Miracle-dz/UniFAR-FTUs huggingface dataset:
A Simple Method for Synthesis of PbS Nanoparticles Using 2-Mercaptoethanol as the Capping Agent

Abstract Lead sulfide nanoparticles were synthesized at room temperature via a simple chemical reaction. In this synthesis, 2-mercaptoethanolwas used as the capping agent and sodium sulfide was used as a sulfur source. The products were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), ultraviolet-visible (UV-vis) spectroscopy and Fourier transform infrared (FT-IR) spectroscopy Electron microscopy study showed that without using a capping agent the bulk PbS is obtained, while adding the mercaptoethanol leads to production of nanoparticles. We found that the electronic absorption spectra as well as the particle sizes depend on the used capping agents. Two exitonic peaks with a large blue shift were observed when mercaptoethanol was used.
Modeling Graph Structure via Relative Position for Text Generation from Knowledge Graphs

We present Graformer, a novel Transformer-based encoder-decoder architecture for graph-to-text generation. With our novel graph self-attention, the encoding of a node relies on all nodes in the input graph - not only direct neighbors - facilitating the detection of global patterns. We represent the relation between two nodes as the length of the shortest path between them. Graformer learns to weight these node-node relations differently for different attention heads, thus virtually learning differently connected views of the input graph. We evaluate Graformer on two popular graph-to-text generation benchmarks, AGENDA and WebNLG, where it achieves strong performance while using many fewer parameters than other approaches.
KonTra at CMCL 2021 Shared Task: Predicting Eye Movements by Combining BERT with Surface, Linguistic and Behavioral Information

This paper describes the submission of the team KonTra to the CMCL 2021 Shared Task on eye-tracking prediction. Our system combines the embeddings extracted from a fine-tuned BERT model with surface, linguistic and behavioral features, resulting in an average mean absolute error of 4.22 across all 5 eye-tracking measures. We show that word length and features representing the expectedness of a word are consistently the strongest predictors across all 5 eye-tracking measures.
Variance-reduced First-order Meta-learning for Natural Language Processing Tasks

First-order meta-learning algorithms have been widely used in practice to learn initial model parameters that can be quickly adapted to new tasks due to their efficiency and effectiveness. However, existing studies find that meta-learner can overfit to some specific adaptation when we have heterogeneous tasks, leading to significantly degraded performance. In Natural Language Processing (NLP) applications, datasets are often diverse and each task has its unique characteristics. Therefore, to address the overfitting issue when applying first-order meta-learning to NLP applications, we propose to reduce the variance of the gradient estimator used in task adaptation. To this end, we develop a variance-reduced first-order meta-learning algorithm. The core of our algorithm is to introduce a novel variance reduction term to the gradient estimation when performing the task adaptation. Experiments on two NLP applications: few-shot text classification and multi-domain dialog state tracking demonstrate the superior performance of our proposed method.
Bot-Adversarial Dialogue for Safe Conversational Agents

Conversational agents trained on large unlabeled corpora of human interactions will learn patterns and mimic behaviors therein, which include offensive or otherwise toxic behavior. We introduce a new human-and-model-in-the-loop framework for evaluating the toxicity of such models, and compare a variety of existing methods in both the cases of non-adversarial and adversarial users that expose their weaknesses. We then go on to propose two novel methods for safe conversational agents, by either training on data from our new human-and-model-in-the-loop framework in a two-stage system, or ”baking-in” safety to the generative model itself. We find our new techniques are (i) safer than existing models; while (ii) maintaining usability metrics such as engagingness relative to state-of-the-art chatbots. In contrast, we expose serious safety issues in existing standard systems like GPT2, DialoGPT, and BlenderBot.
Multi-Hop Transformer for Document-Level Machine Translation

Document-level neural machine translation (NMT) has proven to be of profound value for its effectiveness on capturing contextual information. Nevertheless, existing approaches 1) simply introduce the representations of context sentences without explicitly characterizing the inter-sentence reasoning process; and 2) feed ground-truth target contexts as extra inputs at the training time, thus facing the problem of exposure bias. We approach these problems with an inspiration from human behavior – human translators ordinarily emerge a translation draft in their mind and progressively revise it according to the reasoning in discourse. To this end, we propose a novel Multi-Hop Transformer (MHT) which offers NMT abilities to explicitly model the human-like draft-editing and reasoning process. Specifically, our model serves the sentence-level translation as a draft and properly refines its representations by attending to multiple antecedent sentences iteratively. Experiments on four widely used document translation tasks demonstrate that our method can significantly improve document-level translation performance and can tackle discourse phenomena, such as coreference error and the problem of polysemy.
Fine-grained Post-training for Improving Retrieval-based Dialogue Systems

Retrieval-based dialogue systems display an outstanding performance when pre-trained language models are used, which includes bidirectional encoder representations from transformers (BERT). During the multi-turn response selection, BERT focuses on training the relationship between the context with multiple utterances and the response. However, this method of training is insufficient when considering the relations between each utterance in the context. This leads to a problem of not completely understanding the context flow that is required to select a response. To address this issue, we propose a new fine-grained post-training method that reflects the characteristics of the multi-turn dialogue. Specifically, the model learns the utterance level interactions by training every short context-response pair in a dialogue session. Furthermore, by using a new training objective, the utterance relevance classification, the model understands the semantic relevance and coherence between the dialogue utterances. Experimental results show that our model achieves new state-of-the-art with significant margins on three benchmark datasets. This suggests that the fine-grained post-training method is highly effective for the response selection task.
A Comparative Study on Schema-Guided Dialogue State Tracking

Frame-based state representation is widely used in modern task-oriented dialog systems to model user intentions and slot values. However, a fixed design of domain ontology makes it difficult to extend to new services and APIs. Recent work proposed to use natural language descriptions to define the domain ontology instead of tag names for each intent or slot, thus offering a dynamic set of schema. In this paper, we conduct in-depth comparative studies to understand the use of natural language description for schema in dialog state tracking. Our discussion mainly covers three aspects: encoder architectures, impact of supplementary training, and effective schema description styles. We introduce a set of newly designed bench-marking descriptions and reveal the model robustness on both homogeneous and heterogeneous description styles in training and evaluation.
An Empirical Study of Generating Texts for Search Engine Advertising

Although there are many studies on neural language generation (NLG), few trials are put into the real world, especially in the advertising domain. Generating ads with NLG models can help copywriters in their creation. However, few studies have adequately evaluated the effect of generated ads with actual serving included because it requires a large amount of training data and a particular environment. In this paper, we demonstrate a practical use case of generating ad-text with an NLG model. Specially, we show how to improve the ads’ impact, deploy models to a product, and evaluate the generated ads.
In-Situ High Temperature XRD on U0.54Pu0.46O2-x A Study of the Miscibility Gap

It has been shown in previous studies that a miscibility gap exists in the hypo-stoichiometric region UO2-PuO2-Pu2O3 with one phase poor in oxygen, and the other with an O/M (Oxygen to Metal ratio) close to 2.00. Data on the evolution of this region in temperature, especially in the vicinity of the oxygen content corresponding to the highest temperature at which the gap can be observed, is scarce. A high temperature X-ray diffractometer with a dedicated gas control setup was used to study the described region in-situ. We have observed reflections of the two cubic phases, with one increasing and the other decreasing in intensity during the thermal plateaus lasting up to 20 h. We compare the calculated lattice parameters with literature. We estimated the O/M evolution of our samples from a comparison of phase fractions values obtained by Rietveld refinement and calculations using the Calphad method.
Direct numerical simulation of secondary flows in turbulent boundary layers over streamwise-elongated roughness elements

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Cellular Senescence: Pathogenic Mechanisms in Lung Fibrosis

Pulmonary fibrosis is a chronic and fatal lung disease that significantly impacts the aging population globally. To date, anti-fibrotic, immunosuppressive, and other adjunct therapy demonstrate limited efficacies. Advancing our understanding of the pathogenic mechanisms of lung fibrosis will provide a future path for the cure. Cellular senescence has gained substantial interest in recent decades due to the increased incidence of fibroproliferative lung diseases in the older age group. Furthermore, the pathologic state of cellular senescence that includes maladaptive tissue repair, decreased regeneration, and chronic inflammation resembles key features of progressive lung fibrosis. This review describes regulatory pathways of cellular senescence and discusses the current knowledge on the senescence of critical cellular players of lung fibrosis, including epithelial cells (alveolar type 2 cells, basal cells, etc.), fibroblasts, and immune cells, their phenotypic changes, and the cellular and molecular mechanisms by which these cells contribute to the pathogenesis of pulmonary fibrosis. A few challenges in the field include establishing appropriate in vivo experimental models and identifying senescence-targeted signaling molecules and specific therapies to target senescent cells, known collectively as “senolytic” or “senotherapeutic” agents.
Relating Neural Text Degeneration to Exposure Bias

This work focuses on relating two mysteries in neural-based text generation: exposure bias, and text degeneration. Despite the long time since exposure bias was mentioned and the numerous studies for its remedy, to our knowledge, its impact on text generation has not yet been verified. Text degeneration is a problem that the widely-used pre-trained language model GPT-2 was recently found to suffer from (Holtzman et al., 2020). Motivated by the unknown causation of the text degeneration, in this paper we attempt to relate these two mysteries. Specifically, we first qualitatively and quantitatively identify mistakes made before text degeneration occurs. Then we investigate the significance of the mistakes by inspecting the hidden states in GPT-2. Our results show that text degeneration is likely to be partly caused by exposure bias. We also study the self-reinforcing mechanism of text degeneration, explaining why the mistakes amplify. In sum, our study provides a more concrete foundation for further investigation on exposure bias and text degeneration problems.
A Complete Data Set of Equatorial Projections of Saturn's Energetic Neutral Atom Emissions Observed by Cassini‐INCA

Observations of energetic neutral atoms (ENAs) are a useful tool for analyzing ion and neutral abundances in planetary magnetospheres. They are created when hot plasma, originating for example from magnetic reconnection sites, charge‐exchanges with the ambient neutral population surrounding the planet. The motion of ENAs is not governed by the magnetic field, allowing remote imaging. During the Cassini mission, the Ion and Neutral CAmera (INCA) of the Magnetosphere Imaging Instrument (MIMI) collected vast amounts of hydrogen and oxygen ENA observations of Saturn's magnetosphere from a variety of different viewing geometries. To enable investigations of the morphology and dynamics of Saturn's ring current, it is useful to re‐bin and re‐project the camera‐like views from the spacecraft‐based perspective into a common reference frame. We developed an algorithm projecting INCA's ENA observations into a regular grid in Saturn's equatorial plane. With most neutrals and ions being confined into an equatorial rotating disc, this projection is quite accurate in both spatial location and preservation of ENA intensity, provided the spacecraft is located at large enough elevations. Such projections were performed for all INCA ENA data from the Cassini Saturn tour; the data are available for download together with a Python routine flagging contaminated data and returning detailed spacecraft geometry information. The resulting data set is a good foundation for investigating for example the statistical properties of Saturn's ring current and its complicated dynamics in relation to other remote and in situ observations of, for example, auroral emissions and magnetotail reconnection events.
Clinical features of the Anterior Chamber Depth

Purpose: To determine whether anterior chamber depth (ACD) has any clinical importance as well as the significance in various physiologic and pathologic processes. Methods: Recollection of the most relevant data from the relationships of ACD in eye care were extracted from PubMed, SCOPUS, Google Scholar and ARVO. Forty-two total articles were used in the construct of this review. Results: Anterior chamber depth has a direct proportional relation with axial length, corneal power and anterior chamber angle. It has an inverse proportional relation with age. Shallow anterior chamber depth was associated with and increased risk of endothelial cell density loss after phacoemulsification surgery, less ascorbic acid concentrations that translates to endothelial damage independently of surgery, angle closure glaucoma and angle closure secondary to retinal vein occlusions, trabeculectomy and tube placement complications and IOL formulae selection and refractive outcome inaccuracies. Population studies found this parameter to be different depending on the observed race and high rates of heritability, men were found to have deeper anterior chambers than women across all ethnic groups. Conclusion: ACD plays an influential role in many physiologic and pathologic conditions. These include, IOL lens formula usage, expected refractive outcomes after phacoemulsification surgery, corneal endothelial cell loss, closed angle glaucoma, trabeculectomy and anterior chamber tube endothelial cell loss, closed angle glaucoma, trabeculectomy and anterior chamber tube processes.
Electrochemical Behaviourof Some Amino Acids as Corrosion Inhibitors for Mild Steel in Sweet Brine

The effect of concentration and immersion time of phenylalanine (Phe), leucine (Leu) and valine (Val) for AISI 1018 in sweet brine was studied trough Electrochemical Impedance Spectroscopy (EIS), polarization resistance (Rp), Scanning Electronic Microscope (SEM) and Fourier Transform Infrared Spectroscopy (FTIR) as an ecological and biodegradable alternative as corrosion inhibitors. Electrochemical results showed that amino acids are good corrosion inhibitors according to their charge transfer resistance improvement. The effect of immersion time on corrosion behavior was studied trough 24 h with amino acids concentration variation of 0, 10, 100 and 250 ppm. The preliminary results demonstrated that the three amino acids adsorb over metal surface following the Langmuir adsorption isotherm model, and tend to agglomerate in bulk within time. Keywords: Corrosion Inhibitors; Biodegradable; Amino acids; Sweet brine
Radial variation in bonding performance of preservative-treated wood within Pinus elliottii trees and its relationships with wood density and adhesive penetration

Pinus elliottii wood from different radial locations within trees was used to prepare two-layer preservative-treated wood joints with alkaline copper quaternary preservative and emulsion polymer isocyanates (EPI) or resorcinol-phenol-formaldehyde (RPF) adhesives. The radial variations in shear strength and wood failure percentage were analyzed. Radial variations in wood density and average penetration depth (AP) of adhesives were investigated to establish relationships between shear strengths and characteristics of bonded joints. The shear strengths of EPIand RPF-bonded joints showed similar increasing trends from pith to bark, and they varied in ranges of 2.92 to 8.13 MPa and 2.03 to 7.12 MPa, respectively. The wood failure percentage of EPI joints (93% to 100%) had no significant differences in all the radial locations, but that of RPF joints (60% to 100%) showed a clear decreasing trend from pith to bark. High positive linear correlations were found between the shear strengths and wood density, and Pearson’s correlation coefficients for EPI and RPF were 0.934 (p < 0.01) and 0.931 (p < 0.01), respectively. Wood density is a very important factor influencing radial variations of bonding strengths. The correlation between the AP and shear strength was not significant at the 0.05 level.
Optimal delegation and information transmission under limited awareness

We study the delegation problem between a principal and an agent, who not only has better information about the performance of the available actions but also superior awareness of the set of actions that are actually feasible. We provide conditions under which the agent finds it optimal to leave the principal unaware of relevant options. By doing so, the agent increases the principal's cost of distorting the agent's choices and increases the principal's willingness to grant him higher information rents. We further show that the principal may use the option of renegotiation as a tool to implement actions that are not describable to her at the contracting stage. If the agent renegotiates, his proposal signals information about the payoff state. Due to her limited awareness, the principal makes a coarse inference from the agent's recommendations and, as a result, accepts a large number of the agent's proposals, which ultimately benefits both.
Learning about precipitation orographic enhancement

Precipitation orographic enhancement depends on : is ::: the ::::: result ::: of both synoptic circulation and topography. Since highelevation headwaters are often sparsely instrumented, the magnitude and distribution of this enhancement : , :: as :::: well :: as :::: how :::: they ::::: affect :::::::::: precipitation ::::: lapse ::::: rates, remain poorly understood. Filling this knowledge gap would allow a significant step ahead for hydrologic-forecasting procedures and water management in general. Here, we hypothesized that spatially distributed, manual 5 measurements of snow depth (courses) could provide new insights into this process. We leveraged 11,000+ snow-course data upstream : of : two reservoirs in the Western European Alps (Aosta Valley, Italy) to estimate precipitation orographic enhancement in the form of lapse rates and consequently improve predictions of a snow-hydrologic modeling chain (Flood-PROOFS). We found that Snow Water Equivalent (SWE) above 3000 m ASL was between 2 and 8.5 times higher than recorded cumulative seasonal precipitation below 1000 m ASL, with gradients up to 1000 mm w.e. km−1. Enhancement factors estimated by 10 blending precipitation-gauge and snow-course data were quite consistent between the two hydropower headwaters (median values above 3000 m ASL between 4.1 and 4.8). Including blended gauge-course lapse rates in an iterative precipitationspatialization procedure allowed Flood-PROOFS to remedy underestimations of both :::: both :: of : SWE above 3000 m ASL (up to 50%) and importantly : :::::::::: importantly : :: of : precipitation vs. observed streamflow. Runoff :::::: Annual ::::: runoff : coefficients based on blended lapse rates were also more consistent from year to year that :::: than those based on precipitation gauges alone (standard 15 deviation of 0.06 and 0.19, respectively). Thus, snow courses bear a characteristic signature of orographic precipitation, which opens a window of opportunity for leveraging these data sets to improve our understanding of the mountain water budget. This is all the more important due to their essential role :: the :::::::: essential ::: role :: of :::::::::::: high-elevation :::::::::: headwaters in supporting water security and ecosystem services worldwide. Copyright statement. TEXT 20
The Effect of Hierarchical Word Activities on Written Expression Skills and Writing Anxiety in Turkish Language Education for Foreign Students

In the study, the effect of hierarchical vocabulary activities prepared in accordance with Gass’s Second Language Acquisition Model developed by Ateş (2016) on students’ written expression skills and writing anxiety was investigated. While the determined target words were taught to the experimental group with hierarchical vocabulary activities, the supervised group was kept in a regular diary. The method of the study consists of an experimental design with pre-test-post-test-measurement and supervised group. Experimental and supervised groups consisted of foreign students studying at Inonu-TÖMER in the 2017-2018 academic year. An essay was written to the students as a pre-test and a post-test, and the Writing Anxiety Scale was applied. No significant difference was found between the experimental and supervised groups of the Written Expression Rubric, which is used to score the compositions, in the pre-test and post-tests. It was concluded that the privatized instruction applied to both groups was beneficial for students’ written expressions. There was a significant difference between the experimental and supervised groups in terms of Writing Anxiety Scale pre-test scores. It was determined that the writing anxiety of the experimental group was higher in the pre-test.