Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge-based formalization of cinematic expression and its application to animation

A virtual camera plays an important part in 3D computer animation and virtual environments. Automating the camera behavior can be useful in allowing faster and easier authoring of linear animation. In interactive virtual environments the decisions about camera behavior need to be made in real time, so an automated component is a must. Previous attempts at automating camera behavior can be classified into two approaches: one approach uses geometric constraint optimization, and the other approach tries to capture cinematic idioms and use them as a basis for camera related decisions. We propose a new approach that tries to take the advantages of both approaches; we use a knowledge-based approach for formalizing cinematic knowledge, and symbolic constraint propagation for reasoning and making decisions about camera behavior. We have followed a knowledge acquisition process converting domain expert principles into declarative rules, and our approach uses non-monotonic reasoning in order to distinguish between absolute rules, default rules, preferences, and arbitrary user choices. Our formalization uses abstraction over the cinematic domain, story, space, and time. We have constructed an automated system that converts screenplays in a high-level formal language into animated 3D movies. We have evaluated this system with with various screenplays in different cinematic genres. As virtual environments and artificial worlds are becoming more popular, complex, and long lasting, there is a need for tools that can create such movie summaries automatically. The summarization paradigm involves two parts: automatically understanding what was happening and which parts were interesting, and figuring out how the interesting parts can be presented in the form of a movie. We describe the stages in transforming a chronicle of events into a movie and present an implementation of such a system. We show the results of transforming a log from a life-simulation game into a movie script.
Clinical profile and treatment outcomes of Boerhaave's syndrome: A 13-year experience from an upper gastrointestinal surgical unit.

Objectives IBoerhaave's syndrome (BS) is a rare, but potentially fatal condition, characterized by barogenic esophageal rupture and carries a high mortality. We aimed to study our institutional experience of managing patients with BS. Material and Methods A retrospective review of patients with BS presenting to a tertiary care centre from 2005 to 2018 was carried out in this study. Clinical presentation, diagnostic evaluations, treatments received, and treatment outcomes were studied. Perforations were classified as early (<24 hours) and delayed (>24 hours), based on the time elapsed. Surgical complications were graded using Clavien-Dindo grade. The Pittsburgh perforation severity score was correlated with short-term treatment outcomes. Results Of the 12 patients [male, 75%; mean (range) age, 53 (28-80) years] included, 10 patients had a delayed (>24 hours) presentation. Chest pain was the dominant symptom (58.3%); six patients presented either in shock (n= 1) or with organ failure (n= 3) or both (n= 2). All the perforations were sited in the lower thoracic esophagus, of which three were contained and nine were uncontained. The seal of the perforation was achieved by surgical repair in four patients (primary repair, 2; repair over a T-tube, 2) and endoscopic techniques in four patients (clipping, 1; stenting, 3). Sepsis drainage [surgical, 7 (open-5, minimally-invasive-2); non-surgical, 5] and feeding jejunostomy were performed in all patients. Five (41.7%) patients received a re-intervention. Median (range) hospital stay was 25.5 (12-101) days, 30-day operative morbidity was 50%, and there was one in-hospital death. The Pittsburgh perforation severity score was as follows: 2-5 in two patients and >5 in 10 patients; there were more delayed presentations, increased surgical interventions, post-procedure morbidity, and in-hospital mortality in the latter group, but the differences were statistically not significant. In 11 patients followed-up [median (range):1507 (17-5929) days], there was no disease recurrence, symptomatic reflux or dysphagia. Conclusion Favourable treatment outcomes, including reduced mortality and organ preservation can be achieved for Boerhaave's perforations, through a multimodality approach. Minimally invasive, endoluminal or open surgical techniques may be safely utilized in its management. The Pittsburgh severity score can be a useful clinical tool that can be used to select the initial intervention and to predict treatment outcomes.
APPLICATION OF ROBOTICS IN MINING-A REVIEW

: The deterioration of mining conditions, the rising cost of labor, the limited potential of both humans and traditional mining machines and the lack of skilled workers are some of the reasons to consider robots in mining. Mining is the practice of extracting resources for utilitarian purposes. The new and developed machines used in mining must operate in harsh, dynamic and uncertain environmental conditions. Robotics technology have recently taken on an international favor and become a significant topic in engineering research. Robotics has a great potential for assisting in the complex task of extracting and processing minerals from the ground. Application of robotics in mining are broad and used in operations such as robotic mapping, surveying, excavation, transportation… This paper will provide an overview of the rapidly evolving fields of mining robotics. The development of new theories and techniques and the potential of both these fields in mine design and planning are examined. And there are many reasons to advance the discipline of mining robotics which include the improvement of productivity, safety and lower cost, this paper also discusses about labor problems and environmental sustainability in mining robotics.
Sequence analysis Exodus: sequencing-based pipeline for quantification of pooled variants

Summary: Next-Generation Sequencing is widely used as a tool for identifying and quantifying microorganisms pooled together in either natural or designed samples. However, a prominent obstacle is achieving correct quantiﬁ-cation when the pooled microbes are genetically related. In such cases, the outcome mostly depends on the method used for assigning reads to the individual targets. To address this challenge, we have developed Exodus—a reference-based Python algorithm for quantiﬁcation of genomes, including those that are highly similar, when they are sequenced together in a single mix. To test Exodus’ performance, we generated both empirical and in silico next-generation sequencing data of mixed genomes. When applying Exodus to these data, we observed median error rates varying between 0% and 0.21% as a function of the complexity of the mix. Importantly, no false negatives were recorded, demonstrating that Exodus’ likelihood of missing an existing genome is very low, even if the genome’s relative abundance is low and similar genomes are present in the same mix. Taken together, these data position Exodus as a reliable tool for identifying and quantifying genomes in mixed samples. Exodus is open source and free to use at: https://github.com/ilyavs/exodus. Availability and implementation: Exodus is implemented in Python within a Snakemake framework. It is available on GitHub alongside a docker containing the required dependencies: https://github.com/ilyavs/exodus. The data underlying this article will be shared on reasonable request to the corresponding author.
Navigation Among Humans

As robot are starting to emerge in human everyday environments, it becomes necessary to find ways, in which they can interact and engage seamlessly in the human environments. Open-ended human environments, such as pedestrian streets, shopping centres, hospital corridors, airports etc., are places where robots will start to emerge. Hence, being able to plan motion in these dynamic environments is an important skill for future generations of robots. To be accepted in our everyday human environments, the robots must be able to move naturally, and such that it is both safe, natural and comfortable for the humans in the environment. Imagine a service robot driving around in an airport. The objective of the service robot is: to drive around among the people in the environment; identify people who need assistance; approach them in an appropriate way; interact with the person to help with the needs of the person. This could be showing the way to a gate, providing departure information, checking reservations, giving information about local transportation, etc. This chapter describes algorithms that handle the motion and navigation related problems of this scenario. To enable robots with capabilities for safe and natural motion in human environments like in the above described scenario, there are a number of abilities that the robot must possess. First the robot must be able to sense and find the position of the people in the environment. Also, it is necessary to obtain information about orientation and velocity of each person. Then the robot must be able to find out if a person needs assistance, i.e. the robot needs to establish the intentions of the people. Knowing the motion state and the intention state of the person, this can be used to generate motion, which is adapted according to the person and the situation. But in a crowded human environment, it is not only necessary for the robot to be able to move safe and naturally around one person, but also able to navigate through the environment from one place to another. So in brief, the robot must be able to:
Initial Design of a Generalization of the 6TiSCH Standard to Support Multiple PHY Layers

Résumé : Ce rapport contient des résultats préliminaires d’une étude pour utiliser plusieurs couches physiques dans un même réseau 6TiSCH. Il s’agit d’une première étape dans le but de publier nos travaux complets, sous le titre (en anglais) “Generalized 6TiSCH for an Agile Multi-PHY Wireless Networking”. Ce rapport détaille l’architecture évaluée, et présente les performance de l’approche, en comparaison avec un réseau qui n’utilise qu’une seule couche physique. Mots-clés : Internet des Objects Industriel, Reseautage Agile, 6TiSCH, OpenWSN.
The variable position of Arctic charr (Salvelinus alpinus (L.)) in subarctic lake food webs

Eloranta, Antti The variable position of Arctic charr (Salvelinus alpinus (L.)) in subarctic lake food webs Jyväskylä: University of Jyväskylä, 2013, 49 p. (Jyväskylä Studies in Biological and Environmental Science ISSN 1456-9701; 261) ISBN 978-951-39-5176-4 (nid.) ISBN 978-951-39-5177-1 (PDF) Yhteenveto: Nieriän vaihteleva asema subarktisten järvien ravintoverkoissa Diss. Arctic charr (Salvelinus alpinus (L.)) is a cold-adapted salmonid fish with a wide circumpolar distribution. Due to its wide fundamental trophic niche (i.e. diet and habitat use), Arctic charr can be used to study energy flow as well as competitive and predator–prey interactions in subarctic lakes. The objective of this thesis was to evaluate how the trophic niche of Arctic charr in subarctic lakes is influenced by various biotic (e.g. intraand interspecific resource competition and predation) and abiotic (e.g. seasonality and lake morphometry) factors. Stomach contents and stable isotope analyses indicated marked between-lake and between-individual differences in Arctic charr diet and habitat use. In Saanajärvi, Arctic charr relied mainly on littoral production regardless of season or individual size, probably due to high primary production and lack of sympatric fish species in the littoral zone. In contrast, Arctic charr showed a reduced littoral reliance when the littoral trophic niche was dominated by brown trout, and an increased prevalence of piscivorous foraging when coexisting with planktivorous whitefish and benthivorous grayling. The final results from a total of 17 subarctic lakes indicated that Arctic charr shifted from littoral to a more pelagic trophic niche with increasing lake area and fish species richness. The shift of Arctic charr to a more pelagic piscivorous niche in large lakes was evidently promoted by the existence of small, planktivorous prey fish species and increased interspecific resource competition and/or predation in the littoral zone. The results demonstrate that littoral and pelagic food-web compartments in subarctic lakes can be integrated by generalist Arctic charr, but the energy flow pathways supporting this top consumer can be strongly influenced by prevailing intraand interspecific interactions as well as by the morphometric characteristics of the lakes. The findings highlight that a comprehensive view of food-web structures is fundamental for predicting the likely responses of fish communities and subarctic lake ecosystems to potential environmental changes.
Bibliometric and content analysis of meta-analysis studies in STEM education

The research aims to analyse the most current (2015-2023) literature on STEM education to gain a deeper understanding of current trends, priorities and developments in the field of STEM education. Scientific studies determined within the scope of the study were examined using bibliometric and content analysis methods, which are among the systematic compilation methods. First, access to the WoS database was gained, and a subject-based search was conducted using the keywords "STEM education" and "Meta-analysis. " In this context, 38 scientific studies containing these relevant keywords were found in the search. The collected data were initially analyzed on the basis of descriptive attributes in the WoS. Subsequently, the listed publications were analyzed using the bibliometric analysis tool VOSviewer for bibliographic attributes, including citation counts, co-citation, co-authorship, co-occurrence, and bibliographic coupling analysis types, based on authors, institutions, and countries. Finally, 18 articles from these scientific publications were evaluated through content analysis in terms of various characteristics of following the PRISMA flowchart. The most prominent keywords associated with the examined concepts included technology-related terms like "computer-based learning," "digital game-based learning," "academic achievement," "active learning," and "problem-centered instruction. " Furthermore, the general focus of scientific studies appears to be on determining the impact of a STEM education method on student performance.
to 9-Year-Old Children Sinuses on DTI Parameters of the Brain in 6-The Influence of Nonaerated Paranasal

BACKGROUND AND PURPOSE: DTI is prone to susceptibility artifacts. Air in the paranasal sinuses can cause ﬁ eld inhomogeneity, thus affecting measurements. Children often have mucus in their sinuses or no pneumatization of them. This study investigated the in ﬂ uence of lack of air in the paranasal sinuses on measurements of WM diffusion characteristics. MATERIALS AND METHODS: The study was embedded in the Generation R Study, a prospective population-based birth cohort in Rotterdam (the Netherlands). Brain MR imaging studies (1070 children, 6 – 9years of age) were evaluated for mucosal thickening of the paranasal sinuses. Nonaeration of the paranasal sinuses (modi ﬁ ed Lund-Mackay score) was compared with that in a randomly selected control group. The relationship between nonaerated paranasal sinuses and fractional anisotropy and mean diffusivity in the DTI ﬁ ber tracts was evaluated using ANCOVA and independent t tests. RESULTS: The prevalence of mucosal thickening was 10.2% (109/1070). The mean modi ﬁ ed Lund-Mackay score was 6.87 (SD, 3.76). In 52.3% (57/109), $ 1 paranasal sinus was not pneumatized. The results are reported in effect sizes (Cohen ’ s d ). Lower mean fractional anisotropy values were found in the uncinate fasciculus (right uncinate fasciculus/right frontal sinus, d ¼ – 0.60), superior longitudinal fasciculus (right superior longitudinal fasciculus/right ethmoid sinus, d ¼ – 0.56; right superior longitudinal fasciculus/right sphenoid sinus, d ¼ – 2.09), and cingulate bundle (right cingulum bundle/right sphenoid sinus, d ¼ – 1
Genomic insights and temperature-dependent transcriptional responses of Kosmotoga olearia, a deep-biosphere bacterium that can grow from 20°C to 79°C

1 responses of Kosmotoga olearia, a deep-biosphere bacterium that can 2 grow from 20oC to 79oC 3 Stephen M. J. Pollo, Abigail A. Adebusuyi, Timothy J. Straub, Julia M. Foght, Olga 4 Zhaxybayeva and Camilla L. Nesbø 5 Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada 6 Department of Biological Sciences, Dartmouth College, Hanover, NH, USA 7 Department of Computer Science, Dartmouth College, Hanover, NH, USA 8 Centre for Ecological and Evolutionary Synthesis, Department of Biology, University of Oslo, Oslo, 9 Norway 10 * Correspondence: 11 Corresponding Author 12 nesbo@ualberta.ca 13 Current address: Centre for Research & Innovation, Grande Prairie Regional College, Grande 14 Prairie, AB 15
Online Failure Prediction Through Fault Injection and Machine Learning: Methodology and Case Study

Online Failure Prediction (OFP) is a technique that attempts to predict incoming failures to mitigate their consequences. Machine Learning (ML) has been successfully used to create predictive models for OFP, but failures are rare, and thus failure data are typically not available. Fault injection has been accepted as a viable alternative to generate failure data. However, this raises several challenges, such as how to process the data to create and assess predictive models. The characteristics of fault injection campaigns (e.g., repeated/controlled experiments) and OFP (e.g., autocorrelation) require specific considerations. This work proposes a six-stage methodology for using failure data generated through fault injection to create accurate/representative models for OFP. It overviews the various phases, from generating and processing the data, to creating and assessing the performance of the models up to their deployment, while considering the intrinsic characteristics of the problem. As a case study, we apply the methodology to develop failure predictors for the Linux Operating System (OS). Results show that the proposed methodology led to accurate predictive models that could also generalize to failures that occur under different execution profiles, whilst using traditional techniques resulted in over-optimistic observations.
Multiple detectors-based Bayesian inference and statistics analysis for incipient fault detection

To establish a secure and dependable operational setting for practical industrial processes, it is crucial to detect incipient faults promptly and accurately. In this work, a novel data-driven process monitoring approach is presented for incipient fault detection. First, inspired by the ensemble learning algorithm, multiple Mahalanobis distance (MD) detectors are trained, and corresponding detection indices for each sample are calculated. Second, with Bayesian inference, all the obtained indices are converted to probabilities for samples being faulty. Furthermore, the statistics of probabilities are analyzed by implementing sliding time window technique, ultimately achieving incipient fault detection. Finally, the effectiveness of the proposed strategy is demonstrated by a numerical example and the continuous stirred tank reactor benchmark process. The proposed approach inherits the advantages of ensemble learning, so it has the capability to improve the stability of detection performance. Meanwhile, due to the utilization of Bayesian inference, together with the sliding time window technique, it can better differentiate normal and faulty samples and improve the sensitivity to incipient faults.
Precise Lazy Initialization for Programs with Complex Heap Inputs

Lazy initialization enables symbolic execution for programs with heap-allocated inputs. It starts the program execution with a symbolic heap and concretizes it on demand as the program accesses it. However, the main challenge of lazy initialization is efficiently determining whether the current symbolic heap becomes infeasible with respect to the program’s precondition. Pruning infeasible heaps is crucial to avoid significant runtime overhead and false alarms.In this paper, we propose PLI (Precise Lazy Initialization), an approach that precisely decides whether there exists a concretization of the current symbolic heap that satisfies the program’s precondition. Unlike previous approaches, PLI also takes into account the constraints in the path condition to determine the feasibility of the current symbolic heap. Furthermore, PLI allows preconditions to be specified as standard operational predicates for concrete structures, eliminating the need for additional specifications tailored to symbolic heaps.In our empirical evaluation, PLI demonstrated comparable performance to existing lazy approaches while reducing the number of explored paths by 43% (all infeasible) and eliminating all false alarms in the analysis. Moreover, PLI exhibited faster execution and better scalability compared to "eager" (enumeration-based) approaches, achieving a 67% reduction in explored paths.
Ring Oscillator Aging Characterization in Conventional CMOS Technologies

Ring Oscillators (RO) are widely used to capture the aging in digital circuits of advanced CMOS technology nodes. RO’s also provide critical insights into the aging of conventional CMOS technologies utilizing poly-Si gates and SiO2 or SiON gate dielectrics. The use of an optimized Design-For-Reliability (DFR) RO allows separate measurements decoupling nMOS and pMOS aging of static and dynamic stress. It is shown that Negative Bias Temperature Instability (NBTI) dominates for short stress duration and stress voltages close to operation condition while Channel Hot Carrier (CHC) degradation is the major contributor at high stress voltage.
Stress-Resiliency of AI Implementations on FPGAs

FPGAs have become a popular choice for machine learning acceleration for both cloud and edge devices. While traditional neural networks show impressive performance in classification tasks, Hyperdimensional Computing (HDC) is rapidly emerging as a promising novel machine learning approach for its hardware-friendly inference. In HDC, classes are embedded into high-dimensional vectors during training, and inputs can be classified by computing similarity metrics between class-vectors during inference. HDC inference is especially promoted in terms of its resiliency against errors, attributed to the large inherent redundancy. In this work, we perform a thorough experimental investigation of the fault resiliency of various FPGA-based machine learning implementations under different aspects of stress, comparing HDC with classical neural network approaches. We explore both the amount of faulty classifications as well as system crashes while subjecting the designs to timing stress using overclocking, voltage stress with excessive switching activity, and thermal stress.
Meta-learning-based fault-tolerant controller for aircraft attitude control system

In this study, for aircraft system with actuator fault, a meta-learning-based fault-tolerant control scheme is designed by combining integral reinforcement learning algorithm and fault compensator. For a fault-free system, a neural networks-based integral reinforcement learning algorithm is first developed to solve the Hamilton-Jacobi-Bellman equation, which could directly derive an approximate optimal control law. Then, a fault compensator synthesized into the optimal control law achieve fault-tolerant control of an aircraft system. The meta-learning concept is also adopted to address the unknown and sudden faults of the aircraft system, which can improve the convergence speed of the system state and the practical applicability of the designed fault-tolerant controller. It is proven that the closed-loop system is ultimately uniformly bounded by Lyapunov method. Finally, a simulation example is conducted to verify the proposed scheme's effectiveness.