Simple Description Research Articles

Hydrodynamization in hybrid Bjorken flow attractors

Hybrid fluid models, consisting of two sectors with more weakly and more strongly self-interacting degrees of freedom coupled consistently as in the semi-holographic framework, have been shown to exhibit an attractor surface for Bjorken flow. Retaining only the simple viscid fluid descriptions of both sectors, we find that, on the attractor surface, the hydrodynamization times of both subsectors decrease with increasing total energy density at the respective point of hydrodynamization following a conformal scaling, reach their minimum values, and subsequently rise rapidly. The minimum values are obtained when the respective energy densities are of the order of the inverse of the dimensionful inter-system coupling. Restricting to attractor curves which can be matched to glasma models at a time set by the saturation scale for both p-p and Pb-Pb collisions, we find that the more weakly coupled sector hydrodynamizes much later, and the strongly coupled sector hydrodynamizes earlier in p-p collisions, since the total energy densities at the respective hydrodynamization times of these sectors fall inside and outside of the conformal window. This holds true also for phenomenologically relevant solutions that are significantly away from the attractor surface at the time we match to glasma models.

Effects of non-continuous inverse Compton cooling in blazars

Blazar flares provide a window onto the extreme physical processes occurring in relativistic outflows. Most numerical codes used for modelling blazar emission during flares use a simplified continuous-loss description of particle cooling due to the inverse Compton (IC) process, neglecting non-continuous (discrete) effects that arise in the Klein-Nishina (KN) regime. The significance of such effects has not yet been explored in detail. In this study, we investigate the importance of non-continuous Compton cooling losses and their impact on the electron spectrum and spectral energy distribution (SED) of blazars during high flux states (flares), as well as in the low state. We solve the full transport equation numerically, accounting for large relative jumps in energy by extending our existing blazar flare modelling code EMBLEM. We perform a detailed physical modelling of the brightest gamma -ray flare of the archetypal flat-spectrum radio quasar (FSRQ) 3C\,279 detected in June 2015. We then compare results obtained using the full cooling term and using the continuous-loss approximation. We show that during flaring states of FSRQs characterised by high Compton dominance, the non-continuous cooling can lead to significant modification of the electron spectrum, introducing a range of distinct features, such as low-energy tails, hardening and/or softening, narrow and extended particle excesses, and shifts in the cooling break position . Such distortion translates to differences in the associated SED of up to sim 50<!PCT!>. This highlights the importance of non-continuous effects and the need to consider them in blazar emission models, particularly applied to extreme gamma -ray flares.

Quantifying Shape Transition in Anisotropic Plasmonic Nanoparticles through Geometric Inversion. Application to Gold Bipyramids.

Unraveling the nuanced interplay between the morphology and the optical properties of plasmonic nanoparticles is crucial for targeted applications. Managing the relationship becomes significantly complex when dealing with anisotropic nanoparticles that defy a simple description using parameters like length, width, or aspect ratio. This complexity requires computationally intensive numerical modeling and advanced imaging techniques. To address these challenges, we propose a detailed structural parameter determination of gold nanoparticles using their two-dimensional projections (e.g., micrographs). Employing gold bipyramids (AuBPs) as a model morphology, we can determine their three-dimensional geometry and extract optical features computationally for comparison with the experimental data. To validate our inversion model's effectiveness, we apply it to derive the structural parameters of AuBPs undergoing shape modification through oxidative etching. In summary, our findings allow for the precise characterization of structural parameters for plasmonic nanoparticles during shape transitions, potentially enhancing the comprehension of nanocrystal growth and optimizing plasmonic material design for various applications.

Neutralized Local Entropy and Dimension bounds for Invariant Measures

Abstract We introduce a notion of a point-wise entropy of measures (i.e., local entropy) called neutralized local entropy, and compare it with the Brin-Katok local entropy. We show that the neutralized local entropy coincides with Brin-Katok local entropy almost everywhere. Neutralized local entropy is computed by measuring open sets with a relatively simple geometric description. Our proof uses a measure density lemma for Bowen balls, and a version of a Besicovitch covering lemma for Bowen balls. As an application, we prove a lower point-wise dimension bound for invariant measures, complementing the previously established bounds for upper point-wise dimension.

Image Captioning with Multi-Context Synthetic Data

Image captioning requires numerous annotated image-text pairs, resulting in substantial annotation costs. Recently, large models (e.g. diffusion models and large language models) have excelled in producing high-quality images and text. This potential can be harnessed to create synthetic image-text pairs for training captioning models. Synthetic data can improve cost and time efficiency in data collection, allow for customization to specific domains, bootstrap generalization capability for zero-shot performance, and circumvent privacy concerns associated with real-world data. However, existing methods struggle to attain satisfactory performance solely through synthetic data. We identify the issue as generated images from simple descriptions mostly capture a solitary perspective with limited context, failing to align with the intricate scenes prevalent in real-world imagery. To tackle this, we present an innovative pipeline that introduces multi-context data generation. Beginning with an initial text corpus, our approach employs a large language model to extract multiple sentences portraying the same scene from diverse viewpoints. These sentences are then condensed into a single sentence with multiple contexts. Subsequently, we generate intricate images using the condensed captions through diffusion models. Our model is exclusively trained on synthetic image-text pairs crafted through this process. The effectiveness of our pipeline is validated through experimental results in both the in-domain and cross-domain settings, where it achieves state-of-the-art performance on well-known datasets such as MSCOCO, Flickr30k, and NoCaps.

Capitalism qua development in an era of planetary crisis

ABSTRACT Ecological crisis, massive inequality and war: our world is at a dangerous tipping point. Yet the present global crisis defies simple description and is difficult to explain. To clarify, it is argued that the character of the world crisis today is triple: ecological, economic and political. Underlying all three dimensions is the capitalist form of world society. The consolidation of this capitalist form was accompanied by the emergence of the concept of development within liberal philosophy. During the 20th century, development came to define geographical regions and legitimate capitalist political economy on a world scale. Capitalism came to be taken as development: I call this ‘capitalism qua development’. Because of the triple crisis, capitalism qua development is beginning to undergo a profound change. In an era of rapid global heating and ecological crisis, the tie between capital and development is straining and likely to give way. Capital is becoming more closely attached to the concept of adaptation. I argue that, in the most likely scenario (‘Climate Leviathan’), capitalism qua development will be recast as adaptation. Some signs of this shift are already apparent. Since this is only a likely prospect, three other possibilities – fates of the concept and practice of development – are proposed.

Streamlining FE and BIM Modeling for Historic Buildings with Point Cloud Transformation

ABSTRACT In this paper, a convenient workflow to obtain a simplified lightweight IFC description of a dense point cloud describing a historic building is proposed. The proposed procedure relies on slicing of the point cloud that aims at reconstructing the geometry of the solid. The resulting slices can then be easily employed in the generation of finite element (FE) and architectural building information modeling (BIM) models. In particular, the FE model generation procedure guarantees the obtainment of a conforming solid FE mesh ready to be used for structural purposes. Contextually, the architectural BIM model generation is achieved by slices extrusion to obtain the overall volume of the building. Then, the assembly of independent slice-based meshes is subjected to re-topology to obtain the overall watertight bounding surface of the building. A real historic structure is here used to show the effectiveness of the FE and BIM model generation procedures. Finally, the BIM model generation of an actual historic structure is carried out through the proposed workflow.

Bullshit (Sometimes) Makes the Art (Slightly) More Attractive: A Field Study in Gallery-Goers

Vague, impressive language used in descriptions (bullshit) is thought to make art seem more profound and valuable to the viewer. We studied the effect during art exhibitions in real-life gallery-goers who saw paintings of four artists, each with either simplified, neutral, or bullshitty description. We crafted a typical description of each painting, which we later manipulated in terms of language. A simplified description was modified to be concrete and simplistic, while a bullshitty one was very abstract and vague. After analyzing over 1500 ratings, we found the expressive language of descriptions had a negligible effect on the perceived quality and monetary value of art ( R2 marginal ≤ 1%). We conclude that, at least for experienced gallery-goers, the description accompanying a painting has little influence, and the art speaks for itself.

Glans hypermobility scale (GHS): A simple grading scale and description of a modified glanspexy technique.

Glans hypermobility (GH) is a well-described clinical entity that can have significant implications for cosmesis and function, resulting in sexual dissatisfaction, penile pain, and early device erosion, with an estimated incidence of 0.04% to 10%. We developed a novel grading scale to assess GH severity intraoperatively during primary inflatable penile prosthesis (IPP) placement and describe a modified glanspexy technique to correct GH when encountered during IPP placement. 530 patients who underwent primary IPP placements from two high-volume prosthetic surgeons between February 2018 - November 2019 were retrospectively reviewed in order to identify the incidence of GH. Of these, 139 (26.2%) had hypermobility. Employing our new scaling system, grade 1, 2, and 3 GH was seen in 86 (16.2%), 29 (5.5%), and 24 (4.5%) cases, respectively. Increased implant size correlated with a decreased likelihood of GH incidence. Each increase in implant size by 1 cm decreased the incidence of detecting GH by 11.0% (OR = 0.89; p = 0.015). 11 patients underwent primary GH repair using our described technique. At one-year follow-up, one patient required repeat glanspexy for recurrent bothersome GH and a second patient developed a suture granuloma at the glanspexy incision requiring unilateral cylinder explant. Our modified glanspexy technique can be used to correct GH in any direction and is a useful tool for the prosthetic surgeon's armamentarium.

Octonions, Albert vectors and the group E6(F)

–We present a uniform approach to the construction of the groups of type E 6 over arbitrary fields without using Lie theory. This gives a simple description of the group generators and some of the subgroup structure. In the finite case our approach also permits relatively straightforward computation of the group order.

ChatGPT as an information tool in rhinology. Can we trust each other today?

ChatGPT (Chat-Generative Pre-trained Transformer) has proven to be a powerful information tool on various topics, including healthcare. This system is based on information obtained on the Internet, but this information is not always reliable. Currently, few studies analyze the validity of these responses in rhinology. Our work aims to assess the quality and reliability of the information provided by AI regarding the main rhinological pathologies. We asked to the default ChatGPT version (GPT-3.5) 65 questions about the most prevalent pathologies in rhinology. The focus was learning about the causes, risk factors, treatments, prognosis, and outcomes. We use the Discern questionnaire and a hexagonal radar schema to evaluate the quality of the information. We use Fleiss's kappa statistical analysis to determine the consistency of agreement between different observers. The overall evaluation of the Discern questionnaire resulted in a score of 4.05 (± 0.6). The results in the Reliability section are worse, with an average score of 3.18. (± 1.77). This score is affected by the responses to questions about the source of the information provided. The average score for the Quality section was 3.59 (± 1.18). Fleiss's Kappa shows substantial agreement, with a K of 0.69 (p < 0.001). The ChatGPT answers are accurate and reliable. It generates a simple and understandable description of the pathology for the patient's benefit. Our team considers that ChatGPT could be a useful tool to provide information under prior supervision by a health professional.

Constrained spin systems and KNdS black holes

Kerr-Newman de Sitter (KNdS) spacetimes have a rich thermodynamic structure that involves multiple horizons, and so differs in key respects from asymptotically flat or AdS black holes. In this paper, we show that certain features of KNdS spacetimes can be reproduced by a constrained system of N non-interacting spins in a magnetic field. Both the KNdS and spin systems have bounded energy and entropy, a maximum of the entropy in the interior of the energy range, and a symmetry that maps lower energy states to higher energy states with the same entropy. Consequently, both systems have a temperature that can be positive or negative, where the gravitational temperature is defined analogously to that of the spins. We find that the number of spins N corresponds to 1/Λ for black holes with very small charge q and rotation parameter a, and scales like a2+q2/Λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\sqrt{\\left({a}^2+{q}^2\\right)/\\Lambda} $$\\end{document} for larger values of a and q. These comparisons show that certain key features of the complex thermodynamics of KNdS spacetimes are reproduced by a simple system of noninteracting spins, suggesting that there may be a relatively simple effective description of quantum de Sitter black holes in terms of a finite dimensional Hilbert space.

Causal relationship recognition algorithm based on external semantic and contextual structural features

Identifying the causal relationship of events plays an important role in determining the development of known events and evaluating the possible outcomes of different decisions. At present, neural network models are widely used to identify the relationships between events. Based on obtaining events, researchers distinguish the relationships between events by mining their semantics. However, due to the complexity of events and the dynamic changes in relationships between events, models often cannot fully meet the needs of accurately identifying causal relationships by only learning simple event descriptions in sentences; Moreover, focusing too much on the events themselves often leads to neglecting the structural features of statements and neglecting the impact of specific structural patterns on the relationships between events. In this article, we propose an event masking algorithm that combines external semantics to address the aforementioned issues. In this algorithm, external semantics are first introduced into the statement to enrich the information behind the event, allowing the model to mine the deep connections between events through a wider range of background knowledge; Then, the event masking module is used to enhance the model's extraction of sentence structured features, mining specific contextual representations that are unrelated to the event. The results show that compared to existing neural network algorithms, the algorithm proposed in this paper improves the F1 value of predictions on publicly available datasets by more than 4%.

Artificial Neural Networks for Determining the Empirical Relationship between Meteorological Parameters and High-Level Cloud Characteristics

The special features of the applicability of artificial neural networks to the task of identifying relationships between meteorological parameters of the atmosphere and optical and geometric characteristics of high-level clouds (HLCs) containing ice crystals are investigated. The existing models describing such relationships do not take into account a number of atmospheric effects, in particular, the orientation of crystalline ice particles due to the simplified physical description of the medium, or within the framework of these models, accounting for such dependencies becomes a highly nontrivial task. Neural networks are able to take into account the complex interaction of meteorological parameters with each other, as well as reconstruct almost any dependence of the HLC characteristics on these parameters. In the process of prototyping the software product, the greatest difficulty was in determining the network architecture, the loss function, and the method of supplying the input parameters (attributes). Each of these problems affected the most important issue of neural networks—the overtraining problem, which occurs when the neural network stops summarizing data and starts to tune to them. Dependence on meteorological parameters was revealed for the following quantities: the altitude of the cloud center; elements m22 and m44 of the backscattering phase matrix (BSPM); and the m33 element of BSPM requires further investigation and expansion of the analyzed dataset. Significantly, the result is not affected by the compression method chosen to reduce the data dimensionality. In almost all cases, the random forest method gave a better result than a simple multilayer perceptron.

Reflective Engagement With a Digital Physical Activity Intervention Among People Living With and Beyond Breast Cancer: Mixed Methods Study.

People living with and beyond breast cancer can face internal barriers to physical activity (eg, fatigue and pain). Digital interventions that promote psychological acceptance and motivation may help this population navigate these barriers. The degree to which individuals (1) adhere to intervention protocols and (2) reflect on and internalize intervention content may predict intervention efficacy. The objective of this study was to characterize the nature of reflective processes brought about by an 8-week acceptance- and mindfulness-based physical activity intervention for insufficiently active survivors of breast cancer (n=75). Furthermore, we explored the potential utility of a metric of reflective processes for predicting study outcomes. Of the intervention's 8 weekly modules, 7 (88%) included an item that asked participants to reflect on what they found to be most useful. Two coders conducted directed content analysis on participants' written responses. They assessed each comment's depth of reflection using an existing framework (ranging from 0 to 4, with 0=simple description and 4=fundamental change with consideration of social and ethical issues). The coders identified themes within the various levels of reflection. We fit multiple linear regression models to evaluate whether participants' (1) intervention adherence (ie, number of modules completed) and (2) the mean level of the depth of reflection predicted study outcomes. Participants were aged on average 57.2 (SD 11.2) years, mostly non-Hispanic White (58/75, 77%), and mostly overweight or obese (54/75, 72%). Of the 407 responses to the item prompting personal reflection, 70 (17.2%) were rated as reflection level 0 (ie, description), 247 (60.7%) were level 1 (ie, reflective description), 74 (18.2%) were level 2 (ie, dialogic reflection), 14 (3.4%) were level 3 (ie, transformative reflection), and 2 (0.5%) were level 4 (ie, critical reflection). Lower levels of reflection were characterized by the acquisition of knowledge or expressing intentions. Higher levels were characterized by personal insight, commentary on behavior change processes, and a change of perspective. Intervention adherence was associated with increases in self-reported weekly bouts of muscle-strengthening exercise (B=0.26, SE 0.12, 95% CI 0.02-0.50) and decreases in sleep disturbance (B=-1.04, SE 0.50, 95% CI -0.06 to -2.02). The mean level of reflection was associated with increases in psychological acceptance (B=3.42, SE 1.70, 95% CI 0.09-6.75) and motivation for physical activity (ie, integrated regulation: B=0.55, SE 0.25, 95% CI 0.06-1.04). We identified a useful method for understanding the reflective processes that can occur during digital behavior change interventions serving people living with and beyond breast cancer. Intervention adherence and the depth of reflection each predicted changes in study outcomes. Deeper reflection on intervention content was associated with beneficial changes in the determinants of sustained behavior change. More research is needed to investigate the relations among digital behavior change intervention use, psychological processes, and intervention efficacy.

Shock cooling emission from explosions of red supergiants: II. An analytic model of deviations from blackbody emission

ABSTRACT Light emission in the first hours and days following core-collapse supernovae (SNe) is dominated by the escape of photons from the expanding shock-heated envelope. In a preceding paper, Paper I, we provided a simple analytic description of the time-dependent luminosity, L, and colour temperature, Tcol, valid up to H recombination (T ≈ 0.7 eV), for explosions of red supergiants with convective polytropic envelopes without significant circumstellar medium (CSM). The analytic description was calibrated against ‘grey’ (frequency-independent) photon diffusion numeric calculations. Here, we present the results of a large set of 1D multigroup (frequency-dependent) calculations, for a wide range of progenitor parameters (mass, radius, core/envelope mass ratios, metalicity) and explosion energies, using opacity tables that we constructed (and made publicly available), including the contributions of bound–bound and bound–free transitions. We provide an analytic description of the small, ${\simeq}10\ \hbox{per cent}$ deviations of the spectrum from blackbody at low frequencies, hν &amp;lt; 3Tcol, and an improved (over Paper I) description of ‘line dampening’ for hν &amp;gt; 3Tcol. We show that the effects of deviations from initial polytropic density distribution are small, and so are the effects of ‘expansion opacity’ and deviations from LTE ionization and excitation (within our model assumptions). A recent study of a large set of type II SN observations finds that our model accounts well for the early multiband data of more than 50 per cent of observed SNe (the others are likely affected by thick CSM), enabling the inference of progenitor properties, explosion velocity, and relative extinction.

Field control of quasiparticle decay in a quantum antiferromagnet

Dynamics in a quantum material is described by quantized collective motion: a quasiparticle. The single-quasiparticle description is useful for a basic understanding of the system, whereas a phenomenon beyond the simple description such as quasiparticle decay which affects the current carried by the quasiparticle is an intriguing topic. The instability of the quasiparticle is phenomenologically determined by the magnitude of the repulsive interaction between a single quasiparticle and the two-quasiparticle continuum. Although the phenomenon has been studied in several materials, thermodynamic tuning of the quasiparticle decay in a single material has not yet been investigated. Here we show, by using neutron scattering, magnetic field control of the magnon decay in a quantum antiferromagnet RbFeCl3, where the interaction between the magnon and continuum is tuned by the field. At low fields where the interaction is small, the single magnon decay process is observed. In contrast, at high fields where the interaction exceeds a critical magnitude, the magnon is pushed downwards in energy and its lifetime increases. Our study demonstrates that field control of quasiparticle decay is possible in the system where the two-quasiparticle continuum covers wide momentum-energy space, and the phenomenon of the magnon avoiding decay is ubiquitous.

Reduction of multiple scattering of positively charged ultra relativistic particles channeling in planar fields of single crystals

Based on the theory of multiple scattering of positively charged particles channeled in the planes of a single crystal of silicon, calculations of the rms angle of scattering have been made for particles in the range of momenta 100–7000 GeV/c. As a result of the analysis, we find that the rms angle of multiple particle scattering can be represented by in the form of (Cl)1/w/ph, where p is the particle momentum, l is the crystal thickness and C,w,h are the constant coefficients. We show that these calculation results can be represented in a unified way using the found universal function. This function is shown to be in a good agreement with the experimental data. Furthermore, our analysis is consistent with the effect (predicted by Luboshits and Podgoretsky) of reducing multiple scattering of above-barrier particles moving at small angles to the planes of the crystal. In general, the obtained results give a simple and clear description of the scattering process under consideration.

The Two-Domain Model Utilizing the Effective Pinning Energy for Modeling the Strain-Dependent Magnetic Permeability in Anisotropic Grain-Oriented Electrical Steels.

This paper presents a newly proposed domain wall energy-based model of the 2D strain dependence of relative magnetic permeability in highly grain-oriented anisotropic electrical steels. The model was verified utilizing grain-oriented M120-27s electrical steel sheet samples with magnetic characteristics measured by an automated experimental setup with a magnetic yoke. The model's parameters, identified in the differential evolution-based optimization process, enable a better understanding of the interaction between stress-induced anisotropy and magnetocrystalline anisotropy in electrical steels. Moreover, the consequences of the simplified description of grain-oriented magnetocrystalline anisotropy are clearly visible, which opens up the possibility for further research to improve this description.

Back action evasion in optical lever detection

The optical lever is a centuries old and widely used detection technique employed in applications ranging from consumer products and industrial sensors to precision force microscopes used in scientific research. However, despite the long history, its quantum limits have yet to be explored. In general, any precision optical measurement is accompanied by optical force induced disturbance to the measured object (termed as back action) leading to a standard quantum limit (SQL). Here, we give a simple ray optics description of how such back action can be evaded in optical lever detection. We perform a proof-of-principle experiment demonstrating the mechanism of back action evasion in the classical regime, by developing a lens system that cancels extra tilting of the reflected light off a silicon nitride membrane mechanical resonator caused by laser-pointing-noise-induced optical torques. We achieve a readout noise floor two orders of magnitude lower than the SQL, corresponding to an effective optomechanical cooperativity of 100 without the need for an optical cavity. As the state-of-the-art ultralow dissipation optomechanical systems relevant for quantum sensing are rapidly approaching the level where quantum noise dominates, simple and widely applicable back action evading protocols will be crucial for pushing beyond quantum limits.