Miracle-dz/UniFAR-FTUs huggingface dataset:
Rapid Determination of the ‘Legal Highs’ 4-MMC and 4-MEC by Spectroelectrochemistry: Simultaneous Cyclic Voltammetry and In Situ Surface-Enhanced Raman Spectroscopy

The synthetic cathinones mephedrone (4-MMC) and 4-methylethcathinone (4-MEC) are two designer drugs that represent the rise and fall effect of this drug category within the stimulants market and are still available in several countries around the world. As a result, the qualitative and quantitative determination of ‘legal highs’, and their mixtures, are of great interest. This work explores for the first time the spectroelectrochemical response of these substances by coupling cyclic voltammetry (CV) with Raman spectroscopy in a portable instrument. It was found that the stimulants exhibit a voltammetric response on a gold screen-printed electrode while the surface is simultaneously electro-activated to achieve a periodic surface-enhanced Raman spectroscopy (SERS) substrate with high reproducibility. The proposed method enables a rapid and reliable determination in which both substances can be selectively analyzed through the oxidation waves of the molecules and the characteristic bands of the electrochemical SERS (EC-SERS) spectra. The feasibility and applicability of the method were assessed in simulated seized drug samples and spiked synthetic urine. This time-resolved spectroelectrochemical technique provides a cost-effective and user-friendly tool for onsite screening of synthetic stimulants in matrices with low concentration analytes for forensic applications.
Conservation of magnetite biomineralization genes in all domains of life and implications for magnetic sensing

Significance We present a model of biogenic magnetite formation in eukaryotes and hypothesize this genetic mechanism is used by broad forms of life for geomagnetic sensory perception. Countering previous assertions that salmon olfactory tissues lack biogenic magnetite, we determine that it is present in the form of compact crystal clusters and show that a subset of genes differentially expressed in candidate magnetoreceptor cells, compared to nonmagnetic olfactory cells, are distant homologs to a core suite of genes utilized by magnetotactic bacteria for magnetite biomineralization. This same core gene suite is common to a broad array of eukaryotes and the Asgard clade archaea Lokiarchaeta. Findings have implications for revising our understanding of eukaryote magnetite biomineralization, sensory perception of magnetic fields, and eukaryogenesis. Animals use geomagnetic fields for navigational cues, yet the sensory mechanism underlying magnetic perception remains poorly understood. One idea is that geomagnetic fields are physically transduced by magnetite crystals contained inside specialized receptor cells, but evidence for intracellular, biogenic magnetite in eukaryotes is scant. Certain bacteria produce magnetite crystals inside intracellular compartments, representing the most ancient form of biomineralization known and having evolved prior to emergence of the crown group of eukaryotes, raising the question of whether magnetite biomineralization in eukaryotes and prokaryotes might share a common evolutionary history. Here, we discover that salmonid olfactory epithelium contains magnetite crystals arranged in compact clusters and determine that genes differentially expressed in magnetic olfactory cells, contrasted to nonmagnetic olfactory cells, share ancestry with an ancient prokaryote magnetite biomineralization system, consistent with exaptation for use in eukaryotic magnetoreception. We also show that 11 prokaryote biomineralization genes are universally present among a diverse set of eukaryote taxa and that nine of those genes are present within the Asgard clade of archaea Lokiarchaeota that affiliates with eukaryotes in phylogenomic analysis. Consistent with deep homology, we present an evolutionary genetics hypothesis for magnetite formation among eukaryotes to motivate convergent approaches for examining magnetite-based magnetoreception, molecular origins of matrix-associated biomineralization processes, and eukaryogenesis.
Defect Density Extraction of Gate Oxide Shorts Incorporating Defect Clustering with Pseudo Transistor Arrays

The performance of gate oxide is a critical factor which influences yield and reliability. Gate oxide short (GOS) has become a dominant failure of CMOS in- tegrated circuits. Thus defect density of GOS is a key pa- rameter for yield prediction of nanometer process. In this paper, a new approach for the defect density extraction of GOS incorporating defect clustering, as well as pseudo transistor arrays as test vehicles to collect mass of test- ing data are proposed. Experimental results show that it is in a good agreement with inline e-test data when the extracted defect density of GOS is used to predict yield.
Conceptual Exchanges for Understanding Free-Living and Host-Associated Microbiomes

Whether a microbe is free-living or associated with a host from across the tree of life, its existence depends on a limited number of elements and electron donors and acceptors. Yet divergent approaches have been used by investigators from different fields. ABSTRACT Whether a microbe is free-living or associated with a host from across the tree of life, its existence depends on a limited number of elements and electron donors and acceptors. Yet divergent approaches have been used by investigators from different fields. The “environment first” research tradition emphasizes thermodynamics and biogeochemical principles, including the quantification of redox environments and elemental stoichiometry to identify transformations and thus an underlying microbe. The increasingly common “microbe first” research approach benefits from culturing and/or DNA sequencing methods to first identify a microbe and encoded metabolic functions. Here, the microbe itself serves as an indicator for environmental conditions and transformations. We illustrate the application of both approaches to the study of microbiomes and emphasize how both can reveal the selection of microbial metabolisms across diverse environments, anticipate alterations to microbiomes in host health, and understand the implications of a changing climate for microbial function.
Integration of Machine Translation and Translation Memory: Post-Editing Efforts

The development of Translation Technologies, like Translation Memory and Machine Translation, has completely changed the translation industry and translator’s workflow in the last decades. Nevertheless, TM and MT have been developed separately until very recently. This ongoing project will study the external integration of TM and MT, examining if the productivity and post-editing efforts of translators are higher or lower than using only TM. To this end, we will conduct an experiment where Translation students and professional translators will be asked to translate two short texts; then we will check the post-editing efforts (temporal, technical and cognitive efforts) and the quality of the translated texts.
Transfer Learning with Shallow Decoders: BSC at WMT2021’s Multilingual Low-Resource Translation for Indo-European Languages Shared Task

This paper describes the participation of the BSC team in the WMT2021’s Multilingual Low-Resource Translation for Indo-European Languages Shared Task. The system aims to solve the Subtask 2: Wikipedia cultural heritage articles, which involves translation in four Romance languages: Catalan, Italian, Occitan and Romanian. The submitted system is a multilingual semi-supervised machine translation model. It is based on a pre-trained language model, namely XLM-RoBERTa, that is later fine-tuned with parallel data obtained mostly from OPUS. Unlike other works, we only use XLM to initialize the encoder and randomly initialize a shallow decoder. The reported results are robust and perform well for all tested languages.
TenTrans High-Performance Inference Toolkit for WMT2021 Efficiency Task

The paper describes the TenTrans’s submissions to the WMT 2021 Efficiency Shared Task. We explore training a variety of smaller compact transformer models using the teacher-student setup. Our model is trained by our self-developed open-source multilingual training platform TenTrans-Py. We also release an open-source high-performance inference toolkit for transformer models and the code is written in C++ completely. All additional optimizations are built on top of the inference engine including attention caching, kernel fusion, early-stop, and several other optimizations. In our submissions, the fastest system can translate more than 22,000 tokens per second with a single Tesla P4 while maintaining 38.36 BLEU on En-De newstest2019. Our trained models and more details are available in TenTrans-Decoding competition examples.
Efecto de suplementación en la dieta con BIG EGG® en los parámetros productivos en ponedoras de huevo comercial. Effect of supplementation in diet with BIG EGG in productive parameters in laying hens of commercial eggs

This study was designed to evaluate the effect of inclusion of the product BIG EGG ® on the performance of commercial layers. On the farm El Portal located in the municipality of Rivera in Huila at a height of 700 m above sea level and an average temperature of 28 o C , a total of 21,517 Isa Brown hens of 43 weeks of age entered the evaluation. Hens were distributed in two treatments under a conventional production floor. In treatment 1 (control group) the birds received traditional food, a total of 11,259 hens were intended for this treatment. For treatment 2, birds received the same type of food as treatment 1 plus “On Top” BIG EGG ® at a dose of 2 kg / Ton of food; for this treatment were assigned a total of 10,258 hens. The total testing period was 10 weeks and the variables studied were food intake (g / bird / day), egg production (%), body weight (g), average egg weight (g) and egg mass (gr). The Inclusion of the product BIG EGG ® led to improve productive performance of commercial layers with a level of statistical significance (p<0.05) compared with traditional food that does not include this additive. Production costs reflected efficiency and a positive effect of BIG EGG ® on egg size and production rate, which means greater profitability in the production process.
Effects of Stimulus Frequency on Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces

The stimulus frequency is one of the essential parameters of visual stimuli in steady-state visual evoked potentials (SSVEPs). This study aimed to explore the effects of stimulus frequency on SSVEPs by using the public dataset benchmark. After feature extraction, the SSVEP response intensity was different for the commonly used stimulus frequencies (8∼15.8 Hz), with the relatively lower frequencies of 8∼11 Hz evoking a higher intensity, and the relatively higher frequencies of 14∼16 Hz evoking a lower intensity. Then, the five stimulus frequencies (8.8, 9.8, 8.6, 8.2, and 9.2 Hz) with the highest response intensity and five stimulus frequencies (15.8, 15.6, 15, 15.4, and 15.2 Hz) with the lowest response intensity were selected as the highly sensitive and lowly sensitive frequencies. After target identification by standard canonical correlation analysis (standard CCA) and Filter bank CCA (FBCCA), both the classification accuracy and information transfer rate (ITR) showed a relatively better performance in highly sensitive frequencies than lowly sensitive frequencies. This study proved that there were effects of stimulus frequency on SSVEPs, recommending the stimulus frequencies of 8∼11 Hz in SSVEPs, especially when the number of targets is not so many.
Transcriptional Differences in Luminal Epithelial Cells in Patients with and without Lymph Node Involvement in TNBC

TNBC is an aggressive subtype of breast case which is hard to treat due to the high degree of heterogeneity and higher frequency of metastasis. Luminal progenitor cells (LPs) were previously suggested to play a role in the metastasis in TNBC. In this study, we use a publicly available scRNA-Seq dataset (GSE118389) to address the differences in transcriptional signatures of LPs and luminal epithelial cells in TNBC patients with and without lymph node involvement. When comparing the expression level of genes, we found there is a obvious difference in luminal epithelial cells between TNBC patients with and without lymph node involvement.
Tributes to Ingo Rechenberg (1934 -- 2021)

Ingo Rechenberg was one of the fathers of Evolution Strategies, my main research topic for almost two decades. I had the chance to meet him in 2005 at the GECCO conference in Washington where he presented a tutorial on “Bionik: Building on Biological Evolution”. I discovered a very passionate person who explained how biology, the flight of birds, and nature shaped and inspired his research. When discussing with him afterward, he told me that the biological motivation of the methods in our field is essential for him. He admitted that he never liked the CMA-ES algorithm (that started to be recognized at that time as a powerful method and was invented in his laboratory by Nikolaus Hansen and Andreas Ostermeier) because it is not bio-inspired enough!
The LMU Munich Systems for the WMT21 Unsupervised and Very Low-Resource Translation Task

We present our submissions to the WMT21 shared task in Unsupervised and Very Low Resource machine translation between German and Upper Sorbian, German and Lower Sorbian, and Russian and Chuvash. Our low-resource systems (German↔Upper Sorbian, Russian↔Chuvash) are pre-trained on high-resource pairs of related languages. We fine-tune those systems using the available authentic parallel data and improve by iterated back-translation. The unsupervised German↔Lower Sorbian system is initialized by the best Upper Sorbian system and improved by iterated back-translation using monolingual data only.
Multilingual Machine Translation Evaluation Metrics Fine-tuned on Pseudo-Negative Examples for WMT 2021 Metrics Task

This paper describes our submission to the WMT2021 shared metrics task. Our metric is operative to segment-level and system-level translations. Our belief toward a better metric is to detect a significant error that cannot be missed in the real practice cases of evaluation. For that reason, we used pseudo-negative examples in which attributes of some words are transferred to the reversed attribute words, and we build evaluation models to handle such serious mistakes of translations. We fine-tune a multilingual largely pre-trained model on the provided corpus of past years’ metric task and fine-tune again further on the synthetic negative examples that are derived from the same fine-tune corpus. From the evaluation results of the WMT21’s development corpus, fine-tuning on the pseudo-negatives using WMT15-17 and WMT18-20 metric corpus achieved a better Pearson’s correlation score than the one fine-tuned without negative examples. Our submitted models,hyp+src_hyp+ref and hyp+src_hyp+ref.negative, are the plain model using WMT18-20 and the one additionally fine-tuned on negative samples, respectively.
Tencent AI Lab Machine Translation Systems for the WMT21 Biomedical Translation Task

This paper describes the Tencent AI Lab submission of the WMT2021 shared task on biomedical translation in eight language directions: English-German, English-French, English-Spanish and English-Russian. We utilized different Transformer architectures, pretraining and back-translation strategies to improve translation quality. Concretely, we explore mBART (Liu et al., 2020) to demonstrate the effectiveness of the pretraining strategy. Our submissions (Tencent AI Lab Machine Translation, TMT) in German/French/Spanish⇒English are ranked 1st respectively according to the official evaluation results in terms of BLEU scores.
Adapting Neural Machine Translation for Automatic Post-Editing

Automatic post-editing (APE) models are usedto correct machine translation (MT) system outputs by learning from human post-editing patterns. We present the system used in our submission to the WMT’21 Automatic Post-Editing (APE) English-German (En-De) shared task. We leverage the state-of-the-art MT system (Ng et al., 2019) for this task. For further improvements, we adapt the MT model to the task domain by using WikiMatrix (Schwenket al., 2021) followed by fine-tuning with additional APE samples from previous editions of the shared task (WMT-16,17,18) and ensembling the models. Our systems beat the baseline on TER scores on the WMT’21 test set.
T4T Solution: WMT21 Similar Language Task for the Spanish-Catalan and Spanish-Portuguese Language Pair

The main idea of this solution has been to focus on corpus cleaning and preparation and after that, use an out of box solution (OpenNMT) with its default published transformer model. To prepare the corpus, we have used set of standard tools (as Moses scripts or python packages), but also, among other python scripts, a python custom tokenizer with the ability to replace numbers for variables, solve the upper/lower case issue of the vocabulary and provide good segmentation for most of the punctuation. We also have started a line to clean corpus based on statistical probability estimation of source-target corpus, with unclear results. Also, we have run some tests with syllabical word segmentation, again with unclear results, so at the end, after word sentence tokenization we have used BPE SentencePiece for subword units to feed OpenNMT.
SYSTRAN @ WMT 2021: Terminology Task

This paper describes SYSTRAN submissions to the WMT 2021 terminology shared task. We participate in the English-to-French translation direction with a standard Transformer neural machine translation network that we enhance with the ability to dynamically include terminology constraints, a very common industrial practice. Two state-of-the-art terminology insertion methods are evaluated based (i) on the use of placeholders complemented with morphosyntactic annotation and (ii) on the use of target constraints injected in the source stream. Results show the suitability of the presented approaches in the evaluated scenario where terminology is used in a system trained on generic data only.
Adam Mickiewicz University’s English-Hausa Submissions to the WMT 2021 News Translation Task

This paper presents the Adam Mickiewicz University’s (AMU) submissions to the WMT 2021 News Translation Task. The submissions focus on the English↔Hausa translation directions, which is a low-resource translation scenario between distant languages. Our approach involves thorough data cleaning, transfer learning using a high-resource language pair, iterative training, and utilization of monolingual data via back-translation. We experiment with NMT and PB-SMT approaches alike, using the base Transformer architecture for all of the NMT models while utilizing PB-SMT systems as comparable baseline solutions.
GTCOM Neural Machine Translation Systems for WMT21

This paper describes the Global Tone Communication Co., Ltd.’s submission of the WMT21 shared news translation task. We participate in six directions: English to/from Hausa, Hindi to/from Bengali and Zulu to/from Xhosa. Our submitted systems are unconstrained and focus on multilingual translation odel, backtranslation and forward-translation. We also apply rules and language model to filter monolingual, parallel sentences and synthetic sentences.
MTEQA at WMT21 Metrics Shared Task

In this paper, we describe our submission to the WMT 2021 Metrics Shared Task. We use the automatically-generated questions and answers to evaluate the quality of Machine Translation (MT) systems. Our submission builds upon the recently proposed MTEQA framework. Experiments on WMT20 evaluation datasets show that at the system-level the MTEQA metric achieves performance comparable with other state-of-the-art solutions, while considering only a certain amount of information from the whole translation.
HW-TSC’s Submissions to the WMT21 Biomedical Translation Task

This paper describes the submission of Huawei Translation Service Center (HW-TSC) to WMT21 biomedical translation task in two language pairs: Chinese↔English and German↔English (Our registered team name is HuaweiTSC). Technical details are introduced in this paper, including model framework, data pre-processing method and model enhancement strategies. In addition, using the wmt20 OK-aligned biomedical test set, we compare and analyze system performances under different strategies. On WMT21 biomedical translation task, Our systems in English→Chinese and English→German directions get the highest BLEU scores among all submissions according to the official evaluation results.
Infrastructure investment and economic performance

and ubiquity of persistently modest rates of economic growth, infrastructure investment has once again found itself at the forefront of the public policy agenda. In an effort to boost global growth: Christine Lagarde, head of the International Monetary Fund, suggested that “investing in badly-needed, but well-designed, infrastructure is an obvious area of great potential”; fixing America’s infrastructure is a key element in US President Donald Trump’s plan to “make America great again”; China’s ambition to extend its global footprint is looking to use its infrastructure-led ‘Belt and Road’ initiative to do so; infrastructure is at the heart of EU Commission President JeanClaude Juncker’s Investment Plan for Europe; and the UK government is being urged to increase infrastructure spending to counteract any economic slowdown that might be occasioned by the country’s withdrawal from the European Union (Brexit in more popular parlance). These are just a handful of examples, but whilst few would disagree that infrastructure investment has a potentially positive impact on economic performance, it is the nature and magnitude of such impact that needs to be considered in informing economic policy and its implications for sustainability and other global challenges such as climate change. The positive benefits of infrastructure investment were for many years simply taken for granted by public policy analysts, until the work of Aschauer (1989), Munnell (1992), and others, elevated it to the economic mainstream. Whilst Aschauer’s work established what was perceived to be a statistically significant relationship between investment in infrastructure capital and economic growth, and suggested very high rates of return to such investment, Munnell supported and reinforced the idea that by greasing the wheels of future economic activity, the main beneficial impact of infrastructure investment was improved productivity. In any event, the relationship between infrastructure and economic growth has since been well established, with numerous empirical studies showcasing the positive attributes of investment in public capital. But while infrastructure may lead to higher productivity and output, past and current economic growth also increases the demand for infrastructure services and thereby induces increased supply. In short, it might well be that high GDP and high rates of infrastructure investment are correlated, but the direction of causality is not so clear, which has important implications for policy makers. Although rates of return from infrastructure projects may be significant, they were inevitably exaggerated by early studies because of the high positive correlation with overall economic activity per se. Analysts must control for what econometricians call ‘severe simultaneity bias’ and ‘spurious correlation’. Once this has been done returns are considerably reduced, and this is even before environmental impacts and broader sustainability