Abstract: A wearable automated peritoneal dialysis (“APD”) machine is disclosed herein. In an example, the APD machine includes a delivery system connected to a dialysis fluid container and a catheter connected to a peritoneal cavity of a patient. The delivery system includes a pump for pumping fresh dialysis fluid from the dialysis fluid container to the patient, and pumping used dialysis fluid from the patient to the dialysis fluid container. The delivery system also includes a control unit configured to control the pump using a flow rate measured by a flow sensor and a pressure measured by a pressure sensor. The APD machine also includes a clothing item to be worn by the patient. The clothing item includes a first section to retain the dialysis fluid container, a heating element positioned adjacent to the first section for warming the fresh dialysis fluid, and a second section to retain the delivery system.

Abstract: A system for monitoring flow of fluid through a pipeline is disclosed. The system includes a ferromagnetic strip mounted on an outer surface of the pipeline. A solenoid coil is wound around the ferromagnetic strip and is connectable to a power source. A plurality of magnets is positioned over the solenoid coil, wherein each of the plurality of magnets are positioned at a pre-defined orientation with respect to longitudinal axis of the pipeline. A processing unit is communicatively coupled to the solenoid coil. The processing unit generates a plurality of wave modes in the pipeline by supplying power to the solenoid coil in concurrence with the plurality of magnets and the ferromagnetic strip. The processing unit is configured to receive a plurality of wave signals reflected and are then compared with pre-defined wave signals by the processing unit to monitor flow of fluid through the pipeline.

Abstract: An apparatus includes a cellular culture container having an interior cavity. A cellular culture area is located in the interior cavity. The cellular culture area is configured to receive at least one cell to be cultured. A condensation director is configured to reduce and/or prevent the entry of condensation into the cellular culture area.

Abstract: A resource provisioning service allows users to provision multiple, different network resources in an atomic manner and with a single call to a resource provisioning service. In some instances, the multiple, different network resources comprise individual types of resources that form a portion of one or more cloud-computing platforms. For instance, one or more entities may host and operate a cloud-computing platform that includes different types of network resources, such a storage service, a load balancing service, a compute service, a security service, or any other similar or different type of network-accessible service.

Abstract: Provided is a method for calculating a timestamp associated with a data packet before transcoding of the data packet. The method may include sampling a time of day (TOD) signal to provide a sampled TOD. A previously sampled TOD estimate may be retrieved. An internal TOD estimate may be determined based on the sampled TOD and the previously sampled TOD estimate. A timestamp may be determined based on the internal TOD estimate. A system and computer program product are also disclosed.

Abstract: The present invention discloses a method, a system and a computer program product for data processing and classification. The invention provides warm start and cold start classification tools for classification of data obtained from known or unknown entities. The system and method are also configured to be employed over blockchain based networks.

Abstract: A microfluidic chip is disclosed herein. In a specific embodiment, the microfluidic chip comprises at least one microfluidic reservoir having a wall portion and a heat transfer sealing layer cooperating with the wall portion for receiving a sample to be tested. The heat transfer sealing layer is arranged to be contiguous with the sample to be tested. The microfluidic chip further comprises an active temperature control device arranged to provide structural support to the heat transfer sealing layer and operable to control a temperature of the sample via transmission of heat through the heat transfer sealing layer. A detection module is also disclosed.

Abstract: In one embodiment, a computer networking device calculates a first hash value for an identifier of a group of computing devices, as well as a second hash value for the identifier of the group of computing devices, with each hash value being at least in part on the identifier of the group of computing devices and an identifier of the respective interface. The computer networking device may also analyze the first hash value with respect to the second hash value and select the first interface for association with the identifier of the group of computing devices based at in part on the analyzing. The computer networking device may further store an indication that the identifier of the group of computing devices is associated with the first interface.

Abstract: A system on a chip (SoC) includes a first domain comprising a first processor configured to boot the SoC, and a first debug subsystem, a second domain comprising a second processor, the second domain configurable as either a safety domain or a general-purpose processing domain, and isolation circuitry between the first domain and the second domain. During boot-up of the SoC, the first processor provides code to the second domain which, when executed by the second processor, configures the second domain as either a safety domain or as a general-purpose processing domain.

Abstract: Methods, systems, and apparatus for receiving a request for a risk assessment for a parcel, receiving a set of images for the parcel, the set of images including two or more images, each image having an image scale and an image resolution that is different from other images in the set of images, providing a first-level feature embedding and a second-level feature embedding, the first-level feature embedding being provided by processing a first-level image through a first-level machine learning (ML) model, and the second-level feature embedding being provided by processing a second-level image through a second-level ML model, determining a risk assessment at least partially by processing each of the first-level feature embedding and a second-level feature embedding through a fusion network, and providing a representation of the risk assessment for display.

Abstract: In one embodiment, a computer networking device calculates a first hash value for an identifier of a group of computing devices, as well as a second hash value for the identifier of the group of computing devices, with each hash value being at least in part on the identifier of the group of computing devices and an identifier of the respective interface. The computer networking device may also analyze the first hash value with respect to the second hash value and select the first interface for association with the identifier of the group of computing devices based at in part on the analyzing. The computer networking device may further store an indication that the identifier of the group of computing devices is associated with the first interface.

Abstract: Certain aspects of the present disclosure provide techniques for generalized metric for machine learning model evaluation for unsupervised classification including: for each unsupervised machine learning model of one or more unsupervised machine learning models: generating a first set of synthetic inputs for the model of the one or more unsupervised machine learning models; providing the first set of synthetic inputs to the model trained to output a prediction for each input of the first set of synthetic inputs, wherein the prediction indicates whether the input is of a first class; identifying, based on an output of the model, a second set of synthetic inputs predicted to be of the first class; determining, based on a set of expected normal inputs for the model and the second set of synthetic inputs, an accuracy score for the unsupervised machine learning model; and providing the accuracy score for display.

Abstract: A transportation management system generates routing guidance from an origin location to a destination location by modifying edge weights in a graph of a geographic location to penalize difficult immediate maneuvers. Responsive to receiving a routing request, the system identifies a position of a provider device in a base map having edges representing road segments and nodes representing intersections between road segments. A sub-graph is generated for the edges in the base graph located up to a threshold distance from the origin location, and the system modifies the weight of one or more edges in the sub-graph corresponding to a difficult immediate maneuver. When applying a routing algorithm to generate the routing guidance, the system uses the edge weights of the generated sub-graph for a first portion of the routing guidance and the original edge weights of the base graph for a second portion of the routing guidance.

Abstract: A secure inference over Deep Neural Networks (DNNs) using secure two-party computation to perform privacy-preserving machine learning. The secure inference uses a particular type of comparison that can be used as a building block for various layers in the DNN including, for example, ReLU activations and divisions. The comparison securely computes a Boolean share of a bit representing whether input value x is less than input value y, where x is held by a user of the DNN, and where y is held by a provider of the DNN. Each party computing system parses their input into leaf strings of multiple bits. This is much more efficient than if the leaf strings were individual bits. Accordingly, the secure inference described herein is more readily adapted for using in complex DNNs.

Abstract: A device may receive project management data associated with development of a software product and may process a first portion of the project management data, with first models, to generate timeliness scores and an overall timeliness score for the software product. The device may process a second portion of the project management data, with second models, to generate quality scores and an overall quality score for the software product and may process a third portion of the project management data, with third models, to generate product readiness scores and an overall product readiness score for the software product. The device may utilize a fourth machine learning model, with the overall timeliness score, the overall quality score, and the overall product readiness score, to generate a success probability for the software product and may perform one or more actions based on the success probability for the software product.

Abstract: Techniques are provided for predicting firmware installation failure reasons using machine learning techniques. One method comprises obtaining log data for a user device, wherein the log data is obtained following a failure of a firmware installation on the user device; extracting a plurality of features from the obtained log data; applying the extracted features to a trained machine learning model to obtain a prediction of whether the firmware installation failure is caused by a hardware-related failure or a software-related failure; and performing an automated remedial action based on a result of the prediction. The trained machine learning model can be trained using historical data for multiple user devices that experienced a firmware installation failure, where the historical data comprises a label indicating whether a given failure comprises a hardware-related failure or a software-related failure. The trained machine learning model can be trained and tested using cross-validation techniques.

Abstract: Techniques are disclosed relating to the execution of machine learning models on client devices, particularly in the context of transaction risk evaluation. This reduces computational burden on server systems. In various embodiments, a server system may receive, from a client device, a request to perform a first operation and select a first machine learning model, from a set of machine learning models, to send to the client device. In some embodiments the first machine learning model is executable, by the client device, to generate model output data for the first operation based on one or more encrypted input data values that are encrypted with a cryptographic key inaccessible to the client device. The server system may send the first machine learning model to the client device and then receive, from the client device, a response message that indicates whether the first operation is authorized based on the model output data.

Abstract: A system includes a server and merchant device. The server receives product information for a product scanned by a mobile device. The server stores the product information for the product in a digital cart. The server receives a transaction request from the mobile device, determines that the product is associated with a validation requirement, and transmits a validation request to the merchant device. The server receives, from the merchant device, an indication that the validation requirement is satisfied, processes a transaction, and transmits, to the merchant device, an indication that the transaction is complete. The merchant device receives the validation request, determines that the validation requirement is satisfied, and transmits the indication that the validation requirement is satisfied to the server. The merchant device receives, from the server, the indication that the transaction is complete and displays the indication that the transaction on the display.

Abstract: Images in e-book and other electronic media content can be automatically enhanced for viewing on client devices, using various image categories, attributes, and expected qualities. Should systems and methods determine, based on the categories and attributes, that a given media object does not satisfy at least one rule for optimized presentation of the content on a particular client device, an updated presentation of the content can be generated, possibly in accordance with a predetermined specification. The updating may be in the form of image enhancement through adjustment of the size of the images in the media object. Machine learning schema can assist in recognizing images by category, importance and readability scoring, and in adjusting the images for optimal viewing.