The development of lexical competition in written- and spoken-word competition

Efficient word recognition depends on the ability to overcome competition from overlapping words. The nature of the overlap depends on the input modality: spoken words have temporal overlap from other words that share phonemes in the same positions, whereas written words have spatial overlap from other words with letters in the same places. It is unclear how these differences in input format affect the ability to recognize a word and the types of competitors that become active while doing so. This study investigates word recognition in both modalities in children between 7 and 15. Children complete a visual-world paradigm eye-tracking task that measures competition from words with several types of overlap, using identical word lists between modalities. Results showed correlated developmental changes in the speed of target recognition in both modalities. Additionally, developmental changes were seen in the efficiency of competitor suppression for some competitor types in the spoken modality. These data reveal some developmental continuity in the process of word recognition independent of modality, but also some instances of independence in how competitors are activated. Stimuli, data and analyses from this project are available at: https://osf.io/eav72

Evaluation of JATSdecoder as an automated text extraction tool for statistical results in scientific reports

The extraction of statistical results in scientific reports is beneficial for checking studies on plausibility and reliability. The R package JATSdecoder supports the application of text mining approaches to scientific reports. Its function get.stats() extracts all reported statistical results from text and recomputes p values for most standard test results. The output can be reduced to results with checkable or computable p values only. In this article, get.stats()’s ability to extract, recompute and check statistical results is compared to that of statcheck, which is an already established tool. A manually coded data set, containing the number of statistically significant results in 49 articles, serves as an initial indicator for get.stats()’s and statcheck’s differing detection rates for statistical results. Further 13,531 PDF files by 10 mayor psychological journals, 18,744 XML documents by Frontiers of Psychology and 23,730 articles related to psychological research and published by PLoS One are scanned for statistical results with both algorithms. get.stats() almost replicates the manually extracted number of significant results in 49 PDF articles. get.stats() outperforms the statcheck functions in identifying statistical results in every included journal and input format. Furthermore, the raw results extracted by get.stats() increase statcheck’s detection rate. JATSdecoder’s function get.stats() is a highly general and reliable tool to extract statistical results from text. It copes with a wide range of textual representations of statistical standard results and recomputes p values for two- and one-sided tests. It facilitates manual and automated checks on consistency and completeness of the reported results within a manuscript.

Automating the IRC Analysis within Eyringpy

To analyze the evolution of a chemical property along the reaction path, we have to extract all the necessary information from a set of electronic structure computations. However, this process is time-consuming and prone to human error. Here we introduce IRC-Analysis, a new extension in Eyringpy, to monitor the evolution of chemical properties along the intrinsic reaction coordinate, including complete reaction force analysis. IRC-Analysis collects the entire data set for each point on the reaction coordinate, eliminating human error in data capture and allowing the study of several chemical reactions in seconds, regardless of the complexity of the systems. Eyringpy has a simple input format, and no programming skills are required. A tracer has been included to visualize the evolution of a given chemical property along the reaction coordinate. Several properties can be analyzed at the same time. This version can analysis the evolution of bond distances and angles, Wiberg bond indices, natural charges, dipole moments, and orbital energies (and related properties).

DNNV: A Framework for Deep Neural Network Verification

Despite the large number of sophisticated deep neural network (DNN) verification algorithms, DNN verifier developers, users, and researchers still face several challenges. First, verifier developers must contend with the rapidly changing DNN field to support new DNN operations and property types. Second, verifier users have the burden of selecting a verifier input format to specify their problem. Due to the many input formats, this decision can greatly restrict the verifiers that a user may run. Finally, researchers face difficulties in re-using benchmarks to evaluate and compare verifiers, due to the large number of input formats required to run different verifiers. Existing benchmarks are rarely in formats supported by verifiers other than the one for which the benchmark was introduced. In this work we present DNNV, a framework for reducing the burden on DNN verifier researchers, developers, and users. DNNV standardizes input and output formats, includes a simple yet expressive DSL for specifying DNN properties, and provides powerful simplification and reduction operations to facilitate the application, development, and comparison of DNN verifiers. We show how DNNV increases the support of verifiers for existing benchmarks from 30% to 74%.

METHODS OF XS DATA PREPARATION FOR GEOMETRY WITH FUEL DUMMY

3D deterministic core calculation represents important category of the nuclear fuel cycle and safe Nuclear Power Plant operation. The appropriate solution was not published yet. Data preparation process for non-fuel elements of the core represents the challenge for scientists. This report briefly introduce the problem of the data preparation process and gives the information about new input format for macrocode PARCS (PMAXS). The best homogenization process approach is to prepare data in infinite lattice cell for fuel assemblies, which are placed next to the another fuel assembly. Data for fuel assembly located next to the non-fuel region are better with preparation in the real geometry with the real boundary conditions. Results of the neutron spectra study show that the PMAXS file format is well prepared for the 2 group calculation, but it is not well prepared for the multigroup calculations, however the XSEC file format still gave reasonable results.

TeamTat: a collaborative text annotation tool

Manually annotated data is key to developing text-mining and information-extraction algorithms. However, human annotation requires considerable time, effort and expertise. Given the rapid growth of biomedical literature, it is paramount to build tools that facilitate speed and maintain expert quality. While existing text annotation tools may provide user-friendly interfaces to domain experts, limited support is available for figure display, project management, and multi-user team annotation. In response, we developed TeamTat (https://www.teamtat.org), a web-based annotation tool (local setup available), equipped to manage team annotation projects engagingly and efficiently. TeamTat is a novel tool for managing multi-user, multi-label document annotation, reflecting the entire production life cycle. Project managers can specify annotation schema for entities and relations and select annotator(s) and distribute documents anonymously to prevent bias. Document input format can be plain text, PDF or BioC (uploaded locally or automatically retrieved from PubMed/PMC), and output format is BioC with inline annotations. TeamTat displays figures from the full text for the annotator's convenience. Multiple users can work on the same document independently in their workspaces, and the team manager can track task completion. TeamTat provides corpus quality assessment via inter-annotator agreement statistics, and a user-friendly interface convenient for annotation review and inter-annotator disagreement resolution to improve corpus quality.

DUBS: A Framework for Developing Directory of Useful Benchmarking Sets for Virtual Screening

Benchmarking is a crucial step in evaluating virtual screening methods for drug discovery. One major issue that arises among benchmarking datasets is a lack of a standardized format for representing the protein and ligand structures used to benchmark the virtual screening method. To address this, we introduce the Directory of Useful Benchmarking Sets (DUBS) framework, as a simple and flexible tool to rapidly created benchmarking sets using the protein databank. DUBS uses a simple input text based format along with the Lemon data mining framework to efficiently access and organize data to protein databank and output commonly used inputs for virtual screening software. The simple input format used by DUBS allows users to define their own benchmarking datasets and access the corresponding information directly from the software package. Currently, it only takes DUBS less than 2 minutes to create a benchmark using this format. Since DUBS uses a simple python script, users can easily modify to create more complex benchmarks. We hope that DUBS will be a useful community resource to provide a standardized representation for benchmarking datasets in virtual screening.

Numerical Discrimination of the Generalisation Model from Learnt Weights in Neural Networks

This research demonstrates a method of discriminating the numerical relationships of neural network layer inputs to the layer outputs established from the learnt weights and biases of a neural network's generalisation model. It is demonstrated with a mathematical form of a neural network rather than an image, speech or textual translation application as this provides clarity in the understanding gained from the generalisation model. It is also reliant on the input format but that format is not unlike an image pixel input format and as such the research is applicable to other applications too. The research results have shown that weight and biases can be used to discriminate the mathematical relationships between inputs and make discriminations of what mathematical operators are used between them in the learnt generalisation model. This may be a step towards gaining definitions and understanding for intractable problems that a Neural Network has generalised in a solution. For validating them, or as a mechanism for creating a model used as an alternative to traditional approaches, but derived from a neural network approach as a development tool for solving those problems. The demonstrated method was optimised using learning rate and the number of nodes and in this example achieves a low loss at 7.6e-6, a low Mean Absolute Error at 1e-3 with a high accuracy score of 1.0. But during the experiments a sensitivity to the number of epochs and the use of the random shuffle was discovered, and a comparison with an alternative shuffle using a non-random reordering demonstrated a lower but comparable performance, and is a subject for further research but demonstrated in this "decomposition" class architecture.

A Framework for Constraint Based Local Search using Essence

Structured Neighbourhood Search (SNS) is a framework for constraint-based local search for problems expressed in the Essence abstract constraint specification language. The local search explores a structured neighbourhood, where each state in the neighbourhood preserves a high level structural feature of the problem. SNS derives highly structured problem-specific neighbourhoods automatically and directly from the features of the Essence specification of the problem. Hence, neighbourhoods can represent important structural features of the problem, such as partitions of sets, even if that structure is obscured in the low-level input format required by a constraint solver. SNS expresses each neighbourhood as a constrained optimisation problem, which is solved with a constraint solver. We have implemented SNS, together with automatic generation of neighbourhoods for high level structures, and report high quality results for several optimisation problems.