The optimal transport problem for measures supported on non-Euclidean spaces has recently gained ample interest in diverse applications involving representation learning. In this paper, we focus on circular probabilit...
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Active Visual Exploration (AVE) optimizes the utilization of robotic resources in real-world scenarios by sequentially selecting the most informative observations. However, modern methods require a high computational ...
The transverse momentum distributions of charged hadrons produced in proton-proton collisions at center-of-mass energies(√s)of 0.9 TeV and 2.36 TeV,as measured by the CMS detector at the Large Hadron Collider(LHC),ha...
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The transverse momentum distributions of charged hadrons produced in proton-proton collisions at center-of-mass energies(√s)of 0.9 TeV and 2.36 TeV,as measured by the CMS detector at the Large Hadron Collider(LHC),have been analyzed within various pseudorapidity classes utilizing the thermodynamically consistent Tsallis *** fitting procedure resulted in the key parameters,namely,effective temperature(T),non-extensivity parameter(q),and kinetic freezeout volume(V).Additionally,the mean transverse momentum()and initial temperature(T_(i))of the particle source are determined through the fit function and string percolation method,*** alternative method is employed to calculate the kinetic freezeout temperature(T_(0))and transverse flow velocity(β_(T))from ***,thermodynamic quantities at the freezeout,including energy density(ε),particle density(n),entropy density(s),pressure(P),and squared speed of sound(C_(s)^(2)),are computed using the extracted T and *** is also observed that,with a decrease in pseudorapidity,all thermodynamic quantities except V and q *** trend is attributed to greater energy transfer along the mid pseudorapidity.q increases towards higher values of pseudorapidity,indicating that particles close to the beam axis are far from ***,V remains nearly independent of *** excitation function of these parameters(q)shows a direct(inverse)correlation with collision *** ε,n,s,and P show a strong dependence on collision energies at low *** verification of the thermodynamic inequality ε≥3P suggests the formation of a highly dense droplet-like Quark-Gluon Plasma(QGP).Additionally,the inequality T_(i)>T>T_(0)is explicitly confirmed,aligning with the evolution of the produced fireball.
This study presents a CNN architecture aimed at increasing the accuracy of skin lesion detection by incorporating advanced techniques such as batch normalization data augmentation, dropout layers and data balancing te...
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In the paper, the authors check the behaviour of Bluetooth Low Energy protocol in a popular smart wristband and a microcontroller in the heart rate monitoring application. The measurements were collected using a devel...
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Missing data presents a significant challenge in statistical analysis and machine learning, often resulting in biased outcomes and diminished efficiency. This comprehensive review investigates various imputation techn...
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Missing data presents a significant challenge in statistical analysis and machine learning, often resulting in biased outcomes and diminished efficiency. This comprehensive review investigates various imputation techniques, categorizing them into three primary approaches: deterministic methods, probabilistic models, and machine learning algorithms. Traditional techniques, including mean or mode imputation, regression imputation, and last observation carried forward, are evaluated alongside more contemporary methods such as multiple imputation, expectation-maximization, and deep learning strategies. The strengths and limitations of each approach are outlined. Key considerations for selecting appropriate methods, based on data characteristics and research objectives, are discussed. The importance of evaluating imputation’s impact on subsequent analyses is emphasized. This synthesis of recent advancements and best practices provides researchers with a robust framework for effectively handling missing data, thereby improving the reliability of empirical findings across diverse disciplines.
We present a new graph neural network, the Attention-based Parametric-Kernel augmented Graph Neural Network (APKGNN), developed for node classification tasks. Despite extensive work on modeling multi-faceted relations...
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In everyday life, various exposures at work or at home can lead to skin conditions such as allergies or infections. Skin lesions serve as essential indications, alerting to future issues and requiring immediate care a...
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We consider the problem of control allocation for weakly redundant systems subject to actuator faults. In particular, the design of a suitable allocator will be devised with the aim of compensating for the fault effec...
The Image classification task is an important one in computer vision. In this work, various Deep learning methods for image classification have been extensively studied. For performing the classification and retrieval...
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