Miracle-dz/UniFAR-FTUs huggingface dataset:
Deconvolution in Random Effects Models via Normal Mixtures

Deconvolution in Random Effects Models via Normal Mixtures. (August 2009) Nathaniel A. Litton, B.S., Cornell University; M.S., Texas A&M University Chair of Advisory Committee: Dr. Jeffrey D. Hart This dissertation describes a minimum distance method for density estimation when the variable of interest is not directly observed. It is assumed that the underlying target density can be well approximated by a mixture of normals. The method compares a density estimate of observable data with a density of the observable data induced from assuming the target density can be written as a mixture of normals. The goal is to choose the parameters in the normal mixture that minimize the distance between the density estimate of the observable data and the induced density from the model. The method is applied to the deconvolution problem to estimate the density of Xi when the variable Yi = Xi + Zi, i = 1, . , n, is observed, and the density of Zi is known. Additionally, it is applied to a location random effects model to estimate the density of Zij when the observable quantities are p data sets of size n given by Xij = αi+γZij , i = 1, . , p, j = 1, . , n, where the densities of αi and Zij are both unknown. The performance of the minimum distance approach in the measurement error model is compared with the deconvoluting kernel density estimator of Stefanski and Carroll (1990). In the location random effects model, the minimum distance estimator is compared with the explicit characteristic function inversion method from Hall and Yao (2003). In both models, the methods are compared using simulated and real data sets. In the simulations, performance is evaluated using an integrated squared error
Hybrid algorithm based on particle swarm optimization for solving constrained optimization problems

A hybrid algorithm, PSODE, is proposed by combining particle swarm optimization (PSO) with different evolution (DE), for solving constrained optimization problems. PSODE is a powerful searching algorithm by keeping properly infeasible solution to attracting the swarm to the constraint boundary and by combining with DE to enhance the searching ability. Simulation results on benchmark complex functions with high dimension show that the hybrid algorithm is effective, efficient and fairly robust to initial conditions.
Assessment of spectral vegetation indices for estimating vegetation cover in arid and semiarid shrublands

Forty four spectral vegetation indices based on Enhanced Thematic Mapper Plus (ETM+) data were compared for predicting vegetation cover in Nodushan arid and semi arid shrublands. The efficiency of the indices was evaluated based on calculating the critical error, power of prediction and the prediction interval for the error. MSAVI, TDVI, TVI, CTVI, NDVI, NRVI, IPVI, MND, TTVI, MSR and SRI provided the most accurate estimate of the vegetation cover with an error of less than 10%. MSAVI and TDVI had the lowest critical error (9.3%). The indices based on only near infrared and red bands gave the most accurate prediction of vegetation cover followed by the indices based on only near infrared and green bands (critical error of 10.7–11%), while the indices that use blue and/or shortwave infrared, or only visible (B-G-R) bands provided the least accurate estimate of the vegetation cover (critical error of 25–84%).
EUS and ERCP training in Europe: Time for simulation, optimization, and standardization

starting and/or expanding a family generally takes place during medical residency or post graduate training, pregnancy, lactation and paternity/maternity leave will often lead to curricular interruptions. These interruptions should not be considered a burden; consequently, extension of exposure time should be possible to create an inclusive environment. The development and implementation of a standardized EUS and ERCP training program in Europe is crucial. The study of Campos et al. underlines that current programs vary widely and that basic prerequisites for adequate training are lacking in most cen-ters/countries. The results of this study should serve as a basis to optimize and standardize EUS and ERCP training. Requirements necessary for a dedicated curriculum, preferably in the setting of a dedicated post graduate fellowship, are listed above. Facilities such as a central training course and simulation ‐ based training should ideally be centralized in Europe. Although each country is respon-sible for its own curriculum and trainees, teaching hospitals across Europe should collaborate to guarantee high ‐ quality uniform training programs to ensure competence before independent practice.
Experimental limits on the fidelity of adiabatic geometric phase gates in a single solid-state spin qubit

While it is often thought that the geometric phase is less sensitive to fluctuations in the control fields, a very general feature of adiabatic Hamiltonians is the unavoidable dynamic phase that accompanies the geometric phase. The effect of control field noise during adiabatic geometric quantum gate operations has not been probed experimentally, especially in the canonical spin qubit system that is of interest for quantum information. We present measurement of the Berry phase and carry out adiabatic geometric phase gate in a single solid-state spin qubit associated with the nitrogen-vacancy center in diamond. We manipulate the spin qubit geometrically by careful application of microwave radiation that creates an effective rotating magnetic field, and observe the resulting Berry phase signal via spin echo interferometry. Our results show that control field noise at frequencies higher than the spin echo clock frequency causes decay of the quantum phase, and degrades the fidelity of the geometric phase gate to the classical threshold after a few (∼10) operations. This occurs inspite of the geometric nature of the state preparation, due to unavoidable dynamic contributions. We have carried out systematic analysis and numerical simulations to study the effects of the control field noise and imperfect driving waveforms on the quantum phase gate.
Association of the triglyceride‐glucose index with subclinical left ventricular dysfunction in type 2 diabetes mellitus patients: A retrospective cross‐sectional study

The triglyceride‐glucose (TyG) index is a simple and reliable indicator of insulin resistance, and is associated with the development and poor outcomes of cardiovascular disease. Subclinical left ventricular dysfunction (SLVD) is frequently detected in approximately one‐third of diabetes patients, but it has not been established whether the TyG index correlates with SLVD. We carried out this research to evaluate the relationship between the TyG index and SLVD in type 2 diabetes mellitus patients.
An Improved Image Registration Based on Nonsubsampled Contourlet Transform and Zernike Moments

Feature-based registration is an effective and most widely used image registration method currently. It includes three critical steps, feature extraction, feature matching and transformation parameters estimation. This paper mainly explores the first two steps. In one of Chahira Serief’s paper about image registration, feature points extraction based on nonsubsampled contourlet transform (NSCT) was proposed and feature points matching based on Zernike moments was adopted. The registration accuracy and robustness of his algorithm are acceptable, but it can still be improved. In this paper, an improved scheme of this registration algorithm is proposed. The rotation invariance of NSCT-based feature points extraction is improved, which is beneficial to extract homologous feature points. And the reliability and effectiveness of Zernike momentsbased feature points matching are improved, which can improve the matching accuracy. The improved registration algorithm can realize registration of images related by larger scaling, rotation and translation transformation. The simulation results show that the registration robustness is further improved, and the registration accuracy is still high.
Effect of 12-months testosterone replacement therapy on bone mineral density and markers of bone turnover in testicular cancer survivors – results from a randomized double-blind trial

Abstract Background Testicular cancer survivors (TCS) are at risk of Leydig cell insufficiency, which is a condition characterized by elevated luteinising hormone (LH) in combination with low levels of testosterone. It has been suggested that this condition is associated with impaired metabolic profile and low bone mineral density (BMD). The primary aim of the randomized double-blind trial NCT02991209 was to evaluate metabolic profile after 12-months testosterone replacement therapy (TRT) in TCS with mild Leydig cell insufficiency. Here we present the secondary outcomes of changes in BMD and markers of bone turnover. Methodology In total, 69 TCS with mild Leydig cell insufficiency were randomized 1:1 to 12 months TRT (n = 35) (Tostran, gel, 2%, applied transdermally, with a maximum daily dose of 40 mg) or placebo (n = 34). BMD and markers of bone turnover were evaluated at baseline, after 6- and 12-months TRT, and 3-months post-treatment. Linear mixed effects models were used to analyse changes in BMD, N-terminal propeptide of type 1 procollagen (P1NP) and C-terminal telopeptide of type I collagen (CTX). Results After 12 months treatment, TRT was not associated with a statistically significant difference in BMD compared to placebo; total body BMD: 0.01 g/cm2 (95% confidence interval (CI): −0.01 − 0.02), BMD of the lumbar spine: 0.01 g/cm2, (95% CI: −0.01–0.03), BMD of the left femoral neck: 0.00, (95% CI: −0.01–0.02). TRT was associated with a small but statistically significant increase in P1NP: 11.65 µg/L (95% CI: 3.96, 19.35), while there was no difference in CTX. Conclusion 12 months of TRT did not change BMD, while there was as small and clinically irrelevant increase in P1NP compared to placebo in TCS with mild Leydig cell insufficiency. The findings need validation in a larger cohort.
Exploring self-esteem during expressive writing about trauma predicts decreased depression in people with HIV

ABSTRACT Self-esteem is often negatively impacted by trauma. Low self-esteem has been related to significantly worse depression in people with HIV (PWH). This study explores whether the expression of words related to self-esteem during a 4-session augmented trauma writing intervention predicted post-traumatic stress, depressive symptoms, and health outcomes 6-months later. Ninety-five PWH completed four 30-minute augmented trauma writing sessions in the intervention arm of a randomized controlled trial. One augmented session was devoted to self-esteem. Two individuals coded trauma essays for number of self-esteem words. CD4+ and viral load data were collected, and the Davidson PTSD Scale and the Hamilton Depression Rating Scale were administered at baseline, one-, and six-month follow-up. Greater total self-esteem words were related to lower depressive symptoms at 6-months, controlling for depressive symptoms at study entry, age, race, and education (t(80) = −2.235, ß = −0.239, SE = 0.283, p < 0.05, 95% CI [−1.195, −. 069). Total self-esteem words were not predictive of PTSD, viral load, or CD4+ at 6-months. Exploring self-esteem when writing about and processing a traumatic event could be an important mechanism for decreasing depressive symptoms among PWH. Research is needed to test augmented expressive writing interventions that support efforts to bolster self-esteem in PWH.
Genetic and Genomic Researchers’ Perspectives on Biological Sample Sharing in Collaborative Research in Uganda: A Qualitative Study

Numerous ethical, legal, and social issues arise with biological sample sharing. The study explored the perspectives of genetic and genomic researchers on the sharing of biological samples in international collaborative research. Qualitative in-depth interviews were conducted with 15 researchers. Participants expressed positive attitudes towards biobanking and appreciated the benefits of cross-border sharing of biological samples but noted that this practice had adversely affected local capacity building efforts. There was limited understanding of the ethical and regulatory frameworks governing sample sharing. Researchers emphasized the importance of respecting cultural values in biobanking research. Issues concerning poor governance and inequitable benefit sharing were also raised. There is a need for fair and equitable international collaborations where all researchers are treated with respect and as equal partners.
Ring wormholes and time machines

In the present paper we discuss properties of a model of a ring wormhole, recently proposed by Gibbons and Volkov. Such a wormhole connects two flat spacetimes which are glued through discs of the radius $a$ bounded by the string with negative angle deficit $-2\pi$. The presence of the string's matter violating null energy condition makes the wormhole static and traversable. We study gravitational field of static sources in such a spacetime in the weak field approximation. In particular, we discuss how a field of an oblate thin massive shell surrounding one of the wormhole's mouth is modified by its presence. We also obtain a solution of a similar problem when both mouths of the wormhole are located in the same space. This approximate solution if found for the case when the distance $L$ between these mouths is much larger than the radius $a$ of the ring. We demonstrate that the corresponding locally static gravitational field in such a multiply connected space is non-potential. As a result of this, the proper time gap for the clock's synchronization linearly grows with time and closed timelike curves are formed. This process inevitably transforms such a traversable ring wormhole into a time machine. We estimate the time scale of this process.
Open problems in causal structure learning: A case study of COVID-19 in the UK

Causal machine learning (ML) algorithms recover graphical structures that tell us something about cause-and-effect relationships. The causal representation provided by these algorithms enables transparency and explainability, which is necessary in critical real-world problems. Yet, causal ML has had limited impact in practice compared to associational ML. This paper investigates the challenges of causal ML with application to COVID-19 UK pandemic data. We collate data from various public sources and investigate what the various structure learning algorithms learn from these data. We explore the impact of different data formats on algorithms spanning different classes of learning, and assess the results produced by each algorithm, and groups of algorithms, in terms of graphical structure, model dimensionality, sensitivity analysis, confounding variables, predictive and interventional inference. We use these results to highlight open problems in causal structure learning and directions for future research. To facilitate future work, we make all graphs, models and data sets publicly available online.
Boosting Distributed Machine Learning Training Through Loss-tolerant Transmission Protocol

Distributed Machine Learning (DML) systems are utilized to enhance the speed of model training in data centers (DCs) and edge nodes. The Parameter Server (PS) communication architecture is commonly employed, but it faces severe long-tail latency caused by many-to-one “incast” traffic patterns, negatively impacting training throughput. To address this challenge, we design the Loss-tolerant Transmission Protocol (LTP), which permits partial loss of gradients during synchronization to avoid unneeded retransmission and contributes to faster synchronization per iteration. LTP implements loss-tolerant transmission through out-of-order transmission and out-of-order Acknowledges (ACKs). LTP employs Early Close to adjust the loss-tolerant threshold based on network conditions and bubble-filling for data correction to maintain training accuracy. LTP is implemented by C++ and integrated into PyTorch. Evaluations on a testbed of 8 worker nodes and one PS node demonstrate that LTP can significantly improve DML training task throughput by up to 30x compared to traditional TCP congestion controls, with no sacrifice to final accuracy.
SkillQG: Learning to Generate Question for Reading Comprehension Assessment

We present $\textbf{$\texttt{SkillQG}$}$: a question generation framework with controllable comprehension types for assessing and improving machine reading comprehension models. Existing question generation systems widely differentiate questions by $\textit{literal}$ information such as question words and answer types to generate semantically relevant questions for a given context. However, they rarely consider the $\textit{comprehension}$ nature of questions, i.e. the different comprehension capabilities embodied by different questions. In comparison, our $\texttt{SkillQG}$ is able to tailor a fine-grained assessment and improvement to the capabilities of question answering models built on it. Specifically, we first frame the comprehension type of questions based on a hierarchical skill-based schema, then formulate $\texttt{SkillQG}$ as a skill-conditioned question generator. Furthermore, to improve the controllability of generation, we augment the input text with question focus and skill-specific knowledge, which are constructed by iteratively prompting the pre-trained language models. Empirical results demonstrate that $\texttt{SkillQG}$ outperforms baselines in terms of quality, relevance, and skill-controllability while showing a promising performance boost in downstream question answering task.
A LSTM and Cost-Sensitive Learning-Based Real-Time Warning for Civil Aviation Over-limit

The issue of over-limit during passenger aircraft flights has drawn increasing attention in civil aviation due to its potential safety risks. To address this issue, real-time automated warning systems are essential. In this study, a real-time warning model for civil aviation over-limit is proposed based on QAR data monitoring. Firstly, highly correlated attributes to over-limit are extracted from a vast QAR dataset using the Spearman rank correlation coefficient. Because flight over-limit poses a binary classification problem with unbalanced samples, this paper incorporates cost-sensitive learning in the LSTM model. Finally, the time step length, number of LSTM cells, and learning rate in the LSTM model are optimized using a grid search approach. The model is trained on a real dataset, and its performance is evaluated on a validation set. The experimental results show that the proposed model achieves an F1 score of 0.991 and an accuracy of 0.978, indicating its effectiveness in real-time warning of civil aviation over-limit.
Improved error estimates for a modified exponential Euler method for the semilinear stochastic heat equation with rough initial data

A class of stochastic Besov spaces $B^p L^2(\Omega;\dot H^\alpha(\mathcal{O}))$, $1\le p\le\infty$ and $\alpha\in[-2,2]$, is introduced to characterize the regularity of the noise in the semilinear stochastic heat equation \begin{equation*} {\rm d} u -\Delta u {\rm d} t =f(u) {\rm d} t + {\rm d} W(t) , \end{equation*} under the following conditions for some $\alpha\in(0,1]$: $$ \Big\| \int_0^te^{-(t-s)A}{\rm d} W(s) \Big\|_{L^2(\Omega;L^2(\mathcal{O}))} \le C t^{\frac{\alpha}{2}} \quad\mbox{and}\quad \Big\| \int_0^te^{-(t-s)A}{\rm d} W(s) \Big\|_{B^\infty L^2(\Omega;\dot H^\alpha(\mathcal{O}))}\le C. $$ The conditions above are shown to be satisfied by both trace-class noises (with $\alpha=1$) and one-dimensional space-time white noises (with $\alpha=\frac12$). The latter would fail to satisfy the conditions with $\alpha=\frac12$ if the stochastic Besov norm $\|\cdot\|_{B^\infty L^2(\Omega;\dot H^\alpha(\mathcal{O}))}$ is replaced by the classical Sobolev norm $\|\cdot\|_{L^2(\Omega;\dot H^\alpha(\mathcal{O}))}$, and this often causes reduction of the convergence order in the numerical analysis of the semilinear stochastic heat equation. In this article, the convergence of a modified exponential Euler method, with a spectral method for spatial discretization, is proved to have order $\alpha$ in both time and space for possibly nonsmooth initial data in $L^4(\Omega;\dot{H}^{\beta}(\mathcal{O}))$ with $\beta>-1$, by utilizing the real interpolation properties of the stochastic Besov spaces and a class of locally refined stepsizes to resolve the singularity of the solution at $t=0$.
Shall We Trust All Relational Tuples by Open Information Extraction? A Study on Speculation Detection

Open Information Extraction (OIE) aims to extract factual relational tuples from open-domain sentences. Downstream tasks use the extracted OIE tuples as facts, without examining the certainty of these facts. However, uncertainty/speculation is a common linguistic phenomenon. Existing studies on speculation detection are defined at sentence level, but even if a sentence is determined to be speculative, not all tuples extracted from it may be speculative. In this paper, we propose to study speculations in OIE and aim to determine whether an extracted tuple is speculative. We formally define the research problem of tuple-level speculation detection and conduct a detailed data analysis on the LSOIE dataset which contains labels for speculative tuples. Lastly, we propose a baseline model OIE-Spec for this new research task.
Fairness in Image Search: A Study of Occupational Stereotyping in Image Retrieval and its Debiasing

Multi-modal search engines have experienced significant growth and widespread use in recent years, making them the second most common internet use. While search engine systems offer a range of services, the image search field has recently become a focal point in the information retrieval community, as the adage goes,"a picture is worth a thousand words". Although popular search engines like Google excel at image search accuracy and agility, there is an ongoing debate over whether their search results can be biased in terms of gender, language, demographics, socio-cultural aspects, and stereotypes. This potential for bias can have a significant impact on individuals' perceptions and influence their perspectives. In this paper, we present our study on bias and fairness in web search, with a focus on keyword-based image search. We first discuss several kinds of biases that exist in search systems and why it is important to mitigate them. We narrow down our study to assessing and mitigating occupational stereotypes in image search, which is a prevalent fairness issue in image retrieval. For the assessment of stereotypes, we take gender as an indicator. We explore various open-source and proprietary APIs for gender identification from images. With these, we examine the extent of gender bias in top-tanked image search results obtained for several occupational keywords. To mitigate the bias, we then propose a fairness-aware re-ranking algorithm that optimizes (a) relevance of the search result with the keyword and (b) fairness w.r. t genders identified. We experiment on 100 top-ranked images obtained for 10 occupational keywords and consider random re-ranking and re-ranking based on relevance as baselines. Our experimental results show that the fairness-aware re-ranking algorithm produces rankings with better fairness scores and competitive relevance scores than the baselines.