prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: How does the scoring engine generate a stream of content for the channel?

Read the following text and answer briefly.

Context 1: A system and method for generating a stream of content for a channel  The channel application includes a content categorizer, a scoring engine and a channel engine  The content categorizer categorizes new content items received from heterogeneous data sources  The channel engine identifies a channel category for a user based at least in part on at least one of a historical trend and a user activity  The scoring engine queries the new content items based on the channel category and at least one other channel attribute  The scoring engine retrieves candidate content items that include the channel category and the other channel attribute  The scoring engine then generates a stream of content from the candidate content items for the channel 
This application claims priority under 35 USC §120 to U S  application Ser  No  13/225,209, entitled, “Generating a Stream of Content for a Channel,” filed on Sep  2, 2011, and claims priority under 35 USC §119(e) to U S  Application No  61/424,636, entitled “Scoring Stream Items with Models Based on User Interests” filed Dec  18, 2010, the entireties of which are herein incorporated by reference 
The specification relates to a system and method for generating a stream of content for a channel  In particular, the specification relates to generating a stream of content for a channel based on user interests and historical trends 
Many consumers of digital media have two somewhat contradictory goals: keep apprised of information in the areas they already find interesting and discover new content that is also enjoyable  Keeping apprised of information can become burdensome in the digital age because there is so much information  Hence, there is a need to present the best and most relevant information, without overwhelming the consumer  Furthermore, consumers have varied interests depending on the time of a year or a day  As a result, there is also a need to cater to the time dependent changes in the consumer's interests while presenting information  Similarly, discovering new content is difficult when the consumer is overburdened with existing content 
Prior attempts to solve these problems allow consumers to create personalized sections in feed aggregation websites that are defined by keywords  Often, these personalized sections present any item that includes the keywords even though the item is not of interest to the consumer, per se  In another method, consumers are allowed to manually subscribe to Really Simple Syndication (RSS) feeds from multiple websites  This method often leads to the consumer viewing multiple items which contain redundant information 
In some examples, the specification describes a system and method for generating a stream of content for a channel using a channel application  The channel application includes a processing unit, a model generation engine, a scoring engine, a collaborative filtering engine, a content categorizer, a channel engine, and a user interface engine  The model generation engine generates a model that is used to determine suggestions for channels  The content categorizer categorizes new content items received from heterogeneous data sources  The channel engine identifies a channel category for a user based on at least one of a historical trend and a user activity  The historical trend is at least one of an increase in a number of new content items for a content category, an increase in a number of times one of the new content items is accessed and an event  A scoring engine queries the new content items based on the channel category and at least one other channel attribute  The scoring engine receives candidate content items that include the channel category and the at least one other channel attribute  The scoring engine then generates a stream of content from the candidate content items for the channel  The scoring engine transmits the stream of content to the channel engine, which generates a channel 
In one embodiment, the user interface engine generates a user interface for the user to define the channel category and the channel attribute  The scoring engine queries the new content items based on the user defined channel category and channel attribute and then generates the stream of content  In another embodiment, the channel engine enables the user to subscribe to an existing channel 
In one embodiment, the channel engine enables the user to share the channel with at least one of a friend of the user, a community, a group, and an internet user 
The specification is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings in which like reference numerals are used to refer to similar elements 
FIG  1A is a high-level block diagram illustrating one embodiment of a system for generating a stream of content for a channel 
FIG  1B is a block diagram illustrating one embodiment of a channel application 
FIG  2 is a high-level block diagram illustrating another embodiment of a system for generating a stream of content for a channel 
FIG  3A is a block diagram of one embodiment of the channel engine in more detail 
FIG  3B is a block diagram of one embodiment of the scoring engine in more detail 
FIG  4 is a graphic representation of a user interface that displays the stream of content of a channel 
FIG  5 is a graphic representation of a user interface that allows a user to define or customize a channel 
FIG  6 is a flow diagram of one embodiment of a method for generating a stream of content for a channel 
FIG  7 is a flow diagram of another embodiment of a method for generating a stream of content for a channel 
A system and method for generating a stream of content for a channel is described below  In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the specification  It will be apparent, however, to one skilled in the art that the embodiments can be practiced without these specific details  In other instances, structures and devices are shown in block diagram form in order to avoid obscuring the specification  For example, the specification is described in one embodiment below with reference to user interfaces and particular hardware  However, the description applies to any type of computing device that can receive data and commands, and any peripheral devices providing services 
Some portions of the detailed descriptions that follow are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory  These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art  An algorithm is here, and generally, conceived to be a self consistent sequence of steps leading to a desired result  The steps are those requiring physical manipulations of physical quantities  Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated  It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like 
It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities  Unless specifically stated otherwise as apparent from the following discussion, it is appreciated that throughout the description, discussions utilizing terms such as “processing,” “computing,” “calculating,” “determining,” “displaying,” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices 
The specification also relates to an apparatus for performing the operations herein  This apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer  Such a computer program may be stored in a computer readable storage medium, such as, but not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, flash memories including USB keys with non-volatile memory, or any type of media suitable for storing electronic instructions, each coupled to a computer system bus 
An embodiment can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment containing both hardware and software elements  A preferred embodiment is implemented in software, which includes but is not limited to firmware, resident software, microcode, etc 
Furthermore, an embodiment can take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system  For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device 
FIG  1A illustrates a block diagram of a system 100 for generating a stream of content for a channel according to one embodiment  The system 100 includes user devices 115 a, 115 n that are accessed by users 125 a, 125 n, a social network server 101, a third party server 107, a ratings server 139, an email server 141, an entertainment server 137, and a search server 135  The ratings server 139 includes websites for rating places, people or objects (e g  Google Hotpot)  The entertainment server 137 includes websites with entertaining information, such as news articles  In FIG  1A and the remaining figures, a letter after a reference number, such as “115 a” is a reference to the element having that particular reference number  A reference number in the text without a following letter, such as “115,” is a general reference to any or all instances of the element bearing that reference number  In the illustrated embodiment, these entities are communicatively coupled via a network 105 
In one embodiment, the channel application 103 a is operable on the social network server 101, which is coupled to the network via signal line 104  The social network server 101 also contains a social network application 109 and a social graph 179  Although only one social network server 101 is shown, persons of ordinary skill in the art will recognize that multiple social network servers 101 may be present  A social network is any type of social structure where the users are connected by a common feature, for example, Google+  The common feature includes friendship, family, work, an interest, etc  The common features are provided by one or more social networking systems, such as those included in the system 100, including explicitly-defined relationships and relationships implied by social connections with other online users, where the relationships form a social graph 179  In some examples, the social graph 179 reflects a mapping of these users and how they are related 
In another embodiment, the channel application 103 b is stored on a third-party server 107, which is connected to the network via signal line 106  The third-party server 107 includes software for generating a website (not shown)  In one embodiment, the notifying application generates a user interface that is incorporated into the website  Although only one third-party server 107 is shown, persons of ordinary skill in the art will recognize that multiple third-party servers 107 may be present 
In yet another embodiment, the channel application 103 c is stored on a user device 115 a, which is connected to the network via signal line 108  The user device 115 a is any computing device that includes a memory and a processor, such as a personal computer, a laptop, a smartphone, a cellular phone, a personal digital assistant (PDA), etc  The user 125 a interacts with the user device 115 a via signal line 110  Although only two user devices 115 a, 115 n are illustrated, persons of ordinary skill in the art will recognize that any number of user devices 115 n are available to any number of users 125 n 
The network 105 is a conventional type, wired or wireless, and may have any number of configurations such as a star configuration, token ring configuration or other configurations known to those skilled in the art  Furthermore, the network 105 may comprise a local area network (LAN), a wide area network (WAN) (e g , the Internet), and/or any other interconnected data path across which multiple devices may communicate  In yet another embodiment, the network 105 may be a peer-to-peer network  The network 105 may also be coupled to or includes portions of a telecommunications network for sending data in a variety of different communication protocols  In yet another embodiment, the network 105 includes Bluetooth communication networks or a cellular communications network for sending and receiving data such as via short messaging service (SMS), multimedia messaging service (MMS), hypertext transfer protocol (HTTP), direct data connection, WAP, email, etc  While only one network 105 is coupled to the user devices 115 a, 115 n, the social network server 101, and the third party server 107, in practice any number of networks 105 can be connected to the entities 
The channel application 103 receives data for generating a stream of content for a channel from heterogeneous data sources  In one embodiment, the channel application 103 receives data from a third-party server 107, a social network server 101, user devices 115 a, 115 n, a search server 135 that is coupled to the network 105 via signal line 136, an entertainment server 137 that is coupled to the network 105 via signal line 138, a ratings server 139 that is coupled to the network 105 via signal line 140 and an email server 141 that is coupled to the network 105 via signal line 142  In one embodiment, the search server 135 includes a search engine 143 for retrieving results that match search terms from the Internet  In one embodiment, the search engine 143 is powered by Google®  In one embodiment, the channel application 103 generates a model based on the data from the heterogeneous data sources, identifies a channel category based on a user's activities and historical trends, receives candidate content items that include the channel category from heterogeneous data sources, scores the candidate content items by comparing them to the model, and generates a stream of content for the channel 
Referring now to FIG  1B, the channel application 103 is shown in detail  FIG  1B is a block diagram of a computing device 200 that includes the channel application 103, a memory 237 and a processor 235  In one embodiment, the computing 200 device is a social network server 101  In another embodiment, the computing device 200 is a third party server 107  In yet another embodiment, the computing device 200 is a user device 115 a 
The processor 235 comprises an arithmetic logic unit, a microprocessor, a general purpose controller, or some other processor array to perform computations and provide electronic display signals to a display device  The processor 235 is coupled to the bus 220 for communication with the other components via signal line 236  Processor 235 processes data signals and may comprise various computing architectures including a complex instruction set computer (CISC) architecture, a reduced instruction set computer (RISC) architecture, or an architecture implementing a combination of instruction sets  Although only a single processor is shown in FIG  1B, multiple processors may be included  The processing capability may be limited to supporting the display of images and the capture and transmission of images  The processing capability might be enough to perform more complex tasks, including various types of feature extraction and sampling  It will be obvious to one skilled in the art that other processors, operating systems, sensors, displays, and physical configurations are possible 
The memory 237 stores instructions and/or data that may be executed by processor 235  The memory 237 is coupled to the bus 220 for communication with the other components via signal line 238  The instructions and/or data may comprise code for performing any and/or all of the techniques described herein  The memory 237 may be a dynamic random access memory (DRAM) device, a static random access memory (SRAM) device, flash memory, or some other memory device known in the art  In one embodiment, the memory 237 also includes a non-volatile memory or similar permanent storage device and media such as a hard disk drive, a floppy disk drive, a CD-ROM device, a DVD-ROM device, a DVD-RAM device, a DVD-RW device, a flash memory device, or some other mass storage device known in the art for storing information on a more permanent basis 
In one embodiment, the channel application 103 comprises a processing unit 202, a model generation engine 207, a scoring engine 211, a collaborative filtering engine 217, a content categorizer 250, a channel engine 240, and a user interface engine 260 that are coupled to a bus 220 
The processing unit 202 is software including routines for receiving information about a user's interests, activities and social connections and for storing the information in the memory 237  In one embodiment, the processing unit 202 is a set of instructions executable by the processor 235 to provide the functionality described below for processing the information  In another embodiment, the processing unit 202 is stored in the memory 237 of the computing device 200 and is accessible and executable by the processor 235  In either embodiment, the processing unit 202 is adapted for cooperation and communication with the processor 235, the model generation engine 207, and other components of the computing device 200 via signal line 222 
The processing unit 202 obtains information about users from user input and/or prior actions of a user across a range of heterogeneous data sources including search (such as web, video, news, maps, alerts), entertainment (such as news, video, a personalized homepage, blogs, a reader, gadget subscriptions), social activity (such as interactions through email, profile information, text messaging such as short message service (SMS), microblogs, geographical locations, comments on photos, a social graph and other social networking information), and activity on third-party sites (such as websites that provide ratings, reviews and social networks where users indicate that they approve of content)  This information is obtained, for example, from a user's search history, browsing history and other interactions with the Internet  The processing unit 202 stores the information with a designation of the source of the information 
In one embodiment, there are multiple processing units 202 that each receive data from a different heterogeneous data source  In another embodiment, the user information is received by the same processing unit 202  The processing unit 202 transmits the user information to memory 237 for storage  In one embodiment, the memory 237 partitions the user information from each heterogeneous data source in a separate data storage location  In another embodiment, the user information from heterogeneous data sources is stored in the same location in the memory 237  In yet another embodiment, the memory 237 partitions the model and the stream of content into separate storage locations as well 
The model generation engine 207 is software including routines for retrieving the user information from the memory 237 and generating a model based on the user information  In one embodiment, the model generation engine 207 is a set of instructions executable by the processor 235 to provide the functionality described below for generating the model  In another embodiment, the model generation engine 207 is stored in the memory 237 of the computing device 200 and is accessible and executable by the processor 235  In either embodiment, the model generation engine 207 is adapted for cooperation and communication with the processor 235, the processing unit 202, the scoring engine 211, the channel engine 240 and other components of the computing device 200 via signal line 224 
The model generation engine 207 receives user information from a variety of sources including, for example, queries, clicks, news clicks, gadgets, email interactions, etc , extracts features from the information and generates a model based on the extracted features  The model determines the relevance of items to users, along with floating point values to indicate the extent to which the relevance holds  Examples include liking a source, a primary location and a list of interests  The interests are generated from explicit information and inferred information  Explicit information is derived, for example, from a user's list of interests on a social network or indicating that they liked a particular content item  Inferred information takes into account a user's activities 
The model generation engine 207 will infer that a user is interested in a particular subject, for example, if the subject matter appears in search terms  For example, the model generation engine 207 infers that a user who searches for information about different types of butterflies is interested in butterflies  The model generation engine 207 can even infer information based on the user's friends' activities  For example, content items that interest the user's friends might also interest the user  As a result, in one embodiment, the model includes the user's friends' interests 
In one embodiment, the model generation engine 207 also generates a model that contains several pieces of global meta-information about the user's consumption patterns including how frequently the user consumes the stream of content of a channel and global statistics on how likely the user is to reshare various types of items  Lastly, the model includes a sequence of weights and multipliers that are used to make predictions about the user's likelihood of clicking on, sharing or otherwise engaging with stream items 
The model generation engine 207 generates the model from the user information across the heterogeneous data sources  In one embodiment, the model generation engine 207 builds extensions to the model that employ the patterns of behavior of other users  For example, the model predicts the user's behavior based on the reaction of similar users  All the data that is derived from other users is anonymized before it is incorporated into the model 
In one embodiment, the model generation engine 207 generates a model based on user information, for example, based on the user's search history or third-party accounts  Alternatively, the model generation engine 207 receives periodic updates (one hour, one day, one week, etc ) from the heterogeneous data sources and in turn updates the model 
In yet another embodiment, the model generation engine 207 generates a model each time it receives a request for generating a stream of content for a channel  The advantage of this method is that the newest updates are included and the model is current  The disadvantage is that generating the model and then comparing the candidate content items to the model to generate the stream of content takes more time than comparing the candidate content items to a pre-existing model  The model generation engine 207 transmits the model to memory 237 for storage 
The content categorizer 250 is software including routines for receiving and categorizing new content items from heterogeneous sources according to at least one category and other features  In one embodiment, the content categorizer 250 is a set of instructions executable by the processor 235 to provide the functionality described below for receiving and categorizing new content items  In another embodiment, the content categorizer 250 is stored in the memory 237 of the computing device 200 and is accessible and executable by the processor 235  In either embodiment, the content categorizer 250 is adapted for cooperation and communication with the processor 235, the scoring engine 211 and other components of the computing device 200 via signal line 227 
The content categorizer 250 receives new content items from heterogeneous data sources and annotates them with specific tags, such as features, global scores, etc  In this embodiment, the heterogeneous data sources include a search engine 143, an entertainment server 137, an email server 141, a ratings server 139, a social network server 101, and a third-party server 107  Once the items are annotated, the content categorizer 250 indexes each new content item based on the features and stores the content items in the memory 237  The new content items, in one embodiment, are indexed according to an identification format (MediaType#UniqueItemID, for example, “YOUTUBE#video_id” and “NEWS#doc_id”), an item static feature column that holds an item's static features (title, content, content classification, context, etc ), an item dynamic feature column that holds an item's dynamic features (global_score, number of clicks, number of following, etc ), a source (src) static feature column where the source is a publisher of an item (magazine in news, video uploading in YouTube, etc ) and a src dynamic feature column that holds the source's dynamic features  The content categorizer 250 categorizes the new content items to make their retrieval more efficient and fast 
The channel engine 240 is software including routines for generating a channel for a user  In one embodiment, the channel engine 240 is a set of instructions executable by the processor 235 to provide the functionality described below for generating a channel for a user  In another embodiment, the channel engine 240 is stored in the memory 237 of the computing device 200 and is accessible and executable by the processor 235  In either embodiment, the channel engine 240 is adapted for cooperation and communication with the processor 235, the scoring engine 211, the model generation engine 207, the user interface engine 240, and other components of the computing device 200 via signal line 230 
In one embodiment, the channel engine 240 identifies a channel category for a user based on historical trends and the user's activities, interests and social connections  The channel engine 240 submits a request for a stream of content that includes the channel category and channel attributes to the scoring engine 211  The channel engine 240 then receives a stream of content from the scoring engine 211 and generates the channel  The generated channel is either public or private depending on the user's settings  The channel engine 240 is explained in greater detail below with regard to FIG  3A 
The scoring engine 211 is software including routines for generating a stream of content for a channel  In one embodiment, the scoring engine 211 is a set of instructions executable by the processor 235 to provide the functionality described below for globally scoring content items and for generating a stream of content for a channel  In another embodiment, the scoring engine 211 is stored in the memory 237 of the computing device 200 and is accessible and executable by the processor 235  In either embodiment, the scoring engine 211 is adapted for cooperation and communication with the processor 235, the processing unit 202, the collaborative filtering engine 217, the model generation engine 207, the channel engine 240 and other components of the computing device 200 via signal line 228 
In one embodiment, the scoring engine 211 receives the request from the channel engine 240 and queries the new content items stored in memory 237  In another embodiment, the scoring engine 211 directly queries the heterogeneous data sources  The scoring engine 211 receives candidate content items that include the channel category and the channel attributes  The scoring engine 211 then compares the candidate content items to the model to determine whether the user would find the candidate content items interesting 
In one embodiment, the scoring engine 211 first performs the query and then compares the results to the model to determine whether the user would find them interesting  In another embodiment, these steps are performed simultaneously  In yet another embodiment, the scoring engine 211 compares candidate content items to the model and then filters the results according to the subject matter of the queries  The scoring engine 211 is explained in greater detail below with regard to FIG  3B 
The collaborative filtering engine 217 is software including routines for generating additional candidate content items for the channel through collaborative filtering and transmitting the additional candidate content items to the scoring engine 211 that were derived from collaborative filtering  In one embodiment, the collaborative filtering engine 217 is a set of instructions executable by the processor 235 to provide the functionality described below for generating additional candidate content items for the channel  In another embodiment, the collaborative filtering engine 217 is stored in the memory 237 of the computing device 200 and is accessible and executable by the processor 235  In either embodiment, the collaborative filtering engine 217 is adapted for cooperation and communication with the processor 235, the scoring engine 211 and other components of the computing device via signal line 226 
The collaborative filtering engine 217 obtains additional candidate content items that are socially relevant from a stream of content derived from people with whom the user has a relationship and transmits them to the scoring engine 211  For example, the stream of content is derived from friends in a social network such as the social network application 109 or people that the user frequently emails  The more important that the person appears to be to the user, the more likely that the user will be interested in the candidate content item  Thus, in one embodiment, the collaborative filtering engine 217 applies a weight to candidate content items based on the social relationship of the user to the friend  For example, users that are friends receive higher weights than candidate content items from second generation friends of the user (i e , a friend of a friend)  In one embodiment, the collaborative filtering engine 217 receives information about relationships between users from the social graph 179 
The collaborative filtering engine 217 increases the weights applied to candidate content items from friends when the user positively responds to the items  For example, if the user comments on the item or indicates that the user found the item interesting, the collaborative filtering engine 217 increase the weight so that more candidate content items from the friend become part of the stream of content 
The user interface engine 260 is software including routines for generating a user interface that, when rendered on a browser, displays a channel generated for a user and enables the user to customize the channel  In one embodiment, the user interface engine 260 is a set of instructions executable by the processor 235 to provide the functionality described below for generating a user interface  In another embodiment, the user interface engine 260 is stored in the memory 237 of the computing device 200 and is accessible and executable by the processor 235  In either embodiment, the user interface engine 260 is adapted for cooperation and communication with the processor 235, the channel engine 240 and other components of the computing device 200 via signal line 232 
The user interface engine 260 receives instructions from the channel engine 240 for generating a display  The user interface includes options for viewing a channel, requesting a new channel, modifying the user interests, and following suggested channels 
FIG  2 is a high-level block diagram illustrating another embodiment of a system for generating a stream of content for a channel  In this embodiment, the components of the channel application 103 are divided among various servers so that the information is efficiently processed  The system includes a search server 135, an entertainment server 137, a ratings server 139, an email server 141, a content categorizer 250, a data storage server 265, a model server 255, a scoring server 262, a social network server 101, a user device 115, and a channel application 103 
A content categorizer 250 crawls the heterogeneous data sources (search server 135, entertainment server 137, ratings server 139, and email server 141) are crawled for new content items by the content categorizer 250 or the new content items are directly transmitted to the content categorizer 250 
The content categorizer 250 categorizes the new content items as mentioned above with regards to FIG  1B and stores them in the database 267 of the data storage server 265  The content categorizer 240 also includes a processing unit 202 for processing user information (activities, interests and social connections)  In one embodiment, the processing unit 202 stores the database 267 
In one embodiment, the data storage server 265 dynamically phases out the old content items  For example, news items expire after 24 hours, videos expire after 48 hours and feeds are kept for 24 hours or only the 10 most recent items, whichever is larger, etc 
The content categorizer 250 also transmits the new content items to the scoring server 262 for a global user ranking  The global scores are transmitted from the scoring server 262 to the data storage server 265, which stores the global scores in association with the new content items  The global scores are helpful for organizing the new content items in the data storage server 265 according to the more popular items 
Turning now to the model server 255, the model server 255 receives the user's activity, interests and social connections from the processing unit 202 or the data storage server 265  The model generation engine 207 generates a model based on user input and/or prior actions  The model server 255 transmits a model to the scoring server 262 and the channel application 103 periodically or upon request 
The channel application 103 includes a channel engine 240 and a user interface engine 260  In one embodiment, the channel engine 240 requests the model from the model server 255 and identifies a channel category that a user would find interesting  The channel engine 240 then transmits a request for a stream of content to the scoring server 262  The channel engine 240 receives the stream of content from the scoring server 262 and generates the channel  The user interface engine 260 generates a user interface for displaying a user interface that includes the channel and transmits it to the user device 115  In addition, the user interface engine 260 generates a user interface to allow the user to customize the channel or define a new channel  These user interfaces are explained in greater detail below with regard to FIGS  4-5 
In one embodiment, the channel engine 240 transmits a query based on the channel category to the scoring server 262  The scoring server 262 queries and receives candidate content items from the data storage server 265  The scoring server 262 also queries and receives candidate content items from the social network server 101  The candidate content items from the social network server 101 are pre-scored by the collaborative filtering engine 217 and, in one embodiment, the unread candidate content items are saved to a cache on the social network server 101  These items are saved to a cache because the quantity of social updates can be large enough that performing the scoring during write time enables faster reads 
In one embodiment, the scoring engine 211 requests the model from the model server 255  The scoring server 262 then compares the candidate content items to the model and scores the candidate content items  The scoring engine 211 compares the candidate content items received from the social network server 101 to the model and rescores them according to the model  In another embodiment, the scoring engine 211 scores the candidate content items according to the category and any keywords associated with a channel  In either embodiment, the scoring engine 211 generates a stream of content based on the scored candidate content items and transmits the stream of content to the channel application 103 
Referring now to FIG  3A, one embodiment of a channel engine 240 is shown in more detail  The channel engine 240 includes a historical analyzer 372, a category identifier 374, a subscription module 376 and a channel generator 378 that are each coupled to signal line 230 
The historical analyzer 372 is used to identify when a user will be interested in a particular category  The historical analyzer 372 identifies, for example, a time of the day or a year that a user will be interested in a category by analyzing historical trends associated with the category  In one embodiment, the historical analyzer 372 performs such analyses by measuring the increase or decrease in the number of new content items that are categorized under a content category or by measuring an increase or decrease in the number of times a new content item is accessed  For example, the number of times a tutorial on filing taxes is accessed would be very high during February-April  In another embodiment, the historical analyzer 372 also keeps track of events such as holidays, festivals, etc  Tracking such events is advantageous as, for example, many users might be interested in costume rentals during Halloween or camping during the Memorial Day and July 4th weekends 
The category identifier 374 identifies a channel category for a user based on the user's interests, activities and social connections  In one embodiment, the category identifier 374 requests the model generated by the model generation engine 207 to identify the channel category  For example, the category identifier 374 identifies sports cars as a channel category because it is an explicit interest of the user  The category identifier 374 suggests channels including a source, a category, keywords, a media type, a size of a content item, and a location for a channel  For example, for a user that is interested in foreign politics, especially relations between the United States and China, the category identifier 374 suggests the category of U S  and Chinese relations (e g , entity=“us_china_relations”), keywords such as trade and deficit because the user is particularly interested in the economic aspect of the relationship between China and the United States, a source such as The Economist (source=“economist com”) because the user prefers The Economist over U S  media outlets and the media being news articles because the user does not enjoy viewing videos 
In one embodiment, the category identifier 374 uses the analyses of the historical analyzer 374 for identifying a channel category for the user  This is advantageous as a user who has searched for US taxes might not be interested in knowing about it throughout the year, but it is beneficial for the user to have a separate channel for US taxes during the tax filing season  In yet another embodiment, the category identifier 374 uses contextual cues of the user for identifying channel categories  For example, the category identifier 374 identifies skiing in Switzerland as a channel category because winter sports is listed as an interest of the user and the user's current IP address is in Switzerland 
The subscription module 376 enables a user to subscribe to existing channels that are public  In one embodiment, the subscription module 376 enables a user to subscribe to a pre-defined channel (such as breaking news, most popular videos, updates from a social group, etc )  The channel application 103 generates the stream of content for pre-defined channels based on global scores of the new content items  Subscribing to pre-defined channels such as breaking news is advantageous as it helps the user to keep apprised of current information and discover new interests  Furthermore, because in one embodiment the breaking news channel is personalized since the content items are compared to a model for the user, the breaking news channel is more relevant than simply a list of popular or recent news items 
In another embodiment, the subscription module 376 enables a user to subscribe to another user's channel (a friend, a famous person, etc ) that is public  Subscribing to another user's channel is advantageous because, for example, a user who is interested in the stock market will benefit by viewing the stream of content that is viewed by a famous stock market analyst  In yet another embodiment, the subscription module 376 enables the user to search for channels that are public using the search engine 143  The subscription module 376, suggests such channels that are viewed by other users based on the interests of the user  In another embodiment, the subscription module 376 communicates with the collaborative filtering engine 217 to suggest channels viewed by other users with whom the user has a relationship 
The channel generator 378 submits a request for a stream of content for a channel to the scoring engine 211  The request includes the channel category identified by the category identifier 374 and channel attributes  The channel attributes include any attribute known to a person with ordinary skill in the art such as a source, presence of keywords, absence of keywords, a media type, a location, a time, a size of a content item, a date, etc  In one embodiment, the channel category and the channel attributes are defined by the user  In another embodiment, channel generator 378 defines the channel attributes for the channel category based on the user's preferences and activities  For example, if a user always reads news articles and seldom watches news videos, the channel generator 378 would define the media type for the channel as text based articles  At any point in time, the user can customize both the channel category and the channel attributes  The channel generator 378 then resubmits the request based on the changes made by the user 
In response to the request, the channel generator 378 receives a stream of content from the scoring engine 211 and generates the channel for the user  The generated channel is either public or private depending upon the user's preferences  In one embodiment, the user shares the channel to a community, a group of people or any internet user  The channel is then displayed to the user with an interface generated by the user interface engine 260 
Referring now to FIG  3B, one embodiment of a scoring engine 211 is shown in more detail  The scoring engine 211 includes a query generator 301, a global scorer 302 and a content stream generator 304 that are each coupled to signal line 228 
The global scorer 302 is used to rank new content items that are stored in the data storage server 265 or memory 237 (depending upon the embodiment)  The global scorer 302 uses signals from the different verticals to compute a global user-independent score for each item to approximate its popularity or importance within the stream that produced it  The global scorer 302 normalizes the score across streams so that items from various streams are comparable to aid in generating a quick yet reasonable ranking of items  The global score is a combination of its quality specific to the source stream (depending on the rank of the source, number of known followers of a source, etc ) and its global popularity (trigger rate on universal search, relevance to trending queries, number of clicks, long clicks received, etc ) 
The global scorer 302 transmits the global score to storage where it is associated with the item  The global score helps rank the items for faster retrieval  For example, if the query generated by the query generator 301 includes a request for the top ten items about skiing, those items are already organized in the data storage server 265 or memory 237 according to the global score 
The query generator 301 receives a request for a stream of content for a channel from the channel engine 240  The query generator 301 generates a query based on the channel attributes that are included in the request  The query generator 301 queries the data storage server 265 or memory 237 depending upon the embodiment  The following is an example query generated by the query generator 301: ((Category: Politics) AND (global_score>80) AND (source: NewsWebsite) AND (media type: Text)) 
The content stream generator 304 receives candidate content items that include the channel attributes  The content stream generator 304, for the above mentioned query, receives text based articles that include the channel category politics and have a global score greater than 80  Additionally, the text based articles are from the source NewsWebsite  In one embodiment, the content stream generator 304 generates the stream by ordering the content items in order of their scores  In another embodiment, the content stream generator 304 determines an interestingness of each candidate content item to the user  The content stream generator 304 determines the interestingness by comparing the candidate content items with a model generated for the user by the model generation engine 207 and scoring them 
where p is a property, that is, a setting A=a of the attributes  The latter quantity, Pr(p|user) is approximated from the user's history of interactions with content items as well as the user's search history and other opt-in data  Similarly, the former quantity, Pr(item|p) is approximated by the (suitably weighted) reciprocal of the number of items with property p (e g , if it is expected that p=((Politics) AND (global_score>80) AND (source: NewsWebsite) AND (media type: Text)) to generate 300 items, take Pr(item|p) to be 1/300) 
where the properties p are summed over single-attribute properties (as opposed to all possible settings of an entire collection of attributes), and G is an exponential function of the form G(x)=2(100 x), so that when applied in this form, if there are several values of p for which Pr(item|p) Pr(p|user) is large, the sum of their G-values slowly increases 
Once the scores are calculated, the content stream generator 304 generates a stream of content for the channel that is ordered according to the candidate content item scores  In one embodiment, only the candidate content items that exceed a certain threshold are included in the stream of content for the channel 
Turning now to the user interface engine 260, FIG  4 is a graphic representation 400 of a user interface generated by the user interface engine 260 for displaying the stream of content of a channel  In this example, the user interface 400 also includes channels 405 that are pre-defined, channels 410 that are suggested for the user and channels 415 that are subscribed to by the user  The user can also define new channels and attributes by clicking the link 420 
The example includes the stream of content for the user's soccer channel 425  The stream of content includes news items 445, videos 450 and social network news feeds 455 from the content sources 440 defined by the user  The candidate content items are listed in decreasing order of their scores  The user interface engine 260 lists five candidate content items with the highest scores in the hot items section 430  The remaining candidate content items are listed in the other items section 435  In another embodiment, the entire stream of content is listed in a single section 
FIG  5 is a graphic representation 500 of a user interface that is generated by the user interface engine 260 for a user to define a new channel or customize an existing channel  In this example, the user interface includes all the channel categories 505 that have been either pre-defined, suggested to the user, or subscribed by the user, and the content sources 510 for each channel category  The user customizes a channel by adding or removing content sources for the channel  In one embodiment, the user edits more advanced channel attributes such as media type, size of the content items, etc  by clicking on the link 515  The user makes the channel public, private or restricts it to a group of people by clicking on link 520  Additionally, the user can also define a new channel by adding a new channel category 
Referring now to FIGS  6-7, various embodiments of the method of the specification will be described  FIG  6 is a flow diagram 600 of one embodiment of a method for generating a stream of content for a channel  The channel engine 240 defines 602 a channel category and submits a request for a stream of content  The request includes channel attributes including any of a category, a source, keywords, a media type, a location, a size of a content item, and a date  The channel category is defined based on a model for a user that is generated by the model generation engine 207 or the channel is defined by a user  The scoring engine 211 receives 604 the request including the channel category and generates 606 a stream of content based on the channel category  The channel engine 240 generates 608 a channel with the stream of content and transmits it to the user 
FIG  7 is a flow diagram 700 of another embodiment of a method for generating a stream of content for a channel  The content categorizer 250 categorizes 702 new content items that are received from heterogeneous data sources  The new content items that are received from heterogeneous data sources include, for example, news articles, microblogs, blogs, videos, photos, etc  The content categorizer 250 categorizes the content according to a category and other features  The content categorizer 250 also stores 704 the new content items in a data storage server 265 or a memory 237, depending upon the embodiment  The global scorer 302 generates 706 a global score for each new content item  The category identifier 374 identifies 708 a channel category for a user based on the user's activities and a historical trend identified by the historical analyzer 372  The user's activity includes a search (such as web, video, news, maps, alerts), entertainment (such as news, video, a personalized homepage, blogs, a reader, gadget subscriptions), social activity (such as interactions through email, profile information, text messaging such as short message service (SMS), microblog, comments on photos, a social graph, and other social networking information), and activity on third-party sites (such as websites that provide ratings, reviews and social networks where users indicate that they approve of content) In one embodiment, the category identifier 374 also uses contextual information of the user to identify the channel category 
The query generator 301 generates a query based on the channel category and the channel attributes and queries 710 the new content items stored on the data storage server 265  The content stream generator 304 receives 712 candidate content items that include the channel category and channel attributes  In one embodiment, the content stream generator 304 receives additional candidate content items from the collaborative filtering engine 217 
The content stream generator 304 scores 714 each candidate content item by comparing it to a model generated by the model generation engine 207  The score is calculated by determining an interestingness of the candidate content item to the user  The content stream generator 304 then generates 716 the stream of content based on the scores for each candidate content item  The channel engine 240 then generates 718 a channel with the stream of content and transmits it to the user 
The foregoing description of the embodiments has been presented for the purposes of illustration and description  It is not intended to be exhaustive or to limit the specification to the precise form disclosed  Many modifications and variations are possible in light of the above teaching  It is intended that the scope of the embodiments be limited not by this detailed description, but rather by the claims of this application  As will be understood by those familiar with the art, the examples may be embodied in other specific forms without departing from the spirit or essential characteristics thereof  Likewise, the particular naming and division of the modules, routines, features, attributes, methodologies, and other aspects are not mandatory or significant, and the mechanisms that implement the description or its features may have different names, divisions and/or formats  Furthermore, as will be apparent to one of ordinary skill in the relevant art, the modules, routines, features, attributes, methodologies, and other aspects of the specification can be implemented as software, hardware, firmware, or any combination of the three  Also, wherever a component, an example of which is a module, of the specification is implemented as software, the component can be implemented as a standalone program, as part of a larger program, as a plurality of separate programs, as a statically or dynamically linked library, as a kernel loadable module, as a device driver, and/or in every and any other way known now or in the future to those of ordinary skill in the art of computer programming  Additionally, the specification is in no way limited to implementation in any specific programming language, or for any specific operating system or environment  Accordingly, the disclosure is intended to be illustrative, but not limiting, of the scope of the specification, which is set forth in the following claims 
providing, with the one or more processors, the customized stream of content 
2  The computer-implemented method of claim 1 comprising removing pre-existing content items included in the customized stream of content for the channel 
3  The computer-implemented method of claim 1 wherein the historical trend is one of an increase in a number of the new content items for a content category and an increase in a number of times one of the new content items is accessed 
categorizing the new content items 
5  The computer-implemented method of claim 3 wherein the heterogeneous data sources include at least two from the group of a news article post, a news feed, a social feed, a blog post, a micro-blog post, a photo, a video, an audio, an email message, and a text based message 
6  The computer-implemented method of claim 1 comprising receiving a request from the user to subscribe to an existing channel 
7  The computer-implemented method of claim 1 wherein the channel category is also based on an interest of the user and a connection of the user 
8  The computer-implemented method of claim 1 wherein the user activity is an interaction of the user with an application, wherein the interaction of the user with the application includes providing at least one of a user preference, a user interest, a comment, a tag, and a search 
provide the customized stream of content 
10  The computer program product of claim 9, wherein the computer readable program when executed on the computer also causes the computer to remove pre-existing content items included in the customized stream of content for the channel 
11  The computer program product of claim 9, wherein the historical trend is one of an increase in a number of the new content items for a content category and an increase in a number of times one of the new content items is accessed 
categorize the new content items 
13  The computer program product of claim 12, wherein the heterogeneous data sources include at least two from the group of a news article post, a news feed, a social feed, a blog post, a micro-blog post, a photo, a video, an audio, an email message, and a text based message 
14  The computer program product of claim 9, wherein the computer readable program when executed on the computer also causes the computer to receive a request from the user to subscribe to an existing channel 
15  The computer program product of claim 9, wherein the channel category is also based on an interest of the user and a connection of the user 
16  The computer program product of claim 9, wherein the user activity is an interaction of the user with an application, wherein the interaction of the user with the application includes providing at least one of a user preference, a user interest, a comment, a tag, and a search 
18  The system of claim 17 wherein the system is further configured to remove pre-existing content items included in the customized stream of content for the channel 
19  The system of claim 17 wherein the historical trend is one of an increase in a number of the new content items for a content category and an increase in a number of times one of the new content items is accessed 
21  The system of claim 20 wherein the heterogeneous data sources include at least two from the group of a news article post, a news feed, a social feed, a blog post, a micro-blog post, a photo, a video, an audio, an email message, and a text based message 
22  The system of claim 17 wherein the system is further configured to receive a request from the user to subscribe to an existing channel 
23  The system of claim 17 wherein the channel category is also based on an interest of the user and a connection of the user 
24  The system of claim 17 wherein the user activity is an interaction of the user with an application, wherein the interaction of the user with the application includes providing at least one of a user preference, a user interest, a comment, a tag, and a search 
Adamic et al , "Search in power-law networks," Physical Review E, 2001, vol  64, HP Labs/Stanford University, The American Physical Society 
Boyd, et al , "Social Network Sites: Definition, History, and Scholarship," Journal of Computer-Mediated Communication, International Communication Association, 2008, pp  210-230  8 
MediaSift Ltd , DataSift: Realtime Social Data Mining Platform, Curate and Data Mine the Real Time Web with DataSift, Dedipower, Managed Hosting, May 13, 2011, 1 pg 
Ring Central, Inc , Internet, retrieved at http://www ringcentral com, Apr  19, 2007, 1 pg  28 
Singh et al , "CINEMA: Columbia InterNet Extensible Multimedia Architecture," Department of Computer Science, Columbia University, May 2002 pp  1-83 
Yu et al , "It Takes Variety to Make a World: Diversification in Recommender Systems," 2009, pp  1-11, downloaded from https://openproceedings org/2009/conf/edbt/YuLA09 pdf 
Context 2: Football Club Urartu (, translated Futbolayin Akumb Urartu), commonly known as Urartu, is an Armenian professional football team based in the capital Yerevan that currently plays in the Armenian Premier League  The club won the Armenian Cup three times, in 1992, 2007 and 2016  In 2013–2014, they won the Armenian Premier League for the first time in their history 

In early 2016, the Russia-based Armenian businessman Dzhevan Cheloyants became a co-owner of the club after purchasing the major part of the club shares  The club was known as FC Banants until 1 August 2019, when it was officially renamed FC Urartu 

History

Kotayk
Urartu FC were founded as FC Banants by Sarkis Israelyan on 21 January 1992 in the village of Kotayk, representing the Kotayk Province  He named the club after his native village of Banants (currently known as Bayan)  Between 1992 and 1995, the club was commonly referred to as Banants Kotayk  During the 1992 season, the club won the first Armenian Cup  At the end of the 1995 transitional season, Banants suffered a financial crisis  The club owners decided that it was better to merge the club with FC Kotayk of Abovyan, rather than disband it  In 2001, Banants demerged from FC Kotayk, and was moved from Abovyan to the capital Yerevan 

Yerevan

FC Banants was relocated to Yerevan in 2001  At the beginning of 2003, Banants merged with FC Spartak Yerevan, but was able to limit the name of the new merger to FC Banants  Spartak became Banants's youth academy and later changed the name to Banants-2  Because of the merger, Banants acquired many players from Spartak Yerevan, including Samvel Melkonyan  After the merger, Banants took a more serious approach and have finished highly in the league table ever since  The club managed to lift the Armenian Cup in 2007 
Experience is making way for youth for the 2008 and 2009 seasons



ground_truth: By comparing candidate content items to a model and scoring them.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: When did the club win the Armenian Premier League for the first time?

Read the following text and answer briefly.

Context 2:  The departures of most of the experienced players have left the club's future to the youth  Along with two Ukrainian players, Ugandan international, Noah Kasule, has been signed 

The club headquarters are located on Jivani Street 2 of the Malatia-Sebastia District, Yerevan 

Domestic

European

Stadium

The construction of the Banants Stadium was launched in 2006 in the Malatia-Sebastia District of Yerevan, with the assistance of the FIFA goal programme  It was officially opened in 2008 with a capacity of 3,600 seats  Further developments were implemented later in 2011, when the playing pitch was modernized and the capacity of the stadium was increased up to 4,860 seats (2,760 at the northern stand, 1,500 at the southern stand and 600 at the western stand) 

Training centre/academy
Banants Training Centre is the club's academy base located in the Malatia-Sebastia District of Yerevan  In addition to the main stadium, the centre houses 3 full-size training pitches, mini football pitches as well as an indoor facility  The current technical director of the academy is the former Russian footballer Ilshat Faizulin 

Fans
The most active group of fans is the South West Ultras fan club, mainly composed of residents from several neighbourhoods within the Malatia-Sebastia District of Yerevan, since the club is a de facto representer of the district  Members of the fan club benefit from events organized by the club and many facilities of the Banants training centre, such as the mini football pitch, the club store and other entertainments 

Achievements
 Armenian Premier League
 Winner (1): 2013–14 
 Runner-up (5): 2003, 2006, 2007, 2010, 2018 

 Armenian Cup
 Winner (3): 1992, 2007, 2016 
 Runner-up (6): 2003, 2004, 2008, 2009, 2010, 2021–22

 Armenian Supercup
 Winner (1): 2014 
 Runner-up (5): 2004, 2007, 2009, 2010, 2016 

Current squad

Out on loan

Personnel

Technical staff

Management

Urartu-2

FC Banants' reserve squad play as FC Banants-2 in the Armenian First League  They play their home games at the training field with artificial turf of the Urartu Training Centre 

Managerial history
 Varuzhan Sukiasyan (1992–94)
 Poghos Galstyan (July 1, 1996 – June 30, 1998)
 Oganes Zanazanyan (2001–05)
 Ashot Barseghyan (2005–06)
 Nikolay Kiselyov (2006–07)
 Jan Poštulka (2007)
 Nikolay Kostov (July 1, 2007 – April 8, 2008)
 Nedelcho Matushev (April 8, 2008 – June 30, 2008)
 Kim Splidsboel (2008)
 Armen Gyulbudaghyants (Jan 1, 2009 – Dec 1, 2009)
 Ashot Barseghyan (interim) (2009)
 Stevica Kuzmanovski (Jan 1, 2010 – Dec 31, 2010)
 Rafael Nazaryan (Jan 1, 2011 – Jan 15, 2012)
 Volodymyr Pyatenko (Jan 17, 2013 – June 30, 2013)
 Zsolt Hornyák (July 1, 2013 – May 30, 2015)
 Aram Voskanyan (July 1, 2015 – Oct 11, 2015)
 Tito Ramallo (Oct 12, 2015 – Oct 3, 2016)
 Artur Voskanyan (Oct 3, 2016 – Aug 11, 2018)
 Ilshat Faizulin (Aug 12, 2018 –Nov 24, 2019)
 Aleksandr Grigoryan (Nov 25, 2019 –Mar 10, 2021)
 Robert Arzumanyan (10 March 2021–24 June 2022)
 Dmitri Gunko (27 June 2022–)

References

External links
 Official website 
 Banants at Weltfussball de  

 
Urartu
Urartu
Urartu
Urartu
Context 3: Paper Info

Title: Environmental variability and network structure determine the optimal plasticity mechanisms in embodied agents
Publish Date: Unkown
Author List: Sina Khajehabdollahi (from Department of Computer Science, University of Tübingen)

Figure

Figure2: An outline of the network controlling the foraging agent The sensor layer receives inputs at each time step (the ingredients of the nearest food), which are processed by the plastic layer in the same way as the static sensory network, Fig 1 The output of that network is given as input to the motor network, along with the distance d and angle α to the nearest food, the current velocity v, and energy E of the agent These signals are processed through two hidden layers to the final output of motor commands as the linear and angular acceleration of the agent
Figure4: The evolved parameters θ = (θ 1 ,      , θ 8 ) of the plasticity rule for the reward prediction (a ) and the decision (b ) tasks, for a variety of parameters (p tr = 0 01, d e ∈ 0, 0 1,      , 1, and σ ∈ 0, 0 1,      , 1 in all 100 combinations) Despite the relatively small difference between the tasks, the evolved learning rules differ considerably For visual guidance, the lines connect θs from the same run 
Figure5: a The trajectory of an agent (blue line) in the 2D environment A well-trained agent will approach and consume food with positive values (green dots) and avoid negative food (red dots) b The learning rate of the plastic sensory network eta p grows with the distance between environments d e c  and decreases with the frequency of environmental change d The fitness of an agent (measured as the total food consumed over its lifetime) increases over generations of the EA for both the scalar and binary readouts in the sensory network e The Pearson correlation coefficient of an evolved agent's weights with the ingredient value vector of the current environment (E 1 -blue, E 2 -red) In this example, the agent's weights are anti-correlated with its environment, which is not an issue for performance since the motor network can interpret the inverted signs of food 

abstract

The evolutionary balance between innate and learned behaviors is highly intricate, and different organisms have found different solutions to this problem  We hypothesize that the emergence and exact form of learning behaviors is naturally connected with the statistics of environmental fluctuations and tasks an organism needs to solve 
Here, we study how different aspects of simulated environments shape an evolved synaptic plasticity rule in static and moving artificial agents  We demonstrate that environmental fluctuation and uncertainty control the reliance of artificial organisms on plasticity  Interestingly, the form of the emerging plasticity rule is additionally determined by the details of the task the artificial organisms are aiming to solve 
Moreover, we show that coevolution between static connectivity and interacting plasticity mechanisms in distinct sub-networks changes the function and form of the emerging plasticity rules in embodied agents performing a foraging task  One of the defining features of living organisms is their ability to adapt to their environment and incorporate new information to modify their behavior 
It is unclear how the ability to learn first evolved , but its utility appears evident  Natural environments are too complex for all the necessary information to be hardcoded genetically and more importantly, they keep changing during an organism's lifetime in ways that cannot be anticipated ;   The link between learning and environmental uncertainty and fluctuation has been extensively demonstrated in both natural ; , and artificial environments  
Nevertheless, the ability to learn does not come without costs  For the capacity to learn to be beneficial in evolutionary terms, a costly nurturing period is often required, a phenomenon observed in both biological , and artificial organisms   Additionally, it has been shown that in some complex environments, hardcoded behaviors may be superior to learned ones given limits in the agent's lifetime and envi-ronmental uncertainty ; ;  
The theoretical investigation of the optimal balance between learned and innate behaviors in natural and artificial systems goes back several decades  However, it has recently found also a wide range of applications in applied AI systems ;   Most AI systems are trained for specific tasks, and have no need for modification after their training has been completed 
Still, technological advances and the necessity to solve broad families of tasks make discussions about life-like AI systems relevant to a wide range of potential application areas  Thus the idea of open-ended AI agents that can continually interact with and adapt to changing environments has become particularly appealing 
Many different approaches for introducing lifelong learning in artificial agents have been proposed  Some of them draw direct inspiration from actual biological systems ;   Among them, the most biologically plausible solution is to equip artificial neural networks with some local neural plasticity , similar to the large variety of synaptic plasticity mechanisms ; ; that performs the bulk of the learning in the brains of living organisms  
The artificial plasticity mechanisms can be optimized to modify the connectivity of the artificial neural networks toward solving a particular task  The optimization can use a variety of approaches, most commonly evolutionary computation  The idea of meta-learning or optimizing synaptic plasticity rules to perform specific functions has been recently established as an engineering tool that can compete with stateof-the-art machine learning algorithms on various complex tasks ; ; Pedersen and Risi (2021);  
Additionally, it can be used to reverse engineer actual plasticity mechanisms found in biological neural networks and uncover their functions ;   Here, we study the effect that different factors (environ-arXiv:2303 06734v1 [q-bio NC] 12 Mar 2023 mental fluctuation and reliability, task complexity) have on the form of evolved functional reward-modulated plasticity rules 
We investigate the evolution of plasticity rules in static, single-layer simple networks  Then we increase the complexity by switching to moving agents performing a complex foraging task  In both cases, we study the impact of different environmental parameters on the form of the evolved plasticity mechanisms and the interaction of learned and static network connectivity 
Interestingly, we find that different environmental conditions and different combinations of static and plastic connectivity have a very large impact on the resulting plasticity rules  We imagine an agent who must forage to survive in an environment presenting various types of complex food particles  Each food particle is composed of various amounts and combinations of N ingredients that can have positive (food) or negative (poison) values 
The value of a food particle is a weighted sum of its ingredients  To predict the reward value of a given resource, the agent must learn the values of these ingredients by interacting with the environment  The priors could be generated by genetic memory, but the exact values are subject to change  To introduce environmental variability, we stochastically change the values of the ingredients 
More precisely, we define two ingredient-value distributions E 1 and E 2 and switch between them, with probability p tr for every time step  We control how (dis)similar the environments are by parametrically setting E 2 = (1 − 2d e )E 1 , with d e ∈ [0, 1] serving as a distance proxy for the environments; when d e = 0, the environment remains unchanged, and when d e = 1 the value of each ingredient fully reverses when the environmental transition happens 
For simplicity, we take values of the ingredients in E 1 equally spaced between -1 and 1 (for the visualization, see Fig  )  The static agent receives passively presented food as a vector of ingredients and can assess its compound value using the linear summation of its sensors with the (learned or evolved) weights, see Fig   
The network consists of N sensory neurons that are projecting to a single post-synaptic neuron  At each time step, an input X t = (x 1 ,       , x N ) is presented, were the value x i , i ∈ {1,       , N } represents the quantity of the ingredient i  We draw x i independently form a uniform distribution on the [0, 1] interval (x i ∼ U (0, 1)) 
The value of each ingredient w c i is determined by the environment (E 1 or E 2 )  The postsynaptic neuron outputs a prediction of the food X t value as y t = g(W X T t )  Throughout the paper, g will be either the identity function, in which case the prediction neuron is linear, or a step-function; however, it could be any other nonlinearity, such as a sigmoid or ReLU 
After outputting the prediction, the neuron receives feedback in the form of the real value of the input R t   The real value is computed as R t = W c X T t + ξ, where W c = (w c 1 ,       , w c N ) is the actual value of the ingredients, and ξ is a term summarizing the noise of reward and sensing system ξ ∼ N (0, σ) 
Figure : An outline of the static agent's network  The sensor layer receives inputs representing the quantity of each ingredient of a given food at each time step  The agent computes the prediction of the food's value y t and is then given the true value R t ; it finally uses this information in the plasticity rule to update the weight matrix 
For the evolutionary adjustment of the agent's parameters, the loss of the static agent is the sum of the mean squared errors (MSE) between its prediction y t and the reward R t over the lifetime of the agent  The agent's initial weights are set to the average of the two ingredient value distributions, which is the optimal initial value for the case of symmetric switching of environments that we consider here 
As a next step, we incorporate the sensory network of static agents into embodied agents that can move around in an environment scattered with food  To this end, we merge the static agent's network with a second, non-plastic motor network that is responsible for controlling the motion of the agent in the environment 
Specifically, the original plastic network now provides the agent with information about the value of the nearest food  The embodied agent has additional sensors for the distance from the nearest food, the angle between the current velocity and the nearest food direction, its own velocity, and its own energy level (sum of consumed food values) 
These inputs are processed by two hidden layers (of 30 and 15 neurons) with tanh activation  The network's outputs are angular and linear acceleration, Fig    The embodied agents spawn in a 2D space with periodic boundary conditions along with a number of food particles that are selected such that the mean of the food value distribution is ∼ 0  An agent can eat food by approaching it sufficiently closely, and each time a food particle is eaten, it is The sensor layer receives inputs at each time step (the ingredients of the nearest food), which are processed by the plastic layer in the same way as the static sensory network, Fig   
The output of that network is given as input to the motor network, along with the distance d and angle α to the nearest food, the current velocity v, and energy E of the agent  These signals are processed through two hidden layers to the final output of motor commands as the linear and angular acceleration of the agent re-spawned with the same value somewhere randomly on the grid (following the setup of ) 
After 5000 time steps, the cumulative reward of the agent (the sum of the values of all the food it consumed) is taken as its fitness  During the evolutionary optimization, the parameters for both the motor network (connections) and plastic network (learning rule parameters) are co-evolved, and so agents must simultaneously learn to move and discriminate good/bad food 
Reward-modulated plasticity is one of the most promising explanations for biological credit assignment   In our network, the plasticity rule that updates the weights of the linear sensor network is a rewardmodulated rule which is parameterized as a linear combination of the input, the output, and the reward at each time step:
Additionally, after each plasticity step, the weights are normalized by mean subtraction, an important step for the stabilization of Hebbian-like plasticity rules   We use a genetic algorithm to optimize the learning rate η p and amplitudes of different terms θ = (θ 1 ,       , θ 8 )  The successful plasticity rule after many food presentations must converge to a weight vector that predicts the correct food values (or allows the agent to correctly decide whether to eat a food or avoid it) 
To have comparable results, we divide θ = (θ 1 ,       , θ 8 ) by We then multiply the learning rate η p with θ max to maintain the rule's evolved form unchanged, η norm p = η p • θ max   In the following, we always use normalized η p and θ, omitting norm   To evolve the plasticity rule and the moving agents' motor networks, we use a simple genetic algorithm with elitism  
The agents' parameters are initialized at random (drawn from a Gaussian distribution), then the sensory network is trained by the plasticity rule and finally, the agents are evaluated  After each generation, the bestperforming agents (top 10 % of the population size) are selected and copied into the next generation 
The remaining 90 % of the generation is repopulated with mutated copies of the best-performing agents  We mutate agents by adding independent Gaussian noise (σ = 0 1) to its parameters  To start with, we consider a static agent whose goal is to identify the value of presented food correctly  The static reward-prediction network quickly evolves the parameters of the learning rule, successfully solving the prediction task 
We first look at the evolved learning rate η p , which determines how fast (if at all) the network's weight vector is updated during the lifetime of the agents  We identify three factors that control the learning rate parameter the EA converges to: the distance between the environments, the noisiness of the reward, and the rate of environmental transition 
The first natural factor is the distance d e between the two environments, with a larger distance requiring a higher learning rate, Fig    This is an expected result since the convergence time to the "correct" weights is highly dependent on the initial conditions  If an agent is born at a point very close to optimality, which naturally happens if the environments are similar, the distance it needs to traverse on the fitness landscape is small 
Therefore it can afford to have a small learning rate, which leads to a more stable convergence and is not affected by noise  A second parameter that impacts the learning rate is the variance of the rewards  The reward an agent receives for the plasticity step contains a noise term ξ that is drawn from a zero mean Gaussian distribution with standard deviation σ 
This parameter controls the unreliability of the agent's sensory system, i e , higher σ means that the information the agent gets about the value of the foods it consumes cannot be fully trusted to reflect the actual value of the foods  As σ increases, the learning rate η p decreases, which means that the more unreliable an environment becomes, the less an agent relies on plasticity to update its weights, Fig   
Indeed for some combinations of relatively small distance d e and high reward variance σ, the EA converges to a learning rate of η p ≈ 0  This means that the agent opts to have no adaptation during its lifetime and remain at the mean of the two environments  It is an optimal solution when the expected loss due to ignoring the environmental transitions is, on average, lower than the loss the plastic network will incur by learning via the (often misleading because of the high σ) environmental cues 
A final factor that affects the learning rate the EA will converge to is the frequency of environmental change during an agent's lifetime  Since the environmental change is modeled as a simple, two-state Markov process (Fig  ), the control parameter is the transition probability p tr   When keeping everything else the same, the learning rate rapidly rises as we increase the transition probability from 0, and after reaching a peak, it begins to decline slowly, eventually reaching zero (Fig  ) 
This means that when environmental transition is very rare, agents opt for a very low learning rate, allowing a slow and stable convergence to an environment-appropriate weight vector that leads to very low losses while the agent remains in that environment  As the rate of environmental transition increases, faster learning is required to speed up convergence in order to exploit the (comparatively shorter) stays in each environment 
Finally, as the environmental transition becomes too fast, the agents opt for slower or even no learning, which keeps them ) and the decision (b ) tasks, for a variety of parameters (p tr = 0 01, d e ∈ 0, 0 1,       , 1, and σ ∈ 0, 0 1,       , 1 in all 100 combinations)  Despite the relatively small difference between the tasks, the evolved learning rules differ considerably 
For visual guidance, the lines connect θs from the same run  near the middle of the two environments, ensuring that the average loss of the two environments is minimal (Fig  )  The form of the evolved learning rule depends on the task: Decision vs  Prediction The plasticity parameters θ = (θ 1 ,       , θ 8 ) for the rewardprediction task converge on approximately the same point, regardless of the environmental parameters (Fig  ) 
In particular, θ 3 → 1, θ 5 → −1, θ i → 0 for all other i, and thus the learning rule converges to: Since by definition y t = g(W t X T t ) = W t X T t (g(x) = x in this experiment) and R t = W c X T t + ξ we get: Thus the distribution of ∆W t converges to a distribution with mean 0 and variance depending on η p and σ and W converges to W c  
So this learning rule will match the agent's weight vector with the vector of ingredient values in the environment  We examine the robustness of the learning rule the EA discovers by considering a slight modification of our task  Instead of predicting the expected food value, the agent now needs to decide whether to eat the presented food or not 
This is done by introducing a step-function nonlinearity (g(x) = 1 if x ≥ 1 and 0 otherwise)  Then the output y(t) is computed as: Instead of the MSE loss between prediction and actual value, the fitness of the agent is now defined as the sum of the food values it chose to consume (by giving y t = 1)  Besides these two changes, the setup of the experiments remains exactly the same 
The qualitative relation between η p and parameters of environment d e , σ and p tr is preserved in the changed experiment  However, the resulting learning rule is significantly different (Fig  )  The evolution converges to the following learning rule: In both cases, the rule has the form ∆W t = η p X t [α y R t + β y ] 
Thus, the ∆W t is positive or negative depending on whether the reward R t is above or below a threshold (γ = −β y /α y ) that depends on the output decision of the network (y t = 0 or 1)  Both learning rules (for the reward-prediction and decision tasks) have a clear Hebbian form (coordination of preand post-synaptic activity) and use the incoming reward signal as a threshold 
These similarities indicate some common organizing principles of reward-modulated learning rules, but their significant differences highlight the sensitivity of the optimization process to task details  We now turn to the moving embodied agents in the 2D environment  To optimize these agents, both the motor network's connections and the sensory network's plasticity parameters evolve simultaneously 
Since the motor network is initially random and the agent has to move to find food, the number of interactions an agent experiences in its lifetime can be small, slowing down the learning  However, having the larger motor network also has benefits for evolution because it allows the output of the plastic network to be read out and transformed in different ways, resulting in a broad set of solutions 
The fitness of an agent (measured as the total food consumed over its lifetime) increases over generations of the EA for both the scalar and binary readouts in the sensory network  e  The Pearson correlation coefficient of an evolved agent's weights with the ingredient value vector of the current environment (E 1 -blue, E 2 -red) 
In this example, the agent's weights are anti-correlated with its environment, which is not an issue for performance since the motor network can interpret the inverted signs of food  The agents can solve the task effectively by evolving a functional motor network and a plasticity rule that converges to interpretable weights (Fig  ) 
After ∼ 100 evolutionary steps (Fig  ), the agents can learn the ingredient value distribution using the plastic network and reliably move towards foods with positive values while avoiding the ones with negative values  We compare the dependence of the moving and the static agents on the parameters of the environment: d e and the state transition probability p tr  
At first, in order to simplify the experiment, we set the transition probability to 0, but fixed the initial weights to be the average of E 1 and E 2 , while the real state is E 2   In this experiment, the distance between states d e indicates twice the distance between the agent's initial weights and the optimal weights (the environment's ingredient values) since the agent is initialized at the mean of the two environment distributions 
Same as for the static agent, the learning rate increases with the distance d e (Fig  )  Then, we examine the effect of the environmental transition probability p tr on the evolved learning rate η p   In order for an agent to get sufficient exposure to each environment, we scale down the probability p tr from the equivalent experiment for the static agents 
We find that as the probability of transition increases, the evolved learning rate η p decreases (Fig  )  This fits with the larger trend for the static agent, although there is a clear difference when it comes to the increase for very small transition probabil-ities that were clearly identifiable in the static but not the moving agents 
This could be due to much sparser data and possibly the insufficiently long lifetime of the moving agent (the necessity of scaling makes direct comparisons difficult)  Nevertheless, overall we see that the associations observed in the static agents between environmental distance d e and transition probability p tr and the evolved learning rate η p are largely maintained in the moving agents 
Still, more data would be needed to make any conclusive assertions about the exact effect of these environmental parameters on the emerging plasticity mechanisms  A crucial difference between the static and the moving agents is the function the plasticity has to perform  While in the static agents, the plasticity has to effectively identify the exact value distribution of the environment in order to produce accurate predictions, in the embodied agents, the plasticity has to merely produce a representation of the environment that the motor network can evolve to interpret adequately enough to make decisions about which food to consume 
To illustrate the difference, we plot the Pearson correlation coefficient between an agent's weights and the ingredient values of the environment it is moving in (Fig  )  We use the correlation instead of the MSE loss (which we used for the static agents in Fig  ) because the amplitude of the vector varies a lot for different agents and meaningful The evolved parameters of moving agents' plasticity rule for the g(s) = x, identity (a ) and the step function (Eq 
4) (b ) sensory networks (the environmental parameters here are d e ∈ [0, 1], σ = 0 and p tr = 0 001)  The step function (binary output) network evolved a more structured plasticity rule (e g , θ 3 > 0 for all realizations) than the linear network  Moreover, the learned weights for the identity network (c ) have higher variance and correlate significantly less with the environment's ingredient distribution compared to the learned weights for the thresholded network (d )
conclusions cannot be drawn from the MSE loss  For many agents, the learned weights are consistently anti-correlated with the actual ingredient values (an example of such an agent is shown in Fig  )  This means that the output of the sensory network will have the opposite sign from the actual food value 
While in the static network, this would lead to very bad predictions and high loss, in the foraging task, these agents perform exactly as well as the ones where the weights and ingredients values are positively correlated, since the motor network can simply learn to move towards food for which it gets a negative instead of a positive sensory input 
This additional step of the output of the plastic network going through the motor network before producing any behavior has a strong effect on the plasticity rules that the embodied agents evolve  Specifically, if we look at the emerging rules the top performing agents have evolved (Fig  ), it becomes clear that, unlike the very well-structured rules of the static agents (Fig  ), there is now virtually no discernible pattern or structure 
The difference becomes even clearer if we look at the learned weights (at the end of a simulation) of the best-performing agents (Fig  )  While there is some correlation with the environment's ingredient value distribution, the variance is very large, and they do not seem to converge on the "correct" values in any way 
This is to some extent expected since, unlike the static agents where the network's output has to be exactly correct, driving the evolution of rules that converge to the precise environmental distribution, in the embodied networks, the bulk of the processing is done by the motor network which can evolve to interpret the scalar value of the sensory network's output in a variety of ways 
Thus, as long as the sensory network's plasticity rule co-evolves with the motor network, any plasticity rule that learns to produce consistent information about the value of encountered food can potentially be selected  To further test this assumption, we introduce a bottleneck of information propagation between the sensory and motor networks by using a step-function nonlinearity on the output of the sensory network (Eq 
4)  Similarly to the decision task of the static network, the output of the sensory network now becomes binary  This effectively reduces the flow of information from the sensory to the motor network, forcing the sensory network to consistently decide whether food should be consumed (with the caveat that the motor network can still interpret the binary sign in either of two ways, either consuming food marked with 1 or the ones marked with 0 by the sensory network) 
The agents perform equally well in this variation of the task as before (Fig  ), but now, the evolved plasticity rules seem to be more structured (Fig  )  Moreover, the variance of the learned weights in the bestperforming agents is significantly reduced (Fig  ), which indicates that the bottleneck in the sensory network is in-creasing selection pressure for rules that learn the environment's food distribution accurately 
We find that different sources of variability have a strong impact on the extent to which evolving agents will develop neuronal plasticity mechanisms for adapting to their environment  A diverse environment, a reliable sensory system, and a rate of environmental change that is neither too large nor too small are necessary conditions for an agent to be able to effectively adapt via synaptic plasticity 
Additionally, we find that minor variations of the task an agent has to solve or the parametrization of the network can give rise to significantly different plasticity rules  Our results partially extend to embodied artificial agents performing a foraging task  We show that environmental variability also pushes the development of plasticity in such agents 
Still, in contrast to the static agents, we find that the interaction of a static motor network with a plastic sensory network gives rise to a much greater variety of wellfunctioning learning rules  We propose a potential cause of this degeneracy; as the relatively complex motor network is allowed to read out and process the outputs from the plastic network, any consistent information coming out of these outputs can be potentially interpreted in a behaviorally useful way 
Reducing the information the motor network can extract from the sensory system significantly limits learning rule variability  Our findings on the effect of environmental variability concur with the findings of previous studies that have identified the constraints that environmental variability places on the evolutionary viability of learning behaviors 
We extend these findings in a mechanistic model which uses a biologically plausible learning mechanism (synaptic plasticity)  We show how a simple evolutionary algorithm can optimize the different parameters of a simple reward-modulated plasticity rule for solving simple prediction and decision tasks 
Reward-modulated plasticity has been extensively studied as a plausible mechanism for credit assignment in the brain ; ; and has found several applications in artificial intelligence and robotics tasks ;   Here, we demonstrate how such rules can be very well-tuned to take into account different environmental parameters and produce optimal behavior in simple systems 
Additionally, we demonstrate how the co-evolution of plasticity and static functional connectivity in different subnetworks fundamentally changes the evolutionary pressures on the resulting plasticity rules, allowing for greater diversity in the form of the learning rule and the resulting learned connectivity 
Several studies have demonstrated how, in biological networks, synaptic plasticity heavily interacts with and is driven by network topology   Moreover, it has been recently demonstrated that biological plasticity mechanisms are highly redundant in the sense that any observed neural connectivity or recorded activity can be achieved with a variety of distinct, unrelated learning rules  
This observed redundancy of learning rules in biological settings complements our results and suggests that the function of plasticity rules cannot be studied independently of the connectivity and topology of the networks they are acting on  The optimization of functional plasticity in neural networks is a promising research direction both as a means to understand biological learning processes and as a tool for building more autonomous artificial systems 
Our results suggest that reward-modulated plasticity is highly adaptable to different environments and can be incorporated into larger systems that solve complex tasks  This work studies a simplified toy model of neural network learning in stochastic environments  Future work could be built on this basic framework to examine more complex reward distributions and sources of environmental variability 
Moreover, a greater degree of biological realism could be added by studying more plausible network architectures (multiple plastic layers, recurrent and feedback connections) and more sophisticated plasticity rule parametrizations  Additionally, our foraging simulations were constrained by limited computational resources and were far from exhaustive 
Further experiments can investigate environments with different constraints, food distributions, multiple seasons, more complex motor control systems and interactions of those systems with different sensory networks as well as the inclusion of plasticity on the motor parts of the artificial organisms 



ground_truth: In the 2013-2014 season.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: How does the transition probability of the environment affect the learning rate in the static agent?

Read the following text and answer briefly.

Context 4: 
Myocardium – The muscular tissue of the heart 
New Wall-Motion Abnormalities – Results seen on an echocardiogram test report (see NWMA, below) 
Nitroglycerin – A medicine that helps relax and dilate arteries; often used to treat cardiac chest pain (angina)  Also called NTG or GTN 
NSR – Normal Sinus Rhythm: The characteristic rhythm of the healthy human heart  NSR is considered to be present if the heart rate is in the normal range, the P waves are normal on the EKG/ECG, and the rate does not vary significantly 
NSTEMI – Non-ST-segment-elevation myocardial infarction: The milder form of the two main types of heart attack  An NSTEMI heart attack does not produce an ST-segment elevation seen on an electrocardiogram test (EKG)  See also STEMI 
Nuclear Stress Test – A diagnostic test that usually involves two exercise stress tests, one while you’re exercising on a treadmill/stationary bike or with medication that stresses your heart, and another set while you’re at rest  A nuclear stress test is used to gather information about how well your heart works during physical activity and at rest  See also: Exercise stress test, Nuclear perfusion test, MIBI 
Open heart surgery – Any surgery in which the chest is opened and surgery is done on the heart muscle, valves, coronary arteries, or other parts of the heart (such as the aorta)  See also CABG 
Pacemaker – A surgically implanted electronic device that helps regulate the heartbeat 
PAD – Peripheral Artery Disease: A common circulatory problem in which narrowed arteries reduce blood flow to the limbs, usually to the legs  Symptoms include leg pain when walking (called intermittent claudication) 
PAF – Paroxysmal Atrial Fibrillation: Atrial fibrillation that lasts from a few seconds to days, then stops on its own  See also Atrial Fibrillation 
Palpitations – A noticeably rapid, strong, or irregular heartbeat due to agitation, exertion or illness 
Paroxysmal Atrial Fibrillation – An unusual heart arrhythmia of unknown origin, at one time believed to be associated with an unusual sensitivity to alcohol consumption 
PDA – patent ductus arteriosus: A persistent opening between two major blood vessels leading from the heart  The opening is called ductus arteriosus and is a normal part of a baby’s circulatory system before birth that usually closes shortly after birth  But when it remains open, it’s called a patent ductus arteriosus  If it’s small, it may never need treatment, but a large PDA left untreated can allow poorly oxygenated blood to flow in the wrong direction, weakening the heart muscle and causing heart failure or other complications 
Pericardium: two thin layers of a sac-like tissue that surround the heart, hold it in place and help it work 
PET – Positron Emission Tomography: A non-invasive scanning technique that uses small amounts of radioactive positrons (positively charged particles) to visualize body function and metabolism  In cardiology, PET scans are used to evaluate heart muscle function in patients with coronary artery disease or cardiomyopathy 
PFO – Patent Forman Ovale: An opening between the left and right atria (the upper chambers) of the heart  Everyone has a PFO before birth, but in 1 out of every 3 or 4 people, the opening does not close naturally as it should after birth 
Plaque – A deposit of fatty (and other) substances in the inner lining of the artery wall; it is characteristic of atherosclerosis 
POTS – Postural Orthostatic Tachycardia Syndrome: A disorder that causes an increased heart rate when a person stands upright 
PPCM – Post-partum cardiomyopathy: A form of cardiomyopathy that causes heart failure toward the end of pregnancy or in the months after delivery, in the absence of any other cause of heart failure 
Preeclampsia – a late-pregnancy complication identified by spikes in blood pressure, protein in the urine, possible vision problems  Women who experience pregnancy complications like preeclampsia are at significantly higher risk for heart disease 
Prinzmetal’s Variant Angina – Chest pain caused by a spasm in a coronary artery that supplies blood to the heart muscle 
PSVT – Paroxysmal Supraventricular Tachycardia: – An occasional rapid heart rate (150-250 beats per minute) that is caused by events triggered in areas above the heart’s lower chambers (the ventricles)  “Paroxysmal” means from time to time  See also supraventricular tachycardia (SVT) 
Pulmonary Valve: One of the four valves in the heart, located between the pulmonary artery and the right ventricle of the heart, moves blood toward the lungs and keeps it from sloshing back into the heart 
PV – Pulmonary Vein: A vein carrying oxygenated blood from the lungs to the left atrium of the heart 
PVC – Premature Ventricular Contraction: An early or extra heartbeat that happens when the heart’s lower chambers (the ventricles) contract too soon, out of sequence with the normal heartbeat  In the absence of any underlying heart disease, PVCs do not generally indicate a problem with electrical stability, and are usually benign 
RA – Right Atrium: The right upper chamber of the heart  The right atrium receives de-oxygenated blood from the body through the vena cava and pumps it into the right ventricle which then sends it to the lungs to be oxygenated 
Radial Artery: the artery in the wrist where a thin catheter is inserted through the body’s network of arteries in the arm and eventually into the heart during a procedure to implant a stent  Doctors may also call this transradial access, the transradial approach, or transradial angioplasty  Because it’s associated with fewer complications, this is increasingly considered the default access approach in most countries, except in the U S  where the traditional Femoral Artery (groin) approach is still the most popular access 
RBBB – Right Bundle Branch Block: A delay or obstruction along the pathway that electrical impulses travel to make your heart beat  The delay or blockage occurs on the pathway that sends electrical impulses to the right side of your heart  See also Left Bundle Branch Block 
RCA – Right Coronary Artery: An artery that supplies blood to the right side of the heart 
Restenosis – The re-closing or re-narrowing of an artery after an interventional procedure such as angioplasty or stent placement  Sometimes called “stent failure” 
RHD – Rheumatic Heart Disease: Permanent damage to the valves of the heart caused especially by repeated attacks of rheumatic fever 
RM – Right Main coronary artery: A blood vessel that supplies oxygenated blood to the walls of the heart’s ventricles and the right atrium 
RV – Right Ventricle: The lower right chamber of the heart that receives de-oxygenated blood from the right atrium and pumps it under low pressure into the lungs via the pulmonary artery 
SA – Sinus node: The “natural” pacemaker of the heart  The node is a group of specialized cells in the top of the right atrium which produces the electrical impulses that travel down to eventually reach the ventricular muscle, causing the heart to contract 
SB – Sinus Bradycardia: Abnormally slow heartbeat 
SBP – Systolic Blood Pressure: The highest blood pressure measured in the arteries  It occurs when the heart contracts with each heartbeat  Example: the first number in 120/80 
SCAD – Spontaneous Coronary Artery Dissection: A rare emergency condition that occurs when a tear forms in one of the blood vessels in the heart, causing a heart attack, abnormalities in heart rhythm and/or sudden death  SCAD tends to strike young healthy women with few if any cardiac risk factors 
SD – Septal defect: A hole in the wall of the heart separating the atria (two upper chambers of the heart) or in the wall of the heart separating the ventricles (two lower chambers) 
Sestamibi stress test – See MIBI 
Short QT intervals (SQT): An abnormal heart rhythm where the heart muscle takes a shorter time to recharge between beats  It can cause a variety of complications from fainting and dizziness to sudden cardiac arrest 
Sick Sinus Syndrome (also known as sinus node dysfunction) is caused by an electrical problem in the heart; a group of related heart conditions that can affect how the heart beats, most commonly in older adults, although it can be diagnosed in people of any age  “Sick sinus” refers to the sinoatrial node (see below)  In people with sick sinus syndrome, the SA node does not function normally 
Sinoatrial node (SA): also commonly called the sinus node; it’s a small bundle of neurons situated in the upper part of the wall of the right atrium (the right upper chamber of the heart)  The heart’s electrical impulses are generated there  It’s the normal natural pacemaker of the heart and is responsible for the initiation of each heartbeat 
Spontaneous Coronary Artery Dissection (SCAD) – A rare emergency condition that occurs when a tear forms in one of the blood vessels in the heart, causing a heart attack, abnormalities in heart rhythm and/or sudden death  SCAD tends to strike young healthy women with few if any cardiac risk factors 
SSS – Sick Sinus Syndrome: The failure of the sinus node to regulate the heart’s rhythm 
ST – Sinus Tachycardia: A heart rhythm with elevated rate of impulses originating from the sinoatrial node, defined as greater than 100 beats per minute (bpm) in an average adult  The normal heart rate in the average adult ranges from 60–100 bpm  Also called sinus tach or sinus tachy 
Statins – Any of a class of drugs that lower the levels of low-density lipoproteins (LDL) – the ‘bad’ cholesterol in the blood – by inhibiting the activity of an enzyme involved in the production of cholesterol in the liver  Examples of brand name statins: Lipitor, Crestor, Zocor, Mevacor, Levachol, Lescol, etc  Also available as a cheaper generic form of the drug 
STEMI – ST-elevation heart attack (myocardial infarction)  The more severe form of the two main types of heart attack  A STEMI produces a characteristic elevation in the ST segment on an electrocardiogram (EKG)  The elevated ST segment is how this type of heart attack got its name  See also NSTEMI 
Stent – An implantable device made of expandable, metal mesh (looks a bit like a tiny chicken wire tube) that is placed (by using a balloon catheter) at the site of a narrowing coronary artery during an angioplasty procedure  The stent is then expanded when the balloon fills, the balloon is removed, and the stent is left in place to help keep the artery open  TRIVIA ALERT: the coronary stent was named after Charles Stent (1807-1885), an English dentist who invented a compound to produce dentures and other things like skin grafts and hollow tubes (essentially what a metal coronary stent is)  His real claim to fame occurred when he suggested using his material to coat underwater trans-Atlantic cable, which had broken several times as a result of corrosion by seawater  You’re welcome 
Stint – a common spelling mistake when what you really mean is the word “stent” (see above) 
Stress Echocardiography – A standard echocardiogram test that’s performed while the person exercises on a treadmill or stationary bicycle  This test can be used to visualize the motion of the heart’s walls and pumping action when the heart is stressed, possibly revealing a lack of blood flow that isn’t always apparent on other heart tests  The echocardiogram is performed just before and just after the exercise part of the procedure  See also TTE 
Sudden Cardiac Arrest – The stopping of the heartbeat, usually because of interference with the electrical signal (often associated with coronary heart disease)  Can lead to Sudden Cardiac Death 
Takotsubo Cardiomyopathy – A heart condition that can mimic a heart attack  Sometimes called Broken Heart Syndrome, it is not a heart attack, but it feels just like one, with common symptoms like severe chest pain and shortness of breath  It sometimes follows a severe emotional stress  Over 90% of reported cases are in women ages 58 to 75  Also referred to as Broken Heart Syndrome, stress cardiomyopathy, stress-induced cardiomyopathy or apical ballooning syndrome 
TAVR – Transcatheter aortic valve replacement: A minimally invasive procedure to repair a damaged or diseased aortic valve  A catheter is inserted into an artery in the groin and threaded to the heart  A balloon at the end of the catheter, with a replacement valve folded around it, delivers the new valve to take the place of the old  Also called TAVI (Transcatheter aortic valve implantation) 
Tetralogy of Fallot – A rare condition caused by a combination of four heart defects that are present at birth, affecting the structure of the heart and causing oxygen-poor blood to flow out of the heart and into the rest of the body  Infants and children with Tetralogy of Fallot usually have blue-tinged skin because their blood doesn’t carry enough oxygen  Often diagnosed in infancy, but sometimes not until later in life depending on severity 
Tg – Triglycerides: The most common fatty substance found in the blood; normally stored as an energy source in fat tissue  High triglyceride levels may thicken the blood and make a person more susceptible to clot formation  High triglyceride levels tend to accompany high cholesterol levels and other risk factors for heart disease, such as obesity 
TIA – Transient Ischemic Attack: A stroke-like event that lasts only for a short time and is caused by a temporarily blocked blood vessel 
TEE – Transesophageal echocardiogram: This test involves an ultrasound transducer inserted down the throat into the esophagus in order to take clear images of the heart structures without the interference of the lungs and chest 
Treadmill Stress Test – See Exercise Stress Test 
troponin – a type of cardiac enzyme found in heart muscle, and released into the blood when there is damage to the heart (for example, during a heart attack)  A positive blood test that shows elevated troponin is the preferred test for a suspected heart attack because it is more specific for heart injury than other blood tests, especially the newer high sensitivity troponin tests (hs-cTnT) 
TTE – Transthoracic Echocardiogram: This is the standard echocardiogram, a painless test similar to X-ray, but without the radiation, using a hand-held device called a transducer placed on the chest to transmit high frequency sound waves (ultrasound)  These sound waves bounce off the heart structures, producing images and sounds that can be used by the doctor to detect heart damage and disease 
TV – Tricuspid Valve: One of four one-way valves in the heart, a structure that controls blood flow from the heart’s upper right chamber (the right atrium) into the lower right chamber (the right ventricle) 
UA or USA – Unstable Angina: Chest pain that occurs when diseased blood vessels restrict blood flow to the heart; symptoms are not relieved by rest; considered a dangerous and emergency crisis requiring immediate medical help 
Valves: Your heart has four one-way valves that keep blood flowing in the right direction  Blood enters the heart first through the tricuspid valve, and next goes through the pulmonary valve (sometimes called the pulmonic valve) on its way to the lungs  Then the blood returning from the lungs passes through the mitral (bicuspid) valve and leaves the heart through the aortic valve 
Vasodilator: A drug that causes dilation (widening) of blood vessels 
Vasospasm: A blood vessel spasm that causes sudden constriction, reducing its diameter and blood flow to the heart muscle  See also Prinzmetal’s Variant Angina 
VB – Ventricular Bigeminy: A heart rhythm condition in which the heart experiences two beats of the pulse in rapid succession 
Vena Cava – a large vein that carryies de-oxygenated blood into the heart  There are two in humans, the inferior vena cava (carrying blood from the lower body) and the superior vena cava (carrying blood from the head, arms, and upper body) 
Ventricle – each of the two main chambers of the heart, left and right 
VF – Ventricular Fibrillation: A condition in which the ventricles (two lower chambers of the heart) contract in a rapid, unsynchronized fashion  When fibrillation occurs, the ventricles cannot pump blood throughout the body  Most sudden cardiac deaths are caused by VF or ventricular tachycardia (VT) 
VLDL – Very Low Density Lipoprotein: Molecules made up of mostly triglycerides, cholesterol and proteins  VLDL, also known as the “very bad” cholesterol, carries cholesterol from the liver to organs and tissues in the body  It may lead to low density lipoproteins (LDL), associated with higher heart disease risks  VLDL levels are tricky to measure routinely, and are usually estimated as a percentage of your triglyceride levels  By reducing triglycerides, you are usually also reducing your VLDL levels 
Warfarin – A drug taken to prevent the blood from clotting and to treat blood clots  Warfarin is believed to reduce the risk of blood clots causing strokes or heart attacks  Also known as Coumadin 
Widowmaker heart attack – The type of heart attack I survived, since you asked  A nickname doctors use to describe a severely blocked left main coronary artery or proximal left anterior descending coronary artery of the heart  This term is used because if the artery gets abruptly and completely blocked, it can cause a massive heart attack that will likely lead to sudden cardiac death  Please note the gender imbalance here: despite the number of women like me who do experience this type of cardiac event, doctors are not calling this the widowermaker, after all 
WPW – Wolff-Parkinson-White Syndrome: A condition in which an extra electrical pathway connects the atria (two upper chambers) and the ventricles (two lower chambers)  It may cause a rapid heartbeat 
NOTE FROM CAROLYN: I was very happy when we were able to include this entire glossary in my book, “A Woman’s Guide to Living with Heart Disease“ (Johns Hopkins University Press, 2017) 
Are we missing any important heart acronyms/terms from this list? Let me know!
Please can someone explain something for me  I am a 53 yr old woman and generally fit and healthy  Had 2 ECG’s due to a one off dizzy spell during a stressful time dealing with my fathers terminal diagnosis  The 2nd ECG request did give me concern as i did not know why i had to have one  On 24/01/19 at my doctors appointment she explained that on 3 the leads it showed inverted T waves  And she explained that it may suggest angina  I was so shocked  Wasn’t expecting that  She gave me a GNT (nitroglycerin) spray in case I do get pain and take 75Mg of aspirin  I’m now waiting for a Cardiology referral 
I am so stressed and consumed by what might be wrong  My maternal grandmother had angina and valve issues  Her 3 brothers all had double bypasses  Could I have inherited this? I am not overweight at 63kg and 5 ft 9  I walk 20-25 miles a week at work and general walking here and there  I started HRT (patches evorol 25 -50) in July as menopause pain was making me feel like I was 90 and was getting me down 
I am worried so much now and analysing every ache/ twinge I get  I feel like a hypochondriac at the moment  I’m worried what will happen at the cardiologist and what the test will entail and tell me  I am waiting on cholesterol test which I had on 25/01/19  Can I have inverted T waves and be fine  Please help I am so scared and crying far too much 
Hello Colleen – the first thing is: please take a big deep breath before you read another word here! I’m not a physician so of course cannot comment on your specific case, but I can tell you generally that the definition of “angina” (as this glossary lists above) is “distressing symptoms”, typically chest pain that gets worse with exertion, and goes away with rest  That’s classic stable angina… typically caused by something that’s reducing blood flow to the heart muscle (causing the chest pain of angina) 
A family history that might make a difference for you personally is only in what’s called your ‘first degree’ relatives: for example, if your mother or sister were diagnosed with heart disease before age 65, or if your Dad or brother were diagnosed before age 55, then doctors would consider that you have a family history as a risk factor for heart disease  There’s little if any scientific evidence that a grandparent or uncle’s heart disease history has any effect on your own risk 
It is a very good thing that you’re having further tests and a referral to a cardiologist, if only to ease your mind  There are many reasons for inverted T-waves, ranging from cardiac issues to completely benign conditions  One way of looking at this is choosing to believe that seeing a cardiologist will ease your mind one way or the other – so this is something to look forward to, not dread  If the cardiologist spots something suspicious, a treatment plan will be created  If not, you can wave goodbye and go back to happily living your life 
Try thinking of this cardiology appointment just as you would if your car were making some frightening noises and you were bringing it to your mechanic for a check up  You could work yourself into a complete state worrying ahead of time if the car trouble is going to be serious, or you could look at this appointment as the solution – at last! – to figuring out what’s wrong so the mechanic can recommend the next step 
Thank you for this list of so many definitions provided in plain English  what a valuable resource this is  THANK YOU, I have been looking for translations FOR PATIENTS not med school graduates– like this for three years 
My family doctor had me wear a 24 hr EKG  After reading the results, she has scheduled a scope to look inside my heart by a specialist  Completely forgoing a stress test  Said I have major changes in the EKG, what type of changes could they be looking at? Had LAD STENT INSERTED 7 YRS AGO – WHAT COULD THEY BE LOOKING FOR?
This is a great wealth of information, Carolyn! I looked and did not see my diagnosis, which is aortic stenosis  I looked under aortic as well as stenosis  Did I just miss it somehow?
I learned some new information, I am a bit familiar now, but not when I had my MI, it was like learning a new language  But, my favorite part was seeing SCAD on this list! Thank you 
Thanks and welcome! I was thinking of editing that SCAD definition actually: I suspect that that it isn’t so much that SCAD is “rare”, but it’s more that it’s “rarely correctly diagnosed” 
I totally agree that SCAD is not as rare as I believed for many years  Once awareness is spread to all medical staff, I believe many lives will be saved  Hoping for a brighter future for all SCAD patients 
I hope so too, Cathy  Perhaps when more SCAD studies (like Mayo Clinic’s) are published and read by more and more MDs, it will no longer be “rarely correctly diagnosed” 
It’s great to see IST on here  I was diagnosed with it 9 years ago and the lack of awareness is frustrating 
What a great resource for heart patients and their families!
Thanks so much, Ashley  I recently updated my original 2011 list after the world-famous Cleveland Clinic tweeted their glossary recently and I noticed that their list had a few glaring omissions (like SCAD and Brugada Syndrome) so this made me wonder what my list might be missing, too  Let me know if there’s anything else you think should be included, okay?
How is your health these days? How are you feeling?
New for me too  I have just been diagnosed with A-HCM: Apical Hypertrophic Cardiomyopathy 
I’ll add that one to my list, Kathleen – thanks!
Just saw this, Carolyn, and you’ve compiled a great resource  One note on A-HCM: Present thinking is that it’s due to a genetic modification  Runs in families though sometimes occurs spontaneously  I have not as yet done genetic testing, though it’s been offered 
Thanks Kathleen – like many cardiac diagnoses, it sounds like a moving target… Good luck to you!
This list is great  I’ve just been diagnosed and am utterly overwhelmed  Even in the WomenHeart online support community, I often have no clue most days what others are talking about with all these initials about their heart tests and specific disease  This is VERY helpful, thank you SO MUCH  Love your website which has been a godsend since my diagnosis 



ground_truth: As the transition probability increases, the learning rate initially rises and then declines.