Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-Dimensional Source Selection Based on Topic Modelling

Access to information in multisource environments is facing many problems. One of them is the source selection problem. As more and more sources become available on the internet, how to select the relevant sources that meet the user needs is a big challenge. In this paper, we propose a multi-dimensional source selection approach based on topic modelling, which integrates both the social dimension and the intelligent dimension in order to optimize the source selection according to diﬀerent user interests. Social tagging data is analyzed to discover relevant topics of user interests and latent relationships between users and sources based on topic modelling. By intelligently exploring a large search space of possible solutions, an (optimal) selection of sources is found using an intelligent method (a genetic algorithm). The proposed approach is evaluated on real data sources. The experimental results demonstrate that the proposed approach outperforms state-of-the-art source selection algorithms.
Expressive Scene Graph Generation Using Commonsense Knowledge Infusion for Visual Understanding and Reasoning

Scene graph generation aims to capture the semantic elements in images by modelling objects and their relationships in a structured manner, which are essential for visual understanding and reasoning tasks including image captioning, visual question answering, multimedia event processing, visual storytelling and image retrieval. The existing scene graph generation approaches provide limited performance and expressiveness for higher-level visual understanding and reasoning. This challenge can be mitigated by leveraging commonsense knowledge, such as related facts and background knowledge, about the semantic elements in scene graphs. In this paper, we propose the infusion of diverse commonsense knowledge about the semantic elements in scene graphs to generate rich and expressive scene graphs using a heterogeneous knowledge source that contains commonsense knowledge consolidated from seven diﬀerent knowledge bases. The graph embeddings of the object nodes are used to leverage their structural patterns in the knowledge source to compute similarity metrics for graph reﬁnement and enrichment. We performed experimental and comparative analysis on the benchmark Visual Genome dataset, in which the proposed method achieved a higher recall rate ( R @ K = 29 . 89 , 35 . 4 , 39 . 12 for K = 20 , 50 , 100) as compared to the existing state-of-the-art technique ( R @ K = 25 . 8 , 33 . 3 , 37 . 8 for K = 20 , 50 , 100). The qualitative results of the proposed method in a downstream task of image generation showed that more realistic images are generated using the commonsense knowledge-based scene graphs. These results depict the eﬀectiveness of commonsense knowledge infusion in improving the performance and expressiveness of scene graph generation for visual understanding and reasoning tasks.
Robust Detection of Conversational Groups Using a Voting Scheme and a Memory Process

Studies in human-human interaction have introduced the concept of F-formation to describe the spatial organization of participants during social interaction. This paper aims at detecting such F-formations in images of video sequences. The proposed approach combines a voting scheme in the visual field of each participant and a memory process to make the detection in each frame robust to small, irrelevant changes of participant’s behavior. Results on the MatchNMingle data set demonstrate the good performances of this approach
Identification of cost effective inspection strategies by means of tailored quality tools, cost modeling and simulation

Delivering high quality products is key for successfully competing companies in global markets. Apart from well-defined customer needs high quality products are a consequence of stable manufacturing processes and sound inspection systems. Although aspiring Zero Defects, it remains only a theoretical objective in manufacturing. Failing manufacturing processes paired with inspection errors implies product delivery with nonconformities to customers with inestimable consequences. This is why companies allocate part of their limited budget to investments in (1) process improvement, (2) better product appraisal or (3) a mixture of both. Process improvement may be achieved with the application of quality tools. But complex processes and specialized companies of different industries demand tailored tools. To imperfect manufacturing processes companies find remedies by installing huge product inspection, often upon the entire production volume. With regard to product inspection companies have the choice between three strategies: (1) single inspection, (2) re-inspection of rejected parts and (3) re-inspection of accepted parts. Each strategy implies different consequences on total quality costs. But, if a cost effective strategy is found there may be the need to deviate from it if quality improvement according to progress functions takes place. A methodology is presented which integrates the prioritization of alternatives, cost models, progress functions and simulation. This serves the purpose to determine conditions of a favorable inspection strategy if progress ratios can be assumed for (1) process improvement or (2) better product appraisal. Case studies of the automotive industry are examined by using the elaborated methodology. Results indicate that the prioritization of individual nonconformities to be selected for quality improvement projects with the consideration of progress effects, determine the region of a favorable, cost effective manufacturing inspection strategy. Choosing the right quality improvement option contributes to the minimization of total quality costs. Moreover, the choice of a right inspection strategy should be reconsidered after improvement projects are concluded. This thesis presents work to compliment and extend the scientific knowledge on the topic of total quality management. Both formal methodologies and practical modeling tools are provided to be applied by practitioners such as quality engineers from different manufacturing industries.
Performance and emission evaluation of pure biodiesel from non-edible feedstock and waste oil in a diesel engine

Biodiesel prepared from Jatropha, Moringa and restaurant waste oil were evaluated in a 3.5 kw diesel engine to determine their performance and pollutant emission. The brake specific (BS) emissions across the full load spectrum were benchmarked against the United States Environmental Protection Agency (US, EPA) and the European Union (EU) emission caps. Results showed that the brake specific fuel consumption (BSFC) and brake thermal efficiencies gave marginal differences between conventional diesel fuel (ndiesel) and the biodiesel fuel samples (±4% and ±5 respectively at peak load). Carbon monoxide (CO), unburnt hydrocarbon (UHC) and particulate matter (PM) emissions (in parts per million) showed a decreasing trend with load increase and were lower than those of ndiesel fuel samples. Oxides of nitrogen (NOX) emission for the biodiesel were observed to be lower than those of ndiesel. This was because the 1650 k peak temperature to activate thermal NOX was sparingly breached for the biodiesels. BSHC for all fuel types failed the EPA as well as the EU emission caps under all loading conditions. All tested fuel samples met the regulatory standards at load conditions exceeding 65%. Brake specific particulate matter (BSPM) for all fuel type fell short of the EPA and EU standard, except those of waste oil and jatropha biodiesel, with BSPM below the EU upper limit of 0.85 g/Kwh.
Intake air heating strategy to reduce cold-start emissions from diesel engines

ABSTRACT Diesel engine cold-start performance is influenced by the ambient temperature conditions, engine design, fuel, lubricant and engine operating conditions. This paper investigates the potential of air heating strategy in reducing the particulate emissions from the latest generation of turbocharged direct-injection diesel engines at very cold ambient temperature conditions. New European Driving Cycle (NEDC) tests were conducted at −7 °C ambient temperature conditions for the different intake air temperatures. Intake air heating reduced hydrocarbon emissions by 35 and 55% and nitrogen oxides emissions by 8 and 10% for intake air temperatures of 5 and 15 °C, respectively, during the cold-start phase of the emission driving cycle. The cold-start phase of the NEDC cycle accounted for 1/4 of the total particulates, and the use of intake air heating reduced its proportion to 1/5. More than 50% of the particulates are less than 23 nm in diameter for all the intake air temperature conditions, and the intake air heating reduced the particulate number concentration significantly. The surface area of accumulation mode particles was higher than that of the nucleation mode, and the total particle surface area decreased with the intake air heating.
Enzyme Activity Prediction of Sequence Variants on Novel Substrates using Improved Substrate Encodings and Convolutional Pooling

Protein engineering is currently being revolutionized by deep learning applications, especially through natural language processing (NLP) techniques. It has been shown that state-of-the-art self-supervised language models trained on entire protein databases capture hidden contextual and structural information in amino acid sequences and are capable of improving sequence-to-function predictions. Yet, recent studies have reported that current compound-protein modeling approaches perform poorly on learning interactions between enzymes and substrates of interest within one protein family. We attribute this to low-grade substrate encoding methods and over-compressed sequence representations received by downstream predictive models. In this study, we propose a new substrate-encoding based on Extended Connectivity Fingerprints (ECFPs) and a convolutional-pooling of the sequence embeddings. Through testing on an activity profiling dataset of haloalkanoate dehalogenase superfamily that measures activities of 218 phosphatases against 168 substrates, we show substantial improvements in predictive performances of compound-protein interaction modeling. In addition, we also test the workflow on three other datasets from the halogenase, kinase and aminotransferase families and show that our pipeline achieves good performance on these datasets as well. We further demonstrate the utility of this downstream model architecture by showing that it achieves good performance with six different protein embeddings, including ESM-1b (Rives et al., 2021), TAPE (Rao et al., 2019), ProtBert, ProtAlbert, ProtT5, and ProtXLNet (Elnaggar et al., 2021). This study provides a new workflow for activity prediction on novel substrates that can be used to engineer new enzymes for sustainability applications.
Dextrous motion control of a redundant SCARA robot

A kinematically redundant SCARA type robot with increased manoeuvrability provided by an additional axis is presented. By means of a dextrous motion control approach the extra degree of freedom is used to perform several secondary goals in order to maintain the system availability even in restricted configurations. For this purpose the primary end-effector task is enhanced by an appropriate homogeneous motion from a gradient projection onto the Jacobian null space. Different performance criteria are used to avoid joint limits, singularities or nonconservative joint motions. In order to enable safe manipulation in restricted environments the robot arm is prevented from colliding with work space obstacles represented by artificial potential fields.
Optimization of the morphological pyramid for detecting defects on the surface of ferrite cores

The paper describes the optimization of the thresholds in the morphological pyramid detecting defects on the surface of ferrite cores. A morphological defect detector generates a gray level image, called a map of defects, which specifies the areas of the defects as well as the measure of defectiveness for each pixel. The defects of ferrite cores vary in a very wide range of sizes and shapes. The morphological pyramid makes possible detection of defects at various image resolutions. The result is a pyramid of gray level maps of defects. However, the gray level maps are quite noisy, and it is necessary to reject all the unimportant blobs from the map while maintaining the most important ones which allow one to accept or reject the core. A crucial step in processing the gray level maps of defects is thresholding, which converts a gray level map of defects into a binary map in which the defects include only those pixels for which the measure of defectiveness exceeds a specified threshold. The thresholds have to be chosen for all levels of the pyramid, and they need readjustment whenever the lighting conditions are changed. The genetic algorithm allows one to automatically optimize the thresholds. The main difficulty with the genetic algorithm is that it needs a definition of the fitness function assigning a numerical value to the chromosomes in which the thresholds are encoded. The paper presents a method for conversion of the pictorial information contained in the maps of defects into the numerical value of the fitness function. Experimental results confirm the usefulness of the approach described. The optimization of the pyramid was developed for the possible use by the industrial manufacturer of ferrite cores.
Identification of Basidiobolus species from the common house gecko (Hemidactylus frenatus) and their association with isolates from human basidiobolomycosis

The genus Basidiobolus contains large groups of terrestrial fungi including the etiological agents of human gastrointestinal basidiobolomycosis (GIB). This study aimed to identify Basidiobolus species from the common house gecko and to compare them with human GIB isolates. Gecko and human GIB samples were collected from Muhayil Aseer area, south Saudi Arabia (2017-2019). Isolation of fungi from the gut contents of geckos was performed using Sabouraud dextrose agar incubated aerobically at 30°C for five days. Suspected Basidiobolus species were tentatively identified using routine bench tests and phenotypes were authenticated by phylogenetic analysis of the large subunit ribosomal RNA gene. Isolates (n = 10) were found to have zygomycete-like phenotypic characteristics. In the 28S ribosomal RNA gene phylogenetic tree, the strains assembled in the subclade encompass Basidiobolus spp. along with previously reported isolates from human’ GIB. The strains had a close resemblance with Basidiobolus haptosporus (99.97%) as well as with B. haptosporus var. minor (99.97%). One isolate (L3) falls within the subclade containing B. haptosporus strain NRRL28635. The recovery of similar isolates from both humans and gecko lizards in one geographic region is an important development toward knowing risk factors for GIB.
Assessing the offensive productivity of NHL players using in-game win probabilities

Hockey journalists and statisticians currently lack many of the empirical tools available in other sports. In this paper I introduce a win probability metric for the NHL and use it to develop a new statistic, Added Goal Value, which evaluates player offensive productivity. The metric is the first of its kind to incorporate powerplay information and is the only NHL in-game win probability metric currently available. I show how win probabilities can enhance the narrative around an individual game and can also be used to evaluate playoff series win probabilities. I then introduce Added Goal Value which improves upon traditional offensive player statistics by accounting for game context. A player's AGV has a strong positive correlation between seasons, making it a useful statistic for predicting future offensive productivity. By accounting for the context in which goals are scored, AGV also allows for comparisons to be made between players who have identical goalscoring rates. The work in this paper provides several advances in hockey analytics and also provides a framework for unifying current and future work on Corsi, Fenwick, and other NHL analytics.
Data Valuation in Machine Learning: "Ingredients", Strategies, and Open Challenges

Data valuation in machine learning (ML) is an emerging research area that studies the worth of data in ML. Data valuation is used in collaborative ML to determine a fair compensation for every data owner and in interpretable ML to identify the most responsible, noisy, or misleading training examples. This paper presents a comprehensive technical survey that provides a new formal study of data valuation in ML through its “ingredients” and the corresponding properties, grounds the discussion of common desiderata satisfied by existing data valuation strategies on our proposed ingredients, and identifies open research challenges for designing new ingredients, data valuation strategies, and cost reduction techniques.
Mediated by Monocytes An Innate Response to Allogeneic Nonself

The mammalian innate immune system has evolved diverse strategies to distinguish self from microbial nonself. How the innate immune system distinguishes self-tissues from those of other members of the same species (allogeneic nonself) is less clear. To address this question, we studied the cutaneous hypersensitivity response of lymphocyte-deﬁcient RAG (cid:1) / (cid:1) mice to spleen cells transplanted from either allogeneic or syngeneic RAG (cid:1) / (cid:1) donors. We found that RAG (cid:1) / (cid:1) mice mount a speciﬁc response to allogeneic cells characterized by swelling and inﬁltration of the skin with host monocytes/macrophages and neutrophils. The response required prior priming with allogeneic splenocytes or skin grafts and exhibited features of memory as it could be elicited at least 4 wk after immunization. Neither depletion of host NK cells nor rechallenging immunized mice with F 1 hybrid splenocytes inhibited the response, indicating that the response is not mediated by NK cells. Depletion of host monocytes/macrophages or neutrophils at the time of rechallenge signiﬁcantly diminished the response and, importantly, the adoptive transfer of monocytes from alloimmunized RAG (cid:1) / (cid:1) mice conferred alloimmunity to naive RAG (cid:1) / (cid:1) hosts. Unlike NK- and T cell-dependent allore-sponses, monocyte-mediated alloimmunity could be elicited only when donor and responder mice differed at non-MHC loci. These observations indicate that monocytes mount a response to allogeneic nonself, a function not previously attributed to them, and suggest the existence of mammalian innate allorecognition strategies distinct from detection of missing self-MHC molecules by NK cells.
Mining database and verification of PIK3CB as a marker predicting prognosis and immune infiltration in renal clear cell carcinoma

Abstract Background: Kidney renal clear cell carcinoma (KIRC) was the most prevalent malignancy of urinary system. Phosphatidylinositol 3-kinase pathway exerted a vital function in tumor proliferation, invasion, and survival by integrating extracellular growth signals. Methods: The expression and clinical significance of PIK3CB in KIRC was explored using bioinformatics analysis. And qRT-PCR was performed to verify our results. Results: PIK3CB was downregulated at mRNA and protein level in KIRC. KIRC patients with low PIK3CB expression indicated a worse overall survival, progression free survival, and disease-free survival. A predictive nomogram was constructed and demonstrated that the predicted calibration plots for 1-year, 3-year, and 5-year OS probabilities showed good agreement compared with the actual OS of KIRC patients. Validation research demonstrated a downregulation of PIK3CB in KIRC tissues and a poor overall survival in KIRC patients with low PIK3CB expression. Furthermore, Cox regression analysis revealed that PIK3CB expression was an independent prognostic factor for KIRC. PIK3CB expression showed positive correlation with the abundance of immune cells. Moreover, enrichment analysis revealed that PIK3CB and associated genes were mainly associated with RNA splicing and JAK-STAT signaling pathway. Conclusion: Our study suggested that PIK3CB was a potential biomarker for prognosis and correlated with immune infiltrates in KIRC.
High-Dose Vancomycin in the Treatment of Clostridioides difficile Infection

Background There is significant variability in guideline recommendations regarding the dosing of vancomycin in treating Clostridioides difficile infection (CDI). The aim of this systematic review and meta-analysis was to investigate the efficacy of high-dose vancomycin compared with other current standard of care and other CDI treatment regimens. Methods We conducted a comprehensive literature search in PubMed, the Cochrane Library, Embase, and Web of Science for publications that studied the comparative effectiveness of high-dose vancomycin to other CDI treatment regimens for the period up to July 2020. Results A total of 14 studies, 6 randomized controlled trials (372 participants) and 8 observational controlled studies (952 participants), with a total of 1324 patients were included in the final analysis. There was no statistically significant effect between high-dose vancomycin and other treatment options of CDI on clinical cure/response (odd ratio [OR], 0.70; 95% confidence interval [CI], 0.34 to 1.43; I2 = 59%), clinical relapse and recurrence (OR, 1.01; 95% CI, 0.72–1.42; I2 = 61%), and mortality (OR, 1.03; 95% CI, 0.37–2.84; I2 = 44%). Conclusions High-dose oral vancomycin was not superior to other treatment regimens for the outcomes of clinical response and cure, recurrence, and mortality of CDI.
A retrospective observational study

To determine the feasibility on maintaining oxygenation of high-flow nasal oxygenation (HFNO) with bispectral index-guided intravenous anesthesia for nonintubated interventional bronchoscopy (NIIB). If desaturation happens, the factors influencing intraprocedural desaturation were also analyzed. This is a single-center retrospective study on patients receiving NIIB with HFNO and intravenous anesthesia guided by bispectral index levels to the depth of general anesthesia, which were between 40 and 60. Intraprocedural desaturation (SPO2<90%) and complications (bleeding, delayed discharge, unexpected admission) were collected. Factors affecting desaturation and complicationswere analyzed including patients’ factors (age, American Society of Anesthesiologists classification, bodymass index [BMI]), procedural factors (procedural time, with or without use of cryoprobe), and setting (outpatient or hospitalized). Records of 223 patients receiving NIIB were collected. The NIIB procedures time was 56.1±26.8minute. Sixty patients (26.9%) presented desaturation events. Higher BMI, but not procedure time or setting, was significantly associated with desaturation. The desaturation were resolved after relieving upper airway obstruction but 1 patient required bag–valve–mask ventilation to restore oxygenation. Accidental massive bleeding and intraprocedural desaturation during tracheal and bronchial recannulation with cryoprobes happened in 2 patients and 1 of them was admitted to intensive care unit. HFNO is feasible to maintain oxygenation during NIIB only if there is effective upper airway management especially for patients with higher BMI. Longer procedural time and different setting did not affect the desaturation rate. Complications and unexpected admission were associated with the use of cryoprobes. Abbreviations: ASA = American Society of Anesthesiologists, BIS = bispectral index, BMI = body mass index, HFNO = highflow nasal oxygenation, IB = interventional bronchoscopy, NIIB = nonintubated interventional bronchoscopy, PACU = postanesthesia care unit, TBC = transbronchial cryobiopsy.