Miracle-dz/UniFAR-FTUs huggingface dataset:
Parameter-Efficient Domain Knowledge Integration from Multiple Sources for Biomedical Pre-trained Language Models

Domain-speciﬁc pre-trained language models (PLMs) have achieved great success over various downstream tasks in different domains. However, existing domain-speciﬁc PLMs mostly rely on self-supervised learning over large amounts of domain text, without explicitly integrating domain-speciﬁc knowledge, which can be essential in many domains. Moreover, in knowledge-sensitive ar-eas such as the biomedical domain, knowledge is stored in multiple sources and formats, and existing biomedical PLMs either neglect them or utilize them in a limited manner. In this work, we introduce an architecture to integrate domain knowledge from diverse sources into PLMs in a parameter-efﬁcient way. More speciﬁcally, we propose to encode domain knowledge via adapters , which are small bottleneck feed-forward networks inserted between intermediate transformer layers in PLMs. These knowledge adapters are pre-trained for individual domain knowledge sources and integrated via an attention-based knowledge controller to enrich PLMs. Taking the biomedical domain as a case study, we explore three knowledge-speciﬁc adapters for PLMs based on the UMLS Metathesaurus graph, the Wikipedia articles for diseases, and the semantic grouping information for biomedical concepts. Extensive experiments on different biomedical NLP tasks and datasets demonstrate the beneﬁts of the proposed architecture and the knowledge-speciﬁc adapters across multiple PLMs.
Weakly Supervised Semantic Parsing by Learning from Mistakes

Weakly supervised semantic parsing (WSP) aims at training a parser via utterance-denotation pairs. This task is challenging because it requires (1) searching consistent logical forms in a huge space; and (2) dealing with spurious logical forms. In this work, we propose Learning from Mistakes (L FM ), a simple yet effective learning framework for WSP. L FM utilizes the mistakes made by a parser during searching, i.e., generating logical forms that do not execute to correct denotations, for tackling the two challenges. In a nutshell, L FM additionally trains a parser using utterance-logical form pairs created from mistakes, which can quickly bootstrap the parser to search consistent logical forms. Also, it can motivate the parser to learn the correct mapping between utterances and logical forms, thus dealing with the spuriousness of logical forms. We evaluate L FM on WikiTableQuestions, WikiSQL, and TabFact in the WSP setting. The parser trained with L FM outperforms the previous state-of-the-art semantic parsing approaches on the three datasets. Also, we ﬁnd that L FM can substantially reduce the need for labeled data. Using only 10% of utterance-denotation pairs, the parser achieves 84.2 denotation accuracy on WikiSQL, which is com-petitive with the previous state-of-the-art approaches using 100% labeled data.
Comprehensive Punctuation Restoration for English and Polish

Punctuation restoration is a fundamental re-quirement for the readability of text derived from Automatic Speech Recognition (ASR) systems. Most contemporary solutions are limited to predicting only a few of the most frequently occurring marks, such as periods, commas, and question marks — and only one per word. However, in written language, we deal with a much larger number of punctuation characters (such as parentheses, hyphens, etc.), and their combinations (like parenthesis followed by a dot). Such comprehensive punctuation cannot always be unambiguously reduced to a basic set of the most frequently occurring marks. In this work, we evaluate several methods in the comprehensive punctuation reconstruction task. We conduct experiments on parallel corpora of two different languages, English and Polish — languages with a relatively simple and complex morphology, respectively. We also investigate the inﬂuence of building a model on comprehensive punctuation on the quality of the basic punctuation restoration task.
Glyph Enhanced Chinese Character Pre-Training for Lexical Sememe Prediction

Sememes are deﬁned as the atomic units to describe the semantic meaning of concepts. Due to the difﬁculty of manually annotating sememes and the inconsistency of annotations between experts, the lexical sememe prediction task has been proposed. However, previous methods heavily rely on word or character embeddings, and ignore the ﬁne-grained information. In this paper, we propose a novel pre-training method which is designed to better incorporate the internal information of Chinese character. The G lyph enhanced C hinese C haracter representation ( GCC ) is used to assist sememe prediction. We experiment and evaluate our model on HowNet, which is a fa-mous sememe knowledge base. The experimental results show that our method outperforms existing non-external information models.
External validation of the QCovid risk prediction algorithm for risk of COVID-19 hospitalisation and mortality in adults: national validation cohort study in Scotland

Background The QCovid algorithm is a risk prediction tool that can be used to stratify individuals by risk of COVID-19 hospitalisation and mortality. Version 1 of the algorithm was trained using data covering 10.5 million patients in England in the period 24 January 2020 to 30 April 2020. We carried out an external validation of version 1 of the QCovid algorithm in Scotland. Methods We established a national COVID-19 data platform using individual level data for the population of Scotland (5.4 million residents). Primary care data were linked to reverse-transcription PCR (RT-PCR) virology testing, hospitalisation and mortality data. We assessed the performance of the QCovid algorithm in predicting COVID-19 hospitalisations and deaths in our dataset for two time periods matching the original study: 1 March 2020 to 30 April 2020, and 1 May 2020 to 30 June 2020. Results Our dataset comprised 5 384 819 individuals, representing 99% of the estimated population (5 463 300) resident in Scotland in 2020. The algorithm showed good calibration in the first period, but systematic overestimation of risk in the second period, prior to temporal recalibration. Harrell’s C for deaths in females and males in the first period was 0.95 (95% CI 0.94 to 0.95) and 0.93 (95% CI 0.92 to 0.93), respectively. Harrell’s C for hospitalisations in females and males in the first period was 0.81 (95% CI 0.80 to 0.82) and 0.82 (95% CI 0.81 to 0.82), respectively. Conclusions Version 1 of the QCovid algorithm showed high levels of discrimination in predicting the risk of COVID-19 hospitalisations and deaths in adults resident in Scotland for the original two time periods studied, but is likely to need ongoing recalibration prospectively.
An empirical study of the role of postponement application in reducing supply chain complexity

Postponement has been identified as an important instrument for supply chain management. Complexity is a factor that has wide influence on the performance of supply chains. Although there is not a simple relationship between complexity and performance, performance can be improved through managing or reducing complexity level in many situations. Postponement often brings about changes in the configuration of a supply chain and hence has an impact on supply chain complexity. In this study, we present a tentative classification of supply chain complexity and try to establish a relationship between different supply chain complexity dimensions and postponement applications based existing studies. We then present two cases where postponement is applied in the upstream stages of supply chains in contrast to the mid- and down stream postponement applications found in existing studies. By analysing the manner in which these postponement applications influence complexity and impact on supply chain performance, we try to enrich the concept of postponement on one hand and expand the evidence of the relationship between postponement and complexity. We also try to clarify the relationship between JIT and upstream postponement.
ARTP statement on cardiopulmonary exercise testing 2021

Cardiopulmonary exercise testing (CPET) has become an invaluable tool in healthcare, improving the diagnosis of disease and the quality, efficacy, assessment and safety of treatment across a range of pathologies. CPET’s superior ability to measure the global exercise response of the respiratory, cardiovascular and skeletal muscle systems simultaneously in a time and cost-efficient manner has led to the application of CPET in a range of settings from diagnosis of disease to preoperative assessment. The Association for Respiratory Technology and Physiology Statement on Cardiopulmonary Exercise Testing 2021 provides the practitioner and scientist with an outstanding resource to support and enhance practice, from equipment to testing to leadership, helping them deliver a quality assured service for the benefit of all patient groups.
Applications of statistical techniques to the quality improvement of a 15A circuit breaker

As an initial step to implement total quality management (TQM) in a small manufacturing enterprise (SME) of electrical products, we applied statistical techniques to quantify and evaluate the data collected in order to improve the quality characteristics of a selected product circuit breaker. A circuit breaker will “jump” or the current will be cut short if a current stronger than 15A passes through the equipment. The time required for the circuit breaker to stop the flow of the current is called the trip‐time. Our major concern is to find out how sensitive a 15A circuit breaker is, and how reliable is the device. We were able to confirm several dominant factors that influence the trip‐time of the circuit breaker after conducting several experiments. We used regression analysis to find out the model that best fits the relationship between the current supply and the trip‐time of the 15A circuit breaker. Meanwhile reliability testings of the circuit breaker were performed. Balanced factorial design techniqu.
Beam delivery by adaptive optics for material processing applications using high-power CO2 lasers

The use of high power CO2 lasers for various applications in material and production technologies has increasingly grown, and new applications are on their way to being used in industry. Due to varying beam path lengths, proper beam delivery is essential to obtain constant working conditions when using machines with moving beam guidance. The focussing characteristics of divergent laser beams change with the distance between laser source and processing head. To keep the energy distribution on the workpiece surface on the same level while working with flying optics, the use of deformable mirror systems has proven to be a suitable solution. In this case, a newly-developed system is brought into the beam guidance system to keep the focal spot diameter constant. As far as applications are concerned, where the distance of the focal spot to the surface of the workpiece is of major interest for the performance of the process, these optical devices are also used to tune the focal length. For example collision danger or dynamic limitations of the handling system may mean that the focal spot has to follow the surface outline without keeping the distance between processing head and material surface constant. Besides cutting and shaping by material removal applications, welding is a kind of application where a focus shift without moving the processing head may be advantageous, especially for 3D processes. Therefore, another deformable mirror is installed near to the focusing optics. Investigations have been carried out on the location of the deformable mirror close to the laser source (RS 3000 RF), and inside the processing head.
Partial discharge classification in GIS using the narrow-band UHF method

This paper discusses the use of the narrow-band ultra high frequency (UHF) partial discharge (PD) measuring technique for the recognition and classification of PD in gas insulated substations (GIS). The principle of the identification of PD signals from external disturbances or UHF signals caused by switching operations will be issued. Additionally, the interpretation of the measured signals with the use of orthogonal functions, fuzzy logic and neural networks will be reported. A genetic algorithm will be presented which can be used to determine the optimum topology of the used neural network.
Switched parasitic feeds for parabolic antenna angle diversity

The radiation direction from a parabolic dish antenna can be electronically controlled through the use of a switched parasitic element closely coupled to a dipole feed. Through the application of a dc voltage, the half-wavelength-long parasitic element is switched between open and short circuit using a p-i-n diode at its center. With a parasitic element vertically offset from the horizontal dipole, the performance of the antenna in both switch positions has been optimized. At 1.5 GHz, using a 2.4 m diameter reflector, beam skewing on the order of 1.5° at the −6 dB point can be achieved for a feed element spacing of 0.6 wavelengths. At a parasitic element spacing of 0.5 wavelengths, the main lobe is better than 15 dB above the unshifted null. The analysis of such a structure requires the calculation of the current in both the driven element and the parasitic element and the summation of their fields on the surface of the reflector independently. ©1999 John Wiley & Sons, Inc. Microwave Opt Technol Lett 23: 200–203, 1999.
Compliant MEMS mechanism to extend resolution in Fourier transform spectroscopy

A complaint mechanism to extend resolution in the Fourier Transform Spectroscopy (FTS) technique has been designed, fabricated and tested. The mechanism based on the complaint mechanical design strategy has not mobile parts and was fabricated in MEMS technology in a Bosch Process. When this mechanism is used to displacing the mobile mirror in a FTS setup, an extended range travel for the reference mirror is achieved; thus, the optical path difference and hence the resolving power of the FTS system is increased. The fabricated device has dimensions of 5400x4200x400 Microns at the large, width and thickness respectively, with an aspect ratio about 10. Numerical simulations with ANSYS Software were developed to get the Stress limits and the input and output displacements, the mechanical gain and the resonance frequency of the device. Experimental results in both the forced and dynamical regime are presented. It is found that in the dynamical regime when the device is operated at its resonance frequency it exhibits a higher mechanical gain several times its gain in the forced regime.
Design of double layer frequency selective surfaces with novel multiresonant elements for four‐frequency bands

A double layer frequency selective surface (DFSS) composed of modified square loop, cross dipole, and Jerusalem cross loop patch elements is presented to obtain angle and polarization insensitivity and a broad bandwidth. The presented DFSS reflects the X‐ and K‐band signals, while allowing the C‐ and Ku‐band signals to pass through. In this design, a four‐band FSS (including band‐stop FSS and band‐pass FSS) with a broad bandwidth is obtained by controlling the thickness of the air foam between the FSS layers. An equivalent circuit model is developed to provide insight into the response of the DFSS. Experimental verifications are carried out. The computed results agree well with the measured ones. © 2012 Wiley Periodicals, Inc. Microwave Opt Technol Lett 54:2153–2157, 2012; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.27007
A simplified electrical model of the dye-sensitised photoelectrochemical cell

This paper presents a steady-state electrical model of dye-sensitised solar cells with simplified boundary conditions. The codes are considerably concise and the sub-functions had confirmed good extensibility. This model is utilised to predict the current–voltage characteristics and the energy conversion efficiency based on various design and operating properties. The experimental data from the literature have been used to validate the theoretically fitted j−V characteristics of the presented model. Parametric simulations were conducted to analyse the effect of diffusion/drift, dye loading, and electrode thickness on dye-sensitised solar cell performance. Simulated results confirm that diffusion is the major driving force for electron and ion transport, while the drift of electrons is negligible. The model predicts optimal electrode thickness ranging between 10 and 15° μm which is consistent with the thickness (10 μm) used in general experimental studies published in the literature. Additionally, it is observed that there exists a logarithmic relation between the short-circuit current density and the amount of dye adsorption. This observation suggests that there exists a dominated recombination reaction which is responsible towards the high efficiency of DSCs.
Modelling and Measuring Flow and Wind Turbine Wakes in Large Wind Farms Offshore

Average power losses due to wind turbine wakes are of the order of 10 to 20% of total power output in large offshore wind farms. Accurately quantifying power losses due to wakes is, therefore, an important part of overall wind farm economics. The focus of this research is to compare different types of models from computational fluid dynamics (CFD) to wind farm models in terms of how accurately they represent wake losses when compared with measurements from offshore wind farms. The ultimate objective is to improve modelling of flow for large wind farms in order to optimize wind farm layouts to reduce power losses due to wakes and loads. The research presented is part of the EC-funded UpWind project, which aims to radically improve wind turbine and wind farm models in order to continue to improve the costs of wind energy. Reducing wake losses, or even reduce uncertainties in predicting power losses from wakes, contributes to the overall goal of reduced costs. Here, we assess the state of the art in wake and flow modelling for offshore wind farms, the focus so far has been cases at the Horns Rev wind farm, which indicate that wind farm models require modification to reduce under-prediction of wake losses while CFD models typically over-predict wake losses. Further investigation is underway to determine the causes of these discrepancies. Copyright © 2009 John Wiley & Sons, Ltd.
Coursework Declaration and Feedback Form

Increase in the production of wastewater on the daily basis calls for wastewater treatment which is inevitable in all parts of the world. There are many common ways for treating wastewater like chemical process including ion exchange, neutralisation as well as biological methods like activated sludge anaerobic digestion. This thesis focuses on methanogens, a group of organisms found in the euryarchaeotic phylum involved in the methanogenesis process of anaerobic digestion reactors which is involved in the decomposition of organic matter in the sludge under the absence of oxygen. Methanogenesis is the process of producing methane by breaking down organic matter by methanogens. Specific species of methanogens like Methanosarcina Barkeri, Methanothrix soehngenii and Methanobacterium formicicum, Methanococcus maripaludis are selected from two distinct pathways: the acetolactic and hydrogenotrophic pathway. The former two methanogenic archaea belong to acetolactic pathway while the latter two belong to hydrogenotrophic pathway. A method called whole-genome analysis is adopted where the analysis of the entire genome set of the above-mentioned species is done by downloading the genome from a public repository known as NCBI and then running it in Prokka (to annotate the genomes) as well as Roary (takes the annotated assemblies and calculates whole genome). The annotated data is then integrated into metabolic pathways, these are later on analysed with various visualisation tools. This way the genetic characteristics of all the four species are closely studied to understand their capabilities and potential to produce methane. The methanogens belonging to the same pathway are compared for clarity. But these methanogens may not function well in reactor environments with extreme conditions like varying temperatures, PH levels, acetate stress. The ability of the above said species to resist these extreme conditions are tested in Orion cluster using operating systems like Linux and R scripts for its genetic capacity.
Lidar-based Mapping and Localization for Autonomous Racing

Autonomous racing challenges, since the very beginning of automated driving, have inspired new development and defined state of the art. By this, autonomous racing has strongly contributed to the research field of automated driving and indirectly generated important societal impact, increasing traffic safety by reducing or even avoiding human errors in driving. Accurate, reliable and robust perception is a key factor for driverless vehicles, and lidar sensor is currently one of the most promising sensor technologies used for environmental perception. A lidar sensor is versatile as it can be used for mapping purposes, object detection and for the localization. This makes it predestined for many automotive applications. In this publication, the Autonomous Racing Graz team reveals their approaches for mapping and localization using only a lidar sensor, and the modified software components from Autoware. Developed solutions are applied and validated in two ROBORACE Season Alpha challenges. The entire process, from mapping, over path planning, to online localization, is summarized and discussed.
Asphalt Cement Loss Tangent as Surrogate Performance Indicator for Control of Thermal Cracking

This paper documents and discusses the field validation of a simple performance indicator for specification grading of asphalt cement for thermal cracking. The loss tangent, tan(δ), as defined by the ratio of the viscous over elastic modulus, G”/G’, is able to provide a quantitative measure of the sol and gel nature of asphalt cement. As such, tan(δ) relates closely to how well a material is able to relax stress and is therefore investigated as a surrogate performance indicator for thermal cracking. Asphalt cements from 20 contract sites in Ontario, Canada, were tested in torsion bar geometry to determine their viscoelastic properties. The findings show that tan(δ) was able to distinguish good from poor performers for this set of materials with 95% accuracy, which is a considerable improvement over the current bending beam rheometer protocol. In addition to the use of tan(δ), it is suggested that a measure of the critical strain tolerance in the ductile state could be included to provide a significantly improved performance grading method. Most of the worst performing contracts were found to contain zinc. The presence of this element suggests that the use of waste engine oils in asphalt production is likely widespread, since zinc is a universal additive in engine oil and is never found in straight asphalt cement