Abstract: An iridium/iridium oxide electrode for quantitatively detecting pH in a sulfide ion environment, and a preparation method and use thereof are provided. In the present disclosure, a sensitive layer of the iridium/iridium oxide electrode is coated with a sulfonated polyether ether ketone/ionic liquid (SPEEK/IL) sandwich structure. The SPEEK/IL sandwich structure includes two layers of SPEEK/SiOx/IL composite film and one layer of SPEEK film. The SPEEK film is arranged on an outermost layer. The SPEEK/IL sandwich structure covered on a surface of the electrode has an isolation effect on ions other than hydrogen ions, and especially shows a desirable isolation effect on sulfide ions with a smaller ion size. In this way, the electrode is not easily disturbed by redox substances in a complex aqueous environment.

Abstract: A reference electrode with a sulfonated polyether ether ketone/ionic liquid (SPEEK/IL) sandwich structure, and a preparation method and use thereof are provided. In the reference electrode, an end of a reference electrode main body is provided with the SPEEK/IL sandwich structure; the SPEEK/IL sandwich structure includes two layers of SPEEK/IL composite film and one layer of SPEEK film, and the SPEEK film is arranged at an outermost layer of the SPEEK/IL sandwich structure.

Abstract: An iridium/iridium oxide electrode for quantitatively detecting pH in a sulfide ion environment, and a preparation method and use thereof are provided. In the present disclosure, a sensitive layer of the iridium/iridium oxide electrode is coated with a sulfonated polyether ether ketone/ionic liquid (SPEEK/IL) sandwich structure. The SPEEK/IL sandwich structure includes two layers of SPEEK/SiOx/IL composite film and one layer of SPEEK film. The SPEEK film is arranged on an outermost layer. The SPEEK/IL sandwich structure covered on a surface of the electrode has an isolation effect on ions other than hydrogen ions, and especially shows a desirable isolation effect on sulfide ions with a smaller ion size. In this way, the electrode is not easily disturbed by redox substances in a complex aqueous environment.

Abstract: A method, an apparatus, a computer storage medium and a computer device for geological characteristic constraint-based reservoir parameter prediction are provided. The method includes: selecting dominant seismic attributes according to the relevance between different types of seismic attributes of a target stratum and reservoir parameters (S100); on the basis of the dominant seismic attributes, classifying seismic waveforms of the target stratum by a preset waveform classification network model and according to waveform features, so as to obtain a waveform classification result (S200); taking the waveform classification result as a constraint, and constructing different deep neural network models corresponding to different geological characteristics (S300); fusing different trained deep neural network models into a set of spatially varying neural network prediction model (S500); and predicting the reservoir parameters of the target stratum.

Abstract: Network site users can be selected to receive a communication based on a network site event, such as incomplete registration. A hybrid user interaction machine learning scheme can select a portion of the selected users based on user interaction estimates and network sampling data. The electronic document sent to the users can have portions that undergo two-pass ranking for ordering of content items to be included in the electronic document, such as an email.

Abstract: Network site users can be selected to receive a communication based on a network site event, such as incomplete registration. A hybrid user interaction machine learning scheme can select a portion of the selected users based on user interaction estimates and network sampling data. The electronic document sent to the users can have portions that undergo two-pass ranking for ordering of content items to be included in the electronic document, such as an email.

Abstract: Network site users can be selected to receive a communication based on a network site event, such as incomplete registration. A hybrid user interaction machine learning scheme can select a portion of the selected users based on user interaction estimates and network sampling data. The electronic document sent to the users can have portions that undergo two-pass ranking for ordering of content items to be included in the electronic document, such as an email.

Abstract: Network site users can be selected to receive a communication based on a network site event, such as incomplete registration. A hybrid user interaction machine learning scheme can select a portion of the selected users based on user interaction estimates and network sampling data. The electronic document sent to the users can have portions that undergo two-pass ranking for ordering of content items to be included in the electronic document, such as an email.

Abstract: The disclosure includes a system and method for detecting fine grain copresence between users. The system includes a processor and a memory storing instructions that when executed cause the system to: transmit a wakeup signal to a plurality of devices based on coarse grain location information; send a request to a first device of the plurality of devices to transmit a token using a first communication technology to determine fine grain copresence; receive a first token acknowledgment from a first subset of the plurality of devices; send a request to a second device of the first subset of the plurality of devices to transmit the token using a second communication technology to determine fine grain copresence; receive a second token acknowledgment from a second subset of the plurality of devices; and refine copresence based on receiving the first and second token acknowledgment.

Abstract: The disclosure includes a system and method for detecting fine grain copresence between users. The system includes a processor and a memory storing instructions that when executed cause the system to receive user input regarding copresence detection settings for a first user device, the copresence detection settings comprising a location and/or a user access control list, and determine a current location of the first user device. The system may determine whether copresence detection of the first user device is enabled at the current location based on the copresence detection settings and the current location. Based on determining that copresence detection is enabled, the system may refine copresence and perform an action based on fine grain copresence of the first and second user device.

Abstract: The disclosure includes a system and method for detecting fine grain copresence between users. The system includes a processor and a memory storing instructions that when executed cause the system to: process one or more signals to determine coarse grain location information of a first device and a second device; determine whether the first device and the second device are copresent based on the coarse grain location information; in response to determining that the first device and the second device are copresent based on the coarse grain location information, transmit a signal to the second device to alert the second device to listen for a fine grain copresence token from the first device; and refine copresence based on receiving an indication that the second device has received the fine grain copresence token.

Abstract: Methods and apparatus are directed to geofencing-related heuristics for computing devices. A computing device with a plurality of sensors can receive a plurality of heuristics. Each heuristic can be configured to generate command(s) for the sensors based on one or more heuristic inputs. The heuristic input(s) can include an input related to a geofence. The computing device can receive a plurality of signals from the sensors. The computing device can determine, based on the plurality of signals, an activity class for the computing device. The activity class can specify an activity associated with the computing device. The computing device can select a heuristic from the plurality of heuristics at least based on the activity class. The computing device can execute the selected heuristic to generate the command(s) for the sensors.

Abstract: The present disclosure provides example methods operable by computing device. An example method may include querying a location of a computing device with a first frequency. The method may also include accumulating data on the computing device over a time period. The data may include respective locations of the computing device during the time period with respect to the time of day and at increments based on the first frequency. The method may also include determining a model of the location of the computing device with respect to the time of day based on the accumulated data. The method may also include comparing the current location of the computing device to the model. If the current location of the computing device is consistent with the model, the method may also include querying the location of the computing device with a second frequency.

Abstract: The subject matter of this specification can be implemented in, among other things, a method for determining a wireless access point location. The method includes a step for receiving session data from at least one mobile device, wherein each instance of the received session data includes one or more global positioning system (GPS) data points, one or more sensor data points, and one or more WiFi scan data points, wherein the one or more WiFi scan data points are associated with one or more wireless access points (WAPs). The method also includes a step for calculating a location of at least one of the one or more WAPs using at least a portion from each of the received one or more global positioning system (GPS) data points, the one or more sensor data points, and the one or more WiFi scan data points.

Abstract: The subject matter of this specification can be implemented in, among other things, a method for determining a wireless access point location. The method includes a step for receiving session data from at least one mobile device, wherein each instance of the received session data includes one or more global positioning system (GPS) data points, one or more sensor data points, and one or more WiFi scan data points, wherein the one or more WiFi scan data points are associated with one or more wireless access points (WAPs). The method also includes a step for calculating a location of at least one of the one or more WAPs using at least a portion from each of the received one or more global positioning system (GPS) data points, the one or more sensor data points, and the one or more WiFi scan data points.

Abstract: The disclosure includes a system and method for detecting fine grain copresence between users. The system includes a processor and a memory storing instructions that when executed cause the system to: process one or more signals to determine coarse grain location information of a first device and a second device; determine whether the first device and the second device are copresent based on the coarse grain location information; in response to determining that the first device and the second device are copresent based on the coarse grain location information, transmit a signal to the second device to alert the second device to listen for a fine grain copresence token from the first device; and refine copresence based on receiving an indication that the second device has received the fine grain copresence token.

Abstract: Methods and apparatus are directed to geofencing-related heuristics for computing devices. A computing device with a plurality of sensors can receive a plurality of heuristics. Each heuristic can be configured to generate command(s) for the sensors based on one or more heuristic inputs. The heuristic input(s) can include an input related to a geofence. The computing device can receive a plurality of signals from the sensors. The computing device can determine, based on the plurality of signals, an activity class for the computing device. The activity class can specify an activity associated with the computing device. The computing device can select a heuristic from the plurality of heuristics at least based on the activity class. The computing device can execute the selected heuristic to generate the command(s) for the sensors.

Abstract: Certain implementations of the disclosed technology may include systems, methods, and computer-readable media for wireless geo-fencing. According to an example implementation, a method is provided that may include receiving a set of network identification addresses for a plurality of wireless access points (AP) associated with at least one geo-fence, generating filter coefficients based on the received set of network identification addresses, receiving a network identification address for an in-range wireless access point, generating a test representation of the received network identification address, and determining whether the test representation corresponds a potential member of the received set of network identification addresses associated with the at least one geo-fence.