Abstract: The present disclosure provides a method for manufacturing a thin film transistor, a thin film transistor, and a display apparatus. The method for manufacturing a thin film transistor includes: forming a gate layer on a substrate; forming a gate insulating layer on the gate layer; forming an active layer on the gate insulating layer; forming a source/drain layer on the active layer; and performing a plasma bombardment treatment on a surface of the active layer on which the source/drain layer is formed, and controlling the plasma bombardment treatment to be performed at a gas flow rate of 4K sccm to 70K sccm, at a pressure of 600 mTorr to 1200 mTorr, at a power of 4 KW to 12 KW for a treatment time of 10 s to 60 s.

Abstract: A method and system obtaining positioning data from an object traveling on a plurality of routes; mapping the data into a plurality of points on a digital map; identifying points that are unmatched to the stored route trajectory; obtaining candidate transition points from the unmatched points; aggregating the candidate transition points by applying a clustering algorithm; selecting a first cluster of points and a plurality of second clusters of points, determining a confidence level that the first cluster of points are transition points indicating a transition between the routes, classifying the first cluster of points as a first plurality of traveling points having a first direction in response to the confidence being below a threshold confidence and automatically adjusting the stored route trajectory to indicate that the first cluster of points are on the route trajectory.

Abstract: An amide derivative inhibitor and a preparation method and an application thereof are provided. Specifically, a compound shown in general formula (I), a preparation method for same, a pharmaceutical composition containing the compound, and an application of same as an ASK inhibitor are provided. The disclosed compounds can be used for the treatment of neurodegenerative diseases, cardiovascular diseases, inflammation, autoimmune and metabolic diseases. Each of the substituents in the general formula (I) is as defined in the description.

Abstract: A system and method are provided for identifying security risks in a computer system. The system includes an event stream generator configured to collect system event data from the computer system. The system further includes a query device configured to receive query requests that specify parameters of a query. Each query request includes at least one anomaly model. The query request and the anomaly model are included in a first syntax in which a system event is expressed as {subject-operation-object}. The system further includes a detection device configured to receive at least one query request from the query device and continuously compare the system event data to the anomaly models of the query requests to detect a system event that poses a security risk. The system also includes a reporting device configured to generate an alert for system events that pose a security risk detected by the detection device.

Abstract: Embodiments of the present invention disclose a virtual network device. The virtual network device is configured to implement a broadband network gateway BNG function, and the virtual network device includes a load balance unit and a first forwarding unit; the load balance unit is configured to: receive a first data packet from a first user terminal, determine the first forwarding unit according to the first data packet and a first correspondence, and send the first data packet to the first forwarding unit, where the first correspondence indicates mapping from the first user terminal to the first forwarding unit; and the first forwarding unit is configured to receive and forward the first data packet. In addition, the embodiments of the present invention further disclose a method for implementing load sharing in a virtual network device.

Abstract: A frequency multiplier, a digital phase-locked loop circuit, and a frequency multiplication method, where the frequency multiplier includes a clock controller configured to: receive an output signal from a time-to-digital converter in the digital phase-locked loop circuit, and generate a control signal based on a duty cycle error of the output signal, a clock calibration circuit configured to: receive a reference clock signal, calibrate a duty cycle of the reference clock signal based on the control signal, and output a calibrated clock signal, and a clock frequency multiplier configured to: receive the calibrated clock signal, multiply a frequency of the calibrated clock signal, and output a frequency multiplied signal to the time-to-digital converter.

Abstract: Methods, systems, and storage components for utilizing a deep neural network(s) for classifying at least one medical image. A deep neural network (DNN) can be configured by an image processing component to contain at least one revived neuron, where the revived neuron has an adjusted value based on a focus-learning function that is transferred to the DNN by the image processing component, where the focus-learning function provides the adjustment by updating the DNN with data that contains a corrected classification with respect to at least one normal image sample derived from a medical image, and where the correction is based on the focus-learning function comparing an annotation associated with an abnormal image sample derived from the medical image to another annotation associated with the at least one normal image.

Abstract: Provided herein are compounds, derivatives thereof, composition comprising one or more of said compounds and derivatives, and methods for prevention and/or treatment of microbial infections and/or related diseases or conditions. The present compounds and/or derivatives thereof can be represented by Formula (II): The present methods include administering to a subject an effective amount of one or more compounds of Formula (II). In one embodiment, said microbial infections are bacterial infections. More specifically, said bacterial infections are staphylococcal infections.

Abstract: Systems and a method are provided. A system includes a Temporal Behavior Query Language (TBQL) server having a processor and a memory operably coupled to the processor. The TBQL server configured to construct a TBQL query using a grammar inference technique based on syntactic sugar to expedite query construction. The TBQL server is further configured to execute the TBQL query to generate TBQL query results.

Abstract: Various examples are disclosed for forecasting resource usage and computing capacity utilizing an exponential decay. In some examples, a computing environment can obtain usage measurements from a data stream over a time interval, where the usage measurements describe utilization of computing resource. The computing environment can generate a weight function for individual ones of the usage measurements, where the weight function exponentially decays the usage measurements based on a respective time period at which the usage measurements were obtained. The computing environment can forecast a future capacity of the computing resources based on the usage measurements and the weight function assigned to the individual ones of the usage measurements. The computing environment can further upgrade a forecast engine to use the exponential decay without resetting the forecast engine or its memory.

Abstract: Techniques for neural network training are provided. One computer-implemented method comprises: obtaining, by an electronic device operatively coupled to a processing unit, based on a similarity between two images, a first image and a second image. The computer-implemented method also comprises training, by the electronic device, a neural network based on the first image and the second image such that a distance between a first vector and a second vector generated respectively from the first image and the second image in the trained neural network is associated with the similarity.

Abstract: A method to train a machine learning model for in-vehicle air quality control in a knowledge-based system, executed by one or more computer processors, includes collecting data related to in-vehicle air quality from a plurality of probe cars where the data is collected by various on-board systems in each probe car. The method includes correlating the data related to in-vehicle air quality from each probe car with air quality measurements from each probe car, where the correlation is used to update the machine learning model. The method includes determining a situation when an in-vehicle air quality measurement of the air quality measurements is above a pre-determined in-vehicle air quality level and determining instructions for actions by one or more of the one or more on-board systems in each of the probe cars to maintain an in-vehicle air quality level at or below the pre-determined in-vehicle air quality level.

Abstract: Embodiments are described for minimizing a wait time for a rider after sending a ride request for a vehicle. An example computer-implemented method includes receiving a ride request, the request being for travel from a starting location to a zone in a geographic region during a specified timeslot. The method further includes predicting travel demand based on a number of ride requests in the zone during the specified timeslot. The method further includes requesting transport of one or more vehicles to the zone in response to the predicted number of ride requests when the travel demand is predicted to exceed a number of vehicles in the zone during the specified timeslot.

Abstract: Embodiments are described for minimizing a wait time for a rider after sending a ride request for a vehicle. An example computer-implemented method includes receiving a ride request, the request being for travel from a starting location to a zone in a geographic region during a specified timeslot. The method further includes predicting travel demand based on a number of ride requests in the zone during the specified timeslot. The method further includes requesting transport of one or more vehicles to the zone in response to the predicted number of ride requests when the travel demand is predicted to exceed a number of vehicles in the zone during the specified timeslot.

Abstract: Embodiments of the present invention disclose a virtual network device. The virtual network device is configured to implement a broadband network gateway BNG function, and the virtual network device includes a load balance unit and a first forwarding unit; the load balance unit is configured to: receive a first data packet from a first user terminal, determine the first forwarding unit according to the first data packet and a first correspondence, and send the first data packet to the first forwarding unit, where the first correspondence indicates mapping from the first user terminal to the first forwarding unit; and the first forwarding unit is configured to receive and forward the first data packet. In addition, the embodiments of the present invention further disclose a method for implementing load sharing in a virtual network device.

Abstract: Computational methods and systems that proactively manage usage of computational resources of a distributed computing system are described. A sequence of metric data representing usage of a resource is detrended to obtain a sequence of non-trendy metric data. Stochastic process models, a pulse wave model and a seasonal model of the sequence of non-trendy metric data are computed. When a forecast request is received, a sequence of forecasted metric data is computed over a forecast interval based on the estimated trend and one of the pulse wave or seasonal model that matches the periodicity of the sequence of non-trendy metric data. Alternatively, the sequence of forecasted metric data is computed based on the estimated trend and the stochastic process model with a smallest accumulated residual error. Usage of the resource by virtual objects of the distributed computing system may be adjusted based on the sequence of forecasted metric data.

Abstract: An interconnection unit includes a first connector configured to be coupled to an electronic device. There is a second connector configured to be coupled to a power station and to provide a path to the electronic device via the first connector. There is a low pass filter coupled between the first connector and the second connector and configured to allow the electronic device to receive power from the power station while maintaining data security of the electronic device.

Abstract: Computational methods and systems that proactively manage usage of computational resources of a distributed computing system are described. A sequence of metric data representing usage of a resource is detrended to obtain a sequence of non-trendy metric data. Stochastic process models, a pulse wave model and a seasonal model of the sequence of non-trendy metric data are computed. When a forecast request is received, a sequence of forecasted metric data is computed over a forecast interval based on the estimated trend and one of the pulse wave or seasonal model that matches the periodicity of the sequence of non-trendy metric data. Alternatively, the sequence of forecasted metric data is computed based on the estimated trend and the stochastic process model with a smallest accumulated residual error. Usage of the resource by virtual objects of the distributed computing system may be adjusted based on the sequence of forecasted metric data.

Abstract: Provided herein are compounds, derivatives thereof, composition comprising one or more of said compounds and derivatives, and methods for prevention and/or treatment of microbial infections and/or related diseases or conditions. The present compounds and/or derivatives thereof can be represented by Formula (II): The present methods include administering to a subject an effective amount of one or more compounds of Formula (II). In one embodiment, said microbial infections are bacterial infections. More specifically, said bacterial infections are staphylococcal infections.

Abstract: A method and system of detecting abnormalities captured in a medical image are provided. A medical image is received having one or more lesions and one or more interferences. One or more convolution layers are applied to the medical image to create one or more feature maps, respectively. A region proposal network (RPN) is applied to each of the one or more feature maps to create a bounding box around each lesion and interference. For each bounding box, an object in the bounding box is classified as a lesion, an interference, or a background. Each object that is incorrectly classified as a lesion but determined to be an interference, is stored as a hard-negative example to be part of a training set for a next application of the RPN.