Decoding Of Data Research Articles

Modulating subjective pain perception with decoded Montreal Neurological Institute-space neurofeedback: a proof-of-concept study.

Pain is a complex emotional experience that still remains challenging to manage. Previous functional magnetic resonance imaging (fMRI) studies have associated pain with distributed patterns of brain activity (i.e. brain decoders), but it is still unclear whether these observations reflect causal mechanisms. To address this question, we devised a new neurofeedback approach using real-time decoding of fMRI data to test if modulating pain-related multivoxel fMRI patterns could lead to changes in subjective pain experience. We first showed that subjective pain ratings can indeed be accurately predicted using a real-time decoding approach based on the stimulus intensity independent pain signature (SIIPS) and the neurologic pain signature (NPS). Next, we trained participants (n = 16) in a double-blinded decoded fMRI neurofeedback experiment to up- or downregulate the SIIPS. Our results indicate that participants can learn to downregulate the expression of SIIPS independently from NPS expression. Importantly, the success of this neurofeedback training was associated with the perceived intensity of painful stimulation following the intervention. Taken together, these results indicate that closed-loop brain imaging can be efficiently conducted using a priori fMRI decoders of pain, potentially opening up a new range of applications for decoded neurofeedback, both for clinical and basic science purposes. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

NetHD: Neurally Inspired Integration of Communication and Learning in Hyperspace

The 6G network, the next‐generation communication system, is envisaged to provide unprecedented experience through hyperconnectivity involving everything. The communication should hold artificial intelligence‐centric network infrastructures as interconnecting a swarm of machines. However, existing network systems use orthogonal modulation and costly error correction code; they are very sensitive to noise and rely on many processing layers. These schemes impose significant overhead on low‐power internet of things devices connected to noisy networks. Herein, a hyperdimensional network‐based system, called , is proposed, which enables robust and efficient data communication/learning. exploits a redundant and holographic representation of hyperdimensional computing (HDC) to design highly robust data modulation, enabling two functionalities on transmitted data: 1) an iterative decoding method that translates the vector back to the original data without error correction mechanisms, or 2) a native hyperdimensional learning technique on transmitted data with no need for costly data decoding. A hardware accelerator that supports both data decoding and hyperdimensional learning using a unified accelerator is also developed. The evaluation shows that provides a bit error rate comparable to that of state‐of‐the‐art modulation schemes while achieving 9.4 faster and 27.8 higher energy efficiency compared to state‐of‐the‐art deep learning systems.

Assessing the effectiveness of spatial PCA on SVM-based decoding of EEG data

Principal component analysis (PCA) has been widely employed for dimensionality reduction prior to multivariate pattern classification (decoding) in EEG research. The goal of the present study was to provide an evaluation of the effectiveness of PCA on decoding accuracy (using support vector machines) across a broad range of experimental paradigms. We evaluated several different PCA variations, including group-based and subject-based component decomposition and the application of Varimax rotation or no rotation. We also varied the numbers of PCs that were retained for the decoding analysis. We evaluated the resulting decoding accuracy for seven common event-related potential components (N170, mismatch negativity, N2pc, P3b, N400, lateralized readiness potential, and error-related negativity). We also examined more challenging decoding tasks, including decoding of face identity, facial expression, stimulus location, and stimulus orientation. The datasets also varied in the number and density of electrode sites. Our findings indicated that none of the PCA approaches consistently improved decoding performance related to no PCA, and the application of PCA frequently reduced decoding performance. Researchers should therefore be cautious about using PCA prior to decoding EEG data from similar experimental paradigms, populations, and recording setups.

Intelligent Design of Antithrombotic Peptide Targeting Collagen.

Binding of blood components to collagen was proved to be a key step in thrombus formation. Intelligent Design of Protein Matcher (IDProMat), a neural network model, was then developed based on the principle of seq2seq to design an antithrombotic peptide targeting collagen. The encoding and decoding of peptide sequence data and the interaction patterns of peptide chains at the interface were studied, and then, IDProMat was applied to the design of peptides to cover collagen. The 99.3% decrease in seq2seq loss and 58.3% decrease in MLP loss demonstrated that IDProMat learned the interaction patterns between residues at the binding interface. An efficient peptide, LRWNSYY, was then designed using this model. Validations on its binding on collagen and its inhibition of platelet adhesion were obtained using docking, MD simulations, and experimental approaches.

Graph attention autoencoder model with dual decoder for clustering single-cell RNA sequencing data

Graph attention autoencoder model with dual decoder for clustering single-cell RNA sequencing data

AUDIO AND TEXT TRANSMISSION USING LI-FI TECHNOLOGY

The project "Audio and Text Transmission Using Li-Fi Technology" explores the innovative application of Li-Fi (Light Fidelity) for transmitting both audio and text data. Li-Fi employs light signals, typically from LEDs or lasers, for data transmission, and in this project, it is leveraged to achieve two distinct functionalities: audio and text transmission. For audio transmission, a dedicated Li-Fi system is implemented where an audio signal is modulated onto the light signal emitted by an LED or laser. At the receiver end, multiple amplifiers are employed to retrieve and reproduce the audio signal. This process allows for the wireless transmission of audio data using light waves, offering a unique and potentially efficient alternative to traditional audio communication methods. For text transmission, Li-Fi modules connected to Arduino boards are employed for both the transmitter and receiver. The Arduino-based system facilitates the encoding and decoding of text data into light signals, enabling the wireless transmission of textual information using Li-Fi technology. This dual-functional approach expands the versatility of Li-Fi in communication systems, showcasing its potential for transmitting both audio and text data wirelessly through light Keywords: Arduino, Li Fi Transmitter, Li Fi receiver, LCD

Dynamically Securing the Data by 1O2 Sensitization of Fluorescent Composites with a High Latency and Uncrackable Features

Comprehensive SummaryDynamic fluorescent materials play a crucial role in secure inks for data encryption; however, they are still plagued by issues such as photodegradation, poor latency, and susceptibility to unauthorized access. Herein, we propose a photochemically modulated dynamic fluorescent encryption system based on 1O2 sensitization of fluorescent composites, comprising a 1O2‐sensitive fluorophore (F2) and non‐emissive polymers. After UV irradiation, in‐situ generated 1O2 from the polymer effectively binds with F2 to form endoperoxides (F2EPO), resulting in a significant redshift in emission, up to 150 nm. The 1O2 concentration is closely related to the irradiation time, rendering different fluorescent colors in a time‐gated fashion. Moreover, the emission of F2EPO can be regulated by polymer chemical structure, molecular weight, and crosslinking density. Relying on these merits, we develop a dynamic data encryption method with various non‐emissive polymers as the data storage media, UV light irradiation as the data encoder, and F2 as the data decoder. UV light irradiation of diverse polymer solutions generates 1O2 at different concentrations, effectively encoding the data, which remains invisible under both UV and natural lights. The addition of F2 to these irradiated polymer solutions produces different redshifted fluorescence, enabling secure data decryption. Attributing to the non‐emissive nature of the polymers, time‐gated readout fashion, excellent latency of 1O2, and subtle interactions between 1O2 and F2, this data encryption is nearly undecipherable. This work offers an advantage data encryption approach beyond the reach of conventional fluorophores.

Domain general frontoparietal regions show modality-dependent coding of auditory and visual rules.

A defining feature of human cognition is our ability to respond flexibly to what we see and hear, changing how we respond depending on our current goals. In fact, we can rapidly associate almost any input stimulus with any arbitrary behavioural response. This remarkable ability is thought to depend on a frontoparietal "multiple demand" circuit which is engaged by many types of cognitive demand and widely referred to as domain general. However, it is not clear how responses to multiple input modalities are structured within this system. Domain generality could be achieved by holding information in an abstract form that generalises over input modality, or in a modality-tagged form, which uses similar resources but produces unique codes to represent the information in each modality. We used a stimulus-response task, with conceptually identical rules in two sensory modalities (visual and auditory), to distinguish between these possibilities. Multivariate decoding of functional magnetic resonance imaging data showed that representations of visual and auditory rules recruited overlapping neural resources but were expressed in modality-tagged non-generalisable neural codes. Our data suggest that this frontoparietal system may draw on the same or similar resources to solve multiple tasks, but does not create modality-general representations of task rules, even when those rules are conceptually identical between domains.

Methods for decoding cortical gradients of functional connectivity

Abstract Macroscale gradients have emerged as a central principle for understanding functional brain organization. Previous studies have demonstrated that a principal gradient of connectivity in the human brain exists, with unimodal primary sensorimotor regions situated at one end and transmodal regions associated with the default mode network and representative of abstract functioning at the other. The functional significance and interpretation of macroscale gradients remains a central topic of discussion in the neuroimaging community, with some studies demonstrating that gradients may be described using meta-analytic functional decoding techniques. However, additional methodological development is necessary to fully leverage available meta-analytic methods and resources and quantitatively evaluate their relative performance. Here, we conducted a comprehensive series of analyses to investigate and improve the framework of data-driven, meta-analytic methods, thereby establishing a principled approach for gradient segmentation and functional decoding. We found that a two-segment solution determined by a k-means segmentation approach and an LDA-based meta-analysis combined with the NeuroQuery database was the optimal combination of methods for decoding functional connectivity gradients. Finally, we proposed a method for decoding additional components of the gradient decomposition. The current work aims to provide recommendations on best practices and flexible methods for gradient-based functional decoding of fMRI data.

Parent-Adolescent Communication and Knowledge on Sexual and Reproductive Health Issues Among Adolescents: A Descriptive Cross-Sectional Study from Nepal

Background: The World Health Organization (WHO) defines adolescents as those between the ages of 10 and 19. Parent-adolescent communication is a fundamental process through which parents convey ideas, values, beliefs, expectations, information, and knowledge to their children. Parents are one of the primary stakeholders who plays important roles in protecting adolescents from risky sexual behaviors such as unsafe sex, unwanted pregnancy, substance use, and violence. Methodology: A quantitative, descriptive cross-sectional study using self- administered questionnaire was carried out in sundarharaincha municipality of koshi province among 265 adolescent participants. Simple random sampling was used for selecting schools while purposive sampling was used for participant selection. Excel and SPSS were being used for data coding, decoding, and statistical analysis. Findings: According to the survey, a minority of respondents (31.3%) had inadequate understanding of SRH issues, while the majority (68.7%) had appropriate knowledge of these issues. Additionally, the majority of respondents (70.2%) reported having a sufficient amount of communication with their parents regarding SRH difficulties, whereas the minority (29.8%) reported having a low degree of communication. Conclusion: The survey indicates that adolescents have a good understanding of sexual and reproductive health and effectively communicate with their parents about health-related issues. To improve parents’ adolescent communication, the study suggests conducting community awareness programs and creating a friendly environment between adolescents and parents.

Deep transfer learning-based SSVEP frequency domain decoding method

ObjectiveImproving the decoding accuracy and information transfer rate (ITR) of a steady-state visual evoked potential-based brain-computer interface (SSVEP-BCI) system and narrowing the inter-subject variance are key to the application of the SSVEP-BCI system. To this end, we proposed a deep transfer learning-based SSVEP frequency domain decoding method to improve the decoding performance. MethodsInput data representations with rich spatial and frequency domain features were extracted using filter bank and zero-padding-based fast Fourier transform techniques. A concise and efficient 3-dimensional convolutional neural network (3DCNN) model was designed for feature extraction and decoding of the input data. A transfer learning strategy was proposed to further improve the decoding accuracy and narrow the inter-subject variance. ResultsOur proposed 3DCNN achieves 89.35 % average classification accuracy and 173.02 bits/min ITR on the benchmark dataset with 1 s signal length. On our laboratory dataset, the average classification accuracy and ITR of 3DCNN reach 88.75 % and 120.33 bits/min, respectively, when the signal length is 0.6 s. ConclusionsIn this study, we experimentally confirmed the effectiveness and superiority of our proposed method for SSVEP decoding, which provides a promising decoding method for the application of SSVEP-BCI.

The Recommendation Algorithm Based on Improved Conditional Variational Autoencoder and Constrained Probabilistic Matrix Factorization

An improved recommendation algorithm based on Conditional Variational Autoencoder (CVAE) and Constrained Probabilistic Matrix Factorization (CPMF) is proposed to address the issues of poor recommendation performance in traditional user-based collaborative filtering algorithms caused by data sparsity and suboptimal feature extraction. Firstly, in the data preprocessing stage, a hidden layer is added to CVAE, and random noise is introduced into the hidden layer to constrain the data features, thereby obtaining more accurate latent features and improving the model’s robustness and generative capability. Secondly, the category of items is incorporated as auxiliary information in CVAE to supervise the encoding and decoding of item data. By learning the distribution characteristics of the data, missing values in the rating data can be effectively reconstructed, thereby reducing the sparsity of the rating matrix. Subsequently, the reconstructed data is processed using CPMF, which optimizes the feature extraction performance by imposing constraints on user features. Finally, the prediction rating of a user for an item can be obtained through the matrix product of user and item feature matrices. Experimental results on the MovieLens-100K and MovieLens-1M datasets demonstrate the effectiveness and superiority of the proposed algorithm over four comparative algorithms, as it exhibits significant advantages in terms of root mean square error and mean absolute error metrics.

Emerging Spintronic Materials and Functionalities.

The explosive growth of the information era has put forward urgent requirements for ultrahigh-speed and extremely efficient computations. In direct contrary to charge-based computations, spintronics aims to use spins as information carriers for data storage, transmission, and decoding, to help fully realize electronic device miniaturization and high integration for next-generation computing technologies. Currently, many novel spintronic materials have been developed with unique properties and multifunctionalities, including organic semiconductors (OSCs), organic-inorganic hybrid perovskites (OIHPs), and 2D materials (2DMs). These materials are useful to fulfill the demand for developing diverse and advanced spintronic devices. Herein, these promising materials are systematically reviewed for advanced spintronic applications. Due to the distinct chemical and physical structures of OSCs, OIHPs, and 2DMs, their spintronic properties (spin transport and spin manipulation) are discussed separately. In addition, some multifunctionalities due to photoelectric and chiral-induced spin selectivity (CISS) are overviewed, including the spin-filter effect, spin-photovoltaics, spin-light emitting devices, and spin-transistor functions. Subsequently, challenges and future perspectives of using these multifunctional materials for the development of advanced spintronics are presented.

СЕМАНТИЧНИЙ АСПЕКТ ФОРМУВАННЯ МОТИВАЦІЙНОЇ СПРОМОЖНОСТІ СПОРТИВНОГО КОМПЛЕКСУ ЗАКЛАДУ ВИЩОЇ ОСВІТИ

At the moment, the issue of forming and maintaining the physical and mental health of the population of Ukraine is more relevant than ever. The system of physical education in educational institutions is considered as one of the ways to promote physical culture and sports, as well as to form a healthy lifestyle among the population. However, it is possible to identify two main shortcomings of domestic SCs of higher education institutions, namely: the inconsistency of the existing range of premises and sports spaces with modern needs and the low motivational capacity of the architectural and spatial component of the sports environment of higher education institutions. Thus, we face the issue of forming a motivationally capable environment of the SC of HEIs, which would allow us to increase the interest of young people in physical culture and sports. It should be borne in mind that the work of perception is apperceptive (adding to the perceived) and is mainly determined by the subject's own and borrowed experience. The perception of the architectural (artificial) environment is not reduced to visual assessment, but is formed in relation to the functional content and human needs in the environment of a certain purpose. The functional aspect involves ensuring that the nomenclature of structural elements of the AC of a higher education institution meets the requirements of the training process and a certain level of comfort. The aesthetic aspect involves the analysis of morphological features of the environment in terms of compositional and artistic requirements. The semantic aspect of the formation of the environment of the SC of the HEI involves the analysis of functional and morphological features of the spatial organisation in terms of their semantic significance. The semantics of architecture is defined as sign mental formations embodied in specific forms, due to which the four main functions of the system of meanings are realised: orientation - search and finding of signals; interpretation - separation of signals (content, features) and identification of relations between them; identification of signals - establishing the identity of semantic values; recognition - comparison of the received signals with the system of standards (features) with further decoding of input data. Semantics of form - involves the expression of the embedded ideas and information by means of architectural and compositional expressiveness and the form of the object. Visual accessibility of the training process -provides free access to the visual perception of the functional process of a part of the sports spacesof the complex by unexpected spectators and visitors. Sports symbols - filling the environment withsports symbols is another effective means of informing visitors and casual spectators about thefunctional component of the complex and its focus on sports. The harmonious integration of sportssymbols into the architectural and artistic solution will increase both the information content of theenvironment and its attractiveness. The use of architectural semantics in the formation of thearchitectural environment of the SC of a higher education institution will increase its motivationalcapacity, which will positively affect the process of physical education and the promotion of sportsand a healthy lifestyle.

OFVSD: a Python package of optimized forward variable selection decoder for high-dimensional neuroimaging data.

The complexity and high dimensionality of neuroimaging data pose problems for decoding information with machine learning (ML) models because the number of features is often much larger than the number of observations. Feature selection is one of the crucial steps for determining meaningful target features in decoding; however, optimizing the feature selection from such high-dimensional neuroimaging data has been challenging using conventional ML models. Here, we introduce an efficient and high-performance decoding package incorporating a forward variable selection (FVS) algorithm and hyper-parameter optimization that automatically identifies the best feature pairs for both classification and regression models, where a total of 18 ML models are implemented by default. First, the FVS algorithm evaluates the goodness-of-fit across different models using the k-fold cross-validation step that identifies the best subset of features based on a predefined criterion for each model. Next, the hyperparameters of each ML model are optimized at each forward iteration. Final outputs highlight an optimized number of selected features (brain regions of interest) for each model with its accuracy. Furthermore, the toolbox can be executed in a parallel environment for efficient computation on a typical personal computer. With the optimized forward variable selection decoder (oFVSD) pipeline, we verified the effectiveness of decoding sex classification and age range regression on 1,113 structural magnetic resonance imaging (MRI) datasets. Compared to ML models without the FVS algorithm and with the Boruta algorithm as a variable selection counterpart, we demonstrate that the oFVSD significantly outperformed across all of the ML models over the counterpart models without FVS (approximately 0.20 increase in correlation coefficient, r, with regression models and 8% increase in classification models on average) and with Boruta variable selection algorithm (approximately 0.07 improvement in regression and 4% in classification models). Furthermore, we confirmed the use of parallel computation considerably reduced the computational burden for the high-dimensional MRI data. Altogether, the oFVSD toolbox efficiently and effectively improves the performance of both classification and regression ML models, providing a use case example on MRI datasets. With its flexibility, oFVSD has the potential for many other modalities in neuroimaging. This open-source and freely available Python package makes it a valuable toolbox for research communities seeking improved decoding accuracy.

Impact of mobility on the downlink performance of cell-free massive MIMO systems

In this paper, we analyze the achievable downlink spectral efficiency of cell-free massive multiple input multiple output (CF-mMIMO) systems, accounting for the effects of channel aging (caused by user mobility) and pilot contamination. We consider two cases, one where user equipments (UEs) rely on downlink pilots beamformed by the access points (APs) to estimate downlink channel, and another where UEs utilize statistical channel state information (CSI) for data decoding. For comparison, we also consider cellular mMIMO and derive its achievable spectral efficiency with channel aging and pilot contamination in the above two cases. Our results show that, in CF-mMIMO, downlink training is preferable over statistical CSI when the length of the data sequence is chosen optimally to maximize the spectral efficiency. In cellular mMIMO, however, either one of the two schemes may be better depending on whether user fairness or sum spectral efficiency is prioritized. Furthermore, the CF-mMIMO system generally outperforms cellular mMIMO even after accounting for the effects of channel aging and pilot contamination. Through numerical results, we illustrate the effect of various system parameters such as the maximum user velocity, uplink/downlink pilot lengths, data duration, network densification, and provide interesting insights into the key differences between cell-free and cellular mMIMO systems.

Handheld snapshot multi-spectral camera at tens-of-megapixel resolution

Multi-spectral imaging is a fundamental tool characterizing the constituent energy of scene radiation. However, current multi-spectral video cameras cannot scale up beyond megapixel resolution due to optical constraints and the complexity of the reconstruction algorithms. To circumvent the above issues, we propose a tens-of-megapixel handheld multi-spectral videography approach (THETA), with a proof-of-concept camera achieving 65-megapixel videography of 12 wavebands within visible light range. The high performance is brought by multiple designs: We propose an imaging scheme to fabricate a thin mask for encoding spatio-spectral data using a conventional film camera. Afterwards, a fiber optic plate is introduced for building a compact prototype supporting pixel-wise encoding with a large space-bandwidth product. Finally, a deep-network-based algorithm is adopted for large-scale multi-spectral data decoding, with the coding pattern specially designed to facilitate efficient coarse-to-fine model training. Experimentally, we demonstrate THETA’s advantageous and wide applications in outdoor imaging of large macroscopic scenes.

Joint Activity Detection, Channel Estimation, and Data Decoding for Grant-Free Massive Random Access

In the massive machine-type communication (mMTC) scenario, a large number of devices with sporadic traffic need to access the network on limited radio resources. Recently, grant-free random access has emerged as a promising mechanism for this challenging scenario, but its potential has not been fully unleashed. In particular, the available auxiliary information has not been fully exploited, including the common sparsity pattern in the received pilot and data signal, as well as the channel decoding information. This paper develops advanced receivers in a holistic manner to improve the massive access performance by jointly designing activity detection, channel estimation, and data decoding. To tackle the algorithmic and computational challenges, a turbo structure is adopted at the joint receiver. For performance enhancement, all the received symbols are utilized to jointly estimate the channel state, user activity, and soft data symbols, which effectively exploits the common sparsity pattern. Meanwhile, the extrinsic information from the channel decoder will assist the joint channel estimation and data detection. To reduce the complexity, a low-cost side information (SI)-aided receiver is also proposed, where the channel decoder provides side information to update the estimates on whether a user is active or not. Simulation results show that the turbo receiver is able to reduce the activity detection, channel estimation, and data decoding errors effectively, supporting twice as many active users compared with a separate design that disregards the common sparsity. In addition, the SI-aided receiver notably outperforms the conventional methods with a relatively low complexity.

Energy leakage in OFDM sparse channel estimation: The drawback of OMP and the application of image deblurring

In this paper, in order to reduce the energy leakage caused by the discretized representation in sparse channel estimation for Orthogonal Frequency Division Multiplexing (OFDM) systems, we systematically have analyzed the optimal locations of atoms with discrete delays for each path reconstruction from the perspective of linear fitting theory. Then, we have investigated the adverse effects of the non-ideal inner product function on the iteration in one of the most widely used channel estimation method, Orthogonal Matching Pursuit (OMP). The study shows that the distance between the selected atoms for each path in OMP can be larger than the sampling interval, which prevents OMP-based methods from achieving better performance. To overcome this drawback, the image deblurring-based channel estimation method, in which the channel estimation problem is analogized to one-dimensional image deblurring, was proposed to improve the large compensation distance of traditional OMP. The advantage of the proposed method was validated by the results of numerical simulation and sea trial data decoding.

Decoding semantic relatedness and prediction from EEG: A classification method comparison

Machine-learning (ML) decoding methods have become a valuable tool for analyzing information represented in electroencephalogram (EEG) data. However, a systematic quantitative comparison of the performance of major ML classifiers for the decoding of EEG data in neuroscience studies of cognition is lacking. Using EEG data from two visual word-priming experiments examining well-established N400 effects of prediction and semantic relatedness, we compared the performance of three major ML classifiers that each use different algorithms: support vector machine (SVM), linear discriminant analysis (LDA), and random forest (RF). We separately assessed the performance of each classifier in each experiment using EEG data averaged over cross-validation blocks and using single-trial EEG data by comparing them with analyses of raw decoding accuracy, effect size, and feature importance weights. The results of these analyses demonstrated that SVM outperformed the other ML methods on all measures and in both experiments.