Miracle-dz/UniFAR-FTUs huggingface dataset:
Analytical computation method for electrodynamic forces acting over electrical transformer windings

In prezent există mai multe metode de calcul a forţelor electrodinamice de scurtcircuit din infăsurările transformatoarelor electrice trifazate. Aceste metode fie sunt foarte precise (de exemplu metoda elementului finit) dar necesită un timp indelungat de calcul fie permit un calcul rapid (de exemplu metoda clasică de calcul) insă furnizează rezultate aproximative. Lucrarea de faţă prezintă o metodă rapidă de calcul a acestor forţe, avantajul acesteia fiind că permite obţinerea unor rezultate precise. Pentru a demonstra eficienţa metodei a fost realizată o comparaţie intre rezultatele obţinute cu ajutorul acesteia si rezultatele obţinute cu ajutorul principalelor metode de calcul utilizate in prezent. Different methods are used at present to compute the short-circuit electrodynamic forces exerted over the windings of three-phase electrical transformers. Methods giving quite accurate results (e.g., the finite element method) require significant computing resources and computation time. Classical analytical methods are fast but not so accurate. A fast computation method with accurate results is presented in this paper. Comparisons between the results given by this method and by other well-known methods applied to a three-phase transformer is performed in order to show and discuss the effectiveness of the method.
The coloring problem in the solid-state metal boride carbide ScB2C2: a theoretical analysis

Abstract The electronic properties of the layered ternary metal boride carbide ScB2C2, the structure of which consists of B/C layers made of fused five- and seven-membered rings alternating with scandium sheets, are analyzed. In particular, the respective positions of the B and C atoms (the so-called coloring problem) are tackled using density functional theory, quantum theory of atoms in molecules, and electron localizability indicator calculations. Results reveal that (i) the most stable coloring minimizes the number of B–B and C–C contacts and maximizes the number of boron atoms in the heptagons, (ii) the compound is metallic in character, and (iii) rather important covalent bonding occurs between the metallic sheets and the boron–carbon network.
Unsupervised Anomaly Detection on Node Attributed Networks: A Deep Learning Approach

Anomaly detection has been one of the important issues in social network analysis in recent years due to the crucial role it plays in different applications such as fraud and spammer detection. Using both graph and node characteristics leads to more accurate results in detecting anomalous nodes of node attributed networks. Most of the research works in this field are concentrated on supervised methods for anomaly detection. However, in real-world problems, there is not enough labeled data to use supervised methods for anomaly detection. This paper proposes an unsupervised method for detecting anomalous nodes in node attributed networks. The methods used a two-step deep learning approach. In the first step, structural features of the network are extracted using node2vec; in the next step, Variational AutoEncoder (VAE) is used to detect the anomalies considering both structural and node attributes. The anomalous nodes are recognized by their higher reconstruction loss. Our experimental results on two datasets, BlogCatalog and Flickr, show that the suggested method can compete with the state-of-the-art approaches of anomaly detection in attributed networks. Our method (Deep2NAD) outperforms the state-of-the-art result on the Flickr dataset based on AUC. Moreover, it receives an acceptable AUC over the BlogCatalog dataset in comparison to the state-of-the-art methods.
Practical tools from formal models: the ECHR as a case study

One approach to building legal support systems is to run an executable model of the relevant knowledge through an interface designed to collect information from the user and provide explanations. The usability of such systems depends on the terms used in the law being represented: often only users familiar with the practice and application of the law will be able to provide the required information. Earlier work applied this approach to the European Convention on Human Rights (ECHR). Although the performance of the tool built for that domain was good, the questions posed to the user demanded a good deal of knowledge and experience of the ECHR. Here we use the knowledge of an expert with extensive experience of the ECHR to extend the model, through intermediate levels, to identify questions that are appropriate to the target user. We have undertaken a pilot evaluation in which a small number of lawyers have used the prototype program and provided very positive feedback, showing that they are receptive to AI solutions that give effective, explainable decision support.
A Framework and Tool Support for Managing a Family of Business Process Variants

Business organizations maintain business processes with multiple variants because of varied business requirements of which the support of these multiple business process variants constitutes a big challenge. BPPLE tool demonstration presents an extension of Eclipse BPMN modeller to cope with the modelling, and customization of business processes at build time and run time to compose the business process variants it may have. BPPLE tool is based on the Business Process Product Line Engineering (BPPLE) approach, our proposed approach for managing a family of business process variants. We have applied BPPLE in the scenarios such as the student registration in a higher education institution. Tests carried out showed that BPPLE tool enables the customization of business process models or instances to compose business process variant models or instances, respectively.
CriminelBART: a French Canadian legal language model specialized in criminal law

Learning language representations is a key component in many natural language processing tasks, and their usefulness is most often challenged by specialized target domains and vocabulary. We have witnessed several neural causal language models (CLM) that learn contextual representations such as ELMo [8]. More recently, the Transformer architecture [10] has tremendously improved language representation learning, giving birth to new architectures such as BERT [4], a masked language model, pushing the state-of-the-art of natural language understanding to an unprecedented level of performance on standard benchmarks. Moreover, it has been found that Transformer-based CLM, such as GPT [9], are excellent feature extractors as well as being impressive text generators. BART [7], an architecture combining the backbone of both BERT and GTP proved to be particularly effective at generating text while being competitive in comprehension tasks. BARThez, the French version of BART, was recently introduced as a pre-trained model on a very large monolingual French corpus [6]. In this paper, we introduce CriminelBART, a fine-tuned version of BARThez specialized for criminal law using a French Canadian corpus of legal judgments, and we evaluate its performance on different tasks.
Multidimensional analysis of geosciences literature for knowledge discovery

With the increasing volume of online geosciences data, geoscientists are now facing huge challenges in rapidly discovering and extracting valuable information from a large number of documents. Nowadays, it has become crucial to develop flexible and efficient tools that can help geoscientists to quickly navigate through unstructured texts to reveal hidden patterns and trends. This paper presents a workflow for the multidimensional analysis of geosciences literature. NLP techniques and ontologies are used to automatically identify and extract domain-specific concepts and entities buried in unstructured text. Based on these extracted data, we defined a multidimensional representation form of geosciences text documents which facilitates quantitative and exploratory analysis for knowledge discovery. To illustrate the potential of the proposed workflow, we implemented a pilot system that allows the user to perform multidimensional analysis on large collection of documents through interactive and user-friendly visualizations. We have analyzed the rare earth elements and carbonatites research topic as an example. The obtained visualizations show the usefulness and the efficiency of the proposed system for discovering knowledge and identifying potential research gaps.
Plum2Text: a french plumitifs-descriptions data-to-text dataset for natural language generation

In this paper, we introduce a new French Data-to-Text (D2T) dataset in the legal domain: Plum2Text1. It is made out of plumitifs (docket files) - descriptions pairs that are derived from publicly available documents issued by Canadian criminal courts. The development of Plum2Text is primarily intended to train statistical natural language generation algorithms, in order to make the plumitifs more easily understandable for Canadian citizens. The inputs and outputs of the dataset are unique: on the data side, the values of the table contain long pieces of textual utterance, and on the text side (or reference), it most often consists of a paraphrase of the table values. We describe how we curated the plumitif-description associations by introducing an annotation tool and a methodology specific to the D2T natural language generation task. We do so by using simple yet efficient text classifiers to help the annotator leverage annotated examples during the annotation process. As a matter of privacy, we also illustrate how we are decontextualizing the descriptions.
Sentence classification for contract law cases: a natural language processing approach

Legal practice can gain much from advances in machine learning and A.I. technology. Common law countries such as the United States, the United Kingdom, Canada, and Australia, rely on judicial precedent to decide what law governs new cases and how that law is applied. Judicial precedent is the body of case law that observes how past courts have decided similar cases. Case law is composed of individual case decisions by courts that resolve a particular dispute. When resolving new cases, courts look back on judicial precedent to structure how to decide said new cases. Thus, an extensive knowledge base of how prior cases have been decided under what law is needed in each field of legal practice. Legal research is the practice of identifying relevant case law for a particular set of facts and is essential for any practitioner seeking to make the most persuasive argument for clients. Reviewing case law and identifying relevant cases can be time consuming and imperfect; there is always risk that practitioners overlook relevant and/or new case law that has changed the legal landscape of their practice. Thus practitioners are not only required to retain an extensive knowledge base of relevant case law, but also to keep abreast of changes in case law.
From data to information: automating data science to explore the U.S. court system

The U.S. court system is the nation's arbiter of justice, tasked with the responsibility of ensuring equal protection under the law. But hurdles to information access obscure the inner workings of the system, preventing stakeholders - from legal scholars to journalists and members of the public - from understanding the state of justice in America at scale. There is an ongoing data access argument here: U.S. court records are public data and should be freely available. But open data arguments represent a half-measure; what we really need is open information. This distinction marks the difference between downloading a zip file containing a quarter-million case dockets and getting the real-time answer to a question like "Are pro se parties more or less likely to receive fee waivers? " To help bridge that gap, we introduce a novel platform and user experience that provides users with the tools necessary to explore data and drive analysis via natural language statements. Our approach leverages an ontology configuration that adds domain-relevant data semantics to database schemas to provide support for user guidance and for search and analysis without user-entered code or SQL. The system is embodied in a "natural-language notebook" user experience, and we apply this approach to the space of case docket data from the U.S. federal court system. Additionally, we provide detail on the collection, ingestion and processing of the dockets themselves, including early experiments in the use of language modeling for docket entry classification with an initial focus on motions.
Carbon and Nitrogen Cycling in a Peanut-Cotton-Bahiagrass Rotation

ii Acknowledgements . iv List of Tables . viii List of Figures . x List of Abbreviations . xii I. LITERATURE REVIEW . 13 Introduction . 13 The Sod-Based Rotation. 14 Management Effects on Soil Carbon . 15 Perennial Grass Effect on Soil Carbon . 15 Tillage Effect on Soil Carbon . 16 Grazing Effect on Soil Carbon . 18 Management Effects on Soil Nitrogen . 19 Tillage Effect on Soil Nitrogen . 19 Grazing Effect on Soil Nitrogen . 20 Natural Abundance of Isotopic Carbon . 21 The Greenhouse Effect . 23 Contribution of Agriculture to Greenhouse Gas Emissions . 23 Perennial Grass Effect on Greenhouse Gas Emissions . 25
Targeting Paretic Propulsion and Walking Speed With a Soft Robotic Exosuit: A Consideration-of-Concept Trial

Background: Soft robotic exosuits can facilitate immediate increases in short- and long-distance walking speeds in people with post-stroke hemiparesis. We sought to assess the feasibility and rehabilitative potential of applying propulsion-augmenting exosuits as part of an individualized and progressive training program to retrain faster walking and the underlying propulsive strategy. Methods: A 54-yr old male with chronic hemiparesis completed five daily sessions of Robotic Exosuit Augmented Locomotion (REAL) gait training. REAL training consists of high-intensity, task-specific, and progressively challenging walking practice augmented by a soft robotic exosuit and is designed to facilitate faster walking by way of increased paretic propulsion. Repeated baseline assessments of comfortable walking speed over a 2-year period provided a stable baseline from which the effects of REAL training could be elucidated. Additional outcomes included paretic propulsion, maximum walking speed, and 6-minute walk test distance. Results: Comfortable walking speed was stable at 0.96 m/s prior to training and increased by 0.30 m/s after training. Clinically meaningful increases in maximum walking speed (Δ: 0.30 m/s) and 6-minute walk test distance (Δ: 59 m) were similarly observed. Improvements in paretic peak propulsion (Δ: 2.80 %BW), propulsive power (Δ: 0.41 W/kg), and trailing limb angle (Δ: 6.2 degrees) were observed at comfortable walking speed (p's < 0.05). Likewise, improvements in paretic peak propulsion (Δ: 4.63 %BW) and trailing limb angle (Δ: 4.30 degrees) were observed at maximum walking speed (p's < 0.05). Conclusions: The REAL training program is feasible to implement after stroke and capable of facilitating rapid and meaningful improvements in paretic propulsion, walking speed, and walking distance.
Differences in Gut Microbiome Composition Between Sympatric Wild and Allopatric Laboratory Populations of Omnivorous Cockroaches

Gut microbiome composition is determined by a complex interplay of host genetics, founder’s effects, and host environment. We are using omnivorous cockroaches as a model to disentangle the relative contribution of these factors. Cockroaches are a useful model for host–gut microbiome interactions due to their rich hindgut microbial community, omnivorous diet, and gregarious lifestyle. In this study, we used 16S rRNA sequencing to compare the gut microbial community of allopatric laboratory populations of Periplaneta americana as well as sympatric, wild-caught populations of P. americana and Periplaneta fuliginosa, before and after a 14 day period of acclimatization to a common laboratory environment. Our results showed that the gut microbiome of cockroaches differed by both species and rearing environment. The gut microbiome from the sympatric population of wild-captured cockroaches showed strong separation based on host species. Laboratory-reared and wild-captured cockroaches from the same species also exhibited distinct gut microbiome profiles. Each group of cockroaches had a unique signature of differentially abundant uncharacterized taxa still present after laboratory cultivation. Transition to the laboratory environment resulted in decreased microbiome diversity for both species of wild-caught insects. Interestingly, although laboratory cultivation resulted in similar losses of microbial diversity for both species, it did not cause the gut microbiome of those species to become substantially more similar. These results demonstrate how competing factors impact the gut microbiome and highlight the need for a greater understanding of host–microbiome interactions.
Symbolic Convergence or Divergence? Making Sense of (the Rhetorical) Senses of a University-Wide Organizational Change

This research investigates the extent to which organizational change initiatives may lead to divergent patterns of sensemaking among organizational members. Drawing on the symbolic convergence theory, we performed an in-depth fantasy theme analysis of organization members’ rhetoric around an organizational change at a private university. Our analysis uncovers six fantasy themes and two corresponding fantasy types, which lead to no rhetorical vision. The lack of cognitive convergence between change initiators and change recipients suggests the inherent incompatibility between managerial and employee fantasies around organizational change, barring the exceptions of dual-responsibility change recipients (e.g., faculty members who also assume administrative responsibilities), who tend to adopt the change initiator rhetoric. Overall, this study informs our extant knowledge of change sensemaking with novel theoretical and methodological insights and bears implications for organizational change researchers and practitioners alike.
Emotions Toward Sustainable Innovations: A Matter of Value Congruence

Public resistance to sustainable innovations is oftentimes accompanied by strong negative emotions. Therefore, it is essential to better understand the underlying factors of emotions toward sustainable innovations to facilitate their successful implementation. Based on the Value-Innovation-Congruence model of Emotional responses (VICE model), we argue that positive and negative emotions toward innovations reflect whether innovations are congruent or incongruent with (i.e., support or threaten) people's core values. We tested our reasoning in two experimental studies (N = 114 and N = 246), by asking participants to evaluate innovations whose characteristics were either congruent or incongruent with egoistic values (study 1) or with biospheric values (study 1 and study 2). In line with the VICE model, we found overall that the more an innovation was perceived to have characteristics congruent with these values, and biospheric values in particular, the stronger positive and the weaker negative emotions they experienced toward the innovation, especially the more strongly people endorsed these values. Emotions, in turn, were related with acceptability of innovations. Our findings highlight that emotions toward innovations can have a systematic basis in people's values that can be addressed to ensure responsible decision-making on sustainable innovations.
Deep Gated Hebbian Predictive Coding Accounts for Emergence of Complex Neural Response Properties Along the Visual Cortical Hierarchy

Predictive coding provides a computational paradigm for modeling perceptual processing as the construction of representations accounting for causes of sensory inputs. Here, we developed a scalable, deep network architecture for predictive coding that is trained using a gated Hebbian learning rule and mimics the feedforward and feedback connectivity of the cortex. After training on image datasets, the models formed latent representations in higher areas that allowed reconstruction of the original images. We analyzed low- and high-level properties such as orientation selectivity, object selectivity and sparseness of neuronal populations in the model. As reported experimentally, image selectivity increased systematically across ascending areas in the model hierarchy. Depending on the strength of regularization factors, sparseness also increased from lower to higher areas. The results suggest a rationale as to why experimental results on sparseness across the cortical hierarchy have been inconsistent. Finally, representations for different object classes became more distinguishable from lower to higher areas. Thus, deep neural networks trained using a gated Hebbian formulation of predictive coding can reproduce several properties associated with neuronal responses along the visual cortical hierarchy.
A Systematic Review and Meta-Analysis of the Potential of Millets for Managing and Reducing the Risk of Developing Diabetes Mellitus

Millets (including sorghum) are known to be highly nutritious besides having a low carbon footprint and the ability to survive in high temperatures with minimal water. Millets are widely recognised as having a low Glycaemic Index (GI) helping to manage diabetes. This systematic review and meta-analyzes across the different types of millets and different forms of processing/cooking collated all evidences. Of the 65 studies that were collected globally, 39 studies with 111 observations were used to analyze GI outcomes and 56 studies were used to analyze fasting, post-prandial glucose level, insulin index and HbA1c outcomes in a meta-analysis. It is evident from the descriptive statistics that the mean GI of millets is 52.7 ± 10.3, which is about 36% lower than in typical staples of milled rice (71.7 ± 14.4) and refined wheat (74.2 ± 14.9). The descriptive, meta and regression analyses revealed that Job's tears, fonio, foxtail, barnyard, and teff were the millets with low mean GI (<55) that are more effective (35–79%) in reducing dietary GI than the control samples. Millets with intermediate GI (55–69) are pearl millet, finger millet, kodo millet, little millet, and sorghum which have a 13–35% lower GI than the control with high GI (>69). A meta-analysis also showed that all millets had significantly (p < 0.01) lower GI than white rice, refined wheat, standard glucose or white wheat bread except little millet which had inconsistent data. Long term millet consumption lowered fasting and post-prandial blood glucose levels significantly (p < 0.01) by 12 and 15%, respectively, in diabetic subjects. There was a significant reduction in HbA1c level (from 6.65 ± 0.4 to 5.67 ± 0.4%) among pre-diabetic individuals (p < 0.01) who consumed millets for a long period. Minimally processed millets were 30% more effective in lowering GI of a meal compared to milled rice and refined wheat. In conclusion, millets can be beneficial in managing and reducing the risk of developing diabetes and could therefore be used to design appropriate meals for diabetic and pre-diabetic subjects as well as for non-diabetic people for a preventive approach.