Abstract: An ingestible device (200), comprising: a housing (210, 220) comprising a stop geometry (228) at an exit hole (224); a tissue penetrating member (230) and an actuator that comprises: a pushing portion (260) configured for axial movement from a first to a second position, the pushing portion configured to move the tissue penetrating member from within the housing to a lodged position in tissue, and a holder portion (270) coupled with the pushing portion (260) for initial axial movement with the pushing portion. In the first position the holder portion releasably holds the tissue penetrating member (230) in axial retaining engagement. As the pushing portion (260) moves towards the second position, the tissue penetrating member (230) moves through the exit hole (224) until the holder portion (270) enters into engagement with the stop geometry (228). The pushing portion (260) moves further to release and advance the tissue penetrating member (230).

Abstract: An online system accesses user behavior data and incentive data collected for a user prior to a current time period. The online system trains a behavior prediction model to receive user behavior data for a user and an incentive and output an incentive score using the collected user behavior data. The online system receives one or more candidate incentives generated by an incentive generation model based on the accessed user behavior data and incentive data. The online system applies each candidate incentive to the behavior prediction model to generate an incentive prediction describing a degree of user interaction of the particular user with the online system responsive to offering the candidate incentive to the user. The online system offers one or more candidate incentives to the user based on the determined incentive predictions.

Abstract: An online system augments a dataset in conjunction with a model serving system. The online system accesses a dataset for training a machine-learning model. The online system generates a prompt to generate candidate samples in the training dataset to the model serving system. The online system receives a response comprising one or more candidate samples. The online system compares the one or more candidate samples to at least one existing sample of the dataset to determine whether the one or more candidate samples are within a threshold level of similarity to an existing sample. If a candidate sample received from the machine-learning language model is not within the threshold level of similarity to an existing sample, the online system updates the dataset with the candidate sample.

Abstract: A method for singulating, orientating and distributing tie plates along a section of a track of a railroad is disclosed using a vehicle adapted to travel on rails including a tie plate singulating assembly, a tie plate flipping assembly, a tie plate quarter turn assembly, and a distribution assembly. The tie plate flipping assembly flips shoulder-side down tie plates into a shoulder-side up orientation while maintaining shoulder-side up tie plates in the shoulder-side up orientation. A diverter of the tie plate quarter turn assembly advances tie plates into a rotation inducing abutment resulting in the tie plates turning toward a desired orientation. The distribution assembly distributes tie plates to accumulation slides, controlling distribution with a dealer assembly and orienting discharge assembly to distribute the tie plates in a desired spacing and orientation along the track of a railroad.

Abstract: A system may smartly edit the context of a conversation to be input into a chatbot LLM by using a conversation compression algorithm to prune and compress redundant elements. The system evaluates the conversation context compression algorithm using both a chatbot LLM and an adversarial LLM. The system retrieves a logged conversation and generates a compressed conversation context from the logged conversation. The system generates a synthetic user response by applying the adversarial LLM and generates a test conversation by replacing a user response in the conversation with the synthetic user response. The system generates a compressed context of the test conversation. The system generates a test chatbot LLM response by prompting the chatbot LLM with the compressed context of the test conversation. The system evaluates the conversation context compression algorithm by comparing the test chatbot response with a benchmark chatbot response.

Abstract: A scalable flat in-plane-motion mechanical spring device. The device sincludes a frame with one or more flexures attached to the frame on a first end of the one or more flexures. The device further includes a shuttle with one or more of the one or more flexures attached to the shuttle on a second end of the one or more flexures. The frame, the shuttle, and the one or more flexures may be formed as a single piece.

Abstract: A system for orienting and distributing tie plates along a section of railroad track includes a tie plate flipping assembly. The tie plate flipping assembly flips shoulder-side down tie plates into a shoulder-side up orientation while maintaining shoulder-side up tie plates in the shoulder-side up orientation. One embodiment of the tie plate flipping assembly includes a launch conveyor for launching tie plates against a dampening slide spaced apart from the launch conveyor at a spacing which results in shoulder-side down tie plates flipping over while shoulder-side up tie plates remain shoulder-side up. An alternative tie plate flipping assembly advances tie plates on a longitudinal edge through a vertical guide and past a centered wedge which engages a shoulder side surface of the tie plates conveyed past the wedge causing the tie plates to tip outward and onto a bottom surface.

Abstract: Provided herein are methods, processes, systems, machines and apparatuses for non-invasive assessment of chromosome alterations.

Abstract: A feature management system (the “system”) receives information about a new machine learning (ML) model to be trained. The information includes metadata about the new model. The system applies a trained feature prediction model to the information about the new model and metadata about a plurality of features. The feature prediction model is trained to predict a probability that each of the plurality of features should be selected as an input feature for the new model. The feature management system identifies one or more candidate features based on an output probability score of the feature prediction model. The system presents in a user interface a suggestion to use the one or more candidate features with the new model. The system selects at least one candidate feature and causes the new model to be trained using a set of input features, including the selected candidate feature.

Abstract: A system maintains a data store for managing machine-learning (ML) models and features that are used by the models. The system generates a graph including nodes for each model and a node for each feature, and edges linking models and features that are used by the models. For a new model to be trained, the system receives a proposed feature corresponding to a node in the graph, and identifies one or more candidate features corresponding to nodes in the graph based in part on relevancy scores between the proposed feature with other features corresponding to nodes in the graph. The system presents in a user interface a suggestion to use one or more candidate features with the new model. Responsive to receiving a user selection of at least one candidate feature, the system causes the new model to be trained using the selected candidate feature and the proposed feature.

Abstract: An online system facilitates various functions using machine learning model microservices. A tuning mechanism tunes various configuration parameters for each microservice that control allocation of computing resources and other configurations of physical and/or virtual machines that implement the microservices. Tuning may be performed in part by executing tests under various configurations and evaluating an objective function associated with the different configurations. Furthermore, parameters of the objective function may be set based on a trained learning model that learns baseline parameters and weights of the objective function based on historical data.

Abstract: A variation testing system environment for simulating adaptive experiments of objects is disclosed. An experiment system conducts one or more simulations of an adaptive experiment that includes a plurality of variants of an object. Simulation results based on the one or more simulations are generated that are indicative of at least an estimated amount of time to conduct a real-world adaptive experiment based on the one or more simulations.

Abstract: The present disclosure relates to a method of controlling operation of wind turbine generators (WTGs) in a hybrid power plant including both WTGs and PV modules. The method includes steps of: monitoring at least one operating parameter for one or more of the WTGs; monitoring at least one operating parameter for one or more of the PV modules; and controlling operation of the WTGs in dependence on the monitored operating parameters in order to control blade shadows cast by the WTGs on the PV modules and thereby optimise the power output of the PV modules, for example by reducing the blade shadow area cast on the PV modules.

Abstract: Provided herein are methods, processes, systems, machines and apparatuses for non-invasive assessment of chromosome alterations.

Abstract: The present disclosure is directed to generating datastore checkpoints. In particular, the methods and systems of the present disclosure may generate, within a datastore, data representing multiple checkpoints. Each checkpoint of the checkpoints may correspond to a respective record of the datastore and may represent a common shared value for a field based at least in part on which the datastore is ordered. Based at least in part on the checkpoints, the datastore may be queried to produce one or more responsive records to one or more criteria of the query. Based at least in part on the responsive record(s), training data may be generated. The training data may be utilized for training one or more machine learning (ML) models configured to process input based at least in part on values for the field based at least in part on which the datastore is ordered.

Abstract: Technology provided herein relates in part to methods, processes and apparatuses for non-invasive assessment of genetic variations.

Abstract: Technology provided herein relates in part to methods, processes and apparatuses for non-invasive assessment of genetic variations.

Abstract: Provided herein are methods, processes and apparatuses for non-invasive assessment of genetic variations.

Abstract: Provided herein are methods, processes and apparatuses for non-invasive assessment of genetic variations.