For more than fifty years, taxonomists have proposed numerous alternative definitions of species while they searched for a unique, comprehensive, and persuasive definition. This monograph shows that these efforts have been unnecessary, and indeed have provably been a pursuit of a will o' the wisp because they have failed to recognize the theoretical impossibility of what they seek to accomplish. A clear and rigorous understanding of the logic underlying species definition leads both to a recognition of the inescapable ambiguity that affects the definition of species, and to a framework-relative approach to species definition that is logically compelling, i.e., cannot not be accepted without inconsistency. An appendix reflects upon the conclusions reached, applying them in an intellectually whimsical taxonomic thought experiment that conjectures the possibility of an emerging new human species.

Some critique of usage of sentence parse trees in modern linguistics. Two propositions on constructing trees, as mentioned in the title. Introduction of an English-to-Tatar translator program that is being developed by the author. Precedence by specificity.

A compact alphabetic form of the Morse code is constructed following some simple rules. It is mostly useful in learning the Morse code and using it in writing form.

This paper was originally written for a very different audience around 25 years ago, since when a great deal has changed both in autism research and in the wider world. Thus there was no mention of mercury (of which I knew almost nothing at the time) or of the autism increase (of which no-one else knew much either back then). And also I have become much more experienced in writing than back then. And paper-printed journals were very hostile to “excessive” length such that at least one paragraph got rather more squashed than I am happy to see it now. So for these reasons I am making some minor changes to the writing, which do not change the actual content or meaning but which make for a better reading experience. For the convenience of cynics I will list all those changes at the end. In addition I am adding in some update notes in the text, indicated thus: [2014 Update: blah blah...]

The role of immediate feedback in-group conversations has received scant attention in the recent literature. While studies from the early 1990’s suggested that “added information” in the form of non-verbal cues would allow video conferencing to “augment” the audio-only conference in terms of effectiveness, stunningly little follow-on research has been done reflective of the current state of computer mediated communication, video conferencing, “live walls”, etc. This article contrasts three studies of immediate feedback in in-person settings as the basis for suggesting a new research program – research to look at potential effects of augmenting video-conferencing with an immediate feedback channel.

As Francis Crick said, neuroscience is a data rich but theory poor field, and it is missing a broad framework as in physics. We wish to put forward such a unified framework based on existing evidences. Unexpectedly, it is a very simple statistical model. Specifically, we find that neural mechanisms in the spatial and temporal dimensionalities follow similar statistical laws. And they are usually called neural coding and memory respectively. Moreover, memory can be divided into two types: long-term and short-term (or instantaneous). The instantaneous memory is the foundation of consciousness according to Crick. Then we indicate the physical and biological mechanisms behind these statistical laws. In general, they actually reflect random processes of particles such as ions. Detailed model and supporting evidences can be found in our previous work. And this simple model is really powerful in explaining most psychological phenomenon and advanced intelligence such as language.

We present a novel approach for recognizing what we call targetable named entities; that is, named entities in a targeted set (e.g, movies, books, TV shows). Unlike many other NER systems that need to retrain their statistical models as new entities arrive, our approach does not require such retraining, which makes it more adaptable for types of entities that are frequently updated. For this preliminary study, we focus on one entity type, movie title, using data collected from Twitter. Our system is tested on two evaluation sets, one including only entities corresponding to movies in our training set, and the other excluding any of those entities. Our final model shows F1-scores of 76.19% and 78.70% on these evaluation sets, which gives strong evidence that our approach is completely unbiased to any particular set of entities found during training.

This paper is based on our previous work on neural coding. It is a self-organized model supported by existing evidences. Firstly, we briefly introduce this model in this paper, and then we explain the neural mechanism of language and reasoning with it. Moreover, we find that the position of an area determines its importance. Specifically, language relevant areas are in the capital position of the cortical kingdom. Therefore they are closely related with autonomous consciousness and working memories. In essence, language is a miniature of the real world. Briefly, this paper would like to bridge the gap between molecule mechanism of neurons and advanced functions such as language and reasoning.

Based on existing data, we wish to put forward a biological model of motor system on the neuron scale. Then we indicate its implications in statistics and learning. Specifically, neuron’s firing frequency and synaptic strength are probability estimates in essence. And the lateral inhibition also has statistical implications. From the standpoint of learning, dendritic competition through retrograde messengers is the foundation of conditional reflex and “grandmother cell” coding. And they are the kernel mechanisms of motor learning and sensory-motor integration respectively. Finally, we compare motor system with sensory system. In short, we would like to bridge the gap between molecule evidences and computational models.

The coding mechanism of sensory memory on the neuron scale is one of the most important questions in neuroscience. We have put forward a quantitative neural network model, which is self-organized, self-similar, and self-adaptive, just like an ecosystem following Darwin's theory. According to this model, neural coding is a “mult-to-one”mapping from objects to neurons. And the whole cerebrum is a real-time statistical Turing Machine, with powerful representing and learning ability. This model can reconcile some important disputations, such as: temporal coding versus rate-based coding, grandmother cell versus population coding, and decay theory versus interference theory. And it has also provided explanations for some key questions such as memory consolidation, episodic memory, consciousness, and sentiment. Philosophical significance is indicated at last.