Miracle-dz/UniFAR-FTUs huggingface dataset:
Constrained graph-based semi-supervised learning with higher order regularization

SOUSA, C. A. R. Constrained graph-based semi-supervised learning with higher order regularization. 2017. 158 p. Tese (Doutorado em Ciências – Ciências de Computação e Matemática Computacional) – Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos – SP, 2017. Graph-based semi-supervised learning (SSL) algorithms have been widely studied in the last few years. Most of these algorithms were designed from unconstrained optimization problems using a Laplacian regularizer term as smoothness functional in an attempt to reflect the intrinsic geometric structure of the data’s marginal distribution. Although a number of recent research papers are still focusing on unconstrained methods for graph-based SSL, a recent statistical analysis showed that many of these algorithms may be unstable on transductive regression. Therefore, we focus on providing new constrained methods for graph-based SSL. We begin by analyzing the regularization framework of existing unconstrained methods. Then, we incorporate two normalization constraints into the optimization problem of three of these methods. We show that the proposed optimization problems have closed-form solution. By generalizing one of these constraints to any distribution, we provide generalized methods for constrained graph-based SSL. The proposed methods have a more flexible regularization framework than the corresponding unconstrained methods. More precisely, our methods can deal with any graph Laplacian and use higher order regularization, which is effective on general SSL taks. In order to show the effectiveness of the proposed methods, we provide comprehensive experimental analyses. Specifically, our experiments are subdivided into two parts. In the first part, we evaluate existing graph-based SSL algorithms on time series data to find their weaknesses. In the second part, we evaluate the proposed constrained methods against six state-of-the-art graph-based SSL algorithms on benchmark data sets. Since the widely used best case analysis may hide useful information concerning the SSL algorithms’ performance with respect to parameter selection, we used recently proposed empirical evaluation models to evaluate our results. Our results show that our methods outperforms the competing methods on most parameter settings and graph construction methods. However, we found a few experimental settings in which our methods showed poor performance. In order to facilitate the reproduction of our results, the source codes, data sets, and experimental results are freely available.
EFFECT OF TEMPORAL RANDOMIZATION ON THE INTERACTION OF NORMALIZED AND ANOMALOUS TRANSPORT

The fractional kinetic equations are a natural consequence of non-Gaussian properties in the behavior of many complex systems. We consider the competition between normalized and anomalous transport in the presence of temporal subordination. The anomalous transport is induced by fractional spatial derivatives as occurs in fractional kinetic theory. It is shown that for large time the power tails of the probability density play a dominant role. This supports and extends the Weitzner-Zaslavsky's result obtained in a simpler case. PACS: 05.40. Fb, 02.50. Ey
ENERGY EFFICIENT LIGHTING DESIGN - A CASE STUDY OF JIGME NAMGYEL ENGINEERING COLLEGE

Home Automation Systems are sets of interconnected devices for controlling various functions within a house, such as lighting, heating, air conditioning etc. Mobile devices are suitable for offering a user interface in a Home Automation System because of their portability and diverse capabilities. Wireless Home Automation has been a positive inspiration for new construction as it is easier and effective to use. This Project Report presents the implementation of the wireless technology for controlling the Home Utilities, Home Security System with specific features and door lock system by using scanning technique. In this system, with the help of app, we can control Home Utilities wirelessly that intelligently communicate with one another through a wireless device. In addition, that we have installed motion and gas detector for security purpose with special features like notifying the owner with message and beeping the buzzer. Lastly, we are also installing door lock system for better security using fingerprint that will allow only authorized user to access the home.
Adjoint Formulation Investigations of Benchmark Aerodynamic Design Cases in SU2

We investigate multiple adjoint formulations within the SU2 suite: a continuous adjoint in surface integral form, a continuous adjoint in field integral form, and a discrete adjoint leveraging algorithmic differentiation. The formulations are applied to the computation of drag sensitivities with respect to shape changes for four different external flow test cases, including the NACA 0012, RAE 2822, NACA 4412, and the Ahmed car body configuration. The main objective of this article is to draw meaningful conclusions about the performance of the available adjoint technologies for computing shape design gradients with a particular emphasis on accuracy. While all formulations provide gradients suitable for optimization purposes, we find that the inclusion of mesh effects in the field integral formulation of the continuous adjoint can provide improvements in sensitivity accuracy and robustness over the surface integral formulation.
Series representation of the pricing formula for the European option driven by space-time fractional diffusion

Abstract In this paper, we show that the price of an European call option, whose underlying asset price is driven by the space-time fractional diffusion, can be expressed in terms of rapidly convergent double-series. This series formula is obtained from the Mellin-Barnes representation of the option price with help of residue summation in ℂ2. We also derive the series representation for the associated risk-neutral factors, obtained by Esscher transform of the space-time fractional Green functions.
Completing Einstein’s Spacetime

The four-dimensional character of Einstein’s spacetime is generally accepted in mainstream physics as beyond reasonable doubt correct. However the real problem is when we require scale invariance and that this spacetime be four-dimensional on all scales. It is true that on our classical scale, the 4D decouples into 3D plus one time dimension and that on very large scale only the curvature of spacetime becomes noticeable. However the critical problem is that such spacetime must remain 4D no matter how small the scale we are probing is. This is something of crucial importance for quantum physics. The present work addresses this basic, natural and logical requirement and shows how many contradictory results and shortcomings of relativity and quantum gravity could be eliminated when we “complete” Einstein’s spacetime in such a geometrical gauge invariant way. Concurrently the work serves also as a review of the vast Literature on E-Infinity theory used here.
The Effects of Perilla frutescens Extracts on IgA Nephropathy: A Systematic Review and Meta-Analysis

(1) Background: Chronic renal disorders (CRD) are associated with significant comorbidities and necessitate complex therapeutic management. As time passed, Perilla frutescens (PF) became a promising therapeutic option for CRD. The aim of this systematic review and meta-analysis was to outline the therapeutic effects of PF extracts on various models of immunoglobulin a (IgA) nephropathy; (2) Methods: Medline, Embase, and Cochrane databases were used to find relevant studies. All prospective interventional studies that evaluated the effect of PF extract versus placebo on rat models of chronic renal disorders were assessed according to the international guidelines; (3) Results: Our search yielded 23 unique records, out of which only five were included in the analysis. Our results showed that administration of PF extracts led to a statistically significant reduction in proteinuria and PCNA levels in rats that received high doses of the extract as well as in the PCNA level and DNA synthesis in rats that received low doses of the extract. The evaluated outcomes benefited from a low degree of heterogeneity; (4) Conclusions: Some of the evaluated outcomes were significantly reduced by both high and low doses of extracts from Perilla frutescens. Further studies are needed to determine the exact effect over IgA nephropathy in human subjects.
Community Forums as Amplifiers of Communities’ Voices: Isolated Communities in Puerto Rico

Social determinants of health contribute to health disparities and inequities. We conducted a community forum on the topic of isolation with the objectives of (1) identifying and prioritizing key health-related issues needing attention in isolated communities in Puerto Rico; (2) developing strategies in terms of Policies, Programs, and Practices to address the community priorities we identified. We used the triangulation method for qualitative data, integrating the Colorado State University’s Tri-ethnic Center Model and the Delphi Technique for a better understanding of community health needs and priorities. The five community health-related priorities identified in the community forum were: (1) access to health services (physical and mental); (2) older adults; (3) access to basic services; (4) preparedness for future disasters/emergencies; and (5) COVID-19 and access to vaccination. The Alliance Leaders and Advisory Boards understand that we will work with the priorities of preparedness for future natural disasters/emergencies and COVID-19 and access to vaccination. Fifteen strategies were developed for these priorities and were grouped into five areas that require more attention in order to reduce health disparities. Isolated communities in Puerto Rico present an intersectionality of factors that affect a wide range of health-related risks and outcomes.
Performance Prediction for Deep Learning Models With Pipeline Inference Strategy

For heterogeneous multiprocessor system-on-chips (HMPSoCs), a reasonable pipeline design can significantly improve the inference performance of deep learning (DL) models. The pipeline design optimization can be modeled as a search problem where an accurate prediction model can efficiently speed up the search process. However, the performance prediction of DL models for the pipeline inference strategy is challenging because of the interlayer effect, inference details, and variety of model structures. In this article, we propose TPPNet, a transformer-based model for predicting the inference performance of various DL models with the pipeline inference strategy. TPPNet represents the DL model as an execution sequence with operators and hardware details to extract the hidden factors between layers. Moreover, we apply the multitask learning (MTL) method to accurately predict throughput and latency metrics by constructing a predictive model. To the best of our knowledge, this is the first study dedicated to pipeline inference performance prediction for the DL model on HMPSoCs. We evaluate TPPNet on six well-known DL models using RK3399. The experimental outcomes affirm the high accuracy of TPPNet and its capability to significantly reduce the time overhead associated with pipeline exploration.
Coding Theorems for Delay Sensitive Communication over Burst-Erasure Channels

Coding Theorems for Delay Sensitive Communication over Burst-Erasure Channels Devin Waine-Tak Lui Master of Applied Science Graduate Department of Electrical and Computer Engineering University of Toronto 2011 In this thesis, we consider error-correction codes for systems which have burst erasure channels, but where the packet delay is constrained. The packet delay itself is the time difference between the arrival of a source packet at the encoder and the reconstruction of that source packet at the decoder. While such a framework was introduced by Martinian (2004) and his co-authors, several problems remain open. We make three contributions in this thesis. First we develop a rigorous converse proof for the point-to-point case and thus complete the result of Martinian (2004). Our proof technique is also applied to a multicast channel model and new results are obtained. Secondly we study the case when there are multiple parallel links between the encoder and decoder and obtain the capacity in some special cases. Finally we study a setup when there are multiple source streams, each with a different delay constraint, and obtain capacity results.
Support Vector Machines and Perceptrons: Learning, Optimization, Classification, and Application to Social Networks

This work reviews the state of the art in SVM and perceptron classifiers. A Support Vector Machine (SVM) is easily the most popular tool for dealing with a variety of machine-learning tasks, including classification. SVMs are associated with maximizing the margin between two classes. The concerned optimization problem is a convex optimization guaranteeing a globally optimal solution. The weight vector associated with SVM is obtained by a linear combination of some of the boundary and noisy vectors. Further, when the data are not linearly separable, tuning the coefficient of the regularization term becomes crucial. Even though SVMs have popularized the kernel trick, in most of the practical applications that are high-dimensional, linear SVMs are popularly used. The text examines applications to social and information networks. The work also discusses another popular linear classifier, the perceptron, and compares its performance with that of the SVM in different application areas.
Fine-Tuning Pre-Trained CodeBERT for Code Search in Smart Contract

Smart contracts, which automatically execute on decentralized platforms like Ethereum, require high security and low gas consumption. As a result, developers have a strong demand for semantic code search tools that utilize natural language queries to efficiently search for existing code snippets. However, existing code search models face a semantic gap between code and queries, which requires a large amount of training data. In this paper, we propose a fine-tuning approach to bridge the semantic gap in code search and improve the search accuracy. We collect 80 723 different pairs of from Etherscan.io and use these pairs to fine-tune, validate, and test the pre-trained CodeBERT model. Using the fine-tuned model, we develop a code search engine specifically for smart contracts. We evaluate the Recall@k and Mean Reciprocal Rank (MRR) of the fine-tuned CodeBERT model using different proportions of the fine-tuned data. It is encouraging that even a small amount of fine-tuned data can produce satisfactory results. In addition, we perform a comparative analysis between the fine-tuned CodeBERT model and the two state-of-the-art models. The experimental results show that the fine-tuned CodeBERT model has superior performance in terms of Recall@k and MRR. These findings highlight the effectiveness of our fine-tuning approach and its potential to significantly improve the code search accuracy.
Investigation of the Supply Chain of Mass Timber Systems

How well production systems and their supply chains are designed, configured, and managed affects the delivery of construction projects. Industrialized Construction (IC) and mass timber present a shift from traditional project delivery: they are reshaping existing supply chains and creating new ones within the construction industry. The rapidly increasing number of mass timber projects in North America and the emergence of mass timber supply chains bring the need to study and seek ways to design and improve the production systems that deliver customer value by means of such projects. Accordingly, this paper presents an exploratory case study that describes the characteristics of the mass timber supply chain in North America and the major steps in the process of designing and delivering a mass timber structural system for a multi-story residential building. In addition, we present a list of recommendations for designing and delivering mass timber systems.
Nevada’s Healthcare Crisis: A Severe Shortage of Physicians and Residency Positions

The state of Nevada is home to millions of people and a prominent entertainment industry. However, the state ranks among the lowest in terms of available primary care doctors and general surgeons per capita, resulting in limited access to essential healthcare services and an increased reliance on emergency departments and hospitals. Nevada also faces the challenges posed by an aging physician workforce and a significant proportion of inactive providers. The scarcity of residency positions in Nevada's medical schools drives many graduates to seek residency training opportunities elsewhere, leading to a reduced likelihood of their return to practice within the state. We propose potential solutions, including increased funding for residency positions, prioritizing the retention of medical school graduates through local residency training, and the establishment of interdisciplinary comprehensive academic health centers. These measures are essential to meet the escalating healthcare demands of Nevada's rapidly growing population and to ultimately enhance patient outcomes.
Story generation during appreciating an artwork based on an actual tale (Ugetsu-monogatari)

In this paper, I will show a result of an experiment. The experiment was conducted using an artwork based on an actual tale (Ugetsu-monogatari). Actually the tale is rather old one. I have been conducting experiments to know how people can generate stories from the artworks. Previously I did not use artworks based on actual tales. Only the exception was artworks based on ``Alice in Wonderland. '' These artworks were used to see how Visual Thinking Strategies (VTS) functions during art appreciation. Of course in the experiment, we asked participants to generate stories. However, in this experiment, I tried to see the process of the story generation during art appreciation.
Methods for preferential sampling in geostatistics

Preferential sampling in geostatistics occurs when the locations at which observations are made may depend on the spatial process that underlines the correlation structure of the measurements. We show that previously proposed Monte Carlo estimates for the likelihood function may not be approximating the desired function. Furthermore, we argue that, for preferential sampling of moderate complexity, alternative and widely available numerical methods to approximate the likelihood function produce better results than Monte Carlo methods. We illustrate our findings on the Galicia data set analysed previously in the literature.
Non-Isothermal Crystallization Kinetics of Poly (ɛ-Caprolactone) (PCL) and MgO Incorporated PCL Nanofibers

The study delves into the kinetics of non-isothermal crystallization of Poly (ɛ-caprolactone) (PCL) and MgO-incorporated PCL nanofibers with varying cooling rates. Differential Scanning Calorimetry (DSC-3) was used to acquire crystallization information and investigate the kinetics behavior of the two types of nanofibers under different cooling rates ranging from 0.5–5 K/min. The results show that the crystallization rate decreases at higher crystallization temperatures. Furthermore, the parameters of non-isothermal crystallization kinetics were investigated via several mathematical models, including Jeziorny and Mo’s models. Mo’s approach was suitable to describe the nanofibers’ overall non-isothermal crystallization process. In addition, the Kissinger and Friedman methods were used to calculate the activation energy of bulk-PCL, PCL, and MgO-PCL nanofibers. The result showed that the activation energy of bulk-PCL was comparatively lower than that of nanofibers. The investigation of the kinetics of crystallization plays a crucial role in optimizing manufacturing processes and enhancing the overall performance of nanofibers.
Simulink bus usage in practice: an empirical study

Matlab/Simulink is a graphical modeling environment that has become the de facto standard for the industrial model-based development of embedded systems. Practitioners employ different structuring mechanisms to manage Simulink models’ growing size and complexity. One important architectural element is the so-called bus, which can combine multiple signals into composite ones, thus, reducing a model’s visual complexity. However, when and how to effectively use buses is a non-trivial design problem with several trade-offs. To date, only little guidance exists, often applied in an ad-hoc and subjective manner, leading to suboptimal designs. Using an inductive-deductive research approach, we conducted an exploratory survey among Simulink practitioners and extracted bus usage information from a corpus comprising 433 open-source Simulink models. We elicited 22 hypotheses on bus usage advantages, disadvantages, and best practices from the data, whose validity was later tested through a confirmatory survey. Our findings serve as requirements for static analysis tools and pave the way toward guidelines on bus usage in Simulink.
Single-Electron Tunneling Based Hydrogen Sensor

We present a hydrogen sensor based on single-electron tunneling in two dimensional (2D) hexagonal closed packed arrays of palladium nano-islands. The parameters of the palladium nanoparticles were extracted from the experimental TEM results by the image processing method. We also assumed random offset charges for every palladium nanoparticle. Using the SIMON simulator, the emergence of Coulomb blockade was inspected by studying the current-voltage (IV) characteristics of equivalent circuits consisting of palladium islands and tunneling junctions. After ensuring the emergence of the Coulomb blockade phenomena in these arrays, the possibilities for using these arrays as a hydrogen sensor were studied. The change in the tunneling resistances and capacitances were calculated according to the lattice parameter expansion of the palladium nanoparticles at different pressures of the hydrogen gas. The changes in the IV characteristics were investigated after exposing the arrays into hydrogen gas. The change in the resistance of the arrays before and after the exposure to hydrogen were extracted. According to the results, this configuration shows single-electron tunneling and can be used as a hydrogen gas sensor.
(AlGa)_2O_3 solar-blind photodetectors on sapphire with wider bandgap and improved responsivity

Single crystallinity (AlGa)2O3 solar-blind photodetectors are epitaxially grown on sapphire. Oxygen pressure during the growth has a great impact on the Al composition in (AlGa)2O3, which is investigated utilizing X-ray photoelectron spectroscopy and X-ray diffraction measurements. Measured transmittance spectra and responsivity demonstrate that (AlGa)2O3 photodetectors achieve a wider bandgap compared to a Ga2O3 device. An (Al0.12Ga0.88)2O3 device obtains 10 times higher photocurrent Iphoto than a Ga2O3 photodetector. However, as Al composition increases, significant Iphoto degradation is observed in an (Al0.35Ga0.65)2O3 photodetector. Meanwhile, an (Al0.35Ga0.65)2O3 device exhibits the stronger persistent photoconductivity compared to the Ga2O3 control device. Analysis shows that defect states in a bandgap of (AlGa)2O3 might be associated with the performance change in (AlGa)2O3 photodetectors.