18 papers · ranked by Valyu relevance
Guo-Liang Tian, Xuanyu Liu, Yuanfan Zhao
Although the $\textit{expectation-maximization}$ (EM) algorithm is a powerful optimization tool in statistics, it can only be applied to missing/incomplete data problems or to problems with a latent-variable structure. It is well known that the introduction of latent variables (or the data augmentation) is an art…
Bjoern O. Forsberg, Pranav N.M. Shah, Alister Burt
Cryo electron microscopy (cryo-EM) is used by biological research to visualize biomolecular complexes in 3D, but the heterogeneity of cryo-EM reconstructions is not easily estimated. Current processing paradigms nevertheless exert great effort to reduce flexibility and heterogeneity to improve the quality of the…
Sergio Fuentes del Toro, Josue Aranda-Ruiz, Luca Miceli
Surface electromyography (sEMG) is a critical tool for quantifying muscle activity and inferring biomechanical function, enabling the detection of neuromuscular deficits through the analysis of electrical potential propagation. However, the inherent variability in sEMG signal amplitude, influenced by factors such as…
Kenway Louie
Learning is widely modeled in psychology, neuroscience, and computer science by prediction error-guided reinforcement learning (RL) algorithms. While standard RL assumes linear reward functions, reward-related neural activity is a saturating, nonlinear function of reward; however, the computational and behavioral…
Shaokun Zhao, Feng Sang, Chen Liu, Fei Wang + 5 more
'Chuansheng Chen' 'Jun Wang' 'Xin Li' 'Zhanjun Zhang'] Background Episodic memory (EM) deteriorates as a result of normal aging as well as Alzheimer’s disease. The neural underpinnings of such age-related memory impairments in older individuals are not well-understood. Although previous research has unveiled the…
Kenway Louie, Samuel J. Gershman
Learning is widely modeled in psychology, neuroscience, and computer science by prediction error-guided reinforcement learning (RL) algorithms. While standard RL assumes linear reward functions, reward-related neural activity is a saturating, nonlinear function of reward; however, the computational and behavioral…
Ewa Roszkowska, Mario Martinelli
Normalization is a critical step in Multiple-Criteria Decision Analysis (MCDA) because it transforms heterogeneous criterion values into comparable information. This study examines normalization techniques through the lens of entropy, highlighting how criterion data structure shapes normalization behavior and ranking…
Asif Ali Wagan, Shahnawaz Talpur, Sanam Narejo, Wei Wang
In various fields, including medical science, datasets characterized by uncertainty are generated. Conventional clustering algorithms, designed for deterministic data, often prove inadequate when applied to uncertain data, posing significant challenges. Recent advancements have introduced clustering algorithms based on…
William Ruth
The EM algorithm is a powerful tool for maximum likelihood estimation with missing data. In practice, the calculations required for the EM algorithm are often intractable. We review numerous methods to circumvent this intractability, all of which are based on Monte Carlo simulation. We focus our attention on the Monte…
Zahra AghahosseinaliShirazi, João Pedro A. R. da Silva, Camila P. E. de Souza
'Camila P. E. de Souza'] Nowadays, the confidentiality of data and information is of great importance for many companies and organizations. For this reason, they may prefer not to release exact data, but instead to grant researchers access to approximate data. For example, rather than providing the exact measurements…
Parima Ahmadipour, Omid G. Sani, Bijan Pesaran, Maryam M. Shanechi
Learning dynamical latent state models for multimodal spiking and field potential activity can reveal their collective low-dimensional dynamics and enable better decoding of behavior through multimodal fusion. Toward this goal, developing unsupervised learning methods that are computationally efficient is important…
Jakub Strawa, Jarek Duda
—Data normalization is crucial in machine learning, usually performed by subtracting the mean and dividing by standard deviation, or by rescaling to a fixed range. In copula theory [[1]], popular in finance, there is used normalization to approximately quantiles by transforming x → CDF(x) with estimated CDF/EDF…
Akshat Gupta, Ahmet Şükrü Özdemir, Gopala Anumanchipalli
Comparative Study with RMSNorm Authors: ['Akshat Gupta' 'Ahmet Şükrü Özdemir' 'Gopala Anumanchipalli'] Layer normalization is a pivotal step in the transformer architecture. This paper delves into the less explored geometric implications of this process, examining how LayerNorm influences the norm and orientation of…
Alexandre Benatti, Luciano da Fontoura Costa
The subject of features normalization plays an important central role in data representation, characterization, visualization, analysis, comparison, classification, and modeling, as it can substantially influence and be influenced by all of these activities and respective aspects. The selection of an appropriate…
Anath Rau Krishnan, Mohamad Rizal Hamid, Geoffrey Harvey Tanakinjal, Mohammad Fadhli Asli + 2 more
'Mohammad Fadhli Asli' 'Bonaventure Boniface' 'Mohd Fahmi Ghazali'] Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a popular multi-criteria decision-making method that ranks the available alternatives by examining the ideal-positive and ideal-negative solutions for each decision criterion.…
Authors not listed
A white light-emitting hydrogel was synthesized by embedding N,N’-bis(salicylidene)-(2-(3’,4’-diaminophenyl)benzothiazole) (BTS) into a polyethylene oxide-polypropylene oxide-polyethylene oxide (PEO-PPO-PEO, F127) triblock copolymer hydrogel. BTS undergoes excited state intramolecular proton transfer (ESIPT), which…
Amnon Balanov, Wasim Huleihel, Tamir Bendory
“Einstein from noise” (EfN) is a prominent example of the model bias phenomenon: systematic errors in the statistical model that lead to spurious but consistent estimates. In the EfN experiment, one falsely believes that a set of observations contains noisy, shifted copies of a template signal (e.g., an Einstein…
Authors not listed
Electrochemical impedance spectroscopy (EIS) coupled with distribution of relaxation times (DRT) analysis is a robust framework for characterizing electrochemical systems. However, DRT deconvolution is often plagued by spurious peaks, hindering accurate process identification and quantitative parameter estimation. To…