Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk Factors of Readmission Within 90 Days for Chronic Obstructive Pulmonary Disease Patients with Frailty and Construction of an Early Warning Model

Objective This study aimed to explore the risk factors for readmission within 90 d in Chronic Obstructive Pulmonary Disease (COPD) patients with frailty and construct a clinical warning model. Methods COPD patients with frailty hospitalized in the Department of Respiratory and Critical Care Medicine of Yixing Hospital, Affiliated to Jiangsu University, were retrospectively collected from January 1, 2020, to June 30, 2022. Patients were divided into readmission and control groups according to readmission within 90 d. The clinical data of the two groups were evaluated by univariate and multivariate logistic regression analyses to identify readmission risk factors within 90 d in COPD patients with frailty. Then, a risk quantitative early warning model was constructed. Finally, the model’s prediction efficiency was evaluated, and external verification was carried out. Results The multivariate logistic regression analysis showed that BMI, number of hospitalizations in the past year ≥ 2, CCI, REFS, and 4MGS were independent risk factors for readmission within 90 d in COPD patients with frailty. The early warning model for these patients was established as follows: Logit (p) = −1.896 + (−0.166 × BMI) + (0.969 × number of hospitalizations in the past year ≥ 2) + (0.265 × CCI) + (0.405 × REFS) + (−3.209 × 4MGS), and presented an area under the ROC curve (AUC) of 0.744 [95% CI: 0.687–0.801]. The AUC of the external validation cohort was 0.737 (95% CI: 0.648–0.826), and the AUC of the LACE warning model was 0.657 (95% CI:0.552–0.762). Conclusion The BMI, number of hospitalizations in the past year ≥ 2, CCI, REFS, and 4MGS were independent risk factors for readmission within 90 d in COPD patients with frailty. The early warning model presented a moderate predictive value for assessing the risk of readmission within 90 d in these patients.
Deep Independent Recurrent Neural Network Technique for Modeling Transient Behavior of Nonlinear Circuits

This article introduces a novel macromodeling method based on a recurrent neural network (RNN) called deep independently RNN (DIRNN). The proposed method applies to time-domain modeling of nonlinear circuits and components, resulting in better training. It overcomes the vanishing and exploding gradient problems encountered with conventional RNNs. In conventional RNNs, all neurons in each layer are involved in recurrent connections that cause unnecessary connections, increasing the model’s complexity over time and making it hard to train for long-time sequences. To solve this problem, the proposed DIRNNs neurons are independent of each other in recurrent connections because each neuron only receives connections from its own previous hidden state. The validity of the proposed method is verified by modeling two nonlinear circuit examples, namely, a multistage driver terminated by a multiline interconnect, and an ON-chip voltage generator.
C-Reactive Protein Levels and Risk of Cardiovascular Diseases: A Two-Sample Bidirectional Mendelian Randomization Study

Elevated C-reactive protein (CRP) levels are an indicator of inflammation, a major risk factor for cardiovascular disease (CVD). However, this potential association in observational studies remains inconclusive. We performed a two-sample bidirectional Mendelian randomization (MR) study using publicly available GWAS summary statistics to evaluate the relationship between CRP and CVD. Instrumental variables (IVs) were carefully selected, and multiple approaches were used to make robust conclusions. Horizontal pleiotropy and heterogeneity were evaluated using the MR-Egger intercept and Cochran’s Q-test. The strength of the IVs was determined using F-statistics. The causal effect of CRP on the risk of hypertensive heart disease (HHD) was statistically significant, but we did not observe a significant causal relationship between CRP and the risk of myocardial infarction, coronary artery disease, heart failure, or atherosclerosis. Our primary analyses, after performing outlier correction using MR-PRESSO and the Multivariable MR method, revealed that IVs that increased CRP levels also increased the HHD risk. However, after excluding outlier IVs identified using PhenoScanner, the initial MR results were altered, but the sensitivity analyses remained congruent with the results from the primary analyses. We found no evidence of reverse causation between CVD and CRP. Our findings warrant updated MR studies to confirm the role of CRP as a clinical biomarker for HHD.
Research on vehicle recognition based on deep learning

The increasing number of car ownership aggravates the traffic problem, so the intelligent transportation system is proposed. As a component of intelligent transportation system, vehicle identification system has been studied by researchers all over the world. In this paper, the theoretical knowledge of deep learning is introduced at first, and then the migration training is carried out by using VGG-16, RESNET-50 and the improved neural network based on these two kinds of networks on the self-made model dataset. In the experiment, a verification set was used to verify the training effect, and the training results of the four models were compared in terms of the number of parameters, training time and accuracy. The training results show that the improved RESNET-50 has the best performance, and its accuracy rate reaches 93%.
MULTIVARIATE BINOMIAL AND POISSON DISTRIBUTIONS

An extension of this result to any number of variables has been obtained by Kibble (1945) the extension differing in one respect from the particular case of Mehler. It is well known that Mehler's series is not an isolated result, there being simi lar series, bilinear in Laguerre polynomials, discussed by Hardy (1932), Watson (1933) and Kibble (1941), and series bilinear in certain other orthogonal polynomials discussed by Campbell (1932), Aitken and Gonin (1935). Generalizations of these results, similar to Kibble's generalization of Mehler's have been obtained by the present author (1950). In this paper the series of Aitken and Gonin for a bivariate binomial distribution and the series of Campbell for a bivariate Poisson distribution are extended to any number of variables. It is explained at the end of the paper how the present author's generalizations differ from the particular cases of Aitken and Gronin, and of Campbell in precisely the same way as Kibble's generalization of Mehler's differs from that of Mehler.
Explaining holistic image regressors and classifiers in urban analytics with plausible counterfactuals

Abstract We propose a new form of plausible counterfactual explanation designed to explain the behaviour of computer vision systems used in urban analytics that make predictions based on properties across the entire image, rather than specific regions of it. We illustrate the merits of our approach by explaining computer vision models used to analyse street imagery, which are now widely used in GeoAI and urban analytics. Such explanations are important in urban analytics as researchers and practioners are increasingly reliant on it for decision making. Finally, we perform a user study that demonstrate our approach can be used by non-expert users, who might not be machine learning experts, to be more confident and to better understand the behaviour of image-based classifiers/regressors for street view analysis. Furthermore, the method can potentially be used as an engagement tool to visualise how public spaces can plausibly look like. The limited realism of the counterfactuals is a concern which we hope to improve in the future.
Implementasi Algoritma Modifikasi Transposisi Columnar Pada Bot Telegram

Pesatnya perkembangan internet yang ada saat ini membuat orang memiliki ketergantungan yang tinggi terhadap teknologi informasi dan komunikasi digital melalui internet. Komunikasi digital melalui internet sendiri dapat menimbulkan ancaman terhadap informasi yang dikirimkan diantaranya pencurian data, menyadapan data, dan sabotase data. Oleh karena itu keamanan dalam berkomunikasi melalui internet sangatlah penting. Salah satu cara untuk mengamankan pesan yang akan disampaikan adalah dengan menerapkan teknik kriptografi berupa metode enkripsi data yang hanya diketahui oleh pengirim dan penerima. Penelitian ini bertujuan untuk mengenkripsi pesan teks pada bot telegram menggunakan salah satu teknik kriptografi yaitu algoritma transposisi columnar cipher. Transposisi columnar cipher memiliki kelebihan pada proses kecepatan saat pengimplementasiannya sehingga akan mempermudah proses enkripsi dan deskripsi. Pengujian metode pada proses enkripsi dengan plaintext berupa teks dan kata kunci merupakan karakter huruf memiliki keberhasilan sebesar 100%. Pengujian metode pada proses deskripsi dengan plaintext berupa teks dan kata kunci merupakan karakter huruf memiliki keberhasilan sebesar 86, 67%. Pengujian metode pada proses enkripsi dengan plaintext berupa teks dan kata kunci merupakan karakter angka memiliki keberhasilan sebesar 100%. Pengujian metode pada proses deskripsi dengan plaintext berupa teks dan kata angka merupakan karakter huruf memiliki keberhasilan sebesar 80%. Hasil perbandingan antara plaintext dan hasil deskripsi memiliki nilai akurasi sebesar 83, 33%.
Imagine All the People: Using Tax Data to Build a Representative Sample of the US Population

This paper documents our method of building a representative sample of the US population from tax data, the first step in larger research project measuring changes in the amount and composition of income over the life cycle. We supplement tax return data—which allows us to identify filers and dependent nonfilers—with information return data—which allows us to identify non-dependent nonfilers. The share of the population who are nonfilers increases with age, making their inclusion crucial for measuring elderly income. Our work builds on the existing literature, especially Cilke (2014) and Lurie and Pearce (2021). The data captures a larger share of the population than most previous studies using tax data because, following Lurie and Pearce (2021), we include in our sample individuals identified only on Form 1095, which reports health insurance coverage. Innovations in our method include sampling individuals rather than tax returns and using the individual rather than the tax return or family as the unit of observation. Our method allows us to examine individual income by single year of age—which is difficult, if not impossible, to do for tax returns or families. Inclusive of both filers and nonfilers, roughly 95 percent of the population older than 26 in 2016 has income. Among those with income, median income follows a hump shape over the lifecycle, peaking at $41,000 per individual at age 46. We find that tax data produce a population estimate similar to that of the U.S. Census Bureau (Census) and that the two estimates track closely by age. Our examination of 2010 and 2016 data show that, across years, differences between tax data and Census estimates are more correlated with birth year than they are by age. * This research was conducted as part of the Statistics of Income Joint Research Program. Views presented are those of the authors and do not necessarily represent the views of the Internal Revenue Service or the views of the Investment Company Institute or its members. We thank Kevin Pierce for his assistance with this project.
Research on team role recognition based on multi-network representation learning

Team role research has been a hot research issue in recent years, and a good team background is very important for team role research. A good team is not a simple collection of multiple people but many people with different roles and functions (leadership, group leader, staff), they have close cooperation and strict relationship between superiors and subordinates. Team role research helps us to understand the function and behavior of the members of the team. Compared with the traditional walking method, Mjwalk can walk on multiple independent team networks, showing advantages in multinetwork joint learning. The conv_att method proposed in this paper solves the downstream role recognition task and improves the recognition effect. We evaluate the performance of the Mjwalk and conv_att framework with real team data, and improve f1_score and accuracy.
Lower Limb Biomechanics during the Golf Downswing in Individuals with and without a History of Knee Joint Injury

Although prevention is better than treatment, after a knee injury occurs, the adjustment of the movement technique back to the posture before the injury and the restoration of accuracy is very important for professional and amateur players. This study aimed to compare the differences in lower limb mechanics during the golf downswing between those with and without a history of knee joint injury. A total of 20 professional golfers with single-digit handicaps were recruited for this study, 10 of whom had a knee injury history (KIH+), while another 10 players were without a knee injury history (KIH−). From the 3D analysis, selected kinematic and kinetic parameters during the downswing were analyzed using an independent samples t-test with a significance level of α = 0.05. During the downswing, individuals with KIH+ exhibited a smaller hip flexion angle, smaller ankle abduction angle, and larger ankle adduction/abduction range of motion (ROM). Moreover, there was no significant difference found in the knee joint moment. Athletes with a history of knee injury can adjust the motion angles of their hip and ankle joints (e.g., by avoiding excessive forward leaning of the trunk and maintaining stable foot posture without inward or outward rotation) to minimize the impact of changes in their movement patterns resulting from the injury.
Multi-resolution modelling of terrains by using non restricted quadtree triangulation

The interactive visualization of terrains requires the processing of a high amount of data in real time, usually it is not possible to work with all of them on main in memory. In this work we present, a method which solves the above problem by means of using spatial indices (quadtree). For that, the terrains are divided into square cells, each of them having a quadtree, which will only be reﬁned up to a the detail level depending on the observer position. With this, allaus to reduce the time used for terrains visualization.
Attitude Synchronization of a Group of Rigid Bodies Using Exponential Coordinates

Currently, managing a group of satellites or robot manipulators requires coordinating their motion and work in a cooperative way to complete complex tasks. The attitude motion coordination and synchronization problems are challenging since attitude motion evolves in non-Euclidean spaces. Moreover, the equation of motions of the rigid body are highly nonlinear. This paper studies the attitude synchronization problem of a group of fully actuated rigid bodies over a directed communication topology. To design the synchronization control law, we exploit the cascade structure of the rigid body’s kinematic and dynamic models. First, we propose a kinematic control law that induces attitude synchronization. As a second step, an angular velocity-tracking control law is designed for the dynamic subsystem. We use the exponential coordinates of rotation to describe the body’s attitude. Such coordinates are a natural and minimal parametrization of rotation matrices which almost describe every rotation on the Special Orthogonal group SO(3). We provide simulation results to show the performance of the proposed synchronization controller.
Perturbazioni cosmologiche non lineari in universi curvi

The aim of this disertation is the study of the evolution of cosmological perturbations, beyond the linear order, in universes characterized by non-zero spatial curvature. In particular, we will obtain the general equations for cosmological fluctuations, in Newtonian approximation and the second-order perturbative solutions. Finally, we will calculate the gravitational skewness of the cosmologiacal fields.
A Reconfigurable LDO-Assisted Physically Unclonable Function Achieving a Zero-BER With 14% Masking

This paper presents a physically unclonable function (PUF) based on a ring oscillator (RO) collapse with an improved robustness against temperature variations. To effectively minimize the chances of having temperature-sensitive dark bits, the PUF equips a low-drop-out (LDO) regulator that enables a selection of the supply voltage for each PUF cell, so that uncertainties by temperature change can be minimized whereas those by process mismatch are kept to be large. A reconfigurable RO is also proposed for the PUF cell to effectively transform its shape to have a larger process mismatch. An implementation of the proposed PUF in a 40 nm CMOS shows a bit error rate (BER) of 0.039% in the nominal conditions (0.9 V, 25 °C). The worst-case BER with 0.9 V-to-1.4 V supply and −40 °C -to-125 °C temperature changes is 0.297%. As an indicator of the amount of stability for each PUF cell, the cycles to collapse (CTC) at the nominal conditions is used for the dark bit detection and prescreening. The prescreening reduces the worst-case BER to 0.0092% with 5% masking, to 0.00016% with 10% masking and to zero-error (BER $< 4.88\times 10^{-8}$ in the whole 2048 cells over 10K evaluations) with 14% masking.
PERBANDINGAN AKURASI ENSEMBLE ARIMA DALAM PERAMALAN CURAH HUJAN DI KOTA BATU, MALANG, JAWA TIMUR

Di Indonesia, perubahan pola iklim sangat mempengaruhi produksi pertanian. Salah satu upaya untuk mengatasi dampak perubahan iklim dalam pertanian saat ini adalah melalui pendekatan taktis dengan pengembangan metode dalam pemodelan dan peramalan. Dalam studi ini digunakan metode ARIMA sebagai model individu dan ensemble ARIMA sebagai model kombinasi untuk meramalkan rata-rata curah hujan dasaharian di enam stasiun Kota Batu, Malang, Jawa Timur. Keakuratan model dalam prediksi diukur berdasarkan kriteria RMSE dimana model terbaik yang dipilih adalah model dengan RMSE terkecil. Model ARIMA pada enam stasiun curah hujan menunjukkan akurasi yang lebih baik untuk 54 periode ke depan dibandingkan dengan model Ensemble ARIMA.
Multi-threshhold segmentation and optimization based on Otsu in color image

As it is of deficiency that the conventional Otsu algorithm is only applicable to single-threshold,the algorithm is extended to multi-threshold color image segmentation.Firstly,the selection of meaningful peak values among a group of maximum values is carried out.Then,according to the peak values,the diagram is divided into a certain number of segmentation intervals,in which the threshold is selected.Based on methodology of morphology,the segmentation is optimized.And the influence,as a result of ignoring the characteristics of space,which the threshold technique imposes on noises,is lessened.The results of the experiment indicate that the Otsu algorithm based on multi-threshold color image segmentation is able to implement automatic and rapid multi-threshold segmentation,and is noise-resistant to some extent.
Subset selection algorithms for prediction

In this dissertation, we study the subset selection problem for prediction. It deals with choosing the “best” or “most informative” k-subset from a large set of n > k observable variables, to predict the value of a function or another variable of interest that is related to the observable variables. Natural applications of this problem abound in areas as diverse as medicine, social sciences, economics, numerical analysis, signal processing and sensor networks. There are various mathematical formulations for this problem, depending on the characterization of the best subset and of the dependencies between variables. We study two versions: the first version is a stochastic framework for subset selection of random variables using linear regression, and the second is an adversarial framework for estimating aggregate statistics of a function in the presence of metric-space induced spatial constraints. The goal of this dissertation is to perform an algorithmic analysis of the subset selection problems, characterize natural conditions which make these problems tractable, and explore polynomial-time algorithms with guaranteed optimal or near-optimal solutions. For the stochastic subset selection problem, we explore two broad approaches for designing efficient approximation algorithms. The first approach uses a graph-theoretic framework to characterize the covariance structure of the problem instance, and design efficient algorithms for several classes of covariance graphs. The second approach uses an algebraic framework based on spectral and submodular analysis, to identify conditions under which greedy algorithms can obtain good performance guarantees. For adversarial subset selection, we provide efficient deterministic and randomized sampling strategies and corresponding prediction functions to approximate some commonly used aggregate statistics. For the deterministic setting, we show an interesting connection with common clustering problems, and obtain constant factor approximation algorithms for predicting the average and maximum statistics. For the randomized setting, we obtain a polynomial-time approximation scheme for the problem of finding the optimal randomized algorithm for choosing a single sample to predict the average statistic. We also solve the interesting special case of estimating the integral of a univariate Lipschitz-continuous function over the [0,1] interval using one sample, and design an optimal randomized algorithm in this setting. For several of our subset selection algorithms, we also experimentally validate our theoretical analysis on several real-world data sets.
Retrieval of clear-air atmospheric temperature profiles using AIRS observations

Atmospheric Infrared Sounder(AIRS)is on board the EOS-2/Aqua satellite,launched on May 4,2002.It is now an advanced atmospheric remote sensing instrument with a super high spectral resolution and a global detection coverage in the world.Study on AIRS data processing is of significance in both science and operational application.In the retrieval of atmospheric temperature from the AIRS observations,the eigenvector method is used,and collocated AIRS brightness temperature and corresponding T213 temperature analyses fields are used to compute the regression coefficients.The study includes the following aspects:selection of spectral channels,impacts of surface emissivity and topography,determination of additional predictors,and comparison of retrieval results.Research results indicate that:(1) it is very essential to rationally choose the channels when retrieving atmospheric temperature with the high spectral resolution AIRS data;(2) AIRS data can significant improve the retrieval accuracy of temperature in mid-upper troposphere and at the tropopause,however in the lower troposphere,especially near the surface,the accuracy is not satisfactory;(3) different surface emissivity and topography will affect retrieval result;(4) factors reflecting surface radiation nature,such as surface temperature,surface emissivity,and topography,are selected as additional predictors,which improves the retrieval accuracy near the surface;(5) AIRS retrieval accuracy in this paper is consistent with the AIRS team's.
Application of deep neural network in cost estimation of hydropower projects

Investment estimation is an essential part of hydropower projects. This paper proposes a learning rate control-enabled deep learning neural network model that can be optimized for different data sizes, especially when small. Then, a DNN model with learning rate optimization is constructed based on the existing hydropower project data in China; finally, the practicality and reliability of the learning rate control enabled example calculations to verify the DNN model. According to the results, the learning rate control-enabled DNN model accurately predicts outcomes. Therefore, it can achieve accurate, fast, and adequate investment estimation for large-scale and middle-scale hydropower projects.