A compactification of the algebra of terms

CRT displays aboard U.S. Navy ships use a standardized monochrome Naval Tactical Data System (NTDS) symbol set to represent properties of symbols such as platform type (e.g. Aircraft Carrier, Combat Air), environment (e.g. air, surface, subsurface), and identification (e.g. hostile, friendly). A color symbol set has been proposed in NATO Standardization Agreement 4420, Display Symbology and Colours for NATO Maritime Units (1990). The U.S. Navy is currently considering ratification of this Standardization Agreement (STANAG). Empirical comparisons of operator performance using the NTDS symbology versus those using the color-filled NATO STANAG symbology were conducted. Two additional experimental symbologies were also created. The first, called NTDS Equated, is a color version of the NTDS symbol set, and the second experimental symbol set, called NATO Outline, is a color outline version of the color filled NATO STANAG symbol set. Test subjects were asked to find (hook) specific symbols during a tactically relevant scenario. Time to the first correct hook and percentage of correct hooks were subjected to analyses of variance (ANOVA). Experimental results revealed that the NATO STANAG symbol set outperformed all other symbol sets in terms of symbol recognition time, and outperformed the NTDS Standard symbol set for symbol recognition accuracy as well. The results indicated that tactical information can be transferred more quickly and accurately to watch standers through effective use of symbol coding. Test subjects familiar with the NTDS symbology expressed a preference for the color symbol sets in opinion surveys administered after the experiment. General conclusions resulting from comparisons across symbol sets were that color fill was more effective than color outline, and that operator performance gains were achieved as a result of color coding and greater information content on the symbol. This paper presents the human performance assessment that was conducted, the results, and the implications of the findings for ratification of NATO STANAG 4420.

PurposeThe purpose of this paper is to propose a framework for describing and evaluating the representativeness of a small set of search results extracted from the original results: this is deemed desirable in information retrieval in enterprise information systems.Design/methodology/approachThe paper proposes a combined measure, namely RFβ, to evaluate the extracted small set in terms of the notions of coverage and redundancy. Data experiments were conducted on three different extraction strategies to evaluate the representativeness, i.e. coverage and redundancy.FindingsBoth from intuitive and experimental perspectives, the proposed coverage measure, redundancy measure and RFβ measure could effectively evaluate the representativeness.Research limitations/implicationsThe search results, e.g. in the form of documents and texts, are modeled using a vector space model and cosine similarity. Semantic models and linguistic models could be further introduced into this research to improve the proposed measures.Practical implicationsWith the rapidly growing need for information retrieval in enterprise information systems, the representativeness of search results become more desirable and important for search engine users. The well‐designed representativeness measures will help them achieve satisfactory results.Originality/valueThe originality of the paper lies in the definition of representativeness of a small set of search results extracted from the original results. This focuses on the two aspects of coverage rate and redundancy rate both from intuitive and experimental perspectives.

In this paper, we propose an automatic selection of the bandwidth of the recursive kernel estimators of a regression function defined by the stochastic approximation algorithm. We showed that, using the selected bandwidth and the stepsize which minimize the mean weighted integrated squared error, the recursive estimator will be better than the non‐recursive one for small sample setting in terms of estimation error and computational costs. We corroborated these theoretical results through simulation study and a real dataset.

Uniform Extension Operators for Models and Their Applications

We describe a classifier-enhanced nearest neighbor approach to assigning Medical Subject Headings (MeSH®) to unlabeled documents using a combination of abstract similarities and direct citations to labeled MEDLINE records. The approach frames the classification problem by decomposing it into sets of siblings in the MeSH hierarchy (e.g., training a classifier for predicting “Heterocyclic Compounds, 2-Ring” vs. other “Heterocyclic Compounds”). Preliminary experiments using a small but diverse set of MeSH terms shows the highest performance when using both abstracts and citations compared to each alone, and coupled with a non-naive classifier: 90+% precision and recall with 10fold cross-validation. NLM's Medical Text Indexer (MTI) tool achieves similar overall performance but varies more across the terms tested. For example, MTI performs better on “Heterocyclic Compounds, 2-Ring”, while our approach performs better on Alzheimer Disease and Neuroimaging. Our approach can be applied broadly to documents with abstracts that are similar to (or cite) MEDLINE abstracts, which would help linking and searching across bibliographic databases beyond MEDLINE.

The goal of the paper is to give a systematic way to numerically evaluate the generating function of a periodic multiple polylogarithm using a Chen-Fliess series with a rational generating series. The idea is to realize the corresponding Chen-Fliess series as a bilinear dynamical system. A standard form for such a realization is given. The method is also generalized to the case where the multiple polylogarithm has non-periodic components. This allows one, for instance, to numerically validate the Hoffman conjecture. Finally, a setting in terms of dendriform algebras is provided.

As machine learning is increasingly used to help make decisions, there is a demand for these decisions to be explainable. Arguably, the most explainable machine learning models use decision rules. This paper focuses on decision sets, a type of model with unordered rules, which explains each prediction with a single rule. In order to be easy for humans to understand, these rules must be concise. Earlier work on generating optimal decision sets first minimizes the number of rules, and then minimizes the number of literals, but the resulting rules can often be very large. Here we consider a better measure, namely the total size of the decision set in terms of literals. So we are not driven to a small set of rules which require a large number of literals. We provide the first approach to determine minimum-size decision sets that achieve minimum empirical risk and then investigate sparse alternatives where we trade accuracy for size. By finding optimal solutions we show we can build decision set classifiers that are almost as accurate as the best heuristic methods, but far more concise, and hence more explainable.

Drawn from the Ground. Sound, Sign, and Inscription in Central Australian Sand Stories - By Jennifer Green

The problem of storing permutations in a distributed manner arises in several common scenarios, such as efficient updates of a large, encrypted, or compressed data set. This problem may be addressed in either a combinatorial or a coding approach. The former approach boils down to presenting large sets of permutations with locality, that is, any symbol of the permutation can be computed from a small set of other symbols. In the latter approach, a permutation may be coded in order to achieve locality. Both approaches must present low query complexity to allow the user to find an element efficiently. We discuss both approaches, and give a particular focus to the combinatorial one. In the combinatorial approach, we provide upper and lower bounds for the maximal size of a set of permutations with locality, and provide several simple constructions which attain the upper bound. In cases where the upper bound is not attained, we provide alternative constructions using a variety of tools, such as Reed-Solomon codes, permutation polynomials, and multi-permutations. In addition, several low-rate constructions of particular interest are discussed. In the coding approach we discuss an alternative representation of permutations, present a paradigm for supporting arbitrary powers of the stored permutation, and conclude with a proof of concept that permutations may be stored more efficiently than ordinary strings over the same alphabet.

First-order spectra with one variable

This paper is concerned with the problem of finding a hypothesis in \mathcal{TP}^2 consistent with given positive and negative examples. The hypothesis class \mathcal{TP}^2 consists of all sets of at most two tree patterns and represents the class of unions of at most two tree pattern languages. Especially, we consider the problem from the point of view of the consistency problem for \mathcal{TP}^2. The consistency problem is a problem for deciding whether there exists a consistent hypothesis with given positive and negative examples within some fixed hypothesis space. Efficient solvability of that problem is closely related to the possibility of efficient machine learning or machine discovery. Unfortunately, however, the consistency problem is known to be NPdcomplete for many hypothesis spaces. In this paper, the problem for the class \mathcal{TP}^2 is also shown to be NPdcomplete. In order to overcome this computational hardness, we try to use additional information obtained by making queries. First, we give an algorithm that, using restricted subset queries, solves the consistency problem for \mathcal{TP}^2 in time polynomial in the total size of given positive and negative examples. Next, we show that each subset query made by the algorithm can be replaced by several membership queries under some condition on a set of function symbols. As a result, we have that the consistency problem for \mathcal{TP}^2 is solved in polynomial time using membership queries.

In this paper we propose a concept of term rewriting system with sort priorities, which is simply a partial order on the sorts. According to the partial order and a set of function symbols specified in a system, for every term we define another partial order on the set of all subterms by assigning a priority to each subterm. We also define a subterm to require attention if it is an instance of the left-hand side of some rewrite rule. The procedural meaning of such a rewriting system is that at some stage a subterm is allowed to be rewritten only if it requires attention and no subterm of a higher (or stronger) priority requires attention. This reduction strategy may transform some nonterminating (unrestricted) reduction sequences into terminating ones. We discuss the semantics of our systems and give an application to the operational semantics of recursive programs

Computing a curriculum: descriptor-based domain analysis for educators

Companion animals, food production animals, and wildlife have a significant presence in the lives of modern humans. However, very little empirical research addresses the interaction between humans and the veterinarians who provide health care for these animals. Although similar to the pediatric setting in interactional terms, the veterinary clinic presents a unique set of communication, economic, and ethical dilemmas to be managed through talk in interaction. Available empirical research is largely composed of ethnographic, interview, and coding studies, but a small set of conversation‐analytic studies examine the context in terms of both pet‐directed and client‐directed talk.

The CONTIN method for inverting noisy linear operators was used to calculate the adsorption distribution function from both simulated and experimental adsorption isotherms. Simulated isotherms allowed us to estimate the resolving power of the method and the influence of errors on the distribution function, while experimental isotherms enabled us to validate the adsorption model and its parameters. It was shown that calculation within the condensation approximation region gives perfect recovering of the original distribution if sufficient information is inherent in the input data, that is, in the case of a large data set over a wide range of relative pressures. When only a window of data is available on the adsorption isotherm, it is possible to calculate the distribution restricted to the condensation approximation (CA) region. The influence of peaks outside the CA region may be eliminated by introducing the constant background term in the calculation. The level of error on the isotherm does not affect the resulting distribution up to 10% in the case of a large data set and up to 5% in the case of a small data set. On the basis of analysis of the randomness of the residuals of the fit to the data, it was possible to establish a true parameter of the lateral interaction constant. This approach applied to experimental data has led to the value k1mob = 5.5−5.6 for the HdB mobile adsorption model and k1loc = 3 for the FG localized adsorption model. On the basis of the fact that optimal value of k1mob is closer to the theoretical value, the mobile adsorption model is preferred. The CONTIN method applied to graphitized carbon black revealed four peaks on the adsorption energy distribution function. The peak constrained analysis allowed us to confirm the existence of all original peaks. The distributions calculated from standard nitrogen adsorption data showed that although reference carbons are heterogeneous in different ways, they possess common peaks at 4.9, 6.8, and 8.8 kJ/mol characteristic to graphitized carbon black Sterling FT. Analysis of the heterogeneity of synthetic carbons revealed the changes in adsorption energy distributions (AEDs) that occur during CVD from methane.