Unknown Ground Research Articles

Comparative retrospective study of injuries during military parachuting training and airborne operations from 2013 to 2020

IntroductionFrench paratroopers have been deployed during airborne operations for nearly a century. Parachute operations have increased since the start of Operations Serval and Barkhane in the Sahelo-Saharan strip. These military...

Developing and validating an adaptive multi-layer vehicle trajectory reconstruction method for outlier removal

Trajectory data is vital for traffic flow studies, and aerial photography-based methods are increasingly used to collect such data. However, these datasets often contain errors from various sources, which can be exacerbated by numerical derivative processes. Previous efforts have not fully addressed some of these issues such as consistency, varying length of outlier sequences, and unknown ground truth trends. Moreover, existing validation methods are often indirect and problematic. To address these limitations, we propose an adaptive multi-layer vehicle trajectory reconstruction method, which consists of three modules: the Initial Window Arrangement module to ensure the precise alignment between the reconstruction window and detected outlier fragments, maintaining internal consistency at boundary points; the Window Size Feasibility Test module to adaptively determine the window size according to varying length of outlier sequences, and XGBoost-based Ground Truth Estimation module to be combined with a least-square-based objective function to significantly improve reconstruction accuracy and more closely replicate the underlying trend. Additionally, we introduce the jerk-based reconstruction, which outperforms the acceleration-based reconstruction. To reliably assess and select the best ground truth estimation scheme and objective function, a novel synthetic dataset containing both the ground truth and realistic outlier fragments is proposed. Subsequently, a comparative evaluation of six different outlier removal methods was conducted using Zen Traffic Data. The validation results, utilizing both the synthetic dataset and Zen Traffic Data, demonstrate the exceptional performance of the proposed method across various evaluation perspectives. The good performance of the proposed outlier removal method is further demonstrated by comparing the IDM calibration results using the trajectories with and without outliers being removed. With just one parameter (jerk anomaly threshold), the parameter settings of our method are more objective and generalizable.

Revealing unexpected complex encoding but simple decoding mechanisms in motor cortex via separating behaviorally relevant neural signals.

In motor cortex, behaviorally relevant neural responses are entangled with irrelevant signals, which complicates the study of encoding and decoding mechanisms. It remains unclear whether behaviorally irrelevant signals could conceal some critical truth. One solution is to accurately separate behaviorally relevant and irrelevant signals at both single-neuron and single-trial levels, but this approach remains elusive due to the unknown ground truth of behaviorally relevant signals. Therefore, we propose a framework to define, extract, and validate behaviorally relevant signals. Analyzing separated signals in three monkeys performing different reaching tasks, we found neural responses previously considered to contain little information actually encode rich behavioral information in complex nonlinear ways. These responses are critical for neuronal redundancy and reveal movement behaviors occupy a higher-dimensional neural space than previously expected. Surprisingly, when incorporating often-ignored neural dimensions, behaviorally relevant signals can be decoded linearly with comparable performance to nonlinear decoding, suggesting linear readout may be performed in motor cortex. Our findings prompt that separating behaviorally relevant signals may help uncover more hidden cortical mechanisms.

Impact of the Reference Multiple-Time-Point Dosimetry Protocol on the Validity of Single-Time-Point Dosimetry for [177Lu]Lu-PSMA-I&T Therapy.

Internal dosimetry supports safe and effective patient management during radionuclide therapy. Yet, it is associated with high clinical workload, costs, and patient burden, as patient scans at multiple time points (MTPs) must be acquired. Dosimetry based on imaging at a single time point (STP) has continuously gained popularity. However, MTP protocols, used as a reference to judge the validity of STP dosimetry, differ depending on local requirements and deviate from the unknown patient-specific ground truth pharmacokinetics. The aim of this study was to compare the error and optimum time point for different STP approaches using different reference MTP protocols. Methods: Whole-body SPECT/CT scans of 7 patients (7.4-8.9 GBq of [177Lu]Lu-PSMA-I&T) were scheduled at 24, 48, 72, and 168 h after injection. Sixty lesions, 14 kidneys, and 10 submandibular glands were delineated in the SPECT/CT data. Two curve models, that is, a mono- and a biexponential model, were fitted to the MTP data, in accordance with goodness-of-fit analysis (coefficients of variation, sum of squared errors). Three population-based STP approaches were compared: one method published by Hänscheid etal., one by Jackson etal., and one using population-based effective half-lives in the mono- or biexponential curve models. Percentage differences between STP and MTP dosimetry were evaluated. Results: Goodness-of-fit parameters show that a monoexponential function and a biexponential function with shared population-based parameters and physical tail are reasonable reference models. When comparing both reference models, we observed maximum differences of -44%, -19%, and -28% in the estimated absorbed doses for lesions, kidneys, and salivary glands, respectively. STP dosimetry with an average deviation of less than 10% from MTP dosimetry may be feasible; however, this deviation and the optimum imaging time point showed a dependence on the chosen reference protocol. Conclusion: STP dosimetry for [177Lu]Lu-PSMA therapy is promising to boost the integration of dosimetry into clinical routine. According to our patient cohort, 48 h after injection may be regarded as a compromise for STP dosimetry for lesions and at-risk organs. The results from this analysis show that a common gold standard for dosimetry is desirable to allow for reliable and comparable STP dosimetry.

A spectral approach to Hebbian-like neural networks

We consider the Hopfield neural network as a model of associative memory and we define its neuronal interaction matrix J as a function of a set of K×M binary vectors {ξμ,A}μ=1,...,KA=1,...,M representing a sample of the reality that we want to retrieve. In particular, any item ξμ,A is meant as a corrupted version of an unknown ground pattern ζμ, that is the target of our retrieval process. We consider and compare two definitions for J, referred to as supervised and unsupervised, according to whether the class μ, each example belongs to, is unveiled or not, also, these definitions recover the paradigmatic Hebb's rule under suitable limits. The spectral properties of the resulting matrices are studied and used to inspect the retrieval capabilities of the related models as a function of their control parameters.

Safe Reinforcement Learning-Based Visual Servoing Control for Quadrotors Tracking Unknown Ground Vehicles

Safe Reinforcement Learning-Based Visual Servoing Control for Quadrotors Tracking Unknown Ground Vehicles

칸트 ‘물 자체’의 해석: 교육학적 관점

In this article, I tried to explore the meanings of ‘Ding an Sich’(thing in itself) in Kant’s epistemology and attempted to interpret its epistemological meanings from educational perspective. It is true that Kant's epistemology is a philosophical discussion and does not contain any significant mentions of education. But his epistemology deals with the nature and limitations of knowledge about experience(or phenomena), and it can be said that his epistemology can make an important contributions in its own way to revealing the nature of knowledge as educational content.
 In Kant's epistemology, ‘thing in itself’ has the various status on the context of discussion, such as ‘transcendental material’, ‘transcendental object’, ‘intelligible object’, and ‘transcendental ideal’. Examining these concepts, we find that ‘thing in itself’ can be said to have the status of the ‘unknown ground’ or ‘metaphysical ground’ of knowledges.
 The ‘thing in itself’ as metaphysical ground, can be distincted from the ‘forms’ of knowledge which are belong to the subject, and it refers to the ‘world in itself’ or ‘the world as noumenon’. It can be said that Kant's epistemology shows the ‘twofold structure’ of the world divided into phenomena and ‘thing in itself’. From the perspective of Education, the ‘thing in itself’ of Kant’s epistemology shows that knowledge as educational content has a metaphysical or transcendental ground.

Seismic arrival-time picking on distributed acoustic sensing data using semi-supervised learning

Distributed Acoustic Sensing (DAS) is an emerging technology for earthquake monitoring and subsurface imaging. However, its distinct characteristics, such as unknown ground coupling and high noise level, pose challenges to signal processing. Existing machine learning models optimized for conventional seismic data struggle with DAS data due to its ultra-dense spatial sampling and limited manual labels. We introduce a semi-supervised learning approach to address the phase-picking task of DAS data. We use the pre-trained PhaseNet model to generate noisy labels of P/S arrivals in DAS data and apply the Gaussian mixture model phase association (GaMMA) method to refine these noisy labels and build training datasets. We develop PhaseNet-DAS, a deep learning model designed to process 2D spatio-temporal DAS data to achieve accurate phase picking and efficient earthquake detection. Our study demonstrates a method to develop deep learning models for DAS data, unlocking the potential of integrating DAS in enhancing earthquake monitoring.

Model-based evaluation of spatiotemporal data reduction methods with unknown ground truth through optimal visualization and interpretability metrics.

Optimizing and benchmarking data reduction methods for dynamic or spatial visualization and interpretation (DSVI) face challenges due to many factors, including data complexity, lack of ground truth, time-dependent metrics, dimensionality bias and different visual mappings of the same data. Current studies often focus on independent static visualization or interpretability metrics that require ground truth. To overcome this limitation, we propose the MIBCOVIS framework, a comprehensive and interpretable benchmarking and computational approach. MIBCOVIS enhances the visualization and interpretability of high-dimensional data without relying on ground truth by integrating five robust metrics, including a novel time-ordered Markov-based structural metric, into a semi-supervised hierarchical Bayesian model. The framework assesses method accuracy and considers interaction effects among metric features. We apply MIBCOVIS using linear and nonlinear dimensionality reduction methods to evaluate optimal DSVI for four distinct dynamic and spatial biological processes captured by three single-cell data modalities: CyTOF, scRNA-seq and CODEX. These data vary in complexity based on feature dimensionality, unknown cell types and dynamic or spatial differences. Unlike traditional single-summary score approaches, MIBCOVIS compares accuracy distributions across methods. Our findings underscore the joint evaluation of visualization and interpretability, rather than relying on separate metrics. We reveal that prioritizing average performance can obscure method feature performance. Additionally, we explore the impact of data complexity on visualization and interpretability. Specifically, we provide optimal parameters and features and recommend methods, like the optimized variational contractive autoencoder, for targeted DSVI for various data complexities. MIBCOVIS shows promise for evaluating dynamic single-cell atlases and spatiotemporal data reduction models.

Measuring the Impact of Ease of Use, Usefulness, Attitude, and Enjoyment toward Intention to Play PUBG Mobile

The impact of ease of use, usefulness, attitude, and enjoyment toward intention to play PUBG Mobile as it relates to Indonesians, is explicitly examined in this study’s examination of the mobile gaming era because of the pandemic. This paper investigates the factors that influence an individual’s intention to play PUBG Mobile and the outcome of those factors towards its individuals, as well as the effects of knowledge about gaming behavior to its users. The study’s recommendations are to increase the intention to play mobile games so that the need for enjoyable aspects and the need for outside assistance become more apparent. Through an online survey, 223 individuals provided information for this study. The survey asked for responses from people who had played a mobile game, particularly Player Unknown Battle Ground Mobile version (PUBGM), millennials or members of Generation Z having enrolled in college and who previously had resided in Indonesia, had at least played PUBG Mobile in the past, and had played it more than one year. In this work, statistical analysis is carried out. The outcome demonstrates that perceived ease of use, usefulness, attitude, and perceived enjoyment have an impact toward Intention to play PUBG Mobile. This study closes a gap in the literature by concentrating on the behavioral intention to play PUBG through the playful-consumption experiences of a well-known online multiplayer game.

“FLIPPED ASSESSMENT”: Proposal for a Self-Assessment Method to Improve Learning in the Field of Manufacturing Technologies

Striving toward goal completion and achieving objectives is one of the motors of personal advancement. The path to goal completion is fueled by many reasons, among which motivation stands out as one of the core impulses. Motivation acquires a particularly high relevance in learning, prompting educators to mind its substance when designing not only the material to be imparted but also the approach and the mechanisms to assess knowledge acquisition. The intrinsic nature of motivation might stem from self-realization, thriving in specific goals, or even exploring unknown ground. One of the main teacher–student interactions is the provision of adequate tools to achieve learning outcomes. One of the tools available to teachers is the exercise of extrinsic motivation. This paper proposes and assesses the initial implementation of a student-involved extrinsic motivation method. A pilot group in the Junior year of a Bachelor in Mechanical Engineering program was selected, in which the evaluation system was slightly modified with respect to the system that is normally used. The course selected for the study was a compulsory six European Credit Transfer and Accumulation System (ECTS) course covering production and manufacturing technology. Students were asked to partake in the drafting of questions to assess their own knowledge, hence indirectly increasing their motivation to learn the content. The tentative results obtained with the pilot group appear to be positive and relevant. Students showed a higher engagement during class and reported needing fewer hours of preparation at home (32% reduction). In addition, global satisfaction with the course was improved.

Geometric Analysis of Noisy Low-Rank Matrix Recovery in the Exact Parametrized and the Overparametrized Regimes

The matrix sensing problem is an important low-rank optimization problem that has found a wide range of applications, such as matrix completion, phase synchornization/retrieval, robust principal component analysis (PCA), and power system state estimation. In this work, we focus on the general matrix sensing problem with linear measurements that are corrupted by random noise. We investigate the scenario where the search rank r is equal to the true rank [Formula: see text] of the unknown ground truth (the exact parametrized case), as well as the scenario where r is greater than [Formula: see text] (the overparametrized case). We quantify the role of the restricted isometry property (RIP) in shaping the landscape of the nonconvex factorized formulation and assisting with the success of local search algorithms. First, we develop a global guarantee on the maximum distance between an arbitrary local minimizer of the nonconvex problem and the ground truth under the assumption that the RIP constant is smaller than [Formula: see text]. We then present a local guarantee for problems with an arbitrary RIP constant, which states that any local minimizer is either considerably close to the ground truth or far away from it. More importantly, we prove that this noisy, overparametrized problem exhibits the strict saddle property, which leads to the global convergence of perturbed gradient descent algorithm in polynomial time. The results of this work provide a comprehensive understanding of the geometric landscape of the matrix sensing problem in the noisy and overparametrized regime. Funding: This work was supported by grants from the National Science Foundation, Office of Naval Research, Air Force Office of Scientific Research, and Army Research Office.

Generalized stacked LSTM for the seismic damage evaluation of ductile reinforced concrete buildings

AbstractTo organize accurate and effective emergency responses after an earthquake, it is vital to conduct an early and precise assessment of damage to structures. The use of fragility/vulnerability curves is an advanced evaluation approach for structural damage assessments. However, the analysis based on fragility curves significantly varies depending on soil conditions, ground motion, and structural characteristics. To overcome this issue, a stacked long short‐term memory network was proposed in this research. Unlike previous studies, two input features (acceleration time history in the form of vector and the number of stories in the scalar) are utilized to generalize the results for the same plan building frames with different stories. Three different approaches are presented in this work to link the ground motion time history with the number of stories (2, 4, 8, 12, and 20 stories) in the reinforced concrete building frame, and the networks were tested for unknown ground motions. Of the three approaches, those providing good results were selected for further analysis. For the approaches chosen, the network architectures were changed to a diamond shape and an autoencoder‐like shape with more hidden units (to obtain higher accuracy), which were tested for unknown same plan layout frames. The accuracy obtained using these approaches was significantly high (80%–90%) with a low training time. The proposed model is compared with other techniques and shows significant accuracy. The suggested networks exhibited a number of scenarios for estimating the damage state for unknown ground motions, as well as for unknown frames with various stories. Moreover, the capability of the networks to handle more scalar input features is examined by adding them probabilistically; with additional input variables, the networks predicted the damage state with higher accuracy.

Sampling-based Spotlight SAR Image Reconstruction from Phase History Data for Speckle Reduction and Uncertainty Quantification

Spotlight mode airborne synthetic aperture radar (SAR) is a coherent imaging modality that is an important tool in remote sensing. Existing methods for spotlight SAR image reconstruction from phase history data typically produce a single image estimate which approximates the reflectivity of an unknown ground scene, and therefore provide no quantification of the certainty with which the estimate can be trusted. In addition, speckle affects all coherent imaging modalities causing a degradation of image quality. Many point estimate image reconstruction methods incorrectly treat speckle as additive noise resulting in an unnatural smoothing of the speckle that also reduces image contrast. The purpose of this paper is to address the issues of speckle and uncertainty quantification by introducing a sampling-based approach to SAR image reconstruction directly from phase history data. In particular, a statistical model for speckle as well as a corresponding sparsity technique to reduce it are directly incorporated into the model. Rather than a single point estimate, samples of the resulting joint posterior density are efficiently obtained using a Gibbs sampler, which are in turn used to derive estimates and other statistics which aid in uncertainty quantification. The latter information is particularly important in SAR, where ground truth images even for synthetically created examples are typically unknown. While similar methods have been deployed to process formed images, this paper focuses on the integration of these techniques into image reconstruction from phase history data. An example result using real-world data shows that, when compared with existing methods, the sampling-based approach introduced provides parameter-free estimates with improved contrast and significantly reduced speckle, as well as uncertainty quantification information.

PENGARUH PERSEPSI HARGA DAN PROMOSI TERHADAP MINAT BELI UNKNOWN CASH (UC) DALAM GAMES PLAYER UNKNOWN BATTLE GROUND (PUBG) MOBILE DI MEDAN BARU

The purpose of this researc was to determine the effect of price perception and promotion on buying interest. The number of samples taken was 120 respondents, which purposive sampling method. Data collection was carried out by distributing questionnaires to the sample, namely Game Player Unknown Battle Ground (PUBG) Mobile in Medan Baru who had made purchases and used Unknown Cash (UC). The analysis technique chosen is the IBM SPSS Statistic 23 application. Based on the results of the analysis it was found that: 1) price perception has a positive and significant effect on buying interest in UC in PUBG Mobile Games in Medan Baru. 2) promotion has a positive and significant impact on buying interest in UC in the PUBG Mobile Games in Medan Baru. 3) price perception and promotion simultaneously influence buying interest in UC in PUBG Mobile Games in Medan Baru. In order for the PUBG Corporation to increase consumer buying interest, it is recommended that they provide appropriate prices and quality and increase promotions so that potential consumers are more familiar with and increase buying interest.

TINJAUAN FIKIH EKONOMI TERHADAP PRAKTIK JOKI GAME ONLINE PLAYER UNKNOWN BATTLE GROUND (STUDI KASUS DI DESA PANYABUNGAN III KECAMATAN PANYABUNGAN KABUPATEN MANDAILING NATAL

This article examines the economic fiqh review of the practice of online game jockey player unknown battle ground (PUBG) in Panyabungan III Village, Panyabungan District, Mandailing Natal Regency. The problem is how the legal practice of jockey games online player unknown battle ground (PUBG) in Panyabungan III Village, Panyabungan District, Mandailing Natal Regency. The type of research used is a qualitative approach field research. The source of the data in this study was obtained from interviews with online game jockey players unknown battle ground (PUBG) in Panyabungan III Village, Panyabungan District, Mandailing Natal Regency. The data is processed by analyzing the phenomenon and associated with the study of Islamic economic jurisprudence. The results obtained are in the perspective of economic fiqh, the practice of jockey games online player unknown battle ground (PUBG) in Panyabungan III Village, Panyabungan District, Mandailing Natal Regency using a lease agreement (ijarah) using the services of samsarah. Using the services of a jockey client will feel a loss for himself because his game account will not provide much benefit for him as well as only raising the Tier on his account is just a pleasure, actually in Islam this is an excessive and wasteful act. So it can be concluded that the PUBG Online Game Jockey in Panyabungan III Village, Panyabungan District, Mandailing Natal Regency is haram because it does not meet the pillars and conditions such as containing immoral and haram elements.

A deep learning framework to classify breast density with noisy labels regularization

Background and ObjectiveBreast density assessed from digital mammograms is a biomarker for higher risk of developing breast cancer. Experienced radiologists assess breast density using the Breast Image and Data System (BI-RADS) categories. Supervised learning algorithms have been developed with this objective in mind, however, the performance of these algorithms depends on the quality of the ground-truth information which is usually labeled by expert readers. These labels are noisy approximations of the ground truth, as there is often intra- and inter-reader variability among labels. Thus, it is crucial to provide a reliable method to obtain digital mammograms matching BI-RADS categories. This paper presents RegL (Labels Regularizer), a methodology that includes different image pre-processes to allow both a correct breast segmentation and the enhancement of image quality through an intensity adjustment, thus allowing the use of deep learning to classify the mammograms into BI-RADS categories. The Confusion Matrix (CM) - CNN network used implements an architecture that models each radiologist’s noisy label. The final methodology pipeline was determined after comparing the performance of image pre-processes combined with different DL architectures. MethodsA multi-center study composed of 1395 women whose mammograms were classified into the four BI-RADS categories by three experienced radiologists is presented. A total of 892 mammograms were used as the training corpus, 224 formed the validation corpus, and 279 the test corpus. ResultsThe combination of five networks implementing the RegL methodology achieved the best results among all the models in the test set. The ensemble model obtained an accuracy of (0.85) and a kappa index of 0.71. ConclusionsThe proposed methodology has a similar performance to the experienced radiologists in the classification of digital mammograms into BI-RADS categories. This suggests that the pre-processing steps and modelling of each radiologist’s label allows for a better estimation of the unknown ground truth labels.

Robust ground moving target detection for airborne radar using a novel feature-based machine learning approach

A novel ground-moving target detection method is introduced using a distinguishing target, and clutter feature for airborne radar. The clutter proximity feature is extracted based on the Euclidean distance between a signal pixel and the expected clutter ridge in the angle-Doppler domain. Subsequently, target and clutter pixels are classified based on the extracted features for target detection without actually removing clutters or clutter estimation. The proposed technique is especially suitable for effective airborne radar target detection in the unknown ground clutter. The experimental results have validated the effectiveness of the new approach, which enables ground moving target detection in inhomogeneous clutter.

Software Methodologies and War Strategies

Innovation is permanent in all fields of human activity. Strategy and tactics adopted by one discipline may be used to communicate through disciplines or may be transposed and adapted in other fields. The contemporary activity of software development brought to emerging different methodologies that exercise their influence to other fields and may be applied in other disciplines. On the other hand, as people showed the highest inventiveness in attacking and conquering others, the basic military notions are used across disciplines. Adapting the attacking and war strategies to different fields resulted naturally. The article will examine the trans-disciplinary communication through the Agile incremental methodology and similarities between its application in software development and adopting it in different fields. We'll analyze how to transpose to other fields Agile concepts as adding business value and getting to the business goals, conducting the work the incremental and non-incremental way, and how the basic concepts from Agile are used beyond their original creation, through other disciplines. The article also analyzes war strategies and the possibility to apply them to other disciplines, with extended references to one of the most renowned military treaties, Sun Tzu's The Art of War. We'll investigate what things as initial estimations, attack by stratagem, tactical dispositions, energy, weak points, and strong points, maneuvering, variation in tactics, the army on the march, terrain, arriving on unknown ground, concrete situations on the ground, the use of spies or the attack by fire may mean to communicate across disciplines.

Extended Target Tracking With a Lidar Sensor Using Random Matrices and a Virtual Measurement Model

Random matrices are widely used to estimate the extent of an elliptically contoured object. Usually, it is assumed that the measurements follow a normal distribution, with its standard deviation being proportional to the object’s extent. However, the random matrix approach can filter the center of gravity and the covariance matrix of measurements independently of the measurement model. This work considers the whole chain from data acquisition to the linear Kalman Filter with extension estimation as a reference plant. The input is the (unknown) ground truth (position and extent). The output is the filtered center of gravity and the filtered covariance matrix of the measurement distribution. A virtual measurement model emulates the behavior of the reference plant. The input of the virtual measurement model is adapted using the proposed algorithm until the output parameters of the virtual measurement model match the result of the reference plant. After the adaptation, the input to the virtual measurement model is considered an estimation for position and extent. The main contribution of this paper is the reference model concept and an adaptation algorithm to optimize the input of the virtual measurement model.