November 30, Virtual Event. November 18, Annual Water Conference — In-Person and Online. We believe in the power of good information to build a brighter future for California. Help support our mission. Mark Baldassare , Dean Bonner , Rachel Lawler , and Deja Thomas. Supported with funding from the Arjay and Frances F. Miller Foundation and the James Irvine Foundation. California voters have now received their mail ballots, and the November 8 general election has entered its final stage.

Amid rising prices and economic uncertainty—as well as deep partisan divisions over social and political issues—Californians are processing a great deal of information to help them choose state constitutional officers and state legislators and to make policy decisions about state propositions. The midterm election also features a closely divided Congress, with the likelihood that a few races in California may determine which party controls the US House. These are among the key findings of a statewide survey on state and national issues conducted from October 14 to 23 by the Public Policy Institute of California:.

Today, there is a wide partisan divide: seven in ten Democrats are optimistic about the direction of the state, while 91 percent of Republicans and 59 percent of independents are pessimistic.

Californians are much more pessimistic about the direction of the country than they are about the direction of the state. Majorities across all demographic groups and partisan groups, as well as across regions, are pessimistic about the direction of the United States. A wide partisan divide exists: most Democrats and independents say their financial situation is about the same as a year ago, while solid majorities of Republicans say they are worse off.

Regionally, about half in the San Francisco Bay Area and Los Angeles say they are about the same, while half in the Central Valley say they are worse off; residents elsewhere are divided between being worse off and the same. The shares saying they are worse off decline as educational attainment increases. Strong majorities across partisan groups feel negatively, but Republicans and independents are much more likely than Democrats to say the economy is in poor shape.

Today, majorities across partisan, demographic, and regional groups say they are following news about the gubernatorial election either very or fairly closely. In the upcoming November 8 election, there will be seven state propositions for voters.

Due to time constraints, our survey only asked about three ballot measures: Propositions 26, 27, and For each, we read the proposition number, ballot, and ballot label. Two of the state ballot measures were also included in the September survey Propositions 27 and 30 , while Proposition 26 was not. This measure would allow in-person sports betting at racetracks and tribal casinos, requiring that racetracks and casinos offering sports betting make certain payments to the state to support state regulatory costs.

It also allows roulette and dice games at tribal casinos and adds a new way to enforce certain state gambling laws. Fewer than half of likely voters say the outcome of each of these state propositions is very important to them. Today, 21 percent of likely voters say the outcome of Prop 26 is very important, 31 percent say the outcome of Prop 27 is very important, and 42 percent say the outcome of Prop 30 is very important.

Today, when it comes to the importance of the outcome of Prop 26, one in four or fewer across partisan groups say it is very important to them. About one in three across partisan groups say the outcome of Prop 27 is very important to them. Fewer than half across partisan groups say the outcome of Prop 30 is very important to them. When asked how they would vote if the election for the US House of Representatives were held today, 56 percent of likely voters say they would vote for or lean toward the Democratic candidate, while 39 percent would vote for or lean toward the Republican candidate.

Democratic candidates are preferred by a point margin in Democratic-held districts, while Republican candidates are preferred by a point margin in Republican-held districts. Abortion is another prominent issue in this election. When asked about the importance of abortion rights, 61 percent of likely voters say the issue is very important in determining their vote for Congress and another 20 percent say it is somewhat important; just 17 percent say it is not too or not at all important.

With the controlling party in Congress hanging in the balance, 51 percent of likely voters say they are extremely or very enthusiastic about voting for Congress this year; another 29 percent are somewhat enthusiastic while 19 percent are either not too or not at all enthusiastic. Today, Democrats and Republicans have about equal levels of enthusiasm, while independents are much less likely to be extremely or very enthusiastic.

As Californians prepare to vote in the upcoming midterm election, fewer than half of adults and likely voters are satisfied with the way democracy is working in the United States—and few are very satisfied.

Satisfaction was higher in our February survey when 53 percent of adults and 48 percent of likely voters were satisfied with democracy in America. Today, half of Democrats and about four in ten independents are satisfied, compared to about one in five Republicans.

Notably, four in ten Republicans are not at all satisfied. In addition to the lack of satisfaction with the way democracy is working, Californians are divided about whether Americans of different political positions can still come together and work out their differences.

Forty-nine percent are optimistic, while 46 percent are pessimistic. Today, in a rare moment of bipartisan agreement, about four in ten Democrats, Republicans, and independents are optimistic that Americans of different political views will be able to come together. Notably, in , half or more across parties, regions, and demographic groups were optimistic. Today, about eight in ten Democrats—compared to about half of independents and about one in ten Republicans—approve of Governor Newsom.

Across demographic groups, about half or more approve of how Governor Newsom is handling his job. Approval of Congress among adults has been below 40 percent for all of after seeing a brief run above 40 percent for all of Democrats are far more likely than Republicans to approve of Congress.

Fewer than half across regions and demographic groups approve of Congress. Approval in March was at 44 percent for adults and 39 percent for likely voters. Across demographic groups, about half or more approve among women, younger adults, African Americans, Asian Americans, and Latinos. Views are similar across education and income groups, with just fewer than half approving.

Approval in March was at 41 percent for adults and 36 percent for likely voters. Across regions, approval reaches a majority only in the San Francisco Bay Area. Across demographic groups, approval reaches a majority only among African Americans.

This map highlights the five geographic regions for which we present results; these regions account for approximately 90 percent of the state population. Residents of other geographic areas in gray are included in the results reported for all adults, registered voters, and likely voters, but sample sizes for these less-populous areas are not large enough to report separately.

The PPIC Statewide Survey is directed by Mark Baldassare, president and CEO and survey director at the Public Policy Institute of California. Coauthors of this report include survey analyst Deja Thomas, who was the project manager for this survey; associate survey director and research fellow Dean Bonner; and survey analyst Rachel Lawler.

The Californians and Their Government survey is supported with funding from the Arjay and Frances F. Findings in this report are based on a survey of 1, California adult residents, including 1, interviewed on cell phones and interviewed on landline telephones. The sample included respondents reached by calling back respondents who had previously completed an interview in PPIC Statewide Surveys in the last six months.

Interviews took an average of 19 minutes to complete. Interviewing took place on weekend days and weekday nights from October 14—23, Cell phone interviews were conducted using a computer-generated random sample of cell phone numbers. Additionally, we utilized a registration-based sample RBS of cell phone numbers for adults who are registered to vote in California. All cell phone numbers with California area codes were eligible for selection.

After a cell phone user was reached, the interviewer verified that this person was age 18 or older, a resident of California, and in a safe place to continue the survey e. Cell phone respondents were offered a small reimbursement to help defray the cost of the call. Cell phone interviews were conducted with adults who have cell phone service only and with those who have both cell phone and landline service in the household.

Landline interviews were conducted using a computer-generated random sample of telephone numbers that ensured that both listed and unlisted numbers were called. Additionally, we utilized a registration-based sample RBS of landline phone numbers for adults who are registered to vote in California.

All landline telephone exchanges in California were eligible for selection. For both cell phones and landlines, telephone numbers were called as many as eight times. When no contact with an individual was made, calls to a number were limited to six. Also, to increase our ability to interview Asian American adults, we made up to three additional calls to phone numbers estimated by Survey Sampling International as likely to be associated with Asian American individuals.

Accent on Languages, Inc. Other attributes are called value attributes. Value attributes do not affect inheritance, and elements with value attributes may not have child elements see XML Format. Non-distinguishing attributes are identified by DTD Annotations such as VALUE. For any element in an XML file, an element chain is a resolved [ XPath ] leading from the root to an element, with attributes on each element in alphabetical order.

xml we may have:. An element chain A is an extension of an element chain B if B is equivalent to an initial portion of A. For example, 2 below is an extension of 1. Equivalent, depending on the tree, may not be "identical to". See below for an example. This works because of restrictions on the structure of LDML, including that it does not allow mixed content.

The ordering is the ordering that the element chains are found in the file, and thus determined by the DTD. For example, some of those pairs would be the following. Notice that the first has the null string as element contents. Two LDML element chains are equivalent when they would be identical if all attributes and their values were removed — except for distinguishing attributes. Thus the following are equivalent:.

For any locale ID, a locale chain is an ordered list starting with the root and leading down to the ID. To produce fully resolved locale data file from CLDR for a locale ID L, you start with L, and successively add unique items from the parent locales until you get up to root.

More formally, this can be expressed as the following procedure. For more information, see Process. However, some data that is not explicitly marked as draft may be implicitly draft , either because it inherits it from a parent, or from an enclosing element. Example 2. Suppose that new locale data is added for af Afrikaans. To indicate that all of the data is unconfirmed , the attribute can be added to the top level. However, normally the draft attributes should be canonicalized, which means they are pushed down to leaf nodes as described in Section 5.

If an LDML file does have draft attributes that are not on leaf nodes, the file should be interpreted as if it were the canonicalized version of that file. More formally, here is how to determine whether data for an element chain E is implicitly or explicitly draft, given a locale L. Sections 1, 2, and 4 are simply formalizations of what is in LDML already. Item 3 adds the new element. The validSubLocales in the most specific farthest from root file locale file "wins" through the full resolution step data from more specific files replacing data from less specific ones.

When accessing data based on keywords, the following process is used. Consider the following example:. Note: It is an invariant that the default in root for a given element must always be a value that exists in root. So you can not have the following in root:. For identifiers, such as language codes, script codes, region codes, variant codes, types, keywords, currency symbols or currency display names, the default value is the identifier itself whenever no value is found in the root.

Thus if there is no display name for the region code 'QA' in root, then the display name is simply 'QA'. There are a number of situations where it is useful to be able to find the most likely language, script, or region. For example, given the language "zh" and the region "TW", what is the most likely script? Given the script "Thai" what is the most likely language or region?

Given the region TW, what is the most likely language and script? Conversely, given a locale, it is useful to find out which fields language, script, or region may be superfluous, in the sense that they contain the likely tags. The likelySubtag supplemental data provides default information for computing these values. This data is based on the default content data, the population data, and the suppress-script data in [ BCP47 ].

It is heuristically derived, and may change over time. To look up data in the table, see if a locale matches one of the from attribute values. If so, fetch the corresponding to attribute value. For example, the Chinese data looks like the following:. Note that as of CLDR v24, any field present in the 'from' field is also present in the 'to' field, so an input field will not change in "Add Likely Subtags" operation.

The data and operations can also be used with language tags using [ BCP47 ] syntax, with the appropriate changes. In addition, certain common 'denormalized' language subtags such as 'iw' for 'he' may occur in both the 'from' and 'to' fields.

This allows for implementations that use those denormalized subtags to use the data with only minor changes to the operations. An implementation may choose to exclude language tags with the language subtag "und" from the following operation.

In such a case, only the canonicalization is done. An implementation can declare that it is doing the exclusion, or can take a parameter that controls whether or not to do it. Add Likely Subtags: Given a source locale X, to return a locale Y where the empty subtags have been filled in by the most likely subtags. A subtag is called empty if it is a missing script or region subtag, or it is a base language subtag with the value "und". In the description below, a subscript on a subtag x indicates which tag it is from: xs is in the source, xm is in a match, and xr is in the final result.

The lookup can be optimized. For example, if any of the tags in Step 2 are the same as previous ones in that list, they do not need to be tested. To find the most likely language for a country, or language for a script, use "und" as the language subtag. Remove Likely Subtags: Given a locale, remove any fields that Add Likely Subtags would add. Implementers are often faced with the issue of how to match the user's requested languages with their product's supported languages.

For example, suppose that a product supports {ja-JP, de, zh-TW}. The standard truncation-fallback algorithm does not work well when faced with the complexities of natural language. The language matching data is designed to fill that gap. Stated in those terms, language matching can have the effect of a more complex fallback, such as:. Language matching is used to find the best supported locale ID given a requested list of languages. The requested list could come from different sources, such as the user's list of preferred languages in the OS Settings, or from a browser Accept-Language list.

For example, if my native tongue is English, I can understand Swiss German and German, my French is rusty but usable, and Italian basic, ideally an implementation would allow me to select {gsw, de, fr} as my preferred list of languages, skipping Italian because my comprehension is not good enough for arbitrary content. Language Matching can also be used to get fallback data elements. In many cases, there may not be full data for a particular locale. For example, for a Breton speaker, the best fallback if data is unavailable might be French.

That is, suppose we have found a Breton bundle, but it does not contain translation for the key "CN" for the country China. It is best to return "chine", rather than falling back to the value default language such as Russian and getting "Китай".

The language matching data can be used to get the closest fallback locales of those supported to a given language. When such fallback is used for inherited item lookup, the normal order of inheritance is used for inherited item lookup, except that before using any data from root , the data for the fallback locales would be used if available.

Language matching does not interact with the fallback of resources within the locale-parent chain. For example, suppose that we are looking for the value for a particular path P in nb-NO.

In the absence of aliases, normally the following lookup is used. That is, we first look in nb-NO. If there is no value for P there, then we look in nb. If there is no value for P there, we return the value for P in root or a code value, if there is nothing there. Remember that if there is an alias element along this path, then the lookup may restart with a different path in nb-NO or another locale.

However, suppose that nb-NO has the fallback values [nn da sv en] , derived from language matching. In that case, an implementation may progressively look up each of the listed locales, with the appropriate substitutions, returning the first value that is not found in root. This follows roughly the following pseudocode:. The locales in the fallback list are not used recursively. For example, for the lookup of a path in nb-NO, if fr were a fallback value for da , it would not matter for the above process.

Only the original language matters. The language matching data is intended to be used according to the following algorithm. This is a logical description, and can be optimized for production in many ways.

In this algorithm, the languageMatching data is interpreted as an ordered list. Distances between given pair of subtags can be larger or smaller than the typical distances. For example, the distance between en and en-GB can be greater than those between en-GB and en-IE.

Example: sr-Latn vs. The distances resulting from the table are not linear, but are rather chosen to produce expected results. So a distance of 10 is not necessarily twice as "bad" as a distance of 5. Implementations may want to have a mode where script distances should swamp language distances.

The tables are built such that this can be accomplished by multiplying the language distance by 0. It is typically useful to set the discount factor between successive elements of the desired languages list to be slightly greater than the default region difference.

That avoids the following problem:. This user would expect to get "de", not "fr". In practice, when a user selects a list of preferred languages, they don't include all the regional variants ahead of their second base language. Yet while the user's desired languages really doesn't tell us the priority ranking among their languages, normally the fall-off between the user's languages is substantially greater than regional variants.

Part of this is because 'und' has a special function in BCP 47; it stands in for 'no supplied base language'.

To prevent this from happening, if the desired base language is und, the language matcher should not apply likely subtags to it. For example, suppose that nn-DE and nb-FR are being compared. They are first maximized to nn-Latn-DE and nb-Latn-FR, respectively. The list is searched. The languages are truncated to nn-Latn and nb-Latn, then to nn and nb. Note that language matching is orthogonal to the how closely two languages are related linguistically.

For example, Breton is more closely related to Welsh than to French, but French is the better match because it is more likely that a Breton reader will understand French than Welsh. This also illustrates that the matches are often asymmetric: it is not likely that a French reader will understand Breton. The results may be more understandable by users.

Looking for en-SK, for example, should fall back to something within Europe eg en-GB in preference to something far away and unrelated eg en-SG. Such a closeness metric does not need to be exact; a small amount of data can be used to give an approximate distance between any two regions.

However, any such data must be used carefully; although Hong Kong is closer to India than to the UK, it is unlikely that en-IN would be a better match to en-HK than en-GB would.

The enhanced format for language matching adds structure to enable better matching of languages. The extended structure allows matching to take into account broad similarities that would give better results. Each region in that cluster should be closer to each other than to any other region. And a region outside the cluster should be closer to another region outside that cluster than to one inside.

Note that we use for all of the Americas in the variables above, because en-US should be in the same cluster as es and its contents. In the rules, the percent value These new variables and rules divide up the world into clusters, where items in the same clusters for specific languages get the normal regional difference, and items in different clusters get different weights.

Each cluster can have one or more associated paradigmLocales. These are locales that are preferred within a cluster. Both of {en-GU en} are in a different cluster.

While {en-IN en-GB} are in the same cluster, and the same distance from en-SA, the preference is given to en-GB because it is in the paradigm locales.

It would be possible to express this in rules, but using this mechanism handles these very common cases without bulking up the tables. The paradigmLocales also allow matching to macroregions. But es-MX should match more closely to es than to any of the other es sublocales. There are two kinds of data that can be expressed in LDML: language-dependent data and supplementary data.

In either case, data can be split across multiple files, which can be in multiple directory trees. The status of the data is the same, whether or not data is split. That is, for the purpose of validation and lookup, all of the data for the above ja. xml files is treated as if it was in a single file. The file name must match the identity element. xml must contain the following elements:. Supplemental data can have different root elements, currently: ldmlBCP47 , supplementalData , keyboard , and platform.

Keyboard and platform files are considered distinct. The ldmlBCP47 files and supplementalData files that have the same root are all logically part of the same file; they are simply split into separate files for convenience. Implementations may split the files in different ways, also for their convenience.

The following sections describe the structure of the XML format for language-dependent data. The more precise syntax is in the ldml. dtd file; however, the DTD does not describe all the constraints on the structure. The XML structure is stable over releases. Elements and attributes may be deprecated: they are retained in the DTD but their usage is strongly discouraged. In most cases, an alternate structure is provided for expressing the information. There is only one exception: newer DTDs cannot be used with version 1.

In general, all translatable text in this format is in element contents, while attributes are reserved for types and non-translated information such as numbers or dates.

The reason that attributes are not used for translatable text is that spaces are not preserved, and we cannot predict where spaces may be significant in translated material. For structure elements, there are restrictions to allow for effective inheritance and processing:. Rule elements do not have these restrictions, but also do not inherit, except as an entire block. Items which are ordered have the DTD Annotation ORDERED.

See DTD Annotations and Section 4. For more technical details, see Updating-DTDs. Note that the data in examples given below is purely illustrative, and does not match any particular language. For a more detailed example of this format, see [ Example ]. There is also a DTD for this format, but remember that the DTD alone is not sufficient to understand the semantics, the constraints, nor the interrelationships between the different elements and attributes.

You may wish to have copies of each of these to hand as you proceed through the rest of this document. In particular, all elements allow for draft versions to coexist in the file at the same time. Thus most elements are marked in the DTD as allowing multiple instances. However, unless an element is annotated as ORDERED , or has a distinguishing attribute, it can only occur once as a subelement of a given element.

Thus, for example, the following is illegal even though allowed by the DTD:. There must be only one instance of these per parent, unless there are other distinguishing attributes such as an alt element. In general, LDML data should be in NFC format. Normalization forms are defined by [ UAX15 ]. These elements must not be normalized either to NFC or NFD , or their meaning may be changed. Thus LDML documents must not be normalized as a whole. Lists, such as singleCountries are space-delimited.

That means that they are separated by one or more XML whitespace characters:. This element is designed to allow for arbitrary additional annotation and data that is product-specific. It has one required attribute xmlns , which specifies the XML namespace of the special data. For example, the following used the version 1. The elements in this section are not part of the Locale Data Markup Language 1. Instead, they are special elements used for application-specific data to be stored in the Common Locale Repository.

They may change or be removed in future versions of this document, and are present here more as examples of how to extend the format. Some of these items may move into a future version of the Locale Data Markup Language specification. The above examples are old versions: consult the documentation for the specific application to see which should be used. These DTDs use namespaces and the special element. To include one or more, use the following pattern to import the special DTDs that are used in the file:.

Note: A previous version of this document contained a special element for ISO TR compatibility data. That element has been withdrawn, pending further investigation, since is a Type 1 TR: "when the required support cannot be obtained for the publication of an International Standard, despite repeated effort". See the ballot comments on Comments for details on the defects. For example, most of these patterns make little provision for substantial changes in format when elements are empty, so are not particularly useful in practice.

Compare, for example, the mail-merge capabilities of production software such as Microsoft Word or OpenOffice. Note: While the CLDR specification guarantees backwards compatibility, the definition of specials is up to other organizations. Any assurance of backwards compatibility is up to those organizations. A number of the elements above can have extra information for openoffice.

org , such as the following example:. The contents of any element in root can be replaced by an alias, which points to the path where the data can be found. If not found there, then the resource bundle at "de" will be searched, and so on. If the path attribute is present, then its value is an [ XPath ] that points to a different node in the tree.

The default value if the path is not present is the same position in the tree. All of the attributes in the [ XPath ] must be distinguishing elements. For more details, see Section 4. This special value is equivalent to the locale being resolved. For example, consider the following example, where locale data for 'de' is being resolved:.

The alias in root is logically replaced not by the elements in root itself, but by elements in the 'target' locale. For more details on data resolution, see Section 4. Aliases must be resolved recursively. An alias may point to another path that results in another alias being found, and so on.

For example, looking up Thai buddhist abbreviated months for the locale xx-YY may result in the following chain of aliases being followed:. It is an error to have a circular chain of aliases. That is, a collection of LDML XML documents must not have situations where a sequence of alias lookups including inheritance and lateral inheritance can be followed indefinitely without terminating. Many elements can have a display name.

This is a translated name that can be presented to users when discussing the particular service. For example, a number format, used to format numbers using the conventions of that locale, can have translated name for presentation in GUIs.

Where present, the display names must be unique; that is, two distinct codes would not get the same display name. There is one exception to this: in time zones, where parsing results would give the same GMT offset, the standard and daylight display names can be the same across different time zone IDs. Any translations should follow customary practice for the locale in question. For more information, see [ Data Formats ]. Unfortunately, XML does not have the capability to contain all Unicode code points.

Due to this, in certain instances extra syntax is required to represent those code points that cannot be otherwise represented in element content. The escaping syntax is only defined on a few types of elements, such as in collation or exemplar sets, and uses the appropriate syntax for that type. If this attribute is present, it indicates the status of all the data in this element and any subelements unless they have a contrary draft value , as per the following:. For more information on precisely how these values are computed for any given release, see Data Submission and Vetting Process on the CLDR website.

The draft attribute should only occur on "leaf" elements, and is deprecated elsewhere. For a more formal description of how elements are inherited, and what their draft status is, see Section 4. This attribute labels an alternative value for an element.

The value is a descriptor that indicates what kind of alternative it is, and takes one of the following. proposed should only be present if the draft status is not approved.

It indicates that the data is proposed replacement data that has been added provisionally until the differences between it and the other data can be vetted. For example, suppose that the translation for September for some language is "Settembru", and a bug report is filed that that should be "Settembro". Now assume another bug report comes in, saying that the correct form is actually "Settembre".

Another alternative can be added:. The values for variantname at this time include "variant", "list", "email", "www", "short", and "secondary".

For a more complete description of how draft applies to data, see Section 4. The value of this attribute is a token representing a reference for the information in the element, including standards that it may conform to.

In older versions of CLDR, the value of the attribute was freeform text. That format is deprecated. The reference element may be inherited. xml even though it is not defined there, if it is defined in sv. When attribute specify date ranges, it is usually done with attributes from and to. The from attribute specifies the starting point, and the to attribute specifies the end point. The deprecated time attribute was formerly used to specify time with the deprecated weekEndStart and weekEndEnd elements, which were themselves inherently from or to.

The data format is a restricted ISO format, restricted to the fields year , month , day , hour , minute , and second in that order, with "-" used as a separator between date fields, a space used as the separator between the date and the time fields, and : used as a separator between the time fields.

If the minute or minute and second are absent, they are interpreted as zero. If the hour is also missing, then it is interpreted based on whether the attribute is from or to.

That is, Friday at is the same time as Saturday at Thus when the hour is missing, the from and to are interpreted inclusively: the range includes all of the day mentioned. If the from element is missing, it is assumed to be as far backwards in time as there is data for; if the to element is missing, then it is from this point onwards, with no known end point.

The dates and times are specified in local time, unless otherwise noted. In particular, the metazone values are in UTC also known as GMT. The content of certain elements, such as date or number formats, may consist of several sub-elements with an inherent order for example, the year, month, and day for dates.

In some cases, the order of these sub-elements may be changed depending on the bidirectional context in which the element is embedded. For example, short date formats in languages such as Arabic may contain neutral or weak characters at the beginning or end of the element content. In such a case, the overall order of the sub-elements may change depending on the surrounding text.

Some attribute values or element contents use UnicodeSet notation. A UnicodeSet represents a finite set of Unicode code points and strings, and is defined by lists of code points and strings, Unicode property sets, and set operators, all bounded by square brackets. In this context, a code point means a string consisting of exactly one code point.

Note however that it may deviate from the syntax provided in [ UTS18 ], which is illustrative rather than a requirement. There is one exception to the supported semantics, Section RL2. A UnicodeSet may be cited in specifications outside of the domain of LDML. In such a case, the specification may specify a subset of the syntax provided here. Some constraints on UnicodeSet syntax are not captured by this EBNF.

Notably, property names and values are restricted to those supported by the implementation, and have additional constraints imposed by [ UAX44 ]. In addition, quoted values that resolve to more than one code point are disallowed in ranges of the form char '-' char. Lists are a sequence of strings that may include ranges, which are indicated by a '-' between two code points, as in "a-z".

The sequence start-end specifies the range of all code points from the start to end, inclusive, in Unicode order. For example, [a c d-f m] is equivalent to [a c d e f m].

Whitespace can be freely used for clarity, as [a c d-f m] means the same as [acd-fm]. A string with multiple code points is represented in a list by being surrounded by curly braces, such as in [a-z {ch}].

It can be used with the range notation, as described in Section 5. There is an additional restriction on string ranges in a UnicodeSet: the number of codepoints in the first string of the range must be identical to the number in the second. Thus [{ab}-{c}] and [{ab}-c] are invalid. Outside of single quotes, certain backslashed code point sequences can be used to quote code points:. Anything else following a backslash is mapped to itself, except the property syntax described below, or in an environment where it is defined to have some special meaning.

Any code point formed as the result of a backslash escape loses any special meaning and is treated as a literal. In contrast, Java treats Unicode escapes as just a way to represent arbitrary code points in an ASCII source file, and any resulting code points are not tagged as literals.

Unicode property sets are defined as described in UTS Unicode Regular Expressions [ UTS18 ], Level 1 and RL2. For an example of a concrete implementation of this, see [ ICUUnicodeSet ].

The property names are defined by the PropertyAliases. txt file and the property values by the PropertyValueAliases. txt file. For more information, see [ UAX44 ]. For example, you can match letters by using the POSIX-style syntax:. If the property value is omitted, it is assumed to represent a boolean property with the value "true". The table below shows the two kinds of syntax: POSIX and Perl style.

Also, the table shows the "Negative" version, which is a property that excludes all code points of a given kind. The low-level lists or properties then can be freely combined with the normal set operations union, inverse, difference, and intersection :.

Another example is the set [[ace][bdf] - [abc][def]] , which is not the empty set, but instead equal to [[[[ace] [bdf]] - [abc]] [def]] , which equals [[[abcdef] - [abc]] [def]] , which equals [[def] [def]] , which equals [def]. That is, they must be immediately preceded and immediately followed by a set. For example, the pattern [[:Lu:]-A] is illegal, since it is interpreted as the set [:Lu:] followed by the incomplete range -A.

To specify the set of upper case letters except for 'A', enclose the 'A' in brackets: [[:Lu:]-[A]]. There may be additional, domain-specific requirements for validity of the expansion of the string range. The identity element contains information identifying the target locale for this data, and general information about the version of this data.

The version element provides, in an attribute, the version of this file. The contents of the element can contain textual notes about the changes between this version and the last. This is not to be confused with the version attribute on the ldml element, which tracks the dtd version.

The generation element is now deprecated. It was used to contain the last modified date for the data. This could be in two formats: ISO format, or CVS format illustrated by the example above. The language code is the primary part of the specification of the locale id, with values as described above.

The script code may be used in the identification of written languages, with values described above. The territory code is a common part of the specification of the locale id, with values as described above. The variant code is the tertiary part of the specification of the locale id, with values as described above. When combined according to the rules described in Section 3, Unicode Language and Locale Identifiers , the language element, along with any of the optional script , territory , and variant elements, must identify a known, stable locale identifier.

Otherwise, it is an error. The DTD Annotations in Section 5. The following are restrictions on the format of LDML files to allow for easier parsing and comparison of files. Peer elements have consistent order. That is, if the DTD or this specification requires the following order in an element foo :.

Note that there was one case that had to be corrected in order to make this true. For that reason, pattern occurs twice under currency:. XML files can have a wide variation in textual form, while representing precisely the same data.

By putting the LDML files in the repository into a canonical form, this allows us to use the simple diff tools used widely and in CVS to detect differences when vetting changes, without those tools being confused.

This is not a requirement on other uses of LDML; just simply a way to manage repository data more easily. All end elements except for leaf nodes are on their own line, indented by depth tabs. That is, new IDs are added, but existing ones keep the original form. The TZ timezone database keeps a set of equivalences in the "backward" file. These are used to map other tzids to the canonical form. An element is ordered first by the element name, and then if the element names are identical, by the sorted set of attribute-value pairs.

For the latter, compare the first pair in each in sorted order by attribute pair. If not identical, go to the second pair, and so on. Elements and attributes are ordered according to their order in the respective DTDs. Attribute value comparison is a bit more complicated, and may depend on the attribute and type.

This is currently done with specific ordering tables. Any future additions to the DTD must be structured so as to allow compatibility with this ordering. See also Section 5. The information in a standard DTD is insufficient for use in CLDR.

To make up for that, DTD annotations are added. These are of the form. and are included below the! ELEMENT or! ATTLIST line that they apply to. The current annotations are:. There is additional information in the attributeValueValidity. xml file that is used internally for testing. Every customer is free to make that choice. But of course, many of our larger customers want to make longer-term commitments, want to have a deeper relationship with us, want the economics that come with that commitment.

We're signing more long-term commitments than ever these days. We provide incredible value for our customers, which is what they care about. That kind of analysis would not be feasible, you wouldn't even be able to do that for most companies, on their own premises.

So some of these workloads just become better, become very powerful cost-savings mechanisms, really only possible with advanced analytics that you can run in the cloud. In other cases, just the fact that we have things like our Graviton processors and … run such large capabilities across multiple customers, our use of resources is so much more efficient than others. We are of significant enough scale that we, of course, have good purchasing economics of things like bandwidth and energy and so forth.

So, in general, there's significant cost savings by running on AWS, and that's what our customers are focused on. The margins of our business are going to … fluctuate up and down quarter to quarter. It will depend on what capital projects we've spent on that quarter. Obviously, energy prices are high at the moment, and so there are some quarters that are puts, other quarters there are takes.

The important thing for our customers is the value we provide them compared to what they're used to. And those benefits have been dramatic for years, as evidenced by the customers' adoption of AWS and the fact that we're still growing at the rate we are given the size business that we are.

That adoption speaks louder than any other voice. Do you anticipate a higher percentage of customer workloads moving back on premises than you maybe would have three years ago? Absolutely not. We're a big enough business, if you asked me have you ever seen X, I could probably find one of anything, but the absolute dominant trend is customers dramatically accelerating their move to the cloud.

Moving internal enterprise IT workloads like SAP to the cloud, that's a big trend. Creating new analytics capabilities that many times didn't even exist before and running those in the cloud. More startups than ever are building innovative new businesses in AWS. Our public-sector business continues to grow, serving both federal as well as state and local and educational institutions around the world.

It really is still day one. The opportunity is still very much in front of us, very much in front of our customers, and they continue to see that opportunity and to move rapidly to the cloud. In general, when we look across our worldwide customer base, we see time after time that the most innovation and the most efficient cost structure happens when customers choose one provider, when they're running predominantly on AWS.

A lot of benefits of scale for our customers, including the expertise that they develop on learning one stack and really getting expert, rather than dividing up their expertise and having to go back to basics on the next parallel stack. That being said, many customers are in a hybrid state, where they run IT in different environments.

In some cases, that's by choice; in other cases, it's due to acquisitions, like buying companies and inherited technology. We understand and embrace the fact that it's a messy world in IT, and that many of our customers for years are going to have some of their resources on premises, some on AWS.

Some may have resources that run in other clouds. We want to make that entire hybrid environment as easy and as powerful for customers as possible, so we've actually invested and continue to invest very heavily in these hybrid capabilities.

A lot of customers are using containerized workloads now, and one of the big container technologies is Kubernetes. We have a managed Kubernetes service, Elastic Kubernetes Service, and we have a … distribution of Kubernetes Amazon EKS Distro that customers can take and run on their own premises and even use to boot up resources in another public cloud and have all that be done in a consistent fashion and be able to observe and manage across all those environments.

So we're very committed to providing hybrid capabilities, including running on premises, including running in other clouds, and making the world as easy and as cost-efficient as possible for customers. Can you talk about why you brought Dilip Kumar, who was Amazon's vice president of physical retail and tech, into AWS as vice president applications and how that will play out?

He's a longtime, tenured Amazonian with many, many different roles — important roles — in the company over a many-year period. Dilip has come over to AWS to report directly to me, running an applications group. We do have more and more customers who want to interact with the cloud at a higher level — higher up the stack or more on the application layer.

We talked about Connect, our contact center solution, and we've also built services specifically for the healthcare industry like a data lake for healthcare records called Amazon HealthLake. We've built a lot of industrial services like IoT services for industrial settings, for example, to monitor industrial equipment to understand when it needs preventive maintenance. We have a lot of capabilities we're building that are either for … horizontal use cases like Amazon Connect or industry verticals like automotive, healthcare, financial services.

We see more and more demand for those, and Dilip has come in to really coalesce a lot of teams' capabilities, who will be focusing on those areas. You can expect to see us invest significantly in those areas and to come out with some really exciting innovations.

Would that include going into CRM or ERP or other higher-level, run-your-business applications? I don't think we have immediate plans in those particular areas, but as we've always said, we're going to be completely guided by our customers, and we'll go where our customers tell us it's most important to go next.

It's always been our north star. Correction: This story was updated Nov. Bennett Richardson bennettrich is the president of Protocol. Prior to joining Protocol in , Bennett was executive director of global strategic partnerships at POLITICO, where he led strategic growth efforts including POLITICO's European expansion in Brussels and POLITICO's creative agency POLITICO Focus during his six years with the company.

Prior to POLITICO, Bennett was co-founder and CMO of Hinge, the mobile dating company recently acquired by Match Group. Bennett began his career in digital and social brand marketing working with major brands across tech, energy, and health care at leading marketing and communications agencies including Edelman and GMMB.

Bennett is originally from Portland, Maine, and received his bachelor's degree from Colgate University. Prior to joining Protocol in , he worked on the business desk at The New York Times, where he edited the DealBook newsletter and wrote Bits, the weekly tech newsletter. He has previously worked at MIT Technology Review, Gizmodo, and New Scientist, and has held lectureships at the University of Oxford and Imperial College London.

He also holds a doctorate in engineering from the University of Oxford. We launched Protocol in February to cover the evolving power center of tech. It is with deep sadness that just under three years later, we are winding down the publication.

As of today, we will not publish any more stories. All of our newsletters, apart from our flagship, Source Code, will no longer be sent. Source Code will be published and sent for the next few weeks, but it will also close down in December.

Building this publication has not been easy; as with any small startup organization, it has often been chaotic. But it has also been hugely fulfilling for those involved. We could not be prouder of, or more grateful to, the team we have assembled here over the last three years to build the publication. They are an inspirational group of people who have gone above and beyond, week after week.

Today, we thank them deeply for all the work they have done. We also thank you, our readers, for subscribing to our newsletters and reading our stories. We hope you have enjoyed our work. As companies expand their use of AI beyond running just a few machine learning models, and as larger enterprises go from deploying hundreds of models to thousands and even millions of models, ML practitioners say that they have yet to find what they need from prepackaged MLops systems.

As companies expand their use of AI beyond running just a few machine learning models, ML practitioners say that they have yet to find what they need from prepackaged MLops systems. Kate Kaye is an award-winning multimedia reporter digging deep and telling print, digital and audio stories. She covers AI and data for Protocol.

Her reporting on AI and tech ethics issues has been published in OneZero, Fast Company, MIT Technology Review, CityLab, Ad Age and Digiday and heard on NPR. Kate is the creator of RedTailMedia.

org and is the author of "Campaign ' A Turning Point for Digital Media," a book about how the presidential campaigns used digital media and data. On any given day, Lily AI runs hundreds of machine learning models using computer vision and natural language processing that are customized for its retail and ecommerce clients to make website product recommendations, forecast demand, and plan merchandising.

And he said that while some MLops systems can manage a larger number of models, they might not have desired features such as robust data visualization capabilities or the ability to work on premises rather than in cloud environments.

As companies expand their use of AI beyond running just a few ML models, and as larger enterprises go from deploying hundreds of models to thousands and even millions of models, many machine learning practitioners Protocol interviewed for this story say that they have yet to find what they need from prepackaged MLops systems.

Companies hawking MLops platforms for building and managing machine learning models include tech giants like Amazon, Google, Microsoft, and IBM and lesser-known vendors such as Comet, Cloudera, DataRobot, and Domino Data Lab.

It's actually a complex problem. Intuit also has constructed its own systems for building and monitoring the immense number of ML models it has in production, including models that are customized for each of its QuickBooks software customers.

The model must recognize those distinctions. For instance, Hollman said the company built an ML feature management platform from the ground up. For companies that have been forced to go DIY, building these platforms themselves does not always require forging parts from raw materials. DBS has incorporated open-source tools for coding and application security purposes such as Nexus, Jenkins, Bitbucket, and Confluence to ensure the smooth integration and delivery of ML models, Gupta said.

Intuit has also used open-source tools or components sold by vendors to improve existing in-house systems or solve a particular problem, Hollman said. However, he emphasized the need to be selective about which route to take. I think that the best AI will be a build plus buy. However, creating consistency through the ML lifecycle from model training to deployment to monitoring becomes increasingly difficult as companies cobble together open-source or vendor-built machine learning components, said John Thomas, vice president and distinguished engineer at IBM.

The reality is most people are not there, so you have a whole bunch of different tools. Companies struggling to find suitable off-the-shelf MLops platforms are up against another major challenge, too: finding engineering talent.

Many companies do not have software engineers on staff with the level of expertise necessary to architect systems that can handle large numbers of models or accommodate millions of split-second decision requests, said Abhishek Gupta, founder and principal researcher at Montreal AI Ethics Institute and senior responsible AI leader and expert at Boston Consulting Group. For one thing, smaller companies are competing for talent against big tech firms that offer higher salaries and better resources.

For companies with less-advanced AI operations, shopping at the existing MLops platform marketplace may be good enough, Hollman said. To give you the best possible experience, this site uses cookies. If you continue browsing. you accept our use of cookies. You can review our privacy policy to find out more about the cookies we use.

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This document describes an XML format vocabulary for the exchange of structured locale data. This format is used in the Unicode Common Locale Data Repository. Note: Some links may lead to in-development or older versions of the data files. org for up-to-date CLDR release data. This document has been reviewed by Unicode members and other interested parties, and has been approved for publication by the Unicode Consortium. This is a stable document and may be used as reference material or cited as a normative reference by other specifications.

A Unicode Technical Standard UTS is an independent specification. Conformance to the Unicode Standard does not imply conformance to any UTS. Please submit corrigenda and other comments with the CLDR bug reporting form [ Bugs ]. Related information that is useful in understanding this document is found in the References.

For the latest version of the Unicode Standard see [ Unicode ]. For a list of current Unicode Technical Reports see [ Reports ]. For more information about versions of the Unicode Standard, see [ Versions ]. NOTE: The source for the LDML specification has been converted to GitHub Markdown GFM instead of HTML.

The formatting is now simpler, but some features — such as formatting for table captions — may not be complete by the release date. Improvements in the formatting for the specification may be done after the release, but no substantive changes will be made to the content. Not long ago, computer systems were like separate worlds, isolated from one another.

The internet and related events have changed all that. A single system can be built of many different components, hardware and software, all needing to work together. Many different technologies have been important in bridging the gaps; in the internationalization arena, Unicode has provided a lingua franca for communicating textual data.

However, there remain differences in the locale data used by different systems. The best practice for internationalization is to store and communicate language-neutral data, and format that data for the client. This formatting can take place on any of a number of the components in a system; a server might format data based on the user's locale, or it could be that a client machine does the formatting. The same goes for parsing data, and locale-sensitive analysis of data.

But there remain significant differences across systems and applications in the locale-sensitive data used for such formatting, parsing, and analysis. Many of those differences are simply gratuitous; all within acceptable limits for human beings, but yielding different results. In many other cases there are outright errors. Whatever the cause, the differences can cause discrepancies to creep into a heterogeneous system.

This is especially serious in the case of collation sort-order , where different collation caused not only ordering differences, but also different results of queries!

That is, with a query of customers with names between "Abbot, Cosmo" and "Arnold, James", if different systems have different sort orders, different lists will be returned.

For comparisons across systems formatted as HTML tables, see [ Comparisons ]. Note: There are many different equally valid ways in which data can be judged to be "correct" for a particular locale. The goal for the common locale data is to make it as consistent as possible with existing locale data, and acceptable to users in that locale. This document specifies an XML format for the communication of locale data: the Unicode Locale Data Markup Language LDML.

This provides a common format for systems to interchange locale data so that they can get the same results in the services provided by internationalization libraries.

It also provides a standard format that can allow users to customize the behavior of a system. With it, for example, collation sorting rules can be exchanged, allowing two implementations to exchange a specification of tailored collation rules.

Using the same specification, the two implementations will achieve the same results in comparing strings. Unicode LDML can also be used to let a user encapsulate specialized sorting behavior for a specific domain, or create a customized locale for a minority language. Unicode LDML is also used in the Unicode Common Locale Data Repository CLDR. CLDR uses an open process for reconciling differences between the locale data used on different systems and validating the data, to produce with a useful, common, consistent base of locale data.

For more information, see the Common Locale Data Repository project page [ LocaleProject ]. As LDML is an interchange format, it was designed for ease of maintenance and simplicity of transformation into other formats, above efficiency of run-time lookup and use. Implementations should consider converting LDML data into a more compact format prior to use. There are many ways to use the Unicode LDML format and the data in CLDR, and the Unicode Consortium does not restrict the ways in which the format or data are used.

However, an implementation may also claim conformance to LDML or to CLDR, as follows:. An implementation that claims conformance to this specification shall:. An implementation that claims conformance to Unicode locale or language identifiers shall:. External specifications may also reference particular components of Unicode locale or language identifiers, such as:.

Field X can contain any Unicode region subtag values as given in Unicode Technical Standard Unicode Locale Data Markup Language LDML , excluding grouping codes. Before diving into the XML structure, it is helpful to describe the model behind the structure. People do not have to subscribe to this model to use data in LDML, but they do need to understand it so that the data can be correctly translated into whatever model their implementation uses.

The first issue is basic: what is a locale? In this model, a locale is an identifier id that refers to a set of user preferences that tend to be shared across significant swaths of the world.

Traditionally, the data associated with this id provides support for formatting and parsing of dates, times, numbers, and currencies; for measurement units, for sort-order collation , plus translated names for time zones, languages, countries, and scripts. The data can also include support for text boundaries character, word, line, and sentence , text transformations including transliterations , and other services.

Locale data is not cast in stone: the data used on someone's machine generally may reflect the US format, for example, but preferences can typically set to override particular items, such as setting the date format for In the abstract, locales are simply one of many sets of preferences that, say, a website may want to remember for a particular user.

Locale data in a system may also change over time: country boundaries change; governments and currencies come and go: committees impose new standards; bugs are found and fixed in the source data; and so on.

Thus the data needs to be versioned for stability over time. In general terms, the locale id is a parameter that is supplied to a particular service date formatting, sorting, spell-checking, and so on.

The format in this document does not attempt to represent all the data that could conceivably be used by all possible services. Instead, it collects together data that is in common use in systems and internationalization libraries for basic services.

The main difference among locales is in terms of language; there may also be some differences according to different countries or regions. However, the line between locales and languages , as commonly used in the industry, are rather fuzzy. Note also that the vast majority of the locale data in CLDR is in fact language data; all non-linguistic data is separated out into a separate tree.

For more information, see Section 3. We will speak of data as being "in locale X". That does not imply that a locale is a collection of data; it is simply shorthand for "the set of data associated with the locale id X". Each individual piece of data is called a resource or field , and a tag indicating the key of the resource is called a resource tag.

Unicode LDML uses stable identifiers based on [ BCP47 ] for distinguishing among languages, locales, regions, currencies, time zones, transforms, and so on.

There are many systems for identifiers for these entities. The Unicode LDML identifiers may not match the identifiers used on a particular target system. If so, some process of identifier translation may be required when using LDML data. The BCP 47 extensions -u- and -t- are described in Section 3.

A Unicode language identifier has the following structure provided in EBNF Perl-based. The following table defines syntactically well-formed identifiers: they are not necessarily valid identifiers. For additional validity criteria, see the links on the right.

The semantics of the various subtags is explained in Section 3. Instead, they are intended for certain protocols such as the identification of transliterators or font ScriptLangTag values. For more information on language subtags with 4 letters, see BCP 47 Language Tag to Unicode BCP 47 Locale Identifier. A Unicode locale identifier is composed of a Unicode language identifier plus optional locale extensions.

It has the following structure. The semantics of the U and T extensions are explained in Section 3. Other extensions and private use extensions are supported for pass-through. As is often the case, the complete syntactic constraints are not easily captured by ABNF, so there is a further condition: There cannot be more than one extension with the same singleton -a-, …, -t-, -u-, ….

Note that the private use extension -x- must come after all other extensions. For historical reasons, this is called a Unicode locale identifier.

However, it really functions with few exceptions as a language identifier, and accesses language-based data. Except where it would be unclear, this document uses the term "locale" data loosely to encompass both types of data: for more information, see Section 3. As for terminology, the term code may also be used instead of "subtag", and "territory" instead of "region".

The primary language subtag is also called the base language code. For example, the base language code for "en-US" American English is "en" English. The type may also be referred to as a value or key-value. The identifiers can vary in case and in the separator characters. All identifier field values are case-insensitive.

Although case distinctions do not carry any special meaning, an implementation of LDML should use the casing recommendations in [ BCP47 ], especially when a Unicode locale identifier is used for locale data exchange in software protocols. For example, the canonical form of "en-u-foo-bar-nu-thai-ca-buddhist-kk-true" is "en-u-bar-foo-ca-buddhist-kk-nu-thai".

The attributes "foo" and "bar" in this example are provided only for illustration; no attribute subtags are defined by the current CLDR specification.

Technical Reports. Optional For information about the registration process, meaning, and usage of the 't' extension, see [ RFC ]. For one thing, smaller companies are competing for talent against big tech firms that offer higher salaries and better resources. We're public servants! The above algorithm is a logical statement of the process, but would obviously not be directly suited to production code. On a practical level, if transmitted data is neutral-format, then it is much easier to manipulate the data, debug the processing of the data, and maintain the software connections between components. This convention is used throughout the CLDR.

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