Transformer-Based Downside Risk Forecasting: A Data-Driven Approach with Realized Downward Semi-Variance by Yuping Song, Yuetong Zhang, Po Ning, Jiayi Peng, Chunyu Kao, Liang Hao

“…Compared with traditional econometric, machine learning, and deep learning models, the average increase percentage of RMSE, MAE, MAPE, SMAPE, MBE, and SD indicators is 52.23%, 20.03%, 62.33%, 60.33%, 37.57%, and 18.70%, respectively. (3) In multi-step prediction scenarios, the DM test statistic of the Transformer model is significantly positive, and the prediction accuracy of the Transformer model remains stable as the number of prediction steps increases. (4) Under the impact of external events of COVID-19, the Transformer model has stability, and the addition of emotional factors can effectively improve the prediction accuracy. …”
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Pre-service Teachers’ Noticing of 7th Grade Students’ Errors and Misconceptions about the Subject of Equations by Seval Deniz Kılıç

“…At the end of thestudy, it was found that the success of the attending step was not sufficientfor the other steps.…”
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Numerical solution of a PDE arising from prediction with expert advice by Jeff Calder, Nadejda Drenska, Drisana Mosaphir

“…The problem is a repeated two-person game involving decision-making at each step informed by $n$ experts in an adversarial environment. …”
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Skin Lesion Segmentation in Clinical Images Using Nonnegative Matrix Factorization and Mutual Information by Eliezer Soares Flores, Jacob Scharcanski

“…Skin lesion segmentation is a critical step towards computer-aided diagnosis that is effective in melanoma detection. …”
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Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques. by Colin Griesbach, Andreas Groll, Elisabeth Bergherr

“…Boosting techniques from the field of statistical learning have grown to be a popular tool for estimating and selecting predictor effects in various regression models and can roughly be separated in two general approaches, namely gradient boosting and likelihood-based boosting. …”
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THE EFFECT OF 36 HOURS SLEEP DEPRIVATION ON DRIBBLING SKILLS OF SOCCER PLAYERS by Mehrdad Hefzollesan, Sohrab Ghalehgir, Mostafa Ekrami

“…In this study to assess this skill, the test "Mor - Christian" has been used. In the first step, subjects conducted the dribble test as pre-test after 8 hours sleep a night. 10 days later, to ensure the validity of tests and test results on the learning effect, subjects did the same test again after 8 hours sleep a night. …”
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Localizing Adversarial Attacks To Produces More Imperceptible Noise by Pavan Reddy, Aditya Sanjay Gujral

“… Adversarial attacks in machine learning traditionally focus on global perturbations to input data, yet the potential of localized adversarial noise remains underexplored. …”
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Differentiable Few-view CT-Reconstruction for Arbitrary CT-Trajectories including Prior Knowledge by Linda-Sophie Schneider, Adrian Waldyra, Yipeng Sun, Andreas K. Maier

“…This paper presents a novel two-step pipeline for few-view CT reconstruction that combines discrete prior generation with differentiable optimization. …”
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Imagining the Commoning Library: Alter-Neoliberal Pedagogy in Informational Capitalism by Dimitris Soudias

“…Here, alternative literacies, ways of learning, and ways of being can be prefigured in practice. …”
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Comparison of Patterns Shapes and Patterns Texture for Identification of Malaria Parasites in Microscopic Image by Jani Kusanti, Yusuf Zain Santosa

“…The process of pattern recognition and pattern shapes texture. The last step is to test the identification and classification of plasmodium falciparum parasite into 12 classes using methods Learning Vector Quantization (LVQ). …”
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The Teaching of Initial Multiplication Concepts and Skills in Croatian Textbooks by Goran Trupčević, Anđa Valent

“…To describe it without ignoring certain features of it that appear to be self-evident to an insider, it is necessary to step out of this cultural frame. For that reason, we study the teaching of initial multiplication in Croatia by comparing Croatian mathematical textbooks with textbooks from Singapore, Japan, and England. …”
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Automatic detection of methane emissions in multispectral satellite imagery using a vision transformer by Bertrand Rouet-Leduc, Claudia Hulbert

“…Our model shows an order of magnitude improvement over the state-of-the-art, providing a significant step towards the automated, high resolution detection of methane emissions at a global scale, every few days.…”
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Is My Tree Safe? Recognizing Conditions that Increase the Likelihood of Tree Failure by Drew C. McLean, Andrew K. Koeser, Robert J. Northrop, Gitta Hasing

“…It is a landowner’s responsibility to ensure that the trees on their property are safe. A key step in reducing the potential for tree-related injury or property damage is learning to identify common tree defects associated with increased risk of failure. …”
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Is My Tree Safe? Recognizing Conditions that Increase the Likelihood of Tree Failure by Drew C. McLean, Andrew K. Koeser, Robert J. Northrop, Gitta Hasing

“…It is a landowner’s responsibility to ensure that the trees on their property are safe. A key step in reducing the potential for tree-related injury or property damage is learning to identify common tree defects associated with increased risk of failure. …”
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Fine-tuning neural network quantum states by Riccardo Rende, Sebastian Goldt, Federico Becca, Luciano Loris Viteritti

“…Notably, the computational cost of the fine-tuning step is very low compared to the pretraining stage. …”
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Flood Forecasting for Small Reservoirs Based on Neural Networks by PENG Wei, XIONG Jiayi, JIANG Xianqun, GAO Yueming

“…In flood forecasting,empirical prediction methods report low accuracy,and traditional hydrological models face the problems of large workloads and difficult promotion when they are applied to small reservoirs.Hence,an artificial neural network (ANN) method is introduced,which is equipped with powerful feature-learning capability.It is combined with the genetic algorithm (GA) to find the optimal parameters for flood forecasting of small reservoirs as GA can realize automatic optimization of the time step and hidden-layer neuron nodes in ANN.In this way,parameter search can be targeted,and personalized flood forecasting models can be constructed for each small reservoir.In addition,the flood forecasting models based on the back propagation (BP),long short-term memory (LSTM),and gated recurrent unit (GRU) neural networks are built,and comparisons between simulations and measured data are conducted for the flood process.The results show that the LSTM model has high prediction accuracy and good stability and can learn and simulate the water-level change pattern of the actual flood process,demonstrating better prediction performance than BP and GRU models.…”
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A Hybrid Lightning Search Algorithm-Simplex Method for Global Optimization by Yuting Lu, Yongquan Zhou, Xiuli Wu

“…Simplex method (SM) iteratively optimizes the current worst step leaders to avoid the population searching at the edge, thus improving the convergence accuracy and rate of the algorithm. …”
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Crystal structure generation with autoregressive large language modeling by Luis M. Antunes, Keith T. Butler, Ricardo Grau-Crespo

“…Abstract The generation of plausible crystal structures is often the first step in predicting the structure and properties of a material from its chemical composition. …”
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Deradicalization Through Islamic Education at State Institute for Islamic Studies (IAIN) Tulungagung by Ngainun Naim

“…Deradicalization is an important step to detect, neutralize and restore religious understanding from radical to moderate. …”
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Advanced neural network architectures for tomato leaf disease diagnosis in precision agriculture by Hritwik Ghosh, Irfan Sadiq Rahat, Md. Mintajur Rahman Emon, Md. Jisan Mashrafi, Mohammed Abdul Al Arafat Tanzin, Sachi Nandan Mohanty, Shashi Kant

“…Abstract This study addresses the critical challenge of detecting and classifying tomato leaf diseases using advanced deep learning technologies, a pivotal step in enhancing productivity within precision agriculture. …”
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