Miracle-dz/UniFAR-FTUs huggingface dataset:
LANGMUIR WAVE DECAY IN INHOMOGENEOUS SOLAR WIND PLASMAS: SIMULATION RESULTS

Langmuir turbulence excited by electron flows in solar wind plasmas is studied on the basis of numerical simulations. In particular, nonlinear wave decay processes involving ion-sound (IS) waves are considered in order to understand their dependence on external long-wavelength plasma density fluctuations. In the presence of inhomogeneities, it is shown that the decay processes are localized in space and, due to the differences between the group velocities of Langmuir and IS waves, their duration is limited so that a full nonlinear saturation cannot be achieved. The reflection and the scattering of Langmuir wave packets on the ambient and randomly varying density fluctuations lead to crucial effects impacting the development of the IS wave spectrum. Notably, beatings between forward propagating Langmuir waves and reflected ones result in the parametric generation of waves of noticeable amplitudes and in the amplification of IS waves. These processes, repeated at different space locations, form a series of cascades of wave energy transfer, similar to those studied in the frame of weak turbulence theory. The dynamics of such a cascading mechanism and its influence on the acceleration of the most energetic part of the electron beam are studied. Finally, the role of the decay processes in the shaping of the profiles of the Langmuir wave packets is discussed, and the waveforms calculated are compared with those observed recently on board the spacecraft Solar TErrestrial RElations Observatory and WIND.
GeoGlue: feature matching with self-supervised geometric priors for high-resolution UAV images

ABSTRACT We present GeoGlue, a novel method using high-resolution UAV imagery for accurate feature matching, which is normally challenging due to the complicated scenes. Current feature detection methods are performed without guidance of geometric priors (e.g., geometric lines), lacking enough attention given to salient geometric features which are indispensable for accurate matching due to their stable existence across views. In this work, geometric lines are firstly detected by a CNN-based geometry detector (GD) which is pre-trained in a self-supervised manner through automatically generated images. Then, geometric lines are naturally vectorized based on GD and thus non-significant features can be disregarded as judged by their disordered geometric morphology. A graph attention network (GAT) is utilized for final feature matching, spanning across the image pair with geometric priors informed by GD. Comprehensive experiments show that GeoGlue outperforms other state-of-the-art methods in feature-matching accuracy and performance stability, achieving pose estimation with maximum rotation and translation errors under 1% in challenging scenes from benchmark datasets, Tanks & Temples and ETH3D. This study also proposes the first self-supervised deep-learning model for curved line detection, generating geometric priors for matching so that more attention is put on prominent features and improving the visual effect of 3D reconstruction.
On the Dynamics of Hawaiian Cloud Bands: Comparison of Model Results with Observations and Island Climatology

Abstract This paper presents a detailed comparison study of three-dimensional model results with an aircraft wind field mapping for the island of Hawaii. Model runs were initialized using an aircraft sounding from 1 August 1985, and detailed predictions from the model are compared with observations from that day. The strength and location of the upwind convergence zone were well simulated, as well as the strong deflection and deceleration of the flow around the island and the geometry and location of the upstream cloud bands. The good agreement between the model results and observations supports the results of our previous study in which we show that the flow pattern and associated cloud processes around the island of Hawaii can be understood by considering the flow of a stably stratified fluid around a large three-dimensional obstacle. Model runs with different wind directions showed that increasing northerly tradewind flow resulted in the band clouds moving closer to the shore line, and the large scale .
Slap, Give a Slap, Slap a Slap: Crosslinguistic Diversity in Hitting Event Descriptions

Goal: To show the range of encoding options observed across languages for specific types of events largely arises from crosslinguistically applicable argument realization principles interacting with each language’s lexical and morphosyntactic resources. The case study: The description of hitting events, with a focus on strategies for expressing predicative content and, secondarily, argument realization.
The Conditional Predictive Ordinate for the Normal Distribution

SUMMARY The conditional predictive ordinate (CPO) is a Bayesian diagnostic which detects surprising observations. It has been used in a variety of situations such as univariate samples, the multivariate normal distribution and regression models. Results are presented about the most surprising observation which has minimum CPO. For the multivariate normal distribution it is shown that the most surprising observation must lie at one of the vertices of the convex hull. It is also shown that the observation with maximum Mahalanobis distance from the sample mean must lie on the convex hull. Results are given for the expected number of vertices on the convex hull when the sample is contaminated. An alternative, closely related diagnostic, the ratio ordinate measure, is presented. A numerical comparison of the two measures is given.
Patients Response to Interventional Care for Chronic Pain Study (PRICS): A Cross-Sectional Survey of Community-Based Pain Clinics in Ontario, Canada

Background: Non-image guided injection treatments (“nerve blocks”) are commonly provided in community pain clinics in Ontario for chronic non-cancer pain (CNCP) but remain controversial. Aim: We explored patients’ perspectives of nerve blocks for CNCP. Methods: We administered a 33-item cross-sectional survey to patients living with CNCP pain attending four community-based pain clinics in Ontario, Canada. The survey captured demographic information and asked about patient experiences with nerve blocks. Results: Among 616 patients that were approached, 562 (91%) provided a completed survey. The mean age of respondents was 53 (SD 12), 71% were female, and the majority (57%) reported living with CNCP for more than a decade. Fifty-eight percent had been receiving nerve blocks for their pain for >3 years, 51% on a weekly frequency. Since receiving nerve blocks, patients self-reported a median improvement in pain intensity of 2.5 points (95% CI -2.5 to -3.0) on an 11-point numeric rating scale and 66% reported stopping or reducing prescription medications, including opioids. The majority who were not retired (62%) were receiving disability benefits and were unable to work in any capacity. When asked what impact cessation of nerve blocks would have, most employed patients (52%) reported they would be unable to work, and the majority indicated their ability to function across multiple domains would decrease. Conclusion: Our respondents who received nerve blocks for CNCP attribute important pain relief and functional improvement to this intervention. Randomized trials and clinical practice guidelines are urgently needed to optimize the evidence-based use of nerve blocks for CNCP.
Phenomenological aspects of the E6SSM

The work in this thesis explores various phenomenological aspects of the E6SSM with a particular focus on the inert neutralino sector of the model and on the dark matter implications. The E6SSM is a string theory inspired supersymmetric extension to the Standard Model with an E6 grand unification group. The model provides a solution to the hierarchy problem of the Standard Model, provides an explanation for neutrino mass, and has automatic gauge anomaly cancellation. The inert neutralino sector of the E6SSM and the dark matter that naturally arises from this sector is studied for the first time. Limits on the parameter space from experimental and cosmological observations relating to the inert neutralino dark matter are explored and the consequences for Higgs boson phenomenology are investigated. In plausible scenarios it is found that the couplings of the lightest inert neutralinos to the SM-like Higgs boson are always rather large. This has major implications for Higgs boson collider phenomenology and leads to large spin-independent LSP-nucleon cross-sections. Because of the latter, scenarios in which E6SSM inert neutralinos account for all of the observed dark matter are now severely challenged by recent dark matter direct detection experiment analyses. In plausible scenarios consistent with observations from both cosmology and LEP the lightest inert neutralino is required to have a mass around half of the Z boson mass if it contributes to cold dark matter and this means that tan(? ) cannot be too large. A new variant of the E6SSM called the E6ZS 2 SSM is also presented in which the dark matter scenario is very different to the inert neutralino cold dark matter scenario and in which the presence of supersymmetric massless states in the early universe modifies the expansion rate of the universe prior to Big Bang Nucleosynthesis. The new dark matter scenario is consistent with current observations and the modified expansion rate provides a better explanation of various data than the SM prediction. The prospects for a warm dark matter scenario in the E6SSM are also briefly discussed
Conceptual Modeling of Drag-Augmented Supersonic Retropropulsion for Mars Entry, Descent, and Landing

The development of new decelerator technologies will be required as the payload mass for future Mars landing missions increases beyond the current state-of-the-art capability. This study examines the potential for supersonic retropropulsion applied on entry, descent, and landing vehicles to increase the landed payload mass. This study describes the development of a model characterizing the drag augmentation capabilities of peripheral-nozzle supersonic retropropulsion flow interactions. The model captures the dominant flow physics of pressure conservation through shock cascade structures and predicts an increase in the drag coefficient over the nominal drag coefficient of a 70 deg sphere-cone aeroshell by 14% at high Mach numbers. This study also describes drag-augmented supersonic retropropulsion operation concepts for use in Mars entry, descent, and landing. Drag-augmented supersonic retropropulsion is found to be most effective when used in the region of maximum freestream dynamic pressure. The vehicle .
A GCM Simulation of the Relationship between Tropical-Storm Formation and ENSO

Abstract A low-resolution Geophysical Fluid Dynamics Laboratory (GFDL) general circulation model has been integrated for 15 years. In the course of this experiment, the observed month-to-month sea surface temperature (SST) variations in the tropical Pacific Ocean were incorporated in the lower boundary condition. The output from this model run was used to investigate the influence of El Nin-Southern Oscillation (ENSO) events on the variability of tropical-storm formation. Criteria for detecting tropical cyclogenesis and tropical-storm formation were developed for the model. Tropical storms appearing in the model atmosphere exhibit many typhoonlike characteristics: strong cyclonic vorticity and convergence in the lower troposphere, strong anticyclonic vorticity and divergence near the tropopause, and intense precipitation. It is demonstrated that, despite its coarse resolution, the model is capable of reproducing the observed geographical distribution and seasonal variation of tropical-storm formation. The.
Activism as leadership: Supporting progressive activists within our work

This empirical work is driven by a study on the experiences and motivation of a group of college-aged student activists. Using a cohort of 14 millennial-aged activists who organized their work in response to a rise of right-wing authoritarianism in national and state politics, this paper presents recommendations for how student affairs professionals working in activities-oriented functional areas can leverage these experiences to build capacity around engagement, leadership development, sense of belonging, and work in partnership with these communities to challenge unjust practices within the academy. Further, I argue that in order to do our work of supporting students in their educational ventures, we can no longer claim notions of political neutrality.
Real-Time Flood Forecasting and Information System for the State of Iowa

AbstractThe Iowa Flood Center (IFC), established following the 2008 record floods, has developed a real-time flood forecasting and information dissemination system for use by all Iowans. The system complements the operational forecasting issued by the National Weather Service, is based on sound scientific principles of flood genesis and spatial organization, and includes many technological advances. At its core is a continuous rainfall–runoff model based on landscape decomposition into hillslopes and channel links. Rainfall conversion to runoff is modeled through soil moisture accounting at hillslopes. Channel routing is based on a nonlinear representation of water velocity that considers the discharge amount as well as the upstream drainage area. Mathematically, the model represents a large system of ordinary differential equations organized to follow river network topology. The IFC also developed an efficient numerical solver suitable for high-performance computing architecture. The solver allows the IF.
Design of High-Gain Magneto-Electric Dipole Antenna by Loading a Magneto-Electric Dipole Director

A high-gain magneto-electric dipole (ME-Dipole) antenna has been designed by adding a ME-Dipole director on top of a conventional cavity-backed ME-Dipole antenna. The purpose of adding the ME-Dipole director is to significantly enhance the gain of the antenna. The proposed design has been both measured and simulated, with an average gain of 12.5 and 12.4 dBi, respectively, demonstrating the design's high gain capabilities. Additionally, the measured and simulated impedance bandwidth (VSWR ≤ 2) are 29.21% (1.90–2.55 GHz) and 30.70% (1.93–2.63 GHz), respectively, confirming the wideband nature of the design. Other outstanding performance metrics, such as low cross polarization of −25 dB and the stable antenna gain of 11.8 ± 1.7 dBi as measured and 12.0 ± 1 dBi as simulated, have also been observed in the current design.
Pd(II) and Pt(II) Trinuclear Chelates with Spermidine: Selective Anticancer Activity towards TNBC-Sensitive and -Resistant to Cisplatin

Triple-negative breast cancer (TNBC) is one of the most aggressive forms of breast cancer and constitutes 10–20% of all breast cancer cases. Even though platinum-based drugs such as cisplatin and carboplatin are effective in TNBC patients, their toxicity and development of cancer drug resistance often hamper their clinical use. Hence, novel drug entities with improved tolerability and selectivity profiles, as well as the ability to surpass resistance, are needed. The current study focuses on Pd(II) and Pt(II) trinuclear chelates with spermidine (Pd3Spd2 and Pt3Spd2) for evaluating their antineoplastic activity having been assessed towards (i) cisplatin-resistant TNBC cells (MDA-MB-231/R), (ii) cisplatin-sensitive TNBC cells (MDA-MB-231) and (iii) non-cancerous human breast cells (MCF-12A, to assess the cancer selectivity/selectivity index). Additionally, the complexes’ ability to overcome acquired resistance (resistance index) was determined. This study revealed that Pd3Spd2 activity greatly exceeds that displayed by its Pt analog. In addition, Pd3Spd2 evidenced a similar antiproliferative activity in both sensitive and resistant TNBC cells (IC50 values 4.65–8.99 µM and 9.24–13.34 µM, respectively), with a resistance index lower than 2.3. Moreover, this Pd compound showed a promising selectivity index ratio: >6.28 for MDA-MB-231 cells and >4.59 for MDA-MB-231/R cells. Altogether, the data presently gathered reveal Pd3Spd2 as a new, promising metal-based anticancer agent, which should be further explored for the treatment of TNBC and its cisplatin-resistant forms.
The Association Between Emotional Expressivity in Autobiographies from Early Adulthood and the Risk of Dementia in the Context of Written Language Skills

Background: Risk factors for dementia, such as Alzheimer’s disease, are complex and span a lifetime. Exploring novel factors, such as characteristics of writing, may provide insight into dementia risk. Objective: To investigate the association between emotional expressivity and risk of dementia in the context of a previously identified risk factor, written language skills. Methods: The Nun Study recruited 678 religious sisters aged 75 + years. Of these, 149 U.S.-born participants had archived autobiographies handwritten at a mean age of 22 years. The autobiographies were scored for frequency of emotion word usage and language skills (e.g., idea density). The association of emotional expressivity and a four-level composite variable (combining high/low emotional expressivity and high/low idea density) with dementia was assessed using logistic regression models adjusted for age, education, and apolipoprotein E. Results: Within the composite variable, odds of dementia increased incrementally, with opposing effects of emotional expressivity across the two idea density levels. Compared to the referent category (low emotional expressivity/high idea density), the risk of dementia increased in those with high emotional expressivity/high idea density (OR = 2.73, 95% CI = 1.05–7.08), while those with low emotional expressivity/low idea density had the highest risk (OR = 18.58, 95% CI = 4.01–86.09). Conclusion: Dementia risk is better captured by inclusion of multiple measures relating to characteristics of writing. Emotional expressivity may be protective when individuals are at increased risk due to poor written language skills (i.e., low idea density), but detrimental when not at risk (i.e., high idea density). Our findings indicate that emotional expressivity is a contextually-dependent novel risk factor for dementia.
Machine learning and its impact on psychiatric nosology: Findings from a qualitative study among German and Swiss experts

The increasing integration of Machine Learning (ML) techniques into clinical care, driven in particular by Deep Learning (DL) using Artificial Neural Nets (ANNs), promises to reshape medical practice on various levels and across multiple medical fields. Much recent literature examines the ethical consequences of employing ML within medical and psychiatric practice but the potential impact on psychiatric diagnostic systems has so far not been well-developed. In this article, we aim to explore the challenges that arise from the recent use of ANNs for the old problems of psychiatric nosology. To enable an empirically supported critical reflection on the topic, we conducted semi-structured qualitative interviews with Swiss and German experts in computational psychiatry. Here, we report our findings structured around two themes, namely (1) the possibility of using ML for defining or refining of psychiatric classification, and (2) the desirability of employing ML for psychiatric nosology. We discuss these themes by relating them to recent debates about network theory for psychiatric nosology and show why empirical research in the field should critically reflect on its contribution to psychopathology research. In sum, we argue that beyond technical, regulatory, and ethical challenges, philosophical reflection is crucial to harness the potential of ML in psychiatry.
Observations and Numerical Simulation of Atmospheric Cellular Convection over Mesoscale Topography

Radar and satellite images provide observations of convective rolls and other structures in the convective boundary layer (CBL), but the data are intermittent, and neither radar nor satellite gives a complete picture of roll circulation in the observed cases. As a consequence, numerical modeling is a useful complement to the observations, to investigate the temporal and spatial details of convective rolls. In this paper, observations of convective rolls over the south of England are described. Numerical simulations have been performed to investigate these rolls using the Boundary Layer Above Stationary Inhomogeneous Uneven Surfaces (BLASIUS) model, a relatively simple boundary layer code for flow over topography. The numerical results indicate that most of the features of the convective structures can be successfully reproduced, notably the roll orientation and spacing and the basic features of the cloud field. These features are in good agreement for two case studies, one with distinct rolls and the other with more dispersed convective structures and a time-dependent basic state. The model tends to predict the initial occurrence of rolls later than observed, and this time of occurrence is found to be influenced by model resolution. The presence of low topography (with maximum height on the order of 30% of the CBL depth) may have a small influence on the average orientation and spacing, and the time of initial occurrence of modeled rolls. Local flow anomalies related to the hills are much more pronounced. These anomalies appear to be related to coherent patterns in the model cloud fields, with a tendency for more cloud cover upstream and over hills, and cloud clearing in the lee as a result of descent suppressing convective eddies. When the satellite imagery is combined with topography data, this kind of orographic control of the shallow convection by the topography is evident. The CBL height varies considerably in the early stages of CBL evolution over hilly topography, but when the convection is fully developed the CBL height is almost constant over the domain.
Hypoelliptic Laplacian and probability

The purpose of this paper is to describe the probabilistic aspects underlying the theory of the hypoelliptic Laplacian, as a deformation of the standard elliptic Laplacian. The corresponding diffusion on the total space of the tangent bundle of a Riemannian manifold is a geometric Langevin process, that interpolates between the geometric Brownian motion and the geodesic flow. Connections with the central limit theorem for the occupation measure by the geometric Brownian motion are emphasized. Spectral aspects of the hypoelliptic deformation are also provided on tori. The relevant hypoelliptic deformation of the Laplacian in the case of Riemann surfaces of constant negative curvature is briefly described, in connection with Selberg’s trace formula. Introduction. The purpose of this paper is to describe the probabilistic aspects that underlie the construction of the hypoelliptic Laplacian, at the crossroads of two unrelated theories: index theory, and the central limit theorem. 1. The index of an elliptic operator is the difference of the dimensions of its kernel and its cokernel. It only depends on the principal symbol of the operator. The AtiyahSinger index theorem [AS68a], [AS68b] gives a cohomological formula for the index in terms of characteristic classes. 2. The particular version of the central limit theorem we think of describes the asymptotics of the occupation measure by a geometric Brownian motion as time tends to infinity in terms of a Gaussian random field. This result can also be formulated as the study of the lowest eigenvalue of a family of second order differential operators as a parameter b tends to 0, all the other eigenvalues tending to +∞. A possibly common feature of these two theories is that they are both concerned with small eigenvalues, the zero eigenvalue of certain operators in the case of index theory, and small eigenvalues in the case of the central limit theorem. Before we explain the content of the present paper, we will give the proper perspective to the theory of the hypoelliptic Laplacian. 2010 Mathematics Subject Classification. Primary 35H10, 58J65.