Abstract: Stacked microfeature devices and associated methods of manufacture are disclosed. A package in accordance with one embodiment includes first and second microfeature devices having corresponding first and second bond pad surfaces that face toward each other. First bond pads can be positioned at least proximate to the first bond pad surface and second bond pads can be positioned at least proximate to the second bond pad surface. A package connection site can provide electrical communication between the first microfeature device and components external to the package. A wirebond can be coupled between at least one of the first bond pads and the package connection site, and an electrically conductive link can be coupled between the first microfeature device and at least one of the second bond pads of the second microfeature device. Accordingly, the first microfeature device can form a portion of an electrical link to the second microfeature device.

Abstract: Displaying relevant data subsets of at least one data collection includes providing an input data subset, determining a plurality of partial relevance values for different characteristics of each of the plurality of data subsets of the at least one data collection with respect to the input data subset according to a term frequency of terms in the input data subset and term in each of the plurality of data subsets of the at least one data collection and according to an inverse document frequency of the terms, determining a relevance of each of the plurality of data subsets of the at least one data collection based on the partial relevance values of the different characteristics, and displaying at least a portion of the data subsets of the at least one data collection in order of relevance.

Abstract: Methods and systems for monitoring use, determining risk, and pricing insurance policies for a vehicle having one or more autonomous or semi-autonomous operation features are provided. According to certain aspects, an identity of a vehicle operator may be determined and a vehicle operator profile and/or operating data regarding autonomous operation features of the vehicle may be received after the vehicle operator opts into a rewards program and agrees to share their data. Autonomous operation and vehicle operator risk levels associated with operation of the autonomous or semi-autonomous vehicle may be determined. Based upon the risk levels and/or comparison thereof, one or more autonomous operation features may be disengaged. A preparedness level of the vehicle operator to assume or reassume control of operating the vehicle is determined prior to disengagement. If satisfactory, an alert is presented to the vehicle operator prior to disengagement of the autonomous operation features.

Abstract: Anti-PVRIG and anti-TIGIT antibodies are provided.

Abstract: Vehicles may have autonomous capabilities that permit the vehicle to navigate according to varying levels of participation by a human operator. A human operator may be associated with a driving capability profile and the environment surrounding a vehicle may be associated with an environmental risk profile. A vehicle, or another party in communication with a vehicle, may determine whether, based on the human operator's driving capability profile and the environmental risk profile, it is likely that the vehicle will be navigated more safely if there is a change to the vehicle's autonomous capabilities and a corresponding change to the human operator's driving responsibilities. If it is determined that a change in autonomous vehicle capability is likely to increase safe navigation of the vehicle, an insurer may offer an insurance premium cost reduction if the human operator agrees to the proposed change in autonomous capabilities.

Abstract: Provided herein are methods and compositions for enhancing an immune response and/or for the treatment of an immune-related condition in an individual, comprising disabling myeloid cells using an anti-Triggering Receptor Expressed on Myeloid Cells 1 (TREM1) antibody.

Abstract: Lateral sleep systems for aircraft are disclosed. A first lateral sleep apparatus is positioned on a first side of a divider adjacent a first seat, the lateral sleep apparatus includes a first headrest and a first cradle. The first cradle supports the first headrest and includes a first panel mounting interface coupling the first headrest to a first support ledge of the divider. A second lateral sleep apparatus is positioned on a second side of the divider adjacent a second seat, where the second lateral sleep apparatus includes a second headrest and a second cradle. The second cradle supports the second headrest and includes a second panel mounting interface coupling the second headrest to a second support ledge of the divider.

Abstract: Lateral sleep systems for aircraft are disclosed. A divider is located between a first seat and a second seat of the aircraft. The divider includes a first panel oriented toward the first seat and a second panel oriented toward the second seat. The first panel defines a first support ledge. The second panel defines a second support ledge. A first lateral sleep apparatus positioned on a first side of the divider adjacent the first seat includes a first headrest and a first cradle. The first cradle supports the first headrest and includes a first panel mounting interface coupling the first headrest to the first support ledge. A second lateral sleep apparatus positioned on a second side of the divider adjacent the second seat includes a second headrest and a second cradle. The second cradle supports the second headrest and includes a second panel mounting interface coupling the second headrest to the second support ledge.

Abstract: According to certain aspects, a computer-implemented method for operating an autonomous or semi-autonomous vehicle may be provided. With the customer's permission, an identity of a vehicle operator may be identified and a vehicle operator profile may be retrieved. Operating data regarding autonomous operation features operating the vehicle may be received from vehicle-mounted sensors. When a request to disable an autonomous feature is received, a risk level for the autonomous feature is determined and compared with a driver behavior setting for the autonomous feature stored in the vehicle operator profile. Based upon the risk level comparison, the autonomous vehicle retains control of vehicle or the autonomous feature is disengaged depending upon which is the safer driver—the autonomous vehicle or the vehicle human occupant. As a result, unsafe disengagement of self-driving functionality for autonomous vehicles may be alleviated.

Abstract: The present invention is directed to anti-PVRIG antibodies and methods of using same.

Abstract: Sleep conditions such as moderate-to-severe sleep apnea can be assessed using a multi-night assessments. A respiration signal (e.g., acquired from a sensor strip) can be processed via a computing device. The respiration signal can be segmented and the segments can be classified to identify one or more apnea/hypopnea events. In some examples, some of the segments can be normalized such that each segment input for classification can be of the same size. The identified one or more apnea/hypopnea events can be used to estimate a nightly parameter indicative of a severity of (or presence of) sleep apnea. The nightly parameters from a multi-night period can be used to estimate a multi-night parameter indicative of the severity of (or presence of) sleep apnea. In some examples, quality checks can be performed to filter out some data (e.g., to exclude data from entire nights or exclude a portion of data from individual nights).

Abstract: In a computer-implemented method, alert responsiveness data indicating how responsive a driver is to one or more types of vehicle alerts is received. The method also includes generating or modifying a driver profile associated with the driver, the driver profile including alert responsiveness profile information based upon the alert responsiveness data, and identifying, based at least upon the generated or modified driver profile, a suggested vehicle type. Identifying the suggested vehicle type includes determining that the generated or modified driver profile meets a set of one or more matching criteria associated with the suggested vehicle type at least in part by determining that the alert responsiveness profile information meets one or more criteria indicative of reliability of the suggested vehicle type. The method also includes causing an indication of the suggested vehicle type to be displayed to a user.

Abstract: Anti-PVRIG and anti-TIGIT antibodies are provided.

Abstract: An indication of a selection of an entry associated with a machine learning model is received. One or more interpretation views associated with one or more machine learning models are dynamically updated based on the selected entry.

Abstract: Input data associated with a machine learning model is classified into a plurality of clusters. A plurality of linear surrogate models are generated. One of the plurality of linear surrogate models corresponds to one of the plurality of clusters. A linear surrogate model is configured to output a corresponding prediction based on input data associated with a corresponding cluster. Prediction data associated with the machine learning model and prediction data associated with the plurality of linear surrogate models are outputted.

Abstract: An indication of a selection of an entry associated with a machine learning model is received. One or more interpretation views associated with one or more machine learning models are dynamically updated based on the selected entry.

Abstract: Input data associated with a machine learning model is classified into a plurality of clusters. A plurality of linear surrogate models are generated. One of the plurality of linear surrogate models corresponds to one of the plurality of clusters. A linear surrogate model is configured to output a corresponding prediction based on input data associated with a corresponding cluster. Prediction data associated with the machine learning model and prediction data associated with the plurality of linear surrogate models are outputted.

Abstract: Input data associated with a machine learning model is classified into a plurality of clusters. A plurality of linear surrogate models are generated. One of the plurality of linear surrogate models corresponds to one of the plurality of clusters. A linear surrogate model is configured to output a corresponding prediction based on input data associated with a corresponding cluster. Prediction data associated with the machine learning model and prediction data associated with the plurality of linear surrogate models are outputted.

Abstract: Input data associated with a machine learning model is classified into a plurality of clusters. A plurality of linear surrogate models are generated. One of the plurality of linear surrogate models corresponds to one of the plurality of clusters. A linear surrogate model is configured to output a corresponding prediction based on input data associated with a corresponding cluster. Prediction data associated with the machine learning model and prediction data associated with the plurality of linear surrogate models are outputted.

Abstract: An indication of a selection of an entry associated with a machine learning model is received. One or more interpretation views associated with one or more machine learning models are dynamically updated based on the selected entry.