Miracle-dz/UniFAR-FTUs huggingface dataset:
Determination of normalized extremal quasimodular forms of depth 1 with integral Fourier coefficients

The main purpose of this paper is to determine all normalized extremal quasimodular forms of depth 1 whose Fourier coefficients are integers. By changing the local parameter at infinity from $q=e^{2\pi i \tau}$ to the reciprocal of the elliptic modular $j$-function, we prove that all normalized extremal quasimodular forms of depth 1 have a hypergeometric series expression and that integrality is not affected by this change of parameters. Furthermore, by transforming these hypergeometric series expressions into a certain manageable form related to the Atkin(-like) polynomials and using the lemmas that appeared in the study of $p$-adic hypergeometric series by Dwork and Zudilin, the integrality problem can be reduced to the fact that a polynomial vanishes modulo a prime power, which we prove. We also prove that all extremal quasimodular forms of depth 1 with appropriate weight-dependent leading coefficients have integral Fourier coefficients by focusing on the hypergeometric expression of them.
Dimensionality Reduction for General KDE Mode Finding

Finding the mode of a high dimensional probability distribution $D$ is a fundamental algorithmic problem in statistics and data analysis. There has been particular interest in efficient methods for solving the problem when $D$ is represented as a mixture model or kernel density estimate, although few algorithmic results with worst-case approximation and runtime guarantees are known. In this work, we significantly generalize a result of (LeeLiMusco:2021) on mode approximation for Gaussian mixture models. We develop randomized dimensionality reduction methods for mixtures involving a broader class of kernels, including the popular logistic, sigmoid, and generalized Gaussian kernels. As in Lee et al.'s work, our dimensionality reduction results yield quasi-polynomial algorithms for mode finding with multiplicative accuracy $(1-\epsilon)$ for any $\epsilon>0$. Moreover, when combined with gradient descent, they yield efficient practical heuristics for the problem. In addition to our positive results, we prove a hardness result for box kernels, showing that there is no polynomial time algorithm for finding the mode of a kernel density estimate, unless $\mathit{P} = \mathit{NP}$. Obtaining similar hardness results for kernels used in practice (like Gaussian or logistic kernels) is an interesting future direction.
Notes on Milnor-Witt K-theory

These notes are devoted to the foundations of Milnor-Witt K-theory of fields of arbitrary characteristic and without any perfectness assumptions. Extending the fundamental work of Morel, we establish all its functorial properties as stated in Feld's theory of Milnor-Witt modules, with a special attention about twists. The main new result is a computation of transfers in the general (in particular inseparable) case in terms of Grothendieck (differential) trace maps. These notes are used as the foundation for an expository work on Chow-Witt groups. They are built upon a series of talks given at the Spring School ``Invariants in Algebraic Geometry'', organized by Daniele Faenzi, Adrien Dubouloz and Ronan Terpereau.
FakeSwarm: Improving Fake News Detection with Swarming Characteristics

The proliferation of fake news poses a serious threat to society, as it can misinform and manipulate the public, erode trust in institutions, and undermine democratic processes. To address this issue, we present FakeSwarm, a fake news identification system that leverages the swarming characteristics of fake news. We propose a novel concept of fake news swarming characteristics and design three types of swarm features, including principal component analysis, metric representation, and position encoding, to extract the swarm behavior. We evaluate our system on a public dataset and demonstrate the effectiveness of incorporating swarm features in fake news identification, achieving an f1-score and accuracy over 97% by combining all three types of swarm features. Furthermore, we design an online learning pipeline based on the hypothesis of the temporal distribution pattern of fake news emergence, which is validated on a topic with early emerging fake news and a shortage of text samples, showing that swarm features can significantly improve recall rates in such cases. Our work provides a new perspective and approach to fake news detection and highlights the importance of considering swarming characteristics in detecting fake news.
Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows

Achieving a balance between image quality (precision) and diversity (recall) is a significant challenge in the domain of generative models. Current state-of-the-art models primarily rely on optimizing heuristics, such as the Fr\'echet Inception Distance. While recent developments have introduced principled methods for evaluating precision and recall, they have yet to be successfully integrated into the training of generative models. Our main contribution is a novel training method for generative models, such as Generative Adversarial Networks and Normalizing Flows, which explicitly optimizes a user-defined trade-off between precision and recall. More precisely, we show that achieving a specified precision-recall trade-off corresponds to minimizing a unique $f$-divergence from a family we call the \textit{PR-divergences}. Conversely, any $f$-divergence can be written as a linear combination of PR-divergences and corresponds to a weighted precision-recall trade-off. Through comprehensive evaluations, we show that our approach improves the performance of existing state-of-the-art models like BigGAN in terms of either precision or recall when tested on datasets such as ImageNet.
Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images

Convolutional Neural Networks (CNNs) have shown remarkable progress in medical image segmentation. However, lesion segmentation remains a challenge to state-of-the-art CNN-based algorithms due to the variance in scales and shapes. On the one hand, tiny lesions are hard to be delineated precisely from the medical images which are often of low resolutions. On the other hand, segmenting large-size lesions requires large receptive fields, which exacerbates the first challenge. In this paper, we present a scale-aware super-resolution network to adaptively segment lesions of various sizes from the low-resolution medical images. Our proposed network contains dual branches to simultaneously conduct lesion mask super-resolution and lesion image super-resolution. The image super-resolution branch will provide more detailed features for the segmentation branch, i.e., the mask super-resolution branch, for fine-grained segmentation. Meanwhile, we introduce scale-aware dilated convolution blocks into the multi-task decoders to adaptively adjust the receptive fields of the convolutional kernels according to the lesion sizes. To guide the segmentation branch to learn from richer high-resolution features, we propose a feature affinity module and a scale affinity module to enhance the multi-task learning of the dual branches. On multiple challenging lesion segmentation datasets, our proposed network achieved consistent improvements compared to other state-of-the-art methods.
Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits

The study of collaborative multi-agent bandits has attracted significant attention recently. In light of this, we initiate the study of a new collaborative setting, consisting of $N$ agents such that each agent is learning one of $M$ stochastic multi-armed bandits to minimize their group cumulative regret. We develop decentralized algorithms which facilitate collaboration between the agents under two scenarios. We characterize the performance of these algorithms by deriving the per agent cumulative regret and group regret upper bounds. We also prove lower bounds for the group regret in this setting, which demonstrates the near-optimal behavior of the proposed algorithms.
The clustering of galaxies in hierarchical galaxy formation models

Galaxy clustering encodes information about the values of cosmological parameters and also about the physical processes behind galaxy formation and evolution. The GALFORM semi-analytical model is the theoretical approach we used to model galaxy formation. We start by studying the luminosity dependence of galaxy clustering which is measured accurately in the local Universe. We have compared the clustering predictions of three publicly available galaxy formation models with clustering measurements from the 2dFGRS and found that two new processes need to be included in order to understand the observed clustering. We then study the distribution of cold gas in dark matter haloes central to the processes of galaxy formation. We present the cold gas mass function and its evolution with redshift. We have found that the clustering predicted by the semi-analytic models agrees well with the HIPASS measurements of Meyer et al. (2007). We have calculated effective volume for redshift surveys planned with Square Kilometre Array (SKA) and compared with that of the optical Euclid mission. Finally, we study the clustering of faint extragalactic sources which are one of the foregrounds in PLANCK maps. We predict the clustering of faint extragalactic sources using a hybrid GRASIL+GALFORM+N-body model. We have compared the hybrid scheme with analytic clustering estimates. On large scales the two approaches agree, but for multipoles with l>500 the results differ significantly, with the hybrid approach being the more accurate.
Proactive DP: A Multple Target Optimization Framework for DP-SGD

We introduce a multiple target optimization framework for DP-SGD referred to as pro-active DP. In contrast to traditional DP accountants, which are used to track the expenditure of privacy budgets, the pro-active DP scheme allows one to {\it a-priori} select parameters of DP-SGD based on a fixed privacy budget (in terms of $\epsilon$ and $\delta$) in such a way to optimize the anticipated utility (test accuracy) the most. To achieve this objective, we first propose significant improvements to the moment account method, presenting a closed-form $(\epsilon,\delta)$-DP guarantee that connects all parameters in the DP-SGD setup. Generally, DP-SGD is $(\epsilon\leq 1/2,\delta=1/N)$-DP if $\sigma=\sqrt{2(\epsilon +\ln(1/\delta))/\epsilon}$ with $T$ at least $\approx 2k^2/\epsilon$ and $(2/e)^2k^2-1/2\geq \ln(N)$, where $T$ is the total number of rounds, and $K=kN$ is the total number of gradient computations where $k$ measures $K$ in number of epochs of size $N$ of the local data set. We prove that our expression is close to tight in that if $T$ is more than a constant factor $\approx 4$ smaller than the lower bound $\approx 2k^2/\epsilon$, then the $(\epsilon,\delta)$-DP guarantee is violated. Our enhanced DP theory allows us to create a utility graph and DP calculator. These tools link privacy and utility objectives and search for optimal experiment setups, efficiently taking into account both accuracy and privacy objectives, as well as implementation goals. We furnish a comprehensive implementation flow of our proactive DP, with rigorous experiments to showcase the proof-of-concept.
The Efficacy of Cyclooxygenase-2 Inhibitors for the Male Treatment of Lower Urinary Tract Symptoms: A Systematic Review and Meta-Analysis

To investigate the potential use of cyclooxygenase-2 (COX-2) inhibitors in the treatment of lower urinary tract symptoms (LUTS) in male patients, we conducted a comprehensive meta-analysis. Our study involved the identification and collection of randomized controlled trials (RCTs) from leading databases including PubMed, MEDLINE, EMBASE, and Cochrane Library. The primary objective of this analysis was to evaluate the effectiveness of COX-2 inhibitors for the treatment of LUTS. Our analysis involved six short-term (within 3 months) RCTs involving 707 patients. We found that COX-2 inhibitor treatment significantly improved the International Prostate Symptom Score (IPSS) of patients (mean difference [MD] = −2.99, 95% confidence interval (CI): −3.65 to −2.33, p < . 00001), nocturia frequency (MD = −1.90; 95% CI: −3.18 to −0.61, p = .004), and maximum flow rate (Qmax) (MD = 1.02; 95% CI: 0.06 to 1.98, p = .04). However, no significant differences were found between patients in terms of changes in prostate-specific antigen (PSA) (MD = 0.02; 95% CI: −0.39 to 0.43, p = .92) and total prostate volume (TPV) (MD = −2.93; 95% CI: −6.45 to 0.59, p = .10). Therefore COX-2 inhibitors are an effective treatment for LUTS.
Lysosomal polygenic risk is associated with the severity of neuropathology in Lewy body disease

Abstract Intraneuronal accumulation of misfolded α-synuclein is the pathological hallmark of Parkinson’s disease and dementia with Lewy bodies, often co-occurring with variable degrees of Alzheimer’s disease related neuropathology. Genetic association studies have successfully identified common variants associated with disease risk and phenotypic traits in Lewy body disease, yet little is known about the genetic contribution to neuropathological heterogeneity. Using summary statistics from Parkinson’s disease and Alzheimer’s disease genome-wide association studies, we calculated polygenic risk scores and investigated the relationship with Lewy, amyloid-β and tau pathology. Associations were nominated in neuropathologically defined samples with Lewy body disease from the Netherlands Brain Bank (n = 217) and followed up in an independent sample series from the Mayo Clinic Brain Bank (n = 394). We also generated stratified polygenic risk scores based on single-nucleotide polymorphisms annotated to eight functional pathways or cell types previously implicated in Parkinson’s disease and assessed for association with Lewy pathology in subgroups with and without significant Alzheimer’s disease co-pathology. In an ordinal logistic regression model, the Alzheimer’s disease polygenic risk score was associated with concomitant amyloid-β and tau pathology in both cohorts. Moreover, both cohorts showed a significant association between lysosomal pathway polygenic risk and Lewy pathology, which was more consistent than the association with a general Parkinson’s disease risk score and specific to the subset of samples without significant concomitant Alzheimer’s disease related neuropathology. Our findings provide proof of principle that the specific risk alleles a patient carries for Parkinson’s and Alzheimer’s disease also influence key aspects of the underlying neuropathology in Lewy body disease. The interrelations between genetic architecture and neuropathology are complex, as our results implicate lysosomal risk loci specifically in the subset of samples without Alzheimer’s disease co-pathology. Our findings hold promise that genetic profiling may help predict the vulnerability to specific neuropathologies in Lewy body disease, with potential relevance for the further development of precision medicine in these disorders.
Evaluating femoral augmentation to prevent geriatric hip fracture: A scoping review of experimental methods

Various femoral augmentation designs have been investigated over the past decade for the prevention of geriatric hip fracture. The experimental methods used to evaluate the efficacy of these augmentations have not been critically evaluated or compared in terms of biofidelity, robustness, or ease of application. Such parameters have significant relevance in characterizing future clinical success. In this study we aimed to use a scoping review to summarize the experimental studies that evaluate femoral augmentation approaches, and critically evaluate commonly applied protocols and identify areas for concordance with the clinical situation. We conducted a literature search targeting studies that used experimental test methods to evaluate femoral augmentation to prevent geriatric fragility fracture. A total of 25 studies met the eligibility criteria. The most commonly investigated augmentation to date is the injection of bone cement or another material that cured in situ, and a popular subsequent method for biomechanical evaluation was to load the augmented proximal femur until fracture in a sideways fall configuration. We noted limitations in the clinical relevance of sideways fall scenarios being modeled and large variance in the concordance of many of the studies identified. Our review brings about recommendations for enhancing the fidelity of experimental methods modeling clinical sideways falls, which include an improved representation of soft tissue effects, using outcome metrics beyond load‐to‐failure, and applying loads inertially. Effective augmentations are encouraging for their potential to reduce the burden of hip fracture; however, the likelihood of this success is only as strong as the methods used in their evaluation.
Review of Problems in Application of Supersonic Combustion

Summary The problem of air-breathing engines capable of flying at very high Mach numbers is described briefly. Possible performance of supersonic combustion ramjets is outlined briefly and the supersonic combustion process is described. Two mechanisms of combustion are outlined: one is supersonic combustion controlled by convection process, and the second is controlled by diffusion. The parameters related to the combustion process are discussed in detail. Data and analyses of reaction rates and mixing phenomena are represented; the flame mechanism is discussed, and experimental results are presented.
Patterns of HIV Disclosure and its Impact on the HIV Care Continuum Among Black Men who Have Sex With Men in South Carolina

Addressing the psychosocial concerns of Black men who have sex with men (MSM), such as HIV disclosure, is critical for effective HIV treatment efforts. Black MSM living with HIV experience multiple psychosocial challenges, such as “triple stigma” due to their sexual orientation, racial minority status, and HIV status, which hinder their HIV disclosure and subsequent HIV care-seeking behavior. Our study sought to examine the HIV disclosure patterns and their impact on the HIV care continuum among Black MSM using a qualitative approach. Semi-structured interviews were carried out among 28 Black MSM aged ≥18 years old living in South Carolina. A thematic analysis process was employed for data analysis. The emerging themes of facilitators of HIV disclosure included the sense of personal responsibility to disclose one’s HIV status within the context of a relationship and having other family members living with HIV, while the barriers included fear of family rejection or religious reasons. A few participants preferred to disclose to friends due to the less shame in sharing their status to friends than other confidants (e.g., family members). HIV disclosure was found to be a facilitator for linkage to care and retention in care through social support obtained from disclosure confidants. Helping patients to identify a single person (e.g., family member) to share their HIV status may offer equivalent benefits to wider disclosure. Interventions occurring at multiple levels (e.g., targeting religious groups) and within multiple contexts are needed to promote HIV disclosure and improve clinical outcomes in the Black MSM community.
High Indoleamine 2,3-Dioxygenase Expression along with Low Bridging Integrator-1 Expression in Hepatocellular Carcinoma Patients

Background: Hepatocellular carcinoma (HCC) adopts several tumor immune escape mechanisms; therefore, it has the potential to be targeted by immunotherapy. Indoleamine 2, 3-dioxygenase (IDO) is an immunosuppressive enzyme that has been observed to be overexpressed in HCC patients with poor prognoses. Bridging integrator 1 (Bin1) loss promotes immune escape in cancer by deregulating IDO. Our aim is to investigate IDO expression along with Bin1 expression to find evidence of immunosuppression in HCC patients. Materials and Methods: In this study, we investigated IDO and Bin1 expression in HCC tissue specimens and the correlation of IDO and Bin1 expression with clinicopathological characteristics and prognosis of HCC patients (n=45). Immunohistochemical analysis was performed to analyze the expression of IDO and Bin1. Results: IDO was overexpressed in 38 (84.4%) out of 45 HCC tissue specimens. In addition, tumor size was significantly increased with an increase in the IDO expression (P=0.03). Low Bin1 expression was observed in 27(60%) HCC tissue specimens, whereas the remaining 18(40%) showed high Bin1 expression. Conclusion: Our data showed that expression of IDO along with Bin1 expression could be investigated for clinical evaluation in HCC. IDO might be used as an immunotherapeutic target for HCC. Therefore, further studies in larger patient cohorts are warranted.
Safety and immunogenicity of a bivalent HPV16/18 vaccine in Chinese females

ABSTRACT As global supply is still inadequate to address the worldwide requirements for HPV vaccines, we assessed the safety and immunogenicity of a new bivalent HPV16/18 vaccine. In this randomized, double-blind, placebo-controlled, phase 2 trial, healthy 9–45-year-old Chinese females in three age cohorts (600 aged 9–17 years; 240 aged 18–26 years; 360 aged 27–45 years) were randomized 1:1 to receive three doses (0,2,6 months) of HPV16/18 vaccine or placebo. We measured neutralizing antibodies against HPV 16 and 18 at 7 months and monitored safety to 12 months in all age cohorts; 9–17-year-old girls were monitored for safety and immunogenicity to 48 months. In vaccinees, 99.8% seroconverted for HPV 16 and 18 types at 7 months; respective GMTs of 5827 (95% CI: 5249, 6468) and 4223 (3785, 4713) were significantly (p < .001) higher than controls for all comparisons. GMTs in the 9–17-year-olds, which were significantly higher than in older women at 7 months, gradually declined to 48 months but remained higher than placebo with seropositivity rates maintained at 98.5% and 97.6% against HPV 16 and 18, respectively. Adverse events occurred at similar rates after vaccine and placebo (69.8% vs. 72.5%, p = .308), including solicited local reactions and systemic adverse events which were mainly mild-to-moderate. The bivalent HPV16/18 vaccine was well tolerated and induced high levels of neutralizing antibodies in all age groups which persisted at high levels to 48 months in the 9–17-year-old age group which would be the target for HPV vaccination campaigns.
Prevalence of metabolic syndrome in adolescents based on three diagnostic definitions: a cross-sectional study

ABSTRACT Objective: There is no consensus as to the best criterion for the evaluation of metabolic syndrome (MS), impairing the estimation of its prevalence. This study aims to compare MS estimates using three recommended definitions for adolescents based on a cross-sectional study nested in the Consortium of Brazilian Birth Cohorts in São Luís, Maranhão. Subjects and methods: A total of 2,515 adolescents aged between 18 and 19 years were evaluated. The criteria of International Diabetes Federation (IDF) and National Cholesterol Education Program Panel III (NCEP-ATP) modified by Cook and cols. (2003) and De Ferranti and cols. (2004) defined SM. To compare the estimates of MS prevalence, the chi-square, Fisher’s exact and Cohen’s Kappa index tests were used. Results: Among the 2,064 participants evaluated in the final sample. The prevalence of MS ranged from 4.2% (95% CI: 3.3-5.1) to 10.2% (95% CI: 8.8-11.4). When comparing the estimates of MS prevalence in the total sample and by sex, a statistically significant difference was observed. The agreement between the criteria ranged from 0.42 (CI 95%: 0.35-0.49) to 0.55 (CI 95%: 0.48-0.62) in the total sample, 0.33 (CI 95%: 0.24-0.42) to 0.59 (95%CI: 0.47-0.71) among boys and 0.39 (95% CI: 0.26-0.52) to 0.54 (95% CI: 0.44-0.64) among girls. Conclusion: Different criteria provide different estimates for the prevalence of MS in adolescents, reflecting the importance of establishing a consensus.