Miracle-dz/UniFAR-FTUs huggingface dataset:
Applying Occam’s Razor to Transformer-Based Dependency Parsing: What Works, What Doesn’t, and What is Really Necessary

The introduction of pre-trained transformer-based contextualized word embeddings has led to considerable improvements in the accuracy of graph-based parsers for frameworks such as Universal Dependencies (UD). However, previous works differ in various dimensions, including their choice of pre-trained language models and whether they use LSTM layers. With the aims of disentangling the effects of these choices and identifying a simple yet widely applicable architecture, we introduce STEPS, a new modular graph-based dependency parser. Using STEPS, we perform a series of analyses on the UD corpora of a diverse set of languages. We find that the choice of pre-trained embeddings has by far the greatest impact on parser performance and identify XLM-R as a robust choice across the languages in our study. Adding LSTM layers provides no benefits when using transformer-based embeddings. A multi-task training setup outputting additional UD features may contort results. Taking these insights together, we propose a simple but widely applicable parser architecture and configuration, achieving new state-of-the-art results (in terms of LAS) for 10 out of 12 diverse languages.
Towards Off-Policy Learning for Ranking Policies with Logged Feedback

Probabilistic learning to rank (LTR) has been the dominating approach for optimizing the ranking metric, but cannot maximize long-term rewards. Reinforcement learning models have been proposed to maximize user long-term rewards by formulating the recommendation as a sequential decision-making problem, but could only achieve inferior accuracy compared to LTR counterparts, primarily due to the lack of online interactions and the characteristics of ranking. In this paper, we propose a new off-policy value ranking (VR) algorithm that can simultaneously maximize user long-term rewards and optimize the ranking metric offline for improved sample efficiency in a unified Expectation-Maximization (EM) framework. We theoretically and empirically show that the EM process guides the leaned policy to enjoy the benefit of integration of the future reward and ranking metric, and learn without any online interactions. Extensive offline and online experiments demonstrate the effectiveness of our methods
‘Responsibility to Detect?’: Autonomous Threat Detection and its Implications for Due Diligence in Cyberspace

Private and public organizations have long relied on intrusion detection systems to alert them of malicious activity in their digital networks. These systems were designed to detect threat signatures in static networks or infer anomalous activity based on their security ‘logs’. They are, however, of limited use to detect threats across heterogeneous, modern-day networks, where computing resources are distributed across cloud or routing services. Recent advancements in machine learning (ML) have led to the development of autonomous threat detection (ATD) applications that monitor, evaluate, and respond to malicious activity with minimal human intervention. The use of ‘intelligent’ and programmable algorithms for ATD will reduce incident response times and enhance the capacity of states to detect threats originating from any layer of their territorial information and communications technologies (ICT) infrastructure. This paper argues that ATD technologies will influence the evolution of a due diligence rule for cyberspace by raising the standard of care owed by states to prevent their networks from being used for malicious, transboundary ICT activities. This paper comprises five sections. Section 1 introduces the paper and its central argument. Section 2 outlines broad trends and operational factors pushing public and private entities towards the adoption of ATD. Section 3 offers an overview of a typical ATD application. Section 4 analyses the impact of ATD on the due diligence obligations of states. Section 5 presents the paper’s conclusions.
Is There a Role for Cardiac Rehabilitation After Coronary Artery Bypass Grafting?Response to Niebauer

In Europe, between 1985 to 1989 and 2000 to 2004, a decline of >30% in cardiovascular disease (CVD) and coronary heart disease mortality was observed.1,2 However, according to the latest figures of the European Heart Network, the annual mortality of CVD still exceeds 4 million (ie, 47% of all deaths) in Europe and 1.9 million (ie, 40% of all deaths) in the European Union.2 Furthermore, despite a falling trend in most European countries, CVD remains the most common cause of death in women and men. The financial burden for the European Union economy is estimated to be almost €196 billion a year, of which 54% is attributable to healthcare costs, 24% to productivity losses, and 22% to the informal care of people with CVD. Response by Mendes on p 2537 In the United States, an even greater decrease in cardiovascular mortality of 67% was achieved between 1969 and 2013. In addition, during this time period, age-standardized years of potential life lost per 1000 decreased by 68%. 3 Despite this meaningful improvement, >50 years after the first coronary artery bypass grafting (CABG) in 19644 and almost 40 years after the first percutaneous coronary intervention in 1977,5 the incidence and prevalence of CVD remain high. So does the annual rate of CABG, even though it continues to drop, for example, to 1081 CABGs per 1 million adults in 2007 to 2008 in the United States.6 Obviously, coronary revascularization is widely appreciated for alleviating symptoms and improving prognosis. However, as a structural intervention, it does not address disease progression, which is why revascularization must be paired with measures of secondary prevention to maximize benefit. Given the social and economic burden of coronary artery disease, optimization of evidence-based treatment is paramount. According to current guidelines, treatment of …
A randomized controlled study of intrathecal hyperbaric bupivacaine and hyperbaric bupivacaine with fentanyl in caesarean section, government medical college, Suryapet

--- Background: Spinal anesthesia with hyperbaric bupivacaine and adjuvants such as Fentanyl is now the preferred technique for spinal anesthesia, and is the gold standard for caesarean section. The common procedure is to add such adjuvants in a two-syringe before intrathecal injection into hyperbaric bupivacaine. Drug mixing may alter anesthetic solution density and baricity and may change the distribution of medication in cerebrospinal fluid (CSF). Objectives: In this study, we aimed to assess the effect of comparing block characteristics, intraoperative hemodynamics, and postoperative pain relief in Caesarean section under the subarachnoid block (SAB), following administration of hyperbaric bupivacaine and Fentanyl as ASA category I physical status (Group A & B; 38 (76%)) and II (Group A & B; 12 (24%)) was comparable between all categories and there was no statistically meaningful difference (p=1.0). The mean onset of the sensory block in group B (52.12 ± 2.64 sec.; p<0.001) was comparatively group-A quicker, but the discrepancy was statistically significant. At baseline and in all subsequent measurements (p>0.05), the mean arterial pressure, respiratory rate, and partial oxygen concentration were similar between the two classes. None of the cases in any group experienced nausea, vomiting and respiratory depression. Conclusions: Sequential fentanyl administration reduces the time needed for maximum sensory and motor block accomplishment and substantially prolongs the overall analgesic duration. We found that the sequential procedure did not raise the degree of sedation and occurrence of hypotension or bradycardia as compared with drug administration as a mixture.
GAMIFICACIÓN EN AULAS BILINGÜES DE SECUNDARIA: UNA EXPERIENCIA EDUCATIVA

Este artículo muestra una investigación acción dirigida a mejorar áreas de necesidad de alumnos de Educación Secundaria Obligatoria (ESO) a través de la gamificación. El objetivo principal es analizar el impacto de la gamificación en su puesta en práctica en el aula y determinar la eficacia de esta desde una doble perspectiva: (i) como facilitadora del aprendizaje y (ii) como generadora de actitudes positivas en el grupo-clase. El proyecto ha sido implementado en dos grupos de alumnos de primer año de la ESO en la asignatura de Ciencias Sociales impartida en inglés, bajo la metodología AICLE (Aprendizaje Integrado de Contenidos en Lengua Extranjera). Se observó que los alumnos tenían dificultades en el aprendizaje de fechas históricas en inglés y en la expresión oral debido a una falta de fluidez y confianza a la hora de utilizar la lengua meta. Una vez identificado el área problemática, se diseñaron una serie de sesiones gamificadas para comprobar el papel de esta técnica en el aprendizaje, el aumento de la confianza y motivación y en el desarrollo de habilidades sociales y trabajo en equipo. Los resultados obtenidos, basados en (i) la realización de una prueba de fluidez lectora, (ii) una encuesta distribuida al alumnado y (iii) la propia observación docente, indican que la aplicación de la gamificación mejora el aprendizaje de contenidos y aumenta su interés e implicación en las tareas del aula.
Electronic properties of lightweight intermetallic Li-Al hydrides in the γ-phase

An ab initio electronic structure study of the potential light-weight hydrogen storage materials LiAlH 4 and Li 3 AlH 6 , in their γ-phase, is presented. Electronic band structures and total and partial densities of states for both compounds are calculated by using the WIEN2k simulation code. We estimate an energy bandgap of ca. 3.82 eV for the γ-LiAlH 4 semiconductor and no gap in the γ-Li 3 AlH 6 metal-like compound. In the latter, the valence band is formed by two distinct energy bands originating from hydrogen orbitals hybridization with metal atomic orbitals. Optical properties such as the refractive index 𝑛(𝜔) , the reflectivity 𝑅(𝜔) , the absorption coefficient 𝐼(𝜔) , and energy-loss function 𝐿(𝜔) are determined from the complex dielectric function 𝜀(𝜔) for incident photon energies in the 0-40 eV interval. It is found that these quantities are isotropic for cubic γ-Li 3 AlH 6 , while they are similar but distinguishable in the orthorhombic γ-LiAlH 4 compound. The γ-Li 3 AlH 6 hydride, as an anode in lithium ion cells, is more Li reversible than the γ-LiAlH 4 . Results are discussed and compared with available published data.
Biaffine Dependency and Semantic Graph Parsing for EnhancedUniversal Dependencies

This paper presents the system used in our submission to the IWPT 2021 Shared Task. This year the official evaluation metrics was ELAS, therefore dependency parsing might have been avoided as well as other pipeline stages like POS tagging and lemmatization. We nevertheless chose to deploy a combination of a dependency parser and a graph parser. The dependency parser is a biaffine parser, that uses transformers for representing input sentences, with no other feature. The graph parser is a semantic parser that exploits a similar architecture except for using a sigmoid crossentropy loss function to return multiple values for the predicted arcs. The final output is obtained by merging the output of the two parsers. The dependency parser achieves top or close to top LAS performance with respect to other systems that report results on such metrics, except on low resource languages (Tamil, Estonian, Latvian).
Nutrient signaling in the regulation of human muscle protein synthesis

AMPK and mTOR are important energy and nutrient sensing and signaling proteins in muscle. AMPK activation decreases muscle protein synthesis (MPS) by inhibiting mTOR signaling. Essential amino acids and insulin stimulate mTOR signaling and MPS. We hypothesized that anabolic nutrients would be sensed by both AMPK and mTOR resulting in an acute and potent stimulation of MPS via enhanced translation initiation and elongation.
IFRS experience and earnings quality in the GCC region

PurposeThis paper aims to examine the association between the adoption experience of the International Financial Reporting Standards (IFRS) and the quality of reported earnings in the Gulf Cooperation Council (GCC) region – a region that exhibits several features of emerging economies.Design/methodology/approachThe authors analyse a hand-collected dataset of 222 firms across 4 countries in the GCC region over the period 2012–2017 and measure “IFRS experience” as the number of years since a country has mandatorily adopted the IFRS. In measuring earnings quality, the authors focus on two properties of reported earnings: persistence and accruals quality and employ multivariate regression models based on two-way cluster-robust standard errors and fixed-effects.FindingsThis study’s findings suggest that earnings persistence is decreasing, and discretionary accruals are increasing in IFRS experience in the GCC region over the period 2012–2017. The authors conclude that reported earnings quality has declined following IFRS adoption in this sample.Research limitations/implicationsThe authors contribute to the IFRS literature in the GCC region, which is in its infancy.Practical implicationsThis study’s findings have important policy implications for countries that are about to adopt or are in the early implementation stage of IFRS and suggest that strong enforcement of accounting standards along with improvement in the institutional environments might be needed for improving financial reporting quality.Originality/valueThe authors provide the first cross-country evidence on the relation between IFRS adoption in the GCC region and earnings quality. Moreover, unlike most prior studies, the authors employ a continuous measure that is superior to a binary measure in capturing the effect of IFRS adoption.
Identifying Terrestrial Landscape Character Types in China

Landscape character assessment (LCA) is a widely used tool that integrates natural, cultural, and perceptual attributes to identify and portray landscape. In this study, we used the LCA method to identify the landscape characteristics of China at the national scale. Furthermore, we applied cultural and landscape structural factors along with spatial transmission to improve the identification system. First, we incorporated all the parameters in the assessment. We selected 15 landscape character factors from four factor types including nature, culture, spatial geographic co-ordinates, and landscape structure. These parameters were analysed using multilevel overlay and spatial connection tools in ArcGis 10.2, which resulted in 2307 landscape description units (LDUs). Second, the spatial structure properties of the LDUs were determined using a semivariogram and the moving window method in ArcGis 10.2 and Fragstats 4.2 software, respectively. Third, for visualisation, we applied the principal component analysis (PCA) using the SPSS software and elbow and k-means clustering methods using MATLAB to determine 110 landscape character types (LCTs) of China’s entire terrestrial landscape. Finally, we determined 1483 landscape character areas through semiautomatic segmentation and manual visual correction using eCognition. Based on the unique characteristics of the entire terrestrial landscape of China, a set of ideas and methods for the overall identification of LCTs was proposed. Our findings can be used to optimise territorial spatial planning and landscape protection and management, and promote multiscale land-use studies in China.
Guideline Bias in Wizard-of-Oz Dialogues

NLP models struggle with generalization due to sampling and annotator bias. This paper focuses on a different kind of bias that has received very little attention: guideline bias, i.e., the bias introduced by how our annotator guidelines are formulated. We examine two recently introduced dialogue datasets, CCPE-M and Taskmaster-1, both collected by trained assistants in a Wizard-of-Oz set-up. For CCPE-M, we show how a simple lexical bias for the word like in the guidelines biases the data collection. This bias, in effect, leads to poor performance on data without this bias: a preference elicitation architecture based on BERT suffers a 5.3% absolute drop in performance, when like is replaced with a synonymous phrase, and a 13.2% drop in performance when evaluated on out-of-sample data. For Taskmaster-1, we show how the order in which instructions are resented, biases the data collection.
Challenges of SLAM in Extremely Unstructured Environments: The DLR Planetary Stereo, Solid-State LiDAR, Inertial Dataset

We present the DLR Planetary Stereo, Solid-State LiDAR, Inertial (S3LI) dataset, recorded on Mt. Etna, Sicily, an environment analogous to the Moon and Mars, using a hand-held sensor suite with attributes suitable for implementation on a space-like mobile rover. The environment is characterized by challenging conditions regarding both the visual and structural appearance: severe visual aliasing poses significant limitations to the ability of visual SLAM systems to perform place recognition, while the absence of outstanding structural details, joined with the limited Field-of-View of the utilized Solid-State LiDAR sensor, challenges traditional LiDAR SLAM for the task of pose estimation using point clouds alone. With this data, that covers more than 4 kilometers of travel on soft volcanic slopes, we aim to: 1) provide a tool to expose limitations of state-of-the-art SLAM systems with respect to environments, which are not present in widely available datasets and 2) motivate the development of novel localization and mapping approaches, that rely efficiently on the complementary capabilities of the two sensors.
How Might We Create Better Benchmarks for Speech Recognition?

The applications of automatic speech recognition (ASR) systems are proliferating, in part due to recent significant quality improvements. However, as recent work indicates, even state-of-the-art speech recognition systems – some which deliver impressive benchmark results, struggle to generalize across use cases. We review relevant work, and, hoping to inform future benchmark development, outline a taxonomy of speech recognition use cases, proposed for the next generation of ASR benchmarks. We also survey work on metrics, in addition to the de facto standard Word Error Rate (WER) metric, and we introduce a versatile framework designed to describe interactions between linguistic variation and ASR performance metrics.
Advances in Multigrain Snack Bar Technology and Consumer Expectations: A Review

ABSTRACT Multigrain bars can contain ingredients recommended in daily diets, which are nutritious and beneficial to human health; without white sugar, “harmful” fats, and synthetic additives. This review describes the current nutritional trends in the consumption of snacks and characterizes the most important groups of raw materials for the composition of bars. Many plant materials can be used for the production of multigrain bars, including cereals and their products, oilseeds, nuts, fruit, vegetables, and plant waste, which are primarily a source of fiber, but also other compounds with antioxidant properties. The binding component may be honey, malt extract, fruit or vegetable juice, or purée. Attention was paid to the microbiological quality of the raw materials and their influence on the sensory characteristics of the bars. The pro-health and technological role of fiber is emphasized. In addition to providing nutritionally balanced snacks, these raw materials can be used to shape sensory properties such as color, flavor, aroma, and texture. The most important methods of making bars are indicated. In addition to the common extrusion and baking processes, the use of different drying methods to produce multigrain bars in combination with a wide recipe range can help expand the market for multigrain snack bars.
Effects of Plant Metabolites on the Growth of COVID-19 (Coronavirus Disease-19) Including Omicron Strain

According to recent reports out of India, a new strain of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) B1.1. 529 Omicron virus has emerged. In comparison to the Wuhan (WHU) strain and the delta variant, this variant showed a far stronger effect on the angiotensin converting enzyme2 (ACE2) receptor. There are several medicinal compounds in plant metabolites, and their diverse chemical structures make them ideal for the treatment of serious illnesses. It's possible that some of these could be useful alternative pharmaceuticals, as well as a starting point for the repurposing of existing medications and new chemical discoveries. SARS-CoV-2 infection triggered a worldwide epidemic of the severe acute respiratory syndrome (SARS). There have been trials for different therapies for SARS-CoV-2 and so also there are recent announcements of extensive research into the development of viable medicines for this global health calamity. After a thorough examination of plant-derived treatments for COVID-19, investigators in the current study decided to focus on plant-derived secondary metabolites (PSMs). According to some researchers, new MDR (Multi-Drug Resistant) antibiotics may one day be developed due to the adaptability of secondary metabolites. Identifying plant metabolites that can treat a wide range of viral infections was one of the study's aims. Many natural medications that could be recommended for the treatment of COVID-19 were discovered as a result of this research, including remedies from plant families, viral candidates that are susceptible, antiviral assays, and mechanisms of therapeutic action. The findings of this study will inspire further research and speed up the development of new antiviral plant-based medications.
Application of artificial neural networks in diagnosis of Hepatitis C

Hepatitis C is an inflammatory condition of the liver caused by the hepatitis C virus. Diagnosis of the disease itself is difficult because the incubation period is long, often the disease is initially without some characteristic symptoms, but also due to a lack of laboratory methods. Artificial intelligence is increasingly being used nowadays to make it easier and faster to assess the illness. As hepatitis C is a rising healthcare burden it is of utmost importance to construct effective and reliable screening methods. As AI has already proven useful for diagnosis of a variety of conditions based on clinical parameters, this study focuses on the application of artificial neural network (ANN) for hepatitis C diagnosis. In this study, a database of 1000 respondents divided into two groups was used to develop the ANN: healthy (n = 200) and sick (n = 800). Monitoring parameters were: albumin, alkaline phosphatase, alanine aminotransferase, aspartate aminotransferase, bilirubin, acetylcholinesterase and anti-HCV antibodies. The overall accuracy of the developed ANN was 97,78%, which indicates that the potential of artificial intelligence in diagnosing hepatitis C is enormous, and in the future, attention should be paid to the development of new systems with as much data as possible.