We present a framework for the rational analysis of elemental causal induction – learning about the existence of a relationship between a single cause and effect – based upon causal graphical models. This framework makes precise the distinction between causal structure and causal strength: the difference between asking whether a causal relationship exists and asking how strong that causal relationship might be. We show that two leading rational models of elemental causal induction, ∆P and causal power, both estimate causal strength, and introduce a new rational model, causal support, that assesses causal structure. Causal support predicts several key phenomena of causal induction that cannot be accounted for by other rational models, which we explore through a series of experiments. These phenomena include the complex interaction between ∆P and the base-rate probability of the effect in the absence of the cause, sample size effects, inferences from incomplete contingency tables, and causal learning from rates. Causal support also provides a better account of a number of existing datasets than either ∆P or causal power.

This paper argues that immunological memory is in the same class of associative memories as Kanerva's Sparse Distributed Memory, Albus's Cerebellar Model Arithmetic Computer, and Marr's Theory of the Cerebellar Cortex. This class of memories derives its associative and robust nature from a sparse sampling of a huge input space by recognition units (B and T cells in the immune system) and a distribution of the memory among many independent units (B and T cells in the memory population in the immune system). Keywords: Immunological Memory, Associative Memory, Cross-Reactive Memory, Original Antigenic Sin, Sparse Distributed Memory. 1 Introduction Cowpox vaccination, used to protect humans from smallpox, was the first known deliberate use of associative recall in the immune response (Jenner, 1798). The modern investigation of associative recall began with the observation that antibodies induced during an influenza infection often have greater affinity to prior strains of influenza than t...

We present a method for deriving shape space parameters that are consistent with immunological data, and illustrate the method by deriving shape space parameters for a model of cross-reactive memory. Cross-reactive memory responses occur when the immune system is primed by one strain of a pathogen and challenged with a related, but different, strain. Much of the nature of a cross-reactive response is determined by the quantity and distribution of the memory cells, raised to the primary antigen, that cross-react with the secondary antigen. B cells with above threshold affinity for an antigen lie in a region of shape space that we call a ball of stimulation. In a cross-reactive response, the intersection of the balls of stimulation of the primary and secondary antigens contains the cross-reactive B cells and thus determines the degree of cross-reactivity between the antigens. We derive formulas for the volume of intersection of balls of stimulation in different shape spaces, and show th...

Edward Jenner, who discovered that it is possible to vaccinate against Small Pox using material from Cow Pox, is rightly the man who started the science of immunology. However, over the passage of time many of the details surrounding his astounding discovery have been lost or forgotten. Also, the environment within which Jenner worked as a physician in the countryside, and the state of the art of medicine and society are difficult to appreciate today. It is important to recall that people were still being bled at the time, to relieve the presence of evil humors. Accordingly, this review details Jenner’s discovery and attempts to place it in historical context. Also, the vaccine that Jenner used, which decreased the prevalence of Small Pox worldwide in his own time, and later was used to eradicate Small Pox altogether, is discussed in light of recent data.

The nature of the SIGCSE Symposium has seemingly evolved over the past ten years. Ten years ago the typical Symposium paper focused on the sharing of someone’s interesting or innovative idea on some aspect of the teaching component of being an undergraduate CS instructor. This could be a pedagogic technique, a “nifty ” assignment or even the use of a lecture prop. It was also a primary source for the dissemination of courseware to support CS education (CSEd). The annual gathering at the Symposium was in many respects a large idea swap meet. The most recent Symposia have become infused with the notion of assessment; not techniques for the assessment of students and learning outcomes, but the assessment of the ideas or techniques being presented at the conference. It is seemingly no longer sufficient to develop an interesting or innovative technique/courseware/pedagogic approach, it must also be, using the tools of CSEd research, formally assessed. This panel seeks to explore this phenomenon by examining different perspectives on the question of the relationship between the SIGCSE community in general and the Symposium in particular and formal CSEd research. 2. MICHAEL GOLDWEBER The questions regarding the relationship between CSEd research and the SIGCSE community are: • What is the purpose of the SIGCSE community? • What is CSEd research and who is qualified to undertake CSEd research? • Are SIGCSE sponsored conferences the appropriate forums for the dissemination of CSEd research? Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Copyright 200X ACM X-XXXXX-XX-X/XX/XX...$5.00.

One type of scientific inquiry involves the analysis of large data sets, often using statistical models and formal tests of hypotheses. A moment’s thought, however, shows that there must be other types of scientific inquiry. For instance, something has to be done to answer questions like the following. How should a study be designed? What sorts of data should be collected? What kind of a model is needed? Which hypotheses should be formulated in terms of the model and then tested against the data? The answers to these questions frequently turn on observations, qualitative or quantitative, that give crucial insights into the causal processes of interest. Such observations generate a line of scientific inquiry, or markedly shift the direction of the inquiry by overturning prior hypotheses, or provide striking evidence to confirm hypotheses. They may well stand on their own rather than being subsumed under the systematic data collection and modeling activities mentioned above. Such observations have come to be called “Causal Process Observations”

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We present a framework for the rational analysis of elemental causal induction -- learning about the existence of a relationship between a single cause and effect -- based upon causal graphical models. This framework makes precise the intuitive distinction between causal structure and causal strength: the difference between asking whether or not a causal relationship exists, and asking how strong that causal relationship might be. We show that the two leading rational models of elemental causal induction, #P and causal power, both estimate causal strength, and introduce a new rational model, causal support, that assesses causal structure. Causal support provides a better account of a large number of existing datasets than either #P or causal power. It also predicts several phenomena that cannot be accounted for by other models, which we explore through a series of experiments. These phenomena include the complex interaction between #P and the base-rate probability of the effect in the absence of the cause, sample size effects, inferences from incomplete contingency tables, and causal learning from rates.

Computer science (CS) education research is an emergent area and is still giving birth to a literature. While scholarly and scientific publishing go back to Philosophical Transactions (first published by the Royal Society in England in 1665), one of our oldest dedicated journals Computer Science Education was established less than two decades ago, in 1988. Growth which has led to the emergence of CS education research as an identifiable area has come from various places. Some from sub-specialist areas: the

Since the publication of Edward Jenner's (1) "An inquiry into the causes and effects of the variolae ",Taccinae " in 1798 it has been assumed that certain transformations occur with the viruses of the pox gr,up, either under natural conditions or as the result of experimental manipulation. Particular attention has been focused on the transformation of variola virus to vaccinia virus by animal passage. While variola virus is non-pathogenic on initial inoculation into the skin of domestic and laboratory animals, except in the case of the monkey, there is a considerable amount of evidence indicating that it can be passaged and that by successive passage in the skin of the calf and in the skin or testis of the rabbit it is eventually changed into a virus which resembles vaccinia in producing an extensive dermal reaction in these animals. There is no question that vaccinia virus has often been obtained by this passaging of variolous material. The pertinent question is, however, where the terminal virus originated. As pointed out by Horgan (2) in his extensive review of the situation, the work of this nature has usually been conducted in laboratories