Miracle-dz/UniFAR-FTUs huggingface dataset:
Query2Particles: Knowledge Graph Reasoning with Particle Embeddings

Answering complex logical queries on incomplete knowledge graphs (KGs) with missing edges is a fundamental and important task for knowledge graph reasoning. The query embedding method is proposed to answer these queries by jointly encoding queries and entities to the same embedding space. Then the answer entities are selected according to the similarities between the entity embeddings and the query embedding. As the answers to a complex query are obtained from a combination of logical operations over sub-queries, the embeddings of the answer entities may not always follow a uni-modal distribution in the embedding space. Thus, it is challenging to simultaneously retrieve a set of diverse answers from the embedding space using a single and concentrated query representation such as a vector or a hyper-rectangle. To better cope with queries with diversified answers, we propose Query2Particles (Q2P), a complex KG query answering method. Q2P encodes each query into multiple vectors, named particle embeddings. By doing so, the candidate answers can be retrieved from different areas over the embedding space using the maximal similarities between the entity embeddings and any of the particle embeddings. Meanwhile, the corresponding neural logic operations are defined to support its reasoning over arbitrary first-order logic queries. The experiments show that Query2Particles achieves state-of-the-art performance on the complex query answering tasks on FB15k, FB15K-237, and NELL knowledge graphs.
Clinical Factors Associated with COVID-19 Severity in Chronic Hospitalized Infants and Toddlers: Data from a Center in the West Part of Romania

Background: The risk factors for developing a severe form of COVID-19 in young children are poorly understood. Methods: A single-center retrospective study was conducted to quantify and analyze the clinical risk profile of children admitted to the Pediatric Clinic for Nutritional Recovery. Results: Overall, 51.5% (n = 17) of children were infected with SARS-CoV-2, all symptomatic, and five of them (29.4%) developed a severe form. A positive clinical pulmonary exam was only associated with the severe outcome (OR: 2.00; 95% CI, 0.33–5.66; p = 0.02). Other factors such as age under 3 months, prematurity, birth weight, malnutrition or positive history of congenital cardiac, neurodevelopmental, or genetic diseases, fever, temperature, cough, and digestive symptoms were not found to be significant risk factors. Conclusions: Clinical guidelines based on risk stratification for SARS-CoV-2 infection in children are needed in order to manage, monitor and establish priority access for some groups to high medical care.
Machine Learning Methods leveraging ADFA-LD Dataset for Anomaly Detection in Linux Host Systems

Advancement in network technology and revolution in the global internet transformed the overall Information Technology (IT) infrastructure and its usage. In the era of the Internet of Things (IoT) and the Internet of Everything (IoE), most everyday gadgets and electronic devices are IT-enabled and can be connected over the internet. With the advancements in IT technologies, operating systems also evolved to leverage these advancements. Today's operating systems are more user-friendly and feature-rich to support current IT requirements and provide sophisticated functionalities. On the one hand, these features enabled operating systems accomplish all current requirements, but on the other hand, these modern operating systems increased their attack surface considerably. Intrusion detection systems play a significant role in providing security against the broad spectrum of attacks on host systems. Intrusion detection systems based on anomaly detection have become a prominent research area among diverse areas of cyber security. The traditional approaches for anomaly detection are inadequate to discover the operating system level anomalies. The advancement and research in Machine Learning (ML) based anomaly detection open new opportunities to tackle this challenge. The dataset plays a significant role in ML-based system efficacy. The Australian Defence Force Academy Linux Dataset (ADFA-LD) comprises thousands of normal and attack processes system call traces for the Linux platform. It is the benchmark dataset used for dynamic approach-based anomaly detection. This paper provided a comprehensive and structured study of various research works based on the ADFA-LD for host-based anomaly detection and presented a comparative analysis.
Meat and Muscle Biology ™ A Salute to PVT TIM HiLL: Indispensable Amino Acids and Global Human Health 1

: Students are taught the study mnemonic “ PVT TIM HiLL ” to memorize the 9 indispensable amino acids (IAA): phenylalanine, valine, threonine, tryptophan, isoleucine, methionine, histidine, leucine, and lysine. The Food and Agriculture Organization (FAO) recommended that IAA be treated as individual nutrients on food labels because “ crude protein ” alone does not indicate protein quality. Protein quality — determined by Digestible Indispensable Amino Acid Score (DIAAS) — is calculated for 3 life stages: birth to 6 months, 6 months to 3 years, and 3 years and older. Foods pos-sessing DIAAS greater than 100 are “ excellent ” protein quality, and those with DIAAS between 75 and 99 are “ good, ” whereas foods with DIAAS < 75 cannot make a protein claim. Processing, heating, and/or grinding can decrease or improve plant and animal IAA digestibility. For children 6 months to 3 years of age, ground pork, smoked-cooked bacon, cooked pork leg, cured ham, cooked pork loin, salami, beef/pork bologna, beef jerky, and medium and medium rare beef ribeye steaks can be described as “ excellent. ” A range of research has reported that cooked ground beef can be classified as “ good ” or “ excellent, ” whereas the Impossible ® (Impossible Foods, Redwood City, CA) and Beyond ® (Beyond Meat Inc., El Segundo, CA) meat-alternative burgers and well-done ribeye can be classified as “ good ” sources of IAA for young children. For persons aged > 3 years, all meat categories but cooked ground beef can be classified “ excellent ” sources of IAA. For meat alternatives, Impossible Burger could be classified as “ excellent, ” but Beyond Burger could only claim to be “ good. ” Protein quality claims for individual food ingredients can be diminished when the food is consumed with a lower protein quality item such as a wheat flour bun. To provide meals that are adequate in all IAA, the protein quality in each food item must be determined. Mixed meals must be adjusted for protein quality by combining low-quality proteins (present in cereals and grains) with higher-quality proteins (present in foods of animal origin).
Nonconsensual Distribution of Intimate Images: Exploring the Role of Legal Attitudes in Victimization and Perpetration

In the last 10 years, following widespread outcry among legal scholars and activists, 48 states passed legislation explicitly criminalizing the nonconsensual distribution of intimate images (NCDII) or what is colloquially known as “revenge porn. ” This increased authority granted to criminal justice agencies, coupled with greater media attention to NCDII incidents, may have influenced patterns of victimization and perpetration. Using a survey recently distributed to a sample of young adults (N = 713), we find that NCDII perpetration is strongly related to previous victimization, risky online behaviors, and receipt of unsolicited images. Perceptions of police efficacy in addressing NCDII issues is the strongest predictor of attitudes toward both reporting victimization and the belief that perpetrators will experience some punitive consequence. We also conducted an experiment using vignettes with gender varying victim-offender dyads to explore how gender bias influences attitudes toward punishment for NCDII perpetrators as well as perceptions of “revenge porn” in incidents involving same-sex and mixed-sex couples; we find that respondents are less likely to attribute “revenge porn” or to suggest punitive responses when the perpetrator is female regardless of the gender of the victim. Importantly, we find initial evidence of a new typology of NCDII perpetrator that counters existing research on victim–perpetrator gender dyads: women who nonconsensually disseminate unsolicited intimate images sent by men. Collectively, our findings challenge the efficacy of existing criminal statutes, identify new challenges in effectively legislating against NCDII, and contribute to the body of work on gender-based violence, perceptions of police efficacy, and punitive attitudes.
Estimates of survival from the sighting of marked animals

All the methods which have been presented in recent years for the analysis of capturerecapture data have aimed at estimating total population size, survival rates and dilution rates. These methods, whether deterministic or stochastic, all assume that the animals captured on each occasion are a random sample from the whole population. The proportion of marked animals in each sample then contains information about the size of the population at that instant. Darroch (1958, 1959) has shown for a stochastic model that, in the cases where the population is subject to either death or to immigration but not both, the parameters of the population together with their variances and covariances can be very simply estimated. In the more general cases with both death and immigration, he obtains estimation equations which can readily be solved numerically, and suggests that the variances of the estimates can be obtained by standard asymptotic techniques. For a deterministic survival rate Jolly (1963) obtains explicit formulae for population size and survival and dilution rates, and asymptotic formulae for their variance and covariances. This theory has been derived to meet the case where the animals are sampled completely at random. However, for some species, the markings which are applied to render an animal individually identifiable may be visible without capture. For the past thirteen years zoologists from the University of Aberdeen have been carrying out a study of the fulmar petrel (Fulmarus glacialis) on a small island in Orkney, the details of which have been describedby Carrick & Dunnet (1954) andDunnet, Anderson & Cormack (1963). Throughout this study birds have been caught individually on their nests and have been marked by an identifying set of coloured rings on the leg. These rings are clearly visible in flight, so that in years subsequent to marking the continued presence of a bird in the colony may be established without any capture being effected. New individuals may be marked each year by further capture. The aim of this paper is to examine a model applicable to this biological situation. Clearly no useful inference about the population size is possible, but valid estimates of survival rate can be obtained. In ? 2 and 3 the notation used and the general formulation of the theory are set out. Maximum-likelihood estimates of survival rates are obtained in ? 4, and their asymptotic variances and covariances in ? 5. These are applied to the over-all survival rate and the life span in ? 6. In ? 7, the theory is illustrated for records from the study of fulmars. 2. NOTATION
Pending problems in thermal plasmas and actual development

Thermal plasma processes are now currently used in industry for many applications, each of them using adapted plasma torches or transferred arcs. However, many difficulties still exist in understanding what happens at the electrodes of direct current (dc) arcs. For the latter the material composition is continuously evolving and the arc root behaviour is a three-dimensional transient phenomenon. For RF discharges, the coupling between the plasma and the discharge is much better understood. To model such flows, knowledge of the transport properties is a prerequisite and many works are in progress in this field. The difficulty in modelling lies in the fact that the plasma core is laminar while its fringes are turbulent. Moreover, the turbulence is more of the engulfment type than represented by k-? , Rij. models and the phenomena are even more complex with arcs due to the piston flow generated by arc root fluctuations. Mixing a cold gas with the plasma is the key of many processes. Here also three-dimensional codes are now currently used but it is still difficult to introduce the transient behaviour of the plasma and calculate, fast enough, the-out-of-equilibrium composition and transport coefficients of complex mixtures. Models have been backed by measuring techniques: emission or absorption spectroscopy, enthalpy probes of reduced sizes (d~3? mm) coupled with a mass spectrometer, laser scattering techniques, fast imaging, with or without laser illumination, laser anemometry, fast (50? ns) pyrometry for particles in flight. Such techniques give a much better idea of local and/or transient phenomena, but they also confirm the need for the development of new models.
Integrated model for genomic prediction under additive and non-additive genetic architecture

Using data from genome-wide molecular markers, genomic selection procedures have proved useful for estimating breeding values and phenotypic prediction. The link between an individual genotype and phenotype has been modelled using a number of parametric methods to estimate individual breeding value. It has been observed that parametric methods perform satisfactorily only when the system under study has additive genetic architecture. To capture non-additive (dominance and epistasis) effects, nonparametric approaches have also been developed; however, they typically fall short of capturing additive effects. The idea behind this study is to select the most appropriate model from each parametric and nonparametric category and build an integrated model that can incorporate the best features of both models. It was observed from the results of the current study that GBLUP performed admirably under additive architecture, while SVM’s performance in non-additive architecture was found to be encouraging. A robust model for genomic prediction has been developed in light of these findings, which can handle both additive and epistatic effects simultaneously by minimizing their error variance. The developed integrated model has been assessed using standard evaluation measures like predictive ability and error variance.
The Risk of Emerging of Dengue Fever in Romania, in the Context of Global Warming

(1) Background: Few studies to date have assessed the influences induced by climate change on the spatial distribution and population abundance of Aedes albopictus using the latest climate scenarios. In this study, we updated the current distribution of Ae. albopictus mosquitoes and evaluated the changes in their distribution under future climate conditions, as well as the risk of dengue virus emergence in Romania. (2) Methods: Under the two scenarios: High scenario (HS) when no drastic measures to reduce the effects of global warming will be taken, or they are not effective and low scenario (LS) when very stringent greenhouse control measures will be implemented. (3) Results: The results estimate an increase in temperatures in Romania of up to 2.6 °C in HS and up to 0.4 °C in LS, with an increase in the period of virus replication within the vector from June to October in HS and from May to September in LS. Moreover, in 2022, Ae. albopictus was reported in a new county, where it was not identified at the last monitoring in 2020. (4) Conclusions: The rapid spread of this invasive species and the need to implement monitoring and control programs for the Aedes population in Romania are emphasized.
Mobile Application for Mental Health Using Machine Learning

In present era, mental health has become one of the most neglected, yet critically important, factors of our overall well-being. A large number of people are affected by various types of mental illnesses and mental health disorders. Stress, anxiety, and depression are the most common disorders among children and adolescents in Sri Lanka, and their prevalence has increased over the years, likely to require immediate medical attention. In today’s world, mobile phones and applications play an important role in everyone’s life. With the rapid growth of mental illness, mental health-focused apps and websites have gradually increased globally in recent years. This study aims to develop a mobile application that will primarily serve Sri Lankans with mental health problems, helping them identify their levels of stress, anxiety, and depression (ADS) and receiving advice on how to deal with them. This app’s main objective is to support those who are dealing with mental illnesses and raise awareness of them locally using machine learning and image processing techniques. It does this by serving as a constant reminder of how crucial mental health is and how much of an impact it has on daily life. The GSE Scale, DASS 21 scale has been used to find the users’ mental health illness and the severity of each mental health illness such and Anxiety depression and stress. These methods are put to our mobile application using machine learning techniques such as Decision tree and Random Forest classifiers and uses image processing technologies, CNN machine learning algorithm to offer a variety of activities for reliving stress, depression, and anxiety,
A NOTE ON A RELATION BETWEEN THE WEAK AND STRONG DOMINATION NUMBERS OF A GRAPH

In a graph \(G=(V,E)\) a vertex is said to dominate itself and all its neighbors. A set \(D \subset V\) is a weak (strong, respectively) dominating set of \(G\) if every vertex \(v \in V-S\) is adjacent to a vertex \(u \in D\) such that \(d_G(v) \geq d_G(u)\) (\(d_G(v) \leq d_G(u)\), respectively). The weak (strong, respectively) domination number of \(G\), denoted by \(\gamma_w(G)\) (\(\gamma_s(G)\), respectively), is the minimum cardinality of a weak (strong, respectively) dominating set of \(G\). In this note we show that if \(G\) is a connected graph of order \(n \geq 3\), then \(\gamma_w(G) + t\gamma_s(G) \leq n\), where \(t=3/(\Delta+1)\) if \(G\) is an arbitrary graph, \(t=3/5\) if \(G\) is a block graph, and \(t=2/3\) if \(G\) is a claw free graph.
Efficient Solution to 3D-LiDAR-based Monte Carlo Localization with Fusion of Measurement Model Optimization via Importance Sampling

This paper presents an efficient solution to 3D-LiDAR-based Monte Carlo localization (MCL). MCL robustly works if particles are exactly sampled around the ground truth. An inertial navigation system (INS) can be used for accurate sampling, but many particles are still needed to be used for solving the 3D localization problem even if INS is available. In particular, huge number of particles are necessary if INS is not available and it makes infeasible to perform 3D MCL in terms of the computational cost. Scan matching (SM), that is optimization-based localization, efficiently works even though INS is not available because SM can ignore movement constraints of a robot and/or device in its optimization process. However, SM sometimes determines an infeasible estimate against movement. We consider that MCL and SM have complemental advantages and disadvantages and propose a fusion method of MCL and SM. Because SM is considered as optimization of a measurement model in terms of the probabilistic modeling, we perform measurement model optimization as SM. The optimization result is then used to approximate the measurement model distribution and the approximated distribution is used to sample particles. The sampled particles are fused with MCL via importance sampling. As a result, the advantages of MCL and SM can be simultaneously utilized while mitigating their disadvantages. Experiments are conducted on the KITTI dataset and other two open datasets. Results show that the presented method can be run on a single CPU thread and accurately perform localization even if INS is not available.
AutoEval-Video: An Automatic Benchmark for Assessing Large Vision Language Models in Open-Ended Video Question Answering

We propose a novel and challenging benchmark, AutoEval-Video, to comprehensively evaluate large vision-language models in open-ended video question answering. The comprehensiveness of AutoEval-Video is demonstrated in two aspects: 1) AutoEval-Video constructs open-ended video-questions across 9 skill dimensions, addressing capabilities of perception, comprehension, and generation. 2) AutoEval-Video contains newly collected videos that cover over 40 distinct themes. To efficiently evaluate responses to the open-ended questions, we employ an LLM-based evaluation approach, but instead of merely providing a reference answer, we annotate unique evaluation rules for every single instance (video-question pair). To maximize the robustness of these rules, we develop a novel adversarial annotation mechanism. By using instance-specific rules as prompt, GPT-4, as an automatic evaluator, can achieve a stable evaluation accuracy of around 97.0\%, comparable to the 94.9\% - 97.5\% accuracy of a human evaluator. Furthermore, we assess the performance of eight large vision-language models on AutoEval-Video. Among them, GPT-4V(ision) significantly outperforms other models, achieving an accuracy of 32.2\%. However, there is still substantial room for improvement compared to human accuracy of 72.8\%. By conducting an extensive case study, we uncover several drawbacks of GPT-4V, such as limited temporal and dynamic comprehension, and overly general responses. Code is available at \href{https://github.com/Xiuyuan-Chen/AutoEval-Video}{\color{magenta}https://github.com/Xiuyuan-Chen/AutoEval-Video}.
Three-dimensional Borehole Radar Imaging

Before embarking on any mining operation, it is advantageous to locate the subsurface orebody in three-dimensions with a high resolution (~1m) ahead of actual mining, because this will increase productivity and efficiency. Borehole radar is such an emerging mapping tool in the mining industry, that can be used to image the subsurface orebody with high resolution. 3-D subsurface imaging using two-dimensional aperture synthesis requires many boreholes and is thus not economically feasible in underground orebody imaging. Therefore, the main objective of this thesis is to develop 3-D imaging techniques, using a limited number of boreholes. An interferometric synthetic aperture radar (InSAR) is a well established space-borne/airborne technique for mapping the Earth’s surface. A borehole InSAR simulation study was thus carried out, using a sidelooking antenna configuration to image the subsurface orebodies, such as potholes and cylindrical Kimberlite structures, in 3-D. A performance analysis of an interferometric experiment is presented. In general, borehole radars are a wide-band system, often having bandwidths of 75% of the centre frequency. A 3-D image reconstruction technique was developed by means of correlation-type processing, using magnitude images of multiple boreholes coming from different view angles, which is more suitable for wide-band/ultra wide-band borehole radar signals. The technique was tested by using simulated multiple borehole radar magnitude images, as well as real acoustic images that had been captured in air and water media. The 3-D reconstructed grid spacing derivations are presented for a borehole trajectory fanning outward from the borehole centre. In this thesis, a 40kHz air-based sonar system was used in the laboratory environment to emulate inverse synthetic aperture radar (ISAR) data in the context of a real borehole experiment. A deconvolution processing approach was adopted to range-compress the 40kHz sonar data captured in air. A time domain focusing technique was used to focus the simulated borehole radar as well as the real acoustic data. In the case of homogeneous, isotropic and non-dispersive media, a straight-line wave propagation is considered to process the data in range and azimuth. In a real bore-