucleic acid particle; and (b) one or both of (i) one or more label components attached to the retaining component, and (ii) one or more binding components attached to the retaining component.

Abstract: Techniques are disclosed for anomaly detection based on a predicted value. A search query can be executed over a period of time to produce values for a key performance indicator (KPI), the search query defining the KPI and deriving a value indicative of the performance of a service at a point in time or during a period of time, the value derived from machine data pertaining to one or more entities that provide the service. A graphical user interface (GUI) enabling a user to indicate a sensitivity setting can be displayed. A user input indicating the sensitivity setting can be received via the GUI. Zero or more of the values as anomalies can be identified in consideration of the sensitivity setting indicated by the user input.

Abstract: Object parts (20, 21, 22, 23, 24) are detected in a picture using object detector(s) (3) and part location representations (40, 42, 43, 44) are generated for the detected object parts (20, 22, 23, 24). The size of an object (10) comprising object parts (20, 21, 22, 23, 24) is estimated based on a geometric model and the part location representations (40, 42, 43, 44). Search locations (51) in the picture for a search window (52) having a size based on the estimated size are determined based on the part location representations (40, 42, 43, 44). The search locations (51) are then processed by identifying any detected object part (20, 22, 23) that is within the search window (52) positioned at the search location (51). A homography is estimated by minimizing an error between mapped object part(s) from the geometric model and the identified detected object part(s) (20, 22, 23). If the error is smaller than a threshold value, an object location representation is determined for the object (10).