Abstract: A system uses machine models to estimate trip durations or distance. The system trains a historical model to estimate trip duration using characteristics of past trips. The system trains a real-time model to estimate trip duration using characteristics of recently completed trips. The historical and real-time models may use different time windows of training data to predict estimates, and may be trained to predict an adjustment to an initial trip estimate. A selector model is trained to predict whether the historical model, the real-time model, or a combination of the historical and real-time models will more accurately estimate a trip duration, given features associated with a trip duration request, and the system accordingly uses the models to estimate a trip duration. In some embodiments, the real-time model and the selector may be trained using batch machine learning techniques which allow the models to incorporate new trip data as trips complete.

Abstract: An optical amplifier assembly and a detection method capable of dynamically performing optical time-domain reflection detection. The detection method comprises obtaining signal light intensity detection signals from a first and second photodetectors and sending a control signal to an L-band Raman pump when it is determined that the signal light intensity in the second photodetector is lower than a first preset threshold, so that the L-band Raman pump enters into an optical time-domain reflection detection mode; sending a control signal to the L-band Raman pump when the signal light intensity in the second photodetector is greater than or equal the first preset threshold, so that the L-band Raman pump enters into an L-Band Raman optical fiber amplifier operation mode.

Abstract: A system uses machine models to estimate trip durations or distance. The system trains a historical model to estimate trip duration using characteristics of past trips. The system trains a real-time model to estimate trip duration using characteristics of recently completed trips. The historical and real-time models may use different time windows of training data to predict estimates, and may be trained to predict an adjustment to an initial trip estimate. A selector model is trained to predict whether the historical model, the real-time model, or a combination of the historical and real-time models will more accurately estimate a trip duration, given features associated with a trip duration request, and the system accordingly uses the models to estimate a trip duration. In some embodiments, the real-time model and the selector may be trained using batch machine learning techniques which allow the models to incorporate new trip data as trips complete.

Abstract: A computing system can receive, over one or more networks, location data from the computing devices of user as the user operate throughout a region. For each user, the computing system can determine whether the user is operating a location-spoofing application on the computing device of the user based, at least in part, on the location data received from the computing device of the user.

Abstract: A system uses machine models to estimate trip durations or distance. The system trains a historical model to estimate trip duration using characteristics of past trips. The system trains a real-time model to estimate trip duration using characteristics of recently completed trips. The historical and real-time models may use different time windows of training data to predict estimates, and may be trained to predict an adjustment to an initial trip estimate. A selector model is trained to predict whether the historical model, the real-time model, or a combination of the historical and real-time models will more accurately estimate a trip duration, given features associated with a trip duration request, and the system accordingly uses the models to estimate a trip duration. In some embodiments, the real-time model and the selector may be trained using batch machine learning techniques which allow the models to incorporate new trip data as trips complete.

Abstract: A transport service system determines the accuracy of a map matched trajectory using a forward probability algorithm. A transport vehicle on a trip relays location data to the system. The system uses a map of the corresponding area and the location data to calculate an emission probability, the likelihood of a candidate road being associated with a location data point, and a transition probability, the likelihood of a second state occurring after a first state. The joint probability of the emission and transition probabilities is used to determine a total number of zero forward probability occurrences and an average forward probability associated with the trip. These metrics are used to measure the accuracy of the map matching algorithm for the trip.

Abstract: A transport service system determines the accuracy of a map matched trajectory using a forward probability algorithm. A transport vehicle on a trip relays location data to the system. The system uses a map of the corresponding area and the location data to calculate an emission probability, the likelihood of a candidate road being associated with a location data point, and a transition probability, the likelihood of a second state occurring after a first state. The joint probability of the emission and transition probabilities is used to determine a total number of zero forward probability occurrences and an average forward probability associated with the trip. These metrics are used to measure the accuracy of the map matching algorithm for the trip.

Abstract: Technologies are described for utilizing machine learning (“ML”) to adjust operational characteristics of a computing system based upon detected HID activity. Labeled training data is collected with user consent that includes data describing HID activity and data that identifies user activity taking place on a computing device when the data HID activity took place. A ML model is trained using the labeled training data that can receive data describing current HID activity and identify user activity currently taking place on another computing device based upon the current HID activity. The ML model can then select features of the other computing device that are beneficial to the identified user activity. The ML model can then cause one or more operational characteristics of the other computing device to be adjusted based upon the identified user activity, thereby saving valuable computing resources. A UI can also be presented that describes the identified features.

Abstract: A system uses machine models to estimate trip durations or distance. The system trains a historical model to estimate trip duration using characteristics of past trips. The system trains a real-time model to estimate trip duration using characteristics of recently completed trips. The historical and real-time models may use different time windows of training data to predict estimates, and may be trained to predict an adjustment to an initial trip estimate. A selector model is trained to predict whether the historical model, the real-time model, or a combination of the historical and real-time models will more accurately estimate a trip duration, given features associated with a trip duration request, and the system accordingly uses the models to estimate a trip duration. In some embodiments, the real-time model and the selector may be trained using batch machine learning techniques which allow the models to incorporate new trip data as trips complete.

Abstract: A system uses machine models to estimate trip durations or distance. The system trains a historical model to estimate trip duration using characteristics of past trips. The system trains a real-time model to estimate trip duration using characteristics of recently completed trips. The historical and real-time models may use different time windows of training data to predict estimates, and may be trained to predict an adjustment to an initial trip estimate. A selector model is trained to predict whether the historical model, the real-time model, or a combination of the historical and real-time models will more accurately estimate a trip duration, given features associated with a trip duration request, and the system accordingly uses the models to estimate a trip duration. In some embodiments, the real-time model and the selector may be trained using batch machine learning techniques which allow the models to incorporate new trip data as trips complete.

Abstract: For multipathing using a network of overlays, a set of virtual network interfaces (VNICs) corresponding to a physical network interface (PNIC) is created in a first data processing system. A first virtual network interface (VNIC) from the set of VNICs is bound to a virtual machine (VM) executing in a first data processing environment across a data network from the first data processing system. During a data communication with a second data processing system, data is divided into a first portion and a second portion, the first portion using a first path from the first VNIC to the first VM to the second data processing system, and the second portion using a second path from the PNIC to the second data processing system.

Abstract: For multipathing using a network of overlays, a set of virtual network interfaces (VNICs) corresponding to a physical network interface (PNIC) is created in a first data processing system. A first virtual network interface (VNIC) from the set of VNICs is bound to a virtual machine (VM) executing in a first data processing environment across a data network from the first data processing system. During a data communication with a second data processing system, data is divided into a first portion and a second portion, the first portion using a first path from the first VNIC to the first VM to the second data processing system, and the second portion using a second path from the PNIC to the second data processing system.

Abstract: Embodiments of the present invention provide a neutron spectrometry system, comprising a plurality of semiconductor detector portions arranged in close proximity, wherein the detector portions are arranged in at least two non-parallel axes, wherein each detector portion is arranged to output a detection signal indicative of energy deposited in the detector portion by ionising particles induced in the device by incident neutrons, and a control unit arranged to receive the plurality of detection signals, and to allocate detection signals to one or more of a plurality of channels based on a number of substantially coincident detection signals for determining a spectrum of incident neutrons based thereon.

Abstract: For multipathing using a network of overlays, a set of virtual network interfaces (VNICs) corresponding to a physical network interface (PNIC) is created in a first data processing system. A first virtual network interface (VNIC) from the set of VNICs is bound to a virtual machine (VM) executing in a first data processing environment across a data network from the first data processing system. During a data communication with a second data processing system, data is divided into a first portion and a second portion, the first portion using a first path from the first VNIC to the first VM to the second data processing system, and the second portion using a second path from the PNIC to the second data processing system.

Abstract: An electronic device is provided in the disclosure. The electronic device includes a body, a display unit, and a projecting unit; where the body comprises a first surface and a second surface which intersects the first surface, where the second surface supports the body in a standing position on a support surface at a first angle which is not zero degrees between the second surface and the support surface; the display unit is disposed on the first surface and displays content; and the projecting unit is supported by the body and projects content externally when the body stands on the support surface.

Abstract: A method and apparatus is disclosed herein for load balancing and dynamic scaling for a storage system. In one embodiment, an apparatus comprises a load balancer to direct read requests for objects, received from one or more clients, to at least one of one or more cache nodes based on a global ranking of objects, where each cache node serves the object to a requesting client from its local storage in response to a cache hit or downloads the object from the persistent storage and serves the object to the requesting client in response to a cache miss, and a cache scaler communicably coupled to the load balancer to periodically adjust a number of cache nodes that are active in a cache tier based on performance statistics measured by one or more cache nodes in the cache tier.