Solid-state electronics are semiconductor electronics: electronic equipment that use semiconductor devices such as transistors, diodes and integrated circuits (ICs). The term is also used as an adjective for devices in which semiconductor electronics that have no moving parts replace devices with moving parts, such as the solid-state relay, in which transistor switches are used in place of a moving-arm electromechanical relay, or the solid-state drive (SSD), a type of semiconductor memory used in computers to replace hard disk drives, which store data on rotating disks. == History == The term solid-state became popular at the beginning of the semiconductor era in the 1960s to distinguish this new technology. A semiconductor device works by controlling an electric current consisting of electrons or holes moving within a solid crystalline piece of semiconducting material such as silicon, while the thermionic vacuum tubes it replaced worked by controlling a current of electrons or ions in a vacuum within a sealed tube. Although the first solid-state electronic device was the cat's whisker detector, a crude semiconductor diode invented around 1904, solid-state electronics started with the invention of the transistor in 1947. Before that, all electronic equipment used vacuum tubes, because vacuum tubes were the only electronic components that could amplify—an essential capability in all electronics. The transistor, which was invented by John Bardeen and Walter Houser Brattain while working under William Shockley at Bell Laboratories in 1947, could also amplify, and replaced vacuum tubes. The first transistor hi-fi system was developed by engineers at GE and demonstrated at the University of Philadelphia in 1955. In terms of commercial production, The Fisher TR-1 was the first "all transistor" preamplifier, which became available mid-1956. In 1961, a company named Transis-tronics released a solid-state amplifier, the TEC S-15. The replacement of bulky, fragile, energy-hungry vacuum tubes by transistors in the 1960s and 1970s created a revolution not just in technology but in people's habits, making possible the first truly portable consumer electronics such as the transistor radio, cassette tape player, walkie-talkie and quartz watch, as well as the first practical computers and mobile phones. Other examples of solid state electronic devices are the microprocessor chip, LED lamp, solar cell, charge coupled device (CCD) image sensor used in cameras, and semiconductor laser. Also during the 1960s and 1970s, television set manufacturers switched from vacuum tubes to semiconductors, and advertised sets as "100% solid state" even though the cathode-ray tube (CRT) was still a vacuum tube. It meant only the chassis was 100% solid-state, not including the CRT. Early advertisements spelled out this distinction, but later advertisements assumed the audience had already been educated about it and shortened it to just "100% solid state". LED displays can be said to be truly 100% solid-state.
Orleans (software framework)
Orleans is a cross-platform software framework for building scalable and robust distributed interactive applications based on the .NET Framework or on the more recent .NET. == Overview == Orleans was originally created by the eXtreme Computing Group at Microsoft Research and introduced the virtual actor model as a new approach to building distributed systems for the cloud. Orleans scales from a single on-premises server to highly-available and globally distributed applications in the cloud. The virtual actor model is based on the actor model but has several differences: A virtual actor always exists, it cannot be explicitly created or destroyed. Virtual actors are automatically instantiated. If a server hosting an actor crashes, the next message sent to the actor causes it to be reinstantiated automatically. The server that an actor is on is transparent to the application code. Orleans can automatically create multiple instances of the same stateless actor. Starting with cloud services for the Halo franchise, the framework has been used by a number of cloud services at Microsoft and other companies since 2011. The core Orleans technology was transferred to 343 Industries and is available as open source since January 2015. The source code is licensed under MIT License and hosted on GitHub. Orleans runs on Microsoft Windows, Linux, and macOS and is compatible with .NET Standard 2.0 and above. == Features == Some Orleans features include: Persistence Distributed ACID transactions Streams Timers & Reminders Fault tolerance == Related implementations == The Electronic Arts BioWare division created Project Orbit. It is a Java implementation of virtual actors that was heavily inspired by the Orleans project.
Arabic Speech Corpus
The Arabic Speech Corpus is a Modern Standard Arabic (MSA) speech corpus for speech synthesis. The corpus contains phonetic and orthographic transcriptions of more than 3.7 hours of MSA speech aligned with recorded speech on the phoneme level. The annotations include word stress marks on the individual phonemes. The Arabic Speech Corpus was built as part of a doctoral project by Nawar Halabi at the University of Southampton funded by MicroLinkPC who own an exclusive license to commercialise the corpus, but the corpus is available for strictly non-commercial purposes through the official Arabic Speech Corpus website. It is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. == Purpose == The corpus was mainly built for speech synthesis purposes, specifically Speech Synthesis, but the corpus has been used for building HMM based voices in Arabic. It was also used to automatically align other speech corpora with their phonetic transcript and could be used as part of a larger corpus for training speech recognition systems. == Contents == The package contains the following: 1813 .wav files containing spoken utterances. 1813 .lab files containing text utterances. 1813 .TextGrid files containing the phoneme labels with time stamps of the boundaries where these occur in the .wav files. phonetic-transcript.txt which has the form "[wav_filename]" "[Phoneme Sequence]" in every line. orthographic-transcript.txt which has the form "[wav_filename]" "[Orthographic Transcript]" in every line. Orthography is in Buckwalter Format which is friendlier where there is software that does not read Arabic script. It can be easily converted back to Arabic. There is an extra 18 minutes of fully annotated corpus (separate from above but with the same structure as above) which was used to evaluated the corpus (see PhD thesis). The corpus was also used to prove that using automatically extracted, orthography-based stress marks improve the quality of speech synthesis in MSA.
Blockmodeling linked networks
Blockmodeling linked networks is an approach in blockmodeling in analysing the linked networks. Such approach is based on the generalized multilevel blockmodeling approach. The main objective of this approach is to achieve clustering of the nodes from all involved sets, while at the same time using all available information. At the same time, all one-mode and two-node networks, that are connected, are blockmodeled, which results in obtaining only one clustering, using nodes from each sets. Each cluster ideally contains only nodes from one set, which also allows the modeling of the links among clusters from different sets (through two-mode networks). This approach was introduced by Aleš Žiberna in 2014. Blockmodeling linked networks can be done using: separate analysis: blockmodeling each level separately; conversion approach: converting all one-mode networks to the same level and joining with two-mode networks; a true multilevel approach: one-mode and two-mode networks are blockmodeled at the same time, resulting in one clustering for nodes from each level.
NOMINATE (scaling method)
NOMINATE (an acronym for nominal three-step estimation) is a multidimensional scaling application developed by US political scientists Keith T. Poole and Howard Rosenthal in the early 1980s to analyze preferential and choice data, such as legislative roll-call voting behavior. In its most well-known application, members of the US Congress are placed on a two-dimensional map, with politicians who are ideologically similar (i.e. who often vote the same) being close together. One of these two dimensions corresponds to the familiar left–right political spectrum (liberal–conservative in the United States). As computing capabilities grew, Poole and Rosenthal developed multiple iterations of their NOMINATE procedure: the original D-NOMINATE method, W-NOMINATE, and most recently DW-NOMINATE (for dynamic, weighted NOMINATE). In 2009, Poole and Rosenthal were the first recipients of the Society for Political Methodology's Best Statistical Software Award for their development of NOMINATE. In 2016, the society awarded Poole its Career Achievement Award, stating that "the modern study of the U.S. Congress would be simply unthinkable without NOMINATE legislative roll call voting scores." == Procedure == The main procedure is an application of multidimensional scaling techniques to political choice data. Though there are important technical differences between these types of NOMINATE scaling procedures, all operate under the same fundamental assumptions. First, that alternative choices can be projected on a basic, low-dimensional (often two-dimensional) Euclidean space. Second, within that space, individuals have utility functions which are bell-shaped (normally distributed), and maximized at their ideal point. Because individuals also have symmetric, single-peaked utility functions which center on their ideal point, ideal points represent individuals' most preferred outcomes. That is, individuals most desire outcomes closest their ideal point, and will choose/vote probabilistically for the closest outcome. Ideal points can be recovered from observing choices, with individuals exhibiting similar preferences placed more closely than those behaving dissimilarly. It is helpful to compare this procedure to producing maps based on driving distances between cities. For example, Los Angeles is about 1,800 miles from St. Louis; St. Louis is about 1,200 miles from Miami; and Miami is about 2,700 miles from Los Angeles. From this (dis)similarities data, any map of these three cities should place Miami far from Los Angeles, with St. Louis somewhere in between (though a bit closer to Miami than Los Angeles). Just as cities like Los Angeles and San Francisco would be clustered on a map, NOMINATE places ideologically similar legislators (e.g., liberal Senators Barbara Boxer (D-Calif.) and Al Franken (D-Minn.)) closer to each other, and farther from dissimilar legislators (e.g., conservative Senator Tom Coburn (R-Okla.)) based on the degree of agreement between their roll call voting records. At the heart of the NOMINATE procedures (and other multidimensional scaling methods, such as Poole's Optimal Classification method) are algorithms they utilize to arrange individuals and choices in low dimensional (usually two-dimensional) space. Thus, NOMINATE scores provide "maps" of legislatures. Using NOMINATE procedures to study congressional roll call voting behavior from the First Congress to the present-day, Poole and Rosenthal published Congress: A Political-Economic History of Roll Call Voting in 1997 and the revised edition Ideology and Congress in 2007. In 2009, Poole and Rosenthal were named the first recipients of the Society for Political Methodology's Best Statistical Software Award for their development of NOMINATE, a recognition conferred to "individual(s) for developing statistical software that makes a significant research contribution". In 2016, Keith T. Poole was awarded the Society for Political Methodology's Career Achievement Award. The citation for this award reads, in part, "One can say perfectly correctly, and without any hyperbole: the modern study of the U.S. Congress would be simply unthinkable without NOMINATE legislative roll call voting scores. NOMINATE has produced data that entire bodies of our discipline—and many in the press—have relied on to understand the U.S. Congress." == Dimensions == Poole and Rosenthal demonstrate that—despite the many complexities of congressional representation and politics—roll call voting in both the House and the Senate can be organized and explained by no more than two dimensions throughout the sweep of American history. The first dimension (horizontal or x-axis) is the familiar left-right (or liberal-conservative) spectrum on economic matters. The second dimension (vertical or y-axis) picks up attitudes on cross-cutting, salient issues of the day (which include or have included slavery, bimetallism, civil rights, regional, and social/lifestyle issues). Rosenthal and Poole have initially argued that the first dimension refers to socio-economic matters and the second dimension to race-relations. However, the often confusing and residual nature of the second dimension has led to the second dimension being largely ignored by other researchers. For the most part, congressional voting is uni-dimensional, with most of the variation in voting patterns explained by placement along the liberal-conservative first dimension. While the first dimension of the DW-NOMINATE score is able to predict results at 83% accuracy, the addition of the second dimension only increases accuracy to 85%. Furthermore, the second dimension only provided a significant increase in accuracy for Congresses 1-99. As late as the 1990s, the second dimension was able to measure partisan splits in abortion and gun rights issues. However, a 2017 analysis found that since 1987, the votes of the US Congress had best fit a one-dimensional model, suggesting increasing party polarization after 1987. == Interpretation of nominate scores == For illustrative purposes, consider the following plots which use W-NOMINATE scores to scale members of Congress and uses the probabilistic voting model (in which legislators farther from the "cutting line" between "yea" and "nay" outcomes become more likely to vote in the predicted manner) to illustrate some major Congressional votes in the 1990s. Some of these votes, like the House's vote on President Clinton's welfare reform package (the Personal Responsibility and Work Opportunity Act of 1996) are best modeled through the use of the first (economic liberal-conservative) dimension. On the welfare reform vote, nearly all Republicans joined the moderate-conservative bloc of House Democrats in voting for the bill, while opposition was virtually confined to the most liberal Democrats in the House. The errors (those representatives on the "wrong" side of the cutting line which separates predicted "yeas" and predicted "nays") are generally close to the cutting line, which is what we would expect. A legislator directly on the cutting line is indifferent between voting "yea" and "nay" on the measure. All members are shown on the left panel of the plot, while only errors are shown on the right panel: Economic ideology also dominates the Senate vote on the Balanced Budget Amendment of 1995: On other votes, however, a second dimension (which has recently come to represent attitudes on cultural and lifestyle issues) is important. For example, roll call votes on gun control routinely split party coalitions, with socially conservative "blue dog" Democrats joining most Republicans in opposing additional regulation and socially liberal Republicans joining most Democrats in supporting gun control. The addition of the second dimension accounts for these inter-party differences, and the cutting line is more horizontal than vertical (meaning the cleavage is found on the second dimension rather than the first dimension on these votes) This pattern was evident in the 1991 House vote to require waiting periods on handguns: == Political ideology == DW-NOMINATE scores have been used widely to describe the political ideology of political actors, political parties and political institutions. For instance, a score in the first dimension that is close to either pole means that such score is located at one of the extremes in the liberal-conservative scale. So, a score closer to 1 is described as conservative whereas a score closer to −1 can be described as liberal. Finally, a score at zero or close to zero is described as moderate. == Political polarization == Poole and Rosenthal (beginning with their 1984 article "The Polarization of American Politics") have also used NOMINATE data to show that, since the 1970s, party delegations in Congress have become ideologically homogeneous and distant from one another (a phenomenon known as "polarization"). Using DW-NOMINATE scores (which permit direct comparisons between members of different Congress
Pwnie Awards
The Pwnie Awards are an annual awards ceremony that recognizes both excellence and incompetence in the field of information security, described by SecurityWeek as an event that "recognizes excellence and mocks incompetence in cybersecurity." Winners are selected by a committee of security industry professionals from nominations collected from the information security community. Nominees are announced yearly at Summercon, and the awards themselves are presented at the Black Hat Security Conference. == Origins == The name Pwnie Award is based on the word "pwn", which is hacker slang meaning to "compromise" or "control" based on the previous usage of the word "own" (and it is pronounced similarly). The name "The Pwnie Awards," pronounced as "Pony," is meant to sound like the Tony Awards, an awards ceremony for Broadway theater in New York City. == History == The Pwnie Awards were founded in 2007 by Alexander Sotirov and Dino Dai Zovi following discussions regarding Dino's discovery of a cross-platform QuickTime vulnerability (CVE-2007-2175) and Alexander's discovery of an ANI file processing vulnerability (CVE-2007-0038) in Internet Explorer. == Winners == === 2024 === Most Epic Fail: Crowdstrike for 2024 CrowdStrike incident Best Mobile Bug: Operation Triangulation Lamest Vendor Response: Xiaomi for obstructing Pwn2Own researchers from using their services Best Cryptographic Attack: GoFetch Best Desktop Bug: forcing realtime WebAudio playback in Chrome (CVE-2023-5996) Best Song: Touch Some Grass by UwU Underground Best Privilege Escalation: Windows Streaming Service UAF (CVE-2024-30089) by Valentina Palmiotti (chompie) Best Remote Code Execution: Microsoft Message Queuing (MSMQ) Remote Code Execution Vulnerability (CVE-2024-30080) Most Epic Achievement: Discovery and reverse engineering of the XZ Utils backdoor Most Innovative Research: Let the Cache Cache and Let the WebAssembly Assemble: Knocking’ on Chrome’s Shell by Edouard Bochin, Tao Yan, and Bo Qu Most Underhyped Research: See No Eval: Runtime Dynamic Code Execution in Objective-C === 2023 === Best Desktop Bug: CountExposure! by RyeLv(@b2ahex) Best Cryptographic Attack: Video-based cryptanalysis: Extracting Cryptographic Keys from Video Footage of a Device’s Power LED by Ben Nassi, Etay Iluz, Or Cohen, Ofek Vayner, Dudi Nassi, Boris Zadov, Yuval Elovici Best Song: Clickin’ Most Innovative Research: Inside Apple’s Lightning: Jtagging the iPhone for Fuzzing and Profit Most Under-Hyped Research: Activation Context Cache Poisoning Best Privilege Escalation Bug: URB Excalibur: Slicing Through the Gordian Knot of VMware VM Escapes Best Remote Code Execution Bug: ClamAV RCE Lamest Vendor Response: Three Lessons From Threema: Analysis of a Secure Messenger Most Epic Fail: “Holy fucking bingle, we have the no fly list,” Epic Achievement: Clement Lecigne: 0-days hunter world champion Lifetime Achievement Award: Mudge === 2022 === Lamest Vendor Response: Google's "TAG" response team for "unilaterally shutting down a counterterrorism operation." Epic Achievement: Yuki Chen’s Windows Server-Side RCE Bugs Most Epic Fail: HackerOne Employee Caught Stealing Vulnerability Reports for Personal Gains Best Desktop Bug: Pietro Borrello, Andreas Kogler, Martin Schwarzl, Moritz Lipp, Daniel Gruss, Michael Schwarz for Architecturally Leaking Data from the Microarchitecture Most Innovative Research: Pietro Borrello, Martin Schwarzl, Moritz Lipp, Daniel Gruss, Michael Schwarz for Custom Processing Unit: Tracing and Patching Intel Atom Microcode Best Cryptographic Attack: Hertzbleed: Turning Power Side-Channel Attacks Into Remote Timing Attacks on x86 by Yingchen Wang, Riccardo Paccagnella, Elizabeth Tang He, Hovav Shacham, Christopher Fletcher, David Kohlbrenner Best Remote Code Execution Bug: KunlunLab for Windows RPC Runtime Remote Code Execution (CVE-2022-26809) Best Privilege Escalation Bug: Qidan He of Dawnslab, for Mystique in the House: The Droid Vulnerability Chain That Owns All Your Userspace Best Mobile Bug: FORCEDENTRY Most Under-Hyped Research: Yannay Livneh for Spoofing IP with IPIP Best Song: Dialed Up by Project Mammoth === 2021 === Lamest Vendor Response: Cellebrite, for their response to Moxie, the creator of Signal, reverse-engineering their UFED and accompanying software and reporting a discovered exploit. Epic Achievement: Ilfak Guilfanov, in honor of IDA's 30th Anniversary. Best Privilege Escalation Bug: Baron Samedit of Qualys, for the discovery of a 10-year-old exploit in sudo. Best Song: The Ransomware Song by Forrest Brazeal Best Server-Side Bug: Orange Tsai, for his Microsoft Exchange Server ProxyLogon attack surface discoveries. Best Cryptographic Attack: The NSA for its disclosure of a bug in the verification of signatures in Windows which breaks the certificate trust chain. Most Innovative Research: Enes Göktaş, Kaveh Razavi, Georgios Portokalidis, Herbert Bos, and Cristiano Giuffrida at VUSec for their research on the "BlindSide" Attack. Most Epic Fail: Microsoft, for their failure to fix PrintNightmare. Best Client-Side Bug: Gunnar Alendal's discovery of a buffer overflow on the Samsung Galaxy S20's secure chip. Most Under-Hyped Research: The Qualys Research Team for 21Nails, 21 vulnerabilities in Exim, the Internet's most popular mail server. === 2020 === Best Server-Side Bug: BraveStarr (CVE-2020-10188) – A Fedora 31 netkit telnetd remote exploit (Ronald Huizer') Best Privilege Escalation Bug: checkm8 – A permanent unpatchable USB bootrom exploit for a billion iOS devices. (axi0mX) Epic Achievement: "Remotely Rooting Modern Android Devices" (Guang Gong) Best Cryptographic Attack: Zerologon vulnerability (Tom Tervoort, CVE-2020-1472) Best Client-Side Bug: RCE on Samsung Phones via MMS (CVE-2020-8899 and -16747), a zero click remote execution attack. (Mateusz Jurczyk) Most Under-Hyped Research: Vulnerabilities in System Management Mode (SMM) and Trusted Execution Technology (TXT) (CVE-2019-0151 and -0152) (Gabriel Negreira Barbosa, Rodrigo Rubira Branco, Joe Cihula) Most Innovative Research: TRRespass: When Memory Vendors Tell You Their Chips Are Rowhammer-free, They Are Not. (Pietro Frigo, Emanuele Vannacci, Hasan Hassan, Victor van der Veen, Onur Mutlu, Cristiano Giuffrida, Herbert Bos, Kaveh Razavi) Most Epic Fail: Microsoft; for the implementation of Elliptic-curve signatures which allowed attackers to generate private pairs for public keys of any signer, allowing HTTPS and signed binary spoofing. (CVE-2020-0601) Best Song: Powertrace by Rebekka Aigner, Daniel Gruss, Manuel Weber, Moritz Lipp, Patrick Radkohl, Andreas Kogler, Maria Eichlseder, ElTonno, tunefish, Yuki and Kater Lamest Vendor Response: Daniel J. Bernstein (CVE-2005-1513) === 2019 === Best Server-Side Bug: Orange Tsai and Meh Chang, for their SSL VPN research. Most Innovative Research: Vectorized Emulation Brandon Falk Best Cryptographic Attack: \m/ Dr4g0nbl00d \m/ Mathy Vanhoef, Eyal Ronen Lamest Vendor Response: Bitfi Most Over-hyped Bug: Allegations of Supermicro hardware backdoors, Bloomberg Most Under-hyped Bug: Thrangrycat, (Jatin Kataria, Red Balloon Security) === 2018 === Most Innovative Research: Spectre/Meltdown (Paul Kocher, Jann Horn, Anders Fogh, Daniel Genkin, Daniel Gruss, Werner Haas, Mike Hamburg, Moritz Lipp, Stefan Mangard, Thomas Prescher, Michael Schwarz, Yuval Yarom) Best Privilege Escalation Bug: Spectre/Meltdown (Paul Kocher, Jann Horn, Anders Fogh, Daniel Genkin, Daniel Gruss, Werner Haas, Mike Hamburg, Moritz Lipp, Stefan Mangard, Thomas Prescher, Michael Schwarz, Yuval Yarom) Lifetime Achievement: Michał Zalewski Best Cryptographic Attack: ROBOT - Return Of Bleichenbacher’s Oracle Threat Hanno Böck, Juraj Somorovsky, Craig Young Lamest Vendor Response: Bitfi hardware crypto-wallet, after the "unhackable" device was hacked to extract the keys required to steal coins and rooted to play Doom. === 2017 === Epic Achievement: Federico Bento for Finally getting TIOCSTI ioctl attack fixed Most Innovative Research: ASLR on the line Ben Gras, Kaveh Razavi, Erik Bosman, Herbert Bos, Cristiano Giuffrida Best Privilege Escalation Bug: DRAMMER Victor van der Veen, Yanick Fratantonio, Martina Lindorfer, Daniel Gruss, Clementine Maurice, Giovanni Vigna, Herbert Bos, Kaveh Razavi, Cristiano Giuffrida Best Cryptographic Attack: The first collision for full SHA-1 Marc Stevens, Elie Bursztein, Pierre Karpman, Ange Albertini, Yarik Markov Lamest Vendor Response: Lennart Poettering - for mishandling security vulnerabilities most spectacularly for multiple critical Systemd bugs Best Song: Hello (From the Other Side) - Manuel Weber, Michael Schwarz, Daniel Gruss, Moritz Lipp, Rebekka Aigner === 2016 === Most Innovative Research: Dedup Est Machina: Memory Deduplication as an Advanced Exploitation Vector Erik Bosman, Kaveh Razavi, Herbert Bos, Cristiano Giuffrida Lifetime Achievement: Peiter Zatko aka Mudge Best Cryptographic Attack: DROWN attack Nimrod Aviram et al. Best Song: Cyberlier - Katie Mous
Stochastic block model
The stochastic block model is a generative model for random graphs. This model tends to produce graphs containing communities, subsets of nodes characterized by being connected with one another with particular edge densities. For example, edges may be more common within communities than between communities. Its mathematical formulation was first introduced in 1983 in the field of social network analysis by Paul W. Holland et al. The stochastic block model is important in statistics, machine learning, and network science, where it serves as a useful benchmark for the task of recovering community structure in graph data. == Definition == The stochastic block model takes the following parameters: The number n {\displaystyle n} of vertices; a partition of the vertex set { 1 , … , n } {\displaystyle \{1,\ldots ,n\}} into disjoint subsets C 1 , … , C r {\displaystyle C_{1},\ldots ,C_{r}} , called communities; a symmetric r × r {\displaystyle r\times r} matrix P {\displaystyle P} of edge probabilities. The edge set is then sampled at random as follows: any two vertices u ∈ C i {\displaystyle u\in C_{i}} and v ∈ C j {\displaystyle v\in C_{j}} are connected by an edge with probability P i j {\displaystyle P_{ij}} . An example problem is: given a graph with n {\displaystyle n} vertices, where the edges are sampled as described, recover the groups C 1 , … , C r {\displaystyle C_{1},\ldots ,C_{r}} . == Special cases == If the probability matrix is a constant, in the sense that P i j = p {\displaystyle P_{ij}=p} for all i , j {\displaystyle i,j} , then the result is the Erdős–Rényi model G ( n , p ) {\displaystyle G(n,p)} . This case is degenerate—the partition into communities becomes irrelevant—but it illustrates a close relationship to the Erdős–Rényi model. The planted partition model is the special case that the values of the probability matrix P {\displaystyle P} are a constant p {\displaystyle p} on the diagonal and another constant q {\displaystyle q} off the diagonal. Thus two vertices within the same community share an edge with probability p {\displaystyle p} , while two vertices in different communities share an edge with probability q {\displaystyle q} . Sometimes it is this restricted model that is called the stochastic block model. The case where p > q {\displaystyle p>q} is called an assortative model, while the case p < q {\displaystyle p