The modulation error ratio (MER) is a measure used to quantify the performance of a digital radio (or digital TV) transmitter or receiver in a communications system using digital modulation (such as QAM). A signal sent by an ideal transmitter or received by a receiver would have all constellation points precisely at the ideal locations, however various imperfections in the implementation (such as noise, low image rejection ratio, phase noise, carrier suppression, distortion, etc.) or signal path cause the actual constellation points to deviate from the ideal locations. Transmitter MER can be measured by specialized equipment, which demodulates the received signal in a similar way to how a real radio demodulator does it. Demodulated and detected signal can be used as a reasonably reliable estimate for the ideal transmitted signal in MER calculation. == Definition == An error vector is a vector in the I-Q plane between the ideal constellation point and the point received by the receiver. The Euclidean distance between the two points is its magnitude. The modulation error ratio is equal to the ratio of the root mean square (RMS) power (in Watts) of the reference vector to the power (in Watts) of the error. It is defined in dB as: M E R ( d B ) = 10 log 10 ( P s i g n a l P e r r o r ) {\displaystyle \mathrm {MER(dB)} =10\log _{10}\left({P_{\mathrm {signal} } \over P_{\mathrm {error} }}\right)} where Perror is the RMS power of the error vector, and Psignal is the RMS power of ideal transmitted signal. MER is defined as a percentage in a compatible (but reciprocal) way: M E R ( % ) = P e r r o r P s i g n a l × 100 % {\displaystyle \mathrm {MER(\%)} ={\sqrt {P_{\mathrm {error} } \over P_{\mathrm {signal} }}}\times 100\%} with the same definitions. MER is closely related to error vector magnitude (EVM), but MER is calculated from the average power of the signal. MER is also closely related to signal-to-noise ratio. MER includes all imperfections including deterministic amplitude imbalance, quadrature error and distortion, while noise is random by nature.
Grammatik
Grammatik was the first grammar-checking program for home computers. Aspen Software of Albuquerque, NM, released the earliest version of this diction and style checker for personal computers. It was first released no later than 1981, and was inspired by the Writer's Workbench. Grammatik was first available for the TRS-80, and soon had versions for CP/M and the IBM PC. Reference Software International of San Francisco, California, acquired Grammatik in 1985. Development of Grammatik continued, and it became an actual grammar checker that could detect writing errors beyond simple style checking. Subsequent versions were released for MS-DOS, Windows, Macintosh, and Unix. Grammatik was ultimately acquired by WordPerfect Corporation and is integrated into the WordPerfect word processor.
Daniel Wolfe
Daniel Wolfe (born 1960) is an American activist, advocate, and writer whose work advances health programs and policy that balance scientific research and community expertise. His career has focused on support for community health movements, particularly among groups often regarded as criminal or socially suspect, including gay men and people who use illicit drugs. == Early life == Wolfe was raised between Arizona—including time on Rancho Linda Vista, a commune outside of Tucson—and East Hampton, NY. He received his undergraduate degree in Near Eastern Studies from Princeton University, and following time studying Arabic in Egypt, worked as the junior ghostwriter on the autobiographies of First Lady of Egypt Jehan Sadat and Pakistani Prime Minister Benazir Bhutto. Upon return to New York, he was an assistant at the Council on Foreign Relations to Richard W. Murphy, former US Assistant Secretary of State for Near Eastern and South Asian Affairs. Disagreement with US killing of Iraqi civilians during the 1990 Gulf War—and the rising toll of HIV in NY—moved Wolfe to leave Middle East studies and work full-time on AIDS in 1990. == Education == Wolfe was Community Scholar at the Columbia University Mailman School of Public Healthwhere he received his Masters in Public Health in 2004. He holds a Masters of Philosophy (in history) from Columbia University, and a BA in Near Eastern Studies from Princeton University. He was the recipient of a Charles H. Revson Foundation fellowship for urban leaders who have made a substantial contribution to New York City, and a fellow at the Center for Arabic Studies Abroad in Cairo, Egypt. == AIDS and gay activism == Wolfe was part of the media committee for ACT UP’s 1998 action to seize control of the FDA, and helped organize ACT UP NY’s challenge to Governor Cuomo to do better on the AIDS response and other actions.Wolfe also joined ACT UP colleagues Gregg Bordowitz, David Barr, Richard Elovich, Jean Carlomusto and others to work at Gay Men’s Health Crisis (GMHC), the nation’s first AIDS organization, where he served as director of communications and spokesperson on issues including opposition to NY State cuts to the AIDS budget, the disclosure that Olympic Champion Greg Louganis had HIV, reports of the FBI spying on AIDS activists, and GMHC’s move to offer HIV testing and targeted support to those who were HIV-negative. Wolfe also continued cultural work, making art, performance and video as a member of the gay and lesbian collective GANG with artists and ACT UP members including Zoe Leonard, Suzanne Wright, Loring McAlpin, Wellington Love, Adam Rolston and others, and writing a biography of Lawrence of Arabia for a series for young adults on famous gay men and lesbians in history edited by Martin Duberman. Controversy followed, with North Carolina Senator Jesse Helms waving a GANG piece in an issue of the Movement Research Performance Journal on the floor of Congress to show the "rottenness" of publicly funded art, and a number of schools banning the biography series for young adults from their libraries. Wolfe and others challenged the move as continuing the longstanding and homophobic demand that notable gay men and lesbians stay silent about essential details of their private lives even while being celebrated for their professional achievements. == Gay health == The approval of antiretroviral therapy for HIV in 1996 opened up new space for discussions of gay health beyond HIV, and new directions for Wolfe. Working from hundreds of interviews, surveys, workshops, and with a team of writers, Wolfe was the author of Men Like Us, the Our Bodies, Ourselves-inspired GMHC Complete Guide to Gay Men’s Sexual, Physical, and Emotional Well-being, covering issues from spirituality to sexual health to aging. The move to frame gay health beyond condoms and pills—and to offer a guide to health that “did not need to be translated from the original heterosexual”—was part of a larger gay health movement encompassing wellness and pleasure, and focused less on health disparity than on individual and community resilience. Wolfe was a keynote speaker and workshop leader, along with Eric Rofes, Chris Bartlett, and other organizers, at the first National Gay Men’s Health Summit held in Boulder, Colorado in 2002. Awarded a Charles H. Revson Fellowship for urban leaders in the City of New York, Wolfe became a community scholar at Columbia University’s Center of History and Ethics of Public Health, where he received his MPH in 2003, and was a contributor to Searching Eyes: Privacy, the State, and Disease Surveillance in America. == International harm reduction == Wolfe was Director of International Harm Reduction Development at the Open Society Foundations (2005-2021) where he led grantmaking and advocacy to protect the health and rights of people who use drugs in Eastern Europe, Asia, Africa and the Americas. Wolfe challenged approaches that conditioned support on abstinence or that sought to treat people who use illegal drugs like drugs themselves, as something to be controlled or contained. As with the gay health movement, he advocated a focus on community resilience and strengths, and on supporting individuals and communities to negotiate the balance between risk and pleasure of activities integral to life. Noting what he called the “antisocial behavior of health systems,” Wolfe’s analysis elevated issues such as forced labor and harsh punishment delivered in the name of addiction treatment and rehabilitation, the role of criminalization, imprisonment and stigma in interrupting or impeding HIV treatment, and the bias toward coercive approaches in studying and delivering addiction treatments. He also pointed to defects in national and international drug control policies and human rights violations as a root cause of HIV, hepatitis, and other health challenges faced by people who used drugs. Concrete advocacy supported by Open Society’s International Harm Reduction Development program under his direction included rebuffing US government efforts to force the UN to remove all references to harm reduction in its materials, addition of the addiction treatment medicines methadone and buprenorphine to the World Health Organization’s essential medicines list, and WHO endorsement of lay distribution of the opioid overdose antidote naloxone. Wolfe and OSF colleagues also advocated for new approaches to intellectual property and data sharing in research and development of medicines and vaccines to lower price and improve access to medicines globally to those in need. == AI and patient rights == Reports of patients denied opioid prescriptions based on an algorithm purporting to calculate their risk of overdose led Wolfe to work on AI, first as a resident at the Rockefeller Foundation Bellagio Center, and then as Executive Director of a new UCSF UC Berkeley program pioneering efforts to join AI, clinical and public health practice, and equity. In keeping with his earlier (analog) work on HIV, Wolfe has highlighted concerns about health systems using algorithms to gauge the merit of treatments for those regarded as socially suspect, the importance of moving beyond proprietary, black box algorithms toward an architecture of health data as a public good, and the need to maximize benefit for patients and communities, as well health systems, in the use of large language models.
StepFun
Shanghai Jieyue Xingchen Intelligent Technology Co., Ltd, known as StepFun, is an artificial intelligence (AI) company based in Shanghai, China. It has been dubbed one of China's "AI Tiger" companies by investors. == Background == StepFun was founded in April 2023 by former Microsoft employees. Investors include Tencent, Qiming Venture Partners and Shanghai State-owned Capital Investment. In July 2025 at the World Artificial Intelligence Conference, StepFun announced the "Model-Chip Ecosystem Innovation Alliance" which consisted of Chinese developers of large language models (LLMs) and AI chip manufacturers. This included companies such as Huawei, Biren Technology, Moore Threads and Enflame. Another second alliance named the "Shanghai General Chamber of Commerce AI Committee" was also established that included StepFun, SenseTime, MiniMax, MetaX and Iluvatar CoreX. On 25 February 2026, it was reported that StepFun was seeking an initial public offering on the Hong Kong Stock Exchange. StepFun focuses on multimodal models which are designed to understand multiple types of input data such as text, video and audio. == Products == In July 2024 at the World Artificial Intelligence Conference, StepFun officially launched Step-2, a trillion-parameter LLM, along with the Step-1.5V multimodal model and the Step-1X image generation model. In February 2025, StepFun and Geely jointly announced the open-sourcing of two multimodal large models to global developers. They were Step-Video-T2V and Step-Audio. In July 2025, StepFun released Step 3. The Model-Chip Ecosystem Innovation Alliance aimed to optimize Step 3 for domestic chips. In April 2025, Step-R1-V-Mini was released. It is a multimodal reasoning model designed for visual interpretation and image understanding. In February 2026, Step-3.5-Flash, a mixture-of-experts model with 196 billion parameters and 11 billion active parameters was released under the free and open-source Apache 2.0 license. It supports tool use and a 256k token context window. == Models ==
Mycin
MYCIN was an early backward chaining expert system that used black box to identify bacteria causing severe infections, such as bacteremia and meningitis, and to recommend antibiotics, with the dosage adjusted for patient's body weight — the name derived from the antibiotics themselves, as many antibiotics have the suffix "-mycin". The Mycin system was also used for the diagnosis of blood clotting diseases. MYCIN was developed over five or six years in the early 1970s at Stanford University. It was written in Lisp as the doctoral dissertation of Edward Shortliffe under the direction of Bruce G. Buchanan, Stanley N. Cohen and others. MYCIN emerged from the Stanford Heuristic Programming Project. MYCIN demonstrated the potential for expert systems in building high-performance medical reasoning programs. MYCIN is often viewed as a pioneer in the field of expert systems, even being referred to as the "grandaddy of them all-the one that launched the field" by Dr. Allen Newell. MYCIN led to the EMYCIN expert system shell ("essential MYCIN") for acquiring knowledge, reasoning with it, and explaining the results, without the specific medical knowledge. It can be described as "EMYCIN = Prolog + uncertainty + caching + questions + explanations + contexts - variables". An introduction is in Chapter 16 of Paradigms of Artificial Intelligence Programming (PAIP). == Method == MYCIN operated using a fairly simple inference engine and a knowledge base of ~600 rules by obtaining individual inferential facts identified by experts and encoding such facts as individual production rules. No other AI program at the time contained as much domain-specific knowledge clearly separated from its inference procedures as MYCIN. It would query the physician running the program via a long series of simple yes/no or textual questions. At the end, it provided a list of possible culprit bacteria ranked from high to low based on the probability of each diagnosis, its confidence in each diagnosis' probability, the reasoning behind each diagnosis (that is, MYCIN would also list the questions and rules which led it to rank a diagnosis a particular way), and its recommended course of drug treatment. MYCIN could additionally respond to queries by physicians related to why it asked the user a certain question, how it arrived at a conclusion, and why it did not consider certain factors. The developers performed studies showing that MYCIN's performance was minimally affected by perturbations in the uncertainty metrics associated with individual rules, suggesting that the power in the system was related more to its knowledge representation and reasoning scheme than to the details of its numerical uncertainty model. Some observers felt that it should have been possible to use classical Bayesian statistics. MYCIN's developers argued that this would require either unrealistic assumptions of probabilistic independence, or require the experts to provide estimates for an unfeasibly large number of conditional probabilities. Subsequent studies later showed that the certainty factor model could indeed be interpreted in a probabilistic sense, and highlighted problems with the implied assumptions of such a model. However the modular structure of the system would prove very successful, leading to the development of graphical models such as Bayesian networks. === Context === A context in MYCIN determines what types of objects can be reasoned about. They are similar to variables in Prolog, or environment variables in operating systems. === Evidence combination === In MYCIN it was possible that two or more rules might draw conclusions about a parameter with different weights of evidence. For example, one rule may conclude that the organism in question is E. Coli with a certainty of 0.8 whilst another concludes that it is E. Coli with a certainty of 0.5 or even −0.8. In the event the certainty is less than zero the evidence is actually against the hypothesis. In order to calculate the certainty factor MYCIN combined these weights using the formula below to yield a single certainty factor: C F ( x , y ) = { X + Y − X Y if X , Y > 0 X + Y + X Y if X , Y < 0 X + Y 1 − min ( | X | , | Y | ) otherwise {\displaystyle CF(x,y)={\begin{cases}X+Y-XY&{\text{if }}X,Y>0\\X+Y+XY&{\text{if }}X,Y<0\\{\frac {X+Y}{1-\min(|X|,|Y|)}}&{\text{otherwise}}\end{cases}}} Where X and Y are the certainty factors. This formula can be applied more than once if more than two rules draw conclusions about the same parameter. It is commutative, so it does not matter in which order the weights were combined. The combination formula was designed to have the following desirable properties: −1 can be interpreted as "false", +1 as "true", and 0 as "uncertain". Combining unknown with anything leaves it unchanged. Combining true with anything (except false) gives true. Similarly for false. Combining true and false is a division-by-zero error. Combining +x and -x gives unknown. Combining two positives (except true) gives a larger positive. Similarly for negatives. Combining a positive and a negative gives something in between. === Examples === The following examples come from Chapter 16 of PAIP, which contains an implementation in Common Lisp of a modified and simplified version of MYCIN for pedagogical purposes. A rule, and an English paraphrase generated by the system: == Results == An evaluation of MYCIN was conducted at the Stanford Medical School. The first phase of the evaluation consisted of 10 test cases of diverse origin, chosen by a physician who was not acquainted with MYCIN's methods or knowledge base. These cases were presented to 7 physicians and 1 senior medical student. 10 prescriptions were compiled for each of the cases, 1 recommended by MYCIN, 1 prescribed by the treating physician at the county hospital, and 8 by the aforementioned individuals. The second phase of the evaluation consisted of eight infectious disease specialists being provided the clinical summary and set of 10 prescriptions for each of the 10 cases and tasked to provide their own recommendations for each case and assess the 10 prescriptions. MYCIN received an acceptability rating of 65%, which was comparable to the 42.5% to 62.5% rating of five faculty members. This study is often cited as showing the potential for disagreement about therapeutic decisions, even among experts, when there is no "gold standard" for correct treatment. == Practical use == MYCIN was never actually used in practice. This wasn't because of any weakness in its performance. Some observers raised ethical and legal issues related to the use of computers in medicine, regarding the responsibility of the physicians in case the system gave wrong diagnosis. However, the greatest problem, and the reason that MYCIN was not used in routine practice, was the state of technologies for system integration, especially at the time it was developed. MYCIN was a stand-alone system that required a user to enter all relevant information about a patient by typing in responses to questions MYCIN posed. MYCIN ran on the DEC KI10 PDP-10, supporting a large time-shared system available over the early Internet (ARPANet), before personal computers were developed. MYCIN's greatest influence was accordingly its demonstration of the power of its representation and reasoning approach. Rule-based systems in many non-medical domains were developed in the years that followed MYCIN's introduction of the approach. In the 1980s, expert system "shells" were introduced (including one based on MYCIN, known as E-MYCIN (followed by Knowledge Engineering Environment - KEE)) and supported the development of expert systems in a wide variety of application areas. A difficulty that rose to prominence during the development of MYCIN and subsequent complex expert systems has been the extraction of the necessary knowledge for the inference engine to use from the human expert in the relevant fields into the rule base (the so-called "knowledge acquisition bottleneck").
Globetrooper
Globetrooper is a free travel app known for assisting travelers in finding partners for group trips and world adventures. Globetrooper offers a free social travel platform that helps people find travel partners. == History == Globetrooper was developed and released in 2010 by a couple; Todd Sullivan and Lauren McLeod who are two travel-minded individuals that wanted to make it easier for travelers to plan a journey and see the world. With their backgrounds in business, software & design, and a love for travel, both left the corporate world and launched Globetrooper on Lauren’s birthday 28 March 2010. Globetrooper was first launched as an information portal with a view to making it more social, but after some months, the content quickly grew and changed to the ‘travel partner’ concept.
Unique name assumption
The unique name assumption is a simplifying assumption made in some ontology languages and description logics. In logics with the unique name assumption, different names always refer to different entities in the world. It was included in Ray Reiter's discussion of the closed-world assumption often tacitly included in Database Management Systems (e.g. SQL) in his 1984 article "Towards a logical reconstruction of relational database theory" (in M. L. Brodie, J. Mylopoulos, J. W. Schmidt (editors), Data Modelling in Artificial Intelligence, Database and Programming Languages, Springer, 1984, pages 191–233). The standard ontology language OWL does not make this assumption, but provides explicit constructs to express whether two names denote the same or distinct entities. owl:sameAs is the OWL property that asserts that two given names or identifiers (e.g., URIs) refer to the same individual or entity. owl:differentFrom is the OWL property that asserts that two given names or identifiers (e.g., URIs) refer to different individuals or entities.