Miracle-dz/UniFAR-FTUs huggingface dataset:
Plasma BCAA concentrations during exercise of varied intensities in young healthy men—the impact of endurance training

Background Branched-chain amino acids (BCAA) i.e., leucine (Leu), isoleucine (Ile) and valine (Val) are important amino acids, which metabolism play a role in maintaining system energy homeostasis at rest and during exercise. As recently shown lowering of circulating BCAA level improves insulin sensitivity and cardiac metabolic health. However, little is known concerning the impact of a single bout of incremental exercise and physical training on the changes in blood BCAA. The present study aimed to determine the impact of a gradually increasing exercise intensity—up to maximal oxygen uptake (VO2max) on the changes of the plasma BCAA [∑BCAA]pl, before and after 5-weeks of moderate-intensity endurance training (ET). Methods Ten healthy young, untrained men performed an incremental cycling exercise test up to exhaustion to reach VO2max, before and after ET. Results We have found that exercise of low-to-moderate intensity (up to ∼50% of VO2max lasting about 12 min) had no significant effect on the [∑BCAA]pl, however the exercise of higher intensity (above 70% of VO2max lasting about 10 min) resulted in a pronounced decrease (p < 0.05) in [∑BCAA]pl. The lowering of plasma BCAA when performing exercise of higher intensity was preceded by a significant increase in plasma lactate concentration, showing that a significant attenuation of BCAA during incremental exercise coincides with exercise-induced acceleration of glycogen utilization. In addition, endurance training, which significantly increased power generating capabilities at VO2max (p = 0.004) had no significant impact on the changes of [∑BCAA]pl during this incremental exercise. Conclusion We have concluded that an exercise of moderate intensity of relatively short duration generally has no effect on the [∑BCAA]pl in young, healthy men, whereas significant decrease in [∑BCAA]pl occurs when performing exercise in heavy-intensity domain. The impact of exercise intensity on the plasma BCAA concentration seems to be especially important for patients with cardiometabolic risk undertaken cardiac rehabilitation or recreational activity.
Improving nurses’ knowledge of managing endotracheal tube cuff pressure in intensive care units: A quasi-experimental study

Background Previous studies conducted on nurses’ knowledge regarding endotracheal tube cuff pressure revealed that there were differences in intensive care nurses’ knowledge, leading to varying practices. Aim This study aimed to evaluate how an educational intervention based on the existing evidence-based guidelines, using both passive and active implementation strategies, could improve the knowledge of nurses regarding managing endotracheal tube cuff pressures in Malawian intensive care units. Setting Six functional ICUs (four public and two private) in Malawi. Methods The study followed a quasi-experimental, pre- and post-test design using an educational intervention. Intensive care nurses of six functional intensive care units in Malawi were randomly assigned to two intervention groups. Both groups received a half-day educational session, a printed version of the evidence-based guidelines, a printed and laminated summary of the guidelines and a related algorithm. Additionally, Intervention 2 group received four monitoring visits. Pre- and post-test questionnaires were conducted between February and August 2016. Descriptive and inferential data analyses (a chi-square test and t-test) were utilised. Results An improvement in knowledge was observed on the nursing care practices for the management of endotracheal tube cuff pressure for both groups following the educational intervention, although only the results comparing Intervention 2 group participants indicate that the level of knowledge was significant (t[df = 48] = 2.08, p = 0.043, d = 0.59). Conclusion Implementation of a formal training and mentorship programme for Malawian intensive care nurses would be of great benefit to enhance the knowledge and skills managing endotracheal tube cuff pressure. Follow-up studies would also assist in understanding how guidelines could be implemented most effectively to achieve better knowledge outcomes.
9-Methylgermacrene-B is confirmed as the sex pheromone of the sandfly Lutzomyia longipalpis from Lapinha, Brazil, and the absolute stereochemistry defined as S

The structure of the sex pheromone produced by the male sandfly Lutzomyia longipalpis, from the Lapinha Cave (Minas Gerais State) region of Brazil, previously proposed tentatively as the novel homosesquiterpene 9-methylgermacrene-B, is confirmed and the absolute stereochemistry defined as S by comparing physical and biological properties of the synthetic enantiomers with the natural product.
Structural evolution during the graphitization of polyacrylonitrile‐based carbon fiber as revealed by small‐angle X‐ray scattering

On the basis of a Debye–Bueche correlation length analysis, the small-angle X-ray scattering (SAXS) intensity components due to different scatterers within polyacrylonitrile-based carbon fiber were determined and analyzed separately. According to Guinier's law and other related methods, an intensity component indicating a relatively large scatterer was ascribed to the amorphous structure within the boundaries of fibrils. Results indicated that the amorphous regions decreased in dimension and finally transformed completely into voids as the heat treatment temperature rose to 2773 K. The general trend for microvoids was a systematic change from many small voids to a few large voids, while the local density fluctuation within the samples weakened and finally faded away. In conclusion, the graphitization process of carbon fibers as revealed by SAXS is a systematic evolution from a quasi-two-phase system (fibril, amorphous region and microvoid within the fibril) of high-strength carbon fiber to the true two-phase structure (crystallite and microvoid) of high-modulus graphite fiber.
Electrochemical oxidation of polyamines at diamond thin-film electrodes

The oxidation of five polyamines (ethylenediamine, putrescine, cadaverine, spermine, and spermidine) was investigated at polycrystalline, boron-doped, diamond thin-film electrodes using cyclic voltammetry and flow injection analysis (FIA) with amperometric detection. Cyclic voltammetry of the polyamines was conducted in pH 10 carbonate buffer. Well-resolved oxidation waves with respect to the background signal were observed, and the current−potential curves exhibited a scan rate dependence characteristic of slow desorption kinetics of the polyamine oxidation product. E1/2's of ∼+0.88 V vs Ag/AgCl were measured for all five polyamines. A mechanism is proposed whereby the polyamine oxidation occurs by oxygen transfer from reactive OH radicals. These radicals are produced during the initial stage of oxygen evolution at the nondiamond carbon impurity sites. These sites are believed to be located primarily at the grain boundaries, isolated from one another by the diamond microcrystallites, although the impurit.
A Closer Look at Few-Shot Crosslingual Transfer: Variance, Benchmarks and Baselines

We present a focused study of few-shot crosslingual transfer , a recently proposed NLP scenario: a pretrained multilingual encoder is ﬁrst ﬁnetuned on many annotations in a high resource language (typically English), and then ﬁnetuned on a few annotations (the “few shots”) in a target language. Few-shot transfer brings large improvements over zero-shot transfer. However, we show that it inherently has large variance and it is necessary to report results on multiple sets of few shots for stable results and to guarantee fair comparison of different algorithms. To address this problem, we publish our few-shot sets. In a study of why few-shot learning outperforms zero-shot transfer, we show that large models heavily rely on lexical hints when ﬁnetuned on a few shots and then overﬁt quickly. We evaluate different methods that use few-shot annotations, but do not observe signiﬁcant improvements over the baseline. This calls for better ways of utilizing the few-shot annotations.
Variational balance models for the three-dimensional Euler–Boussinesq equations with full Coriolis force

We derive a semi-geostrophic variational balance model for the three-dimensional Euler--Boussinesq equations on the non-traditional $f$-plane under the rigid lid approximation. The model is obtained by a small Rossby number expansion in the Hamilton principle, with no other approximations made. We allow for a fully non-hydrostatic flow and do not neglect the horizontal components of the Coriolis parameter, i.e., we do not make the so-called"traditional approximation". The resulting balance models have the same structure as the"$L_1$ balance model"for the primitive equations: a kinematic balance relation, the prognostic equation for the three-dimensional tracer field, and an additional prognostic equation for a scalar field over the two-dimensional horizontal domain which is linked to the undetermined constant of integration in the thermal wind relation. The balance relation is elliptic under the assumption of stable stratification and sufficiently small fluctuations in all prognostic fields.
NeuralMagicEye: Learning to See and Understand the Scene Behind an Autostereogram

An autostereogram, a.k. a. magic eye image, is a single-image stereogram that can create visual illusions of 3D scenes from 2D textures. This paper studies an interesting question that whether a deep CNN can be trained to recover the depth behind an autostereogram and understand its content. The key to the autostereogram magic lies in the stereopsis - to solve such a problem, a model has to learn to discover and estimate disparity from the quasi-periodic textures. We show that deep CNNs embedded with disparity convolution, a novel convolutional layer proposed in this paper that simulates stereopsis and encodes disparity, can nicely solve such a problem after being sufficiently trained on a large 3D object dataset in a self-supervised fashion. We refer to our method as ``NeuralMagicEye''. Experiments show that our method can accurately recover the depth behind autostereograms with rich details and gradient smoothness. Experiments also show the completely different working mechanisms for autostereogram perception between neural networks and human eyes. We hope this research can help people with visual impairments and those who have trouble viewing autostereograms. Our code is available at \url{https://jiupinjia.github. io/neuralmagiceye/}.
Deep Learning Towards Edge Computing: Neural Networks Straight from Compressed Data

Due to the popularization and grow in computational power of mobile phones, as well as advances in artiﬁcial intelligence, many intelligent applications have been developed, meaningfully enriching people’s life. For this reason, there is a growing interest in the area of edge intelligence, that aims to push the computation of data to the edges of the network, in order to make those applications more eﬃcient and secure. Many intelligent applications rely on deep learning models, like convolutional neural networks (CNNs). Over the past decade, they have achieved state-of-the-art performance in many computer vision tasks. To increase the performance of these methods, the trend has been to use increasingly deeper architectures and with more parameters, leading to a high computational cost. Indeed, this is one of the main problems faced by deep architectures, limiting their applicability in domains with limited computational resources, like edge devices. To alleviate the computational complexity, we propose a deep neural network capable of learning straight from the relevant information pertaining to visual content readily available in the compressed representation used for image and video storage and transmission. The novelty of our approach is that it was designed to operate directly on frequency domain data, learning with DCT co-eﬃcients rather than RGB pixels. This enables to save high computational load in full decoding the data stream and therefore greatly speed up the processing time, which has become a big bottleneck of deep learning. We evaluated our network on two challenging tasks: (1) image classiﬁcation on the ImageNet dataset and (2) video classiﬁcation on the UCF-101 and HMDB-51 datasets. Our results demonstrate comparable eﬀectiveness to the state-of-the-art methods in terms of accuracy, with the advantage of being more computationally eﬃcient.
Satisfiability Problems on Sums of Kripke Frames

We consider the operation of sum on Kripke frames, where a family of frames-summands is indexed by elements of another frame. In many cases, the modal logic of sums inherits the finite model property and decidability from the modal logic of summands [Babenyshev and Rybakov 2010; Shapirovsky 2018]. In this paper we show that, under a general condition, the satisfiability problem on sums is polynomial space Turing reducible to the satisfiability problem on summands. In particular, for many modal logics decidability in PSpace is an immediate corollary from the semantic characterization of the logic.
The Preparation of Asphalt Emulsions with Dissymmetric Gemini Quaternary Ammonium Salts Cationic Surfactants

In this study, dissymmetric Gemini quaternary ammonium salts cationic surfactant was synthesized and characterized based on which asphalt emulsions were prepared. To this end, the resulting performance evaluation results have been compared with standards. It was found that the asphalt emulsions prepared in the presence of a main emulsifier of 0.8 wt%, an assistant emulsifier of 0.2 wt%, pH value of 7.0, and a stabilizer of 0.15 wt% exhibited good storage stability, and its quality met the related standard requirements.
TrustMAE: A Noise-Resilient Defect Classification Framework using Memory-Augmented Auto-Encoders with Trust Regions

In this paper, we propose a framework called Trust-MAE to address the problem of product defect classification. Instead of relying on defective images that are difficult to collect and laborious to label, our framework can accept datasets with unlabeled images. Moreover, unlike most anomaly detection methods, our approach is robust against noises, or defective images, in the training dataset. Our framework uses a memory-augmented auto-encoder with a sparse memory addressing scheme to avoid over-generalizing the auto-encoder, and a novel trust-region memory updating scheme to keep the noises away from the memory slots. The result is a framework that can reconstruct defect-free images and identify the defective regions using a perceptual distance network. When compared against various state-of-the-art baselines, our approach performs competitively under noise-free MVTec datasets. More importantly, it remains effective at a noise level up to 40% while significantly outperforming other baselines.
kōan: A Corrected CBOW Implementation

It is a common belief in the NLP community that continuous bag-of-words (CBOW) word embeddings tend to underperform skip-gram (SG) embeddings. We ﬁnd that this belief is founded less on theoretical differences in their training objectives but more on faulty CBOW implementations in standard software libraries such as the ofﬁcial implementation word2vec.c [Mikolov et al., 2013b] and Gensim [ ˇReh˚uˇrek and Sojka, 2010]. We show that our correct implementation of CBOW yields word embeddings that are fully competitive with SG on various intrinsic and extrinsic tasks while being more than three times as fast to train. We release our implementation, k¯oan 1 , at https://github.com/ bloomberg/koan .
BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining

In this paper, we propose BANG, a new pretraining model to Bridge the gap between Autoregressive (AR) and Non-autoregressive (NAR) Generation. AR and NAR generation can be uniformly regarded as to what extent previous tokens can be attended, and BANG bridges AR and NAR generation by designing a novel model structure for large-scale pretraining. The pretrained BANG model can simultaneously support AR, NAR and semi-NAR generation to meet different requirements. Experiments on question generation (SQuAD 1.1), summarization (XSum) and dialogue generation (PersonaChat) show that BANG improves NAR and semi-NAR performance significantly as well as attaining comparable performance with strong AR pretrained models. Compared with the semi-NAR strong baselines, BANG achieves absolute improvements of 14.01 and 5.24 in the overall scores of SQuAD 1.1 and XSum, respectively. In addition, BANG achieves absolute improvements of 10.73, 6.39 and 5.90 in the overall scores of SQuAD, XSUM and PersonaChat respectively compared with the strong NAR baselines.
Existence and NonExistence for the Collision-Induced Breakage Equation

Abstract. A mathematical model for collision-induced breakage is considered. Existence of weak solutions to the continuous nonlinear collision-induced breakage equation is shown for a large class of unbounded collision kernels and daughter distribution functions, assuming the collision kernel K to be given by K(x, y) = xy + xy with α ≤ β ≤ 1. When α + β ∈ [1, 2], it is shown that there exists at least one weak mass-conserving solution for all times. In contrast, when α + β ∈ [0, 1) and α ≥ 0, global mass-conserving weak solutions do not exist, though such solutions are constructed on a finite time interval depending on the initial condition. The question of uniqueness is also considered. Finally, for α < 0 and a specific daughter distribution function, the non-existence of mass-conserving solutions is also established.
Understanding and Improving Lexical Choice in Non-Autoregressive Translation

Knowledge distillation (KD) is essential for training non-autoregressive translation (NAT) models by reducing the complexity of the raw data with an autoregressive teacher model. In this study, we empirically show that as a side effect of this training, the lexical choice errors on low-frequency words are propagated to the NAT model from the teacher model. To alleviate this problem, we propose to expose the raw data to NAT models to restore the useful information of low-frequency words, which are missed in the distilled data. To this end, we introduce an extra Kullback-Leibler divergence term derived by comparing the lexical choice of NAT model and that embedded in the raw data. Experimental results across language pairs and model architectures demonstrate the effectiveness and universality of the proposed approach. Extensive analyses confirm our claim that our approach improves performance by reducing the lexical choice errors on low-frequency words. Encouragingly, our approach pushes the SOTA NAT performance on the WMT14 English-German and WMT16 Romanian-English datasets up to 27.8 and 33.8 BLEU points, respectively. The source code will be released.
Out of Order: How important is the sequential order of words in a sentence in Natural Language Understanding tasks?

Do state-of-the-art natural language understanding models care about word order - one of the most important characteristics of a sequence? Not always! We found 75% to 90% of the correct predictions of BERT-based classifiers, trained on many GLUE tasks, remain constant after input words are randomly shuffled. Despite BERT embeddings are famously contextual, the contribution of each individual word to downstream tasks is almost unchanged even after the word's context is shuffled. BERT-based models are able to exploit superficial cues (e.g. the sentiment of keywords in sentiment analysis; or the word-wise similarity between sequence-pair inputs in natural language inference) to make correct decisions when tokens are arranged in random orders. Encouraging classifiers to capture word order information improves the performance on most GLUE tasks, SQuAD 2.0 and out-of-samples. Our work suggests that many GLUE tasks are not challenging machines to understand the meaning of a sentence.
CMV-BERT: Contrastive multi-vocab pretraining of BERT

In this work, we represent CMV-BERT, which improves the pretraining of a language model via two ingredients: (a) contrastive learning, which is well studied in the area of computer vision; (b) multiple vocabularies, one of which is fine-grained and the other is coarse-grained. The two methods both provide different views of an original sentence, and both are shown to be beneficial. Downstream tasks demonstrate our proposed CMV-BERT are effective in improving the pretrained language models.
The Impact of Climate on Economic and Financial Cycles: A Markov-switching Panel Approach

This paper examines the impact of climate shocks on 13 European economies analysing jointly business and financial cycles, in different phases and disentangling the effects for different sector channels. A Bayesian Panel Markov-switching framework is proposed to jointly estimate the impact of extreme weather events on the economies as well as the interaction between business and financial cycles. Results from the empirical analysis suggest that extreme weather events impact asymmetrically across the different phases of the economy and heterogeneously across the EU countries. Moreover, we highlight how the manufacturing output, a component of the industrial production index, constitutes the main channel through which climate shocks impact the EU economies.
ERNIE-Doc: A Retrospective Long-Document Modeling Transformer

Transformers are not suited for processing long documents, due to their quadratically increasing memory and time consumption. Simply truncating a long document or applying the sparse attention mechanism will incur the context fragmentation problem or lead to an inferior modeling capability against comparable model sizes. In this paper, we propose ERNIE-Doc, a document-level language pretraining model based on Recurrence Transformers. Two well-designed techniques, namely the retrospective feed mechanism and the enhanced recurrence mechanism, enable ERNIE-Doc, which has a much longer effective context length, to capture the contextual information of a complete document. We pretrain ERNIE-Doc to explicitly learn the relationships among segments with an additional document-aware segment-reordering objective. Various experiments were conducted on both English and Chinese document-level tasks. ERNIE-Doc improved the state-of-the-art language modeling result of perplexity to 16.8 on WikiText-103. Moreover, it outperformed competitive pretraining models by a large margin on most language understanding tasks, such as text classification and question answering.