Miracle-dz/UniFAR-FTUs huggingface dataset:
Image fusion based on shift invariant shearlet transform and stacked sparse autoencoder

Stacked sparse autoencoder is an efficient unsupervised feature extraction method, which has excellent ability in representation of complex data. Besides, shift invariant shearlet transform is a state-of-the-art multiscale decomposition tool, which is superior to traditional tools in many aspects. Motivated by the advantages mentioned above, a novel stacked sparse autoencoder and shift invariant shearlet transform-based image fusion method is proposed. First, the source images are decomposed into low- and high-frequency subbands by shift invariant shearlet transform; second, a two-layer stacked sparse autoencoder is adopted as a feature extraction method to get deep and sparse representation of high-frequency subbands; third, a stacked sparse autoencoder feature-based choose-max fusion rule is proposed to fuse the high-frequency subband coefficients; then, a weighted average fusion rule is adopted to merge the low-frequency subband coefficients; finally, the fused image is obtained by inverse shift invariant shearlet transform. Experimental results show the proposed method is superior to the conventional methods both in terms of subjective and objective evaluations.
Coefficients of the asymptotic distribution of the kolmogorov-smirnov statistic when parameters are estimated

If the hypothesized distribution function contains unknown parameters the distribution of the Kolmogorov-Smirnov goodness-of-fit test statistic and that of the empirical process depend on the hypothesized distribution type and sometimes on the values of the parameters. A simple rule is obtained about when the empirical process depends on the values of the unknown parameters. The asymptotic distribution of the K-S statistic is defined bytwo coefficients which are computed for 11 distributions. For 5 other families the 0necessary formulae are supplied. A comparatively simple algorithm is given for the calculations. Further simplification is possible forasymmetric distribution. Asymptotic critical values and significance levels are compared to those obtained in Monte-Carlo studies.
High temperature 1 MHz capacitance-voltage method for evaluation of border traps in 4H-SiC MOS system

In this work, border traps located in SiO2 at different depths in 4H-SiC MOS system are evaluated by a simple and effective method based on capacitance-voltage (C-V) measurements. This method estimates the border traps between two adjacent depths through C-V measurement at various frequencies at room and elevated temperatures. By comparison of these two C-V characteristics, the correlation between time constant of border traps and temperatures is obtained. Then the border trap density is determined by integration of capacitance difference against gate voltage at the regions where border traps dominate. The results reveal that border trap concentration a few nanometers away from the interface increases exponentially towards the interface, which is in good agreement with previous work. It has been proved that high temperature 1 MHz C-V method is effective for border trap evaluation.
Drafting in long-track speed skating team pursuit on the ice rink

ABSTRACT Drafting is distinctive for team pursuit races in long-track speed skating. This study aims to compare the impact of drafting on physical intensity (heart rate [HR]) and perceived intensity (ratings of perceived exertion [RPE]) per drafting position. Eighteen skilled male (n = 9) and female (n = 9) skaters (20.0 ± 4.8 years) skated three trials, in first, second or third position, with consistent average velocity (F2,10 = 2.30, p = 0.15, ηp 2 = 0.32). Differences in HR and RPE (Borg CR−10 scale) were compared within-subjects (three positions) using a repeated-measures ANOVA (p < 0.05). Compared to the first position, HR was lower in the second (benefit 3.2%) and third (benefit 4.7%) position and lower in third compared to second position (benefit 1.5%), observed in 10 skaters (F2,28 = 28.9, p < 0.001, ηp 2= 0.67). RPE was lower when comparing second (benefit 18.5%) and third (benefit 16.8%) position to first (F1.3,22.1 = 7.02, p < 0.05, ηp 2= 0.29) and similar for third and second positions. , observed in 8 skaters. Even though the physical intensity was lower when drafting in third versus second position, the perceived intensity was equal. There were large interindividual differences between skaters. Coaches are advised to adopt a multidimensional, tailored approach when selecting and training skaters for a team pursuit.
Data migration from SQL to NoSQL using snapshot- Livestream migration

The process of moving data from a source database to a destination database is known as data migration. For a variety of reasons, including higher data handling capacity, improved speed, and scalability, many businesses are choosing to convert their databases from one kind (e.g., RDBMS) to another (e.g., NoSQL). Sqoop [3], mongoimport [2], and mongify [1] are a few techniques and technologies that have been developed to help with this transition from RDBMS to NoSQL databases. NoSQL databases use different models, as opposed to the relational model employed by RDBMS, including document, graph, and key-value. Large data volumes were the main focus of the design of NoSQL databases. The database migration model we provide in this paper can effectively transfer both real-time and historical data in parallel. Our Java-based model focuses on transferring data from MongoDB, a document-oriented NoSQL database, to MySQL, an RDBMS. The prototype we created can migrate both live data and a snapshot of the database at a particular moment in time simultaneously. Our experimental evaluation shows that, in terms of performance for both snapshot and live data migration, our model beats competing approaches.
A model for the effect of pressure gradient on turbulent axisymmetric wakes

Turbulent axisymmetric wakes under pressure gradient have received little attention in the literature, in spite of their fundamental and practical importance, for example, in the case of wind turbine wakes over topography. In this paper, we develop an analytical framework to analyse turbulent axisymmetric wakes under different pressure gradient conditions. Specifically, we develop a model to predict how an arbitrary imposed pressure gradient perturbs the evolution of the zero-pressure-gradient wake. The starting point of the model is the basic mean conservation of the streamwise momentum equation. We take advantage of the self-similarity of the wake velocity deficit and the assumption that the ratio of the maximum velocity deficit to the wake width is independent of the pressure gradient; such an assumption is supported experimentally for planar wakes, and numerically for axisymmetric wakes in this study. Furthermore, an asymptotic solution for the problem is also derived. The problem is considered for both an axisymmetric strain and a planar strain. The inputs to the model are the imposed pressure gradient and the wake width in the zero-pressure-gradient case. To validate the model results, a set of large-eddy simulations (LES) are performed. Comparing the evolution of the maximum velocity deficit and the wake width, the model results and the LES data show good agreement. Similarly to planar wakes, it is observed that the axisymmetric wake recovers faster in the favourable pressure gradient compared with the adverse one.
A Global Optimization Approach Based on Opinion Formation in Complex Networks

Population-based metaheuristic optimization techniques have numerous applications in science and engineering. In this paper, we introduce a novel population-based binary optimization method, built upon consensus formation in interacting multi-agent systems. Agents, each associated with an opinion vector, are linked together through a network structure. The agents influence each other by performing interactions, and as a result their opinions evolve. Opinion vectors hold solutions to the problem, and at the same time, store additional information on agents’ interaction experience. The agents communicate and work collectively to solve an optimization task. In this study, we consider a specific opinion update rule and various underlying interaction network topologies. Results of the experiments, conducted on a number of benchmark cost functions, show that a dynamical ring topology, designed for our specific purpose, leads to the best performance compared to other network topologies. We also compare the performance of the proposed optimization algorithm with classical and state-of-the-art population-based optimizers, namely, Genetic Algorithm, Binary Hybrid Topology Particle Swarm Optimization, Selectively Informed Particle Swarm Optimization, Binary Learning Differential Evolution, and Discrete Artificial Bee Colony. Comparisons based on experimental analysis reveal that the proposed consensus-based optimizer is the top-performer.
Prospects and Challenges of Large Language Models in the Field of Intelligent Building

: At the end of November 2022, the ChatGPT released by OpenAI Inc. performed excellently and quickly became popular worldwide. Despite some shortcomings, Large Language Models (LLM) represented by Generative Pre-trained Transformer (GPT) is here to stay, leading the way for the new generation of Natural Language Processing (NLP) technique. This commentary presents the potential benefits and challenges of the applications of large language models, from the viewpoint of intelligent building. We briefly discuss the history and current state of large language models and their shortcomings. We then highlight how these models can be used to improve the daily maintenance of intelligent building. With regard to challenges, we address some vital problems to be solved before deployment and argue that large language models in intelligent building require maintenance staff to develop sets of competencies and literacies necessary to both understand the technology as well as the maintenance and maneuver of intelligent building. In addition, a clear strategy within intelligent building troops with a strong focus on AI talents construction and training dataset annotation are required to integrate and take full advantage of large language models in the daily maintenance. We conclude with recommendations for how to address these challenges and prepare for further applications of LLM in the field of intelligent building in the future.
On the effect of boundaries on confined vortex matter systems

In this thesis, we investigate the effects of boundaries on confined superconducting vortex matter systems. We are primarily interested in understanding the properties of the so-called `row-drop' transition in a density-driven, confined flow channel, reported by J. Watkins. We present numerical and analytic calculations that reveal chaos, structural progression, and novel energetic behaviour. Firstly, we study a cylindrical geometry of confined vortices. Vortices align to form a regular structure that wraps around the cylinder in a commensurate fashion. We find a set of analytic equations and solve them exactly to find all possible lattices. Combining numerical results with energetic arguments enables us to construct the phase diagram of structural ground states for arbitrary circumference and density. Following this, we generalise the results from the cylinder to a numerical study of a conical geometry of flowing vortices. We are able to accurately predict the lattice structures given the geometry of the cone and the density of vortices on its surface. By developing the theory of lattice structures on the cylinder, we propose a way of generating a single flowing lattice on the cone, subject to a source and sink reservoir of vortices at either end. Numerical simulations confirm this proposition. Finally, we simulate vortices confined to a narrow, constricted channel constructed out of static, pinned vortices. By carefully controlling system parameters, we observe the emergence of chaotic behaviour centred at the aperture of the constriction. We present results diagrammatically and analyse them using established techniques to extract Lyapunov exponents from time series data.
Glycation Inhibition of Bovine Serum Albumin by Extracts of Momordica charantia L. using Spectroscopic and Computational Methods

Background: Momordica charantia (M. charantia) has been used in traditional medicine for the management of complications associated with diabetes mellitus. Several phytochemicals with different pharmacological properties have been previously identified from the botanical; however, the mechanisms of actions of this plant vis-à-vis inhibition of non-enzymatic protein glycation are not known. This study aimed at understanding the putative mechanisms underlying the antiglycation properties of M. charantia extracts experimental and theoretical approaches. Methods: The antiglycation properties of the plant were evaluated by studying the inhibitory actions of methanol and aqueous extracts on glucose-induced glycation of Bovine Serum Albumin (BSA) and protein aggregation. The mode of binding of identified phenolics of the botanical with BSA, amyloid beta-peptide (1–42) and 3D amyloid beta (1–42) fibrils were also investigated. Results: The in vitro experimental properties of the extracts showed that the extracts could prevent inductions of protein glycation and protein folding. The molecular docking analyses revealed that phenolics had better binding affinities with chlorogenic acid showing the highest binding score (−7.13±0.04 kcal/mol) towards BSA than glucose and their respective interactions with BSA could prevent glucose-induced protein aggregation. Conclusion: Consequently, the results of this study provide insight into the probable mechanisms of actions of the extracts of M. charantia against the inhibition of advanced glycation end products formation.
H∞ model reduction for T‐S fuzzy systems over finite frequency ranges

This paper investigates the H∞ model reduction problem over finite frequency ranges for continuous time Takagi‐Sugeno (T‐S) fuzzy systems. Given an asymptotically stable system, our aim is to find a stable reduced‐order system in such a way that the error of the transfer function between the original system and the reduced‐order one is bounded over a finite frequency range. By using the Finsler's lemma, new sufficient conditions in terms of linear matrix inequalities are derived in different frequency ranges. These conditions provide extra degrees of freedom in the optimization of the H∞ performance when the frequency range of noises is known beforehand. Finally, we demonstrate via numerical examples that our method can achieve much smaller approximation error than existing approaches.
Moral Individualism in Modern Politics: A New Measure Inspired by Political Theory

We reexamine the concept of individualism and its political implications. While both political scientists and social psychologists agree that individualism is a core value for many Americans, political science has primarily associated the concept with views about economic mobility. Building upon insights from political theory, we argue that a narrow focus on economics overlooks key elements of individualism and its relation to political life. With the help of five distinct datasets collected between 2018 and 2022 (combined N = 12,169), we develop a new index that emphasizes autonomy from authority, which we label moral individualism. We show how it and other dimensions of individualism explain interactions with the political world, including responses to the COVID-19 pandemic. Across multiple indicators, moral individualists were far less likely to engage in collective action or pursue other behaviors meant to assist the community. For example, even after controlling for the effects of ideology and partisanship, moral individualism reduced the probability of wearing a mask during the first year of the pandemic by approximately 30 percentage points.
Partially Identified Treatment Effects for Generalizability

ABSTRACT Recent methods to improve generalizations from nonrandom samples typically invoke assumptions such as the strong ignorability of sample selection, which is challenging to meet in practice. Although researchers acknowledge the difficulty in meeting this assumption, point estimates are still provided and used without considering alternative assumptions. We compare the point identifying assumption of strong ignorability of sample selection with two alternative assumptions—bounded sample variation and monotone treatment response—that partially identify the parameter of interest, yielding interval estimates. Additionally, we explore the role that population data frames play in contributing identifying power for the interval estimates. We situate the comparison around causal generalization with nonrandom samples by applying the assumptions to a cluster randomized trial in education. Bounds on the population average treatment effect are derived under the alternative assumptions and the case when no assumptions are made on the data. While comparing the bounds, we discuss the plausibility of each alternative assumption and the practical trade-offs. We highlight the importance of thoughtfully considering the role that assumptions play in causal generalization by illustrating the differences in inferences from different assumptions.
Protecting Medical Data on the Internet of Things with an Integrated Chaotic-GIFT Lightweight Encryption Algorithm

The secure transmission of medical data is crucial for the protection of patients' privacy and confidentiality. With the advent of IoT in healthcare, medical data is being transmitted over networks that are vulnerable to cyberattacks. Therefore, there is an urgent need for lightweight yet secure encryption algorithms that can protect medical data in transit. In this paper, we propose an integrated Chaotic-GIFT algorithm for lightweight and robust encryption of medical data transmitted over IoT networks. The proposed algorithm combines the chaos theory with a lightweight block cipher to provide secure and efficient encryption of medical data. The Chaotic-GIFT algorithm employs bit-level shuffling and substitution of medical images to provide encryption, while the chaotic sequence generated by the logistic map is used as the cryptographic key for added security. The proposed Chaotic-GIFT algorithm provides a lightweight and efficient solution for the secure transmission of medical data over IoT networks. Evaluation of the algorithm's effectiveness was conducted using multiple metrics including encryption and decryption time, throughput, avalanche effect, non-linearity analysis, and correlation coefficient.
Brain Connectivity Models for Functional Magnetic Resonance Imaging

When analyzing data from functional magnetic resonance imaging, different mathematical models are used. This article briefly describes the most important ones – starting with the so-called balloon model describing the hemodynamic response during brain activity, the General Linear Model widely used for functional localization of brain areas involved with certain stimuli, and Dynamic Causal Modelling as a framework for investigating dynamic brain connectivity models.
The effectiveness of machine learning‐based multi‐model ensemble predictions of CMIP6 in Western Ghats of India

The popularity of cutting‐edge machine learning ensemble approaches has solved many climate change research and prediction issues. The six top‐performing GCMs obtained from Technique for Order Preference by Similarity to an Ideal Solution were ensembled using seven machine learning ensemble methods such as Random Forest Regressor (RFR), Support Vector Regressor (SVR), Linear Regression (LR), Adaptive Boosting Regressor (AdaBoost), Extreme Gradient Boosting Regressor (XGBR), Extra Tree Regressor (ETR), Multi‐Layer Perceptron neural network (MLP) and simple Arithmetic Mean (AM) over the diverse geo‐climatic basins. Precipitation is best simulated by EC‐Earth3 and BCC‐CSM2‐MR. Maximum temperature by MPI‐ESM1‐2‐HR, EC‐Earth3‐Veg, INM‐CM5‐0 and MPI‐ESM1‐2‐LR. Minimum temperature by INM‐CM5‐0 and MPI‐ESM1‐2‐LR model. The MME of XGBR and RFR stand out for their superior performance across all six basins, with exceptional performance over the per‐humid basins, while AdaBoost, SVR and the AM underperform. Examining the interseasonal variability of the simulated MMEs over the basins highlights the reliability of these MME models. The anticipated change in maximum and minimum temperature in the SSP245 and SSP585 in the future horizon corroborates the undeniable rise in temperature by all the MMEs with a dramatic change in future temperature in AM and AdaBoost in precipitation with a factor of two rises in the far future over the recent past. Though climate change is expected to increase precipitation, atmospheric stabilization over the Ghats will affect the spatiotemporal distribution of precipitation. We recommend a comprehensive testing and validation approach to generate ensembles in regional investigations involving complicated and diverse precipitation mechanisms.
Delayed Bandit Online Learning with Unknown Delays

This paper studies bandit learning problems with delayed feedback, which included multi-armed bandit (MAB) and bandit convex optimization (BCO). Given only function value information (a.k.a. bandit feedback), algorithms for both MAB and BCO typically rely on (possibly randomized) gradient estimators based on function values, and then feed them into well-studied gradient-based algorithms. Different from existing works however, the setting considered here is more challenging, where the bandit feedback is not only delayed but also the presence of its delay is not revealed to the learner. Existing algorithms for delayed MAB and BCO become intractable in this setting. To tackle such challenging settings, DEXP3 and DBGD have been developed for MAB and BCO, respectively. Leveraging a unified analysis framework, it is established that both DEXP3 and DBGD guarantee an ${\cal O}\big( \sqrt{T+D} \big)$ regret over $T$ time slots with $D$ being the overall delay accumulated over slots. The new regret bounds match those in full information settings.
Noninvasive Online Capacitor Monitoring Method for Three-Level Converter Based on Active Neutral-Point Current Adjustment

Capacitor monitoring is one of the effective measures to assess the health status of power converters. Although some conventional methods have been proposed, they require adding auxiliary current sensors, changing operation conditions, or deteriorating the output performance. The lack of noninvasive online capacitor monitoring methods is a great problem in an ac/dc/ac system. To address this urgent need, in this article, we propose a truly noninvasive online capacitor monitoring method specifically for three-level neutral-point-clamped converters. The word “noninvasive” means no additional sensors, no changes to operation conditions, and little influence on the output performance. It monitors capacitors by actively adjusting the neutral-point (NP) current to induce a ripple in the NP voltage. Then, it reconstructs the injected NP current based on the switching states and three-phase currents, and obtains the response ripple in the NP voltage through a recursive least square (RLS) filter. Finally, according to the relationship between the injected NP current and the response ripple in the NP voltage, it estimates the capacitance simply via an RLS observer. Both numerical simulation and experimental results are provided as well to verify the correctness and feasibility of the proposed method.