Miracle-dz/UniFAR-FTUs huggingface dataset:
Advanced control of managed pressure drilling

Automation of managed pressure drilling (MPD) enhances the safety and increases efficiency of drilling and that drives the development of controllers and observers for MPD. The objective is to maintain the bottom hole pressure (BHP) within the pressure window formed by the reservoir pressure and fracture pressure and also to reject kicks. Practical MPD automation solutions must address the nonlinearities and uncertainties caused by the variations in mud flow rate, choke opening, friction factor, mud density, etc. It is also desired that if pressure constraints are violated the controller must take appropriate actions to reject the ensuing kick. The objectives are addressed by developing two controllers: a gain switching robust controller and a nonlinear model predictive controller (NMPC). The robust gain switching controller is designed using H1 loop shaping technique, which was implemented using high gain bumpless transfer and 2D look up table. Six candidate controllers were designed in such a way they preserve robustness and performance for different choke openings and flow rates. It is demonstrated that uniform performance is maintained under different operating conditions and the controllers are able to reject kicks using pressure control and maintain BHP during drill pipe extension. The NMPC was designed to regulate the BHP and contain the outlet flow rate within certain tunable threshold. The important feature of that controller is that it can reject kicks without requiring any switching and thus there is no scope for shattering due to switching between pressure and flow control. That is achieved by exploiting the constraint handling capability of NMPC. Active set method was used for computing control inputs. It is demonstrated that NMPC is able to contain kicks and maintain BHP during drill pipe extension.
INDUSTRIE 4.0 - Automation in weft knitting technology

Industry 4.0 applies to the knitting industry. Regarding the knitting process retrofitting activities are executed mostly manually by an operator on the basis on the operator's experience. In doing so, the knitted fabric is not necessarily produced in the most efficient way regarding process speed and fabric quality aspects. The knitting division at ITA is concentrating on project activities regarding automation and Industry 4.0. ITA is working on analysing the correspondences of the knitting process parameters and their influence on the fabric quality. By using e.g. the augmented reality technology, the operator will be supported when setting up the knitting machine in case of product or pattern change - or in case of an intervention when production errors occur. Furthermore, the RFID-Technology offers great possibilities to ensure information flow between sub-processes of the fragmented textile process chain. ITA is using RFID-chips to save yarn production information and connect the information to the fabric producing machine control. In addition, ITA is currently working on integrating image processing systems into the large circular knitting machine in order to ensure online-quality measurement of the knitted fabrics. This will lead to a self-optimizing and selflearning knitting machine.
Eco-design of a small size industrial fan for ceramic tile cooling

In this paper, the one-dimensional design and the three-dimensional numerical analysis through computational fluid dynamics simulations of a small size fan are carried out. The fan in consideration provides the airflow used for controlling the surface thermal gradient of ceramic tiles between the dryer and the digital printing stage. An experimental campaign performed on a backward-curved centrifugal fan prototype with an optimized air blowing device demonstrates that fan performances (efficiency and air flow rate) meet the eco-design demands and the air velocity field at the blowing device outlet is suitable to obtain the expected heat exchange. The new cooling system can also reduce acoustic emissions up to 3 dB(A) with respect to the existing one.
GUI for Photovoltaic (PV) array Monitoring System

This paper presents the GUI design of the photovoltaic array monitoring system. The system monitor solar irradiance, temperature, short-circuit current, open-circuit voltage, maximum power, voltage and current at maximum power, fill factor and also efficiency of PV array. It was designed on ATmega8535 microcontroller. The GUI display the graph and the data can be saved Microsoft excel format. The calibration of sensors feature is included in GUI. The GUI was developed using VB.Net.
Social correlates of variation in urinary oxytocin concentrations of mantled howler monkeys Alouatta palliata (Gray, 1849) (Primates: Atelidae): A preliminary assessment

There is growing evidence that non-human primate sociality is linked to proximate neuroendocrine mechanisms. Arginine vasopressin, endorphins, and oxytocin may be involved in such mechanisms. Here, we perform a preliminary analysis of the social correlates of variation in urinary oxytocin concentrations in mantled howler monkeys (Alouatta palliata). From January to December 2017, we studied 13 adult mantled howler monkeys belonging to two groups. We recorded the occurrence of social interactions (547.5 observation hours); assessed proximity among group members (2,194 instantaneous recordings); and collected 172 urine samples. Urine samples were assayed (ELISA) for oxytocin concentrations, which were corrected for specific gravity, resulting in 54 analyzed samples. Hormone concentrations increased by approximately 62% when individuals were involved in affiliative interactions. With respect to when no affiliative interactions occurred, urinary oxytocin concentrations were higher when dyads with a low-quality social relationship affiliated. These results agree with previous contentions that this hormone is linked to participation in affiliative interactions. Convergence between these and results from other taxa supports the hypothesis that the biological mechanisms that allow for primate sociality are shared among species.
Somatic Mutation Profiling in Head and Neck Paragangliomas

Abstract Context Head and neck paragangliomas (HNPGLs) are rare neoplasms with a high degree of heritability. Paragangliomas present as polygenic diseases caused by combined alterations in multiple genes; however, many driver changes remain unknown. Objective The objective of the study was to analyze somatic mutation profiles in HNPGLs. Methods Whole-exome sequencing of 42 tumors and matched normal tissues obtained from Russian patients with HNPGLs was carried out. Somatic mutation profiling included variant calling and utilizing MutSig and SigProfiler packages. Results 57% of patients harbored germline and somatic variants in paraganglioma (PGL) susceptibility genes or potentially related genes. Somatic variants in novel genes were found in 17% of patients without mutations in any known PGL-related genes. The studied cohort was characterized by 6 significantly mutated genes: SDHD, BCAS4, SLC25A14, RBM3, TP53, and ASCC1, as well as 4 COSMIC single base substitutions (SBS)-96 mutational signatures (SBS5, SBS29, SBS1, and SBS7b). Tumors with germline variants specifically displayed SBS11 and SBS19, when an SBS33-specific mutational signature was identified for cases without those. Beta allele frequency analysis of copy number variations revealed loss of heterozygosity of the wild-type allele in 1 patient with germline mutation c.287-2A>G in the SDHB gene. In patients with germline mutation c.A305G in the SDHD gene, frequent potential loss of chromosome 11 was observed. Conclusion These results give an understanding of somatic changes and the mutational landscape associated with HNPGLs and are important for the identification of molecular mechanisms involved in tumor development.
Relationship of Functional Outcome With Sarcopenia and Objectively Measured Physical Activity in Patients With Stroke Undergoing Rehabilitation.

This study aimed to investigate the relationship of Functional Independence Measure for motor function (FIM-M) with sarcopenia, and physical activity in patients with stroke undergoing rehabilitation. This cross-sectional study included patients with stroke at a single convalescent rehabilitation hospital. Sarcopenia was diagnosed based on the Asia Working Group for Sarcopenia 2019 criteria. Physical activity was measured as the duration of light-intensity physical activity and moderate to vigorous physical activity using a triaxial accelerometer. Of 80 patients (median age: 72.0 years), 46 (57.5%) were diagnosed with sarcopenia. In multivariate linear regression analysis, FIM-M score was significantly associated with sarcopenia (β = -0.15, p = .043) and light-intensity physical activity (β = 0.55, p < .001). In another model, FIM-M score was significantly associated with moderate to vigorous physical activity (β = 0.27, p = .002) but not with sarcopenia. This study demonstrated that FIM-M was partially associated with sarcopenia and associated with physical activity regardless of intensity in patients with stroke.
The aging human lung mucosa: A proteomics study.

The older adult population, estimated to double by 2050, is at increased risk of respiratory infections and other pulmonary diseases. Biochemical changes in the lung alveolar lining fluid (ALF) and in alveolar compartment cells can alter local immune responses as we age, generating opportunities for invading pathogens to establish successful infections. Indeed, the lung alveolar space of older adults is a pro-inflammatory, pro-oxidative, dysregulated environment that remains understudied. We performed an exploratory, quantitative proteomic profiling of the soluble proteins present in ALF, developing insight into molecular fingerprints, pathways, and regulatory networks that characterize the alveolar space in old age, comparing it to that of younger individuals. We identified 457 proteins that were significantly differentially expressed in older adult ALF, including increased production of matrix metalloproteinases, markers of cellular senescence, antimicrobials, and proteins of neutrophilic granule origin, among others, suggesting that neutrophils in the lungs of older adults could be potential contributors to the dysregulated alveolar environment with increasing age. Finally, we describe a hypothetical regulatory network mediated by the Serum Response Factor that could explain the neutrophilic profile observed in the older adult population.
Identifying Chinese Opinion Expressions with Extremely-Noisy Crowdsourcing Annotations

Recent works of opinion expression identification (OEI) rely heavily on the quality and scale of the manually-constructed training corpus, which could be extremely difficult to satisfy. Crowdsourcing is one practical solution for this problem, aiming to create a large-scale but quality-unguaranteed corpus. In this work, we investigate Chinese OEI with extremely-noisy crowdsourcing annotations, constructing a dataset at a very low cost. Following Zhang el al. (2021), we train the annotator-adapter model by regarding all annotations as gold-standard in terms of crowd annotators, and test the model by using a synthetic expert, which is a mixture of all annotators. As this annotator-mixture for testing is never modeled explicitly in the training phase, we propose to generate synthetic training samples by a pertinent mixup strategy to make the training and testing highly consistent. The simulation experiments on our constructed dataset show that crowdsourcing is highly promising for OEI, and our proposed annotator-mixup can further enhance the crowdsourcing modeling.
ICDBigBird: A Contextual Embedding Model for ICD Code Classification

The International Classification of Diseases (ICD) system is the international standard for classifying diseases and procedures during a healthcare encounter and is widely used for healthcare reporting and management purposes. Assigning correct codes for clinical procedures is important for clinical, operational and financial decision-making in healthcare. Contextual word embedding models have achieved state-of-the-art results in multiple NLP tasks. However, these models have yet to achieve state-of-the-art results in the ICD classification task since one of their main disadvantages is that they can only process documents that contain a small number of tokens which is rarely the case with real patient notes. In this paper, we introduce ICDBigBird a BigBird-based model which can integrate a Graph Convolutional Network (GCN), that takes advantage of the relations between ICD codes in order to create ‘enriched’ representations of their embeddings, with a BigBird contextual model that can process larger documents. Our experiments on a real-world clinical dataset demonstrate the effectiveness of our BigBird-based model on the ICD classification task as it outperforms the previous state-of-the-art models.
Constructing Trinomial Models Based on Cubature Method on Wiener Space: Applications to Pricing Financial Derivatives

This contribution deals with an extension to our developed novel cubature methods of degrees 5 on Wiener space. In our previous studies, we have shown that the cubature formula is exact for all multiple Stratonovich integrals up to dimension equal to the degree. In fact, cubature method reduces solving a stochastic diﬀerential equation to solving a ﬁnite set of ordinary diﬀerential equations. Now, we apply the above methods to construct trinomial models and to price diﬀerent ﬁnancial derivatives. We will compare our numerical solutions with the Black’s and Black–Scholes models’ analytical solutions. The constructed model has practical usage in pricing American-style derivatives and can be extended to more sophis-ticated stochastic models.
Implicit Channel Charting with Application to UAV-aided Localization

Traditional localization algorithms based on features such as time difference of arrival are impaired by non-line of sight propagation, which negatively affects the consistency that they expect among distance estimates. Instead, fingerprinting localization is robust to these propagation conditions but requires the costly collection of large data sets. To alleviate these limitations, the present paper capitalizes on the recently-proposed notion of channel charting to learn the geometry of the space that contains the channel state information (CSI) measurements collected by the nodes to be localized. The proposed algorithm utilizes a deep neural network that learns distances between pairs of nodes using their measured CSI. Unlike standard channel charting approaches, this algorithm directly works with the physical geometry and therefore only implicitly learns the geometry of the radio domain. Simulation results demonstrate that the proposed algorithm outperforms its competitors and allows accurate localization in emergency scenarios using an unmanned aerial vehicle.
Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB Images

In this paper, we present a generalizable model-free 6-DoF object pose estimator called Gen6D. Existing generalizable pose estimators either need high-quality object models or require additional depth maps or object masks in test time, which significantly limits their application scope. In contrast, our pose estimator only requires some posed images of the unseen object and is able to accurately predict the poses of the object in arbitrary environments. Gen6D consists of an object detector, a viewpoint selector and a pose refiner, all of which do not require the 3D object model and can generalize to unseen objects. Experiments show that Gen6D achieves state-of-the-art results on two model-free datasets: the MOPED dataset and a new GenMOP dataset collected by us. In addition, on the LINEMOD dataset, Gen6D achieves competitive results compared with instance-specific pose estimators. Project page: https://liuyuan-pal.github. io/Gen6D/.
Taygete at SemEval-2022 Task 4: RoBERTa based models for detecting Patronising and Condescending Language

This work describes the development of different models to detect patronising and condescending language within extracts of news articles as part of the SemEval 2022 competition (Task-4). This work explores different models based on the pre-trained RoBERTa language model coupled with LSTM and CNN layers. The best models achieved 15^{th} rank with an F1-score of 0.5924 for subtask-A and 12^{th} in subtask-B with a macro-F1 score of 0.3763.
Out-of-Domain Evaluation of Finnish Dependency Parsing

The prevailing practice in the academia is to evaluate the model performance on in-domain evaluation data typically set aside from the training corpus. However, in many real world applications the data on which the model is applied may very substantially differ from the characteristics of the training data. In this paper, we focus on Finnish out-of-domain parsing by introducing a novel UD Finnish-OOD out-of-domain treebank including five very distinct data sources (web documents, clinical, online discussions, tweets, and poetry), and a total of 19,382 syntactic words in 2,122 sentences released under the Universal Dependencies framework. Together with the new treebank, we present extensive out-of-domain parsing evaluation utilizing the available section-level information from three different Finnish UD treebanks (TDT, PUD, OOD). Compared to the previously existing treebanks, the new Finnish-OOD is shown include sections more challenging for the general parser, creating an interesting evaluation setting and yielding valuable information for those applying the parser outside of its training domain.
STD: A Seasonal-Trend-Dispersion Decomposition of Time Series

The decomposition of a time series is an essential task that helps to understand its very nature. It facilitates the analysis and forecasting of complex time series expressing various hidden components such as the trend, seasonal components, cyclic components and irregular fluctuations. Therefore, it is crucial in many fields for forecasting and decision-making processes. In recent years, many methods of time series decomposition have been developed, which extract and reveal different time series properties. Unfortunately, they neglect a very important property, i.e., time series variance. To deal with heteroscedasticity in time series, the method proposed in this work – a seasonal-trend-dispersion decomposition (STD) – extracts the trend, seasonal component and component related to the dispersion of the time series. We define STD decomposition in two ways: with and without an irregular component. We show how STD can be used for time series analysis and forecasting.
Scalable symmetric Tucker tensor decomposition

We study the best low-rank Tucker decomposition of symmetric tensors. The motivating application is decomposing higher-order multivariate moments. Moment tensors have special structure and are important to various data science problems. We advocate for projected gradient descent (PGD) method and higher-order eigenvalue decomposition (HOEVD) approximation as computation schemes. Most importantly, we develop scalable adaptations of the basic PGD and HOEVD methods to decompose sample moment tensors. With the help of implicit and streaming techniques, we evade the overhead cost of building and storing the moment tensor. Such reductions make computing the Tucker decomposition realizable for large data instances in high dimensions. Numerical experiments demonstrate the efficiency of the algorithms and the applicability of moment tensor decompositions to real-world datasets. Finally we study the convergence on the Grassmannian manifold, and prove that the update sequence derived by the PGD solver achieves first- and second-order criticality.
Meet Your Favorite Character: Open-domain Chatbot Mimicking Fictional Characters with only a Few Utterances

In this paper, we consider mimicking fictional characters as a promising direction for building engaging conversation models. To this end, we present a new practical task where only a few utterances of each fictional character are available to generate responses mimicking them. Furthermore, we propose a new method named Pseudo Dialog Prompting (PDP) that generates responses by leveraging the power of large-scale language models with prompts containing the target character’s utterances. To better reflect the style of the character, PDP builds the prompts in the form of dialog that includes the character’s utterances as dialog history. Since only utterances of the characters are available in the proposed task, PDP matches each utterance with an appropriate pseudo-context from a predefined set of context candidates using a retrieval model. Through human and automatic evaluation, we show that PDP generates responses that better reflect the style of fictional characters than baseline methods.
IBM Quantum Platforms: A Quantum Battery Perspective

We characterize for the first time the performances of IBM quantum chips as quantum batteries, specifically addressing the single-qubit Armonk processor. By exploiting the Pulse access enabled to some of the IBM Quantum processors via the Qiskit package, we investigate the advantages and limitations of different profiles for classical drives used to charge these miniaturized batteries, establishing the optimal compromise between charging time and stored energy. Moreover, we consider the role played by various possible initial conditions on the functioning of the quantum batteries. As the main result of our analysis, we observe that unavoidable errors occurring in the initialization phase of the qubit, which can be detrimental for quantum computing applications, only marginally affect energy transfer and storage. This can lead counter-intuitively to improvements of the performances. This is a strong indication of the fact that IBM quantum devices are already in the proper range of parameters to be considered as good and stable quantum batteries comparable to state-of-the-art devices recently discussed in the literature.