Miracle-dz/UniFAR-FTUs huggingface dataset:
Logic of Combinatory Logic

We develop a classical propositional logic for reasoning about combinatory logic. We define its syntax, axiomatic system and semantics. The syntax and axiomatic system are presented based on classical propositional logic, with typed combinatory terms as basic propositions, along with the semantics based on applicative structures extended with special elements corresponding to primitive combinators. Both the equational theory of untyped combinatory logic and the proposed axiomatic system are proved to be sound and complete w.r. t. the given semantics. In addition, we prove that combinatory logic is sound and complete w.r. t. the given semantics.
Dynamic Maxflow via Dynamic Interior Point Methods

In this paper we provide an algorithm for maintaining a (1−є)-approximate maximum flow in a dynamic, capacitated graph undergoing edge insertions. Over a sequence of m insertions to an n-node graph where every edge has capacity O(poly(m)) our algorithm runs in time O(m √n · є−1). To obtain this result we design dynamic data structures for the more general problem of detecting when the value of the minimum cost circulation in a dynamic graph undergoing edge insertions achieves value at most F (exactly) for a given threshold F. Over a sequence m insertions to an n-node graph where every edge has capacity O(poly(m)) and cost O(poly(m)) we solve this thresholded minimum cost flow problem in O(m √n). Both of our algorithms succeed with high probability against an adaptive adversary. We obtain these results by dynamizing the recent interior point method by [Chen et al. FOCS 2022] used to obtain an almost linear time algorithm for minimum cost flow, and introducing a new dynamic data structure for maintaining minimum ratio cycles in an undirected graph that succeeds with high probability against adaptive adversaries.
Cross-model Fairness: Empirical Study of Fairness and Ethics Under Model Multiplicity

While data-driven predictive models are a strictly technological construct, they may operate within a social context in which benign engineering choices entail implicit, indirect and unexpected real-life consequences. Fairness of such systems -- pertaining both to individuals and groups -- is one relevant consideration in this space; it arises when data capture protected characteristics upon which people may be discriminated. To date, this notion has predominantly been studied for a fixed model, often under different classification thresholds, striving to identify and eradicate undesirable, discriminative and possibly unlawful aspects of its operation. Here, we backtrack on this fixed model assumption to propose and explore a novel definition of cross-model fairness where individuals can be harmed when one predictor is chosen ad hoc from a group of equally-well performing models, i.e., in view of utility-based model multiplicity. Since a person may be classified differently across models that are otherwise considered equivalent, this individual could argue for a predictor granting them the most favourable outcome, employing which may have adverse effects on others. We introduce this scenario with a two-dimensional example and linear classification; then, we present a comprehensive empirical study based on real-life predictive models and data sets that are popular with the algorithmic fairness community; finally, we investigate analytical properties of cross-model fairness and its ramifications in a broader context. Our findings suggest that such unfairness can be readily found in the real life and it may be difficult to mitigate by technical means alone as doing so is likely to degrade predictive performance.
When symmetries are enough: collapsing the bounded width hierarchy for infinite-domain CSPs

We prove that relational structures admitting speciﬁc polymorphisms (namely, canonical pseudo-WNU operations of all arities n ≥ 3) have low relational width. This implies a collapse of the bounded width hierarchy for numerous classes of inﬁnite-domain CSPs studied in the literature. Moreover, we obtain a characterization of bounded width for ﬁrst-order reducts of unary structures and a characterization of MMSNP sentences that are equivalent to a Datalog program, answering a question posed by Bienvenu et al. . In particular, the bounded width hierarchy collapses in those cases as well. Our results extend the scope of theorems of Barto and Kozik characterizing bounded width for ﬁnite structures, and show the applicability of inﬁnite-domain CSPs to other ﬁelds.
Effect of Electrical Discharge on Separation Processes and Shocks Position in Supersonic Airflow.

The paper analyses and discusses the results of resent experimental works on influence of non-uniform plasma formation on characteristics of near-wall layers and separated areas in high-speed airflow. Fresh experimental results are being presented, which are obtained at condition of small-scale model duct. Two different aerodynamic situations are investigated: surface electrical discharge in a free stream and surface discharge in separated zone behind the wall step. The discharge properties and structure are studied as well as the change of the gasdynamic structure of the flow due to electric discharge excitation. Images of the shocks interaction with the plasma overlayer observed by the Schlieren method are shown. The effect of the flow instability damping by the plasma layer is demonstrated. Application of the surface type of electric discharge for the flow modification in ducts, inlets and combustors is discussed. The work has been fulfilled in frames of Advance Flow/Flight Control (AFFC) concept.
Diagnosis of the Role of Vertical Deformation in a Two-Dimensional Primitive Equation Model of Upper-Level Frontogenesis

Abstract Equations are developed for the temporal rates of change of the magnitudes of vector gradients of potential temperature and absolute momentum projected onto vertical planes transverse to straight frontal zones. Subject to restrictions involving two-dimensionality, the temporal rates of change of the magnitudes of gradients of potential temperature and absolute momentum can be partitioned into effects due to the geostrophic and along-front ageostrophic flow (both of which are taken to be nondivergent) and effects due to the divergence and deformation in the cross-front plane associated with the transverse ageostrophic flow. The terms involving the transverse ageostrophic circulation are of the same mathematical form and analogous in a kinematic sense to the divergence and deformation terms involving the horizontal wind field in Petterssen's classic equation for frontogenesis in the potential temperature field. The proposed form of the frontogenesis equation for the magnitude of the potential tempe.
Tunable-focus flat liquid crystal spherical lens

A tunable-focus spherical lens using two flat substrates and inhomogeneous electric field over a homogeneous liquid crystal (LC) layer is demonstrated. The top flat substrate has an imbedded spherical indium–tin–oxide (ITO) electrode and the bottom has a planar ITO electrode on its inner surface. The inhomogeneous electric field generates a centrosymmetric gradient refractive index profile within the LC layer which causes the focusing behavior. The focal length of the LC lens can be tuned continuously from infinity to 0.6 m by the applied voltage.
Magnetic field of a dipole and the dipole–dipole interaction

With a data-acquisition system and sensors commercially available, it is easy to determine magnetic fields produced by permanent magnets and to study the dipole–dipole interaction for different separations and angular positions of the magnets. For sufficiently large distances, the results confirm the 1/R3 law for the magnetic field and the 1/R4 law for the interaction force between two dipoles, as well as their angular dependences.
In vivo measurement of human brain material properties under quasi-static loading

Computational modelling of the brain requires accurate representation of the tissues concerned. Mechanical testing has numerous challenges, in particular for low strain rates, like neurosurgery, where redistribution of fluid is biomechanically important. A finite-element (FE) model was generated in FEBio, incorporating a spring element/fluid–structure interaction representation of the pia–arachnoid complex (PAC). The model was loaded to represent gravity in prone and supine positions. Material parameter identification and sensitivity analysis were performed using statistical software, comparing the FE results to human in vivo measurements. Results for the brain Ogden parameters µ, α and k yielded values of 670 Pa, −19 and 148 kPa, supporting values reported in the literature. Values of the order of 1.2 MPa and 7.7 kPa were obtained for stiffness of the pia mater and out-of-plane tensile stiffness of the PAC, respectively. Positional brain shift was found to be non-rigid and largely driven by redistribution of fluid within the tissue. To the best of our knowledge, this is the first study using in vivo human data and gravitational loading in order to estimate the material properties of intracranial tissues. This model could now be applied to reduce the impact of positional brain shift in stereotactic neurosurgery.
Is Asian tiger mosquito (Aedes albopictus) going to become homodynamic in Southern Europe in the next decades due to climate change?

The Asian tiger mosquito, Aedes albopictus, competent vector of several arboviruses, poses significant impact on human health worldwide. Although global warming is a driver of A. albopictus range expansion, few studies focused on its effects on homodynamicity (i.e. the ability to breed all-year-round), a key factor of vectorial capacity and a primary condition for an Aedes-borne disease to become endemic in temperate areas. Data from a 4-year monitoring network set in Central Italy and records from weather stations were used to assess winter adult activity and weekly minimum temperatures. Winter oviposition occurred in 38 localities with a seasonal mean photoperiod of 9.7 : 14.3 (L : D) h. Positive collections (87) occurred with an average minimum temperature of the two and three weeks before sampling of approximately 4°C. According to these evidences and considering the climate projections of three global climate models and three shared socio-economic pathways for the next three 20-year periods (from 2021 to 2080), the minimum temperature of January will increase enough to allow an all-year-round oviposition of A. albopictus in several areas of the Mediterranean Basin. Due to vector homodynamicity, Aedes-borne diseases could become endemic in Southern Europe by the end of the twenty-first century, worsening the burden on human health.
Unidirectional Homogenous Alignment of Smectic Liquid Crystal under Shear Stress

Homogenous alignment of smectic liquid crystal is demonstrated by applying bidirectional shear with controlled amplitude and frequency. For shear amplitudes smaller than 10 μm and at the frequency of 100Hz, the smectic layer was found to align in the direction of shear. The quality of alignment was evaluated from the contrast ratio obtained when the sample was placed between crossed polarizers and set at 0° and 45° to the transmission axes. A monotonic increase in the alignment quality was found with increasing shear amplitude.
Mean-Variance Hedging in Large Financial Markets

Abstract We consider a mean-variance hedging (MVH) problem for an arbitrage-free large financial market, that is, a financial market with countably many risky assets modelled by a sequence of continuous semimartingales. By using the stochastic integration theory for a sequence of semimartingales developed in De Donno and Pratelli [6], we extend the results about change of numéraire and MVH of Gourieroux et al. [12] to this setting. We also consider, for all n ≥ 1, the market formed by the first n risky assets and study the solutions to the corresponding n-dimensional MVH problem and their behaviour when n tends to infinity.
Staphylococcus aureus Fibronectin-Binding Proteins Contribute to Colonization of the Female Reproductive Tract

Methicillin-resistant Staphylococcus aureus (MRSA) is an opportunistic pathogen and frequent colonizer of human skin and mucosal membranes, including the vagina, with vaginal colonization reaching nearly 25% in some pregnant populations. MRSA vaginal colonization can lead to aerobic vaginitis (AV), and during pregnancy, bacterial ascension into the upper reproductive tract can lead to adverse birth outcomes. ABSTRACT Methicillin-resistant Staphylococcus aureus (MRSA) is an opportunistic pathogen and frequent colonizer of human skin and mucosal membranes, including the vagina, with vaginal colonization reaching nearly 25% in some pregnant populations. MRSA vaginal colonization can lead to aerobic vaginitis (AV), and during pregnancy, bacterial ascension into the upper reproductive tract can lead to adverse birth outcomes. USA300, the most prominent MRSA lineage to colonize pregnant individuals, is a robust biofilm former and causative agent of invasive infections; however, little is known about how it colonizes and ascends in the female reproductive tract (FRT). Our previous studies showed that a MRSA mutant of seven fibrinogen-binding adhesins was deficient in FRT epithelial attachment and colonization. Using both monolayer and multilayer air-liquid interface cell culture models, we determine that one class of these adhesins, the fibronectin binding proteins (FnBPA and FnBPB), are critical for association with human vaginal epithelial cells (hVECs) and hVEC invasion through interactions with α5β1 integrin. We observe that both FnBPs are important for biofilm formation as single and double fnbAB mutants exhibit reduced biofilm formation on hVECs. Using heterologous expression of fnbA and fnbB in Staphylococcus carnosus, FnBPs are also found to be sufficient for hVEC cellular association, invasion, and biofilm formation. In addition, we found that an ΔfnbAB mutant displays attenuated ascension in our murine vaginal colonization model. Better understanding of MRSA FRT colonization and ascension can ultimately inform treatment strategies to limit MRSA vaginal burden or prevent ascension, especially during pregnancy and in those prone to AV.
Schlafmedizin für den Hals-Nasen-Ohren-Facharzt

Schnarchen und Atempausen im Schlaf treten sowohl bei Erwachsenen als auch bei Kindern häufig auf und führen Betroffene und Angehörige zum HNO(Hals-Nasen-Ohren)-Arzt. Ihre Betreuung setzt schlafmedizinische Grundkenntnisse voraus, um die geschilderten Symptome den relevanten Schlafstörungen zuordnen und die notwendigen diagnostischen und therapeutischen Schritte einleiten zu können.
An implicit algorithm for a rate-dependent ductile failure model

An implicit numerical algorithm has been developed for a rate-dependent model for damage and failure of ductile materials under high-rate dynamic loading [F. L. Addessio and J. N. Johnson, J. Appl. Phys. 74, 1640 (1993)]. Over each time step, the algorithm first implicitly determines the equilibrium state on a Gurson surface, and then calculates the final state by solving viscous relaxation equations, also implicitly. Numerical examples are given to demonstrate the key features of the algorithm. Compared to the explicit algorithm used previously, the current algorithm allows significantly larger time steps that can be used in the analysis. As the viscosity of the material vanishes, the results of the rate-dependent model are shown here to converge to that of the corresponding rate-independent model, a result not achieved with the explicit algorithm.
Missing Data Repairing for Bearing Vibrations using Generative Adversarial Networks

A vibration data repair method based on Generative Adversarial Networks (GAN) is proposed to resolve the problem of incomplete data acquisition of bearing vibration data under certain circumstances (sensor failure, extreme environments, etc.), which leads to errors in data analysis. We use a GAN framework, combined with an Auto Encoder (AE), to become an Auto Encoder-Generative Adversarial Networks (AE-GAN) to generate synthetic data related to data interpolation. First, an Auto Encoder is introduced in the generator of the GAN to reconstruct the input with missing data by encoding and decoding. Then, the reconstructed data is continuously trained adversarially with the original data in the discriminator of the Generative Adversarial Networks. Finally, enabling the proposed model to generate interpolated data close to the actual data. The algorithm validity with the bearing vibration dataset from the IEEE PHM 2012 Predictive Challenge was verified, and the results showed that: for missing vibration datasets, the AE-GAN algorithm has better repair accuracy and convergence speed than traditional algorithms; the model is more stable for GAN training because of the addition of Auto Encoder; providing new ideas for deep learning research on industrial data.
Nonlinear Kalman Filter Based Shop Floor RFID Data Fusion Algorithm

Radio frequency identification (RFID) technology is one of the main means to obtain the location data of production elements such as personnel and materials in intelligent workshops, but its positioning accuracy has many uncertainties. In order to map the transportation trajectory of workshop materials in digital space more accurately, this paper adopts a nonlinear Kalman filter-based RFID data fusion algorithm. Firstly, the good estimation performance of nonlinear filters such as extended Kalman filter (EKF) and unscented Kalman filter (UKF) is utilized, and the motion process is determined by combining with the target dynamics model thus forming the fusion algorithm, and finally the data from multiple RFID readers are fused for path estimation and the final approximate trajectory is obtained. In the simulation experiments, after repeated experiments and comparison experiments with particle filter (PF) and Gauss-Hermite Kalman filter (GHKF) algorithms, it is found that the UKF-based fusion algorithm proves to have higher accuracy, and the EKF-based fusion algorithm has less computing time. In addition, the fusion performance of both methods is excellent in RFID readers sufficiency areas.
Research on Sleep EEG Signals Based on IOTA

As a nonlinear analysis method based on permutation, internal composition alignment (IOTA) algorithm can study the coupling between systems by calculating the coupling coefficient between two time series. In this paper, the internal composition alignment (IOTA) algorithm is used to study the sleep EEG signals generated by the human body in different sleep periods. Firstly, the IOTA coefficients between different time series calculated by this method are used as nodes to construct the sleep function networks in different sleep periods, and the statistical characteristics of networks such as node degree and clustering coefficient are selected to compare different sleep networks. The results show that the IOTA coefficient and the node average degree and aggregation coefficient of EEG network in NREM-I period are higher than those in awake period, indicating that the complexity of EEG network in NREM-I period is higher than that in awake period, and that the coupling degree in NREM-I period is also higher than that in awake period. This experiment proves the effectiveness of IOTA algorithm for analyzing sleep function network. This algorithm can be used to study sleep EEG staging. At the same time, it also provides an important reference for the research, clinical diagnosis and treatment of sleep diseases in the future.
Adaptive real-time interactive rendering of gigantic multi-resolution models

Real-time visualization of large-scale surface models is still a challenging problem. When using multiple levels of details (LOD), the main issues are popping between levels and/or cracks between level parts. We present a scheme (both mesh preprocessing and real-time rendering), which avoids both of these issues. Vertex interpolation between child and parent is used to achieve crack and popping free rendering. We implemented and tested our method on a modest laptop PC, and could render scanned models of multiples tens of million triangles at optimal visual quality and interactive frame rate.
Tomato Disease Identification Application Based on EfficientNetV2

In each growth stage of tomato, there are many kinds of diseases, and artificial identification of crop diseases is easily interfered by subjective and environmental factors. Accurate identification of tomato diseases requires the guidance of agricultural experts, which consumes a lot of manpower and material resources. Some agricultural pest identification software exists on the market at present, but their identification accuracy is somewhat unstable. In this paper, nine common tomato diseases were identified accurately, based on EfficientNetV2 transfer learning, the value of mAP can reach 0.98 and the value of lost can reach 0.061. Meanwhile, the reasoning model is deployed to the cloud server, and the model is called based on the Flask framework, so as to infer and identify the photos uploaded by the Android application and return the results. The actual application test and comparative analysis show that the recognition accuracy of the recognition system designed in this paper is significantly higher than that of the existing commercial application software of the same type.
Models for the remnants of recurrent novae – III. Comparison with the X-ray observations of RS Ophiuchi (1985)

Ealiers papers of this series developed a model for the interaction of ejecta with a circumstellar medium which was principally applied to the 1985 outburst of the recurrent nova RS Ophiuchi. Here the X-ray emission predicted by this model is compared in detail to EXOSAT observations of the outburst. Fits to the first observations 55 days after outburst lead to values for model parameters which are consistent with existing estimates : outburst energy ⇒ 1.1 × 10 43 erg; ejecta mass ⇒ 1.1 × 10 -6 M ⊙ ; and mass-loss rate into the wind of the red giant component ⇒ 6 × 10 12 g/cm. Once these parameters are fixed the model predicts the subsequent evolution of the X-ray emission
Lacunary Polynomial Spline Interpolation

A special form of the Birkhoff interpolation problem is investigated. We prove an existence theorem for certain types of interpolation which, in a particular case, reduces to a theorem of Meir and Sharma for $(0,2)$ interpolation by $C^3 $ piecewise quintics. The method of proof enables us to obtain $L_\infty $-estimates for the error in interpolating smooth functions. These error bounds are shown to be sharp by means of a Baire category argument.
Dielectric metrology with coaxial sensors

Coaxial sensors are used in applications that require accurate and traceable measurements of complex permittivity. For example, coaxial sensors are often used for measurement of the complex permittivity of tissue equivalent materials (TEMs) used in specific absorption rate (SAR) measurements of exposure to RF fields. It is therefore important that well-founded metrological techniques for their use are developed and published. Although there are many published papers on coaxial sensors, few discuss the experimental techniques required to obtain the most accurate results. In this paper experimental approaches for obtaining the most accurate measurements are described. Common pitfalls with the technique are discussed. A Monte Carlo modelling (MCM) technique is used to provide estimates of uncertainty which are compared to those of measurements on reference liquids made with 3.5 mm, 7 mm and 15.1 mm diameter sensors. The MCM technique allows uncertainties to be estimated when measuring dielectrics for which there are no reference materials that have comparable properties, for example for TEMs at frequencies below 100 MHz.