In statistics, a maximum-entropy Markov model (MEMM), or conditional Markov model (CMM), is a graphical model for sequence labeling that combines features of hidden Markov models (HMMs) and maximum entropy (MaxEnt) models. An MEMM is a discriminative model that extends a standard maximum entropy classifier by assuming that the unknown values to be learnt are connected in a Markov chain rather than being conditionally independent of each other. MEMMs find applications in natural language processing, specifically in part-of-speech tagging and information extraction. == Model == Suppose we have a sequence of observations O 1 , … , O n {\displaystyle O_{1},\dots ,O_{n}} that we seek to tag with the labels S 1 , … , S n {\displaystyle S_{1},\dots ,S_{n}} that maximize the conditional probability P ( S 1 , … , S n ∣ O 1 , … , O n ) {\displaystyle P(S_{1},\dots ,S_{n}\mid O_{1},\dots ,O_{n})} . In a MEMM, this probability is factored into Markov transition probabilities, where the probability of transitioning to a particular label depends only on the observation at that position and the previous position's label: P ( S 1 , … , S n ∣ O 1 , … , O n ) = ∏ t = 1 n P ( S t ∣ S t − 1 , O t ) . {\displaystyle P(S_{1},\dots ,S_{n}\mid O_{1},\dots ,O_{n})=\prod _{t=1}^{n}P(S_{t}\mid S_{t-1},O_{t}).} Each of these transition probabilities comes from the same general distribution P ( s ∣ s ′ , o ) {\displaystyle P(s\mid s',o)} . For each possible label value of the previous label s ′ {\displaystyle s'} , the probability of a certain label s {\displaystyle s} is modeled in the same way as a maximum entropy classifier: P ( s ∣ s ′ , o ) = P s ′ ( s ∣ o ) = 1 Z ( o , s ′ ) exp ( ∑ a λ a f a ( o , s ) ) . {\displaystyle P(s\mid s',o)=P_{s'}(s\mid o)={\frac {1}{Z(o,s')}}\exp \left(\sum _{a}\lambda _{a}f_{a}(o,s)\right).} Here, the f a ( o , s ) {\displaystyle f_{a}(o,s)} are real-valued or categorical feature-functions, and Z ( o , s ′ ) {\displaystyle Z(o,s')} is a normalization term ensuring that the distribution sums to one. This form for the distribution corresponds to the maximum entropy probability distribution satisfying the constraint that the empirical expectation for the feature is equal to the expectation given the model: E e [ f a ( o , s ) ] = E p [ f a ( o , s ) ] for all a . {\displaystyle \operatorname {E} _{e}\left[f_{a}(o,s)\right]=\operatorname {E} _{p}\left[f_{a}(o,s)\right]\quad {\text{ for all }}a.} The parameters λ a {\displaystyle \lambda _{a}} can be estimated using generalized iterative scaling. Furthermore, a variant of the Baum–Welch algorithm, which is used for training HMMs, can be used to estimate parameters when training data has incomplete or missing labels. The optimal state sequence S 1 , … , S n {\displaystyle S_{1},\dots ,S_{n}} can be found using a very similar Viterbi algorithm to the one used for HMMs. The dynamic program uses the forward probability: α t + 1 ( s ) = ∑ s ′ ∈ S α t ( s ′ ) P s ′ ( s ∣ o t + 1 ) . {\displaystyle \alpha _{t+1}(s)=\sum _{s'\in S}\alpha _{t}(s')P_{s'}(s\mid o_{t+1}).} == Strengths and weaknesses == An advantage of MEMMs rather than HMMs for sequence tagging is that they offer increased freedom in choosing features to represent observations. In sequence tagging situations, it is useful to use domain knowledge to design special-purpose features. In the original paper introducing MEMMs, the authors write that "when trying to extract previously unseen company names from a newswire article, the identity of a word alone is not very predictive; however, knowing that the word is capitalized, that is a noun, that it is used in an appositive, and that it appears near the top of the article would all be quite predictive (in conjunction with the context provided by the state-transition structure)." Useful sequence tagging features, such as these, are often non-independent. Maximum entropy models do not assume independence between features, but generative observation models used in HMMs do. Therefore, MEMMs allow the user to specify many correlated, but informative features. Another advantage of MEMMs versus HMMs and conditional random fields (CRFs) is that training can be considerably more efficient. In HMMs and CRFs, one needs to use some version of the forward–backward algorithm as an inner loop in training. However, in MEMMs, estimating the parameters of the maximum-entropy distributions used for the transition probabilities can be done for each transition distribution in isolation. A drawback of MEMMs is that they potentially suffer from the "label bias problem," where states with low-entropy transition distributions "effectively ignore their observations." Conditional random fields were designed to overcome this weakness, which had already been recognised in the context of neural network-based Markov models in the early 1990s. Another source of label bias is that training is always done with respect to known previous tags, so the model struggles at test time when there is uncertainty in the previous tag.
Auto-defrost
Auto-defrost, automatic defrost or self-defrosting is a technique which regularly defrosts the evaporator in a refrigerator or freezer. Appliances using this technique are often called frost free, frostless, or no-frost. == Mechanism == The defrost mechanism in a refrigerator heats the cooling element (evaporator coil) for a short period of time and melts the frost that has formed on it. The resulting water drains through a duct at the back of the unit. Defrosting is controlled by an electric or electronic timer. For every 6, 8, 10, 12 or 24 hours of compressor operation, it turns on a defrost heater for 15 minutes to half an hour. The defrost heater, having a typical power rating of 350W to 600W, is often mounted just below the evaporator in top and bottom-freezer models. It can also be located below and in the middle of the evaporator in side-by-side models. It may be protected from short circuits by means of fusible links. In older refrigerators, the timer runs continuously. In newer designs, the timer only runs while the compressor runs, so the longer the refrigerator door is closed, the less time the heater will run for and the more energy is saved. A defrost thermostat opens the heater circuit when the evaporator temperature rises above a preset temperature, 40°F (5°C) or more, thereby preventing excessive heating of the freezer compartment. The defrost timer is such that either the compressor or the defrost heater is on, but not both at the same time. Inside the freezer, air is circulated by means of one or more fans. In a typical design cold air from the freezer compartment is ducted to the fresh food compartment and circulated back into the freezer compartment. Air circulation helps sublimate any ice or frost that may form on frozen items in the freezer compartment. While defrosting, this fan is stopped to prevent heated-up air from reaching the food compartment. Instead of the normal cooling elements being embedded in the freezer liner, auto-defrost elements are behind or beneath the liner. This allows them to be heated for short periods of time to dispose of frost, without heating the contents of the freezer. Alternatively, some systems use the hot gas in the condenser to defrost the evaporator. This is done by means of a circuit that is cross-linked by a three-way valve. The hot gas quickly heats up the evaporator and defrosts it. This system is primarily used in commercial applications such as ice-cream displays. == Application == While this technique was originally applied to the refrigerator compartment, it was later used for freezer compartment as well. A combined refrigerator/freezer which applies self-defrosting to the refrigerator compartment only is usually called "partial frost free" or semi-automatic defrost (some brands call these "Auto Defrost" while Frigidaire referred to their semi-automatic models as "Cycla-Matic," Kelvinator often named these models as "Cyclic Defrost" ). These refrigerators usually have a pan underneath where water from the melted frost in the refrigerator section evaporates. Freezers with automatic defrosting and combined refrigerator/freezer units which also apply self defrosting to their freezer compartment are called "frost free". The latter usually feature an air connection between the two compartments with the air passage to the refrigerator compartment regulated by a damper. By this means, a controlled portion of the air coming from the freezer reaches the refrigerator. Some older models have no air circulation between their freezer and refrigerator sections. Instead, they use an independent cooling system (for example: an evaporator coil with a defrost heater and a circulating fan in the freezer and a cold-plate or open-coil evaporator in the refrigerator. "Frost-Free" refrigerator/freezer units usually use a heating element to defrost their evaporators, a pan to collect and evaporate water from the frost that melts from the cold plate and/or evaporator coil, a timer which turns off the compressor and turns on the defrost element usually from once to 4 times a day for periods usually ranging from 15 to 30 minutes, a defrost limiter thermostat that turns off the heating element before the temperature rises too much while the timer is still in its defrost phase. Some models also feature a drain heater to prevent ice from blocking the drain. Other early types of refrigerators also use hot gas defrost instead of electric heaters. These reverse the evaporator and condenser sides for the defrost cycle. Some newer refrigerator/freezer models have a computer that monitors how many times each door is opened and uses this data to control defrost scheduling thereby reducing power use. == Advantages == No need to manually defrost the frost buildup, therefore power consumption will not increase with time. Food packaging is easier to see. Most frozen food will not stick together. Smells are limited, especially in total frost-free appliances because the air always circulates. Better temperature management. == Disadvantages == The system can be more expensive to run when usage is high and if the fan continues or starts to run when the door is opened. A thermal cutout safety device is required to prevent overheating of the heating element. Increased electrical and mechanical complexity compared to a basic upright freezer or chest freezer, making it more prone to component failure. The temperature of the freezer contents rises during the defrosting cycles, especially if there is a light load in the freezer. This can cause "freezer burn" on articles placed in the freezer, from partially defrosting, then re-freezing On hot, humid days condensation will sometimes form around the refrigerator doors. Defrosting may not be completed by the time the defrost timer cycles back to normal operation (especially in hot, humid conditions with frequent door openings), leaving ice/frost on the evaporator coils. This condition can lead to "icing" which will interfere with the operation of the refrigerator. In laboratories, self-defrosting freezers must not be used to store certain delicate reagents such as enzymes, because the temperature cycling can degrade them. In addition, water can evaporate out of containers that do not have a very tight seal, altering the concentration of the reagents. Self-defrosting freezers should never be used to store flammable chemicals.
Artificial empathy
Artificial empathy or computational empathy is the development of AI systems—such as companion robots or virtual agents—that can detect emotions and respond to them in an empathic way. Although such technology can be perceived as scary or threatening, it could also have a significant advantage over humans for roles in which emotional expression can be important, such as in the health care sector. An October 2025 review and meta-analysis in the British Medical Bulletin found that AI chatbots were rated as showing more empathy than human healthcare professionals in 13 of 15 studies that compared them. Care-givers who perform emotional labor above and beyond the requirements of paid labor can experience chronic stress or burnout, and can become desensitized to patients. Artificial empathy could also help the socialization of care-givers, or serve as role model for emotional detachment. A broader definition of artificial empathy is "the ability of nonhuman models to predict a person's internal state (e.g., cognitive, affective, physical) given the signals (s)he emits (e.g., facial expression, voice, gesture) or to predict a person's reaction (including, but not limited to internal states) when he or she is exposed to a given set of stimuli (e.g., facial expression, voice, gesture, graphics, music, etc.)". A 2025 study reported that some multimodal large language models can recognize basic facial expressions with human-level accuracy on a commonly used research dataset of posed facial expressions. == Areas of research == There are a variety of philosophical, theoretical, and applicative questions related to artificial empathy. For example: Which conditions would have to be met for a robot to respond competently to a human emotion? What models of empathy can or should be applied to Social and Assistive Robotics? Must the interaction of humans with robots imitate affective interaction between humans? Can a robot help science learn about affective development of humans? Would robots create unforeseen categories of inauthentic relations? What relations with robots can be considered authentic? How can we assess artificial empathy in AI systems? == Examples of artificial empathy research and practice == People often communicate and make decisions based on inferences about each other's internal states (e.g., emotional, cognitive, and physical states) that are in turn based on signals emitted by the person such as facial expression, body gesture, voice, and words. Broadly speaking, artificial empathy focuses on developing non-human models that achieve similar objectives using similar data. === Streams of artificial empathy research === Artificial empathy has been applied in various research disciplines, including artificial intelligence and business. Two main streams of research in this domain are: the use of nonhuman models to predict a person's internal state (e.g., cognitive, affective, physical) given the signals he or she emits (e.g., facial expression, voice, gesture) the use of nonhuman models to predict a person's reaction when he or she is exposed to a given set of stimuli (e.g., facial expression, voice, gesture, graphics, music, etc.). Research on affective computing, such as emotional speech recognition and facial expression detection, falls within the first stream of artificial empathy. Contexts that have been studied include oral interviews, call centers, human-computer interaction, sales pitches, and financial reporting. The second stream of artificial empathy has been researched more in marketing contexts, such as advertising, branding, customer reviews, in-store recommendation systems, movies, and online dating. === Artificial empathy applications in practice === With the increasing volume of visual, audio, and text data in commerce, many business applications for artificial empathy have followed. For example, Affectiva analyses viewers' facial expressions from video recordings while they are watching video advertisements in order to optimize the content design of video ads. Software like HireVue, BarRaiser, a hiring intelligence firm, helps firms make recruitment decisions by analyzing audio and video information from candidates' video interviews. Lapetus Solutions develops a model to estimate an individual's longevity, health status, and disease susceptibility from a face photo. Their technology has been applied in the insurance industry. == Artificial empathy and human services == Although artificial intelligence cannot yet replace social workers themselves, the technology has been deployed in that field. Florida State University published a study about Artificial Intelligence being used in the human services field. The research used computer algorithms to analyze health records for combinations of risk factors that could predict a future suicide attempt. The article reports, "machine learning—a future frontier for artificial intelligence—can predict with 80% to 90% accuracy whether someone will attempt suicide as far off as two years into the future. The algorithms become even more accurate as a person's suicide attempt gets closer. For example, the accuracy climbs to 92% one week before a suicide attempt when artificial intelligence focuses on general hospital patients". Such algorithmic machines can help social workers. Social work operates on a cycle of engagement, assessment, intervention, and evaluation with clients. Earlier assessment for risk of suicide can lead to earlier interventions and prevention, therefore saving lives. The system would learn, analyze, and detect risk factors, alerting the clinician of a patient's suicide risk score (analogous to a patient's cardiovascular risk score). Then, social workers could step in for further assessment and preventive intervention.
NoSQL
NoSQL (originally meaning "not only SQL" or "non-relational") refers to a type of database design that stores and retrieves data differently from the traditional table-based structure of relational databases. Unlike relational databases, which organize data into rows and columns like a spreadsheet, NoSQL databases use a single data structure—such as key–value pairs, wide columns, graphs, or documents—to hold information. Since this non-relational design does not require a fixed schema, it scales easily to manage large, often unstructured datasets. NoSQL systems are sometimes called "Not only SQL" because they can support SQL-like query languages or work alongside SQL databases in polyglot-persistent setups, where multiple database types are combined. Non-relational databases date back to the late 1960s, but the term "NoSQL" emerged in the early 2000s, spurred by the needs of Web 2.0 companies like social media platforms. NoSQL databases are popular in big data and real-time web applications due to their simple design, ability to scale across clusters of machines (called horizontal scaling), and precise control over data availability. These structures can speed up certain tasks and are often considered more adaptable than fixed database tables. However, many NoSQL systems prioritize speed and availability over strict consistency (per the CAP theorem), using eventual consistency—where updates reach all nodes eventually, typically within milliseconds, but may cause brief delays in accessing the latest data, known as stale reads. While most lack full ACID transaction support, some, like MongoDB, include it as a key feature. == Barriers to adoption == Barriers to wider NoSQL adoption include their use of low-level query languages instead of SQL, inability to perform ad hoc joins across tables, lack of standardized interfaces, and significant investments already made in relational databases. Some NoSQL systems risk losing data through lost writes or other forms, though features like write-ahead logging—a method to record changes before they’re applied—can help prevent this. For distributed transaction processing across multiple databases, keeping data consistent is a challenge for both NoSQL and relational systems, as relational databases cannot enforce rules linking separate databases, and few systems support both ACID transactions and X/Open XA standards for managing distributed updates. Limitations within the interface environment are overcome using semantic virtualization protocols, such that NoSQL services are accessible to most operating systems. == History == The term NoSQL was used by Carlo Strozzi in 1998 to name his lightweight Strozzi NoSQL open-source relational database that did not expose the standard Structured Query Language (SQL) interface, but was still relational. His NoSQL RDBMS is distinct from the around-2009 general concept of NoSQL databases. Strozzi suggests that, because the current NoSQL movement "departs from the relational model altogether, it should therefore have been called more appropriately 'NoREL'", referring to "not relational". Johan Oskarsson, then a developer at Last.fm, reintroduced the term NoSQL in early 2009 when he organized an event to discuss "open-source distributed, non-relational databases". The name attempted to label the emergence of an increasing number of non-relational, distributed data stores, including open source clones of Google's Bigtable/MapReduce and Amazon's DynamoDB. == Types and examples == There are various ways to classify NoSQL databases, with different categories and subcategories, some of which overlap. What follows is a non-exhaustive classification by data model, with examples: === Key–value store === Key–value (KV) stores use the associative array (also called a map or dictionary) as their fundamental data model. In this model, data is represented as a collection of key–value pairs, such that each possible key appears at most once in the collection. The key–value model is one of the simplest non-trivial data models, and richer data models are often implemented as an extension of it. The key–value model can be extended to a discretely ordered model that maintains keys in lexicographic order. This extension is computationally powerful, in that it can efficiently retrieve selective key ranges. Key–value stores can use consistency models ranging from eventual consistency to serializability. Some databases support ordering of keys. There are various hardware implementations, and some users store data in memory (RAM), while others on solid-state drives (SSD) or rotating disks (aka hard disk drive (HDD)). === Document store === The central concept of a document store is that of a "document". While the details of this definition differ among document-oriented databases, they all assume that documents encapsulate and encode data (or information) in some standard formats or encodings. Encodings in use include XML, YAML, and JSON and binary forms like BSON. Documents are addressed in the database via a unique key that represents that document. Another defining characteristic of a document-oriented database is an API or query language to retrieve documents based on their contents. Different implementations offer different ways of organizing and/or grouping documents: Collections Tags Non-visible metadata Directory hierarchies Compared to relational databases, collections could be considered analogous to tables and documents analogous to records. But they are different – every record in a table has the same sequence of fields, while documents in a collection may have fields that are completely different. === Graph === Graph databases are designed for data whose relations are well represented as a graph consisting of elements connected by a finite number of relations. Examples of data include social relations, public transport links, road maps, network topologies, etc. Graph databases and their query language == Performance == The performance of NoSQL databases is usually evaluated using the metric of throughput, which is measured as operations per second. Performance evaluation must pay attention to the right benchmarks such as production configurations, parameters of the databases, anticipated data volume, and concurrent user workloads. Ben Scofield rated different categories of NoSQL databases as follows: Performance and scalability comparisons are most commonly done using the YCSB benchmark. == Handling relational data == Since most NoSQL databases lack ability for joins in queries, the database schema generally needs to be designed differently. There are three main techniques for handling relational data in a NoSQL database. (See table join and ACID support for NoSQL databases that support joins.) === Multiple queries === Instead of retrieving all the data with one query, it is common to do several queries to get the desired data. NoSQL queries are often faster than traditional SQL queries, so the cost of additional queries may be acceptable. If an excessive number of queries would be necessary, one of the other two approaches is more appropriate. === Caching, replication and non-normalized data === Instead of only storing foreign keys, it is common to store actual foreign values along with the model's data. For example, each blog comment might include the username in addition to a user id, thus providing easy access to the username without requiring another lookup. When a username changes, however, this will now need to be changed in many places in the database. Thus this approach works better when reads are much more common than writes. === Nesting data === With document databases like MongoDB it is common to put more data in a smaller number of collections. For example, in a blogging application, one might choose to store comments within the blog post document, so that with a single retrieval one gets all the comments. Thus in this approach a single document contains all the data needed for a specific task. == ACID and join support == A database is marked as supporting ACID properties (atomicity, consistency, isolation, durability) or join operations if the documentation for the database makes that claim. However, this doesn't necessarily mean that the capability is fully supported in a manner similar to most SQL databases. == Query optimization and indexing in NoSQL databases == Different NoSQL databases, such as DynamoDB, MongoDB, Cassandra, Couchbase, HBase, and Redis, exhibit varying behaviors when querying non-indexed fields. Many perform full-table or collection scans for such queries, applying filtering operations after retrieving data. However, modern NoSQL databases often incorporate advanced features to optimize query performance. For example, MongoDB supports compound indexes and query-optimization strategies, Cassandra offers secondary indexes and materialized views, and Redis employs custom indexing mechanisms tailored to specific use cases. Systems like El
Data janitor
A data janitor is a person who works to take big data and condense it into useful amounts of information. Also known as a "data wrangler", a data janitor sifts through data for companies in the information technology industry. A multitude of start-ups rely on large amounts of data, so a data janitor works to help these businesses with this basic, but difficult process of interpreting data. While it is a commonly held belief that data janitor work is fully automated, many data scientists are employed primarily as data janitors. The information technology industry has been increasingly turning towards new sources of data gathered on consumers, so data janitors have become more commonplace in recent years.
Switch (app)
Switch was a mobile-only job-matching app that connected candidates directly to hiring managers. Candidates could upload their resumes and connect their social and professional media profiles, but remain anonymous while searching. Users received a daily set of job recommendations that fit their backgrounds and salary criteria, and swipe right to apply. Employers post many jobs on Switch directly, which eliminates the need for third-party job boards and recruiters, and connects job seekers to hiring managers. Switch reveals a candidate’s identity to one employer at a time, only after the candidate matches with that employer. When candidates and employers match, they can chat within the app. Switch is available for iOS, with an Android version in development. == History == === Founding === Yarden Tadmor founded Switch in New York City in January 2014. For the first 10 months, Tadmor funded the company himself. By December 2014, Switch had raised $1.4 million in funding from venture capitals firms Metamorphic Ventures, SG VC, BAM and Rhodium. Tadmor's inspiration for Switch came after being frustrated by his experience both as a job seeker, and also as a supervisor hiring at numerous technology startup companies. Tadmor has said of Switch, “We operate on the five-second resume principle, which is usually the amount of time a recruiter spends on a resume. They scan through the typical data points and move on.” Switch was designed for passive job seekers to browse openings discreetly and connect quickly. Originally, Switch served only the New York metro area technology sector while in early beta, but Tadmor always intended to expand into national coverage. Soon, the company started including all major metropolitan markets across the U.S. In May 2015, Switch announced it would start sourcing tech and media jobs from all the job boards available online. Later in 2015, Switch began to post jobs in smaller urban areas. The company also expanded industries and jobs to include restaurant staff, retail sales, healthcare, nursing and education. Tadmor subsequently founded Livekick, a one-on-one private fitness and yoga instruction company, based in New York. == Operation == In May 2015, Switch reported generating over 400,000 job applications. The company said that nine of the 50 largest websites in the U.S. were using the service. It had grown its customer base to thousands of companies in a few months from launch including Microsoft, Amazon, Facebook, IBM, Yahoo!, eBay, DropBox, SoundCloud, and Wikipedia. John Cline, software development manager at eBay, told ABC’s Good Morning America that Switch is now his “main way of finding new prospective employees.” Switch uses a double opt-in technique, meaning job seekers and employers must both say yes before moving forward. They also use swiping technology and intelligent matching algorithms to connect job seekers and employers. The user experience is different for each group, but the major attraction for both sides is the speed at which they can be connected. === Features === Swipe is a major aspect of the Switch user experience. Job seekers swipe to apply to jobs, or left to pass on positions. Employers respond and swipe right to reciprocate interest, or left to eliminate the candidate. Direct connection between job seekers and employers allows hiring managers and job seekers to start an immediate conversation. Hiring managers can message with job seekers within the app, and both parties can quickly vet one another and decide whether to move forward. Easy profile creation from social media and in-app profile editing helps job seekers focus on finding a job. === Users === Job Seekers can either load their profile manually or pull in professional credentials from social media. They can post validated photos on their Facebook account. Switch’s matching algorithm analyzes the job seeker’s location, experience, and skills to bring them jobs they may be interested in. Job seekers swipe to apply and, if the employer shows interest too, only then does Switch’s system reveal the job seeker’s identity to the corporate recruiter or hiring manager. The job seeker and hiring manager can then chat through the app. Employers behave similarly to job seekers. Hiring managers or corporate recruiters sign up online, add open positions, then view Switch-recommended candidates or wait for job seekers to swipe right. Employers can select relevant job seekers by swiping right on their profiles, then chat directly in the app. === Subscriptions === The app is currently free for users and employers. == Company overview == === Financials === Switch closed out its seed round in May 2015 with $2 million in seed round funding. Investors include Marker VC, Metamorphic, Rhodium, 500 Startups, BAM, SG VC and Marcel Legrand. In a July 2015 interview with Tadmor, he claimed that Switch had raised $2.4 million to date. == Reception == Thanks to its swipe technology and double opt-in make-up, the media often refers to Switch as the Tinder for jobs. Switch has received features in lists and app reviews as an effective tool to improve your digital job search, particularly on the mobile platform. “It’s minimal effort to connect with relevant matches,” said Good Morning America workplace contributor Tory Johnson. “Which is what everybody wants to find.”
Manhattan address algorithm
The Manhattan address algorithm is a series of formulas used to estimate the closest east–west cross street for building numbers on north–south avenues in the New York City borough of Manhattan. == Algorithm == To find the approximate number of the closest cross street, divide the building number by a divisor (generally 20) and add (or subtract) the "tricky number" from the table below: For the north–south avenues, there are typically 20 address numbers between consecutive east–west streets (10 on either side of the avenue). A standard land lot on each avenue was originally 20 feet (6.1 m) wide, and there is about 200 feet (61 m) between each pair of east–west streets, for ten land lots between each pair of streets. The exceptions are Riverside Drive, as well as Fifth Avenue and Central Park West between 59th and 110th streets, which use a divisor of 10. These avenues all have buildings only on one side of the street, with a park on the other side. The "tricky number" often corresponds to a street near the southern end of the avenue. There are some notable exceptions: York Avenue address numbers are continuations of Avenue A address numbers, since the avenue was originally called Avenue A. East End Avenue address numbers are continuations of Avenue B address numbers, since the avenue was originally called Avenue B. Sixth Avenue and Broadway start south of Houston Street, the southern boundary of the Manhattan street numbering system. Although Park Avenue's southern terminus is at 32nd Street, a homeowner at 34th Street wanted the address "1 Park Avenue" (this was later changed). === Examples === For example, if you are at 62 Avenue B, 62 ÷ 20 ≈ 3 {\displaystyle 62\div 20\approx 3} , then add the "tricky number" 3 {\displaystyle 3} to give 6 {\displaystyle 6} . The nearest cross street to 62 Avenue B is East 6th Street. If you are at 78 Riverside Drive, 78 ÷ 10 ≈ 8 {\displaystyle 78\div 10\approx 8} , then add the "tricky number" 72 {\displaystyle 72} to give 80 {\displaystyle 80} . The nearest cross street to 78 Riverside Drive is West 80th Street. If you are at 501 5th Avenue, 501 ÷ 20 ≈ 25 {\displaystyle 501\div 20\approx 25} , then add the "tricky number" 18 {\displaystyle 18} to give 43 {\displaystyle 43} . The nearest cross street to 501 5th Avenue is actually 42nd Street, not 43rd Street, as the Manhattan address algorithm only gives approximate answers.