Knowledge-Based Schematics Drafting: Aesthetic Configuration as a Design Task

Depicting an electrical circuit by a schematic is a tedious task that is a good candidate for automation. Programs that draft schematics with the usual algorithmic approach do not fully exploit knowledge of circuit function, relying mainly on the circuit topology. The extra-topological circuit characteristics are what an engineer uses to understand a schematic; human drafters take these characteristics into account when drawing a schematic. This document presents a knowledge base and an architecture for drafting arithmetic digital circuits having a single theme. The relevance and limitations of this architecture and knowledge base for other types of circuit are explored. It is argued that the task of schematics drafting is one of aesthetic design. The affect of aesthetic criteria on the program architecture is discussed. The circuit layout constraint language, the program's search regimen, and the backtracking scheme are highlighted and explained in detail.MIT Artificial Intelligence Laborator

Spatio-Temporal Reasoning and Linear Inequalities

Time and space are sufficiently similar to warrant in certain cases a common representation in AI problem-solving systems. What is represented is often the constraints that hold between objects, and a concern is the overall consistency of a set of constraints. This paper scrutinizes two current approaches to spatio-temporal reasoning. The suitableness of Allen's temporal algebra for constraint networks is influenced directly by the mathematical properties of the algebra. These properties are extracted by a formulation as a network of set-theoretic relations, such that some previous theorems due to Montanari apply. Some new theorems concerning consistency of these temporal constraint networks are also presented

Learning retrodictive knowledge from scientific laws : the case of chemical kinetics

Abstract: "We consider the problem of extracting retrodictive knowledge from scientific laws used ordinarily only to predict. In particular, a method is developed which synthesizes rules of experiment-interpretation from the basic law of chemical kinetics. Previous work in AI on transforming predictive knowledge into convenient retrodictive knowledge has been within the subfield of diagnosis. The current work extends the idea to the domain of elucidation of causal mechanism. Refutation rules are synthesized by discovering invariants within a parameterized system of equations. The choice of invariants to look for is guided by four criteria.A principle of stable refutation, based on the character of experimental data, is derived from the non-rescindible nature of refutation. Three other criteria contribute to the practicality, generality, and reliability of the rules. The invariants chosen are tested by systematic sampling of a system parameter-space. Hence, the rules, which check that an invariant holds for experimental data, are established by induction from simulation data. The synthesized rules serve in practice as reliable disconfirmatory evidence, rather than refutations, due to their inductive origin as well as to the uncertainty of experimental data. The rules will be applied within the context of ongoing work on elucidation of chemical-reaction networks."</p

Concise, Intelligible, and Approximate Profiling of Multiple Classes

When a dataset involves multiple classes, there is often a need to express the key contrasting features among these classes in humanly understandable terms, that is, to profile the classes. Commonly, one class is contrasted from the rest by aggregating the latter into a pseudo-class; alternatively, classes are treated separately without coordinating their profiles with those of the other classes. We introduce the concise all pairs profiling (CAPP) method for concise, intelligible, and approximate profiling of large classifications. The method compares all classes pairwise and then minimizes the overall number of features needed to guarantee that each pair of classes is contrasted by at least one feature. Then each class profile gets its own minimized list of features, annotated with how these features contrast the class from the others. Significant applications to social and natural science are demonstrated

Treatment Delivery Preferences Associated With Type of Mental Disorder and Perceived Treatment Barriers Among Mexican University Students

PURPOSE: Although Internet-based electronic health (eHealth) interventions could potentially reduce mental health disparities, especially in college students in under-resourced countries, little is known about the relative acceptability of eHealth versus in-person treatment modalities and the treatment barriers associated with a preference for one type over the other. METHODS: Participants were from the 2018-2019 cohort of the University Project for Healthy Students (PUERTAS), a Web-based survey of incoming first-year students in Mexico and part of the World Mental Health International College Student Survey initiative. A total of 7,849 first-year students, 54.73% female, from five Mexican universities participated. We estimated correlates of preference for eHealth delivery over in-person modalities with a multivariate logistic regression. RESULTS: Thirty-eight percent of students prefer in-person services, 36% showed no preference for in-person over eHealth, 19% prefer not to use services of any kind, and 7% preferred eHealth over in-person treatment delivery. Being embarrassed, worried about harm to one's academic career, wanting to handle problems on one's own, beliefs about treatment efficacy, having depression, and having attention-deficient hyperactivity disorder were associated with a clear preference for eHealth delivery methods with odds ratios ranging from 1.47 to 2.59. CONCLUSIONS: Although more students preferred in-person services over eHealth, those reporting attitudinal barriers (i.e., embarrassment, stigma, wanting to handle problems on one's own, and beliefs about treatment efficacy) and with depression or attention-deficit hyperactivity disorder had a greater preference for eHealth interventions suggesting these are students to whom eHealth interventions could be targeted to alleviate symptoms and/or as a bridge to future in-person treatment.status: publishe

Evaluation of a quality improvement intervention to reduce anastomotic leak following right colectomy (EAGLE): pragmatic, batched stepped-wedge, cluster-randomized trial in 64 countries

Background Anastomotic leak affects 8 per cent of patients after right colectomy with a 10-fold increased risk of postoperative death. The EAGLE study aimed to develop and test whether an international, standardized quality improvement intervention could reduce anastomotic leaks. Methods The internationally intended protocol, iteratively co-developed by a multistage Delphi process, comprised an online educational module introducing risk stratification, an intraoperative checklist, and harmonized surgical techniques. Clusters (hospital teams) were randomized to one of three arms with varied sequences of intervention/data collection by a derived stepped-wedge batch design (at least 18 hospital teams per batch). Patients were blinded to the study allocation. Low- and middle-income country enrolment was encouraged. The primary outcome (assessed by intention to treat) was anastomotic leak rate, and subgroup analyses by module completion (at least 80 per cent of surgeons, high engagement; less than 50 per cent, low engagement) were preplanned. Results A total 355 hospital teams registered, with 332 from 64 countries (39.2 per cent low and middle income) included in the final analysis. The online modules were completed by half of the surgeons (2143 of 4411). The primary analysis included 3039 of the 3268 patients recruited (206 patients had no anastomosis and 23 were lost to follow-up), with anastomotic leaks arising before and after the intervention in 10.1 and 9.6 per cent respectively (adjusted OR 0.87, 95 per cent c.i. 0.59 to 1.30; P = 0.498). The proportion of surgeons completing the educational modules was an influence: the leak rate decreased from 12.2 per cent (61 of 500) before intervention to 5.1 per cent (24 of 473) after intervention in high-engagement centres (adjusted OR 0.36, 0.20 to 0.64; P &lt; 0.001), but this was not observed in low-engagement hospitals (8.3 per cent (59 of 714) and 13.8 per cent (61 of 443) respectively; adjusted OR 2.09, 1.31 to 3.31). Conclusion Completion of globally available digital training by engaged teams can alter anastomotic leak rates. Registration number: NCT04270721 (http://www.clinicaltrials.gov)