Miracle-dz/UniFAR-FTUs huggingface dataset:
24-Hour Profiles of 11-Oxygenated C19 Steroids and Δ5-Steroid Sulfates during Oral and Continuous Subcutaneous Glucocorticoids in 21-Hydroxylase Deficiency

Background Optimal management of androgen excess in 21-hydroxylase deficiency (21OHD) remains challenging. 11-oxygenated-C19 steroids (11-oxyandrogens) have emerged as promising biomarkers of disease control, but data regarding their response to treatment are lacking. Objective To compare the dynamic response of a broad set of steroids to both conventional oral glucocorticoids (OG) and circadian cortisol replacement via continuous subcutaneous hydrocortisone infusion (CSHI) in patients with 21OHD based on 24-hour serial sampling. Participants and Methods We studied 8 adults (5 women), ages 19-43 years, with poorly controlled classic 21OHD who participated in a single-center open-label phase I–II study comparing OG with CSHI. We used mass spectrometry to measure 15 steroids (including 11-oxyandrogens and Δ5 steroid sulfates) in serum samples obtained every 2 h for 24 h after 3 months of stable OG, and 6 months into ongoing CSHI. Results In response to OG therapy, androstenedione, testosterone (T), and their four 11-oxyandrogen metabolites:11β-hydroxyandrostenedione, 11-ketoandrostenedione, 11β-hydroxytestosterone and 11-ketotestosterone (11KT) demonstrated a delayed decline in serum concentrations, and they achieved a nadir between 0100-0300. Unlike DHEAS, which had little diurnal variation, pregnenolone sulfate (PregS) and 17-hydoxypregnenolone sulfate peaked in early morning and declined progressively throughout the day. CSHI dampened the early ACTH and androgen rise, allowing the ACTH-driven adrenal steroids to return closer to baseline before mid-day. 11KT concentrations displayed the most consistent difference between OG and CSHI across all time segments. While T was lowered by CSHI as compared with OG in women, T increased in men, suggesting an improvement of the testicular function in parallel with 21OHD control in men. Conclusion 11-oxyandrogens and PregS could serve as biomarkers of disease control in 21OHD. The development of normative data for these promising novel biomarkers must consider their diurnal variability.
Effects of Zinc Oxide Nanoparticles Synthesized Using Aspergillus niger on Carbapenem-Resistant Klebsiella pneumonia In Vitro and In Vivo

Currently, the mortality rate in Saudi Arabia’s ICUs is increasing due to the spread of Klebsiella pneumoniae carbapenemase (KPC)-producing bacteria. This study was carried out to evaluate the ability of biologically synthesized zinc oxide nanoparticles (ZnO-NPs) using Aspergillus niger to overcome carbapenem-resistant K. pneumoniae (KPC) in vitro and in vivo. ZnO-NPs were synthesized via a biological method and characterized using UV–Vis spectroscopy, Zetasizer and zeta potential analyses, x-ray diffraction spectroscopy, Fourier transform infrared spectroscopy, scanning electron microscopy (SEM), and energy-dispersive x-ray spectroscopy (EDX). In vitro sensitivity of KPC to ZnO-NPs was identified using the well diffusion method. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were determined by a macro-dilution method. The morphological alteration of KPC cells after ZnO-NPs treatment was observed by SEM. The in vivo susceptibility of KPC cells to ZnO-NPs ointment was evaluated using wound healing in experimental rats. The chemical characterization findings showed the formation, stability, shape, and size of the synthesized nanoparticles. The MIC and MBC were 0.7 and 1.8 mg/ml, respectively. The in vivo results displayed reduced inflammation and wound re-epithelialization of KPC-infected rats. These findings demonstrated that ZnO-NPs have great potential to be developed as antibacterial agents.
"Be nice to your wife! The restaurants are closed": Can Gender Stereotype Detection Improve Sexism Classification?

In this paper, we focus on the detection of sexist hate speech against women in tweets studying for the ﬁrst time the impact of gender stereotype detection on sexism classiﬁcation. We propose: (1) the ﬁrst dataset annotated for gender stereotype detection, (2) a new method for data augmentation based on sentence similarity with multilingual external datasets, and (3) a set of deep learning experiments ﬁrst to detect gender stereotypes and then, to use this auxiliary task for sexism detection. Although the presence of stereotypes does not necessarily entail hateful content, our results show that sexism classiﬁcation can deﬁnitively beneﬁt from gender stereotype detection.
Methane Productivity of the Major Animal Waste Types

ABSTRACT METHANE production using animal waste is becoming economically feasible for intermediate to large scale animal production operations. No general economic assessment can be made, however, since each individual application of the technology must be evaluated as construction methods, labor costs and alternative energy values vary from one production facility to another. The methane yield from a fermentation plant, however, will be relatively constant from site to site, given the same waste type and operating conditions. The expected methane yield from a fermentation plant is needed to evaluate the site-specific economics and for on-site energy use planning and development. The methane productivity of the four major animal waste types has been studied by computer simulation using a validated dynamic mathematical model of the methane fermentation process. Methane productivity is reported on two bases: (a) yield per unit total solids loaded (m^CH4/Mg TS) and (b) yield per unit animal live weight (mCH4/Mg LW-day). Results indicate that there are large differences between the waste types and that poultry waste produces the highest yield for animal LW while dairy waste is the least productive on a LW and TS basis.
A Discourse-Aware Graph Neural Network for Emotion Recognition in Multi-Party Conversation

Emotion recognition in multi-party conversation (ERMC) is becoming increasingly popular as an emerging research topic in natural language processing. Prior research focuses on exploring sequential information but ignores the discourse structures of conversations. In this paper, we investigate the importance of discourse structures in handling informative contextual cues and speaker-speciﬁc features for ERMC. To this end, we propose a discourse-aware graph neural network (ERMC-DisGCN) for ERMC. In particular, we design a relational convolution to lever the self-speaker dependency of interlocutors to propagate contextual information. Further-more, we exploit a gated convolution to select more informative cues for ERMC from dependent utterances. The experimental results show our method outperforms multiple baselines, illustrating that discourse structures are of great value to ERMC.
Past, Present, and Future: Conversational Emotion Recognition through Structural Modeling of Psychological Knowledge

Conversational Emotion Recognition (CER) is a task to predict the emotion of an utterance in the context of a conversation. Although modeling the conversational context and interactions between speakers has been studied broadly, it is important to consider the speaker’s psychological state, which controls the action and intention of the speaker. The state-of-the-art method introduces CommonSense Knowledge (CSK) to model psychological states in a sequential way (forwards and backwards). However, it ignores the structural psychological interactions between utterances. In this paper, we propose a pSychological-Knowledge-Aware Interaction Graph (SKAIG). In the locally connected graph, the targeted utterance will be enhanced with the information of action inferred from the past context and intention implied by the future context. The utterance is self-connected to consider the present effect from itself. Furthermore, we utilize CSK to enrich edges with knowledge representations and process the SKAIG with a graph transformer. Our method achieves state-of-the-art and competitive performance on four popu-lar CER datasets.
Neural Media Bias Detection Using Distant Supervision With BABE - Bias Annotations By Experts

Media coverage has a substantial effect on the public perception of events. Nevertheless, media outlets are often biased. One way to bias news articles is by altering the word choice. The automatic identification of bias by word choice is challenging, primarily due to the lack of a gold standard data set and high context dependencies. This paper presents BABE, a robust and diverse data set created by trained experts, for media bias research. We also analyze why expert labeling is essential within this domain. Our data set offers better annotation quality and higher inter-annotator agreement than existing work. It consists of 3,700 sentences balanced among topics and outlets, containing media bias labels on the word and sentence level. Based on our data, we also introduce a way to detect bias-inducing sentences in news articles automatically. Our best performing BERT-based model is pre-trained on a larger corpus consisting of distant labels. Fine-tuning and evaluating the model on our proposed supervised data set, we achieve a macro F1-score of 0.804, outperforming existing methods.
Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing

While neural networks are ubiquitous in state-of-the-art semantic parsers, it has been shown that most standard models suffer from dramatic performance losses when faced with compositionally out-of-distribution (OOD) data. Recently several methods have been proposed to improve compositional generalization in semantic parsing. In this work we instead focus on the problem of detecting compositionally OOD examples with neural semantic parsers, which, to the best of our knowledge, has not been investigated before. We investigate several strong yet simple methods for OOD detection based on predictive uncertainty. The experimental results demon-strate that these techniques perform well on the standard SCAN and CFQ datasets. Moreover, we show that OOD detection can be further improved by using a heterogeneous ensemble.
Internal mobile phone antenna array for LTE/WWAN and LTE MIMO operations

An antenna array comprising a main antenna for eight‐band LTE/WWAN operation and an auxiliary antenna to combine with the main antenna for LTE MIMO operation in the mobile phone is presented. Both the main and auxiliary antennas are disposed at the bottom edge of the system circuit board of the mobile phone and separated by a protruded ground. The main and auxiliary antennas can respectively fit in two small no‐ground regions of 15 × 30 mm2 and 15 × 20 mm2 on the system circuit board, while the protruded ground in‐between the two antennas has a width of 10 mm for mounting a USB data port. The main antenna covers the 704–960/1710–2690 MHz bands for eight‐band LTE/WWAN operation, and the auxiliary antenna covers the 704–787/2300–2690 MHz bands such that the main and auxiliary antennas together can perform LTE MIMO operation in the LTE700/2300/2500 bands. The proposed antenna array is fabricated and tested. Results are presented and discussed. © 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett, 2011; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.26038
Retrieve, Discriminate and Rewrite: A Simple and Effective Framework for Obtaining Affective Response in Retrieval-Based Chatbots

Obtaining affective response is a key step in building empathetic dialogue systems. This task has been studied a lot in generation-based chatbots, but the related research in retrievalbased chatbots is still in the early stage. Existing works in retrieval-based chatbots are based on Retrieve-and-Rerank framework, which have a common problem of satisfying affect label at the expense of response quality. To address this problem, we propose a simple and effective Retrieve-Discriminate-Rewrite framework. The framework replaces the reranking mechanism with a new discriminate-andrewrite mechanism, which predicts the affect label of the retrieved high-quality response via discrimination module and further rewrites the affect unsatisfied response via rewriting module. This can not only guarantee the quality of the response, but also satisfy the given affect label. In addition, another challenge for this line of research is the lack of an off-theshelf affective response dataset. To address this problem and test our proposed framework, we annotate a Sentimental Douban Conversation Corpus based on the original Douban Conversation Corpus. Experimental results show that our proposed framework is effective and outperforms competitive baselines.
Semantic Alignment with Calibrated Similarity for Multilingual Sentence Embedding

Measuring the similarity score between a pair of sentences in different languages is the essential requisite for multilingual sentence embedding methods. Predicting the similarity score consists of two sub-tasks, which are monolingual similarity evaluation and multi-lingual sentence retrieval. However, conventional methods have mainly tackled only one of the sub-tasks and therefore showed biased performances. In this paper, we suggest a novel and strong method for multilingual sentence embedding, which shows performance improvement on both sub-tasks, consequently resulting in robust predictions of multilingual similarity scores. The suggested method consists of two parts: to learn semantic similarity of sentences in the pivot language and then to extend the learned semantic structure to different languages. To align semantic structures across different languages, we introduce a teacher-student network. The teacher network distills the knowledge of the pivot language to different languages of the student network. During the distillation, the parameters of the teacher network are updated with the slow-moving average. Together with the distillation and the parameter updating, the semantic structure of the student network can be directly aligned across different languages while preserving the ability to measure the semantic similarity. Thus, the multilingual training method drives performance improvement on multilingual similarity evaluation. The suggested model achieves the state-of-the-art performance on extended STS 2017 multilingual similarity evaluation as well as two sub-tasks, which are extended STS 2017 monolingual similarity evaluation and Tatoeba multilingual retrieval in 14 languages.
Are Factuality Checkers Reliable? Adversarial Meta-evaluation of Factuality in Summarization

With the continuous upgrading of the summarization systems driven by deep neural networks, researchers have higher requirements on the quality of the generated summaries, which should be not only ﬂuent and infor-mative but also factually correct. As a result, the ﬁeld of factual evaluation has developed rapidly recently. Despite its initial progress in evaluating generated summaries, the meta-evaluation methodologies of factuality metrics are limited in their opacity , leading to the insufﬁcient understanding of factuality metrics’ relative advantages and their applicability. In this paper, we present an adversarial meta-evaluation methodology that allows us to (i) diagnose the ﬁne-grained strengths and weaknesses of 6 existing top-performing metrics over 24 diagnostic test datasets, (ii) search for directions for further improvement by data augmentation. Our observations from this work motivate us to propose several calls for future research. We make all codes, diagnostic test datasets, trained factuality models available: https://github.com/zide05/ AdvFact .
Language Clustering for Multilingual Named Entity Recognition

Recent work in multilingual natural language processing has shown progress in various tasks such as natural language inference and joint multilingual translation. Despite success in learning across many languages, challenges arise where multilingual training regimes of-ten boost performance on some languages at the expense of others. For multilingual named entity recognition (NER) we propose a simple technique that groups similar languages together by using embeddings from a pre-trained masked language model, and automatically discovering language clusters in this embedding space. Speciﬁcally, we ﬁne-tune an XLM-Roberta model on a language identiﬁcation task, and use embeddings from this model for clustering. We conduct experiments on 15 diverse languages in the WikiAnn dataset and show our technique largely outperforms three baselines: (1) training a multilingual model jointly on all available languages, (2) training one monolingual model per language, and (3) grouping languages by linguistic family. We also conduct analyses showing meaning-ful multilingual transfer for low-resource languages (Swahili and Yoruba), despite being automatically grouped with other seemingly dis-parate languages.
T3-Vis: visual analytic for Training and fine-Tuning Transformers in NLP

Transformers are the dominant architecture in NLP, but their training and fine-tuning is still very challenging. In this paper, we present the design and implementation of a visual analytic framework for assisting researchers in such process, by providing them with valuable insights about the model’s intrinsic properties and behaviours. Our framework offers an intuitive overview that allows the user to explore different facets of the model (e.g., hidden states, attention) through interactive visualization, and allows a suite of built-in algorithms that compute the importance of model components and different parts of the input sequence. Case studies and feedback from a user focus group indicate that the framework is useful, and suggest several improvements. Our framework is available at: https://github.com/raymondzmc/T3-Vis.
Crack Growth in Ferroelectric Ceramics Driven by Cyclic Polarization Switching

Ferroelectric ceramics are susceptible to stable crack growth under cyclic electric field of high magnitude. The stresses originate from mismatch strains induced by the electric field around inhomogeneities, such as processing flaws, terminated electrodes and the cracks themselves. The phenomenon is studied using a lead lanthanum zirconate-titanate ceramic. A flaw is indented on the surface of a ceramic sample. Subject to a cyclic electric field of magnitude exceeding the coercive field, cracks eminate from the flaw, in the direction perpendicular to the voltage drop. Tests relevant to the phenomenon are also performed. Polarization and strain as functions of applied electric field are measured at various temperatures and applied stresses. Discharge is demonstrated through an air gap between two ceramic plates subjected to a voltage drop. Models are presented that explain the cyclic nature of the crack growth.
Shear Capacity of Hybrid Plate Girders

The AASHTO LRFD Bridge Design Specifications, in versions up to and including the 2003 interim, limit the shear resistance of hybrid steel I-girders to the shear buckling or shear yield load and prevent consideration of the additional capacity due to tension field action, which homogeneous girders are allowed to include. This limitation severely affected the economy of girders utilizing high-performance steel, whose optimum configuration is often hybrid. Therefore, an experimental investigation was initiated by the National Bridge Research Organization at the University of Nebraska-Lincoln to address the limitation on the consideration of tension field action in hybrid girders. This paper presents the findings of that research. Eight simply supported steel I-girders were designed, constructed, and loaded to failure to investigate their failure mechanisms and shear capacities. All girders tested were capable of supporting loads greater than those predicted, considering full contribution from tension field .
Optimal operation of multiquality water distribution systems: unsteady conditions

This paper describes the methodology and application of a genetic algorithm scheme tailor-made to EPANET, for optimizing the operation of a water distribution system under unsteady water quality conditions. The water distribution system consists of sources of different qualities, treatment facilities, tanks, pipes, control valves, and pumping stations. The objective is to minimize the total cost of pumping and treating the water for a selected operational time horizon, while delivering the consumers the required quantities at acceptable qualities and pressures. The decision variables for each of the time steps that encompass the total operational time horizon include: the scheduling of the pumping units, settings of the control valves, and treatment removal ratios at the treatment facilities. The constraints are: head and concentrations at the consumer nodes, maximum removal ratios at the treatment facilities, maximum allowable amounts of water withdrawals at the sources, and returning at the end of the operational time horizon to a prescribed total volume in the tanks. The model is explored through two example applications.
A FIELD STUDY ON BRAKING RESPONSES DURING DRIVING. I. TRIGGERING AND MODULATION

The present experiment was carried out to explore the response of driving subjects to emergency braking. The field trial consisted of driving behind a leading vehicle while the following drivers' responses were recorded by telemetry. A group of 51 individuals performed a series of trials at two driving speeds (60 and 80km/h), two following distances (6 and 12 m), and two braking conditions (real and dummy braking). Not all of these subjects completed all conditions or the minimum number of trials. The dependent variables were the total braking time (TBT) and its subcomponents: braking reaction time (BRT), and accelerator-to-brake movement time (MT). These data were analysed in three separate three-way ANOVAs with repeated measures on all factors. The results showed that when subjects were not aware of the forthcoming braking, the distance and braking conditions had major effects on all dependent variables. At the shorter following distance drivers reacted and moved faster. Similarly, when the brakes were .
Linking Structure and Parametric Geometry

The Karamba plug-in developed by Clemens Preisinger in collaboration with Bollinger + Grohmann Engineers has been developed to predict the behaviour of structures under external loads. Intended to be used by architects rather than being solely confined to an engineering setting, it enables a seamless flow of data between structural and geometric models. Preisinger here describes the program's evolution and application.