Miracle-dz/UniFAR-FTUs huggingface dataset:
On Computability of Computable Problems

Computational problems are classified into computable and uncomputable problems.If there exists an effective procedure (algorithm) to compute a problem then the problem is computable otherwise it is uncomputable.Turing machines can execute any algorithm therefore every computable problem is Turing computable.There are some variants of Turing machine that appear computationally more powerful but all these variants have been proven equally powerful.The main objective of this work is to revisit and examine the computational power of different variants of Turing machines at very fine-grain level.We achieve this objective by constructing a transform technique for Turing computable problems that transforms computable problems into another type of problems, and then we try to compute the transformed problems through different variants of Turing machine.This paper shows the existence of a realizable computational scheme that can establish a framework to analyze computational characteristics of different variants of Turing machine at infinitesimal scale.
Exploring the Potential of Large Language Models in Artistic Creation: Collaboration and Reflection on Creative Programming

Recently, the potential of large language models (LLMs) has been widely used in assisting programming. However, current research does not explore the artist potential of LLMs in creative coding within artist and AI collaboration. Our work probes the reflection type of artists in the creation process with such collaboration. We compare two common collaboration approaches: invoking the entire program and multiple subtasks. Our findings exhibit artists' different stimulated reflections in two different methods. Our finding also shows the correlation of reflection type with user performance, user satisfaction, and subjective experience in two collaborations through conducting two methods, including experimental data and qualitative interviews. In this sense, our work reveals the artistic potential of LLM in creative coding. Meanwhile, we provide a critical lens of human-AI collaboration from the artists' perspective and expound design suggestions for future work of AI-assisted creative tasks.
Influence of insulin sensitivity on food cue evoked functional brain connectivity in children

Objective Insulin resistance during childhood is a risk factor for developing type 2 diabetes and other health problems later in life. Studies in adults have shown that insulin resistance affects regional and network activity in the brain which are vital for behavior, e.g. ingestion and metabolic control. To date, no study has investigated whether brain responses to food cues in children are associated with peripheral insulin sensitivity. Methods We included 53 children (36 girls) between the age of 7-11 years, who underwent an oral Glucose Tolerance Test (oGTT) to estimate peripheral insulin sensitivity (ISI). Brain responses were measured using functional magnetic resonance imaging (fMRI) before and after glucose ingestion. We compared food-cue task-based activity and functional connectivity (FC) between children with low and high ISI, adjusted for age and BMIz. Results Independent of prandial state (i.e., glucose ingestion), children with lower ISI showed higher FC between the anterior insula and caudate and lower FC between the posterior insula and mid temporal cortex than children with higher ISI. Sex differences were found based on prandial state and peripheral insulin sensitivity in the insular FC. No differences were found on whole-brain food-cue reactivity. Conclusions Children with low peripheral insulin sensitivity showed differences in food cue evoked response particularly in insula functional connectivity. These differences might influence eating behavior and future risk of developing diabetes.
Epigenome and early selection determine the tumour-immune evolutionary trajectory of colorectal cancer

Immune system control is a major hurdle that cancer evolution must circumvent. The relative timing and evolutionary dynamics of subclones that have escaped immune control remain incompletely characterized, and how immune-mediated selection shapes the epigenome has received little attention. Here, we infer the genome- and epigenome-driven evolutionary dynamics of tumour-immune coevolution within primary colorectal cancers (CRCs). We utilise our existing CRC multi-region multi-omic dataset that we supplement with high-resolution spatially-resolved neoantigen sequencing data and highly multiplexed imaging of the tumour microenvironment (TME). Analysis of somatic chromatin accessibility alterations (SCAAs) reveals frequent somatic loss of accessibility at antigen presenting genes, and that SCAAs contribute to silencing of neoantigens. We observe that strong immune escape and exclusion occur at the outset of CRC formation, and that within tumours, including at the microscopic level of individual tumour glands, additional immune escape alterations have negligible consequences for the immunophenotype of cancer cells. Further minor immuno-editing occurs during local invasion and is associated with TME reorganisation, but that evolutionary bottleneck is relatively weak. Collectively, we show that immune evasion in CRC follows a “Big Bang” evolutionary pattern, whereby genetic, epigenetic and TME-driven immune evasion acquired by the time of transformation defines subsequent cancer-immune evolution.
Sensing the bactericidal and bacteriostatic antimicrobial mode of action using Raman-Deuterium stable isotope probing (DSIP)

The mode of actions of antibiotics can be broadly classified as bacteriostatic and bactericidal. The bacteriostatic mode leads to the arrested growth of the cells while the bactericidal mode causes cell death. In this work, we report the applicability of Deuterium stable isotope probing (DSIP) in combination with Raman spectroscopy (Raman DSIP) for discrimination among antibiotics on the basis of their mode of action at community level. We optimized the concentration of deuterium oxide required for metabolic activity monitoring without compromising the microbial growth. We also identified a novel carbon-deuterium Raman metabolic qualitative spectral marker in the biofingerprint region. This can be used for early identification of the antibiotic’s mode of action. Our results explores the new perspective which supports the utility of Deuterium based vibrational tags in the field of clinical spectroscopy. Understanding the antibiotic’s mode of action on bacterial cells in a short and objective manner can significantly enhance the clinical management abilities of infectious diseases and may also help in personalised antimicrobial therapy.
Chapter 6 : Modeling the Runtime Integrity of Cloud Servers : a Scoped Invariant Perspective

One of the underpinnings of cloud computing security is the trustworthiness of individual cloud servers. Due to the on-going discovery of runtime software vulnerabilities like buffer overflows, it is critical to be able to gauge the trustworthiness of a cloud server as it operates. The purpose of this chapter is to discuss trust enhancing technologies in cloud computing, specifically remote attestation of cloud servers. We will discuss how remote attestation can provide higher assurance that cloud providers can be trusted to properly handle a customer’s computation and/or data. Then we will focus on the modeling of the runtime integrity of a cloud server, which determines the level of assurance that remote attestation can offer. Specifically, we propose scoped invariants as a primitive for analyzing the software system for its integrity properties. We report our experience with the modeling and detection of scoped invariants for the Xen Virtual Machine Manager.
ALIN results for OAEI 2021

4 Alin is a system for interactive ontology matching. The ALIN version participating in OAEI 2021 applies natural language processing techniques (NLP) to standardize the concept names of the ontologies that participate in the matching process, like last year’s version. In this version, we modiﬁed the grammars related to the ontologies to improve the quality of the alignments. This article describes the participation of Alin at OAEI 2021 and discusses its results.
Conceptual Scaling of RDFS Ontologies

Conceptual scaling is a method, developed in the framework of Formal Concept Analysis (FCA), that transforms a many-valued context (a collection of objects, described by attributes with values, such as name, size or color) into a formal context (a binary schema from which formal concepts are derived). Relational scaling extends conceptual scaling, by taking relations between objects into account. Previous work applies relational scaling to a relational database, transforming the relational database into a relational structure. Relational structures play the role of formal contexts in a relational variant of FCA. In this paper, we present a similar approach, which applies relational scaling to RDFS ontologies.
Multi-Person Visual Focus of Attention from Head Pose and Meeting Contextual Cues

This paper presents a model for visual focus of attention (VFOA ) and conversational estimation in meetings from audio-visual perceptual cues. Rather than independently recognizing the VFOA of each participant from his own head pose, we propose to recognize participants’ VFOA jointly in order to introduce context dependent interaction models that relates to group activity and the social dynamics of communication. To this end, we designed a dynamic Bayesian network (DBN) , whose hidden states are the joint VFOA of all participants, and the meeting conversational events. The observation used to infer the hidden states are the people’s head poses and speaking status. Interaction models are introduced in the DBN by the use of the conversational events, and the projection screen activity which are contextual cues that aﬀect the temporal evolution of the joint VFOA sequence, allowing us to model group dynamics that accounts for people’s tendency to share the same focus, or to have their VFOA driven by contextual cues such as projection screen activity or conversational events. The model is rigorously evaluated on a publicly available dataset of 4 real meetings of a total duration of 1h30 minutes.
Using BERT Models to Automatically Classify Domain Concepts into DOLCE Top-Level Concepts: A Study of the OAEI Ontologies

Top-level ontologies provide a set of foundational concepts that have a well-founded philosophical meaning, being a useful tool in ontology engineering. However, in practice, few domain ontologies integrate top-level concepts. One of the difficulties refers to the selection of appropriate top-level concepts. This paper presents an analysis of top-level categories of a set of well-known domain ontologies from ontology matching benchmarks. Our main hypothesis is that training classification models using only concept comments (i.e., rdfs:comment ) from top-level concepts can improve reported results in the literature. We then consider the best classifiers to estimate the distribution of concepts from Ontology Alignment Evaluation Initiative (OAEI) ontologies aligned to DOLCE top-level concepts.
ALIN results for OAEI 2017

ALIN is an ontology alignment system specialized in the interactive alignment of ontologies. Its main characteristic is the selection of correspondences to be shown to the expert, depending on the previous feedbacks given by the expert. This selection is based on semantic and structural characteristics. ALIN has obtained the alignment with the highest quality in the interactive tracking for Conference data set. This paper describes its configuration for the OAEI 2017 competition and discusses its results.
NET Institute* www.NETinst.org

We use variation in wind speeds at sur(cid:133)ng locations in Switzerland as exogenous shifters of users(cid:146)propensity to post content about their sur(cid:133)ng activity onto an online social network. We exploit this variation to test whether users(cid:146)social ties on the network have a causal e⁄ect on their content generation, and whether content generation in turn has a causal e⁄ect on the users(cid:146)ability to form social ties. Economically significant causal e⁄ects of this kind can produce positive feedback that generate multiplier e⁄ects to interventions that subsidize tie formation. We argue these interventions can therefore be the basis of a strategy by the (cid:133)rm to indirectly facilitate content generation on the site. The exogenous variation provided by wind speeds enable us to measure this feedback empirically and to assess the return on investment from such policies. We use a detailed dataset from an online social network that comprises the complete history of social tie formation and content generation on the site. The richness of the data enable us to control for several spurious confounds that have typically plagued empirical analysis of social interactions. Our results show evidence of signi(cid:133)cant positive feed-back in user content generation. We discuss the implications of the estimates for the management of the content and the growth of the network.
Seismic modeling of fluid substitution in Redwater Reef, Alberta

The objective of this study is to analyze and undertake seismic modeling of CO2 saturation of the Devonian Leduc reservoir characterization at the Redwater reef by using Gassmann fluid substitution seismic modeling. This method was applied to the available wells inside the reef. Zero-offset synthetics were created for these wells before and after fluid substitution. A distinct P-wave velocity decrease occurs from 0% to about 40% of CO2 saturation. From around 40% to 100% of CO2 saturation, the P-wave velocity starts increasing slightly, while the S-wave velocity increases almost linearly with the CO2 saturation increase. There are slight changes in amplitudes between the wet in-situ reservoir reflections and the fluid substitution modeling reflections of Leduc formation. A time shift is observed at the base of the Leduc reservoir. The maximum time shift at the base of the Leduc reservoir and the highest amplitude difference changes are recognized at around 40% of CO2 saturations.
Detection of non-tuberculous mycobacteria in native wildlife species at conservation risk of Argentina

Introduction Non-tuberculous Mycobacteria (NTM) are mainly environmental but can cause opportunistic infections and diseases in humans and animals. Livestock and wild animals can be infected with NTM. In Argentina, there are native wild species facing conservation risks, and they are the focus of protection and reintroduction projects designed to preserve biodiversity in various ecoregions. The aim of this study was to report the presence of NTM in samples collected from four endangered native wild species from nine Argentine provinces, as part of their pre-release health assessment. Methods A total of 165 samples from giant anteater, peccary, tapir and pampas deer were obtained, these included either bronchoalveolar or endotracheal lavages, or oropharyngeal, nasopharyngeal or tracheal swabs. Bacteriological culture followed by molecular identification and sequencing were performed. Results A total of 27 NTM were detected, including Mycobacterium avium subsp. hominissuis, M. intracellulare, M. terrae, M. gordonense, M. kumamotonense, M. fortuitum, M. saskatchewanense, and M. genavense. Results revealed a 16,36% NTM recovery rate, with the giant anteater showing the highest prevalence among the mammals under study. Discussion In Argentina, due to extensive production systems, the interaction between domestic and wild species sharing the same environment is frequent, increasing the exposure of all the species to these NTM. In this way, the transmission of infectious agents from one to another is feasible. Moreover, NTMs might interfere with the diagnosis of bovine tuberculosis and paratuberculosis. These findings emphasize the importance of active health surveillance in conservation programs. It highlights the need to address NTM epidemiology in wildlife and its impact on conservation and public health.
Data-driven time-frequency analysis of seismic data using regularized nonstationary autoregression

Time-frequency decomposition can capture the nonstationary character of seismic data. In this paper, we propose a new method of time-frequency analysis based on regularized nonstationary autoregression coupled with Hilbert-Huang spectrum (RNARHHS). RNARHHS is an empirical-modedecomposition like method but uses regularized nonstationary autoregression to construct its intrinsic mode functions (IMFs). Examples of synthetic and field seismic data show that this method achieves high time-frequency resolution and can detect low-frequency anomalies.
Inpatient vs. Outpatient: A Systematic Review of Information Needs throughout the Heart Failure Patient Journey

The objective of this systematic review was to identify and describe information needs for individuals with heart failure (HF) throughout their patient journey. Six databases were searched (APA PsycINFO, CINAHL Ultimate, Embase, Emcare Nursing, Medline ALL, and Web of Science Core Collection) from inception to February 2023. Search strategies were developed utilizing the PICO framework. Potential studies of any methodological design were considered for inclusion through a snowball hand search. Data from the included articles were extracted by a reviewer, and the extraction accuracy was independently cross-checked by another author. Quality appraisal was assessed using the Mixed-Methods Appraisal Tool. A narrative synthesis was used to analyze all the outcomes according to the Synthesis Without Meta-analysis reporting guidelines. Twenty-five studies (15 quantitative and 10 qualitative) were included. Socioeconomic, cultural, and demographic factors influencing information needs were considered. The top three information needs for outpatients included general HF information, signs and symptoms and disease management strategies. For inpatients, medications, risk factors, and general HF were reported as the top needs. These divergent needs emphasize the importance of tailored education at different stages. Additionally, the review identified gaps in global representation, with limited studies from Africa and South America, underscoring the need for inclusive research. The findings caution against overgeneralization due to varied reporting methods. Practical implications call for culturally sensitive interventions to address nuanced HF patients’ needs, while future research must prioritize standardized reporting, consider diverse patient journey timepoints, and minimize biases for enhanced reliability and applicability.
EVOLUTION OF RENDERING BASED ON RADIANCE FIELDS. THE PALERMO CASE STUDY FOR A COMPARISON BETWEEN NERF AND GAUSSIAN SPLATTING

Abstract. In recent years there has been a rapid diffusion of new digitization methodologies based on radiance fields and the implementation of new rendering processes and learning systems based on neural networks. The article focuses on these new tools and how they can be used for the knowledge and dissemination of Cultural Heritage. A case study is then described regarding the video acquisition of a noble chapel of the Cemetery of Santa Maria dei Rotoli in Palermo to promote knowledge of ‘fragile’ artefacts, exposed to the risk of radical transformation or degradation, and thus protecting their conservation. The research aims to compare the first results obtained through the NeRF and Gaussian Splatting methodology which constitute the current state of the art of this type of processing; both the source algorithms (Nerfacto and 3D Gaussian Splatting) and the Luma AI web app were used, and data management was studied using third-party software such as Blender 3D, Unreal Engine 5.0 and the playcanvas game engine. The results obtained with this case study are of particular interest, above all for the processing of data useful for the visualization of heritage starting from unconventional acquisitions.
Characterization and engineering of plastic-degrading polyesterases jmPE13 and jmPE14 from Pseudomonas bacterium

Polyester plastics are widely used in daily life, but also cause a large amount of waste. Degradation by microbial enzymes is the most promising way for the biobased upcycling of the wastes. However, there is still a shortage of high-performance enzymes, and more efficient polyester hydrolases need to be developed. Here we identified two polyester hydrolases, jmPE13 and jmPE14, from a previously isolated strain Pseudomonas sp. JM16B3. The proteins were recombinantly expressed and purified in E. coli, and their enzymatic properties were characterized. JmPE13 and jmPE14 showed hydrolytic activity towards polyethylene terephthalate (PET) and Poly (butylene adipate-co-terephthalate) (PBAT) at medium temperatures. The enzyme activity and stability of jmPE13 were further improved to 3- and 1.5-fold, respectively, by rational design. The results of our research can be helpful for further engineering of more efficient polyester plastic hydrolases and their industrial applications.
Investigation of the switching and domain wall behaviour in BiFeO3 thin films and mapping of the phase diagram of BiFeO3-LaFeO3 superlattices

Ma these se concentre sur un materiau nomme BiFeO3, l’un des rares multiferroiques a temperature ambiante, affichant ferroelasticite, ferroelectricite, ainsi qu’antiferromagnetisme. Le point de depart de cette recherche repose sur le travail intensif experimental et theorique sur ce materiau, ainsi que sur les progres de la croissance des couches minces qui permettent non seulement le controle au niveau quasi-atomique de la qualite du film, mais aussi de la configuration des domaines ferroelectriques/ferroelastiques. Ce controle nous a permis d’examiner de plus pres des parois de domaines, tant d’un point de vue fondamental avec une approche statistique des interfaces elastiques ancrees, qu’en fonction de leurs nouvelles proprietes fonctionnelles qui pourraient etre utilisee dans des applications futures. De plus, il est aussi possible de fabriquer des heterostructures epitaxiales de couches minces avec des interfaces bien definies, qui pourraient donner lieu a de nouveaux phenomenes physiques.
Predictive Analytics in Mental Health Leveraging LLM Embeddings and Machine Learning Models for Social Media Analysis

The prevalence of stress-related disorders has increased significantly in recent years, necessitating scalable methods to identify affected individuals. This paper proposes a novel approach utilizing large language models (LLMs), with a focus on OpenAI's generative pre-trained transformer (GPT-3) embeddings and machine learning (ML) algorithms to classify social media posts as indicative or not of stress disorders. The aim is to create a preliminary screening tool leveraging online textual data. GPT-3 embeddings transformed posts into vector representations capturing semantic meaning and linguistic nuances. Various models, including support vector machines, random forests, XGBoost, KNN, and neural networks, were trained on a dataset of >10,000 labeled social media posts. The top model, a support vector machine, achieved 83% accuracy in classifying posts displaying signs of stress.