Empirical Comparison Research Articles

Empirical comparison of deep learning models for fNIRS pain decoding.

Pain assessment is extremely important in patients unable to communicate and it is often done by clinical judgement. However, assessing pain using observable indicators can be challenging for clinicians due to the subjective perceptions, individual differences in pain expression, and potential confounding factors. Therefore, the need for an objective pain assessment method that can assist medical practitioners. Functional near-infrared spectroscopy (fNIRS) has shown promising results to assess the neural function in response of nociception and pain. Previous studies have explored the use of machine learning with hand-crafted features in the assessment of pain. In this study, we aim to expand previous studies by exploring the use of deep learning models Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and (CNN-LSTM) to automatically extract features from fNIRS data and by comparing these with classical machine learning models using hand-crafted features. The results showed that the deep learning models exhibited favourable results in the identification of different types of pain in our experiment using only fNIRS input data. The combination of CNN and LSTM in a hybrid model (CNN-LSTM) exhibited the highest performance (accuracy = 91.2%) in our problem setting. Statistical analysis using one-way ANOVA with Tukey's (post-hoc) test performed on accuracies showed that the deep learning models significantly improved accuracy performance as compared to the baseline models. Overall, deep learning models showed their potential to learn features automatically without relying on manually-extracted features and the CNN-LSTM model could be used as a possible method of assessment of pain in non-verbal patients. Future research is needed to evaluate the generalisation of this method of pain assessment on independent populations and in real-life scenarios.

Prediction of Electricity Generation Using Onshore Wind and Solar Energy in Germany

Renewable energy production is one of the most important strategies to reduce the emission of greenhouse gases. However, wind and solar energy especially depend on time-varying properties of the environment, such as weather. Hence, for the control and stabilization of electricity grids, the accurate forecasting of energy production from renewable energy sources is essential. This study provides an empirical comparison of the forecasting accuracy of electricity generation from renewable energy sources by different deep learning methods, including five different Transformer-based forecasting models based on weather data. The models are compared with the long short-term memory (LSTM) and Autoregressive Integrated Moving Average (ARIMA) models as a baseline. The accuracy of these models is evaluated across diverse forecast periods, and the impact of utilizing selected weather data versus all available data on predictive performance is investigated. Distinct performance patterns emerge among the Transformer-based models, with Autoformer and FEDformer exhibiting suboptimal results for this task, especially when utilizing a comprehensive set of weather parameters. In contrast, the Informer model demonstrates superior predictive capabilities for onshore wind power and photovoltaic (PV) power production. The Informer model consistently performs well in predicting both onshore wind and PV energy. Notably, the LSTM model outperforms all other models across various categories. This research emphasizes the significance of selectively using weather parameters for improved performance compared to employing all parameters and a time reference. We show that the suitability and performance of a prediction model can vary significantly, depending on the specific forecasting task and the data that are provided to the model.

Norterpene Cyclic Peroxides from the Marine Sponge Diacarnus spinipoculum, Inhibitors of Transient Receptor Potential Ankyrin 1.

Bioassay-guided isolation of the extract from the marine sponge Diacarnus spinipoculum showing inhibitory activity against human transient receptor potential ankyrin 1 (hTRPA1) resulted in the isolation of 12 norditerpene cyclic peroxides (1-12) and eight norsesterterpene cyclic peroxides (13-20). Among these, 10 (5-7, 11, 12, 16-20) are unprecedented analogs. Compounds with either a hydroxy (5, 11) or a methoxy (6, 12) group attached to the cyclohexanone moiety were obtained as epimeric mixtures at C-11, while compounds 4, 6, 10, and 12 are likely the artifacts of isolation. The absolute configurations of the new compounds were established based on an NMR-based empirical method and comparison of specific rotation values. Mosher ester analysis revealed the absolute configurations of compounds 17-20. The inhibitory activity of the isolated compounds against hTRPA1 varied significantly depending on their structures, with the norsesterterpenoid 19 displaying the most potent activity (IC50 2.0 μM).

Assessing the clinical and cost-effectiveness of inpatient mental health rehabilitation services provided by the NHS and independent sector (ACER): protocol

BackgroundMental health rehabilitation services provide specialist treatment to people with particularly severe and complex problems. In 2018, the Care Quality Commission reported that over half the 4,400 mental health inpatient rehabilitation beds in England were provided by the independent sector. They raised concerns that the length of stay and cost of independent sector care was double that of the NHS and that their services tended to be provided much further from people’s homes. However, there has been no research comparing the two sectors and we therefore do not know if these concerns are justified. The ACER Study (Assessing the Clinical and cost-Effectiveness of inpatient mental health Rehabilitation services provided by the NHS and independent sector) is a national programme of research in England, funded from 2021 to 2026, that aims to investigate differences in inpatient mental health rehabilitation provided by the NHS and independent sector in terms of: patient characteristics; service quality; patient, carer and staff experiences; clinical and cost effectiveness.MethodsACER comprises a:1) detailed survey of NHS and independent sector inpatient mental health rehabilitation services across England; 2) qualitative investigation of patient, family, staff and commissioners’ experiences of the two sectors; 3) cohort study comparing clinical outcomes in the two sectors over 18 months; 4) comprehensive national comparison of inpatient service use in the two sectors, using instrumental variable analysis of routinely collected healthcare data over 18 months; 5) health economic evaluation of the relative cost-effectiveness of the two sectors. In Components 3 and 4, our primary outcome is ‘successful rehabilitation’ defined as a) being discharged from the inpatient rehabilitation unit without readmission and b) inpatient service use over the 18 months.DiscussionThe ACER study will deliver the first empirical comparison of the clinical and cost-effectiveness of NHS and independent sector inpatient mental health rehabilitation services.Trial registrationISRCTN17381762 retrospectively registered.

An empirical comparison of statistical methods for multiple cut-off diagnostic test accuracy meta-analysis of the Edinburgh postnatal depression scale (EPDS) depression screening tool using published results vs individual participant data

BackgroundSelective reporting of results from only well-performing cut-offs leads to biased estimates of accuracy in primary studies of questionnaire-based screening tools and in meta-analyses that synthesize results. Individual participant data meta-analysis (IPDMA) of sensitivity and specificity at each cut-off via bivariate random-effects models (BREMs) can overcome this problem. However, IPDMA is laborious and depends on the ability to successfully obtain primary datasets, and BREMs ignore the correlation between cut-offs within primary studies.MethodsWe compared the performance of three recent multiple cut-off models developed by Steinhauser et al., Jones et al., and Hoyer and Kuss, that account for missing cut-offs when meta-analyzing diagnostic accuracy studies with multiple cut-offs, to BREMs fitted at each cut-off. We used data from 22 studies of the accuracy of the Edinburgh Postnatal Depression Scale (EPDS; 4475 participants, 758 major depression cases). We fitted each of the three multiple cut-off models and BREMs to a dataset with results from only published cut-offs from each study (published data) and an IPD dataset with results for all cut-offs (full IPD data). We estimated pooled sensitivity and specificity with 95% confidence intervals (CIs) for each cut-off and the area under the curve.ResultsCompared to the BREMs fitted to the full IPD data, the Steinhauser et al., Jones et al., and Hoyer and Kuss models fitted to the published data produced similar receiver operating characteristic curves; though, the Hoyer and Kuss model had lower area under the curve, mainly due to estimating slightly lower sensitivity at lower cut-offs. When fitting the three multiple cut-off models to the full IPD data, a similar pattern of results was observed. Importantly, all models had similar 95% CIs for sensitivity and specificity, and the CI width increased with cut-off levels for sensitivity and decreased with an increasing cut-off for specificity, even the BREMs which treat each cut-off separately.ConclusionsMultiple cut-off models appear to be the favorable methods when only published data are available. While collecting IPD is expensive and time consuming, IPD can facilitate subgroup analyses that cannot be conducted with published data only.

The Road to Oligarchic Peace: Comparing the Antebellum United States and Ukraine

This article endeavours to answer the question as to what accounts for the preservation of peace in the United States and Ukraine in 1850 and 2004 when there was a potential for an armed conflict. I argue that parallels can be drawn between the events taking place in both countries in the antebellum period, and, through a detailed empirical comparison, it can be demonstrated that peace endures when, during a supreme moment of crisis, an “oligarchic peace”, that is a compromise, is negotiated at the level of national and regional elites that ensures representation and protection of property rights for the key elites.

Estimation of Cochlear Frequency Selectivity Using a Convolution Model of Forward-Masked Compound Action Potentials.

Frequency selectivity is a fundamental property of the peripheral auditory system; however, the invasiveness of auditory nerve (AN) experiments limits its study in the human ear. Compound action potentials (CAPs) associated with forward masking have been suggested as an alternative to assess cochlear frequency selectivity. Previous methods relied on an empirical comparison of AN and CAP tuning curves in animal models, arguably not taking full advantage of the information contained in forward-masked CAP waveforms. To improve the estimation of cochlear frequency selectivity based on the CAP, we introduce a convolution model to fit forward-masked CAP waveforms. The model generates masking patterns that, when convolved with a unitary response, can predict the masking of the CAP waveform induced by Gaussian noise maskers. Model parameters, including those characterizing frequency selectivity, are fine-tuned by minimizing waveform prediction errors across numerous masking conditions, yielding robust estimates. The method was applied to click-evoked CAPs at the round window of anesthetized chinchillas using notched-noise maskers with various notch widths and attenuations. The estimated quality factor Q10 as a function of center frequency is shown to closely match the average quality factor obtained from AN fiber tuning curves, without the need for an empirical correction factor. This study establishes a moderately invasive method for estimating cochlear frequency selectivity with potential applicability to other animal species or humans. Beyond the estimation of frequency selectivity, the proposed model proved to be remarkably accurate in fitting forward-masked CAP responses and could be extended to study more complex aspects of cochlear signal processing (e.g., compressive nonlinearities).

An empirical comparison between gradient boosting methods and cox’s proportional hazards model for right-censored survival data

Gradient boosting methods become popular in recent years to analyze right-censored survival data where Cox’s proportional hazards model is the widely used statistical model. However, there are very limited studies on the differences between the two approaches for right-censored survival data. We compare two boosting methods with Cox’s proportional hazards model in this paper: one is the gradient boosting decision tree and the other is gradient boosting with component-wise linear models. The differences between the two boosting methods are presented. A simulation study is conducted to investigate the performance of the three methods in practice where only the main effects of covariates are included. The results show that the boosting methods outperform Cox’s proportional hazards model in both the relative and absolute risk estimation in the proportional hazards model except when Cox’s proportional hazards model is fully specified with nonlinear and interaction covariates effects. It indicates that the boosting methods, particularly the gradient boosting decision tree, is a very competitive method for right-censored survival data if complicated covariate effects may exist but are unknown to the investigator. We illustrate an application of the boosting methods with real data analysis.

Volatility prediction comparison via robust volatility proxies: An empirical deviation perspective

Volatility forecasting is crucial to risk management and portfolio construction. One particular challenge of assessing volatility forecasts is how to construct a robust proxy for the unknown true volatility. In this work, we show that the empirical loss comparison between two volatility predictors hinges on the deviation of the volatility proxy from the true volatility. We then establish non-asymptotic deviation bounds for three robust volatility proxies, two of which are based on clipped data, and the third of which is based on exponentially weighted Huber loss minimization. In particular, in order for the Huber approach to adapt to non-stationary financial returns, we propose to solve a tuning-free weighted Huber loss minimization problem to jointly estimate the volatility and the optimal robustification parameter at each time point. We then inflate this robustification parameter and use it to update the volatility proxy to achieve optimal balance between the bias and variance of the global empirical loss. We also extend this Huber method to construct volatility predictors. Finally, we exploit the proposed robust volatility proxy to compare different volatility predictors on the Bitcoin market data and calibrated synthetic data. It turns out that when the sample size is limited, applying the robust volatility proxy gives more consistent and stable evaluation of volatility forecasts.

MetaQuad: shared informative variants discovery in metagenomic samples.

Strain-level analysis of metagenomic data has garnered significant interest in recent years. Microbial single nucleotide polymorphisms (SNPs) are genomic variants that can reflect strain-level differences within a microbial species. The diversity and emergence of SNPs in microbial genomes may reveal evolutionary history and environmental adaptation in microbial populations. However, efficient discovery of shared polymorphic variants in a large collection metagenomic samples remains a computational challenge. MetaQuad utilizes a density-based clustering technique to effectively distinguish between shared variants and non-polymorphic sites using shotgun metagenomic data. Empirical comparisons with other state-of-the-art methods show that MetaQuad significantly reduces the number of false positive SNPs without greatly affecting the true positive rate. We used MetaQuad to identify antibiotic-associated variants in patients who underwent Helicobacter pylori eradication therapy. MetaQuad detected 7591 variants across 529 antibiotic resistance genes. The nucleotide diversity of some genes is increased 6 weeks after antibiotic treatment, potentially indicating the role of these genes in specific antibiotic treatments. MetaQuad is an open-source Python package available via https://github.com/holab-hku/MetaQuad.

An empirical comparison between ESG 100 & sectoral indices with perspective of circular economy

An empirical comparison between ESG 100 & sectoral indices with perspective of circular economy

Mutual Information Guided Diffusion for Zero-Shot Cross-Modality Medical Image Translation.

Cross-modality data translation has attracted great interest in medical image computing. Deep generative models show performance improvement in addressing related challenges. Nevertheless, as a fundamental challenge in image translation, the problem of zero-shot learning cross-modality image translation with fidelity remains unanswered. To bridge this gap, we propose a novel unsupervised zero-shot learning method called Mutual Information guided Diffusion Model, which learns to translate an unseen source image to the target modality by leveraging the inherent statistical consistency of Mutual Information between different modalities. To overcome the prohibitive high dimensional Mutual Information calculation, we propose a differentiable local-wise mutual information layer for conditioning the iterative denoising process. The Local-wise-Mutual-Information-Layer captures identical cross-modality features in the statistical domain, offering diffusion guidance without relying on direct mappings between the source and target domains. This advantage allows our method to adapt to changing source domains without the need for retraining, making it highly practical when sufficient labeled source domain data is not available. We demonstrate the superior performance of MIDiffusion in zero-shot cross-modality translation tasks through empirical comparisons with other generative models, including adversarial-based and diffusion-based models. Finally, we showcase the real-world application of MIDiffusion in 3D zero-shot learning-based cross-modality image segmentation tasks.

Демократична модель трансформації культурного ландшафту міста

Background. The analysis of the city's cultural landscape allows investigation of features and the context of local culture. The hybrid organization of the city as a media-architectural complex, and the involvement of city residents in the processes of making administrative decisions in various forms create a field for a democratic model of transformation of the cities' cultural landscapes. This model provides not just residents' free access to the city space but also their active participation in making decisions about its use and making changes. Methods. The methods of empirical analysis, systematization, generalization, and comparison of the obtained data, as well as the description of the obtained results, were used. Results. The cultural landscape of the city is the result of the interaction of various actors – local self-government, public organizations, enterprises, creative industries, and residents. Changes in politics, economy, and society can contribute to the development of new ideas and approaches to the cultural landscape of the city that reflect current needs and values. The democratic model of a city's cultural landscape development requires an effective communication system between all actors. The media play here a key role by shaping the perception, idea, and public opinion of urban space, and influencing decision-making in this domain. In the context of the studied model of the city's cultural landscape development, the media have an important task of ensuring openness and availability of information about various initiatives and projects implemented in the city. They help involve citizens in discussions, consultations, and public debates on city development issues, and promote the formation of common development goals and strategies. Conclusions. It was defined that the development of a democratic model of the city's cultural landscape transformation is important for ensuring sustainable urban development and improving the residents' quality of life. The media play an important role in creating an open and transparent urban environment that promotes the active participation of citizens in decision-making processes and shaping the city's cultural landscape. Their role is to ensure wide access to information, facilitate an open exchange of views and ideas, and support dialogue between all stakeholders in urban development. Local residents as social actors can make deep changes in the city's cultural landscape structure. This plays a positive role for local democracies and the culture of interaction because local actors are the most interested party when a local-level problem acquires a high level of complexity.

An Empirical Comparison of Immediate and Delayed Rewards in Multi-Objective Q-Learning

An Empirical Comparison of Immediate and Delayed Rewards in Multi-Objective Q-Learning

An empirical comparison of Hindi-BERT and MuRIL for hate speech detection on social media platforms in Hindi language

An empirical comparison of Hindi-BERT and MuRIL for hate speech detection on social media platforms in Hindi language

RD-OpenMax: Rethinking OpenMax for Robust Realistic Open-Set Recognition.

Open-set recognition (OSR) toward a practical open-world setting has attracted increasing research attention in recent years. However, existing OSR settings are either too idealized or focus on specific scenes such as long-tailed distribution and few-shot samples, which fail to capture the complexity of real-world scenarios. In this article, we propose a realistic OSR (ROSR) setting that covers a diverse range of challenging and real-world scenarios, including fine-grained cases with strong semantic correlation and a large number of species, few-shot samples, long-tailed sample distribution, dynamic inputs (e.g., images, spatio-temporal, and multimodal signals) and cross-domain adaptation. In particular, we rethink the simple and basic OpenMax for the ROSR setting and introduce a novel method, regularized discriminative OpenMax (RD-OpenMax), to handle the challenges in the ROSR setting. RD-OpenMax improves upon the basic OpenMax approach by introducing a covariance attention-based covariance pooling (CACP) module as a global aggregation step before the deep architecture's classifier. This module explores rich statistical information on features and provides discriminative distance scores for OpenMax. To address the instability of extreme value theory (EVT) estimation due to insufficient training samples under few-shot and long-tailed scenarios, we propose a regularized EVT (REVT) method based on Monte Carlo sampling to recalibrate the distribution of distance scores. As such, our RD-OpenMax performs a REVT model of distance scores generated by discriminative CACP representations to distinguish known classes and recognize unknown ones effectively and robustly. Extensive experiments are conducted on more than ten visual benchmarks across several scenarios, and the empirical comparisons show that the ROSR setting challenges existing state-of-the-art OSR approaches. Moreover, our RD-OpenMax clearly outperforms its counterparts under the ROSR setting while performing favorably against state-of-the-arts under the traditional OSR setting.

Enhancing System Reliability and Battery Longevity through Integrated Energy Sources and Algorithmic Optimization

This research examines the enhancement of low power electronic systems, such as low power wide area networks (LPWANs), by incorporating a mix of renewable energy sources like solar and wind in China. It addresses the challenges posed by the variability of these sources due to climatic and temporal factors by merging diverse energy harvesting (EH) methods to bolster power stability and availability. The study assesses the most effective combination of solar, wind, and rain EH for steady power supply. A novel algorithm is introduced for energy input management based on real‐time resource availability, with the goal of prolonging battery life by avoiding complete charge or discharge cycles. The feasibility of this multisource approach in possibly reducing the need for extra energy storage is evaluated. The investigation includes simulations using Chinese weather data to explore different combinations of energy sources. A real‐life system utilizing solar and wind energy, guided by the developed algorithm, was also implemented for empirical comparison. Findings suggest that solar and wind energies have a higher power yield than rain harvesting, with actual wind energy collection often falling short of simulated forecasts. The solar–wind energy mix achieves a 99% system availability and facilitates a more compact design owing to their high power density. Although the input‐switching algorithm lessens the frequency of complete battery drainage, a small degree of energy storage is still essential for maintaining system reliability. This integrated EH method, compared to single‐source options, allows for a smaller energy storage requirement. Moreover, the algorithm limits battery charging to 80%, significantly enhancing battery lifespan.

Self-Supervised Learning for Recommender Systems: A Survey

In recent years, neural architecture-based recommender systems have achieved tremendous success, but they still fall short of expectation when dealing with highly sparse data. Self-supervised learning (SSL), as an emerging technique for learning from unlabeled data, has attracted considerable attention as a potential solution to this issue. This survey paper presents a systematic and timely review of research efforts on self-supervised recommendation (SSR). Specifically, we propose an exclusive definition of SSR, on top of which we develop a comprehensive taxonomy to divide existing SSR methods into four categories: contrastive, generative, predictive, and hybrid. For each category, we elucidate its concept and formulation, the involved methods, as well as its pros and cons. Furthermore, to facilitate empirical comparison, we release an open-source library SELFRec ( <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Coder-Yu/SELFRec</uri> ), which incorporates a wide range of SSR models and benchmark datasets. Through rigorous experiments using this library, we derive and report some significant findings regarding the selection of self-supervised signals for enhancing recommendation. Finally, we shed light on the limitations in the current research and outline the future research directions.

Comparison and Analysis of the Effectiveness of the State-of-the-Art in Option and Financial Pricing Models

Contemporarily, various models have been developed and used to price the returns of an asset or an option for decades. This paper will discuss and compare two option pricing models (the BSM model and the Monte Carlo Simulation) and three financial pricing models (the CAPM, the FF3 Model, and the FF5 Model). It will start with discussing the history, assumptions, and formulae of each model. Then, it will lead into how each model has changed over time, whether that be changes from the researchers themselves or other researchers within the community. Additionally, specifics of the change will be addressed, and the paper will outline what the new model has done to improve upon those concerns. Eventually, an empirical comparison will contrast the effectiveness and accuracy of each model. Ultimately, it will be seen that the BSM model and the MCS are useful in different time frames and the FF5 model is superior to the original financial pricing models as it builds on the original models’ limitations. Even so, the BSM model, MCS, and the FF5 model still have their own limitations that vary from reality and can be improved on in future research. These results shed light on guiding further exploration of newer and more refined models, methodologies, and approaches aimed to enhance our ability to make informed decisions and manage risks with greater precision.

Assessing dispersion stability of alumina colloids via thermal characterization of the sediment layer

Colloidal suspensions incorporating particles are pivotal in diverse industries because of their distinct properties. However, challenges such as particle aggregation and sedimentation have impeded their widespread application. Existing techniques for assessing dispersion stability have limitations with respect to the particle type, concentration, and suspension properties. This study focuses on an innovative approach for assessing the dispersion stability and presents a streamlined method for analyzing the particle size and distribution. A theoretical model considering gravity, buoyancy, and viscous forces acting on the particles was developed to calculate the time-varying thickness of the sediment layer at the bottom of the sample colloid. The 3ω method is employed to measure thermal resistance changes at the sample droplet's bottom due to sedimentation. An empirical comparison utilizing Al2O3 particles in deionized water validates the efficacy of the method in understanding sedimentation mechanisms. In addition, a stability constant was proposed to quantitatively compare sedimentation phenomena, factoring in mixing methods, time, particle concentration, and shelf time. This study enhanced the quantification of the dispersion stability of colloids by enabling swift and precise assessment of particle concentration, size, and distribution inside a sample droplet.