Search

There is a clear difference in runtime performance between native applications that use augmented/virtual reality (AR/VR) device-specific hardware and comparable web-based implementations. Here we show that WebAssembly (Wasm) offers a promising developer solution that can bring near-native low latency performance to web-based applications, enabling hardware-agnostic interoperability at scale through portable bytecode that runs on any WiFi or cellular data network-enabled AR/VR device. Many software application areas have begun to realize Wasm's potential as a key enabling technology, but it has yet to establish a robust presence in the AR/VR domain. When considering the limitations of current web-based AR/VR development technologies such as WebXR, which provides an existing application programming interface (API) that enables AR/VR capabilities for web-based programs, Wasm can resolve critical issues faced with just-in-time (JIT) compilation, slow run-times, large file sizes and big data, among other challenges. Existing applications using Wasm-based WebXR are sparse but growing, and the potential for porting native applications to use this emerging framework will benefit the web-based AR/VR application space and bring it closer to its native counterparts in terms of performance. Taken together, this kind of standardized "write-once-deploy-everywhere" software framework for AR/VR applications has the potential to consolidate user experiences across different head-mounted displays and other compatible hardware devices to ultimately create an interoperable AR/VR ecosystem., Comment: 9 pages, 2 figures

We address the need for expanding the presence of the Lisp family of programming languages in bioinformatics and computational biology research. Languages of this family, like Common Lisp, Scheme, or Clojure, facilitate the creation of powerful and flexible software models that are 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 (DSL): languages which 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 AI research in bioinformatics and computational biology., Comment: 9 pages

Zipf's law predicts a power-law relationship between word rank and frequency in language communication systems, and is widely reported in texts yet remains enigmatic as to its origins. Computer simulations have shown that language communication systems emerge at an abrupt phase transition in the fidelity of mappings between symbols and objects. Since the phase transition approximates the Heaviside or step function, we show that Zipfian scaling emerges asymptotically at high rank based on the Laplace transform. We thereby demonstrate that Zipf's law gradually emerges from the moment of phase transition in communicative systems. We show that this power-law scaling behavior explains the emergence of natural languages at phase transitions. We find that the emergence of Zipf's law during language communication suggests that the use of rare words in a lexicon is critical for the construction of an effective communicative system at the phase transition., Comment: 6 pages, 3 figures

The nature of the quantitative distribution of the 64 DNA codons in the human genome has been an issue of debate for over a decade. Some groups have proposed that the quantitative distribution of the DNA codons ordered as a rank-frequency plot follows a well-known power law called Zipf's law. Others have shown that the DNA codon distribution is best fitted to an exponential function. However, the reason for such scaling behavior has not yet been addressed. In the present study, we demonstrate that the nonlinearity of the DNA codon distribution is a direct consequence of the frequency recurrence of the codon usage (i.e., the repetitiveness of codon usage frequencies at the whole genome level). We discover that if frequency recurrence is absent from the human genome, the frequency of occurrence of codons scales linearly with the codon rank. We also show that DNA codons of both low and high frequency of occurrence in the genome are best fitted by an exponential function and provide strong evidence to suggest that the coding region of the human genome does not follow Zipf's law. Information-theoretic methods and entropy calculations are applied to the DNA codon distribution and a new approach, called the lariat method, is proposed to quantitatively analyze the DNA codon distribution in Homo sapiens., Comment: 8 pages, 5 figures