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The use of sequencing information in software specification for verification
Software requirements specifications, virtual machine definitions, and algorithmic design all place constraints on the sequence of operations that are permissible during a program's execution. This paper discusses how these constraints can be captured and used to aid in the program verification process. The sequencing constraints can be expressed as a grammar over the alphabet of program operations. Several techniques can be used in support of testing or verification based on these specifications. Dynamic aalysis and static analysis are considered here. The automatic generation of some of these aids is feasible; the means of doing so is described
Digital signal processing: the impact of convergence on education, society and design flow
Design and development of real-time, memory and processor hungry digital signal processing systems has for decades been accomplished on general-purpose microprocessors. Increasing needs for high-performance DSP systems made these microprocessors unattractive for such implementations. Various attempts to improve the performance of these systems resulted in the use of dedicated digital signal processing devices like DSP processors and the former heavyweight champion of electronics design â Application Specific Integrated Circuits.
The advent of RAM-based Field Programmable Gate Arrays has changed the DSP design flow. Software algorithmic designers can now take their DSP algorithms right from inception to hardware implementation, thanks to the increasing availability of software/hardware design flow or hardware/software co-design. This has led to a demand in the industry for graduates with good skills in both Electrical Engineering and Computer Science. This paper evaluates the impact of technology on DSP-based designs, hardware design languages, and how graduate/undergraduate courses have changed to suit this transition
Curriculum Guidelines for Undergraduate Programs in Data Science
The Park City Math Institute (PCMI) 2016 Summer Undergraduate Faculty Program
met for the purpose of composing guidelines for undergraduate programs in Data
Science. The group consisted of 25 undergraduate faculty from a variety of
institutions in the U.S., primarily from the disciplines of mathematics,
statistics and computer science. These guidelines are meant to provide some
structure for institutions planning for or revising a major in Data Science
Estimating the Algorithmic Complexity of Stock Markets
Randomness and regularities in Finance are usually treated in probabilistic
terms. In this paper, we develop a completely different approach in using a
non-probabilistic framework based on the algorithmic information theory
initially developed by Kolmogorov (1965). We present some elements of this
theory and show why it is particularly relevant to Finance, and potentially to
other sub-fields of Economics as well. We develop a generic method to estimate
the Kolmogorov complexity of numeric series. This approach is based on an
iterative "regularity erasing procedure" implemented to use lossless
compression algorithms on financial data. Examples are provided with both
simulated and real-world financial time series. The contributions of this
article are twofold. The first one is methodological : we show that some
structural regularities, invisible with classical statistical tests, can be
detected by this algorithmic method. The second one consists in illustrations
on the daily Dow-Jones Index suggesting that beyond several well-known
regularities, hidden structure may in this index remain to be identified
Structure emerges faster during cultural transmission in children than in adults
How does childrenâs limited processing capacity affect cultural transmission of complex information? We show that over the course of iterated reproduction of two-dimensional random dot patterns transmission accuracy increased to a similar extent in 5- to 8-year-old children and adults whereas algorithmic complexity decreased faster in children. Thus, children require more structure to render complex inputs learnable. In line with the Less-Is-More hypothesis, we interpret this as evidence that childrenâs processing limitations affecting working memory capacity and executive control constrain the ability to represent and generate complexity, which, in turn, facilitates emergence of structure. This underscores the importance of investigating the role of children in the transmission of complex cultural traits
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