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We present a rationale for expanding the presence of the Lisp family of programming languages in bioinformatics and computational biology research. Put simply, Lisp-family languages enable programmers to more quickly write programs that run faster than in other languages. Languages such as Common Lisp, Scheme and Clojure facilitate the creation of powerful and flexible software that is required for complex and rapidly evolving domains like biology. We will point out several important key features that distinguish languages of the Lisp family from other programming languages, and we will explain how these features can aid researchers in becoming more productive and creating better code. We will also show how these features make these languages ideal tools for artificial intelligence and machine learning applications. We will specifically stress the advantages of domain-specific languages (DSLs): languages that are specialized to a particular area, and thus not only facilitate easier research problem formulation, but also aid in the establishment of standards and best programming practices as applied to the specific research field at hand. DSLs are particularly easy to build in Common Lisp, the most comprehensive Lisp dialect, which is commonly referred to as the ‘programmable programming language’. We are convinced that Lisp grants programmers unprecedented power to build increasingly sophisticated artificial intelligence systems that may ultimately transform machine learning and artificial intelligence research in bioinformatics and computational biology.

In 1914, Felix Hausdorff published an elegant proof that almost all numbers are simply normal in base 2. We generalize this proof to show that almost all numbers are normal. The result is arguably the most elementary proof for this theorem so far and should be accessible to undergraduates in their first year.

We describe an approach which facilitates and makes explicit the organization of the knowledge necessary to map multisensor system requirements onto an appropriate assembly of algorithms, processors, sensors, and actuators. We have previously introduced the multisensor kernel system and logical sensor specifications as a means for high-level specification of multisensor systems. The main goals of such a characterization are to develop a coherent treatment of multisensor information, to allow system reconfiguration for both fault tolerance and dynamic response to environmental conditions, and to permit the explicit description of control.In this paper we show how logical sensors can be incorporated into an object-based approach for the interpretation of 3D structure. Considering the inherent difficulties in interpreting general configurations of lines in space, and considering the ubiquitousness of special line configurations in manufactured environments and objects, we advocate the use of computational units tuned to the occurrence of special configurations. The organized use of these units circumvents the inherent difficulties in interpreting general configurations of lines. After a brief examination of the problem of interpreting general configurations of lines in space, a number of computational units are proposed which are naturally derived from angular relations. The process of propagation (which allows interpretation to spread over the image) is also advocated. Such computational units and processes, which are simple and efficient, can be conveniently organized in a rule-based framework where the occurrence of the various special configurations can be tested. The multisensor knowledge system provides such a framework.