Abstract: The present invention relates to a molecular marker, a specific primer pair, and an identification method of the high-quality Ganoderma lucidum strain HMGIM-M624. The high-quality Ganoderma lucidum strain HMGIM-M624 was preserved in Guangdong Microbial Culture Collection Center (address: 5th Floor, No. 59 Building of No. 100 Yard, Mid. Xianlie Road, Guangzhou City) with the preservation number of GDMCC No: 60889 on Nov. 7, 2019. The molecular marker is an InDel molecular marker. The high-quality Ganoderma lucidum strain HMGIM-M624 has a base deletion of CATGCTGTA at the 246451th-246460th site of the chromosome sca34. The present invention provides reagents for detecting the molecular marker of the high-quality Ganoderma lucidum strain HMGIM-M624. The reagents can distinguish the high-quality Ganoderma lucidum strain HMGIM-M624 from the other 11 G. lucidum strains for commercial cultivation, thus specifically identifying the high-quality Ganoderma lucidum strain HMGIM-M624.

Abstract: The embodiments of the present invention disclose a line protection method and a related apparatus for a flexible grounding system of a power distribution network. The method comprises outputting a first compensation voltage by a voltage source of a flexible grounding device of each line of a target power distribution network in the case where a ground fault occurs in the target power distribution network; starting to calculate the zero-sequence impedance of each line in real time by the zero-sequence impedance protection device; after the first compensation voltage is output for a first output duration, outputting a second compensation voltage; calculating the zero-sequence impedance of each line in real time by the line zero-sequence impedance protection device; determining a fault line by the zero-sequence impedance protection device, and cutting off the fault line to isolate the ground fault after a trip for a preset time delay.

Abstract: The embodiments of the present invention disclose a method and a related device for locating a ring power network fault. The method comprises: expanding in each ring line segment with an arbitrary point as a coordinate origin in a ring power network to construct a plurality of reference coordinate systems; acquiring monitoring point coordinate information and wavehead arrival time information of each of the monitoring point positions; acquiring assumed fault point information; acquiring a traveling wave propagation speed; constructing a plurality of distributed fault location equation sets according to the monitoring point coordinate information, the wavehead arrival time information, the assumed fault point information and the traveling wave propagation speed in each of the reference coordinate systems; and determining an actual fault position according to a solution result of each of the distributed fault location equation sets and the assumed fault section.

Abstract: A method and a system for locating a fault of a complex power line are provided. Several monitoring points on the complex power line can be obtained by providing several traveling wave head monitoring sensors on the complex power line. The complex power line is transformed into coordinates in conjunction with an arrival moment and a location corresponding to the monitoring points, thereby establishing a trunk line fault-locating equation related to a line location, the arrival moment, and a traveling wave propagation speed, from which a first fault point can thus be derived simply and quickly. If a fault point is located on a branch line, a branch line fault-locating equation is then established to derive a second fault point. Furthermore, the first fault point and the second fault point serve as a basis for deriving a target fault point of the complex power line.

Abstract: An online system distributes content items describing events to one or more users of the online system. The online system receives, an event from a third-party system, the event associated with one or more content items. The online system determines a vector representation of users based on a first neural network and a vector representation of an event based on a second neural network. The online system jointly trains the first neural network and second neural network based on labels describing user entity relationships. The online system determines a likelihood of attendance of an event by a user based on a distance between the vector representation of the user and the vector representation of the entity. The online system provides the content associated with the event to users of the online system based on the likelihood of attendance of the event by the users.

Abstract: An online system causes a graphical user interface to display at a client device. The graphical user interface includes a story field that displays ephemeral content items which are created within a threshold time period and are automatically removed after the time period. The online system uses a computer learned model to rank the selected ephemeral content items for display. The display of a set of ephemeral content items is associated with a session. The computer learned model is trained with sample sets that use an entire past session that includes a plurality of ephemeral content items. The computer model proposes a ranked order of the content items in the past session. Based on the past user actions performed on the past content items, a normalized discounted cumulative gain is determined for the past session. The computer model is trained to optimize the normalized discounted cumulative gain.

Abstract: A social networking system validates content items based on policies regarding use of content items. The social networking system balances the validation process of content items with at least two validation queues, each having a priority value and some number of content items. A validation queue is selected based on the priority values. From the selected validation queue, a content item is determined to be valid or invalid. A validation efficiency for each queue is calculated based on the validated content items from each queue. The social networking system dynamically adjusts the priority value of each queue based on the validation efficiencies. The dynamic adjustment may be periodic. The social networking system may withhold invalid content items from users.

Abstract: The disclosed embodiments provide a method and system for performing regularized model adaptation for in-session recommendations. During operation, the system obtains, from a server, a first global version of a statistical model. During a first user session with a user, the system improves a performance of the statistical model by using the first global version to output one or more recommendations to the user and using the first global version and user feedback from the user to create a first personalized version of the statistical model. At an end of the first user session, the system transmits an update containing a difference between the first personalized version and the first global version to the server for use in producing a second global version of the statistical model by the server.

Abstract: A system and method for managing asynchronously receiving updates and merging updates into global versions of a statistical model using version control are disclosed. During operation, the system transmits a first global version of a statistical model to a set of client computer systems. Next, the system obtains, from a first subset of the client computer systems, a first set of updates to the first global version. The system then merges the first set of updates into a second global version of the statistical model. Finally, the system transmits the second global version to the client computer systems asynchronously from receiving a second set of updates to the first and/or second global versions from a second subset of the client computer systems.

Abstract: An online system distributes content items describing events to one or more users of the online system. The online system receives, an event from a third-party system, the event associated with one or more content items. The online system determines a vector representation of users based on a first neural network and a vector representation of an event based on a second neural network. The online system jointly trains the first neural network and second neural network based on labels describing user entity relationships. The online system determines a likelihood of attendance of an event by a user based on a distance between the vector representation of the user and the vector representation of the entity. The online system provides the content associated with the event to users of the online system based on the likelihood of attendance of the event by the users.

Abstract: Learning to rank modeling in the context of an on-line social network is described. A learning to rank model can learn from pairwise preference (e.g., job posting A is more relevant than job posting B for a particular member profile) thus directly optimizing for the rank order of job postings for each member profile. With ranking position taken into consideration during training, top-ranked job postings may be treated by a recommendation system as being of more importance than lower-ranked job postings. In addition, a learning to rank approach may also result in an equal optimization across all member profiles and help minimize bias towards those member profiles that have been paired with a larger number of job postings.

Abstract: Learning to rank modeling in the context of an on-line social network is described. A learning to rank model can learn from pairwise preference (e.g., job posting A is more relevant than job posting B for a particular member profile) thus directly optimizing for the rank order of job postings for each member profile. With ranking position taken into consideration during training, top-ranked job postings may be treated by a recommendation system as being of more importance than lower-ranked job postings. In addition, a learning to rank approach may also result in an equal optimization across all member profiles and help minimize bias towards those member profiles that have been paired with a larger number of job postings.

Abstract: The disclosed embodiments provide a system and method for performing version control for asynchronous distributed machine learning. During operation, the system transmits a first global version of a statistical model to a set of client computer systems. Next, the system obtains, from a first subset of the client computer systems, a first set of updates to the first global version. The system then merges the first set of updates into a second global version of the statistical model. Finally, the system transmits the second global version to the client computer systems asynchronously from receiving a second set of updates to the first and/or second global versions from a second subset of the client computer systems.

Abstract: The disclosed embodiments provide a method and system for performing regularized model adaptation for in-session recommendations. During operation, the system obtains, from a server, a first global version of a statistical model. During a first user session with a user, the system improves a performance of the statistical model by using the first global version to output one or more recommendations to the user and using the first global version and user feedback from the user to create a first personalized version of the statistical model. At an end of the first user session, the system transmits an update containing a difference between the first personalized version and the first global version to the server for use in producing a second global version of the statistical model by the server.

Abstract: Techniques are provided for determining one or more recommendations to a user based on job change indications. In one approach, job change indications are tracked to generate associations between job information, such as an association between a user's job title at one time and the user's job title at a later time or an association between a user's job skill and a job skill of a job position to which the user applied. A mapping (such as a translation model) is updated based on the associations. The mapping is used to determine one or more recommendations for a particular user, where the recommendations may be job openings for the particular user, candidate users for the particular user to recruit, suggestions to edit a profile of the particular user, or possible query expansions for a query of the particular user.

Abstract: Nonlinear featurization of decision trees for linear regression modeling in the context of an on-line social network is described. A computer-implemented converter is provided that is capable of reading a decision tree structure that is included in the learning to rank algorithm and convert each path from root to a leaf into an s-expression. The s-expressions are used as additional features to train a logistic regression model.

Abstract: Learning to rank modeling in the context of an on-line social network is described. A learning to rank model can learn from pairwise preference (e.g., job posting A is more relevant than job posting B for a particular member profile) thus directly optimizing for the rank order of job postings for each member profile. With ranking position taken into consideration during training, top-ranked job postings may be treated by a recommendation system as being of more importance than lower-ranked job postings. In addition, a learning to rank approach may also result in an equal optimization across all member profiles and help minimize bias towards those member profiles that have been paired with a larger number of job postings.

Abstract: A system stores member and job posting pairs in a database, which indicate that a job has been presented to a member, that the member has viewed the job posting, and that the member has applied for the job. The system extracts member attributes from a member profile and job attributes from a job posting file, and creates attribute pairs from the extracted member attributes and the extracted job attributes. The system creates an attribute tracking table, and for each job attribute in the attribute tracking table, create ratios relating to jobs that have been shown to members, jobs that members have viewed, and jobs for which members have applied. The system also determines trends between particular member attributes and particular job attributes. The ratios and trends are provided to a job recommendation engine that uses them in determining jobs to recommend to members.

Abstract: Disclosed is an alanine racemase chiral binaphthol derivative having the ability to recognize amino alcohols selectively on the basis of chirality and transform amino acids from an L-form into a D-form. Methods for the optical resolution of amino acid or amino alcohol and for the optical transformation of D- and L-forms of amino acids using the binaphthol derivative are also provided.

Abstract: Disclosed is an alanine racemase chiral binaphthol derivative having the ability to recognize amino alcohols selectively on the basis of chirality and transform amino acids from an L-form into a D-form. Methods for the optical resolution of amino acid or amino alcohol and for the optical transformation of D- and L-forms of amino acids using the binaphthol derivative are also provided.