Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring Interrater Agreement for Ratings of a Single Target

Researchers assessing interrater agreement for ratings of a single target have increasingly used the rWG(j) index, but have found it can display irregular behavior. Mathematical analyses show this problem arises from the use of random response, operationalized by the variance of a uniform distribution (sEU), for the baseline of comparison. These analyses suggest that researchers should continue to use rWG(j) as a summary measure of interrater agreement, but should use maximum dissensus as a reference distribution for computing rWG(j). Although values of s2 can be descriptively misleading, they provide an important inferential baseline. Thus, sEU should be used in computing x2 tests of the departure of the observed response variance from random responding. Researchers should also examine interrater agreement as a theoretical variable in its own right, investigating the causes and consequences of rater dissensus.
Institutions, foreign direct investment and growth: a hierarchical Bayesian approach

Summary. We investigate empirically the existence of a heterogeneous relationship between foreign direct investment (FDI) and economic growth across developing countries. We argue that, across countries, differences in institutional quality are correlated with heterogeneous absorptive capacities and hence a heterogeneous FDI–growth relationship. Our empirical results show substantial heterogeneity in the FDI–growth relationship. We find that controlling for certain measures of institutional quality reduces the degree of heterogeneity. These findings question the orthodox assumption of a homogeneous return to FDI in the existing empirical literature and highlight the importance of specific aspects of institutional quality in the FDI–growth relationship.
Comprehensive Review of Cardiovascular Disease Risk in Nonalcoholic Fatty Liver Disease

Nonalcoholic Fatty Liver Disease (NAFLD) is a growing global phenomenon, and its damaging effects in terms of cardiovascular disease (CVD) risk are becoming more apparent. NAFLD is estimated to affect around one quarter of the world population and is often comorbid with other metabolic disorders including diabetes mellitus, hypertension, coronary artery disease, and metabolic syndrome. In this review, we examine the current evidence describing the many ways that NAFLD itself increases CVD risk. We also discuss the emerging and complex biochemical relationship between NAFLD and its common comorbid conditions, and how they coalesce to increase CVD risk. With NAFLD’s rising prevalence and deleterious effects on the cardiovascular system, a complete understanding of the disease must be undertaken, as well as effective strategies to prevent and treat its common comorbid conditions.
3D Self-Motion Tracking Services: Coalescence of mmWave Beam Orientations and Phase Information

The profound integration of sensing functionalities is seen as a major step stone towards unleashing the full potential of 6G, yet recent advances in current networks already offer new opportunities for sensing. This is especially true for the mmWave domain which offers a suitable environment for sensing services, e.g. due to the ability to detect and determine the angles of available link opportunities. Whereas previous work devised a fine 3D motion tracking by combining phase measurements along with several co-deployed nodes' links to the mmWave network, this work instead exploits multiple available propagation paths. We observe sub-10 $\mu\mathrm{m}$ 3D motion tracking accuracy for the proposed single user equipment (UE) enhancement, mirroring the conventional multi-UE-based approach performance. However, our detailed error analysis finds that multipath may turn from friend to foe if undesired components are not suppressed sufficiently, as these amplify the effects of phase distortions due to channel noise and hardware imperfections. Our evaluation further yields that the technique is sensitive to erroneous propagation path angle information.
Pacing Patterns of Half-Marathon Runners: An analysis of ten years of results from Gothenburg Half Marathon

The Gothenburg Half Marathon is one of the world’s largest half marathon races with over 40 000 participants each year. In order to reduce the number of runners risking over-straining, injury, or collapse, we would like to provide runners with advice to appropriately plan their pacing. Many participants are older or without extensive training experience and may particularly benefit from such pacing assistance. Our aim is to provide this with the help of machine learning. We first analyze a large publicly available dataset of results from the years 2010 - 2019 (n = 423 496) to identify pacing patterns related to age, sex, ability, and temperature of the race day. These features are then used to train machine learning models for predicting runner’s finish time and to identify which runners are at risk of making severe pacing errors and which ones seem set to pace well. We find that prediction of finish time improves over the current baseline, while identification of pacing patterns correctly identifies over 70% of runners at risk of severe slowdowns, albeit with many false positives.
VERIFICA EMPIRICA ED ANALISI CRITICA DEL MODELLO MISTURA BIVARIATA

Bivariate mixture models, introduced by Tauchen-Pitts, try to esplain the relationship between return volatility and trading volume in financial markets, by stochastic changes in a single latent variable, representing the number of information arrivals. In this article, dynamic bivariate mixture models that allow for autocorrelation in the latent variable are represented by nonlinear state space systems, nonlinearity being due to the measurement equation. The parametres are estimated by simulated maximum likelihood and the latent variable by simulated non linear filter, both being based on the same rejection sampling algorithm. The results, based on italian daily stock market data, reveal that dynamic bivariate mixture models neither can explain the autocorrelation, nor can account for the persistence in return variance.
Under the Hood of MARVEL

—A growing trend in repackaging attacks exploits the Android virtualization technique, in which malicious code can run together with the victim app in a virtual container. In such a scenario, the attacker can directly build a malicious container capable of hosting the victim app instead of tampering with it, thus neglecting any anti-repackaging protection developed so far. To mitigate this issue, we proposed MARVEL [26], the ﬁrst methodology that allows preventing both traditional and virtualization-based repackaging attacks. MARVEL relies on the virtualization technique to build a secure virtual environment where protected apps can run and be checked at runtime. To assess the viability and reliability of our protection scheme, we implemented it in two tools: MARVELoid to protect the plugins, and a container app (Trusted Container) that enforces the protection at runtime. In this paper, we describe the automatic testing pipeline used in the experimental campaign conducted to evaluate the MARVEL implementation in terms of correctness , performance , and effectiveness . Moreover, we perform an in-depth analysis of the dataset used during the evaluation and discuss the open challenges. The ﬁrst set of experiments aims to evaluate the protection performed by MARVELoid, verifying its viability and reliability. To do so, we protected 4000 apps with 24 different conﬁgurations of the MARVELoid protection parameters (i.e., 96k protection combinations). MARVELoid was able to inject the protection into 97.3% of the cases, with a processing time of 98 seconds per app on average. Then, we evaluate the runtime overhead introduced by our solution, which turned out to have a negligible impact in terms of average CPU ( < 5%) and memory overhead ( < 0.5%).
AN INTRODUCTION TO GRAPHICAL DESIGNS

A graphical design is a generalization of a quadrature rule to the domain of graphs. Informally, a quadrature rule is a set of points that represent a continuous domain well in the context of numerical integration. That is, if we would like to average a suitably chosen function over the entire domain, it should suffice to know what values the function takes on the quadrature points. An example of a quadrature rule on a sphere is a spherical t-design, which is a subset of points {x1, . , xN} ⊂ Sd−1 = {x ∈ R : ∥x∥2 = 1} and weights αi ∈ R chosen so that
Linking Early Detection/Treatment of Parkinson’s Disease using Deep Learning Techniques

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder that causes uncontrollable movements and difficulty with balance and coordination. It is highly important for early detection of Parkinson’s Disease for patients to receive proper treatment. This paper aims to present a preliminary data mining procedure that help Parkinson’s Disease patients slow down their progression of the disease while helping early detection of the disease. For early non-invasive treatment, our research first analyses the early symptoms of Parkinson’s Disease, designs/selects a proper demo video, let the user follow the demo to exercise and upload his exercise video to our deep learning APP: LaBelle. LaBelle utilizing MediaPipe Pose to identify, analyze, and store data about the poses and movements of both demo and the user, calculates the angles created between different joints and major body parts. LaBelle’s AI model uses a K-means clustering algorithm to create a group of clusters for both demo and the user dataset. Using the two sets of clusters, LaBelle identifies the key frames in the user video and searches the demo cluster set for a matching set of properties and frames. It evaluates the differences between the paired frames and produces a final score as well as feedback on the poses that need improving. Meanwhile, if the user is willing to donate their exercise data, he can simply input his age, whether he is a PD patient (maybe for how long) anonymously. Then his data can be stored into our customized dataset, used in data mining for Parkinson’s Disease prediction, which involves building/training our deep learning CNN model and help early detection of Parkinson’s Disease.
A dynamic programming approach to the dominating set problem on k trees

Dynamic programming has long been established as an important technique for demonstrating the existence of polynomial time algorithms for various discrete optimization problems. In this paper we extend the normal paradigm of dynamic programming to allow a polynomial number of optimal solutions to be computed for each subproblem. This technique yields a polynomial time algorithm for the dominating set problem on k-trees, where k is fixed. It is also shown that the dominating set problem is NP-complete for k-trees where k is arbitrary.
Star formation and some aspects of the chemical evolution of the Galaxy

On montre que le taux de formation stellaire est relie a la densite de surface de l'hydrogene moleculaire et qu'il est reste pratiquement constant pendant toute la duree de vie du disque. On propose un modele qui permet de resoudre le probleme des naines G, du gradient de metallicite et de la relation âge-metallicite
Large-eddy simulation of heat transfer from impinging slot jets

Nusselt number distributions are presented for impinging jet flow of an array of slot nozzles (rectangular jets). The tools to calculate the present turbulent flow are large-eddy simulation (LES) using a dynamic subgrid stress model and the direct numerical simulation (DNS). The numerical code has been validated by comparing computed Nusselt number distributions on Ike impingement plate for two-dimensional flow with experimental results. A comparison between LES using a logarithmic law of the wall and the DNS shows good agreement of Nusselt number in the Reynolds number range of 600–3000. The velocity profile at the feed tube exit strongly influences the maximum heat transfer at the stagnation point.
Practical Phase Retrieval Based On Theoretical Models For Multi-Dimensional Band-Limited Signals

The analytic properties of two-dimensional band-limited functions are discussed. In practice, only a limited number of intensity samples are available, and so we choose to model the spectrum as a finite degree polynomial. The set of reducible finite degree multi-variate polynomials is of measure zero and unique recovery from noise free Fourier magnitude is expected in almost all cases. We pursue a new algorithm based on finding the complex zeros of 1-D lines of the data set which requires only that the intensity is sampled at twice the Nyquist rate or greater. All solutions compatible with the Fourier magnitude samples are generated, including ambiguities should they exist. An exact solution to the phase retrieval problem, given a polynomial model, may be regarded as factorization. We discuss the relationship of this approach to factorization and iterative procedures and describe problems arising from data truncation and the presence of noise.
Real-life trends of anticoagulant prescribing practices for pulmonary embolism – results of a single-center study based on the experience of a multi-profile clinical hospital

Acute pulmonary embolism (APE) is one of the main causes of cardiovascular deaths and anticoagulant treatment plays a key role in preventing recurrent episodes, chronic thromboembolic pulmonary hypertension (CTEPH), and deaths. The aim of this study is to assess the real-life trends and to determine factors associated with the choice of anticoagulation therapy in patients with APE. This is a single center prospective open study. We followed 178 consecutive patients admitted to the tertiary clinical center with APE proven with computed tomography (CT) scan within period of 24 months. A total number of 178 patients with APE were enrolled in the study. 48.9% of subjects were hospitalized in cardiology department. As a prolonged anticoagulant therapy 35.7% of study cohort received direct oral anticoagulants (DOACs), 35.1% LMWH, and 29.2% vitamin K antagonists (VKA), respectively. No statistically significant differences were found between the departments regarding frequency of prescribing anticoagulants (p=0.15). The multivariable analysis showed that oral anti-coagulants (OACs) were less likely to be prescribed than LMWH in patients with malignancy, history of major bleeding, serious medical condition and altered mental status. OACs were preferred over LMWH in symptoms of deep vein thrombosis (DVT). VKA were significantly less likely to be chosen than DOACs in patients with history of orthopaedics procedure. After six months anticoagulation therapy was discontinued in 24.3% of patients. Concluding, the form of anticoagulant therapy was associated with the presence of chronic diseases. LMWH was prescribed in high bleeding risk patients more frequently.
Influence of Environmental Factors on Prey Discrimination of Bait-Attracted White Sharks from Gansbaai, South Africa

Simple Summary Predator–prey interactions can be influenced by environmental factors and depend on the sensory capabilities of a predator and its prey. Environmental factors influence the prey discrimination of white sharks in Gansbaai during their passive prey predatory behavior. Tide range is the most important factor that influences the white sharks’ prey choice, followed by underwater visibility, water temperature, and sea conditions. With high tide, better underwater visibility, cooler water temperature, and better sea conditions, sharks choose the energetical richer prey, the seal-shaped decoy, instead of the tuna bait. This study confirms the importance of visual ability in mature and immature white sharks with dietary shifts in their feeding pattern. Abstract The influence of environmental factors on prey discrimination of bait-attracted white sharks was studied over a six-year period (2008–2013) at Dyer Island Nature Reserve (Gansbaai, South Africa). Across 240 bait-attracted feeding events observed in this period, both immature and mature white sharks were attracted by the seal-shaped decoy rather than the tuna bait, except for the years 2008 and 2011. Tide ranges, underwater visibility, water temperature, and sea conditions were, in decreasing order, the factors which drove white sharks to select the seal-shaped decoy. High tide lowered the minimum depth from which sharks could approach seals close to the shore, while extended visibility helped the sharks in making predatory choices towards the more energy-rich prey source, the odorless seal-shaped decoy. On the contrary, warmer water is associated with an increase in phytoplankton that reduces underwater visibility and increases the diversity of teleosts including tuna—a known prey of white sharks—driving the sharks to favor the tuna bait. Overall, sea conditions were almost always slightly rough, ensuring a good average underwater visibility. Recommendations for future research work at this site are presented.
Freja observations of heating and precipitation of positive ions

The experiments on board Freja are designed to measure auroral particle energization processes with very high temporal and spatial resolution. One main scientific objective is to study ion heating transverse to the magnetic field lines in the auroral region. The Freja orbit with an inclination of 63° allows us to make detailed measurements in the nightside auroral oval during all disturbance levels. We concentrate here on two different observations of transverse ion energization at an altitude of about 1700 km in the northern hemisphere auroral region. The three-dimensional ion mass spectrograph has shown that both heavy and light ions are heated to energies most often in the range from a few eV to some hundred eV. Transversely heated ions are, however, also seen up to the present high energy limit of the hot plasma instrument, 4.5 keV. Ion conics are produced in regions with anisotropic electron fluxes as well as in regions of intense keV proton precipitation. Waves above the lower hybrid frequency are observed in the events presented in this report. These waves may play an important role in the ion heating process. The Freja data indicate that the waves are generated in different ways in these events. Thus, this preliminary investigation confirms that several scenarios are needed to explain the heating of ionospheric plasma and shows some of the possibilities for future studies.
The next generation of NWP: explicit forecasts of convection using the weather research and forecasting (WRF) model

The performance of daily convection forecasts from 13 May to 9 July 2003 using the Weather Research and Forecast (WRF) model is investigated. Although forecasts using 10‐km grid spacing and parameterized convection are not lacking in prediction of convective rainfall, fully explicit forecasts with a 4‐km grid spacing more often predict identifiable mesoscale convective systems (MCSs) that correspond to observed systems in time and space. Furthermore, the explicit forecasts more accurately predict the number of MCSs daily and type of organization (termed convective system mode). The explicit treatment of convection in NWP does not necessarily provide a better point specific‐forecast, but rather a more accurate depiction of the physics of convective systems. Copyright © 2004 Royal Meteorological Society
Neural network prediction of the flow field in a periodic domain with hyper-neural network parametrization

Computational fluid dynamics (CFD) is a powerful tool for numerical simulations of various processes in the industry. Unfortunately, there is still a very challenging task in terms of computational demands. Some numerical simulations are unrealizable by traditional CFD methods with today’s computational resources. The use of neural networks to solve CFD problems can overcome this problem because neural networks can predict the flow field very fast, [1, 2]. This contribution aims to describe a developed neural network architecture, which can quickly predict a steady flow field in a blade cascade. The presented neural network consists of an autoencoder, which transforms the coordinates of the inter-blade area grid points onto flow field variables. The internal convolution layers were modified to include a periodic boundary condition. Furthermore, an approach for parametrization of the autoencoder for various Reynolds numbers was proposed. This approach uses another dense neural network, called hypernet, which maps a given Reynolds number onto autoencoder weights. Within this study, the autoencoder was trained for three different Reynolds numbers Re = 100, 500, 1000 on the train set of 200 randomly generated blade profiles. The resulting autoencoder weights were used for the training of the hypernet. The key part of this work is the study of the behavior of the autoencoder on unseen profiles for weights generated by hypernet for Reynolds numbers Re = 250, 750, which were not included in the training set.
A method to determine the Young's modulus of thin-film elements assisted by dark-field electron holography

We present a method to determine the isotropic elastic modulus of nanometer-thick films of unknown or imprecise microstructure and composition. First, the mesoscopic stress of the film is determined using Stoney's method. Then, after fabricating film-stripes by lithography, dark-field electron holography is used to image the strain fields (3 nm spatial resolution, ∼2 × 10−4 precision) resulting from the local interactions between the stripes and an underlying silicon crystal. By comparing the experimental results with finite element method modeling, we deduce Young's modulus of the film. Silicon nitride films on Si substrates are presented as a model system.
The revival–collapse phenomenon in the higher-order fluctuations of quadrature field components of the multiphoton Jaynes–Cummings model

For a system of multiphoton Jaynes–Cummings model, which represents the interaction of a two-level atom with the radiation field, we study the connection between the atomic inversion, in particular the occurrence of revival–collapse phenomenon (RCP), and the higher-order fluctuation factors. We assume that the atom and the field are initially prepared in the excited and the k-photon coherent states, respectively. We show that there is a class of states for which the higher-order fluctuation factor can provide RCP similar to that involved in the corresponding atomic inversion. Moreover, for initial coherent light we prove that the higher-order fluctuation factor of the three-photon transition can provide RCP similar to that occurring in the atomic inversion of the one-photon transition. As an example we discuss the fourth-order fluctuation in detail.
A three-manifold invariant derived from the universal Vassiliev-Kontsevich invariant

We construct a three manifold invariant from the universal Vassiliev-Kontsevich (UVK) invariant , which includes Lescop’s generalization of the Casson-Walker invariant. The UVK invariant 2 has values in the space M of chord diagrams subject to the four term relation [2, 1, 3]. Here, we construct a three manifold invariant by taking Kirby move (Fig. 1) invariant part from the UVK invariant of framed links constructed in [3]. We modify so that it has a good property with respect to the KII moves. Let Z,(U) -1, which is the factor introduced in [3] to normalize the effect of maximal and minimal point.s. For an /-component link L, let (L) (L) # (1, 1, ---, l). This means that we connect-sum to each string of y(L). Then, we take a certain quotient d of so that the image of z(L) is stab! e under the KII moves. Let A’(L) denote this image of zz(L), then A’(L) factors the Jones-Witten invariant in [6, 9] except the normalization factor for the KI moves. We study a low degree part of A’(L) concretely, and, after normalizing for the KI moves, we show that A’(L) includes the order of the first homology and Casson’s invariant. For a Z/rZhomology 3-sphere (r" odd prime), the JonesWitten invariant dominates the Casson-Walker invariant [7]. In this short note, we expose the results and the idea of proofs. The detail will be given elsewhere.