Abstract: A method, which is applied to a UE, for controlling the transmission power of each of positioning (POS) sounding reference signals (SRSs) across a subset of multiple component carriers (CCs) in carrier aggregation is provided. A SRS configuration for a subset of the CCs is received. Pathlosses corresponding to SRS transmission in the subset of the CCs are measured. Pathloss measurements corresponding to the subset of the CCs are processed to obtain a single pathloss across the subset of the CCs. The transmission power of each of the POS-SRSs across the subset of the CCs is determined based on the single pathloss. Each of the POS-SRSs are transmitted with the transmission power through the CCs.

Abstract: Various embodiments of the present technology include an improved system for measuring the impacts of feature rollouts on machine utilization and service performance. More specifically, embodiments of the present technology include an exposure control system based on randomized machine assignments and a corresponding score card for comparing machine utilization metrics. In an embodiment, a computing apparatus identifies a new feature in a codebase, wherein the codebase has been updated with the new feature across multiple resources, enables the new feature for a target group of the multiple resources while keeping the new feature dormant for a control group of the multiple resources, collects performance information for the target group and the control group for a time period, and generates a visualization of one or more differences between the performance information for the target group and the performance information for the control group for the time period.

Abstract: At least some example embodiments provide a system-state monitoring method and device and a storage medium. The method includes determining a standard operation mode of a system, the standard operation mode including a plurality of operation states of the system in a unit time period. The method further includes determining, according to current operation data of the system, a current operation mode of the system and determining, by comparing the current operation mode with the standard operation mode, whether or not the system is in the standard operation mode. The plurality of operation states of the system are determined to be the standard operation mode, such that changes in system operation patterns can be readily detected, thereby facilitating a timely adjustment of the monitored system or peripheral mechanisms in cooperation therewith and improving system performance.

Abstract: An autonomous off-road vehicle (AOV) accesses a pile plan map indicating a plurality of locations within a geographic area at which piles are to be installed. The AOV generates an obstacle map indicating locations of obstacles within the geographic area. The AOV autonomously navigates to a first location of the plurality of locations using the pile plan map. In response to driving a pile into the ground at the first location, the AOV modifies the obstacle map to include a representation of the pile at the first location. The AOV autonomously navigates to a second location of the plurality of locations using the pile plan map. In response to driving a pile into ground at the second location, the AOV modifies the obstacle map that includes the representation of the pile at the first location to further include a representation of the pile at the second location.

Abstract: A prediction tool for judging drug sensitivity and long-term prognosis of liver cancer based on gene detection provided. The present application statistically analyzes an aerobic glycolysis pathway gene related to liver cancer prognosis in TCGA data, and adopts LASSO regression analysis to simplify a prognosis-related gene on this basis, so as to establish a prediction tool based on the aerobic glycolysis pathway gene, referred to as an aerobic glycolysis index. The index is validated in a plurality of public databases and clinical samples from a Sir Run Run Shaw Hospital, and it is found that the index can accurately predict sensitivity and long-term prognosis of liver cancer patients to sorafenib therapy. The present application can effectively screen liver cancer patients sensitive to the sorafenib therapy, and provides a new idea for precise and comprehensive treatment of the liver cancer patients.

Abstract: This application provides a flexible screen cover. The flexible screen cover includes a protective layer, a main layer, and a buffer layer. A modulus ratio of the main layer to the buffer layer is 8 to 180000. The buffer layer can be disposed at any proper position in the flexible screen. A modulus ratio of an adjacent layer to a buffer layer is 2 to 500000. The collapsible electronic device includes a housing and a flexible screen, and the flexible screen is installed in the housing. This application can improve impact resistance performance of the flexible screen.

Abstract: A pile plan map indicating a plurality of locations in a geographic area at which piles are to be installed is accessed. A first set of locations is identified from the plurality of locations and a first set of piles to be driven into the ground at the first set of locations using the pile plan map is identified. An order for driving the first set of piles into the ground is identified and a pile type for each of the first set of piles is identified. Basket assembly instructions are generated for assembling the first set of piles into a basket based on the identified order and the identified pile types. The first set of piles are assembled autonomously or manually into the basket based on the generated basket assembly instructions.

Abstract: Various embodiments of the teachings herein include an environment prediction method based on a target available model. An example method comprises: generating a training sample based on predetermined environment data; using the training sample to perform training based on a fluid dynamics model and a Gaussian simulation model, to obtain a target available model; and based on real environment data, using the target available model to determine a real environment prediction value of a time-related pollution concentration sequence for a calibration position.

Abstract: This description provides an autonomous or semi-autonomous excavation vehicle that is capable determining a route between a start point and an end point in a site and navigating over the route. The sensors collect any or more of spatial, imaging, measurement, and location data to detect an obstacle between two locations within the site. Based on the collected data and identified obstacles, the excavation vehicle generates unobstructed routes circumventing the obstacles, obstructed routes traveling through the obstacles, and instructions for removing certain modifiable obstacles. The excavation vehicle determines and selects the shortest route of the unobstructed and obstructed route and navigates over the selected path to move within the site.

Abstract: An autonomous earth moving system can determine a desired state for a portion of the EMV including at least one control surface. Then the EMV selects a set of control signals for moving the portion of the EMV from the current state to the desired state using a machine learning model trained to generate control signals for moving the portion of the EMV to the desired state based on the current state. After the EMV executes the selected set of control signals, the system measures an updated state of the portion of the EMV. In some cases, this updated state of the EMV is used to iteratively update the machine learning model using an online learning process.

Abstract: When an EMV performs an action comprising moving a tool of the EMV through soil or other material, the EMV can measure a current speed of the tool through the material and a current kinematic pressure exerted on the tool by the material. Using the measured current speed and kinematic pressure, the EMV system can use a machine learned model to determine one or more soil parameters of the material. The EMV can then make decisions based on the soil parameters, such as by selecting a tool speed for the EMV based on the determined soil parameters.

Abstract: This description provides an autonomous or semi-autonomous excavation vehicle that is capable of determining a route between a start point and an end point in a site and navigating over the route. The sensors collect any or more of spatial, imaging, measurement, and location data to detect an obstacle between two locations within the site. Based on the collected data and identified obstacles, the excavation vehicle generates unobstructed routes circumventing the obstacles, obstructed routes traveling through the obstacles, and instructions for removing certain modifiable obstacles. The excavation vehicle determines and selects the shortest route of the unobstructed and obstructed route and navigates over the selected path to move within the site.

Abstract: In some implementations, the EMV uses a calibration to inform autonomous control over the EMV. To calibrate an EMV, the system first selects a calibration action comprising a control signal for actuating a control surface of the EMV. Then, using a calibration model comprising a machine learning model trained based on one or more previous calibration actions taken by the EMV, the system predicts a response of the control surface to the control signal of the calibration action. After the EMV executes the control signal to perform the calibration action, the EMV system monitors the actual response of the control signal and uses that to update the calibration model based on a comparison between the predicted and monitored states of the control surface.

Abstract: An earth moving vehicle (EMV) autonomously performs an earth moving operation within a dig site. If the EMV determines that a state of the EMV or the dig site triggers a triggering condition associated with a pause in the autonomous behavior of the EMV, the EMV determines a risk associated with the state or triggering condition. If the risk is greater than a first threshold, the EMV continues the autonomous performance and notifies a remote operator that the triggering condition was triggered. If the risk is greater than the first threshold risk but less than a second threshold risk, the EMV is configured to operate in a default state before continuing and notifying the remote operator. If the risk is greater than the second threshold risk, the EMV notifies the remote operator of the state pauses the performance until feedback is received from the remote operator.

Abstract: Example methods and systems for service request handling with protocol translation are described. In one example, in response to intercepting a service request from a virtualized computing instance, a computer system may generate and send a translated service request towards a service node. The translated service request may be generated by performing a first translation of the service request from a service protocol to a data exchange protocol supported by both a client node running on the computer system and a service node. In response to receiving the service response that is generated according to the data exchange protocol from the service node, the computer system may generate and send a translated service response towards the virtualized computing instance. The translated service response may be generated by performing a second translation of the service response from the data exchange protocol to the service protocol.

Abstract: This description provides an autonomous or semi-autonomous excavation vehicle that is capable of navigating through a dig site and carrying out an excavation routine using a system of sensors physically mounted to the excavation vehicle. The sensors collects any one or more of spatial, imaging, measurement, and location data representing the status of the excavation vehicle and its surrounding environment. Based on the collected data, the excavation vehicle executes instructions to carry out an excavation routine. The excavation vehicle is also able to carry out numerous other tasks, such as checking the volume of excavated earth in an excavation tool, and helping prepare a digital terrain model of the site as part of a process for creating the excavation routine.

Abstract: The invention is directed towards compounds (e.g., Formulae (I)-(IX)), their mechanism of action, processes to prepare the compounds, methods of activating quorum sensing signaling activity, and methods of treating diseases and disorders using the compounds described herein (e.g., Formulae (I)-(IX)).

Abstract: This description provides an autonomous or semi-autonomous excavation vehicle that is capable determining a route between a start point and an end point in a site and navigating over the route. The sensors collect any or more of spatial, imaging, measurement, and location data to detect an obstacle between two locations within the site. Based on the collected data and identified obstacles, the excavation vehicle generates unobstructed routes circumventing the obstacles, obstructed routes traveling through the obstacles, and instructions for removing certain modifiable obstacles. The excavation vehicle determines and selects the shortest route of the unobstructed and obstructed route and navigates over the selected path to move within the site.

Abstract: An autonomous earth moving system can determine a desired state for a portion of the EMV including at least one control surface. Then the EMV selects a set of control signals for moving the portion of the EMV from the current state to the desired state using a machine learning model trained to generate control signals for moving the portion of the EMV to the desired state based on the current state. After the EMV executes the selected set of control signals, the system measures an updated state of the portion of the EMV. In some cases, this updated state of the EMV is used to iteratively update the machine learning model using an online learning process.

Abstract: When an EMV performs an action comprising moving a tool of the EMV through soil or other material, the EMV can measure a current speed of the tool through the material and a current kinematic pressure exerted on the tool by the material. Using the measured current speed and kinematic pressure, the EMV system can use a machine learned model to determine one or more soil parameters of the material. The EMV can then make decisions based on the soil parameters, such as by selecting a tool speed for the EMV based on the determined soil parameters.