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Throughput capacity of large ad hoc networks has been shown to scale adversely with the size of network $n$. However the need for the nodes to find or repair routes has not been analyzed in this context. In this paper, we explicitly take route discovery into account and obtain the scaling law for the throughput capacity under general assumptions on the network environment, node behavior, and the quality of route discovery algorithms. We also discuss a number of possible scenarios and show that the need for route discovery may change the scaling for the throughput capacity dramatically., Comment: extended version, originally published in conference (IEEE GLOBECOM '05)

The ability to have the same experience for different user groups (i.e., accessibility) is one of the most important characteristics of Web-based systems. The same is true for Knowledge Graph Question Answering (KGQA) systems that provide the access to Semantic Web data via natural language interface. While following our research agenda on the multilingual aspect of accessibility of KGQA systems, we identified several ongoing challenges. One of them is the lack of multilingual KGQA benchmarks. In this work, we extend one of the most popular KGQA benchmarks - QALD-9 by introducing high-quality questions' translations to 8 languages provided by native speakers, and transferring the SPARQL queries of QALD-9 from DBpedia to Wikidata, s.t., the usability and relevance of the dataset is strongly increased. Five of the languages - Armenian, Ukrainian, Lithuanian, Bashkir and Belarusian - to our best knowledge were never considered in KGQA research community before. The latter two of the languages are considered as "endangered" by UNESCO. We call the extended dataset QALD-9-plus and made it available online https://github.com/Perevalov/qald_9_plus.

Data-driven systems need to be evaluated to establish trust in the scientific approach and its applicability. In particular, this is true for Knowledge Graph (KG) Question Answering (QA), where complex data structures are made accessible via natural-language interfaces. Evaluating the capabilities of these systems has been a driver for the community for more than ten years while establishing different KGQA benchmark datasets. However, comparing different approaches is cumbersome. The lack of existing and curated leaderboards leads to a missing global view over the research field and could inject mistrust into the results. In particular, the latest and most-used datasets in the KGQA community, LC-QuAD and QALD, miss providing central and up-to-date points of trust. In this paper, we survey and analyze a wide range of evaluation results with significant coverage of 100 publications and 98 systems from the last decade. We provide a new central and open leaderboard for any KGQA benchmark dataset as a focal point for the community - https://kgqa.github.io/leaderboard. Our analysis highlights existing problems during the evaluation of KGQA systems. Thus, we will point to possible improvements for future evaluations.

Software with natural-language user interfaces has an ever-increasing importance. However, the quality of the included Question Answering (QA) functionality is still not sufficient regarding the number of questions that are answered correctly. In our work, we address the research problem of how the QA quality of a given system can be improved just by evaluating the natural-language input (i.e., the user's question) and output (i.e., the system's answer). Our main contribution is an approach capable of identifying wrong answers provided by a QA system. Hence, filtering incorrect answers from a list of answer candidates is leading to a highly improved QA quality. In particular, our approach has shown its potential while removing in many cases the majority of incorrect answers, which increases the QA quality significantly in comparison to the non-filtered output of a system.

This work describes the update of NO along with the incorporation of NO$_{2}$ and N$_{2}$O to the HITEMP database. Where appropriate, the HITRAN line lists for the same molecules have also been updated. This work brings the current number of molecules provided by HITEMP to seven. The initial line lists originating from \textit{ab initio} and semi-empirical methods for each molecule have been carefully validated against available observations and, where necessary, adjustments have been made to match observations. We anticipate this work will be applied to a variety of high-temperature environments including astronomical applications, combustion monitoring, and non-local thermodynamic equilibrium conditions.

Question-answering systems and voice assistants are becoming major part of client service departments of many organizations, helping them to reduce the labor costs of staff. In many such systems, there is always natural language understanding module that solves intent classification task. This task is complicated because of its case-dependency - every subject area has its own semantic kernel. The state of art approaches for intent classification are different machine learning and deep learning methods that use text vector representations as input. The basic vector representation models such as Bag of words and TF-IDF generate sparse matrixes, which are becoming very big as the amount of input data grows. Modern methods such as word2vec and FastText use neural networks to evaluate word embeddings with fixed dimension size. As we are developing a question-answering system for students and enrollees of the Perm National Research Polytechnic University, we have faced the problem of user's intent detection. The subject area of our system is very specific, that is why there is a lack of training data. This aspect makes intent classification task more challenging for using state of the art deep learning methods. In this paper, we propose an approach of the questions embeddings representation based on calculation of Shannon entropy.The goal of the approach is to produce low dimensional question vectors as neural approaches do and to outperform related methods, described above in condition of small dataset. We evaluate and compare our model with existing ones using logistic regression and dataset that contains questions asked by students and enrollees. The data is labeled into six classes. Experimental comparison of proposed approach and other models revealed that proposed model performed better in the given task., Comment: Proceedings of International Conference on Applied Innovation in IT

A search for sub-GeV dark matter produced from collisions of the Fermilab 8 GeV Booster protons with a steel beam dump was performed by the MiniBooNE-DM Collaboration using data from $1.86 \times 10^{20}$ protons on target in a dedicated run. The MiniBooNE detector, consisting of 818 tons of mineral oil and located 490 meters downstream of the beam dump, is sensitive to a variety of dark matter initiated scattering reactions. Three dark matter interactions are considered for this analysis: elastic scattering off nucleons, inelastic neutral pion production, and elastic scattering off electrons. Multiple data sets were used to constrain flux and systematic errors, and time-of-flight information was employed to increase sensitivity to higher dark matter masses. No excess from the background predictions was observed, and 90$\%$ confidence level limits were set on the vector portal and leptophobic dark matter models. New parameter space is excluded in the vector portal dark matter model with a dark matter mass between 5 and 50$\,\mathrm{MeV}\,c^{-2}$. The reduced neutrino flux allowed to test if the MiniBooNE neutrino excess scales with the production of neutrinos. No excess of neutrino oscillation events were measured ruling out models that scale solely by number of protons on target independent of beam configuration at 4.6$\sigma$., Comment: 19 pages, 25 figures, Data release: http://www-boone.fnal.gov/for_physicists/data_release/dark_matter_prd/ v2 Updated to published version