Emotion norms for 6000 Polish word meanings with a direct mapping to the Polish wordnet

Emotion lexicons are useful in research across various disciplines, but the availability of such resources remains limited for most languages. While existing emotion lexicons typically comprise words, it is a particular meaning of a word (rather than the word itself) that conveys emotion. To mitigate this issue, we present the Emotion Meanings dataset, a novel dataset of 6000 Polish word meanings. The word meanings are derived from the Polish wordnet (plWordNet), a large semantic network interlinking words by means of lexical and conceptual relations. The word meanings were manually rated for valence and arousal, along with a variety of basic emotion categories (anger, disgust, fear, sadness, anticipation, happiness, surprise, and trust). The annotations were found to be highly reliable, as demonstrated by the similarity between data collected in two independent samples: unsupervised (n = 21,317) and supervised (n = 561). Although we found the annotations to be relatively stable for female, male, younger, and older participants, we share both summary data and individual data to enable emotion research on different demographically specific subgroups. The word meanings are further accompanied by the relevant metadata, derived from open-source linguistic resources. Direct mapping to Princeton WordNet makes the dataset suitable for research on multiple languages. Altogether, this dataset provides a versatile resource that can be employed for emotion research in psychology, cognitive science, psycholinguistics, computational linguistics, and natural language processing.

HillTau: A fast, compact abstraction for model reduction in biochemical signaling networks

Signaling networks mediate many aspects of cellular function. The conventional, mechanistically motivated approach to modeling such networks is through mass-action chemistry, which maps directly to biological entities and facilitates experimental tests and predictions. However such models are complex, need many parameters, and are computationally costly. Here we introduce the HillTau form for signaling models. HillTau retains the direct mapping to biological observables, but it uses far fewer parameters, and is 100 to over 1000 times faster than ODE-based methods. In the HillTau formalism, the steady-state concentration of signaling molecules is approximated by the Hill equation, and the dynamics by a time-course tau. We demonstrate its use in implementing several biochemical motifs, including association, inhibition, feedforward and feedback inhibition, bistability, oscillations, and a synaptic switch obeying the BCM rule. The major use-cases for HillTau are system abstraction, model reduction, scaffolds for data-driven optimization, and fast approximations to complex cellular signaling.

An Implementation of Deep-Connections for Multi-Level Modeling

<p>Traditional object-oriented programming languages only support two logical domain classification levels, i.e. classes and objects. However, if the problem involves more than two classification levels, then to model a multi-level scenario within two classification levels, a mapping approach is required which introduces accidental complexity and destroys the desirable property of “direct mapping”. Therefore “Multi-level modeling” was proposed. It supports an unbounded number of classification levels, that can support “direct mapping” without introducing accidental complexity. Many supporting features have been proposed for “multi-level” modeling such as “deep instantiation”, potency, clabjects, etc. To date most of the research effort was focusing on the entities (clabjects), while the relationships between entities were receiving much less attention and remained under-explored. The “Melanee” tool was developed to support multi-level modeling both for academics and practitioners. “Melanee” supports an unbounded number of classification levels for domain modeling and it treats relationships like clabjects. It mainly supports “constructive modeling” by creating models using a “top-down” approach, whereas “explanatory modeling”, which is creating models using “bottom-up” approach, is not well supported and lacks support to ensure the integrity of the created models. Hence, to further explore relationships in multi-level modeling and to provide a better modeling environment, there are two main focuses in this thesis: First, based on existing, I further explore relationships between entities and extend the LML (Level Agnostic Modeling Language) supported by Melanee accordingly. Second, I extend Melanee’s functionality to support “explanatory modeling”. Considering that Melanee is an open source tool I first discuss Melanee’s structure and its principles in order contribute to future extensions to Melanee. The knowledge of Melanee is currently known by its principle developer, Ralph Gerbig, with whom I had contacts in the beginning phase of the “deep-connection” development for advices. Next I use the work proposed in the paper “A Unifying Approach to Connections for Multi-Level Modeling” by Atkinson et al. as a foundation and stepping stone, to further explore relationships between entities. I extended Melanee to support the “Deep-connections” feature by adding potency to connections and their monikers, and further allow connections to have “deep-multiplicities”. I developed these features, as well as respective validation functions to ensure the well-formedness of models. Then I extended LML so that user-specified type names can be used to indicate the names of types for clabjects. Instead of relying on modelers to fully manually define type- of classification relations between different levels, I introduce “connection conformance” and “entity conformance” to introduce classification support to Melanee. Potentially matching types are calculated and ordered per their matching scores. Respective suggestions to modelers including messages for each possible matching type about how to fix the current connection instance so that it matches the potential type whenever applicable. The suggestions are made available as so-called “quick-fixes” and I extended this approach with a second-stage dialog that allows modelers to select amongst many fix alternatives. Finally, I evaluate my design using model sets taken from existing papers and a systematic exploration involving 57 different scenarios.</p>

An Implementation of Deep-Connections for Multi-Level Modeling

<p>Traditional object-oriented programming languages only support two logical domain classification levels, i.e. classes and objects. However, if the problem involves more than two classification levels, then to model a multi-level scenario within two classification levels, a mapping approach is required which introduces accidental complexity and destroys the desirable property of “direct mapping”. Therefore “Multi-level modeling” was proposed. It supports an unbounded number of classification levels, that can support “direct mapping” without introducing accidental complexity. Many supporting features have been proposed for “multi-level” modeling such as “deep instantiation”, potency, clabjects, etc. To date most of the research effort was focusing on the entities (clabjects), while the relationships between entities were receiving much less attention and remained under-explored. The “Melanee” tool was developed to support multi-level modeling both for academics and practitioners. “Melanee” supports an unbounded number of classification levels for domain modeling and it treats relationships like clabjects. It mainly supports “constructive modeling” by creating models using a “top-down” approach, whereas “explanatory modeling”, which is creating models using “bottom-up” approach, is not well supported and lacks support to ensure the integrity of the created models. Hence, to further explore relationships in multi-level modeling and to provide a better modeling environment, there are two main focuses in this thesis: First, based on existing, I further explore relationships between entities and extend the LML (Level Agnostic Modeling Language) supported by Melanee accordingly. Second, I extend Melanee’s functionality to support “explanatory modeling”. Considering that Melanee is an open source tool I first discuss Melanee’s structure and its principles in order contribute to future extensions to Melanee. The knowledge of Melanee is currently known by its principle developer, Ralph Gerbig, with whom I had contacts in the beginning phase of the “deep-connection” development for advices. Next I use the work proposed in the paper “A Unifying Approach to Connections for Multi-Level Modeling” by Atkinson et al. as a foundation and stepping stone, to further explore relationships between entities. I extended Melanee to support the “Deep-connections” feature by adding potency to connections and their monikers, and further allow connections to have “deep-multiplicities”. I developed these features, as well as respective validation functions to ensure the well-formedness of models. Then I extended LML so that user-specified type names can be used to indicate the names of types for clabjects. Instead of relying on modelers to fully manually define type- of classification relations between different levels, I introduce “connection conformance” and “entity conformance” to introduce classification support to Melanee. Potentially matching types are calculated and ordered per their matching scores. Respective suggestions to modelers including messages for each possible matching type about how to fix the current connection instance so that it matches the potential type whenever applicable. The suggestions are made available as so-called “quick-fixes” and I extended this approach with a second-stage dialog that allows modelers to select amongst many fix alternatives. Finally, I evaluate my design using model sets taken from existing papers and a systematic exploration involving 57 different scenarios.</p>

Establishing Multivariate Specification Regions for Incoming Raw Materials Using Projection to Latent Structure Models: Comparison Between Direct Mapping and Model Inversion

Increasing raw material variability is challenging for many industries since it adversely impacts final product quality. Establishing multivariate specification regions for selecting incoming lot of raw materials is a key solution to mitigate this issue. Two data-driven approaches emerge from the literature for defining these specifications in the latent space of Projection to Latent Structure (PLS) models. The first is based on a direct mapping of good quality final product and associated lots of raw materials in the latent space, followed by selection of boundaries that minimize or best balance type I and II errors. The second rather defines specification regions by inverting the PLS model for each point lying on final product acceptance limits. The objective of this paper is to compare both methods to determine their advantages and drawbacks, and to assess their classification performance in presence of different levels of correlation between the quality attributes. The comparative analysis is performed using simulated raw materials and product quality data generated under multiple scenarios where product quality attributes have different degrees of collinearity. First, a simple case is proposed using one quality attribute to illustrate the methods. Then, the impact of collinearity is studied. It is shown that in most cases, correlation between the quality variable does not seem to influence classification performance except when the variables are highly correlated. A summary of the main advantages and disadvantages of both approaches is provided to guide the selection of the most appropriate approach for establishing multivariate specification regions for a given application.

Modeling Routing Processes through Network Theory: A Grammar to Define RDEVS Simulation Models

The Routed DEVS (RDEVS) formalism provides a reasonable formalization for the simulation of routing processes. In this paper, we introduce a context-free grammar for the definition of routing processes as a particular case of a constrained network model. Such grammar is based on a metamodel that defines the semantics over the syntactical elements. This metamodel allows a direct mapping between its concepts and RDEVS simulation models. A Java implementation is provided for the grammar as a plug-in for Eclipse IDE. The main benefit of this software tool is the feasibility of getting a simulation model without having programming skills.

Intraoperative Neuromonitoring for Tethered Cord Surgery in Infants: Challenges and Outcome

<b><i>Objective:</i></b> The aim of this study was to evaluate the efficacy and safety of intraoperative neuromonitoring (IONM) in surgery for tethered cord in infants. <b><i>Materials and Methods:</i></b> The study included 87 infants who underwent surgery for closed spinal dysraphism under IONM. Their preoperative neurological and urological statuses were compared with postoperative status clinically. The study design was prospective, and the study’s duration was from January 2011 to February 2020. IONM was performed (TcMEP and direct mapping) with an Xltek Protektor 32 IOM system, Natus Neurology/medical Inc., Middleton, USA. Statistical analysis in the form of χ² is conducted using SPSS. <b><i>Results:</i></b> Overall, among 87 patients, clinical improvement was seen in 28 (28/29) patients with motor deficits, 17 (17/24) with bladder deficits, and 18 (18/24) with bowel deficits. The monitorability for motor and sphincter was 97.3% and 90.7%, respectively. The sensitivity of IONM in predicting new motor deficit was 100%, whereas the specificity was 100%. The negative predictive value of predicting motor deficit was 100%, with a diagnostic accuracy of 100%. There were no complications in this cohort related to the IONM. <b><i>Conclusions:</i></b> The study has highlighted that the use of IONM is sensitive in identifying motor injury in infants with reliable outcome correlation. Assessment, monitoring, and outcome correlation of bladder and sphincteric functions are a challenge in this cohort.

Phonological evidence for morpho-syntactic structure in Athapaskan

Athapaskan verbal morphology appears to violate the Mirror Principle in multiple ways and, thus, the ordering of affixes in these languages has resisted a straightforward analysis. We adopt a new morphological tool of Iterative Root Prefixation, which allows for a more direct mapping from syntax to morphology in languages of this profile. Apparent violations of affix ordering that remain, namely the puzzling placement of the transitive and causative morphemes, are argued to be explained by overriding phonological constraints.

Performing arithmetic using a neural network trained on images of digit permutation pairs

In this paper, a neural network is trained to perform simple arithmetic using images of concatenated handwritten digit pairs. A convolutional neural network was trained with images consisting of two side-by-side handwritten digits, where the image’s label is the summation of the two digits contained in the combined image. Crucially, the network was tested on permutation pairs that were not present during training in an effort to see if the network could learn the task of addition, as opposed to simply mapping images to labels. A dataset was generated for all possible permutation pairs of length 2 for the digits 0–9 using MNIST as a basis for the images, with one thousand samples generated for each permutation pair. For testing the network, samples generated from previously unseen permutation pairs were fed into the trained network, and its predictions measured. Results were encouraging, with the network achieving an accuracy of over 90% on some permutation train/test splits. This suggests that the network learned at first digit recognition, and subsequently the further task of addition based on the two recognised digits. As far as the authors are aware, no previous work has concentrated on learning a mathematical operation in this way. This paper is an attempt to demonstrate that a network can learn more than a direct mapping from image to label, but is learning to analyse two separate regions of an image and combining what was recognised to produce the final output label.