Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of the COVID-19 Pandemic on the Psychological Health of Patients Who Underwent Liver Transplantation Due to Hepatocellular Carcinoma

Background: The primary aim of this study was to compare liver transplant (LT) recipients with and without hepatocellular carcinoma (HCC) in terms of COVID-19-related depression, anxiety, and stress. Method: A total of 504 LT recipients with (HCC group; n = 252) and without HCC (non-HCC group; n = 252) were included in the present case–control study. Depression Anxiety Stress Scales (DASS-21) and Coronavirus Anxiety Scale (CAS) were used to evaluate the depression, stress, and anxiety levels of LT patients. DASS-21 total and CAS-SF scores were determined as the primary outcomes of the study. Poisson regression and negative binomial regression models were used to predict the DASS and CAS scores. The incidence rate ratio (IRR) was used as a coefficient. Both groups were also compared in terms of awareness of the COVID-19 vaccine. Results: Poisson regression and negative binomial regression analyses for DASS-21 total and CAS-SF scales showed that the negative binomial regression method was the appropriate model for both scales. According to this model, it was determined that the following independent variables increased the DASS-21 total score: non-HCC (IRR: 1.26; p = 0.031), female gender (IRR: 1.29; p = 0.036), presence of chronic disease (IRR: 1.65; p < 0.001), exposure to COVID-19 (IRR: 1.63; p < 0.001), and nonvaccination (IRR: 1.50; p = 0.002). On the other hand, it was determined that the following independent variables increased the CAS score: female gender (IRR:1.75; p = 0.014) and exposure to COVID-19 (IRR: 1.51; p = 0.048). Significant differences were found between the HCC and non-HCC groups in terms of median DASS-21 total (p < 0.001) and CAS-SF (p = 0.002) scores. Cronbach’s alpha internal consistency coefficients of DASS-21 total and CAS-SF scales were calculated to be 0.823 and 0.783, respectively. Conclusion: This study showed that the variables including patients without HCC, female gender, having a chronic disease, being exposed to COVID-19, and not being vaccinated against COVID-19 increased anxiety, depression, and stress. High internal consistency coefficients obtained from both scales indicate that these results are reliable.
ROBUST H_∞ CONTROL FOR LINEAR DELAY SYSTEMS WITH TIME-VARYING UNCERTAINTIES

This paper focuses on the problem of robust H ∞ state feedback controller design for linear delay systems with time varying uncertainties. Based on the notion of quadratic H ∞ performance, it is proved that if there exists a robust H ∞ dynamic state feedback controller, then there must exist a robust H ∞ static state feedback controller. Sufficient conditions for the existence of robust H ∞ static state feedback controllers are given in terms of linear matrix inequalities, and the design method is also presented. The systems in consideration are more general than those considered in the references. Finally, an example is provided to demonstrate the effectiveness of the proposed method.
Investigation of the MPI Signal's Dependency on Ferrofluid Concentration

Langevin’s single particle model (SPM) yields a linear relation between magnetic particle concentration c and higher harmonic amplitudes in magnetic particle imaging (MPI). In reality, the MPI signal is nonlinearly related to c, as demonstrated via simulation and experiment. Depending on the actual field of application, this may have severe impact on MPI. Inadequacy of paramagnetic theory The SPM is a well-established ferrofluid magnetization theory in the field of MPI [1]. Due to its paramagnetic nature, it describes noninteracting magnetic particles. Therefore, it is valid only in the limit of particle concentration c ! 0. In a real ferrofluid, particles interact magnetically. The smaller the distance between single magnetic moments, the more they support each other in the process of alignment to an external magnetic field H. Hence, the magnetization curve M(H) nonlinearly depends on c: increasing c leads to a steeper curve; saturation is reached for weaker H. This eect is ignored by the SPM. The impact of particle interaction can lead to drastic deviations from the SPM, as visualized in Figure 1.
The Psychology of Invention in the Mathematical Field.

Preface to the Paperback EditionForewordIntroductionIGeneral Views and Inquiries1IIDiscussions on Unconsciousness21IIIThe Unconscious and Discovery29IVThe Preparation Stage. Logic and Chance43VThe Later Conscious Work56VIDiscovery as a Synthesis. The Help of Signs64VIIDifferent Kinds of Mathematical Minds100VIIIParadoxical Cases of Intuition116IXThe General Direction of Research124Final Remarks133App. I137App. II142
Dreamlet based Gaussian packet migration

Gaussian packets are high-frequency asymptotic solutions of the wave equation. Gaussian packets have both Gaussian shaped time-frequency localization and space-direction localization. Its evolution can be obtained by ray tracing and dynamic ray equations. Through investigating into the relationship between Gaussian packets and Dreamlet atoms, we proposed and tested an effective decomposition method of seismic data (surface reflection profiling) into Gaussian packets. We test the poststack depth migration to the datasets of a 4-layer model and the 2D SEG-EAGE model to demonstrate the method’s validity and accuracy. From the results, we see that the Dreamlet based Gaussian packet migration can obtain the correct main features of the subsurface structures with properly smoothed velocity model. This shows the potential of Gaussian packet migration as the fast method of preliminary investigation.
A Wideband Circularly Polarized Filtering Antenna Based on Slot-Patch Structure

This letter presents a wideband circularly polarized (CP) antenna with filtering radiation response based on a slot-patch structure. The proposed CP filtering antenna is composed of a top patch, a middle microstrip-to- (“+”-shape) slot structure, and a bottom metallic reflector. The middle layer not only functions as a slot-coupled feeding structure but also operates as a driven slot CP antenna. The top and bottom layers are used to reduce the slot antenna backside radiation and enhance the antenna axial ratio (AR) bandwidth. In addition, a diagonal slot etched on the patch and an additional E-shaped slot on the middle layer can generate the lower- and upper-band radiation zeros, respectively. For verification, the antenna is implemented. The measured results show that the overlapping bandwidth of the impedance and AR bandwidth is 46.9% (3.35–5.40 GHz). The measured average CP gain is about 7.6 dBic. High frequency selectivity and better than 10 dB out-of-band radiation rejection level is obtained. Compared to the conventional filtering CP design method, which employs the filter or resonator feeding circuit, the proposed design features a simple structure, higher realized gain and wider AR/impedance overlapping bandwidth.
Shu: visualization of high-dimensional biological pathways

Abstract Summary Shu is a visualization tool that integrates diverse data types into a metabolic map, with a focus on supporting multiple conditions and visualizing distributions. The goal is to provide a unified platform for handling the growing volume of multi-omics data, leveraging the metabolic maps developed by the metabolic modeling community. In addition, shu offers a streamlined python API, based on the Grammar of Graphics, for easy integration with data pipelines. Availability and implementation Freely available at https://github.com/biosustain/shu under MIT/Apache 2.0 license. Binaries are available in the release page of the repository and the web application is deployed at https://biosustain.github. io/shu.
EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deployments

EvalRS aims to bring together practitioners from industry and academia to foster a debate on rounded evaluation of recommender systems, with a focus on real-world impact across a multitude of deployment scenarios. Recommender systems are often evaluated only through accuracy metrics, which fall short of fully characterizing their generalization capabilities and miss important aspects, such as fairness, bias, usefulness, informativeness. This workshop builds on the success of last year's workshop at CIKM, but with a broader scope and an interactive format.
Cultural-aware Machine Learning based Analysis of COVID-19 Vaccine Hesitancy

Understanding the COVID-19 vaccine hesitancy, such as who and why, is very crucial since a large-scale vaccine adoption remains as one of the most efficient methods of controlling the pandemic. Such an understanding also provides insights into designing successful vaccination campaigns for future pandemics. Unfortunately, there are many factors involving in deciding whether to take the vaccine, especially from the cultural point of view. To obtain these goals, we design a novel culture-aware machine learning (ML) model, based on our new data collection, for predicting vaccination willingness. We further analyze the most important features which contribute to the ML model's predictions using advanced AI explainers such as the Probabilistic Graphical Model (PGM) and Shapley Additive Explanations (SHAP). These analyses reveal the key factors that most likely impact the vaccine adoption decisions. Our findings show that Hispanic and African American are most likely impacted by cultural characteristics such as religions and ethnic affiliation, whereas the vaccine trust and approval influence the Asian communities the most. Our results also show that cultural characteristics, rumors, and political affiliation are associated with increased vaccine rejection.
Just Tell Me: Prompt Engineering in Business Process Management

GPT-3 and several other language models (LMs) can effectively address various natural language processing (NLP) tasks, including machine translation and text summarization. Recently, they have also been successfully employed in the business process management (BPM) domain, e.g., for predictive process monitoring and process extraction from text. This, however, typically requires fine-tuning the employed LM, which, among others, necessitates large amounts of suitable training data. A possible solution to this problem is the use of prompt engineering, which leverages pre-trained LMs without fine-tuning them. Recognizing this, we argue that prompt engineering can help bring the capabilities of LMs to BPM research. We use this position paper to develop a research agenda for the use of prompt engineering for BPM research by identifying the associated potentials and challenges.
Dynamic Variable Selection in High-Dimensional Predictive Regressions

We develop methodology and theory for a general Bayesian approach towards dynamic variable selection in high-dimensional regression models with time-varying parameters. Specifically, we propose a variational inference scheme which features dynamic sparsity-inducing properties so that different subsets of ``active'' predictors can be identified over different time periods. We compare our modeling framework against established static and dynamic variable selection methods both in simulation and within the context of two common problems in macroeconomics and finance: inflation forecasting and equity returns predictability. The results show that our approach helps to tease out more accurately the dynamic impact of different predictors over time. This translates into significant gains in terms of out-of-sample point and density forecasting accuracy. We believe our results highlight the importance of taking a dynamic approach towards variable selection for economic modeling and forecasting.
Data-Driven Modeling for Transonic Aeroelastic Analysis

Aeroelasticity in the transonic regime is challenging because of the strongly nonlinear phenomena involved in the formation of shock waves and flow separation. In this work, we introduce a computationally efficient framework for accurate transonic aeroelastic analysis. We use dynamic mode decomposition with control to extract surrogate models from high-fidelity computational fluid dynamics (CFD) simulations. Instead of identifying models of the full flowfield or focusing on global performance indices, we directly predict the pressure distribution on the body surface. The learned surrogate models provide information about the system’s stability and can be used for control synthesis and response studies. Specific techniques are introduced to avoid spurious instabilities of the aerodynamic model. We use the high-fidelity CFD code SU2 to generate data and test our method on the benchmark supercritical wing. Our Python-based software is fully open source and will be included in the SU2 package to streamline the workflow from defining the high-fidelity aerodynamic model to creating a surrogate model for flutter analysis.
Uncertainty-Aware Vehicle Energy Efficiency Prediction Using an Ensemble of Neural Networks

The transportation sector accounts for about 25% of global greenhouse gas emissions. Therefore, an improvement of energy efficiency in the traffic sector is crucial to reduce the carbon footprint. Efficiency is typically measured in terms of energy use per traveled distance, e.g., liters of fuel per kilometer. Leading factors that impact the energy efficiency are the type of vehicle, environment, driver behavior, and weather conditions. These varying factors introduce uncertainty in estimating vehicles’ energy efficiency. We propose in this article an ensemble learning approach based on deep neural networks that is designed to reduce the predictive uncertainty and to output measures of such uncertainty. We evaluated it using the publicly available Vehicle Energy Dataset and compared it with several baselines per vehicle and energy type. The results showed a high predictive performance, and they allowed us to output a measure of predictive uncertainty.
Self-Supervised Scene Dynamic Recovery from Rolling Shutter Images and Events

Scene Dynamic Recovery (SDR) by inverting distorted Rolling Shutter (RS) images to an undistorted high frame-rate Global Shutter (GS) video is a severely ill-posed problem due to the missing temporal dynamic information in both RS intra-frame scanlines and inter-frame exposures, particularly when prior knowledge about camera/object motions is unavailable. Commonly used artificial assumptions on scenes/motions and data-specific characteristics are prone to producing sub-optimal solutions in real-world scenarios. To address this challenge, we propose an event-based SDR network within a self-supervised learning paradigm, i.e., SelfUnroll. We leverage the extremely high temporal resolution of event cameras to provide accurate inter/intra-frame dynamic information. Specifically, an Event-based Inter/intra-frame Compensator (E-IC) is proposed to predict the per-pixel dynamic between arbitrary time intervals, including the temporal transition and spatial translation. Exploring connections in terms of RS-RS, RS-GS, and GS-RS, we explicitly formulate mutual constraints with the proposed E-IC, resulting in supervisions without ground-truth GS images. Extensive evaluations over synthetic and real datasets demonstrate that the proposed method achieves state-of-the-art and shows remarkable performance for event-based RS2GS inversion in real-world scenarios. The dataset and code are available at https://w3un.github. io/selfunroll/.
Exploring Causes of Demographic Variations In Face Recognition Accuracy

In recent years, media reports have called out bias and racism in face recognition technology. We review experimental results exploring several speculated causes for asymmetric cross-demographic performance. We consider accuracy differences as represented by variations in non-mated (impostor) and / or mated (genuine) distributions for 1-to-1 face matching. Possible causes explored include differences in skin tone, face size and shape, imbalance in number of identities and images in the training data, and amount of face visible in the test data ("face pixels"). We find that demographic differences in face pixel information of the test images appear to most directly impact the resultant differences in face recognition accuracy.
Epitaxial ferroelectric hafnia stabilized by symmetry constraints

Ferroelectric memories experienced a revival in the last decade due to the discovery of ferroelectricity in HfO$_2$-based nanometer-thick thin films. These films exhibit exceptional silicon compatibility, overcoming the scaling and integration obstacles that impeded perovskite ferroelectrics' use in high-density integrated circuits. The exact phase responsible for ferroelectricity in hafnia films remains debated with no single factor identified that could stabilize the ferroelectric phase thermodynamically. Here, supported by density functional theory (DFT) high-throughput (HT) calculations that screen a broad range of epitaxial conditions, we demonstrate conclusively that specific epitaxial conditions achievable with common substrates such as yttria-stabilized zirconia (YSZ) and SrTiO$_3$ can favor the polar Pca2$_1$ phase thermodynamically over other polar phases such as R3m and Pmn2$_1$ and nonpolar P2$_1$/c phase. The substrate's symmetry constraint-induced shear strain is crucial for the preference of Pca2$_1$. The strain-stability phase diagrams resolve experiment-theory discrepancies and can guide the improvement of ferroelectric properties of epitaxial hafnia thin films.
Study on Soft Robotic Pinniped Locomotion

Legged locomotion is a highly promising but under–researched subfield within the field of soft robotics. The compliant limbs of soft-limbed robots offer numerous benefits, including the ability to regulate impacts, tolerate falls, and navigate through tight spaces. These robots have the potential to be used for various applications, such as search and rescue, inspection, surveillance, and more. The state-of-the-art still faces many challenges, including limited degrees of freedom, a lack of diversity in gait trajectories, insufficient limb dexterity, and limited payload capabilities. To address these challenges, we develop a modular soft-limbed robot that can mimic the locomotion of pinnipeds. By using a modular design approach, we aim to create a robot that has improved degrees of freedom, gait trajectory diversity, limb dexterity, and payload capabilities. We derive a complete floating-base kinematic model of the proposed robot and use it to generate and experimentally validate a variety of locomotion gaits. Results show that the proposed robot is capable of replicating these gaits effectively. We compare the locomotion trajectories under different gait parameters against our modeling results to demonstrate the validity of our proposed gait models.
Extremum Seeking Regulator for Nonlinear Systems with Unknown Control Directions and an Uncertain Exosystem

—This paper proposes a solution to the practical robust output regulation problem for a class of nonlinear systems with unknown control directions and uncertain exosystem dynamics. The concurrence of the unknown control directions and uncertain parameters in both the system dynamics and the exosystem pose a signiﬁcant challenge to solve this problem. Moreover, in light of the nonlinear internal model approach, this paper converts the robust, practical output regulation problem into a robust non-adaptive stabilization problem for the aug- mented system with integral Input-to-State Stable (iISS) inverse dynamics. By employing an extremum-seeking control approach, the construction of the control laws avoids the use of Nussbaum-type gain techniques to handle the practical robust output regulation problem subject to time-varying control directions. The stability of the non-adaptive output regulation design is proven via a Lie bracket averaging technique where uniform ultimate boundedness of the closed-loop signals is guaranteed. As a result, the estimation and tracking errors converge to zero exponentially, provided that the frequency of the dither signal goes to inﬁnity. Finally, a numerical example with unknown coefﬁcients is provided to illustrate the validity of the theoretical results.
Fermionic Gaussian projected entangled pair states in 
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: Rotations and relativistic limits

Fermionic Gaussian Projected Entangled Pair States are fermionic tensor network state constructions which describe the physics of ground states of non-interacting fermionic Hamiltonians. As non-interacting states, one may study and analyze them very efficiently, in both analytical and numerical means. Recently it was shown that they may be used as the starting point - after applying so-called PEPS gauging mechanisms - for variational study of lattice gauge theories. This is done using sign-problem free variational Monte-Carlo. In this work we show how to generalize such states from two to three spatial dimensions, focusing on spin representations and requirements of lattice rotations. We present constructions which are crucial for the application of the above mentioned variational Monte-Carlo techniques for studying non-perturbative lattice gauge theory physics, with fermionic matter, in $2+1$-d and $3+1$-d models. Thus, the constructions presented here are crucial for the study of non-trivial lattice gauge theories with fermionic tensor network states.
Uncertainty-Aware Null Space Networks for Data-Consistent Image Reconstruction

Reconstructing an image from noisy and incomplete measurements is a central task in several image processing applications. In recent years, state-of-the-art reconstruction methods have been developed based on recent advances in deep learning. Especially for highly underdetermined problems, maintaining data consistency is a key goal. This can be achieved either by iterative network architectures or by a subsequent projection of the network reconstruction. However, for such approaches to be used in safety-critical domains such as medical imaging, the network reconstruction should not only provide the user with a reconstructed image, but also with some level of confidence in the reconstruction. In order to meet these two key requirements, this paper combines deep null-space networks with uncertainty quantification. Evaluation of the proposed method includes image reconstruction from undersampled Radon measurements on a toy CT dataset and accelerated MRI reconstruction on the fastMRI dataset. This work is the first approach to solving inverse problems that additionally models data-dependent uncertainty by estimating an input-dependent scale map, providing a robust assessment of reconstruction quality.
On the stability of numerical schemes modeling non-Newtonian fluids

We study various numerical schemes for differential models of non-newtonian fluids, like the viscoelastic polymer fluids. Our motivation is to give some tracks to understand at least some of the numerical instabilities that have been observed for decades by rheologists in simulations, which are generically known as High-Weissenberg Number Problems. Our starting point among many differential viscoelastic models is the prototypical Oldroyd-B model, for which a free energy estimation holds [3]. The free energy is indeed the right positive quantity that allows to control the long-time behaviour of solutions to a Cauchy problem for the Oldroyd-B model [4]. We will show under which assumptions a free energy estimate is also satisfied by discretizations of the Oldroyd-B model [1,2]. Some numerical observations obtained with different schemes will support our viewpoint that it may be important for the stability of numerical schemes to satisfy a discrete free energy estimation.