Krishna K. Agarwal

Empirical Explorations of SYNCHEM.

During the past several years, a substantial body of experience has accumulated in the use of SYNCHEM, a large-scale program which is able to discover synthesis routes for relatively complex organic structures without on-line guidance on the part of its chemist user. These results indicate that the approach to computer-directed organic synthesis route discovery embodied in the program has been valid and reasonable, and that SYNCHEM is likely to be fruitful from the point of view of its intended users as well as for our research objectives in artificial intelligence. The experiments have revealed a number of insufficiencies in the program as well. Most of these are rectified in SYNCHEM2, a revised version of the program which includes, among other improvements, a more highly developed synthesis search algorithm and the routine consideration of stereochemistry.

Efficient sorting with CCD's and magnetic bubble memories

The use of sorting in various computer-related applications such as database management, is well established. Normally, sorting is implemented by employing a combination of hardware and software techniques. In this correspondence the authors examine the utility of serial-shifting nature of the charge coupled device (CCD) and the magnetic bubble memories for achieving low-cost sorting within the secondary storage. The radix-sort method has been found to be very suitable for this purpose. Five different memory organizations implementing radix sort have been described in detail and their relative merits are also outlined.

Graph embedding in SYNCHEM2, an expert system for organic synthesis discovery

Abstract Graph embedding (subgraph isomorphism) is an NP-complete problem of great theoretical and practical importance in the sciences, especially chemistry and computer science. This paper presents positive test results for techniques to speed embedding by modeling graphs with subroutines, precalculating edge tables, turning recursion into iteration, and using search-ordering heuristics. The expert system synchem 2 searches for synthesis routes of organic molecules without the online guidance of a user, and this paper examines how embedding information helps to implement the central operations of synchem 2: selection, application, and evaluation of chemical reactions. The paper also outlines the architecture of synchem 2, analyzes the computational time complexity of embedding and related problems in graph isomorphism and canonical chemical naming, and suggests topics and techniques for further research.

Rectification behaviour of some metal ion–metal interfaces and concentration cells

The rectification behaviour of three metal ion–metal interfaces and 38 concentration cells was studied. The rectification in Al∣Al3+∣Al was 35% (−0.4 to +0.80 V d.c.) between 2.0–5.0 V a.c. and for Zn∣Zn2+ ⋮ Al3+∣Al cell was 20% (+0.20 to −0.30 V d.c.). Its negative d.c. potential showed some similarity to a tunnel diode. 20% rectification was obtained when each of Al, Zn, Mg half-cell was coupled with I−, I2∣Pt half-cell and Al half-cell was coupled with Fe3+, Fe2+∣Pt half-cell. When the Zn half-cell was associated with Cr3+, Cr2O72−∣Pt half-cell the rectification was 15%, whereas the rectification in all other concentration cells varied from 1 to 12%. The possibility of obtaining much higher percentage of rectification can be explored in a large number of other metal ion–metal interfaces and concentration cells which can be assembled in a similar manner using the table of standard reduction potentials. The characteristics of a concentration cell can be varied by change in concentration of metal ion, redox ratio, variation of pH, temperature, effect of different additives to the cell solution, irradiation of electrode surface etc. Consequently, it will affect the percentage of rectification which may be of some use in commercial applications.

Parallel implementation of an extended functional programming language on cellular tree and data flow architectures

Extended Functional Programming language (EFP) la an extended version of FP [BJ78] which can simply manipulate the operations on Syntactically Represented Data Structures (SRDS) [SR87,TS86, TS87]. In EFP, a complex operation such as insertion of a node h a binary tree will be expressed in a set of parallel operations which are much simpler. Both Cellular Tree Architecture (CTA) [MG80] and Data Flow Architecture (DFA) [RM83] can be used to implement the operations on SRDS (in particular) in parallel. CTA, which has a full binary tree structure, containa an array of independent cells which are the leavea of the tree. Each cell will be involved with only one B*1 of the SRDS . Since these cells are independent, they can perform the operations on these symbols in parallel. So in CTA, we get parallelism within the operations as well as between the operations. In case of DFA, each complex operation will be converted to a sequence of primitive operations which may be performed in parallel. This sequence of operations will be represented by a basic block of instructions [RM83]. Each instruction represents a primitive operation. All the instructions with the number of dependencies equal to zero can be performed in parallel (if there are enough processors ). Only one processor is involved with the execution of each instruction. So there is no parallelism within the operation but only between the operations. In this paper these two computer architectures have been briefly described and the implementation of some operations on SRDS bas been demonstrated. Finally, these implementations on these two computer architectures have been compared and contracted by discussing some advantages and disadvantages of sach method. 1. Cellular Tree Architectmw Gyula Mag’o has designed the cellular tree architecture (CTA) which directly executes the Functional Programming language originated by Backus and which appears especially suitable for VLSI implementation. This section briefly describes this architecture. The following sections give some examples which show how the functions defined in EFP can be implemented on this architecture. Permission to copy without fee ell or pert of this meteriei is granted providad that the copies are not made or distributed for direct commercial advantage, the ACM copyri~ht notice and the title of the publication and its date appear, and notice is given that copyingis bypermissionof tha Association for Computing Mechinery. To copy otherwise, or to republish, requiresa fee and/or specific permission. e 1992ACM().89791-502.)(/92/0()02/0890.<.

Solving Problems in Robotics with Semantic Networks

1 .GJj For more information about this architecture, the interested reader can refer to [MG80, MG79a, 14G79b]. 1.1 Strnctnral Aspect of the Ambitectnre In this section a machine (CTA) will be described which is a binary tree of cells possibly with additional connections between leaves of the tree to form a linear array from the leavee as shown in the follcwing figure from[MG80] : This machine (Cellular Tree Machine] directly executes the Functional Programming language by distributing program symbols across the cells of the tree and it appears especially suitable for VLSI implemental ion because it is built of a large number of simple similar and small parts. Cells of one kind (L) form a linear array which normally hold the program text and cells of another kind (T) form a full hinary tree and they perform processing functions. The leaves of the tree are called L cells and collectively are called the L array. The non-leaf cells of the tree are called T cells. All the T cells are identical except for the root which serves aa the 1/0 port. All the L cells are identical. Since Tone has demonstrated [MG80] that the connections between L cells can be eliminated without losing tbe capability of the machine, those connection have been represented by broken lines. 1.2 cell Orgeniaetion Since both L and T cells execute simple operations they do not need to hold stacks, queues and the like. They only need to have a few dozen registers as local storage. So both L and T cells are rather small and consequently, as VLSI technology advances, the whole aubtrees of cells may be put on a single chip. The following figure from [MGSO] shows an attractive way of embedding a binary tree of cells in a plane within a single chip or acroas several chips. Such a machine is easily expandable to handle any long object.

Python for CS1, CS2 and beyond

Robot problems are examined in the context of semantic networks which are used to represent the state of a problem and the operators useful for solving it. Graph transformation algorithms are discussed as an aid to problem solving. Although these form only a small subset of the first-order predicate calculus based systems, considerations such as subgoal circularity, partially specified states and multiple manipulators sharing the same environment may warrant this simplification.