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In a context of increasing globalization and digitalization, single windows for trade have assumed a fundamental role as a tool to streamline and facilitate international business operations. This scientific article focuses on the analysis of the most recent trends in the implementation of single windows for trade, highlighting four areas of particular interest that are shaping the current landscape in this field. The first trend is related to the implementation of a national single window in a country instead of implementing various "limited" single windows. The following trend involves including various sectors of services in the national single window, from regulatory services to private services, in the logistics and transportation sphere. The third trend is related to the transformation of national systems into global trade facilitation platforms that allow uninterrupted trade. Compared to the single window, trade platforms offer multiple solutions and are capable of covering a wide range of goods, services, and international trade participants not only in a specific sector but across the entire supply chain. These trends represent an ongoing evolution in the pursuit of simplifying and enhancing international trade, adapting it to the changing demands of the globalized economy., Em um contexto de crescente globalização e digitalização, as janelas únicas de comércio assumiram um papel fundamental como ferramenta para simplificar e facilitar as operações comerciais internacionais. Este artigo científico se concentra na análise das tendências mais recentes na implementação de janelas únicas de comércio, destacando quatro áreas de interesse particular que estão moldando o cenário atual nesse campo. A primeira tendência está relacionada à implementação de uma janela única nacional em um país, em vez de implementar várias janelas únicas "limitadas". A tendência seguinte envolve a inclusão de diversos setores de serviços na janela única nacional, desde serviços regulatórios até serviços privados, na esfera da logística e transporte. A terceira tendência está relacionada à transformação de sistemas nacionais em plataformas globais de facilitação do comércio que permitem o comércio ininterrupto. Em comparação com a janela única, as plataformas de comércio oferecem várias soluções e são capazes de abranger uma ampla gama de bens, serviços e participantes do comércio internacional, não apenas em um setor específico, mas em toda a cadeia de suprimentos. Essas tendências representam uma evolução contínua na busca por simplificar e aprimorar o comércio internacional, adaptando-o às demandas em constante mudança da economia globalizada., En un contexto de creciente globalización y digitalización, las ventanillas únicas para el comercio han adquirido un papel fundamental como herramienta para agilizar y facilitar las operaciones comerciales a nivel internacional. Este artículo científico se centra en el análisis de las tendencias más recientes en la implementación de las ventanillas únicas para el comercio, poniendo de relieve cuatro áreas de particular interés que están moldeando la actualidad de este campo. La primera tendencia se relaciona con la aplicación de una ventanilla única nacional en un país en vez de implementar distintas ventanillas únicas «limitados». La siguiente tendencia implica incluir diversos sectores de servicios en la ventanilla única nacional, desde los servicios regulatorios hasta los servicios privados, en la esfera de la logística y el transporte. La tercera tendencia se relaciona con la transformación de los sistemas nacionales en las plataformas globales de facilitación del comercio que permiten realizar el comercio sin interrupción, en comparación con la ventanilla única, las plataformas para el comercio ofrecen múltiples soluciones y capaces de abarcar un gran número de bienes, servicios y participantes del comercio internacional no sólo en un sector específico, sino abarcar toda la cadena de suministro. Estas tendencias representan una evolución constante en la búsqueda de simplificar y mejorar el comercio internacional, adaptándolo a las demandas cambiantes de la economía globalizada.

Metagenomics is gradually being implemented for diagnosing infectious diseases. However, in-depth protocol comparisons for viral detection have been limited to individual sets of experimental workflows and laboratories. In this study, we present a benchmark of metagenomics protocols used in clinical diagnostic laboratories initiated by the European Society for Clinical Virology (ESCV) Network on NGS (ENNGS). A mock viral reference panel was designed to mimic low biomass clinical specimens. The panel was used to assess the performance of twelve metagenomic wet lab protocols currently in use in the diagnostic laboratories of participating ENNGS member institutions. Both Illumina and Nanopore, shotgun and targeted capture probe protocols were included. Performance metrics sensitivity, specificity, and quantitative potential were assessed using a central bioinformatics pipeline. Overall, viral pathogens with loads down to 104 copies/ml (corresponding to CT values of 31 in our PCR assays) were detected by all the evaluated metagenomic wet lab protocols. In contrast, lower abundant mixed viruses of CT values of 35 and higher were detected only by a minority of the protocols. Considering the reference panel as the gold standard, optimal thresholds to define a positive result were determined per protocol, based on the horizontal genome coverage. Implementing these thresholds, sensitivity and specificity of the protocols ranged from 67 to 100 % and 87 to 100 %, respectively. A variety of metagenomic protocols are currently in use in clinical diagnostic laboratories. Detection of low abundant viral pathogens and mixed infections remains a challenge, implying the need for standardization of metagenomic analysis for use in clinical settings.

The intricate relationship between human decision-making and emotions, particularly guilt and regret, has significant implications on behavior and well-being. Yet, these emotions subtle distinctions and interplay are often overlooked in computational models. This paper introduces a dataset tailored to dissect the relationship between guilt and regret and their unique textual markers, filling a notable gap in affective computing research. Our approach treats guilt and regret recognition as a binary classification task and employs three machine learning and six transformer-based deep learning techniques to benchmark the newly created dataset. The study further implements innovative reasoning methods like chain-of-thought and tree-of-thought to assess the models interpretive logic. The results indicate a clear performance edge for transformer-based models, achieving a 90.4% macro F1 score compared to the 85.3% scored by the best machine learning classifier, demonstrating their superior capability in distinguishing complex emotional states.

In recent years, language models and deep learning techniques have revolutionized natural language processing tasks, including emotion detection. However, the specific emotion of guilt has received limited attention in this field. In this research, we explore the applicability of three transformer-based language models for detecting guilt in text and compare their performance for general emotion detection and guilt detection. Our proposed model outformed BERT and RoBERTa models by two and one points respectively. Additionally, we analyze the challenges in developing accurate guilt-detection models and evaluate our model's effectiveness in detecting related emotions like "shame" through qualitative analysis of results.

This study delves into the relationship between emotional trends from X platform data and the market dynamics of well-known cryptocurrencies Cardano, Binance, Fantom, Matic, and Ripple over the period from October 2022 to March 2023. Leveraging SenticNet, we identified emotions like Fear and Anxiety, Rage and Anger, Grief and Sadness, Delight and Pleasantness, Enthusiasm and Eagerness, and Delight and Joy. Following data extraction, we segmented each month into bi-weekly intervals, replicating this process for price data obtained from Finance-Yahoo. Consequently, a comparative analysis was conducted, establishing connections between emotional trends observed across bi-weekly intervals and cryptocurrency prices, uncovering significant correlations between emotional sentiments and coin valuations.

Detection of malignant lesions on mammography images is extremely important for early breast cancer diagnosis. In clinical practice, images are acquired from two different angles, and radiologists can fully utilize information from both views, simultaneously locating the same lesion. However, for automatic detection approaches such information fusion remains a challenge. In this paper, we propose a new model called MAMM-Net, which allows the processing of both mammography views simultaneously by sharing information not only on an object level, as seen in existing works, but also on a feature level. MAMM-Net's key component is the Fusion Layer, based on deformable attention and designed to increase detection precision while keeping high recall. Our experiments show superior performance on the public DDSM dataset compared to the previous state-of-the-art model, while introducing new helpful features such as lesion annotation on pixel-level and classification of lesions malignancy., Comment: Accepted at ISBI 2024 (21st IEEE International Symposium on Biomedical Imaging)

We consider a new type of ${\cal N}=4$, $d=1$ semi-dynamical multiplet based on the non-linear version of the mirror multiplet ${\bf (3, 4, 1)}$, with the triplet of bosonic physical fields parametrizing a three-dimensional sphere $S^3$ of the radius $R$. The limit $R\to\infty$ amounts to the contraction $S^3\to\mathbb{R}^3$ and leads to the linear mirror multiplet ${\bf (3, 4, 1)}$. Spin degrees of freedom described by a Wess-Zumino action specify a two-dimensional surface embedded in the sphere $S^3$. A pair of the examples considered correspond to the round and squashed ``fuzzy'' 2-spheres. We couple the squashed 2-sphere model to the dynamical mirror multiplet ${\bf (2, 4, 2)}$. A notable feature of this coupling is the dependence of the squashing parameter on the bosonic fields $z, \bar z$ of the chiral multiplet., Comment: 1+17 pages

Catalyst optimization processes typically rely on inductive and qualitative assumptions of chemists based on screening data. While machine learning models using molecular properties or calculated 3D structures enable quantitative data evaluation, costly quantum chemical calculations are often required. In contrast, readily available binary fingerprint descriptors are time- and cost-efficient, but their predictive performance remains insufficient. Here, we describe a machine learning model based on fragment descriptors, which are fine-tuned for asymmetric catalysis and represent cyclic or polyaromatic hydrocarbons, enabling robust and efficient virtual screening. Using training data with only moderate selectivities, we designed theoretically and validated experimentally new catalysts showing higher selectivities in a challenging asymmetric tetrahydropyran synthesis.

Catalyst optimization processes typically rely on inductive and qualitative assumptions of chemists based on screening data. While machine learning models using molecular properties or calculated 3D structures enable quantitative data evaluation, costly quantum chemical calculations are often required. In contrast, readily available binary fingerprint descriptors are time- and cost-efficient, but their predictive performance remains insufficient. Here, we describe a machine learning model based on fragment descriptors, which are fine-tuned for asymmetric catalysis and represent cyclic or polyaromatic hydrocarbons, enabling robust and efficient virtual screening. Using training data with only moderate selectivities, we designed theoretically and validated experimentally new catalysts showing higher selectivities in a challenging asymmetric tetrahydropyran synthesis.

Coronaviruses express a papain-like protease (PLpro) that is required for replicase polyprotein maturation and also serves as a deubiquitinating enzyme (DUB). In this study, using a Middle East respiratory syndrome virus (MERS-CoV) PLpro modified virus in which the DUB is selectively inactivated, we show that the PLpro DUB is an important MERS-CoV interferon antagonist and virulence factor. Although the DUB-negative rMERS-CoVMA replicates robustly in the lungs of human dipeptidyl peptidase 4 knock-in (hDPP4 KI) mice, it does not cause clinical symptoms. Interestingly, a single intranasal vaccination with DUB-negative rMERS-CoVMA induces strong and sustained neutralizing antibody responses and sterilizing immunity after a lethal wt virus challenge. The survival of naïve animals also significantly increases when sera from animals vaccinated with the DUB-negative rMERS-CoVMA are passively transferred, prior to receiving a lethal virus dose. These data demonstrate that DUB-negative coronaviruses could be the basis of effective modified live attenuated vaccines.

This paper presents clinical results of wireless portable dynamic light scattering sensors that implement laser Doppler flowmetry signal processing. It has been verified that the technology can detect microvascular changes associated with diabetes and ageing in volunteers. Studies were conducted primarily on wrist skin. Wavelet continuous spectrum calculation was used to analyse the obtained time series of blood perfusion recordings with respect to the main physiological frequency ranges of vasomotions. In patients with type 2 diabetes, the area under the continuous wavelet spectrum in the endothelial, neurogenic, myogenic, and cardio frequency ranges showed significant diagnostic value for the identification of microvascular changes. Aside from spectral analysis, autocorrelation parameters were also calculated for microcirculatory blood flow oscillations. The groups of elderly volunteers and patients with type 2 diabetes, in comparison with the control group of younger healthy volunteers, showed a statistically significant decrease of the normalised autocorrelation function in time scales up to 10 s. A set of identified parameters was used to test machine learning algorithms to classify the studied groups of young controls, elderly controls, and diabetic patients. Our conclusion describes and discusses the classification metrics that were found to be most effective.

This paper presents clinical results of wireless portable dynamic light scattering sensors that implement laser Doppler flowmetry signal processing. It has been verified that the technology can detect microvascular changes associated with diabetes and ageing in volunteers. Studies were conducted primarily on wrist skin. Wavelet continuous spectrum calculation was used to analyse the obtained time series of blood perfusion recordings with respect to the main physiological frequency ranges of vasomotions. In patients with type 2 diabetes, the area under the continuous wavelet spectrum in the endothelial, neurogenic, myogenic, and cardio frequency ranges showed significant diagnostic value for the identification of microvascular changes. Aside from spectral analysis, autocorrelation parameters were also calculated for microcirculatory blood flow oscillations. The groups of elderly volunteers and patients with type 2 diabetes, in comparison with the control group of younger healthy volunteers, showed a statistically significant decrease of the normalised autocorrelation function in time scales up to 10 s. A set of identified parameters was used to test machine learning algorithms to classify the studied groups of young controls, elderly controls, and diabetic patients. Our conclusion describes and discusses the classification metrics that were found to be most effective.

The extent to which severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) break through infection- or vaccine-induced immunity is not well understood. We analyzed 28,578 sequenced SARS-CoV-2 samples from individuals with known immune status obtained through national community testing in the Netherlands from March to August 2021. We found evidence of an increased risk of infection by the Beta (B.1.351), Gamma (P.1), or Delta (B.1.617.2) variants compared with the Alpha (B.1.1.7) variant after vaccination. No clear differences were found between vaccines. However, the effect was larger in the first 14 to 59 days after complete vaccination compared with ≥60 days. In contrast to vaccine-induced immunity, there was no increased risk for reinfection with Beta, Gamma, or Delta variants relative to the Alpha variant in individuals with infection-induced immunity.

The use of transfer learning methods is largely responsible for the present breakthrough in Natural Learning Processing (NLP) tasks across multiple domains. In order to solve the problem of sentiment detection, we examined the performance of four different types of well-known state-of-the-art transformer models for text classification. Models such as Bidirectional Encoder Representations from Transformers (BERT), Robustly Optimized BERT Pre-training Approach (RoBERTa), a distilled version of BERT (DistilBERT), and a large bidirectional neural network architecture (XLNet) were proposed. The performance of the four models that were used to detect disaster in the text was compared. All the models performed well enough, indicating that transformer-based models are suitable for the detection of disaster in text. The RoBERTa transformer model performs best on the test dataset with a score of 82.6% and is highly recommended for quality predictions. Furthermore, we discovered that the learning algorithms' performance was influenced by the pre-processing techniques, the nature of words in the vocabulary, unbalanced labeling, and the model parameters., Comment: This submission has been removed from arXiv because the submitter did not have the authority to grant the license at the time of submission

We introduce a novel Natural Language Processing (NLP) task called Guilt detection, which focuses on detecting guilt in text. We identify guilt as a complex and vital emotion that has not been previously studied in NLP, and we aim to provide a more fine-grained analysis of it. To address the lack of publicly available corpora for guilt detection, we created VIC, a dataset containing 4622 texts from three existing emotion detection datasets that we binarized into guilt and no-guilt classes. We experimented with traditional machine learning methods using bag-of-words and term frequency-inverse document frequency features, achieving a 72% f1 score with the highest-performing model. Our study provides a first step towards understanding guilt in text and opens the door for future research in this area.

In this paper, we investigate the issue of hate speech by presenting a novel task of translating hate speech into non-hate speech text while preserving its meaning. As a case study, we use Spanish texts. We provide a dataset and several baselines as a starting point for further research in the task. We evaluated our baseline results using multiple metrics, including BLEU scores. The aim of this study is to contribute to the development of more effective methods for reducing the spread of hate speech in online communities.

This paper describes CIC NLP's submission to the AmericasNLP 2023 Shared Task on machine translation systems for indigenous languages of the Americas. We present the system descriptions for three methods. We used two multilingual models, namely M2M-100 and mBART50, and one bilingual (one-to-one) -- Helsinki NLP Spanish-English translation model, and experimented with different transfer learning setups. We experimented with 11 languages from America and report the setups we used as well as the results we achieved. Overall, the mBART setup was able to improve upon the baseline for three out of the eleven languages., Comment: Accepted to Third Workshop on NLP for Indigenous Languages of the Americas

In this paper, we present a parallel Spanish-Mazatec and Spanish-Mixtec corpus for machine translation (MT) tasks, where Mazatec and Mixtec are two indigenous Mexican languages. We evaluated the usability of the collected corpus using three different approaches: transformer, transfer learning, and fine-tuning pre-trained multilingual MT models. Fine-tuning the Facebook M2M100-48 model outperformed the other approaches, with BLEU scores of 12.09 and 22.25 for Mazatec-Spanish and Spanish-Mazatec translations, respectively, and 16.75 and 22.15 for Mixtec-Spanish and Spanish-Mixtec translations, respectively. The findings show that the dataset size (9,799 sentences in Mazatec and 13,235 sentences in Mixtec) affects translation performance and that indigenous languages work better when used as target languages. The findings emphasize the importance of creating parallel corpora for indigenous languages and fine-tuning models for low-resource translation tasks. Future research will investigate zero-shot and few-shot learning approaches to further improve translation performance in low-resource settings. The dataset and scripts are available at \url{https://github.com/atnafuatx/Machine-Translation-Resources}, Comment: Accepted to Third Workshop on NLP for Indigenous Languages of the Americas

BACKGROUND Host-response biomarkers to differentiate bacterial from viral etiology in children with respiratory infections have shown high accuracies, but are understudied in Mycoplasma pneumoniae (Mp) infections. METHODS We compared BV scores (0-34 indicating viral, and 66-100 indicating bacterial etiology), TRAIL (pg/mL), IP-10 (pg/mL), and CRP (mg/L) serum levels between Mp positive (Mp+) and negative (Mp-) community-acquired pneumonia (CAP). We performed receiver operating characteristic (ROC) curve analyses for clinical features and biomarkers. RESULTS Of 80 CAP patients (median age 6.3 years, 57.5% male), 26 were Mp + CAP. By comparing Mp + CAP with Mp-CAP patients, BV scores were lower (median 14.0, IQR 3.0-27.8 vs. 54.0, IQR 12.0-84.8; P = 0.0008), TRAIL levels were higher (86.5, IQR 67.4-123.0 vs. 65.5, IQR 42.5-103.9; P = 0.025), CRP levels were lower (12.9, IQR 4.0-22.3 vs. 36.7, IQR 13.0-132.8; P = 0.0019), and IP-10 levels were comparable (366.0, IQR 150.2-603.8 vs. 331.0, IQR 154.3-878.8; P = 0.73). ROC analyses yielded a comparable discriminatory accuracy for the combination of age, fever duration, respiratory symptoms duration, with either procalcitonin or BV (AUC 0.87 vs. 0.86, P = 0.94). CONCLUSIONS Children with Mp + CAP have atypically low, viral levels of the BV score, underscoring the complementary role of microbiological testing.

This study aims to discuss the existence and uniqueness of solution of fuzzy Volterra integral equation with piecewise continuous kernel. Such problems appears in many balance problems for hereditary dynamic systems, e.g. in electric load leveling. The method of successive approximations is applied and the main theorems are proved based on the method. Some examples are discussed and the results are presented for different values of $\mu$ by plotting several graphs., Comment: Presented on DYSC-2023, submitted to Fuzzy Sets and Systems

In natural language processing (NLP), lexical function is a concept to unambiguously represent semantic and syntactic features of words and phrases in text first crafted in the Meaning-Text Theory. Hierarchical classification of lexical functions involves organizing these features into a tree-like hierarchy of categories or labels. This is a challenging task as it requires a good understanding of the context and the relationships among words and phrases in text. It also needs large amounts of labeled data to train language models effectively. In this paper, we present a dataset of most frequent Spanish verb-noun collocations and sentences where they occur, each collocation is assigned to one of 37 lexical functions defined as classes for a hierarchical classification task. Each class represents a relation between the noun and the verb in a collocation involving their semantic and syntactic features. We combine the classes in a tree-based structure, and introduce classification objectives for each level of the structure. The dataset was created by dependency tree parsing and matching of the phrases in Spanish news. We provide baselines and data splits for each objective.

The Volterra integral-functional series is the classic approach for nonlinear black box dynamical systems modeling. It is widely employed in many domains including radiophysics, aerodynamics, electronic and electrical engineering and many other. Identifying the time-varying functional parameters, also known as Volterra kernels, poses a difficulty due to the curse of dimensionality. This refers to the exponential growth in the number of model parameters as the complexity of the input-output response increases. The least squares method (LSM) is widely acknowledged as the standard approach for tackling the issue of identifying parameters. Unfortunately, the LSM suffers with many drawbacks such as the sensitivity to outliers causing biased estimation, multicollinearity, overfitting and inefficiency with large datasets. This paper presents alternative approach based on direct estimation of the Volterra kernels using the collocation method. Two model examples are studied. It is found that the collocation method presents a promising alternative for optimization, surpassing the traditional least squares method when it comes to the Volterra kernels identification including the case when input and output signals suffer from considerable measurement errors.

The DIRAC experiment at CERN investigated in the reaction $\rm{p}(24~\rm{GeV}/c) + Ni$ the particle pairs $K^+K^-, \pi^+ \pi^-$ and $p \bar{p}$ with relative momentum $Q$ in the pair system less than 100 MeV/c. Because of background influence studies, DIRAC explored three subsamples of $K^+K^-$ pairs, obtained by subtracting -- using time-of-flight (TOF) technique -- background from initial $Q$ distributions with $K^+K^-$ sample fractions more than 70\%, 50\% and 30\%. The corresponding pair distributions in $Q$ and in its longitudinal projection $Q_L$ were analyzed first in a Coulomb model, which takes into account only Coulomb final state interaction (FSI) and assuming point-like pair production. This Coulomb model analysis leads to a $K^+K^-$ yield increase of about four at $Q_L=0.5$ MeV/c compared to 100 MeV/c. In order to study contributions from strong interaction, a second more sophisticated model was applied, considering besides Coulomb FSI also strong FSI via the resonances $f_0(980)$ and $a_0(980)$ and a variable distance $r^*$ between the produced $K$ mesons. This analysis was based on three different parameter sets for the pair production. For the 70\% subsample and with best parameters, $3680\pm 370$ $K^+K^-$ pairs was found to be compared to $3900\pm 410$ $K^+K^-$ extracted by means of the Coulomb model. Knowing the efficiency of the TOF cut for background suppression, the total number of detected $K^+K^-$ pairs was evaluated to be around $40000\pm 10\%$, which agrees with the result from the 30\% subsample. The $K^+K^-$ pair number in the 50\% subsample differs from the two other values by about three standard deviations, confirming -- as discussed in the paper -- that experimental data in this subsample is less reliable.

The DIRAC experiment at CERN investigated in the reaction $\rm{p}(24~\rm{GeV}/c) + Ni$ the particle pairs $K^+K^-, \pi^+ \pi^-$ and $p \bar{p}$ with relative momentum $Q$ in the pair system less than 100 MeV/c. Because of background influence studies, DIRAC explored three subsamples of $K^+K^-$ pairs, obtained by subtracting -- using time-of-flight (TOF) technique -- background from initial $Q$ distributions with $K^+K^-$ sample fractions more than 70\%, 50\% and 30\%. The corresponding pair distributions in $Q$ and in its longitudinal projection $Q_L$ were analyzed first in a Coulomb model, which takes into account only Coulomb final state interaction (FSI) and assuming point-like pair production. This Coulomb model analysis leads to a $K^+K^-$ yield increase of about four at $Q_L=0.5$ MeV/c compared to 100 MeV/c. In order to study contributions from strong interaction, a second more sophisticated model was applied, considering besides Coulomb FSI also strong FSI via the resonances $f_0(980)$ and $a_0(980)$ and a variable distance $r^*$ between the produced $K$ mesons. This analysis was based on three different parameter sets for the pair production. For the 70\% subsample and with best parameters, $3680\pm 370$ $K^+K^-$ pairs was found to be compared to $3900\pm 410$ $K^+K^-$ extracted by means of the Coulomb model. Knowing the efficiency of the TOF cut for background suppression, the total number of detected $K^+K^-$ pairs was evaluated to be around $40000\pm 10\%$, which agrees with the result from the 30\% subsample. The $K^+K^-$ pair number in the 50\% subsample differs from the two other values by about three standard deviations, confirming -- as discussed in the paper -- that experimental data in this subsample is less reliable.

RNA-binding proteins (RBPs) play diverse roles in regulating co-transcriptional RNA-processing and chromatin functions, but our knowledge of the repertoire of chromatin-associated RBPs (caRBPs) and their interactions with chromatin remains limited. Here, we developed SPACE (Silica Particle Assisted Chromatin Enrichment) to isolate global and regional chromatin components with high specificity and sensitivity, and SPACEmap to identify the chromatin-contact regions in proteins. Applied to mouse embryonic stem cells, SPACE identified 1459 chromatin-associated proteins, ∼48% of which are annotated as RBPs, indicating their dual roles in chromatin and RNA-binding. Additionally, SPACEmap stringently verified chromatin-binding of 403 RBPs and identified their chromatin-contact regions. Notably, SPACEmap showed that about 40% of the caRBPs bind chromatin by intrinsically disordered regions (IDRs). Studying SPACE and total proteome dynamics from mES cells grown in 2iL and serum medium indicates significant correlation (R = 0.62). One of the most dynamic caRBPs is Dazl, which we find co-localized with PRC2 at transcription start sites of genes that are distinct from Dazl mRNA binding. Dazl and other PRC2-colocalised caRBPs are rich in intrinsically disordered regions (IDRs), which could contribute to the formation and regulation of phase-separated PRC condensates. Together, our approach provides an unprecedented insight into IDR-mediated interactions and caRBPs with moonlighting functions in native chromatin., source:https://academic.oup.com/nar/article/49/22/13092/6454268?login=true

The rising interest in the development of new wearable electronic devices for mobile healthcare can be exemplified by great opportunities for the development of new methods for broader use of blood perfusion monitoring for the purposes of precision medicine. A fine analysis of capillary blood flow structure and its rhythmic oscillations in the time and frequency domains, coupled with a new possibility of round-the-clock monitoring, can provide valuable diagnostic information about the state of microvascular blood flow. In this chapter, we review recent advances in the development and clinical applications of a new class of wearable devices for blood perfusion monitoring and diagnosis of microvascular complications in type 2 diabetes.

Advances in the development of ultra-compact semiconductor lasers have opened up new possibilities for the development of wearable devices implementing biophotonic technologies, such as laser Doppler flowmetry (LDF) and fluorescence spectroscopy (FS). This work is aimed to evaluate the correlation between diagnostic parameters simultaneously registered by a newly developed wearable device and a standard bedside fiber-based technique. Experimental studies with healthy volunteers using the occlusion test showed a high correlation between the parameters recorded by the two devices.,,,,,,,,.

In this paper, we present a study of regret and its expression on social media platforms. Specifically, we present a novel dataset of Reddit texts that have been classified into three classes: Regret by Action, Regret by Inaction, and No Regret. We then use this dataset to investigate the language used to express regret on Reddit and to identify the domains of text that are most commonly associated with regret. Our findings show that Reddit users are most likely to express regret for past actions, particularly in the domain of relationships. We also found that deep learning models using GloVe embedding outperformed other models in all experiments, indicating the effectiveness of GloVe for representing the meaning and context of words in the domain of regret. Overall, our study provides valuable insights into the nature and prevalence of regret on social media, as well as the potential of deep learning and word embeddings for analyzing and understanding emotional language in online text. These findings have implications for the development of natural language processing algorithms and the design of social media platforms that support emotional expression and communication.

Using code-mixed data in natural language processing (NLP) research currently gets a lot of attention. Language identification of social media code-mixed text has been an interesting problem of study in recent years due to the advancement and influences of social media in communication. This paper presents the Instituto Polit\'ecnico Nacional, Centro de Investigaci\'on en Computaci\'on (CIC) team's system description paper for the CoLI-Kanglish shared task at ICON2022. In this paper, we propose the use of a Transformer based model for word-level language identification in code-mixed Kannada English texts. The proposed model on the CoLI-Kenglish dataset achieves a weighted F1-score of 0.84 and a macro F1-score of 0.61.

Sarcasm detection is an essential task that can help identify the actual sentiment in user-generated data, such as discussion forums or tweets. Sarcasm is a sophisticated form of linguistic expression because its surface meaning usually contradicts its inner, deeper meaning. Such incongruity is the essential component of sarcasm, however, it makes sarcasm detection quite a challenging task. In this paper, we propose a model, that incorporates different features to capture the incongruity intrinsic to sarcasm. We use a pre-trained transformer and CNN to capture context features, and we use transformers pre-trained on emotions detection and sentiment analysis tasks. Our approach outperformed previous state-of-the-art results on four datasets from social networking platforms and online media.

The task of automatically identifying a language used in a given text is called Language Identification (LI). India is a multilingual country and many Indians especially youths are comfortable with Hindi and English, in addition to their local languages. Hence, they often use more than one language to post their comments on social media. Texts containing more than one language are called "code-mixed texts" and are a good source of input for LI. Languages in these texts may be mixed at sentence level, word level or even at sub-word level. LI at word level is a sequence labeling problem where each and every word in a sentence is tagged with one of the languages in the predefined set of languages. In order to address word level LI in code-mixed Kannada-English (Kn-En) texts, this work presents i) the construction of code-mixed Kn-En dataset called CoLI-Kenglish dataset, ii) code-mixed Kn-En embedding and iii) learning models using Machine Learning (ML), Deep Learning (DL) and Transfer Learning (TL) approaches. Code-mixed Kn-En texts are extracted from Kannada YouTube video comments to construct CoLI-Kenglish dataset and code-mixed Kn-En embedding. The words in CoLI-Kenglish dataset are grouped into six major categories, namely, "Kannada", "English", "Mixed-language", "Name", "Location" and "Other". The learning models, namely, CoLI-vectors and CoLI-ngrams based on ML, CoLI-BiLSTM based on DL and CoLI-ULMFiT based on TL approaches are built and evaluated using CoLI-Kenglish dataset. The performances of the learning models illustrated, the superiority of CoLI-ngrams model, compared to other models with a macro average F1-score of 0.64. However, the results of all the learning models were quite competitive with each other.

Viral metagenomics is increasingly applied in clinical diagnostic settings for detection of pathogenic viruses. While several benchmarking studies have been published on the use of metagenomic classifiers for abundance and diversity profiling of bacterial populations, studies on the comparative performance of the classifiers for virus pathogen detection are scarce. In this study, metagenomic data sets (n = 88) from a clinical cohort of patients with respiratory complaints were used for comparison of the performance of five taxonomic classifiers: Centrifuge, Clark, Kaiju, Kraken2, and Genome Detective. A total of 1144 positive and negative PCR results for a total of 13 respiratory viruses were used as gold standard. Sensitivity and specificity of these classifiers ranged from 83 to 100% and 90 to 99%, respectively, and was dependent on the classification level and data pre-processing. Exclusion of human reads generally resulted in increased specificity. Normalization of read counts for genome length resulted in a minor effect on overall performance, however it negatively affected the detection of targets with read counts around detection level. Correlation of sequence read counts with PCR Ct-values varied per classifier, data pre-processing (R2 range 15.1–63.4%), and per virus, with outliers up to 3 log10 reads magnitude beyond the predicted read count for viruses with high sequence diversity. In this benchmarking study, sensitivity and specificity were within the ranges of use for diagnostic practice when the cut-off for defining a positive result was considered per classifier.

Machine translation (MT) is one of the main tasks in natural language processing whose objective is to translate texts automatically from one natural language to another. Nowadays, using deep neural networks for MT tasks has received great attention. These networks require lots of data to learn abstract representations of the input and store it in continuous vectors. This paper presents the first relatively large-scale Amharic-English parallel sentence dataset. Using these compiled data, we build bi-directional Amharic-English translation models by fine-tuning the existing Facebook M2M100 pre-trained model achieving a BLEU score of 37.79 in Amharic-English 32.74 in English-Amharic translation. Additionally, we explore the effects of Amharic homophone normalization on the machine translation task. The results show that the normalization of Amharic homophone characters increases the performance of Amharic-English machine translation in both directions.

Hope is characterized as openness of spirit toward the future, a desire, expectation, and wish for something to happen or to be true that remarkably affects human's state of mind, emotions, behaviors, and decisions. Hope is usually associated with concepts of desired expectations and possibility/probability concerning the future. Despite its importance, hope has rarely been studied as a social media analysis task. This paper presents a hope speech dataset that classifies each tweet first into "Hope" and "Not Hope", then into three fine-grained hope categories: "Generalized Hope", "Realistic Hope", and "Unrealistic Hope" (along with "Not Hope"). English tweets in the first half of 2022 were collected to build this dataset. Furthermore, we describe our annotation process and guidelines in detail and discuss the challenges of classifying hope and the limitations of the existing hope speech detection corpora. In addition, we reported several baselines based on different learning approaches, such as traditional machine learning, deep learning, and transformers, to benchmark our dataset. We evaluated our baselines using weighted-averaged and macro-averaged F1-scores. Observations show that a strict process for annotator selection and detailed annotation guidelines enhanced the dataset's quality. This strict annotation process resulted in promising performance for simple machine learning classifiers with only bi-grams; however, binary and multiclass hope speech detection results reveal that contextual embedding models have higher performance in this dataset., Comment: 20 pages, 9 figures

Acknowledged as one of the most successful online cooperative projects in human society, Wikipedia has obtained rapid growth in recent years and desires continuously to expand content and disseminate knowledge values for everyone globally. The shortage of volunteers brings to Wikipedia many issues, including developing content for over 300 languages at the present. Therefore, the benefit that machines can automatically generate content to reduce human efforts on Wikipedia language projects could be considerable. In this paper, we propose our mapping process for the task of converting Wikidata statements to natural language text (WS2T) for Wikipedia projects at the sentence level. The main step is to organize statements, represented as a group of quadruples and triples, and then to map them to corresponding sentences in English Wikipedia. We evaluate the output corpus in various aspects: sentence structure analysis, noise filtering, and relationships between sentence components based on word embedding models. The results are helpful not only for the data-to-text generation task but also for other relevant works in the field., Comment: 29 pages

This paper portrays the method of UAV magnetometry survey data interpolation. The method accommodates the fact that this kind of data has a spatial distribution of the samples along a series of straight lines (similar to maritime tacks), which is a prominent characteristic of many kinds of UAV surveys. The interpolation relies on the very basic Nearest Neighbours algorithm, although augmented with a Machine Learning approach. Such an approach enables the error of less than 5 percent by intelligently adjusting the Nearest Neighbour algorithm parameters. The method was pilot tested on geomagnetic data with Borok Geomagnetic Observatory UAV aeromagnetic survey data.

First Rosenblatt's theorem about omnipotence of shallow networks states that elementary perceptrons can solve any classification problem if there are no discrepancies in the training set. Minsky and Papert considered elementary perceptrons with restrictions on the neural inputs: a bounded number of connections or a relatively small diameter of the receptive field for each neuron at the hidden layer. They proved that under these constraints, an elementary perceptron cannot solve some problems, such as the connectivity of input images or the parity of pixels in them. In this note, we demonstrated first Rosenblatt's theorem at work, showed how an elementary perceptron can solve a version of the travel maze problem, and analysed the complexity of that solution. We constructed also a deep network algorithm for the same problem. It is much more efficient. The shallow network uses an exponentially large number of neurons on the hidden layer (Rosenblatt's $A$-elements), whereas for the deep network the second order polynomial complexity is sufficient. We demonstrated that for the same complex problem deep network can be much smaller and reveal a heuristic behind this effect.

It's by now folklore that to understand the activity pattern of a user in an online social network (OSN) platform, one needs to look at his friends or the ones he follows. The common perception is that these friends exert influence on the user, effecting his decision whether to re-share content or not. Hinging upon this intuition, a variety of models were developed to predict how information propagates in OSN, similar to the way infection spreads in the population. In this paper, we revisit this world view and arrive at new conclusions. Given a set of users $V$, we study the task of predicting whether a user $u \in V$ will re-share content by some $v \in V$ at the following time window given the activity of all the users in $V$ in the previous time window. We design several algorithms for this task, ranging from a simple greedy algorithm that only learns $u$'s conditional probability distribution, ignoring the rest of $V$, to a convolutional neural network-based algorithm that receives the activity of all of $V$, but does not receive explicitly the social link structure. We tested our algorithms on four datasets that we collected from Twitter, each revolving around a different popular topic in 2020. The best performance, average F1-score of 0.86 over the four datasets, was achieved by the convolutional neural network. The simple, social-link ignorant, algorithm achieved an average F1-score of 0.78.

This paper gives the overview of the first shared task at FIRE 2020 on fake news detection in the Urdu language. This is a binary classification task in which the goal is to identify fake news using a dataset composed of 900 annotated news articles for training and 400 news articles for testing. The dataset contains news in five domains: (i) Health, (ii) Sports, (iii) Showbiz, (iv) Technology, and (v) Business. 42 teams from 6 different countries (India, China, Egypt, Germany, Pakistan, and the UK) registered for the task. 9 teams submitted their experimental results. The participants used various machine learning methods ranging from feature-based traditional machine learning to neural network techniques. The best performing system achieved an F-score value of 0.90, showing that the BERT-based approach outperforms other machine learning classifiers., Comment: arXiv admin note: substantial text overlap with arXiv:2207.11893

This overview paper describes the first shared task on fake news detection in Urdu language. The task was posed as a binary classification task, in which the goal is to differentiate between real and fake news. We provided a dataset divided into 900 annotated news articles for training and 400 news articles for testing. The dataset contained news in five domains: (i) Health, (ii) Sports, (iii) Showbiz, (iv) Technology, and (v) Business. 42 teams from 6 different countries (India, China, Egypt, Germany, Pakistan, and the UK) registered for the task. 9 teams submitted their experimental results. The participants used various machine learning methods ranging from feature-based traditional machine learning to neural networks techniques. The best performing system achieved an F-score value of 0.90, showing that the BERT-based approach outperforms other machine learning techniques

With the growth of social media platform influence, the effect of their misuse becomes more and more impactful. The importance of automatic detection of threatening and abusive language can not be overestimated. However, most of the existing studies and state-of-the-art methods focus on English as the target language, with limited work on low- and medium-resource languages. In this paper, we present two shared tasks of abusive and threatening language detection for the Urdu language which has more than 170 million speakers worldwide. Both are posed as binary classification tasks where participating systems are required to classify tweets in Urdu into two classes, namely: (i) Abusive and Non-Abusive for the first task, and (ii) Threatening and Non-Threatening for the second. We present two manually annotated datasets containing tweets labelled as (i) Abusive and Non-Abusive, and (ii) Threatening and Non-Threatening. The abusive dataset contains 2400 annotated tweets in the train part and 1100 annotated tweets in the test part. The threatening dataset contains 6000 annotated tweets in the train part and 3950 annotated tweets in the test part. We also provide logistic regression and BERT-based baseline classifiers for both tasks. In this shared task, 21 teams from six countries registered for participation (India, Pakistan, China, Malaysia, United Arab Emirates, and Taiwan), 10 teams submitted their runs for Subtask A, which is Abusive Language Detection and 9 teams submitted their runs for Subtask B, which is Threatening Language detection, and seven teams submitted their technical reports. The best performing system achieved an F1-score value of 0.880 for Subtask A and 0.545 for Subtask B. For both subtasks, m-Bert based transformer model showed the best performance.

Both policy and research benefit from a better understanding of individuals' jobs. However, as large-scale administrative records are increasingly employed to represent labor market activity, new automatic methods to classify jobs will become necessary. We developed an automatic job offers classifier using a dataset collected from the largest job bank of Mexico known as Bumeran https://www.bumeran.com.mx/ Last visited: 19-01-2022.. We applied machine learning algorithms such as Support Vector Machines, Naive-Bayes, Logistic Regression, Random Forest, and deep learning Long-Short Term Memory (LSTM). Using these algorithms, we trained multi-class models to classify job offers in one of the 23 classes (not uniformly distributed): Sales, Administration, Call Center, Technology, Trades, Human Resources, Logistics, Marketing, Health, Gastronomy, Financing, Secretary, Production, Engineering, Education, Design, Legal, Construction, Insurance, Communication, Management, Foreign Trade, and Mining. We used the SMOTE, Geometric-SMOTE, and ADASYN synthetic oversampling algorithms to handle imbalanced classes. The proposed convolutional neural network architecture achieved the best results when applied the Geometric-SMOTE algorithm., Comment: 13 pages, 2 figures, 8th World Conference On Soft Computing

This study reports the second shared task named as UrduFake@FIRE2021 on identifying fake news detection in Urdu language. This is a binary classification problem in which the task is to classify a given news article into two classes: (i) real news, or (ii) fake news. In this shared task, 34 teams from 7 different countries (China, Egypt, Israel, India, Mexico, Pakistan, and UAE) registered to participate in the shared task, 18 teams submitted their experimental results and 11 teams submitted their technical reports. The proposed systems were based on various count-based features and used different classifiers as well as neural network architectures. The stochastic gradient descent (SGD) algorithm outperformed other classifiers and achieved 0.679 F-score.

Automatic detection of fake news is a highly important task in the contemporary world. This study reports the 2nd shared task called UrduFake@FIRE2021 on identifying fake news detection in Urdu. The goal of the shared task is to motivate the community to come up with efficient methods for solving this vital problem, particularly for the Urdu language. The task is posed as a binary classification problem to label a given news article as a real or a fake news article. The organizers provide a dataset comprising news in five domains: (i) Health, (ii) Sports, (iii) Showbiz, (iv) Technology, and (v) Business, split into training and testing sets. The training set contains 1300 annotated news articles -- 750 real news, 550 fake news, while the testing set contains 300 news articles -- 200 real, 100 fake news. 34 teams from 7 different countries (China, Egypt, Israel, India, Mexico, Pakistan, and UAE) registered to participate in the UrduFake@FIRE2021 shared task. Out of those, 18 teams submitted their experimental results, and 11 of those submitted their technical reports, which is substantially higher compared to the UrduFake shared task in 2020 when only 6 teams submitted their technical reports. The technical reports submitted by the participants demonstrated different data representation techniques ranging from count-based BoW features to word vector embeddings as well as the use of numerous machine learning algorithms ranging from traditional SVM to various neural network architectures including Transformers such as BERT and RoBERTa. In this year's competition, the best performing system obtained an F1-macro score of 0.679, which is lower than the past year's best result of 0.907 F1-macro. Admittedly, while training sets from the past and the current years overlap to a large extent, the testing set provided this year is completely different.

Given the current social distance restrictions across the world, most individuals now use social media as their major medium of communication. Millions of people suffering from mental diseases have been isolated due to this, and they are unable to get help in person. They have become more reliant on online venues to express themselves and seek advice on dealing with their mental disorders. According to the World health organization (WHO), approximately 450 million people are affected. Mental illnesses, such as depression, anxiety, etc., are immensely common and have affected an individuals' physical health. Recently Artificial Intelligence (AI) methods have been presented to help mental health providers, including psychiatrists and psychologists, in decision making based on patients' authentic information (e.g., medical records, behavioral data, social media utilization, etc.). AI innovations have demonstrated predominant execution in numerous real-world applications broadening from computer vision to healthcare. This study analyzes unstructured user data on the Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD. We trained traditional machine learning, deep learning, and transfer learning multi-class models to detect mental disorders of individuals. This effort will benefit the public health system by automating the detection process and informing appropriate authorities about people who require emergency assistance., Comment: 11 pages, 2 figures, 8th World Conference On Soft Computing

Angelman syndrome (AS) is a neurodevelopmental disorder caused by loss of expression of the maternal copy of the UBE3A gene. Individuals with AS have a multifaceted behavioral phenotype consisting of deficits in motor function, epilepsy, cognitive impairment, sleep abnormalities, as well as other comorbidities. Effectively modeling this behavioral profile and measuring behavioral improvement will be crucial for the success of ongoing and future clinical trials. Foundational studies have defined an array of behavioral phenotypes in the AS mouse model. However, no single behavioral test is able to fully capture the complex nature of AS—in mice, or in children. We performed multidimensional analysis (principal component analysis + k-means clustering) to quantify the performance of AS model mice (n = 148) and wild-type littermates (n = 138) across eight behavioral domains. This approach correctly predicted the genotype of mice based on their behavioral profile with ~95% accuracy, and remained effective with reasonable sample sizes (n = ~12–15). Multidimensional analysis was effective using different combinations of behavioral inputs and was able to detect behavioral improvement as a function of treatment in AS model mice. Overall, multidimensional behavioral analysis provides a tool for evaluating the effectiveness of preclinical treatments for AS. Multidimensional analysis of behavior may also be applied to rodent models of related neurodevelopmental disorders, and may be particularly valuable for disorders where individual behavioral tests are less reliable than in AS.

Chromosome 15q11.2-q13.1 duplication syndrome (Dup15q syndrome) is a severe neurodevelopmental disorder characterized by intellectual disability, impaired motor coordination, and autism spectrum disorder. Chromosomal multiplication of the UBE3A gene is presumed to be the primary driver of Dup15q pathophysiology, given that UBE3A exhibits maternal monoallelic expression in neurons and that maternal duplications typically yield far more severe neurodevelopmental outcomes than paternal duplications. However, studies into the pathogenic effects of UBE3A overexpression in mice have yielded conflicting results. Here, we investigated the neurodevelopmental impact of Ube3a gene overdosage using bacterial artificial chromosome-based transgenic mouse models (Ube3aOE) that recapitulate the increases in Ube3a copy number most often observed in Dup15q. In contrast to previously published Ube3a overexpression models, Ube3aOE mice were indistinguishable from wild-type controls on a number of molecular and behavioral measures, despite suffering increased mortality when challenged with seizures, a phenotype reminiscent of sudden unexpected death in epilepsy. Collectively, our data support a model wherein pathogenic synergy between UBE3A and other overexpressed 15q11.2-q13.1 genes is required for full penetrance of Dup15q syndrome phenotypes.

Oblivious data processing has been an on and off topic for the last decade or so. It provides great opportunities for secure data management and processing, especially in the cloud. At the same time, modern computing resources seem to be affordable enough to allow for practical use of homomorphic cryptography. Yet, the availability of products that offer practical homomorphic data processing is extremely scarce. As part of a project aimed at developing a practical homomorphic data management platform, we have conducted an extensive study of homomorphic cryptosystems' performance, the results of which are presented in this work. For this work we chose the following five cryptosystems: fully homomorphic HElib and SEAL, somewhat fully homomorphic PyAono, and partially homomorphic Paillier and ElGamal. In the discussion of the aggregated results, we suggest that partially homomorphic cryptosystems could be used today in certain practical applications, whereas time has not yet come for the fully homomorphic ones.

Trade costs directly affect the ability of countries to use trade as the engine of inclusive economic growth for sustainable development, especially through integration into regional and global value chains. The world community has now reached a consensus on the importance of reducing trade costs and that trade facilitation can bring significant benefits to the economy. The article investigates the features of the new World Trade Organization Trade Facilitation Agreement, Los costos comerciales afectan directamente la capacidad de los países de utilizar el comercio como el motor del crecimiento económico inclusivo que favorece el desarrollo sostenible, especialmente mediante la integración en las cadenas regionales y mundiales de valor. En el presente la comunidad mundial ha llegado a un consenso sobre la importancia de reducir los costos comerciales y de que la facilitación del comercio puede generar importantes beneficios a la economía. En el artículo se investigan los rasgos del nuevo Acuerdo de Facilitación del Comercio de la Organización Mundial del Comercio