Window Size Research Articles

The Droplet Search Algorithm for Kernel Scheduling

Kernel scheduling is the problem of finding the most efficient implementation for a computational kernel. Identifying this implementation involves experimenting with the parameters of compiler optimizations, such as the size of tiling windows and unrolling factors. This article shows that it is possible to organize these parameters as points in a coordinate space. The function that maps these points to the running time of kernels, in general, will not determine a convex surface. However, this article provides empirical evidence that the origin of this surface (an unoptimized kernel) and its global optimum (the fastest kernel) reside on a convex region. We call this hypothesis the “droplet expectation.” Consequently, a search method based on the Coordinate Descent algorithm tends to find the optimal kernel configuration quickly if the hypothesis holds. This approach—called Droplet Search—has been available in Apache TVM since April of 2023. Experimental results with six large deep learning models on various computing devices (ARM, Intel, AMD, and NVIDIA) indicate that Droplet Search is not only as effective as other AutoTVM search techniques but also 2 to 10 times faster. Moreover, models generated by Droplet Search are competitive with those produced by TVM’s AutoScheduler (Ansor), despite the latter using 4 to 5 times more code transformations than AutoTVM.

Application of Simulation-Based Metrics to Improve the Daylight Performance of a Secondary School, An Approach for Green Building Designers and Architects

Visual comfort affects the quality of classrooms as well as student learning. A practice-oriented approach discovers how the gap between academic research and its application in building design can be addressed. Nevertheless, the physical characteristics design of daylighting systems, including window sizes, shapes, dimensions, and materials, are considered fundamental challenges for their practicability. In this study, the physical characteristics design of daylighting systems, including window sizes, shapes, dimensions, and materials, are considered in a designed sample school, and the daylight metrics were analyzed to achieve more trustworthy and applicable daylighting systems. Grasshopper (Honeybee-Ladybug), as a parametric control method, was applied to simulate the daylighting quality for various educational spaces in a secondary school in Sanandaj City, based on average ‘Daylight Factor’, ‘Daylight Autonomy’, ‘Useful Daylight Illuminance’, and ‘Annual Sunlight Exposure’. These metrics were examined to discover the relationship between window size and positions on visual comfort. The results indicate daylighting assessments are a solid approach to revising the architectural design mistakes at the primary designing phase. Architects and other building designers or energy consumption assessors can apply the design improvement process to present more sufficient and successful architectural details. This is a fundamental step toward the implementation of green buildings.

Machine Learning Methods and Visual Observations to Categorize Behavior of Grazing Cattle Using Accelerometer Signals.

Accelerometers worn by animals produce distinct behavioral signatures, which can be classified accurately using machine learning methods such as random forest decision trees. The objective of this study was to identify accelerometer signal separation among parsimonious behaviors. We achieved this objective by (1) describing functional differences in accelerometer signals among discrete behaviors, (2) identifying the optimal window size for signal pre-processing, and (3) demonstrating the number of observations required to achieve the desired level of model accuracy,. Crossbred steers (Bos taurus indicus; n = 10) were fitted with GPS collars containing a video camera and tri-axial accelerometers (read-rate = 40 Hz). Distinct behaviors from accelerometer signals, particularly for grazing, were apparent because of the head-down posture. Increasing the smoothing window size to 10 s improved classification accuracy (p < 0.05), but reducing the number of observations below 50% resulted in a decrease in accuracy for all behaviors (p < 0.05). In-pasture observation increased accuracy and precision (0.05 and 0.08 percent, respectively) compared with animal-borne collar video observations.

Unveiling AI-Generated Financial Text: A Computational Approach Using Natural Language Processing and Generative Artificial Intelligence

This study is an in-depth exploration of the nascent field of Natural Language Processing (NLP) and generative Artificial Intelligence (AI), and it concentrates on the vital task of distinguishing between human-generated text and content that has been produced by AI models. Particularly, this research pioneers the identification of financial text derived from AI models such as ChatGPT and paraphrasing tools like QuillBot. While our primary focus is on financial content, we have also pinpointed texts generated by paragraph rewriting tools and utilized ChatGPT for various contexts this multiclass identification was missing in previous studies. In this paper, we use a comprehensive feature extraction methodology that combines TF–IDF with Word2Vec, along with individual feature extraction methods. Importantly, combining a Random Forest model with Word2Vec results in impressive outcomes. Moreover, this study investigates the significance of the window size parameters in the Word2Vec approach, revealing that a window size of one produces outstanding scores across various metrics, including accuracy, precision, recall and the F1 measure, all reaching a notable value of 0.74. In addition to this, our developed model performs well in classification, attaining AUC values of 0.94 for the ‘GPT’ class; 0.77 for the ‘Quil’ class; and 0.89 for the ‘Real’ class. We also achieved an accuracy of 0.72, precision of 0.71, recall of 0.72, and F1 of 0.71 for our extended prepared dataset. This study contributes significantly to the evolving landscape of AI text identification, providing valuable insights and promising directions for future research.

FetchEEG: a hybrid approach combining feature extraction and temporal-channel joint attention for EEG-based emotion classification

Objective. Electroencephalogram (EEG) analysis has always been an important tool in neural engineering, and the recognition and classification of human emotions are one of the important tasks in neural engineering. EEG data, obtained from electrodes placed on the scalp, represent a valuable resource of information for brain activity analysis and emotion recognition. Feature extraction methods have shown promising results, but recent trends have shifted toward end-to-end methods based on deep learning. However, these approaches often overlook channel representations, and their complex structures pose certain challenges to model fitting. Approach. To address these challenges, this paper proposes a hybrid approach named FetchEEG that combines feature extraction and temporal-channel joint attention. Leveraging the advantages of both traditional feature extraction and deep learning, the FetchEEG adopts a multi-head self-attention mechanism to extract representations between different time moments and channels simultaneously. The joint representations are then concatenated and classified using fully-connected layers for emotion recognition. The performance of the FetchEEG is verified by comparison experiments on a self-developed dataset and two public datasets. Main results. In both subject-dependent and subject-independent experiments, the FetchEEG demonstrates better performance and stronger generalization ability than the state-of-the-art methods on all datasets. Moreover, the performance of the FetchEEG is analyzed for different sliding window sizes and overlap rates in the feature extraction module. The sensitivity of emotion recognition is investigated for three- and five-frequency-band scenarios. Significance. FetchEEG is a novel hybrid method based on EEG for emotion classification, which combines EEG feature extraction with Transformer neural networks. It has achieved state-of-the-art performance on both self-developed datasets and multiple public datasets, with significantly higher training efficiency compared to end-to-end methods, demonstrating its effectiveness and feasibility.

Tokamak edge localized mode onset prediction with deep neural network and pedestal turbulence

A neural network, BES-ELMnet, predicting a quasi-periodic disruptive eruption of the plasma energy and particles known as edge localized mode (ELM) onset is developed with observed pedestal turbulence from the beam emission spectroscopy system in DIII-D. BES-ELMnet has convolutional and fully-connected layers, taking two-dimensional plasma fluctuations with a temporal window of size 128 µs and generating a scalar output which can be interpreted as a probability of the upcoming ELM onset. As approximately labeled inter-ELM broadband ( 15kHz⩽f⩽150kHz ) fluctuations are given to the network, BES-ELMnet learns by itself ELM-related precursors arising before the onsets through supervised learning scheme. BES-ELMnet achieves the gradually increasing ELM onset probabilities between two consecutive ELMs during the inter-ELM phases and can forecast the first ELM onsets which occur after the high confinement mode transition. We further investigate the network generality in terms of the selected frequency band to ensure the use of BES-ELMnet for various operation regimes without changing the trained architecture. Therefore, our novel prediction method will enhance a proactive high confinement mode control of fusion-grade plasmas.

Synthesis, characterization, hysteretic two-step CO2 adsorption, and fluorescence properties of two isoreticular mixed-ligand pillared-layer frameworks

Two isostructural and isomorphrous metal–organic frameworks (MOFs), namely [Zn2(Brtpt)2 (bpBTD)]n (1) and {[Zn2 (ntpt)2 (bpBTD)]∙solvent}n (2), where bpBTD = bis(pyridin-4-yl)benzothiadiazole, H2Brtpt = 2-bromoterephthalic acid, H2ntpt = 2-nitroterephthalic acid, with thermal stability of 355 °C for 1 and 265 °C for 2 were hydro (solvo)thermally synthesized in DMF/H2O media at 100 °C. Single-crystal X-ray diffraction analysis revealed that 1 and 2 both possess 2-fold interpenetrating pillared-layer frameworks showing pcu net topology with the point symbol of 41,263. The sufficient extra-framework void spaces are approximately 30.1 % for 1 and 32.0 % for 2 with open channels along the b axis, where the window sizes are ca. 5.51 × 3.15 Å2 in edge for the former and ca. 5.47 × 3.44 Å2 and 3.12 × 3.44 Å2 in edge for the latter. Gas adsorption isotherms revealed that thermally activated 1 and 2 both hardly adsorb N2 with very low N2 uptakes at 77 K. Comparably, the CO2 adsorption isotherms of thermally activated 1 and 2 both revealed gate-opening (two-step) sorption behaviors with an apparent hysteretic adsorption–desorption process, where the maximum uptakes are 135.4 and 171.5 cm3 g−1 STP for 1 and 2, respectively, at 195 K and P/P0 = 1. Photoluminescence studies showed that 1 and 2 both emitted bpBTD-dominated ligand-centered emissions, being as cyan and pearl aqua color fluorescence centered at λem = 467 and 495 nm, respectively, in solid-state and as cyan color fluorescence centered at λem = 455 and 458 nm, respectively, in H2O suspensions.

Quantitative Evaluation of the Pore and Window Sizes of Tissue Engineering Scaffolds on Scanning Electron Microscope Images Using Deep Learning.

The morphological characteristics of tissue engineering scaffolds, such as pore and window diameters, are crucial, as they directly impact cell-material interactions, attachment, spreading, infiltration of the cells, degradation rate and the mechanical properties of the scaffolds. Scanning electron microscopy (SEM) is one of the most commonly used techniques for characterizing the microarchitecture of tissue engineering scaffolds due to its advantages, such as being easily accessible and having a short examination time. However, SEM images provide qualitative data that need to be manually measured using software such as ImageJ to quantify the morphological features of the scaffolds. As it is not practical to measure each pore/window in the SEM images as it requires extensive time and effort, only the number of pores/windows is measured and assumed to represent the whole sample, which may cause user bias. Additionally, depending on the number of samples and groups, a study may require measuring thousands of samples and the human error rate may increase. To overcome such problems, in this study, a deep learning model (Pore D2) was developed to quantify the morphological features (such as the pore size and window size) of the open-porous scaffolds automatically for the first time. The developed algorithm was tested on emulsion-templated scaffolds fabricated under different fabrication conditions, such as changing mixing speed, temperature, and surfactant concentration, which resulted in scaffolds with various morphologies. Along with the developed model, blind manual measurements were taken, and the results showed that the developed tool is capable of quantifying pore and window sizes with a high accuracy. Quantifying the morphological features of scaffolds fabricated under different circumstances and controlling these features enable us to engineer tissue engineering scaffolds precisely for specific applications. Pore D2, an open-source software, is available for everyone at the following link: https://github.com/ilaydakaraca/PoreD2.

Relative truth value acquisition method of heterogeneous surface based on the Improved Point Spread Function

Abstract. The acquisition of pixel-scale relative truth value is important for remote sensing validation.The Point Spread Function (PSF) has been widely used in the field of the acquisition of relative truth value of heterogeneous surface. In this study, we propose an Improved Point Spread Function (IPSF) based on the Median Pixel Variability Weighted (MPVW) method and PSF to acquire relative truth value of heterogeneous surface. Firstly, the size of variance and clustering window are confirmed based on the pixel scale information of the satellite product to be validated. Secondly, the PSF suitable for heterogeneous surface is selected from 5 PSFs. Thirdly, the IPSF is constructed according to the MPVW and the PSF suitable for heterogeneous surface. Finally, the IPSF is used to acquire relative true value at pixel-scale of heterogeneous surface from airborne hyperspectral image. This study shows: (1) Good correlation between relative true value obtained using IPSF and reference value is indicated as R2 values among 256 channels reach 0.985, structural similarity (SSIM) ranges from 0.992 to 0.998, and peak signal noise ratio (PSNR) more than 35dB. (2) Better accuracy performance is observed in the acquisition of relative truth value of heterogeneous surface for IPSF than PSF. Compared with conventional PSF, the R2, PSNR and SSIM of the result of IPSF are increased by about 1.34%, 9% and 0.7% on the average. (3) Significant advantage of IPSF is also reported in reducing the deviation compared to PSF, as the average root mean square error (RMSEave) is reduced by 30.37% and the average mean absolute error (MAEave) is reduced by 35.98%, respectively. (4) Comparing the RMSE and MAE of PSF and IPSF result, the RMSE and MAE of the result of IPSF become smaller. The RMSE decreases from 3 ~ 195 to 2 ~ 131. The MAE changes from 2 ~ 138 to 1 ~ 89. Overall, the IPSF proposed in this paper can effectively calculate the relative true value of heterogeneous surface, which can provide reference for the validation of multi-spectral and hyper-spectral satellite products.

Genome-wide association study in Chickpea (Cicer arietinum L.) for yield and nutritional components

The increase in the global human population and the accompanying challenges in meeting nutritional needs amidst climate change are a worldwide concern. Widespread protein and micronutrient deficiencies contribute to a significant number of individuals experiencing malnutrition, leading to severe health repercussions. This issue can be addressed through genomics-assisted breeding, particularly in enhancing the nutritional profile of vital staple crops like chickpea (Cicer arietinum L.). Chickpea, beyond being a rich source of protein, provides a diverse nutritional spectrum encompassing carbohydrates, fats, and minerals. To explore and improve the genetic basis of nutritional traits in chickpea, a study was conducted using 93 kabuli-type single plant derived lines and five cultivars in 2018, 2022, and 2023. Genotyping by sequencing revealed a total of 165K single nucleotide polymorphisms (SNPs) within this kabuli chickpea mini-core collection. After filtering for a minor allele frequency greater than 5%, 113,512 SNPs were utilized, distributed across eight chromosomes of the chickpea genome. Marker-trait associations were analyzed using genome wide association study, leading to the identification of 27 significantly associated SNPs from across all eight chromosomes linked to three seed nutritional concentrations and 100-seed weight. To unravel the molecular mechanisms governing seed protein, fiber, fat concentrations, and 100-seed weight, 31 candidate genes were determined within a 30 kb window size. This comprehensive approach holds promise for advancing crop breeding strategies to combat malnutrition and improve global food security.

Radial basis point interpolation for strain field calculation in digital image correlation

In order to extract smooth and accurate strain fields from the noisy displacement fields obtained by digital image correlation (DIC), a point interpolation meshless (PIM) method with a radial basis function (RBF) is introduced for full-field strain calculation, which overcomes the problems of slow calculation speed and unstable matrix inverse calculation of the element-free Galerkin method (EFG). The radial basis point interpolation method (RPIM) with three different radial basis functions and the moving least squares (MLS) and pointwise least squares (PLS) methods are compared by analyzing and validating the strain fields with high-strain gradients in simulation experiments. The results indicate that the RPIM is nearly 80% more computationally efficient than the MLS method when a larger support domain is used, and the efficiency of the RPIM is nearly 26% higher than that of the MLS method when a smaller support domain is used; the strain calculation accuracy is slightly lower than that of the MLS method by 0.3–0.5%, but the stability of the calculation is significantly improved. In contrast with the PLS method, which is easily affected by the noise and the size of the strain calculation window, the RPIM is insensitive to the displacement noise and the size of the support domain and can obtain a similar calculation accuracy. The RPIM with multiquadric (MQ) radial basis functions performs well in balancing the computational accuracy and efficiency and is insensitive to shape parameters. The application cases show that the method can effectively compute the strain field at the crack tip, validating its applicability to the study of the plastic region at the crack tip. In conclusion, the proposed RPIM-based method provides an accurate, practical, and robust approach for full-field strain measurements.

Comparative performance of intensive care mortality prediction models based on manually curated versus automatically extracted electronic health record data

IntroductionBenchmarking intensive care units for audit and feedback is frequently based on comparing actual mortality versus predicted mortality. Traditionally, mortality prediction models rely on a limited number of input variables and significant manual data entry and curation. Using automatically extracted electronic health record data may be a promising alternative. However, adequate data on comparative performance between these approaches is currently lacking. MethodsThe AmsterdamUMCdb intensive care database was used to construct a baseline APACHE IV in-hospital mortality model based on data typically available through manual data curation. Subsequently, new in-hospital mortality models were systematically developed and evaluated. New models differed with respect to the extent of automatic variable extraction, classification method, recalibration usage and the size of collection window. ResultsA total of 13 models were developed based on data from 5,077 admissions divided into a train (80%) and test (20%) cohort. Adding variables or extending collection windows only marginally improved discrimination and calibration. An XGBoost model using only automatically extracted variables, and therefore no acute or chronic diagnoses, was the best performing automated model with an AUC of 0.89 and a Brier score of 0.10. DiscussionPerformance of intensive care mortality prediction models based on manually curated versus automatically extracted electronic health record data is similar. Importantly, our results suggest that variables typically requiring manual curation, such as diagnosis at admission and comorbidities, may not be necessary for accurate mortality prediction. These proof-of-concept results require replication using multi-centre data.

Exploring the impact of windows on musicians’ experience: a neuroarchitecture perspective

ABSTRACT Performing music can be an enjoyable activity or a source of anxiety and discomfort. Previous studies suggest that it is possible to create spaces that promote well-being by applying knowledge from neuroscience to architectural design. However, this application remains unexplored in the context of music practice spaces. In this study, a virtual replica of a real conservatoire classroom was used to investigate the effect of window size and views through windows on the perception of the built environment. A sample of 61 musicians was recruited, and through questionnaires and eye tracking, participants’ responses to environmental changes while playing their instruments were studied. The results of the experiment indicated a clear preference for larger windows, which significantly contributed to a more positive environment, perceived as more open, light, inviting, calming and spacious. In addition, eye tracking data suggested a reduction in anxiety levels, as evidenced by reduced gaze velocity and saccade ratios. These results demonstrate that windows and related variables such as daylight and views can have a significant impact on the experience of musicians during solo performance. This highlights the need to re-evaluate design considerations in music practice spaces, emphasising the crucial role of environmental elements in enhancing musicians’ well-being.

Intoxication due to Δ9-tetrahydrocannabinol is characterized by disrupted prefrontal cortex activity

Neural states of impairment from intoxicating substances, including cannabis, are poorly understood. Cannabinoid 1 receptors, the main target of Δ9-tetrahydrocannabinol (THC), the primary intoxicating cannabinoid in cannabis, are densely localized within prefrontal cortex; therefore, prefrontal brain regions are key locations to examine brain changes that characterize acute intoxication. We conducted a double-blind, randomized, cross-over study in adults, aged 18–55 years, who use cannabis regularly, to determine the effects of acute intoxication on prefrontal cortex resting-state measures, assessed with portable functional near-infrared spectroscopy. Participants received oral THC (10–80 mg, individually dosed to overcome tolerance and achieve acute intoxication) and identical placebo, randomized for order; 185 adults were randomized and 128 completed both study days and had usable data. THC was associated with expected increases in subjective intoxication ratings (ES = 35.30, p < 0.001) and heart rate (ES = 11.15, p = 0.001). THC was associated with decreased correlations and anticorrelations in static resting-state functional connectivity within the prefrontal cortex relative to placebo, with weakest correlations and anticorrelations among those who reported greater severity of intoxication (RSFC between medial PFC-ventromedial PFC and DEQ scores, r = 0.32, p < 0.001; RSFC between bilateral mPFC and DEQ scores, r = –0.28, p = 0.001). Relative to placebo, THC was associated with increased variability (or reduced stability) in dynamic resting-state functional connectivity of the prefrontal cortex at p = 0.001, consistent across a range of window sizes. Finally, using frequency power spectrum analyses, we observed that relative to placebo, THC was associated with widespread reduced spectral power within the prefrontal cortex across the 0.073–0.1 Hz frequency range at p < 0.039. These neural features suggest a disruptive influence of THC on the neural dynamics of the prefrontal cortex and may underlie cognitive impairing effects of THC that are detectable with portable imaging. This study is registered in Clinicaltrials.gov (NCT03655717).

Hyperspectral anomaly detection based on adaptive background dictionary construction and collaborative representation

ABSTRACT Collaborative representation-based (CR) methods have received widespread attention in hyperspectral anomaly detection, but the results are greatly affected by the quality of the background dictionary. Abnormal pixels and abnormal-mixed pixels in the background dictionary may affect the accuracy of linear representation and make its performance poor. To address the above problem, an adaptive background dictionary construction-based anomaly detection method is proposed. To ensure the purity of the background dictionary, abnormal pixels and abnormal-mixed pixels around each test pixel are removed adaptively through clustering and pure pixel extraction. Furthermore, the saliency weight of the test pixel is calculated through the pixels in the inner window and weighted into the linear representation process to improve the robustness of the method. Experimental results on four hyperspectral datasets show that the proposed method performs better than other CR methods and traditional detectors, and it can reduce the dependence of anomaly detection performance on dual window size.

Deep-learning-based stock market prediction incorporating ESG sentiment and technical indicators

As sustainability emerges as a crucial factor in the development of modern enterprises, integrating environmental, social, and governance (ESG) information into financial assessments has become essential. ESG indicators serve as important metrics in evaluating a company’s sustainable practices and governance effectiveness, influencing investor trust and future growth potential, ultimately affecting stock prices. This study proposes an innovative approach that combines ESG sentiment index extracted from news with technical indicators to predict the S&P 500 index. By utilizing a deep learning model and exploring optimal window sizes, the study explores the best model through mean absolute percentage error (MAPE) as an evaluation metric. Additionally, an ablation test clarifies the influence of ESG and its causality with the S&P 500 index. The experimental results demonstrate improved predictive accuracy when considering ESG sentiment compared to relying solely on technical indicators or historical data. This comprehensive methodology enhances the advantage of stock price prediction by integrating technical indicators, which consider short-term fluctuations, with ESG information, providing long-term effects. Furthermore, it offers valuable insights for investors and financial market experts, validating the necessity to consider ESG for financial assets and introducing a new perspective to develop investment strategies and decision-making processes.

Graphite counter electrode modified Tropaeolin‐O photo‐sensitized photogalvanic cells for solar power and storage

AbstractAn optimization, photo‐stability, and hysteresis property of the Graphite counter electrode‐modified Tropaeolin‐O (TPO) photo‐sensitized photogalvanic (PG) cells has been investigated. A complex H‐shaped cell design, a costly and delicate saturated calomel electrode (counter electrode), and a heavy sensitizer molecule (dye having high molecular weight, low diffusivity, and low photo‐stability) have been exploited for fabricating most of the PG cells so far. All these factors are not suitable for the fabrication of durable and cheap PG cells. Therefore, in the present study, the highly conductive/catalytically active robust graphite electrode with TPO dye photosensitizer (having a low molecular weight, higher diffusivity, and higher photo‐stability) has been exploited with diffusion‐friendly low cost and a simple transparent cylindrical glass tube. The cheap and robust graphite counter electrode has been exploited for optimization and long‐term study of the TPO photo‐sensitized PG cells. The observed electrical output is potential 676 mV, current 2000 µA, and power 340.0 μW. The power, current, and efficiency, have been found quite independent of the illumination window size. The potential and current have been observed to be quite stable over a long time during illumination, and the same has been supported by the hysteresis study.

Efficient Decoding of Affective States from Video-elicited EEG Signals: An Empirical Investigation

Affect decoding through brain-computer interfacing (BCI) holds great potential to capture users’ feelings and emotional responses via non-invasive electroencephalogram (EEG) sensing. Yet, little research has been conducted to understand efficient decoding when users are exposed to dynamic audiovisual contents. In this regard, we study EEG-based affect decoding from videos in arousal and valence classification tasks, considering the impact of signal length, window size for feature extraction, and frequency bands. We train both classic Machine Learning models (SVMs and k -NNs) and modern Deep Learning models (FCNNs and GTNs). Our results show that: (1) affect can be effectively decoded using less than 1 minute of EEG signal; (2) temporal windows of 6 and 10 seconds provide the best classification performance for classic Machine Learning models but Deep Learning models benefit from much shorter windows of 2 seconds; and (3) any model trained on the Beta band alone achieves similar (sometimes better) performance than when trained on all frequency bands. Taken together, our results indicate that affect decoding can work in more realistic conditions than currently assumed, thus becoming a viable technology for creating better interfaces and user models.

Kaczmarz Projection Algorithms with Rank Two Gain Update

New Kaczmarz algorithms with rank two gain update, extended orthogonality property and forgetting mechanism which includes both exponential and instantaneous forgetting (implemented via a proper choice of the forgetting factor and the window size) are introduced and systematically associated in this paper with well-known Kaczmarz algorithms with rank one update. The parameter convergence was proved using Lyapunov method and convergence of the inverse of the information matrix can be used for further performance improvement. The performance of new algorithms is examined in the problem of estimation of the grid events in the presence of significant harmonic emissions.

Static sphere observers and geodesics in Schwarzschild-de Sitter spacetime

We analyze null- and spacelike radial geodesics in Schwarzschild-de Sitter spacetime connecting two conjugate static sphere observers, i.e. free-falling observers at a fixed radius in between the two horizons. We explicitly determine the changes in the causal structure with respect to these natural observers as a result of the inward bending of the black hole singularity, as well as the outward bending of asymptotic infinity. Notably, the inward and outward bending changes as a function of the black hole mass, first increasing towards a maximum and then decreasing to vanish in the extreme Nariai limit. For a generic mass of the black hole this implies the existence of finite size (temporal) windows for the presence of symmetric radial geodesics between the static sphere observers probing the interior region of the black hole, as well as the exterior de Sitter region. We determine the size of the interior (black hole) and exterior (de Sitter) temporal windows in 4, 5 and 6 spacetime dimensions, finding that they are equal in D = 5, and compute the proper lengths of the symmetric radial geodesics. We comment on the implications for information exchange and the potential role of the symmetric radial geodesics in a geodesic approximation of static sphere correlators in Schwarzschild-de Sitter spacetime.