Abstract: The invention relates to a terahertz dual-comb spectrum stabilization system, including an optical loop for coupling a first optical frequency comb signal (OFCS) to a second OFCS; and a frequency mixer for performing frequency mixing on the coupled first OFCS and second OFCS to generate a dual-comb signal. A local oscillator signal end of the frequency mixer is connected to a multi-chain frequency multiplication chain, the multi-chain frequency multiplication chain is used for performing frequency multiplication processing on a radio frequency signal, a local oscillator frequency in terahertz frequency range is generated the local oscillator frequency in terahertz frequency range is respectively mixed with the first OFCS and the second OFCS in the frequency mixer, and two down-converted OFCSs are generated in microwave frequency range. One line of comb teeth of the two down-converted OFCSs are respectively locked to obtain a stable multi-heterodyne dual-comb spectrum.

Abstract: An optimized design method and system for carbon dioxide geological storage parameters of a depleted gas reservoir is provided, including: collecting data of a depleted gas reservoir in which carbon dioxide geological storage is to be carried out, and establishing a numerical simulation model of the depleted gas reservoir; carrying out fitting of production performance history to obtain current state information; simulating and predicting production performance data after carbon dioxide injection into depleted oil and gas reservoirs under different combinations of well pattern parameters and injection parameters using a numerical simulation technology; calculating a parameter value representing a uniform pressure rise according to the production performance data; updating well pattern parameters and injection parameters using a genetic algorithm; repeating above steps until an iterative convergence condition is met; and determining an optimal combination of carbon dioxide injection process parameters accordin

Abstract: A crystal form of a pyridine nitrogen oxide compound and the use thereof. Specifically, the present invention relates to a crystal form of a compound of formula (I), a pharmaceutical composition and the use thereof.

Abstract: A drive system for an industrial handling vehicle comprises an electric motor, a first pump, for example, a plunger pump, and a second pump, for example, a gear pump. The first pump is configured to provide hydraulic power for a travelling device of the industrial handling vehicle. The first pump and the travelling device are in a first hydraulic circuit. The second pump is configured to provide hydraulic power for a brake device and/or a steering device and/or a handling device of the industrial handling vehicle. The second pump, the brake device, the steering device, and the handling device are in a second hydraulic circuit isolated from the first hydraulic circuit. The electric motor is operatively coupled to both the first pump and the second pump, to drive them to operate.

Abstract: A bot traffic detection system detects scripted network traffic. The bot traffic detection system may use a one-sided unsupervised machine learning technique to estimate distributions for human, non-scripted traffic (clean distributions). The clean distributions may be dynamically updated based on the latest traffic patterns. To estimate the clean distributions the bot traffic detection system may identify, for a certain subset of network traffic, feature values of the certain subset of network traffic that do not include bot traffic (clean buckets). Using clean traffic may provide more robust and stable behavior that can be tracked over time. Using the clean distributions, the bot traffic detection system may generate a rules table that indicates a likelihood that network traffic with a given combination of feature values is scripted network traffic. The bot traffic detection system may apply the rules table in real time to identify scripted network traffic.

Abstract: Disclosed is an all-electric fixed underfloor wheel sets lathe, comprising: a frame, two rails provided on the frame, two lathe tools provided on the frame and two sets of wheel driving devices provided on the frame, wherein each set of the wheel driving devices comprises leveling brackets, drive assemblies provided on the leveling brackets, lifting assemblies provided on the leveling brackets and leveling assemblies provided on the leveling brackets, the lifting assemblies and the leveling assemblies are fixedly connected to the frame, the drive assemblies are configured for driving wheels to rotate and the lifting assemblies are configured for lifting the leveling brackets.

Abstract: The present application relates to a Favipiravir compound represented by Formula I, a geometric isomer, a pharmaceutically acceptable salt, a solvate and/or a hydrate thereof, and a pharmaceutical composition comprising the compound for treating a coronavirus infection.

Abstract: Examples described herein generally relate to a computer device including a memory, and at least one processor configured to evaluate a change to a user interface. The computer device monitor user interactions with the user interface prior to and after a change to the user interface. The monitoring includes collecting result metric data per user. The computer device divides users into a treated group and a control group based on whether each user engages in a particular interaction. The computer device generates a result metric time series for the treated group and a partitioned result metric time series for the control group. The computer device estimates a conditional distribution of the result metric and a counterfactual behavior using a Bayesian machine learning model based on the result metric time series. The computer device determines a treatment effect of the change to the user interface on the result metric data.

Abstract: A bot traffic detection system detects scripted network traffic. The bot traffic detection system may use a one-sided unsupervised machine learning technique to estimate distributions for human, non-scripted traffic (clean distributions). The clean distributions may be dynamically updated based on the latest traffic patterns. To estimate the clean distributions the bot traffic detection system may identify, for a certain subset of network traffic, feature values of the certain subset of network traffic that do not include bot traffic (clean buckets). Using clean traffic may provide more robust and stable behavior that can be tracked over time. Using the clean distributions, the bot traffic detection system may generate a rules table that indicates a likelihood that network traffic with a given combination of feature values is scripted network traffic. The bot traffic detection system may apply the rules table in real time to identify scripted network traffic.

Abstract: The present application relates to a Favipiravir compound represented by Formula I, a geometric isomer, a pharmaceutically acceptable salt, a solvate and/or a hydrate thereof, and a pharmaceutical composition comprising the compound for treating a coronavirus infection.

Abstract: The present application relates to a Favipiravir compound represented by Formula I, a geometric isomer, a pharmaceutically acceptable salt, a solvate and/or a hydrate thereof, and a pharmaceutical composition comprising the compound for treating a coronavirus infection.

Abstract: Examples described herein generally relate to a computer device including a memory, and at least one processor configured to evaluate a change to a user interface. The computer device monitor user interactions with the user interface prior to and after a change to the user interface. The monitoring includes collecting result metric data per user. The computer device divides users into a treated group and a control group based on whether each user engages in a particular interaction. The computer device generates a result metric time series for the treated group and a partitioned result metric time series for the control group. The computer device estimates a conditional distribution of the result metric and a counterfactual behavior using a Bayesian machine learning model based on the result metric time series. The computer device determines a treatment effect of the change to the user interface on the result metric data.

Abstract: A method for measuring quality of a service running on a terminal includes sending, by the terminal, a measurement request message to a network management device, where the measurement request message requests the network management device to measure the quality of the service, receiving, by the terminal, measurement configuration information from the network management device in response to the measurement request message, coloring a packet of a service stream within a preset measurement period according to the measurement configuration information to obtain a colored packet, and sending, by the terminal, at least one of quantity information or time stamp information of the colored packet to the network management device. Hence, the quality of the service running on the terminal is comprehensively measured.

Abstract: Methods and systems for modeling host behavior in a network include determining a first probability function for observing each of a set of process-level events at a first host based on embedding vectors for the first event and the first host. A second probability function is determined for the first host issuing each of a set of network-level events connecting to a second host based on embedding vectors for the first host and the second host. The first and second probability functions are maximized to determine a set of likely process-level and network-level events for the first host. A security action is performed based on the modeled host behavior.

Abstract: Methods and systems for detecting host community include modeling a target host's behavior based on historical events recorded at the target host. One or more original peer hosts having behavior similar to the target host's behavior are found by determining a distance in a latent space that embeds the historical events between events of the target host and events of the one or more original peer hosts. A security management action is performed based on behavior of the target host and the determined one or more original peer hosts.

Abstract: Systems and methods for determining a risk level of a host in a network include modeling a target host's behavior based on historical events recorded at the target host. One or more original peer hosts having behavior similar to the target host's behavior are determined. An anomaly score for the target host is determined based on how the target host's behavior changes relative to behavior of the one or more original peer hosts over time. A security management action is performed based on the anomaly score.

Abstract: Systems and methods for determining a risk level of a host in a network include modeling a target host's behavior based on historical events recorded at the target host. One or more original peer hosts having behavior similar to the target host's behavior are determined. An anomaly score for the target host is determined based on how the target host's behavior changes relative to behavior of the one or more original peer hosts over time. A security management action is performed based on the anomaly score.

Abstract: Methods and systems for modeling host behavior in a network include determining a first probability function for observing each of a set of process-level events at a first host based on embedding vectors for the first event and the first host. A second probability function is determined for the first host issuing each of a set of network-level events connecting to a second host based on embedding vectors for the first host and the second host. The first and second probability functions are maximized to determine a set of likely process-level and network-level events for the first host. A security action is performed based on the modeled host behavior.

Abstract: Methods and systems for detecting host community include modeling a target host's behavior based on historical events recorded at the target host. One or more original peer hosts having behavior similar to the target host's behavior are found by determining a distance in a latent space that embeds the historical events between events of the target host and events of the one or more original peer hosts. A security management action is performed based on behavior of the target host and the determined one or more original peer hosts.